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Eliminating Transition State Calculations for Faster and More Accurate Reactivity Prediction in Sulfa-Michael Additions Relevant to Human Health and the Environment
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Eliminating Transition State Calculations for Faster and More Accurate Reactivity Prediction in Sulfa-Michael Additions Relevant to Human Health and the Environment
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Cite this: ACS Omega 2022, 7, 30, 26945–26951
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https://doi.org/10.1021/acsomega.2c03739
Published July 21, 2022

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Abstract

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Fast and accurate computational approaches to predicting reactivity in sulfa-Michael additions are required for high-throughput screening in toxicology (e.g., predicting excess aquatic toxicity and skin sensitization), chemical synthesis, covalent drug design (e.g., targeting cysteine), and data set generation for machine learning. The kinetic glutathione chemoassay is a time-consuming in chemico method used to extract kinetic data in the form of log(kGSH) for organic electrophiles. In this work, we use density functional theory to compare the use of transition states (TSs) and enolate intermediate structures following C–S bond formation in the prediction of log(kGSH) for a diverse group of 1,4 Michael acceptors. Despite the widespread use of transition state calculations in the literature to predict sulfa-Michael reactivity, we observe that intermediate structures show much better performance for the prediction of log(kGSH), are faster to calculate, and easier to obtain than TSs. Furthermore, we show how linear combinations of atomic charges from the isolated Michael acceptors can further improve predictions, even when using inexpensive semiempirical quantum chemistry methods. Our models can be used widely in the chemical sciences (e.g., in the prediction of toxicity relevant to the environment and human health, synthesis planning, and the design of cysteine-targeting covalent inhibitors), and represent a low-cost, sustainable approach to reactivity assessment.

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Introduction

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Michael addition reactions, characterized by nucleophilic attack at the β-carbon of an α,β-unsaturated carbonyl compound, have been widely used in synthesis for generating a variety of carbon-nucleophile bond types (e.g., C–S, C–N, and C–C bonds). (1−3) The sulfa-Michael addition in particular is a highly important reaction given its extensive use in organic synthesis (4−6) pharmacology, (7,8) toxicology, (9,10) and materials science. (11) Therefore, the ability to assess and predict the reactivity of Michael acceptors (MAs) toward sulfur nucleophiles is of paramount importance across a range of disciplines. In chemical synthesis, predicting rates of reaction between MAs and a given nucleophile would provide low-cost, quantitative predictions for novel synthetic transformations. In toxicology, such predictions could be used in the chemical risk assessment of aquatic toxicity and skin sensitization, while aligning with the increasingly important “Green” toxicology approach. (12−14) Lastly, in drug discovery, such predictions could be used in the design of cysteine-targeting, targeted covalent inhibitors (TCIs). TCIs are an emerging class of compounds in drug discovery and recently, three FDA-approved drugs (Afatanib, Ibrutinib, and Osimertinib) were designed to react irreversibly with a sulfur-containing cysteine residue through a hetero-Michael addition. (15,16)
In 2006, Schultz et al. presented a pioneering framework for modeling reactions between an electrophilic toxicant and a biological macromolecule, many of which are nucleophilic. (17) This framework proposed the use of model nucleophiles to better understand reactive toxicity and the ultimate biological effects associated with its existence. Traditionally, MA reactivity has been assessed using chemoassays, with the kinetic glutathione chemoassay developed by Böhme et al. being the most widely used to examine sulfa-Michael additions. (18) This method has been used extensively to obtain second-order rate constants [L mol–1 min–1] to assess the reactivity of different α,β-unsaturated carbonyl compounds and uses glutathione (GSH) as the nucleophile (Figure 1). (19) In 2016, Schürmann and co-workers compiled, to the best of our knowledge, the largest known experimental kinetic glutathione assay rate data set for α,β-unsaturated carbonyl compounds. (20) As we move toward a more sustainable future, it is vital that in silico approaches are developed that align with the principles of green chemistry. Predictive in silico approaches provide advantages in the design of safer chemicals and less hazardous syntheses, along with reducing harmful waste products that are commonly utilized in experimental methods. Thus, to reduce the environmental, financial, and time-based costs attached to chemoassays, there have been numerous attempts to develop quantum chemical transition state (TS) approaches to the prediction of MA reactivity data sets. In 2010, Cronin and co-workers performed TS calculations for 22 MAs reacting with methanethiolate. (21) They presented a QSAR regression equation for the prediction of log(kGSH) that used the activation barrier ΔE‡PCM for both the forward and backward reaction, and the solvent accessible surface area (SASA), resulting in an impressive squared Pearson correlation coefficient (r2) of 0.90 between their calculated and experimental values for log(kGSH):

Figure 1

Figure 1. Generalized reaction mechanism between glutathione and a 1,4 Michael acceptor. “Glut” represents a glutathione residue minus its sulfur atom.

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Although this work did provide strong regression statistics, multiple descriptors were required, and when only ΔE‡PCM and the SASA were utilized as features, a relatively poor correlation (r2 = 0.51) was observed. In the same year, Schürmann and co-workers published a set of multidescriptor models based on ground state features such as a charge-limited local electrophilicity index, a σ-bond energy (obtained from natural bond order analysis), and SASAs for both the α- and β-carbons. (22) Overall, their models showed strong statistics, with a four-descriptor model showing the best performance (r2 = 0.93). In 2011, Mulliner et al. used TS calculations to predict log(kGSH) for 35 α,β-unsaturated carbonyl compounds. (23) They also performed calculations under the PCM solvent model to examine the effect of implicit solvation on the prediction of log(kGSH). For solvated and nonsolvated models, respectively, they obtained regression equations with r2 = 0.76 and r2 = 0.68 between calculated and experimental log(kGSH) values. Following this in 2013, the same authors calculated reaction barriers (ΔE‡) for a set of esters, and from ΔE‡, calculated log(kGSH) using their previously developed regression model from 2011. This study examined the use of log(kGSH) and hydrophobicity (in the form of log(Kow), where Kow is the octanol–water partition coefficient) for the prediction of 50% growth inhibition ofT. pyriformis (log(EC50)). It is thus clear that TSs have commonly been used in assessing MA reactivity, but far fewer studies have examined high energy intermediate structures (HEI, see Figure 2) and their ability to predict reactivity. (24) In 2013, Enoch and Roberts performed DFT calculations on a data set of 26 MAs and calculated their ΔEHEI values upon reaction with methanethiolate. (25) With the full data set, their results showed a poor correlation (r2 = 0.02) between skin sensitization potency (pEC3) and intermediate energies upon linear regression analysis. Upon refining the data set and removing some problematic compounds from the analysis, their results showed improved correlation (r2 = 0.43) for a single descriptor model using intermediate energies. This was further improved by adding a SASA descriptor, resulting in a multivariate regression model with strong statistics (r2 = 0.79). Further in 2016, Ebbrell et al. developed an in silico profiler for the prediction of RC50 values using intermediate MA structures, where RC50 is the concentration of electrophiles needed to reduce the concentration of GSH by 50%. Their results showed that a single descriptor model using ΔEHEI resulted in modest regression statistics (n = 54, r2 = 0.52). However, this model was greatly improved (n = 41, r2 = 0.87) by adopting a multivariate approach and including an additional SASA descriptor. (10) In 2017, Ebbrell et al. presented a thorough analysis on how their previously developed fragment profiler could be used in the prediction of aquatic toxicity (towardT. pyriformis) and skin sensitization potential. (26) This study highlighted the crucial role of ΔEHEI regression models for the prediction of important toxicological endpoints such as EC50/pEC50. Thus, it is clear that intermediates and ΔEHEI are of great use and importance in predictive toxicology. Furthermore, intermediate structures are particularly desirable from a practical standpoint; TS calculations are not only computationally costly but are challenging for both experts and nonexperts to perform. (27) Compared with TSs, intermediate structure calculations involve optimization to a minimum, which is typically less costly, easier to automate (e.g., for data set generation in machine learning (ML)), and much easier to perform. (24) In this work, we find that easily calculable intermediate energies perform better than TSs in the prediction of log(kGSH). Furthermore, we find that linear regression models built with very inexpensive features from semiempirical quantum mechanical (SQM) calculations can make reliable predictions of log(kGSH) comparable to methods using DFT calculations, even when only reactant-derived features are used.

Figure 2

Figure 2. GSH-MA reaction pathway showing the structure of the reactants, intermediate, TS, and product.

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Materials and Methods

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Kinetic glutathione assay data (log(kGSH)) for 23 1,4 MAs were taken from the work published by Böhme et al., providing experimental rate data for nine esters, seven aldehydes, and seven ketones (see Figure 3 and Table S1 in the Supporting Information). (20) DFT calculations were performed with Gaussian 16 (Rev. A.03) (28) at the M06-2X/def2-TZVPP level of theory under the IEFPCM implicit solvation model (water), which has been used extensively for modeling organic reactions. (29−32) Implicit solvation in water was chosen to simulate the experimental conditions used in the kinetic glutathione chemoassay. In line with previous studies, and to ensure computational feasibility, methanethiolate was used as a model nucleophile in all calculations. (10,25) All regression models were developed via the Scikit-learn Python package. (33) Activation barriers and intermediate energy differences were calculated according to:
where ΔG‡ is the activation free energy, GTS is the TS free energy, GMA is the free energy of a MA, ΔGHEI is the free energy difference between the intermediate and the reactant state, GInt is the free energy of the intermediate structure, and Gnuc is the free energy of methanethiolate. For full computational details, see the Supporting Information.

Figure 3

Figure 3. Visualizing log(kGSH) for the 23 compounds included in this study. Red indicates a higher reactivity with glutathione, while green indicates lower reactivity. Blue compounds indicate that minor truncation was performed (see the SI for full details).

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Results and Discussion

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In total, across the data set, 94 reactant ground states, 226 TSs, and 229 intermediate structures were obtained and verified as either minima or first-order saddle points on the M06-2X/def2-TZVPP-IEFPCM (water) potential energy surface. Two compounds were minorly truncated in this work; 1-pentene-3-one to methyl vinyl ketone and trans-2-pentenal to but-2-enal. Previous literature demonstrates that conformational freedom is often neglected in the construction of QSAR models that use descriptors derived from quantum chemical methods. (34−36) However, a more thorough exploration of chemical space is needed. To illustrate this, six reactant conformers were obtained for 2-ethylacrolein, and the lowest energy structure was 3.26 kcal/mol lower in energy than the highest energy conformation. Thus, if single conformations are examined, large errors can be introduced in the calculation of thermochemical data. A further example includes the two reactant conformers obtained for methyl methacrylate; a difference of 7.73 kcal/mol was obtained between the two conformers. Thus, it is quite clear that without direct consideration of molecular flexibility, calculated activation barriers and relative intermediate energies can vary considerably.

TS Calculations

Across the data set, experimental rate constants ranged from 3.1 to −2.15 log units (see Figure 3). The more positive a value of log(kGSH), the faster the reaction between an MA and methanethiolate, and thus, the corresponding MA is more reactive. Our calculated activation energies ranged from 9.7 to 16.1 kcal/mol across all compounds, with esters showing the largest range among the three groups. A single descriptor linear regression analysis showed a poor correlation between log(kGSH) and ΔG‡ (Figure 4), with a squared Pearson correlation coefficient (r2) of 0.49 between the predicted and measured log(kGSH). The resultant model demonstrates that TSs and free energies of activation provide a poor prediction of log(kGSH) in the MA data set, with a relatively large test set mean absolute error (MAE) of 0.69 log units. These results show good agreement with previous work by Schwöbel et al., where it was shown that at the B3LYP/6-31G** level of theory, no global model for esters, ketones, and aldehydes exists that utilizes activation energies derived from TSs. (21) Additionally, it has been previously reported that DFT can prove problematic in the study of charged ionic TSs due to large errors in the calculated reactivity parameters. (37) It is also known that most implicit solvent models define the solvent cavity as a set of overlapping spheres with fixed atomic radii. These radii are experimentally calculated, and for IEFPCM, the Bondi data set is used; for each atom, the atomic radius is calculated through multiplication of a constant (1.2) by the Bondi atomic van der Waals radius. (38,39) These empirically determined radii use solvation free energy training sets for parameterization, and TSs are often not included in the initial training set, thereby introducing error when TSs are examined with implicit solvent models. (40) Additionally, radii of the specific atoms involved in the bond-forming/breaking process (e.g., S–C from our study) are often poorly defined in TSs for this reason. It must also be noted that from a practical perspective, explicit solvation is too costly and would eliminate the easy-to-use nature of our models.

Figure 4

Figure 4. Linear regression of log(kGSH) on the activation energy derived from transition state structures (M06-2X/def2-TZVPP-IEFPCM (water)) was performed. Predicted log(kGSH) is plotted against measured log(kGSH).

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Therefore, our work, combined with previous literature, indicates that a poor correlation between log(kGSH) and activation energy is likely to be independent of both basis set and DFT functional. However, to further corroborate this, benchmarking studies could be performed to thoroughly examine the role that level of theory and solvation method plays in this problem. Upon examination of our results and previously published work, it is clear that single-descriptor ΔG‡ models do not provide a sufficient description of MA reactivity toward sulfur nucleophiles and should not be used for making predictions about the reactivity of MAs with glutathione.

Intermediate Structures

The computed stability of the enolate intermediates after C–S bond formation, which closely resembles the TSs in line with Hammond’s postulate, may be used to accurately predict the reactivity of MAs toward thiol containing compounds. (41) Across the data set, calculated intermediate energies ΔGHEI varied from 2.4 to 13.3 kcal/mol. Intermediate energies for ketones ranged from 2.4 to 11.6 kcal/mol, aldehydes ranged from 2.8 to 7.5 kcal/mol, and esters ranged from 6.2 to 13.3 kcal/mol. A linear regression analysis with ΔGHEI resulted in a significantly improved model compared to TS barriers (Figure 5), with r2= 0.76 and a test set MAE of 0.48 log units. This is a noteworthy result; generally, it would be expected that TS barriers correlate more strongly with kinetic data such as log(kGSH), if the rate-determining step has been modeled. As highlighted by Schürmann and co-workers, the rate-determining step is the addition of methanethiolate to the MA, and the protonation step (see Figure 2, TS-2) is expected to be fast and not rate-limiting. (22) However, to ensure we have focused our calculations on the rate-determining step (TS-1), we calculated reaction barriers of the protonation step (ΔG‡prot) for six structures in our data set; two esters, two ketones, and two aldehydes, with a fast and slow reacting compound being chosen for each (see Table S2). Methanethiol was the proton source as per the work by Northrop and co-workers. (42) All calculated barriers for the protonation step were lower than the corresponding barrier for the addition of methanethiolate by an average value of 6.41 kcal/mol, thus confirming it highly likely that thiolate addition is the rate-determining step. From these results, it remains clear that, in line with the Hammond postulate, calculation of ΔGHEI can provide a fast, high-throughput measure of 1,4 MA reactivity toward methanethiolate (and thus, thiol containing compounds), with our models permitting strong quantitative predictions to be made for initial, preliminary reactivity assessments. As detailed above, it is likely that the deficiencies of DFT TS modeling account for why our intermediate models perform better than those using TS-derived features.

Figure 5

Figure 5. Linear regression of log(kGSH) on the intermediate energy differences (M06-2X/def2-TZVPP-IEFPCM (water)). Predicted log(kGSH) is plotted against measured log(kGSH).

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A key advantage in the development of this model is its simplicity. A single variable regression model that uses calculated intermediate energies is very simple to use for both experts and nonexperts. TS searching is a nontrivial task, involving specialist knowledge and a high degree of human input to arrive at suitable structures. Optimization of only reactant and intermediate structures is significantly easier for the nonspecialist to make predictions. Minima, such as the intermediate structures presented here, converge more readily, thereby providing a great practical advantage for making predictions. (43) Successfully obtaining a set of TSs for a single structure can take many rounds of re-optimization, while intermediate geometries can be readily obtained after a single calculation. A generalized workflow for obtaining intermediate structures was presented in one of our previous studies, and can be equally applied in the context of this study. (24) Our model presents a fast, easy-to-use method for predicting the reactivity of 1,4 MAs with thiol containing compounds.

Multivariate Models

To see if the intermediate or TS models could be improved upon, a number of multivariate linear regression models were also generated using key Mulliken atomic charges, APT atomic charges (Figure 6), and several other chemical features (see Supporting Information) for all MAs, intermediates, and TSs. With the aim to create an easy-to-use method, Mulliken and APT charges, which are readily extracted from Gaussian output files and are thus very user friendly for the nonexpert performing calculations, were selected. The best results were obtained using MA features only, with r2 = 0.88 and an MAE of 0.35 log units via a three-descriptor model combining the Mulliken atomic charges of the α- and β-carbons and the carbonyl oxygen (Figure 7). Not only are these results a significant improvement on the ΔGHEI model, but with fewer atoms and less conformational flexibility than the corresponding intermediates, calculations on reactant MA structures are even more trivial.

Figure 6

Figure 6. Mulliken and APT atomic charges were calculated for highlighted atoms only.

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Figure 7

Figure 7. Linear regression of log(kGSH) on key atomic charges of the MA (M06-2X/def2-TZVPP-IEFPCM (water)). Predicted log(kGSH) is plotted against measured log(kGSH).

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Semiempirical Models

Finally, we reoptimized all MAs and intermediates using the SQM AM1 method (44) and generated multivariate linear models using the same chemical features as above. Because SQM calculations are typically orders of magnitude faster than DFT, regression models derived from SQM calculations could substantially reduce the computational expense of predictions. A result of particular significance is the negative relationship observed between log(kGSH) and the Mulliken charges (of C1 and C2) in the regression equation (see Figure 8). Although counterintuitive according to chemical intuition, previous literature indicates that MAs have a propensity to show interesting charge behavior on the α- and β-carbons. Spencer et al. used 13C NMR chemical shifts to examine the effect of electron-withdrawing groups (EWGs) at different positions on the aryl group of methyl cinnamates upon reaction with GSH. (45) The presence of EWGs resulted in an increased rate of reaction with GSH, with the chemical shifts of the β-carbon being shifted slightly upfield (less positively charged). Additionally, although downfield shifts were observed for the α-carbon resonances with an increasing rate of reaction, no obvious statistical relationship was apparent between the rate of GSH addition and the α-carbon chemical shifts. When ortho-hydroxyl substitution on the aryl group was considered, a significant observation of an upfield shift of 3–6 ppm was observed at the β-carbon. These observations suggest that the electron distribution (and thus charge) at the α- and β-carbons can behave in an unusual way in the context of sulfa-Michael addition, and they agree with our results. Further to this point, the regression equation demonstrates a positive relationship between log(kGSH) and the APT charge on the carbonyl oxygen. It is possible that due to the highly electronegative nature of oxygen, an increasingly positive charge on the carbonyl oxygen is indicative of less overall electron density in the conjugated α,β-unsaturated carbonyl substructure. Thus, with lower electron density, the MA electrophile should become more receptive to nucleophilic attack, and the rate of reaction would increase as the charge becomes increasingly positive on oxygen. Similar to the model presented in the previous section, the best performing model utilized atomic charges on the α- and β-carbons and the carbonyl oxygen of the MA, with r2 = 0.89 and an MAE of 0.37 log units (Figure 8). This provides very similar performance to the DFT MA model, and is better than the single feature ΔGHEI model but reduces the need for time-consuming DFT calculations. It is significant to note that a combination of models with differing levels of theory, opens up their use to a wider audience with differing computational resources.

Figure 8

Figure 8. Linear regression of log(kGSH) on key atomic charges of the MA (AM1). Predicted log(kGSH) is plotted against measured log(kGSH).

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Conclusions

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In this work, TS, intermediate, and reactant structures were explored in reactivity predictions (log(kGSH)) for a group of 23 sulfa-Michael additions. Such an approach to predicting sulfa-Michael reactivity is desirable for high-throughput screening in chemical synthesis, toxicology (aquatic toxicity and skin sensitization prediction), drug discovery (targeted covalent inhibitor design), and reactivity data set generation for ML. Further, our models provide a practical advantage in shifting the focus toward more sustainable approaches to chemical reactivity assessment. TSs were first considered, and activation free energies showed poor predictive performance in regression analyses toward log(kGSH) (r2 = 0.49, MAE = 0.69 log units). Intermediate enolate structures that follow the C–S bond-forming TSs were also considered and showed stronger predictive performance in regression analyses toward log(kGSH) (r2 = 0.76, MAE = 0.48 log units). Intermediate structures provide two key advantages over TSs: they are easier to compute and provide increased calculation speeds. Using linear combinations of purely reactant-derived chemical features, thus simplifying the calculations even further, resulted in noticeable improvements to our models with respective squared Pearson correlation coefficients and MAEs of 0.88 and 0.35 log units with DFT, and 0.89 and 0.37 log units with SQM. The models presented here are fast and easy-to-use methods for predicting log(kGSH).

Supporting Information

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All data supporting this study is provided in the Supporting Information. The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acsomega.2c03739.

  • Full computational methods, model statistics and for each compound, energies, free energies, Cartesian coordinates, and the number of imaginary frequencies (PDF)

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Most electronic Supporting Information files are available without a subscription to ACS Web Editions. Such files may be downloaded by article for research use (if there is a public use license linked to the relevant article, that license may permit other uses). Permission may be obtained from ACS for other uses through requests via the RightsLink permission system: http://pubs.acs.org/page/copyright/permissions.html.

Author Information

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  • Corresponding Author
  • Authors
    • Piers A. Townsend - Centre for Sustainable Chemical Technologies, Department of Chemistry, University of Bath, Claverton Down, Bath BA2 7AY, U.K.;  Department of Chemistry, University of Bath, Claverton Down, Bath BA2 7AY, U.K.;  Orcidhttps://orcid.org/0000-0002-7164-7958
    • Elliot H. E. Farrar - Department of Chemistry, University of Bath, Claverton Down, Bath BA2 7AY, U.K.;  Orcidhttps://orcid.org/0000-0003-3350-2907
  • Author Contributions

    The manuscript was written through contributions of all authors. All authors have given approval to the final version of the manuscript.

  • Funding

    This work was supported by the Engineering and Physical Sciences Research Council (EP/L016354/1 for PAT and EP/R513155/1 for EHEF) and the University of Bath.

  • Notes
    The authors declare no competing financial interest.

    All conformational searches were performed with the Maestro Suite using Schrodinger’s Macromodel (Ver. 12.3, https://www.schrodinger.com/products/macromodel). All DFT calculations were performed using Gaussian 16 (Rev A.03, https://gaussian.com/gaussian16/). Thermochemistry was obtained using the freely available GoodVibes package (Ver. 2.0.3, https://github.com/patonlab/GoodVibes/releases/tag/v2.0.3). The Cartesian coordinates of each structure have been included in the Supporting Information.

Acknowledgments

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Part of this work was completed using the Balena HPC service at the University of Bath (https://www.bath.ac.uk/corporate-information/balena-hpc-cluster/).

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    A detailed evaluation of the kinetics of the thiol-Michael reaction between hexanethiol and hexyl acrylate is described. It is shown that primary amines are more effective catalysts than either secondary or tertiary amines with, for example, quant. conversion being achieved within 500 s in the case of hexylamine with an apparent rate const. of 53.4 mol L-1 s-1 at a catalyst loading of 0.057 mol %. Certain tertiary phosphines, and esp. tri-n-propylphosphine and dimethylphenylphosphine, are shown to be even more effective species even at concns. 2 orders of magnitude lower than employed for hexylamine and performed in soln. with quant. conversions reached within ca. 100 s for both species and apparent rate consts. of 1810 and 431 mol L-1 s-1, resp. The nature of the thiol is also demonstrated to be an important consideration with mercaptoglycolate and mercaptopropionate esters being significantly more reactive than hexanethiol with reactivity mirroring the pKa of the thiols. Likewise, it is shown that the structure of the activated ene is also crucial with the degree of activation and ene-substitution pattern being important features in detg. reactivity. In terms of reaction with hexanethiol in the presence of hexylamine as catalyst, it is shown that propylmaleimide > di-Et fumarate > di-Et maleate > dimethylacrylamide > acrylonitrile > Et crotonate > Et cinnamate > Et methacrylate.
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  5. 5
    Nair, D. P.; Podgórski, M.; Chatani, S.; Gong, T.; Xi, W.; Fenoli, C. R.; Bowman, C. N. The Thiol-Michael Addition Click Reaction: A Powerful and Widely Used Tool in Materials Chemistry. Chem. Mater. 2014, 26, 724– 744,  DOI: 10.1021/cm402180t
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    The thiol-Michael addition click reaction. A powerful and widely used tool in materials chemistry
    Nair, Devatha P.; Podgorski, Maciej; Chatani, Shunsuke; Gong, Tao; Xi, Weixian; Fenoli, Christopher R.; Bowman, Christopher N.
    Chemistry of Materials (2014), 26 (1), 724-744CODEN: CMATEX; ISSN:0897-4756. (American Chemical Society)
    A review. The key attribute of the thiol-Michael addn. reaction that makes it a prized tool in materials science is its modular click nature, which allows for the implementation of this highly efficient, green reaction in applications that vary from small mol. synthesis to in situ polymer modifications in biol. systems to the surface functionalization of material coatings. Over the past few decades, interest in the thiol-Michael addn. reaction increased dramatically, as is evidenced by the no. of studies that were dedicated to elucidating different aspects of the reaction that range from an in-depth anal. aimed at understanding the mechanistic pathways of the reaction to synthetic studies that have examd. modifying mol. structures with the aim of yielding highly efficient thiol-Michael reaction monomers. This review examd. the reaction mechanisms, the substrates and catalysts used in the reaction, and the subsequent implementation of the thiol-Michael reaction in materials science over the years, with particular emphasis on the recent developments in the arena over the past decade.
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  6. 6
    Chauhan, P.; Mahajan, S.; Enders, D. Organocatalytic Carbon-Sulfur Bond-Forming Reactions. Chem. Rev. 2014, 114, 8807– 8864,  DOI: 10.1021/cr500235v
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    Organocatalytic Carbon-Sulfur Bond-Forming Reactions
    Chauhan, Pankaj; Mahajan, Suruchi; Enders, Dieter
    Chemical Reviews (Washington, DC, United States) (2014), 114 (18), 8807-8864CODEN: CHREAY; ISSN:0009-2665. (American Chemical Society)
    A review. Organocatalysis, which uses small org. mols. to catalyze org. transformations, is a relatively new and rapidly growing field within the domain of catalytic asym. and nonenantioselective synthesis. The use of small org. mols. as catalysts offers several fundamental advantages over the metal- and biocatalysts, as they can easily be obtained from readily available materials, they are insensitive to air and moisture, they are robust, less toxic, and they can also provide both enantiomers of bioactive compds. such as drugs and natural products, with high enantioselectivity. The organocatalysts not only promote simple C-C, C-N, C-O, C-P, and C-S bond formations, but they also facilitate more complex domino/cascade reactions via multicomponent one-pot protocols. The organocatalytic C-C and carbon-heteroatom bond formations have been reviewed from time to time. Although several examples of organocatalyzed C-S bond formations existed in the literature even before the renaissance of organocatalysis in the year 2000 and a significant growth has been witnessed in recent years, however, this topic has not been reviewed so far in a general way.
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  7. 7
    Duplan, V.; Hoshino, M.; Li, W.; Honda, T.; Fujita, M. In Situ Observation of Thiol Michael Addition to a Reversible Covalent Drug in a Crystalline Sponge. Angew. Chem. Int. Ed. 2016, 128, 5003– 5007,  DOI: 10.1002/ange.201509801
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    Sun, Y.; Liu, H.; Cheng, L.; Zhu, S.; Cai, C.; Yang, T.; Yang, L.; Ding, P. Thiol Michael Addition Reaction: A Facile Tool for Introducing Peptides into Polymer-Based Gene Delivery Systems. Polym. Int. 2018, 67, 25– 31,  DOI: 10.1002/pi.5490
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    Thiol Michael addition reaction: a facile tool for introducing peptides into polymer-based gene delivery systems
    Sun, Yanping; Liu, Hui; Cheng, Lin; Zhu, Shimeng; Cai, Cuifang; Yang, Tianzhi; Yang, Li; Ding, Pingtian
    Polymer International (2018), 67 (1), 25-31CODEN: PLYIEI; ISSN:0959-8103. (John Wiley & Sons Ltd.)
    Polymers, as widely used non-viral gene carriers, suffer from high cytotoxicity and relatively low transfection efficiency. Such crucial drawbacks of polymers could be solved by incorporating short and bioactive peptides. The resulting synthetic polymer-peptide conjugates can not only maintain their own special characteristics, but also gain novel characteristics far beyond those of their parent polymer and peptide components to overcome barriers to gene delivery. There are various chemoselective reactions applied in the synthesis of polymer-peptide conjugates, such as Heck, Sonogashira and Suzuki coupling, Diels-Alder cycloaddn., click chem., Staudinger ligation, reductive alkylation and oxime/hydrazone chem. Among them, thiol-ene click reactions, including thiol-ene radical and thiol Michael addn. reactions, are common methods for prepg. peptide-polymer conjugates. In this review, we focus on thiol Michael addn. reactions, elaborate on their mechanisms and highlight their applications in the synthesis of polymer-peptide conjugates for gene delivery. © 2017 Society of Chem. Industry.
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  9. 9
    Furuhama, A.; Aoki, Y.; Shiraishi, H. Consideration of Reactivity to Acute Fish Toxicity of α,β-Unsaturated Carbonyl Ketones and Aldehydes. SAR QSAR Environ. Res. 2012, 23, 169– 184,  DOI: 10.1080/1062936X.2011.636381
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    9
    Consideration of reactivity to acute fish toxicity of α,β-unsaturated carbonyl ketones and aldehydes
    Furuhama, A.; Aoki, Y.; Shiraishi, H.
    SAR and QSAR in Environmental Research (2012), 23 (1-2), 169-184CODEN: SQERED; ISSN:1026-776X. (Taylor & Francis Ltd.)
    To understand the key factor for fish toxicity of 11 α,β-unsatd. carbonyl aldehydes and ketones, we used quantum chem. calcns. to investigate their Michael reactions with methanethiol or glutathione. We used two reaction schemes, with and without an explicit water mol. (Scheme-1wat and Scheme-0wat, resp.), to account for the effects of a catalytic water mol. on the reaction pathway. We detd. the energies of the reactants, transition states (TS), and products, as well as the activation energies of the reactions. The acute fish toxicities of nine of the carbonyl compds. were evaluated to correlate with their hydrophobicities; no correlation was obsd. for acrolein and crotonaldehyde. The most toxic compd., acrolein, had the lowest activation energy. The activation energy of the reaction could be estd. with Scheme-1wat but not with Scheme-0wat. The complexity of the reaction pathways of the compds. was reflected in the difficulty of the TS structure searches when Scheme-1wat was used with the polarizable continuum model. The theor. estns. of activation energies of α,β-unsatd. carbonyl compds. with catalytic mols. or groups including hydrogen-bond networks may complement traditional tools for predicting the acute aquatic toxicities of compds. that cannot be easily obtained exptl.
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  10. 10
    Ebbrell, D. J.; Madden, J. C.; Cronin, M. T. D.; Schultz, T. W.; Enoch, S. J. Development of a Fragment-Based in Silico Profiler for Michael Addition Thiol Reactivity. Chem. Res. Toxicol. 2016, 29, 1073– 1081,  DOI: 10.1021/acs.chemrestox.6b00099
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    10
    Development of a Fragment-Based in Silico Profiler for Michael Addition Thiol Reactivity
    Ebbrell, David J.; Madden, Judith C.; Cronin, Mark T. D.; Schultz, Terry W.; Enoch, Steven J.
    Chemical Research in Toxicology (2016), 29 (6), 1073-1081CODEN: CRTOEC; ISSN:0893-228X. (American Chemical Society)
    The Adverse Outcome Pathway (AOP) paradigm details the existing knowledge that links the initial interaction between a chem. and a biol. system, termed the mol. initiating event (MIE), through a series of intermediate events, to an adverse effect. An important example of a well-defined MIE is the formation of a covalent bond between a biol. nucleophile and an electrophilic compd. This particular MIE has been assocd. with various toxicol. end points such as acute aquatic toxicity, skin sensitization, and respiratory sensitization. This study has investigated the calcd. parameters that are required to predict the rate of chem. bond formation (reactivity) of a dataset of Michael acceptors. Reactivity of these compds. toward glutathione was predicted using a combination of a calcd. activation energy value (Eact), calcd. using d. functional theory (DFT) calcn. at the B3YLP/6-31G+(d) level of theory, and solvent-accessible surface area values (SAS) at the α carbon. To further develop the method, a fragment-based algorithm was developed enabling the reactivity to be predicted for Michael acceptors without the need to perform the time-consuming DFT calcns. Results showed the developed fragment method was successful in predicting the reactivity of the Michael acceptors excluding two sets of chems.: volatile esters with an extended substituent at the β-carbon and chems. contg. a conjugated benzene ring as part of the polarizing group. Addnl. the study also demonstrated the ease with which the approach can be extended to other chem. classes by the calcn. of addnl. fragments and their assocd. Eact and SAS values. The resulting method is likely to be of use in regulatory toxicol. tools where an understanding of covalent bond formation as a potential MIE is important within the AOP paradigm.
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  11. 11
    Chatani, S.; Wang, C.; Podgórski, M.; Bowman, C. N. Triple Shape Memory Materials Incorporating Two Distinct Polymer Networks Formed by Selective Thiol-Michael Addition Reactions. Macromolecules 2014, 47, 4949– 4954,  DOI: 10.1021/ma501028a
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    11
    Triple Shape Memory Materials Incorporating Two Distinct Polymer Networks Formed by Selective Thiol-Michael Addition Reactions
    Chatani, Shunsuke; Wang, Chen; Podgorski, Maciej; Bowman, Christopher N.
    Macromolecules (Washington, DC, United States) (2014), 47 (15), 4949-4954CODEN: MAMOBX; ISSN:0024-9297. (American Chemical Society)
    We present a composite material composed of dual polymer networks uniquely formed from a single reaction type and catalyst but involving monomers with dramatically different reactivities. This powerful new approach to creating polymer networks produces two narrow glass transition, homogeneous networks sequentially from a single reaction but with all monomers present and uniformly mixed prior to any polymn. These materials exhibit a triple shape memory effect based on the dual polymer networks, which were both formed using the thiol-Michael addn. reaction. Two multifunctional thiol monomers (i.e., mercaptoacetate (MA) and mercaptopropionate (MP)) and two multifunctional vinyls (i.e., vinyl sulfone (V) and acrylate (A)) were polymd. in situ using a nucleophilic initiator. The MA-V polymer network (Tg = 55 °C) was generated first assocd. with the higher functional group reactivities followed by the formation of the MP-A network (Tg = 10 °C) which was confirmed by FT-IR, SEM, DMA, and a sep. prepd. composite polymer consisting of MA-V particles embedded in an MP-A matrix. The triple shape memory effect was characterized using DMA, and it was demonstrated that the shapes could be programmed either by a one-step (single temp.) or a two-step method (two different temps.). This material was able to hold its transitional shape for an extended time period (>1 h) at intermediate temp. (20 °C) between its two Tgs, mainly due to narrow transitions of two sep. networks. This new approach to obtain dual polymer networks with distinct transitions and characteristics is simple and robust, thus enabling applications in areas such as triple shape memory polymers, biomedical materials, and composites.
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  12. 12
    Hahn, M. E. Mechanistic Research in Aquatic Toxicology: Perspectives and Future Directions. Aquat. Toxicol. 2011, 105, 67– 71,  DOI: 10.1016/j.aquatox.2011.06.001
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    12
    Mechanistic research in aquatic toxicology: Perspectives and future directions
    Hahn, Mark E.
    Aquatic Toxicology (2011), 105 (Suppl.), 67-71CODEN: AQTODG; ISSN:0166-445X. (Elsevier B.V.)
    A review. On the 30th anniversary of the journal, I provide a perspective on some of the questions and opportunities for new understanding that will interest aquatic toxicologists during the next 30 years. I focus on mechanisms of toxicity involving transcription factors, signalling pathways, and gene networks involved in toxic and adaptive responses in aquatic animals. Prominent questions address the value of a toxicity pathways approach in aquatic systems, issues involving extrapolation among species, identification of susceptibility genes and useful biomarkers of adverse effect, new emerging contaminants, the importance of epigenetic mechanisms, effects of multiple stressors, evolutionary toxicol., and the relative roles of tech. and conceptual limitations to our understanding of chem. effects on aquatic systems.
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  13. 13
    Patlewicz, G.; Aptula, A. O.; Uriarte, E.; Roberts, D. W.; Kern, P. S.; Gerberick, G. F.; Kimber, I.; Dearman, R. J.; Ryan, C. A.; Basketter, D. A. An Evaluation of Selected Global (Q)SARs/Expert Systems for the Prediction of Skin Sensitisation Potential. SAR QSAR Environ. Res. 2007, 18, 515– 541,  DOI: 10.1080/10629360701427872
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    13
    An evaluation of selected global (Q)SARs/expert systems for the prediction of skin sensitization potential
    Patlewicz, G.; Aptula, A. O.; Uriarte, E.; Roberts, D. W.; Kern, P. S.; Gerberick, G. F.; Kimber, I.; Dearman, R. J.; Ryan, C. A.; Basketter, D. A.
    SAR and QSAR in Environmental Research (2007), 18 (5-6), 515-541CODEN: SQERED; ISSN:1062-936X. (Taylor & Francis Ltd.)
    Skin sensitization potential is an endpoint that needs to be assessed within the framework of existing and forthcoming legislation. At present, skin sensitization hazard is normally identified using in vivo test methods, the favored approach being the local lymph node assay (LLNA). This method can also provide a measure of relative skin sensitizing potency which is essential for assessing and managing human health risks. One potential alternative approach to skin sensitization hazard identification is the use of (Quant.) structure activity relationships ((Q)SARs) coupled with appropriate documentation and performance characteristics. This represents a major challenge. Current thinking is that (Q)SARs might best be employed as part of a battery of approaches that collectively provide information on skin sensitization hazard. A no. of (Q)SARs and expert systems have been developed and are described in the literature. Here we focus on three models (TOPKAT, Derek for Windows and TOPS-MODE), and evaluate their performance against a recently published dataset of 211 chems. The current strengths and limitations of one of these models is highlighted, together with modifications that could be made to improve its performance. Of the models/expert systems evaluated, none performed sufficiently well to act as a stand alone tool for hazard identification.
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  14. 14
    Townsend, P. A.; Grayson, M. N. Density Functional Theory in the Prediction of Mutagenicity: A Perspective. Chem. Res. Toxicol. 2021, 34, 179– 188,  DOI: 10.1021/acs.chemrestox.0c00113
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    14
    Density Functional Theory in the Prediction of Mutagenicity: A Perspective
    Townsend, Piers A.; Grayson, Matthew N.
    Chemical Research in Toxicology (2021), 34 (2), 179-188CODEN: CRTOEC; ISSN:0893-228X. (American Chemical Society)
    A review. As a field, computational toxicol. is concerned with using in silico models to predict and understand the origins of toxicity. It is fast, relatively inexpensive, and avoids the ethical conundrum of using animals in scientific experimentation. In this perspective, we discuss the importance of computational models in toxicol., with a specific focus on the different model types that can be used in predictive toxicol. approaches toward mutagenicity (SARs and QSARs). We then focus on how quantum chem. methods, such as d. functional theory (DFT), have previously been used in the prediction of mutagenicity. It is then discussed how DFT allows for the development of new chem. descriptors that focus on capturing the steric and energetic effects that influence toxicol. reactions. We hope to demonstrate the role that DFT plays in understanding the fundamental, intrinsic chem. of toxicol. reactions in predictive toxicol.
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  15. 15
    Jackson, P. A.; Widen, J. C.; Harki, D. A.; Brummond, K. M. Covalent Modifiers: A Chemical Perspective on the Reactivity of α,β-Unsaturated Carbonyls with Thiols via Hetero-Michael Addition Reactions. J. Med. Chem. 2017, 60, 839– 885,  DOI: 10.1021/acs.jmedchem.6b00788
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    15
    Covalent Modifiers: A Chemical Perspective on the Reactivity of α,β-Unsaturated Carbonyls with Thiols via Hetero-Michael Addition Reactions
    Jackson, Paul A.; Widen, John C.; Harki, Daniel A.; Brummond, Kay M.
    Journal of Medicinal Chemistry (2017), 60 (3), 839-885CODEN: JMCMAR; ISSN:0022-2623. (American Chemical Society)
    A review. Although Michael acceptors display a potent and broad spectrum of bioactivity, they have largely been ignored in drug discovery because of their presumed indiscriminate reactivity. As such, a dearth of information exists relevant to the thiol reactivity of natural products and their analogs possessing this moiety. In the midst of recently approved acrylamide-contg. drugs, it is clear that a good understanding of the hetero-Michael addn. reaction and the relative reactivities of biol. thiols with Michael acceptors under physiol. conditions is needed for the design and use of these compds. as biol. tools and potential therapeutics. This Perspective provides information that will contribute to this understanding, such as kinetics of thiol addn. reactions, bioactivities, as well as steric and electronic factors that influence the electrophilicity and reversibility of Michael acceptors. This Perspective is focused on α,β-unsatd. carbonyls given their preponderance in bioactive natural products.
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  16. 16
    Baillie, T. A. Targeted Covalent Inhibitors for Drug Design. Angew. Chem. Int. Ed. 2016, 55, 13408– 13421,  DOI: 10.1002/anie.201601091
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    16
    Targeted Covalent Inhibitors for Drug Design
    Baillie, Thomas A.
    Angewandte Chemie, International Edition (2016), 55 (43), 13408-13421CODEN: ACIEF5; ISSN:1433-7851. (Wiley-VCH Verlag GmbH & Co. KGaA)
    In contrast to the traditional mechanism of drug action that relies on the reversible, noncovalent interaction of a ligand with its biol. target, a targeted covalent inhibitor (TCI) is designed such that the initial, reversible assocn. is followed by the formation of a covalent bond between an electrophile on the ligand and a nucleophilic center in the protein. Although this approach offers a variety of potential benefits (high potency and extended duration of action), concerns over the possible toxicol. consequences of protein haptenization have hindered the development of the TCI concept. Recently, approaches to mitigate the risk of serious adverse reactions to this new class of agent have emerged, thus stimulating interest in the field and leading to authorization of the first cadre of TCIs to be marketed. The covalent inhibitor approach is rapidly gaining acceptance as a valuable tool in drug discovery, and is poised to make a major impact on the design of enzyme inhibitors and receptor modulators.
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  17. 17
    Schultz, T. W.; Carlson, R. E.; Cronin, M. T. D.; Hermens, J. L. M.; Johnson, R.; O’Brien, P. J.; Roberts, D. W.; Siraki, A.; Wallace, K. B.; Veith, G. D. A Conceptual Framework for Predicting the Toxicity of Reactive Chemicals: Modeling Soft Electrophilicity. SAR QSAR Environ. Res. 2006, 17, 413– 428,  DOI: 10.1080/10629360600884371
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    17
    A conceptual framework for predicting the toxicity of reactive chemicals: modeling soft electrophilicity
    Schultz, T. W.; Carlson, R. E.; Cronin, M. T. D.; Hermens, J. L. M.; Johnson, R.; O'Brien, P. J.; Roberts, D. W.; Siraki, A.; Wallace, K. B.; Veith, G. D.
    SAR and QSAR in Environmental Research (2006), 17 (4), 413-428CODEN: SQERED; ISSN:1062-936X. (Taylor & Francis Ltd.)
    Although the literature is replete with QSAR models developed for many toxic effects caused by reversible chem. interactions, the development of QSARs for the toxic effects of reactive chems. lacks a consistent approach. While limitations exit, an appropriate starting-point for modeling reactive toxicity is the applicability of the general rules of org. chem. reactions and the assocn. of these reactions to cellular targets of importance in toxicol. The identification of plausible "mol. initiating events" based on covalent reactions with nucleophiles in proteins and DNA provides the unifying concept for a framework for reactive toxicity. This paper outlines the proposed framework for reactive toxicity. Empirical measures of the chem. reactivity of xenobiotics with a model nucleophile (thiol) are used to simulate the relative rates at which a reactive chem. is likely to bind irreversibly to cellular targets. These measures of intrinsic reactivity serve as correlates to a variety of toxic effects; what's more they appear to be more appropriate endpoints for QSAR modeling than the toxicity endpoints themselves.
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  18. 18
    Böhme, A.; Thaens, D.; Paschke, A.; Schürmann, G. Kinetic Glutathione Chemoassay to Quantify Thiol Reactivity of Organic Electrophiless - Application to α,β-Unsaturated Ketones, Acrylates, and Propiolates. Chem. Res. Toxicol. 2009, 22, 742– 750,  DOI: 10.1021/tx800492x
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    18
    Kinetic Glutathione Chemoassay To Quantify Thiol Reactivity of Organic Electrophiles-Application to α,β-Unsaturated Ketones, Acrylates, and Propiolates
    Bohme, Alexander; Thaens, Diana; Paschke, Albrecht; Schuurmann, Gerrit
    Chemical Research in Toxicology (2009), 22 (4), 742-750CODEN: CRTOEC; ISSN:0893-228X. (American Chemical Society)
    Glutathione (GSH) is a soft nucleophile and, as such, can be used to sense the reactivity of electrophilic agents toward the thiol group and other electron-rich sites of mol. structures. A new kinetic GSH chemoassay is introduced that employs a photometric method to quantify GSH loss and enables an efficient detn. of second-order rate consts., kGSH, of the reaction between electrophilic substances and GSH. Comparison with an existing 2 h static assay shows that the new kinetic variant is superior with respect to the detectable range of electrophilic reactivity and to confounding factors such as addnl. GSH loss due to oxidn. Anal. of the chemoassay degrdn. kinetics provides insight into the characteristic reaction times and the contributions of GSH-electrophile Michael addn. and GSH oxidn. to the overall GSH loss. For 15 α,β-unsatd. ketones, nine acrylates, and two propiolates acting as Michael acceptors, the measured kGSH values span ca. 5 orders of magnitude. Moreover, log kGSH correlates with the compds.' toxicity toward the ciliates Tetrahymena pyriformis in terms of 48 h log EC50 (50% growth inhibition) values, yielding a squared correlation coeff. (R2) of 0.91 and a root-mean-square error of 0.30 log units. It shows that for these and related compds., aquatic toxicity is driven by electrophilic reactivity. The findings demonstrate that the kinetic GSH chemoassay can be used as an efficient tool to analyze, interpret, and predict correspondingly reactive toxicity in the context of qual. and quant. structure-activity relationship studies and as a nonanimal tool of integrated testing strategies for REACH to screen compds. for excess toxicity.
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    Schultz, T. W.; Yarbrough, J. W.; Johnson, E. L. Structure-Activity Relationships for Reactivity of Carbonyl-Containing Compounds with Glutathione. SAR QSAR Environ. Res. 2005, 16, 313– 322,  DOI: 10.1080/10659360500204152
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    19
    Structure-activity relationships for reactivity of carbonyl-containing compounds with glutathione
    Schultz, T. W.; Yarbrough, J. W.; Johnson, E. L.
    SAR and QSAR in Environmental Research (2005), 16 (4), 313-322CODEN: SQERED; ISSN:1062-936X. (Taylor & Francis Ltd.)
    For toxicol.-based structure-activity relationships to advance, will require a better understanding of mol. reactivity. A rapid and inexpensive spectrophotometric assay for detg. the reactive to glutathione (GSH) was developed and used to det. GSH reactivity (reactGSH) data for 21 aliph. derivs. of esters, ketones and aldehydes. From these data, a series of structure-activity relationships were evaluated. The structure feature assocd. with reactGSH was an acetylenic or olefinic moiety conjugated to a carbonyl group (i.e. polarized α,β-unsatn.). This structure conveys the capacity to undergo a covalent interaction with the thiol group of cysteine (i.e. Michael- addn.). Quant. reactGSH of the α,β-unsatd. carbonyl compds. is reliant upon the specific mol. structure with several tendencies obsd. Specifically, it was noted that for α,β-unsatd. carbonyl compds.: (1) the acetylenic-substituted derivs. were more reactive than the corresponding olefinic-substituted ones; (2) terminal vinyl-substituted derivs. was more reactive than the internal vinylene-substituted ones; (3) Me substitution on the vinyl carbon atoms diminishes reactivity and methyl-substitution on the carbon atom farthest from the carbonyl group causes a larger redn.; (4) derivs. with carbon-carbon double bond on the end of the mol. (i.e. vinyl ketone) were more reactive than one with the carbon-oxygen bond at the end of the mol. (i.e. aldehyde) and (5) the ester with an addnl. unsatd. vinyl groups were more reactive than the deriv. having an unsatd. Et group.
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  20. 20
    Böhme, A.; Laqua, A.; Schürmann, G. Chemoavailability of Organic Electrophiles: Impact of Hydrophobicity and Reactivity on Their Aquatic Excess Toxicity. Chem. Res. Toxicol. 2016, 29, 952– 962,  DOI: 10.1021/acs.chemrestox.5b00398
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    20
    Chemoavailability of Organic Electrophiles: Impact of Hydrophobicity and Reactivity on Their Aquatic Excess Toxicity
    Bohme Alexander; Laqua Anja; Schuurmann Gerrit; Laqua Anja; Schuurmann Gerrit
    Chemical research in toxicology (2016), 29 (6), 952-62 ISSN:.
    Organic electrophiles have been recognized as important components of the exposome that can be characterized as cumulative totality of exposure in the organism in response to environmental perturbation. For such compounds, chemical reactivity may contribute significantly to the toxicological profile through covalent attacks at nucleophilic sites of peptides such as glutathione (GSH), proteins, lipid components, and the DNA and RNA. Employing a Michael acceptor set of 58 α,β-unsaturated carbonyls with 15 ketones, 18 aldehydes, and 25 esters, the hydrophobicity and reactivity contributions to their toxicity enhancement Te over baseline narcosis with the ciliates Tetrahymena pyriformis is analyzed through a conceptual model, featuring toxicokinetic phase transfer steps and the reactive molecular initiating event (MIE) at endogenous target sites exposed to water-rich or water-poor compartments. To this end, hydrophobicity was quantified by the octanol/water partition coefficient, Kow, electrophilic reactivity through second-order rate constants of reaction with GSH in a kinetic chemoassay, kGSH, and Te as the ratio of narcosis-level vs experimental concentration yielding 50% growth inhibition of the ciliates within 48 h of exposure. The observed decrease of log Te with increasing log Kow can be traced back to a rate-determining impact of the toxicant transfer from the membrane to the intracellular cytosol. Moreover, the recently introduced concept of chemoavailability is shown to enable, from knowledge of log Kow and log kGSH alone, a screening-level discrimination between reactive and hydrophobic MIEs triggering predominantly alone or in parallel respective adverse outcome pathways (AOPs) including the diffusion-control limit of reactive MIE saturation. As such, chemoavailability may aid in evaluating prevalent MIEs expected for a given organic electrophile and in assessing its toxicological profile within AOP schemes addressing aquatic toxicity.
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  21. 21
    Schwöbel, J. A. H.; Madden, J. C.; Cronin, M. T. D. Examination of Michael Addition Reactivity towards Glutathione by Transition-State Calculations. SAR QSAR Environ. Res. 2010, 21, 693– 710,  DOI: 10.1080/1062936X.2010.528943
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    Examination of Michael addition reactivity towards glutathione by transition-state calculations
    Schwobel, J. A. H.; Madden, J. C.; Cronin, M. T. D.
    SAR and QSAR in Environmental Research (2010), 21 (7-8), 693-710CODEN: SQERED; ISSN:1062-936X. (Taylor & Francis Ltd.)
    Kinetic rate consts. (kGSH) for the reaction of compds. acting as Michael acceptors with glutathione (GSH) were modelled by quantum chem. transition-state calcns. at the B3LYP/6-31G** and B3LYP/TZVP level. The data set included α,β-unsatd. aldehydes, ketones and esters, with double bonds and triple bonds, linear and cyclic systems, both with and without substituents in the α-position. Predicted values for kGSH were found to be in good agreement with exptl. kGSH values. Factors affecting rate consts. have been elucidated, esp. solvent effects and the influence of steric hindrance. Solvent effects were examd. by adding explicit solvent mols. to the system and by using a polarizable continuum solvent model. Detailed anal. of transition-state energies shows that the reaction is reversible. The reactive enolic intermediate plays an important role in Michael addn. to GSH, while the subsequent keto-enol-tautomerism is not rate limiting.
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  22. 22
    Schwöbel, J. A. H.; Wondrousch, D.; Koleva, Y. K.; Madden, J. C.; Cronin, M. T. D.; Schürmann, G. Prediction of Michael-Type Acceptor Reactivity toward Glutathione. Chem. Res. Toxicol. 2010, 23, 1576– 1585,  DOI: 10.1021/tx100172x
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    Prediction of Michael-Type Acceptor Reactivity toward Glutathione
    Schwobel, Johannes A. H.; Wondrousch, Dominik; Koleva, Yana K.; Madden, Judith C.; Cronin, Mark T. D.; Schuurmann, Gerrit
    Chemical Research in Toxicology (2010), 23 (10), 1576-1585CODEN: CRTOEC; ISSN:0893-228X. (American Chemical Society)
    A model has been developed to predict the kinetic rate consts. (kGSH) of α,β-unsatd. Michael acceptor compds. for their reaction with glutathione (GSH). The model uses the local charge-limited electrophilicity index ωq at the β-carbon atom as a descriptor of reactivity, a descriptor for resonance stabilization of the transition state, and one for steric hindrance at the reaction sites involved. Overall, the Michael addn. model performs well (r2 = 0.91; rms = 0.34). It includes various classes of compds. with double and triple bonds, linear and cyclic systems, and compds. with and without substituents in the α-position. Comparison of exptl. and predicted rate consts. demonstrates even better performance of the model for individual classes of compds. (e.g., for aldehydes, r2 = 0.97 and rms = 0.15; for ketones, r2 = 0.95 and rms = 0.35). The model also allows for the prediction of the RC50 values from the Schultz chemoassay, the accuracy being close to the interlab. exptl. error. Furthermore, kGSH and assocd. RC50 values can be predicted in cases where exptl. measurements are not possible or restricted, for example, because of low soly. or high volatility. The model has the potential to provide information to assist in the assessment and categorization of toxicants and in the application of integrated testing strategies.
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    Mulliner, D.; Wondrousch, D.; Schürmann, G. Predicting Michael-Acceptor Reactivity and Toxicity through Quantum Chemical Transition-State Calculations. Org. Biomol. Chem. 2011, 9, 8400– 8412,  DOI: 10.1039/c1ob06065a
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    Predicting Michael-acceptor reactivity and toxicity through quantum chemical transition-state calculations
    Mulliner, Denis; Wondrousch, Dominik; Schueuermann, Gerrit
    Organic & Biomolecular Chemistry (2011), 9 (24), 8400-8412CODEN: OBCRAK; ISSN:1477-0520. (Royal Society of Chemistry)
    The electrophilic reactivity of Michael acceptors is an important determinant of their toxicity. For a set of 35 α,β-unsatd. aldehydes, ketones and esters with exptl. rate consts. of their reaction with glutathione (GSH), kGSH, quantum chem. transition-state calcns. of the corresponding Michael addn. of the model nucleophile methane thiol (CH3SH) have been performed at the B3LYP/6-31G** level, focusing on the 1,2-olefin addn. pathway without and with initial protonation. Inclusion of Boltzmann-weighting of conformational flexibility yields intrinsic reaction barriers ΔE‡ that for the case of initial protonation correctly reflect the structural variation of kGSH across all three compd. classes, except that they fail to account for a systematic (essentially incremental) decrease in reactivity upon α-substitution. By contrast, the redn. in kGSH through β-substitution is well captured by ΔE‡. Empirical correction for the α-substitution effect yields a high squared correlation coeff. (r2 = 0.96) for the quantum chem. prediction of log kGSH, thus enabling an in silico screening of the toxicity-relevant electrophilicity of α,β-unsatd. carbonyls. The latter is demonstrated through application of the calcn. scheme for a larger set of 46 Michael-acceptor aldehydes, ketones and esters with exptl. values for their toxicity toward the ciliates Tetrahymena pyriformis in terms of 50% growth inhibition values after 48 h exposure (EC50). The developed approach may add in the predictive hazard evaluation of α,β-unsatd. carbonyls such as for the European REACH (Registration, Evaluation, Authorization and Restriction of Chems.) Directive, enabling in particular an early identification of toxicity-relevant Michael-acceptor reactivity.
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    Townsend, P. A.; Grayson, M. N. Reactivity Prediction in Aza-Michael Additions without Transition State Calculations: The Ames Test for Mutagenicity. Chem. Commun. 2020, 56, 13661– 13664,  DOI: 10.1039/d0cc05681b
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    Reactivity prediction in aza-Michael additions without transition state calculations: the Ames test for mutagenicity
    Townsend, Piers A.; Grayson, Matthew N.
    Chemical Communications (Cambridge, United Kingdom) (2020), 56 (88), 13661-13664CODEN: CHCOFS; ISSN:1359-7345. (Royal Society of Chemistry)
    Animal testing remains a contentious ethical issue in predictive toxicol. Thus, a fast, versatile, low-cost quantum chem. model is presented for predicting the risk of Ames mutagenicity in a series of 1,4 Michael acceptor type compds. This framework eliminates the need for transition state calcns., and uses an intermediate structure to probe the reactivity of aza-Michael acceptors. This model can be used in a variety of settings e.g., the design of targeted covalent inhibitors and polyketide biosyntheses.
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    Enoch, S. J.; Roberts, D. W. Predicting Skin Sensitization Potency for Michael Acceptors in the LLNA Using Quantum Mechanics Calculations. Chem. Res. Toxicol. 2013, 26, 767– 774,  DOI: 10.1021/tx4000655
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    Predicting Skin Sensitization Potency for Michael Acceptors in the LLNA Using Quantum Mechanics Calculations
    Enoch, S. J.; Roberts, D. W.
    Chemical Research in Toxicology (2013), 26 (5), 767-774CODEN: CRTOEC; ISSN:0893-228X. (American Chemical Society)
    This study outlines the development of a series of quant. mechanistic models enabling skin sensitization potency in the LLNA to be predicted for direct acting Michael acceptors. These models utilized several computational descriptors based on knowledge of the Michael addn. reaction mechanism. The key descriptor was calcd. using d. functional theory and modeled the stability of the reaction intermediate. A second descriptor relating to the available surface area at the site of the reaction was also found to be important. Several poorly predicted compds. were identified, and in all cases, these could be rationalized mechanistically. The anal. of these compds. allowed a well-defined mechanistically driven applicability domain to be developed. The study showed that in silico quant. mechanistic models, with a well-defined applicability domain, can be used to predict skin sensitization potency in the LLNA. The approach presented has the potential to be of use as part of a wt. of evidence approach for predicting skin sensitization without the use of animals in risk assessment.
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  26. 26
    Ebbrell, D. J.; Madden, J. C.; Cronin, M. T. D.; Schultz, T. W.; Enoch, S. J. Validation of a Fragment-Based Profiler for Thiol Reactivity for the Prediction of Toxicity: Skin Sensitization and Tetrahymena Pyriformis. Chem. Res. Toxicol. 2017, 30, 604– 613,  DOI: 10.1021/acs.chemrestox.6b00361
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    Validation of a Fragment-Based Profiler for Thiol Reactivity for the Prediction of Toxicity: Skin Sensitization and Tetrahymena pyriformis
    Ebbrell, David J.; Madden, Judith C.; Cronin, Mark T. D.; Schultz, Terry W.; Enoch, Steven J.
    Chemical Research in Toxicology (2017), 30 (2), 604-613CODEN: CRTOEC; ISSN:0893-228X. (American Chemical Society)
    This study outlines the use of a recently developed fragment-based thiol reactivity profiler for Michael acceptors to predict toxicity towards Tetrahymena pyriformis and skin sensitization potency as detd. in the Local Lymph Node Assay (LLNA). The results showed that the calcd. reactivity parameter from the profiler, -log RC50(calc), was capable of predicting toxicity for both endpoints with excellent statistics. However, the study highlighted the importance of a well-defined applicability domain for each endpoint. In terms of Tetrahymena pyriformis this domain was defined in terms of how fast or slowly a given Michael acceptor reacts with thiol leading to two sep. quant. structure-activity models. The first, for fast reacting chems. required only 1Log RC50(calc) as a descriptor, while the second required the addn. of a descriptor for hydrophobicity. Modeling of the LLNA required only a single descriptor, -log RC50(calc), enabling potency to be predicted. The applicability domain excluded chems. capable of undergoing polymn. and those that were predicted to be volatile. The modeling results for both endpoints, using the 1log RC50(calc) value from the profiler, were in keeping with previously published studies that have utilized exptl. detd. measurements of reactivity. This results demonstrate the output from the fragment-based thiol reactivity profiler can be used to develop quant. structure-activity relationship models where reactivity towards thiol is a driver of toxicity.
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    Ayala, P.; Schlegel, H. A Combined Method for Determining Reaction Paths, Minima and Transition State Geometries. J. Chem. Phys. 1997, 107, 375– 384,  DOI: 10.1063/1.474398
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    A combined method for determining reaction paths, minima, and transition state geometries
    Ayala, Philippe Y.; Schlegel, H. Bernhard
    Journal of Chemical Physics (1997), 107 (2), 375-384CODEN: JCPSA6; ISSN:0021-9606. (American Institute of Physics)
    Mapping out a reaction mechanism involves optimizing the reactants and products, finding the transition state and following the reaction path connecting them. Transition states can be difficult to locate and reaction paths can be expensive to follow. We describe an efficient algorithm for detg. the transition state, min. and reaction path in a single procedure. Starting with an approx. path represented by N points, the path is iteratively relaxed until one of the N points reached the transition state, the end points optimize to min. and the remaining points converged to a second order approxn. of the steepest descent path. The method appears to be more reliable than conventional transition state optimization algorithms, and requires only energies and gradients, but not second deriv. calcns. The procedure is illustrated by application to a no. of model reactions. In most cases, the reaction mechanism can be described well using 5 to 7 points to represent the transition state, the min. and the path. The computational cost of relaxing the path is less than or comparable to the cost of std. techniques for finding the transition state and the min., detg. the transition vector and following the reaction path on both sides of the transition state.
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    Frisch, M. J.; Trucks, G. W.; Schlegel, H. B.; Scuseria, G. E.; Robb, M. A.; Cheeseman, J. R.; Scalmani, G.; Barone, V.; Mennucci, B.; Petersson, G. A.; Nakatsuji, H.; Caricato, M.; Li, X.; Hratchian, H. P.; Izmaylov, A. F.; Bloino, J.; Zheng, J.; Sonnenberg, J. L.; Hada, M.; Ehara, M.; Toyota, K.; Fukuda, R.; Hasegawa, J.; Ishida, M.; Nakajima, T.; Honda, Y.; Kitao, O.; Nakai, H.; Vreven, T.; Montgomery, J. A.; Peralta, J. E.; Ogliaro, F.; Bearpark, M.; Heyd, J. J.; Brothers, E.; Kudin, K. N.; Staroverov, V. N.; Kobayashi, R.; Normand, J.; Raghavachari, K.; Rendell, J. C. A.; Burant, S.; Iyengar, S.; Tomasi, J.; Cossi, M.; Rega, N.; Millam, J. M.; Klene, M.; Knox, J. E.; Cross, J. B.; Bakken, V.; Adamo, C.; Jaramillo, J.; Gomperts, R.; Stratmann, R. E.; Yazyev, O.; Austin, A. J.; Cammi, R.; Pomelli, C.; Ochterski, J. W.; Martin, R. L.; Morokuma, K.; Zakrzewski, V. G.; Voth, G. A.; Salvador, P.; Dannenberg, J. J.; Dapprich, S.; Daniels, A. D.; Farkas, O.; Foresman, J. B.; Ortiz, J. V.; Cioslowski, J.; Fox, D. J. Gaussian 16, Revision A.03; Gaussian, Inc.: Wallingford, CT, 2016.
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    Lam, Y. H.; Grayson, M. N.; Holland, M. C.; Simon, A.; Houk, K. N. Theory and Modeling of Asymmetric Catalytic Reactions. Acc. Chem. Res. 2016, 49, 750– 762,  DOI: 10.1021/acs.accounts.6b00006
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    Theory and Modeling of Asymmetric Catalytic Reactions
    Lam, Yu-hong; Grayson, Matthew N.; Holland, Mareike C.; Simon, Adam; Houk, K. N.
    Accounts of Chemical Research (2016), 49 (4), 750-762CODEN: ACHRE4; ISSN:0001-4842. (American Chemical Society)
    Modern d. functional theory and powerful contemporary computers have made it possible to explore complex reactions of value in org. synthesis. We describe recent explorations of mechanisms and origins of stereoselectivities with d. functional theory calcns. The specific functionals and basis sets that are routinely used in computational studies of stereoselectivities of org. and organometallic reactions in our group are described, followed by our recent studies that uncovered the origins of stereocontrol in reactions catalyzed by (1) vicinal diamines, including cinchona alkaloid-derived primary amines, (2) vicinal amidophosphines, and (3) organo-transition-metal complexes. Two common cyclic models account for the stereoselectivity of aldol reactions of metal enolates (Zimmerman-Traxler) or those catalyzed by the organocatalyst proline (Houk-List). Three other models were derived from computational studies described in this Account.Cinchona alkaloid-derived primary amines and other vicinal diamines are venerable asym. organocatalysts. For α-fluorinations and a variety of aldol reactions, vicinal diamines form enamines at one terminal amine and activate electrophilically with NH+ or NF+ at the other. We found that the stereocontrolling transition states are cyclic and that their conformational preferences are responsible for the obsd. stereoselectivity. In fluorinations, the chair seven-membered cyclic transition states is highly favored, just as the Zimmerman-Traxler chair six-membered aldol transition state controls stereoselectivity. In aldol reactions with vicinal diamine catalysts, the crown transition states are favored, both in the prototype and in an exptl. example, shown in the graphic. We found that low-energy conformations of cyclic transition states occur and control stereoselectivities in these reactions. Another class of bifunctional organocatalysts, the vicinal amidophosphines, catalyzes the (3 + 2) annulation reaction of allenes with activated olefins. Stereocontrol here is due to an intermol. hydrogen bond that activates the electrophilic partner in this reaction. We have also studied complex organometallic catalysts. Krische's ruthenium-catalyzed asym. hydrohydroxyalkylation of butadiene involves two chiral ligands at Ru, a chiral diphosphine and a chiral phosphate. The size of this combination strains the limits of modern computations with over 160 atoms, multiple significant steps, and a variety of ligand coordinations and conformations possible. We found that carbon-carbon bond formation occurs via a chair Zimmerman-Traxler-type transition structure and that a formyl CH···O hydrogen bond from aldehyde CH to phosphate oxygen, as well as steric interactions of the two chiral ligands, control the stereoselectivity.
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    Fordham, J. M.; Grayson, M. N.; Aggarwal, V. K. Vinylidene Homologation of Boronic Esters and Its Application to the Synthesis of the Proposed Structure of Machillene. Angew. Chem. Int. Ed. 2019, 131, 15412– 15416,  DOI: 10.1002/ange.201907617
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    Falcone, B. N.; Grayson, M. N.; Rodriguez, J. B. Mechanistic Insights into a Chiral Phosphoric Acid-Catalyzed Asymmetric Pinacol Rearrangement. J. Org. Chem. 2018, 83, 14683– 14687,  DOI: 10.1021/acs.joc.8b02812
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    Mechanistic Insights into a Chiral Phosphoric Acid-Catalyzed Asymmetric Pinacol Rearrangement
    Falcone, Bruno N.; Grayson, Matthew N.; Rodriguez, Juan B.
    Journal of Organic Chemistry (2018), 83 (23), 14683-14687CODEN: JOCEAH; ISSN:0022-3263. (American Chemical Society)
    The first catalytic enantioselective pinacol rearrangement was reported by Antilla and co-workers in 2010. The reaction was catalyzed by a chiral phosphoric acid and resulted in high levels of enantioselectivity (up to 96% ee). The present study uses d. functional theory to investigate the mechanism and origins of stereoselectivity of this important reaction and to explain the difference in selectivity between different catalysts. An OH···O hydrogen bond between the intermediate indolyl alc. and the phosphate group from the catalyst together with a CH···O hydrogen bond between the indole and the phosphate group were obsd. in the preferred activation mode for the stereodetermining [1,2]-aryl shift. A stronger CH···O interaction in the major transition state was found to contribute to the high levels of enantioselectivity. A more bulky catalyst (TRIP) was found to impede the formation of the key CH···O interaction, leading to lower levels of enantioselectivity.
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    Townsend, P. A.; Grayson, M. N. Density Functional Theory Transition-State Modeling for the Prediction of Ames Mutagenicity in 1,4 Michael Acceptors. J. Chem. Inf. Model. 2019, 59, 5099– 5103,  DOI: 10.1021/acs.jcim.9b00966
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    Density Functional Theory Transition-State Modeling for the Prediction of Ames Mutagenicity in 1,4 Michael Acceptors
    Townsend, Piers A.; Grayson, Matthew N.
    Journal of Chemical Information and Modeling (2019), 59 (12), 5099-5103CODEN: JCISD8; ISSN:1549-9596. (American Chemical Society)
    Assessing the safety of new chems., without introducing the need for animal testing, is a task of great importance. The Ames test, a widely used bioassay to assess mutagenicity, can be an expensive, wasteful process with animal-derived reagents. Existing in silico methods for the prediction of Ames test results are traditionally based on chem. category formation and can lead to false pos. predictions. Category formation also neglects the intrinsic chem. assocd. with DNA reactivity. Activation energies and HOMO/LUMO energies for thirty 1,4 Michael acceptors were calcd. using a model nucleobase and were further used to predict the Ames test result of these compds. The proposed model builds upon existing work and examines the fundamental toxicant-target interactions using d. functional theory transition-state modeling. The results show that Michael acceptors with activation energies <20.7 kcal/mol and LUMO energies < -1.85 eV are likely to act as direct mutagens upon exposure to DNA.
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    Pedregosa, F.; Varoquaux, G.; Gramfort, A.; Michel, V.; Thirion, B.; Grisel, O.; Blondel, M.; Prettenhofer, P.; Weiss, R.; Dubourg, V.; Vanderplas, J.; Passos, A.; Cournapeau, D.; Brucher, M.; Perrot, M.; Duchesnay, É. Scikit-Learn: Machine Learning in Python. J. Mach. Learn. Res. 2011, 12, 2825– 2830,  DOI: 10.5555/1953048.2078195
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    Pasha, F. A.; Srivastava, H. K.; Singh, P. P. Comparative QSAR Study of Phenol Derivatives with the Help of Density Functional Theory. Bioorg. Med. Chem. 2005, 13, 6823– 6829,  DOI: 10.1016/j.bmc.2005.07.064
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    Comparative QSAR study of phenol derivatives with the help of density functional theory
    Pasha, F. A.; Srivastava, H. K.; Singh, P. P.
    Bioorganic & Medicinal Chemistry (2005), 13 (24), 6823-6829CODEN: BMECEP; ISSN:0968-0896. (Elsevier B.V.)
    Quantum chem. reactivity descriptors based QSAR study of 50 phenol derivs. is presented in this paper. Four different methods have been employed to certify the reliability of QSAR study. The mol. wt., hardness, chem. potential, total energy, and electrophilicity index provide valuable information and have a significant role in the assessment of the toxicity of phenols. The first model has been drawn up with the help of AM1 calcns. and in this model the correlation coeff. r2 is 0.88 and the cross-validation coeff. r2cv is 0.78. Second and third models have been designed with the PM3 and PM5 calcns., resp. The values of correlation coeff. r2 and cross-validation coeff. r2cv in the second case are 0.85 and .070, while in the third case they are 0.85 and 0.71. Finally, the DFT calcns. have been made for the same series of compds. by using a B88-PW91 GGA energy functional with the DZVP basis set. The DFT models have a higher predictive power than AM1, PM3, and PM5 methods, and the reliability of this model is clear from its correlation coeff. r2 0.91 and cross-validation coeff. r2cv 0.88. This study is also helpful in detg. the effect of any particular phenol deriv. of this series over Tetrahymena pyriformis.
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  • Abstract

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    Figure 1

    Figure 1. Generalized reaction mechanism between glutathione and a 1,4 Michael acceptor. “Glut” represents a glutathione residue minus its sulfur atom.

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    Figure 2

    Figure 2. GSH-MA reaction pathway showing the structure of the reactants, intermediate, TS, and product.

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    Figure 3

    Figure 3. Visualizing log(kGSH) for the 23 compounds included in this study. Red indicates a higher reactivity with glutathione, while green indicates lower reactivity. Blue compounds indicate that minor truncation was performed (see the SI for full details).

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    Figure 4

    Figure 4. Linear regression of log(kGSH) on the activation energy derived from transition state structures (M06-2X/def2-TZVPP-IEFPCM (water)) was performed. Predicted log(kGSH) is plotted against measured log(kGSH).

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    Figure 5

    Figure 5. Linear regression of log(kGSH) on the intermediate energy differences (M06-2X/def2-TZVPP-IEFPCM (water)). Predicted log(kGSH) is plotted against measured log(kGSH).

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    Figure 6

    Figure 6. Mulliken and APT atomic charges were calculated for highlighted atoms only.

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    Figure 7

    Figure 7. Linear regression of log(kGSH) on key atomic charges of the MA (M06-2X/def2-TZVPP-IEFPCM (water)). Predicted log(kGSH) is plotted against measured log(kGSH).

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    Figure 8

    Figure 8. Linear regression of log(kGSH) on key atomic charges of the MA (AM1). Predicted log(kGSH) is plotted against measured log(kGSH).

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      Chan, Justin W.; Hoyle, Charles E.; Lowe, Andrew B.; Bowman, Mark
      Macromolecules (Washington, DC, United States) (2010), 43 (15), 6381-6388CODEN: MAMOBX; ISSN:0024-9297. (American Chemical Society)
      A detailed evaluation of the kinetics of the thiol-Michael reaction between hexanethiol and hexyl acrylate is described. It is shown that primary amines are more effective catalysts than either secondary or tertiary amines with, for example, quant. conversion being achieved within 500 s in the case of hexylamine with an apparent rate const. of 53.4 mol L-1 s-1 at a catalyst loading of 0.057 mol %. Certain tertiary phosphines, and esp. tri-n-propylphosphine and dimethylphenylphosphine, are shown to be even more effective species even at concns. 2 orders of magnitude lower than employed for hexylamine and performed in soln. with quant. conversions reached within ca. 100 s for both species and apparent rate consts. of 1810 and 431 mol L-1 s-1, resp. The nature of the thiol is also demonstrated to be an important consideration with mercaptoglycolate and mercaptopropionate esters being significantly more reactive than hexanethiol with reactivity mirroring the pKa of the thiols. Likewise, it is shown that the structure of the activated ene is also crucial with the degree of activation and ene-substitution pattern being important features in detg. reactivity. In terms of reaction with hexanethiol in the presence of hexylamine as catalyst, it is shown that propylmaleimide > di-Et fumarate > di-Et maleate > dimethylacrylamide > acrylonitrile > Et crotonate > Et cinnamate > Et methacrylate.
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    5. 5
      Nair, D. P.; Podgórski, M.; Chatani, S.; Gong, T.; Xi, W.; Fenoli, C. R.; Bowman, C. N. The Thiol-Michael Addition Click Reaction: A Powerful and Widely Used Tool in Materials Chemistry. Chem. Mater. 2014, 26, 724– 744,  DOI: 10.1021/cm402180t
      5
      The thiol-Michael addition click reaction. A powerful and widely used tool in materials chemistry
      Nair, Devatha P.; Podgorski, Maciej; Chatani, Shunsuke; Gong, Tao; Xi, Weixian; Fenoli, Christopher R.; Bowman, Christopher N.
      Chemistry of Materials (2014), 26 (1), 724-744CODEN: CMATEX; ISSN:0897-4756. (American Chemical Society)
      A review. The key attribute of the thiol-Michael addn. reaction that makes it a prized tool in materials science is its modular click nature, which allows for the implementation of this highly efficient, green reaction in applications that vary from small mol. synthesis to in situ polymer modifications in biol. systems to the surface functionalization of material coatings. Over the past few decades, interest in the thiol-Michael addn. reaction increased dramatically, as is evidenced by the no. of studies that were dedicated to elucidating different aspects of the reaction that range from an in-depth anal. aimed at understanding the mechanistic pathways of the reaction to synthetic studies that have examd. modifying mol. structures with the aim of yielding highly efficient thiol-Michael reaction monomers. This review examd. the reaction mechanisms, the substrates and catalysts used in the reaction, and the subsequent implementation of the thiol-Michael reaction in materials science over the years, with particular emphasis on the recent developments in the arena over the past decade.
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    6. 6
      Chauhan, P.; Mahajan, S.; Enders, D. Organocatalytic Carbon-Sulfur Bond-Forming Reactions. Chem. Rev. 2014, 114, 8807– 8864,  DOI: 10.1021/cr500235v
      6
      Organocatalytic Carbon-Sulfur Bond-Forming Reactions
      Chauhan, Pankaj; Mahajan, Suruchi; Enders, Dieter
      Chemical Reviews (Washington, DC, United States) (2014), 114 (18), 8807-8864CODEN: CHREAY; ISSN:0009-2665. (American Chemical Society)
      A review. Organocatalysis, which uses small org. mols. to catalyze org. transformations, is a relatively new and rapidly growing field within the domain of catalytic asym. and nonenantioselective synthesis. The use of small org. mols. as catalysts offers several fundamental advantages over the metal- and biocatalysts, as they can easily be obtained from readily available materials, they are insensitive to air and moisture, they are robust, less toxic, and they can also provide both enantiomers of bioactive compds. such as drugs and natural products, with high enantioselectivity. The organocatalysts not only promote simple C-C, C-N, C-O, C-P, and C-S bond formations, but they also facilitate more complex domino/cascade reactions via multicomponent one-pot protocols. The organocatalytic C-C and carbon-heteroatom bond formations have been reviewed from time to time. Although several examples of organocatalyzed C-S bond formations existed in the literature even before the renaissance of organocatalysis in the year 2000 and a significant growth has been witnessed in recent years, however, this topic has not been reviewed so far in a general way.
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    7. 7
      Duplan, V.; Hoshino, M.; Li, W.; Honda, T.; Fujita, M. In Situ Observation of Thiol Michael Addition to a Reversible Covalent Drug in a Crystalline Sponge. Angew. Chem. Int. Ed. 2016, 128, 5003– 5007,  DOI: 10.1002/ange.201509801
      There is no corresponding record for this reference.
    8. 8
      Sun, Y.; Liu, H.; Cheng, L.; Zhu, S.; Cai, C.; Yang, T.; Yang, L.; Ding, P. Thiol Michael Addition Reaction: A Facile Tool for Introducing Peptides into Polymer-Based Gene Delivery Systems. Polym. Int. 2018, 67, 25– 31,  DOI: 10.1002/pi.5490
      8
      Thiol Michael addition reaction: a facile tool for introducing peptides into polymer-based gene delivery systems
      Sun, Yanping; Liu, Hui; Cheng, Lin; Zhu, Shimeng; Cai, Cuifang; Yang, Tianzhi; Yang, Li; Ding, Pingtian
      Polymer International (2018), 67 (1), 25-31CODEN: PLYIEI; ISSN:0959-8103. (John Wiley & Sons Ltd.)
      Polymers, as widely used non-viral gene carriers, suffer from high cytotoxicity and relatively low transfection efficiency. Such crucial drawbacks of polymers could be solved by incorporating short and bioactive peptides. The resulting synthetic polymer-peptide conjugates can not only maintain their own special characteristics, but also gain novel characteristics far beyond those of their parent polymer and peptide components to overcome barriers to gene delivery. There are various chemoselective reactions applied in the synthesis of polymer-peptide conjugates, such as Heck, Sonogashira and Suzuki coupling, Diels-Alder cycloaddn., click chem., Staudinger ligation, reductive alkylation and oxime/hydrazone chem. Among them, thiol-ene click reactions, including thiol-ene radical and thiol Michael addn. reactions, are common methods for prepg. peptide-polymer conjugates. In this review, we focus on thiol Michael addn. reactions, elaborate on their mechanisms and highlight their applications in the synthesis of polymer-peptide conjugates for gene delivery. © 2017 Society of Chem. Industry.
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    9. 9
      Furuhama, A.; Aoki, Y.; Shiraishi, H. Consideration of Reactivity to Acute Fish Toxicity of α,β-Unsaturated Carbonyl Ketones and Aldehydes. SAR QSAR Environ. Res. 2012, 23, 169– 184,  DOI: 10.1080/1062936X.2011.636381
      9
      Consideration of reactivity to acute fish toxicity of α,β-unsaturated carbonyl ketones and aldehydes
      Furuhama, A.; Aoki, Y.; Shiraishi, H.
      SAR and QSAR in Environmental Research (2012), 23 (1-2), 169-184CODEN: SQERED; ISSN:1026-776X. (Taylor & Francis Ltd.)
      To understand the key factor for fish toxicity of 11 α,β-unsatd. carbonyl aldehydes and ketones, we used quantum chem. calcns. to investigate their Michael reactions with methanethiol or glutathione. We used two reaction schemes, with and without an explicit water mol. (Scheme-1wat and Scheme-0wat, resp.), to account for the effects of a catalytic water mol. on the reaction pathway. We detd. the energies of the reactants, transition states (TS), and products, as well as the activation energies of the reactions. The acute fish toxicities of nine of the carbonyl compds. were evaluated to correlate with their hydrophobicities; no correlation was obsd. for acrolein and crotonaldehyde. The most toxic compd., acrolein, had the lowest activation energy. The activation energy of the reaction could be estd. with Scheme-1wat but not with Scheme-0wat. The complexity of the reaction pathways of the compds. was reflected in the difficulty of the TS structure searches when Scheme-1wat was used with the polarizable continuum model. The theor. estns. of activation energies of α,β-unsatd. carbonyl compds. with catalytic mols. or groups including hydrogen-bond networks may complement traditional tools for predicting the acute aquatic toxicities of compds. that cannot be easily obtained exptl.
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    10. 10
      Ebbrell, D. J.; Madden, J. C.; Cronin, M. T. D.; Schultz, T. W.; Enoch, S. J. Development of a Fragment-Based in Silico Profiler for Michael Addition Thiol Reactivity. Chem. Res. Toxicol. 2016, 29, 1073– 1081,  DOI: 10.1021/acs.chemrestox.6b00099
      10
      Development of a Fragment-Based in Silico Profiler for Michael Addition Thiol Reactivity
      Ebbrell, David J.; Madden, Judith C.; Cronin, Mark T. D.; Schultz, Terry W.; Enoch, Steven J.
      Chemical Research in Toxicology (2016), 29 (6), 1073-1081CODEN: CRTOEC; ISSN:0893-228X. (American Chemical Society)
      The Adverse Outcome Pathway (AOP) paradigm details the existing knowledge that links the initial interaction between a chem. and a biol. system, termed the mol. initiating event (MIE), through a series of intermediate events, to an adverse effect. An important example of a well-defined MIE is the formation of a covalent bond between a biol. nucleophile and an electrophilic compd. This particular MIE has been assocd. with various toxicol. end points such as acute aquatic toxicity, skin sensitization, and respiratory sensitization. This study has investigated the calcd. parameters that are required to predict the rate of chem. bond formation (reactivity) of a dataset of Michael acceptors. Reactivity of these compds. toward glutathione was predicted using a combination of a calcd. activation energy value (Eact), calcd. using d. functional theory (DFT) calcn. at the B3YLP/6-31G+(d) level of theory, and solvent-accessible surface area values (SAS) at the α carbon. To further develop the method, a fragment-based algorithm was developed enabling the reactivity to be predicted for Michael acceptors without the need to perform the time-consuming DFT calcns. Results showed the developed fragment method was successful in predicting the reactivity of the Michael acceptors excluding two sets of chems.: volatile esters with an extended substituent at the β-carbon and chems. contg. a conjugated benzene ring as part of the polarizing group. Addnl. the study also demonstrated the ease with which the approach can be extended to other chem. classes by the calcn. of addnl. fragments and their assocd. Eact and SAS values. The resulting method is likely to be of use in regulatory toxicol. tools where an understanding of covalent bond formation as a potential MIE is important within the AOP paradigm.
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    11. 11
      Chatani, S.; Wang, C.; Podgórski, M.; Bowman, C. N. Triple Shape Memory Materials Incorporating Two Distinct Polymer Networks Formed by Selective Thiol-Michael Addition Reactions. Macromolecules 2014, 47, 4949– 4954,  DOI: 10.1021/ma501028a
      11
      Triple Shape Memory Materials Incorporating Two Distinct Polymer Networks Formed by Selective Thiol-Michael Addition Reactions
      Chatani, Shunsuke; Wang, Chen; Podgorski, Maciej; Bowman, Christopher N.
      Macromolecules (Washington, DC, United States) (2014), 47 (15), 4949-4954CODEN: MAMOBX; ISSN:0024-9297. (American Chemical Society)
      We present a composite material composed of dual polymer networks uniquely formed from a single reaction type and catalyst but involving monomers with dramatically different reactivities. This powerful new approach to creating polymer networks produces two narrow glass transition, homogeneous networks sequentially from a single reaction but with all monomers present and uniformly mixed prior to any polymn. These materials exhibit a triple shape memory effect based on the dual polymer networks, which were both formed using the thiol-Michael addn. reaction. Two multifunctional thiol monomers (i.e., mercaptoacetate (MA) and mercaptopropionate (MP)) and two multifunctional vinyls (i.e., vinyl sulfone (V) and acrylate (A)) were polymd. in situ using a nucleophilic initiator. The MA-V polymer network (Tg = 55 °C) was generated first assocd. with the higher functional group reactivities followed by the formation of the MP-A network (Tg = 10 °C) which was confirmed by FT-IR, SEM, DMA, and a sep. prepd. composite polymer consisting of MA-V particles embedded in an MP-A matrix. The triple shape memory effect was characterized using DMA, and it was demonstrated that the shapes could be programmed either by a one-step (single temp.) or a two-step method (two different temps.). This material was able to hold its transitional shape for an extended time period (>1 h) at intermediate temp. (20 °C) between its two Tgs, mainly due to narrow transitions of two sep. networks. This new approach to obtain dual polymer networks with distinct transitions and characteristics is simple and robust, thus enabling applications in areas such as triple shape memory polymers, biomedical materials, and composites.
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    12. 12
      Hahn, M. E. Mechanistic Research in Aquatic Toxicology: Perspectives and Future Directions. Aquat. Toxicol. 2011, 105, 67– 71,  DOI: 10.1016/j.aquatox.2011.06.001
      12
      Mechanistic research in aquatic toxicology: Perspectives and future directions
      Hahn, Mark E.
      Aquatic Toxicology (2011), 105 (Suppl.), 67-71CODEN: AQTODG; ISSN:0166-445X. (Elsevier B.V.)
      A review. On the 30th anniversary of the journal, I provide a perspective on some of the questions and opportunities for new understanding that will interest aquatic toxicologists during the next 30 years. I focus on mechanisms of toxicity involving transcription factors, signalling pathways, and gene networks involved in toxic and adaptive responses in aquatic animals. Prominent questions address the value of a toxicity pathways approach in aquatic systems, issues involving extrapolation among species, identification of susceptibility genes and useful biomarkers of adverse effect, new emerging contaminants, the importance of epigenetic mechanisms, effects of multiple stressors, evolutionary toxicol., and the relative roles of tech. and conceptual limitations to our understanding of chem. effects on aquatic systems.
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    13. 13
      Patlewicz, G.; Aptula, A. O.; Uriarte, E.; Roberts, D. W.; Kern, P. S.; Gerberick, G. F.; Kimber, I.; Dearman, R. J.; Ryan, C. A.; Basketter, D. A. An Evaluation of Selected Global (Q)SARs/Expert Systems for the Prediction of Skin Sensitisation Potential. SAR QSAR Environ. Res. 2007, 18, 515– 541,  DOI: 10.1080/10629360701427872
      13
      An evaluation of selected global (Q)SARs/expert systems for the prediction of skin sensitization potential
      Patlewicz, G.; Aptula, A. O.; Uriarte, E.; Roberts, D. W.; Kern, P. S.; Gerberick, G. F.; Kimber, I.; Dearman, R. J.; Ryan, C. A.; Basketter, D. A.
      SAR and QSAR in Environmental Research (2007), 18 (5-6), 515-541CODEN: SQERED; ISSN:1062-936X. (Taylor & Francis Ltd.)
      Skin sensitization potential is an endpoint that needs to be assessed within the framework of existing and forthcoming legislation. At present, skin sensitization hazard is normally identified using in vivo test methods, the favored approach being the local lymph node assay (LLNA). This method can also provide a measure of relative skin sensitizing potency which is essential for assessing and managing human health risks. One potential alternative approach to skin sensitization hazard identification is the use of (Quant.) structure activity relationships ((Q)SARs) coupled with appropriate documentation and performance characteristics. This represents a major challenge. Current thinking is that (Q)SARs might best be employed as part of a battery of approaches that collectively provide information on skin sensitization hazard. A no. of (Q)SARs and expert systems have been developed and are described in the literature. Here we focus on three models (TOPKAT, Derek for Windows and TOPS-MODE), and evaluate their performance against a recently published dataset of 211 chems. The current strengths and limitations of one of these models is highlighted, together with modifications that could be made to improve its performance. Of the models/expert systems evaluated, none performed sufficiently well to act as a stand alone tool for hazard identification.
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    14. 14
      Townsend, P. A.; Grayson, M. N. Density Functional Theory in the Prediction of Mutagenicity: A Perspective. Chem. Res. Toxicol. 2021, 34, 179– 188,  DOI: 10.1021/acs.chemrestox.0c00113
      14
      Density Functional Theory in the Prediction of Mutagenicity: A Perspective
      Townsend, Piers A.; Grayson, Matthew N.
      Chemical Research in Toxicology (2021), 34 (2), 179-188CODEN: CRTOEC; ISSN:0893-228X. (American Chemical Society)
      A review. As a field, computational toxicol. is concerned with using in silico models to predict and understand the origins of toxicity. It is fast, relatively inexpensive, and avoids the ethical conundrum of using animals in scientific experimentation. In this perspective, we discuss the importance of computational models in toxicol., with a specific focus on the different model types that can be used in predictive toxicol. approaches toward mutagenicity (SARs and QSARs). We then focus on how quantum chem. methods, such as d. functional theory (DFT), have previously been used in the prediction of mutagenicity. It is then discussed how DFT allows for the development of new chem. descriptors that focus on capturing the steric and energetic effects that influence toxicol. reactions. We hope to demonstrate the role that DFT plays in understanding the fundamental, intrinsic chem. of toxicol. reactions in predictive toxicol.
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    15. 15
      Jackson, P. A.; Widen, J. C.; Harki, D. A.; Brummond, K. M. Covalent Modifiers: A Chemical Perspective on the Reactivity of α,β-Unsaturated Carbonyls with Thiols via Hetero-Michael Addition Reactions. J. Med. Chem. 2017, 60, 839– 885,  DOI: 10.1021/acs.jmedchem.6b00788
      15
      Covalent Modifiers: A Chemical Perspective on the Reactivity of α,β-Unsaturated Carbonyls with Thiols via Hetero-Michael Addition Reactions
      Jackson, Paul A.; Widen, John C.; Harki, Daniel A.; Brummond, Kay M.
      Journal of Medicinal Chemistry (2017), 60 (3), 839-885CODEN: JMCMAR; ISSN:0022-2623. (American Chemical Society)
      A review. Although Michael acceptors display a potent and broad spectrum of bioactivity, they have largely been ignored in drug discovery because of their presumed indiscriminate reactivity. As such, a dearth of information exists relevant to the thiol reactivity of natural products and their analogs possessing this moiety. In the midst of recently approved acrylamide-contg. drugs, it is clear that a good understanding of the hetero-Michael addn. reaction and the relative reactivities of biol. thiols with Michael acceptors under physiol. conditions is needed for the design and use of these compds. as biol. tools and potential therapeutics. This Perspective provides information that will contribute to this understanding, such as kinetics of thiol addn. reactions, bioactivities, as well as steric and electronic factors that influence the electrophilicity and reversibility of Michael acceptors. This Perspective is focused on α,β-unsatd. carbonyls given their preponderance in bioactive natural products.
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    16. 16
      Baillie, T. A. Targeted Covalent Inhibitors for Drug Design. Angew. Chem. Int. Ed. 2016, 55, 13408– 13421,  DOI: 10.1002/anie.201601091
      16
      Targeted Covalent Inhibitors for Drug Design
      Baillie, Thomas A.
      Angewandte Chemie, International Edition (2016), 55 (43), 13408-13421CODEN: ACIEF5; ISSN:1433-7851. (Wiley-VCH Verlag GmbH & Co. KGaA)
      In contrast to the traditional mechanism of drug action that relies on the reversible, noncovalent interaction of a ligand with its biol. target, a targeted covalent inhibitor (TCI) is designed such that the initial, reversible assocn. is followed by the formation of a covalent bond between an electrophile on the ligand and a nucleophilic center in the protein. Although this approach offers a variety of potential benefits (high potency and extended duration of action), concerns over the possible toxicol. consequences of protein haptenization have hindered the development of the TCI concept. Recently, approaches to mitigate the risk of serious adverse reactions to this new class of agent have emerged, thus stimulating interest in the field and leading to authorization of the first cadre of TCIs to be marketed. The covalent inhibitor approach is rapidly gaining acceptance as a valuable tool in drug discovery, and is poised to make a major impact on the design of enzyme inhibitors and receptor modulators.
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    17. 17
      Schultz, T. W.; Carlson, R. E.; Cronin, M. T. D.; Hermens, J. L. M.; Johnson, R.; O’Brien, P. J.; Roberts, D. W.; Siraki, A.; Wallace, K. B.; Veith, G. D. A Conceptual Framework for Predicting the Toxicity of Reactive Chemicals: Modeling Soft Electrophilicity. SAR QSAR Environ. Res. 2006, 17, 413– 428,  DOI: 10.1080/10629360600884371
      17
      A conceptual framework for predicting the toxicity of reactive chemicals: modeling soft electrophilicity
      Schultz, T. W.; Carlson, R. E.; Cronin, M. T. D.; Hermens, J. L. M.; Johnson, R.; O'Brien, P. J.; Roberts, D. W.; Siraki, A.; Wallace, K. B.; Veith, G. D.
      SAR and QSAR in Environmental Research (2006), 17 (4), 413-428CODEN: SQERED; ISSN:1062-936X. (Taylor & Francis Ltd.)
      Although the literature is replete with QSAR models developed for many toxic effects caused by reversible chem. interactions, the development of QSARs for the toxic effects of reactive chems. lacks a consistent approach. While limitations exit, an appropriate starting-point for modeling reactive toxicity is the applicability of the general rules of org. chem. reactions and the assocn. of these reactions to cellular targets of importance in toxicol. The identification of plausible "mol. initiating events" based on covalent reactions with nucleophiles in proteins and DNA provides the unifying concept for a framework for reactive toxicity. This paper outlines the proposed framework for reactive toxicity. Empirical measures of the chem. reactivity of xenobiotics with a model nucleophile (thiol) are used to simulate the relative rates at which a reactive chem. is likely to bind irreversibly to cellular targets. These measures of intrinsic reactivity serve as correlates to a variety of toxic effects; what's more they appear to be more appropriate endpoints for QSAR modeling than the toxicity endpoints themselves.
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    18. 18
      Böhme, A.; Thaens, D.; Paschke, A.; Schürmann, G. Kinetic Glutathione Chemoassay to Quantify Thiol Reactivity of Organic Electrophiless - Application to α,β-Unsaturated Ketones, Acrylates, and Propiolates. Chem. Res. Toxicol. 2009, 22, 742– 750,  DOI: 10.1021/tx800492x
      18
      Kinetic Glutathione Chemoassay To Quantify Thiol Reactivity of Organic Electrophiles-Application to α,β-Unsaturated Ketones, Acrylates, and Propiolates
      Bohme, Alexander; Thaens, Diana; Paschke, Albrecht; Schuurmann, Gerrit
      Chemical Research in Toxicology (2009), 22 (4), 742-750CODEN: CRTOEC; ISSN:0893-228X. (American Chemical Society)
      Glutathione (GSH) is a soft nucleophile and, as such, can be used to sense the reactivity of electrophilic agents toward the thiol group and other electron-rich sites of mol. structures. A new kinetic GSH chemoassay is introduced that employs a photometric method to quantify GSH loss and enables an efficient detn. of second-order rate consts., kGSH, of the reaction between electrophilic substances and GSH. Comparison with an existing 2 h static assay shows that the new kinetic variant is superior with respect to the detectable range of electrophilic reactivity and to confounding factors such as addnl. GSH loss due to oxidn. Anal. of the chemoassay degrdn. kinetics provides insight into the characteristic reaction times and the contributions of GSH-electrophile Michael addn. and GSH oxidn. to the overall GSH loss. For 15 α,β-unsatd. ketones, nine acrylates, and two propiolates acting as Michael acceptors, the measured kGSH values span ca. 5 orders of magnitude. Moreover, log kGSH correlates with the compds.' toxicity toward the ciliates Tetrahymena pyriformis in terms of 48 h log EC50 (50% growth inhibition) values, yielding a squared correlation coeff. (R2) of 0.91 and a root-mean-square error of 0.30 log units. It shows that for these and related compds., aquatic toxicity is driven by electrophilic reactivity. The findings demonstrate that the kinetic GSH chemoassay can be used as an efficient tool to analyze, interpret, and predict correspondingly reactive toxicity in the context of qual. and quant. structure-activity relationship studies and as a nonanimal tool of integrated testing strategies for REACH to screen compds. for excess toxicity.
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    19. 19
      Schultz, T. W.; Yarbrough, J. W.; Johnson, E. L. Structure-Activity Relationships for Reactivity of Carbonyl-Containing Compounds with Glutathione. SAR QSAR Environ. Res. 2005, 16, 313– 322,  DOI: 10.1080/10659360500204152
      19
      Structure-activity relationships for reactivity of carbonyl-containing compounds with glutathione
      Schultz, T. W.; Yarbrough, J. W.; Johnson, E. L.
      SAR and QSAR in Environmental Research (2005), 16 (4), 313-322CODEN: SQERED; ISSN:1062-936X. (Taylor & Francis Ltd.)
      For toxicol.-based structure-activity relationships to advance, will require a better understanding of mol. reactivity. A rapid and inexpensive spectrophotometric assay for detg. the reactive to glutathione (GSH) was developed and used to det. GSH reactivity (reactGSH) data for 21 aliph. derivs. of esters, ketones and aldehydes. From these data, a series of structure-activity relationships were evaluated. The structure feature assocd. with reactGSH was an acetylenic or olefinic moiety conjugated to a carbonyl group (i.e. polarized α,β-unsatn.). This structure conveys the capacity to undergo a covalent interaction with the thiol group of cysteine (i.e. Michael- addn.). Quant. reactGSH of the α,β-unsatd. carbonyl compds. is reliant upon the specific mol. structure with several tendencies obsd. Specifically, it was noted that for α,β-unsatd. carbonyl compds.: (1) the acetylenic-substituted derivs. were more reactive than the corresponding olefinic-substituted ones; (2) terminal vinyl-substituted derivs. was more reactive than the internal vinylene-substituted ones; (3) Me substitution on the vinyl carbon atoms diminishes reactivity and methyl-substitution on the carbon atom farthest from the carbonyl group causes a larger redn.; (4) derivs. with carbon-carbon double bond on the end of the mol. (i.e. vinyl ketone) were more reactive than one with the carbon-oxygen bond at the end of the mol. (i.e. aldehyde) and (5) the ester with an addnl. unsatd. vinyl groups were more reactive than the deriv. having an unsatd. Et group.
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    20. 20
      Böhme, A.; Laqua, A.; Schürmann, G. Chemoavailability of Organic Electrophiles: Impact of Hydrophobicity and Reactivity on Their Aquatic Excess Toxicity. Chem. Res. Toxicol. 2016, 29, 952– 962,  DOI: 10.1021/acs.chemrestox.5b00398
      20
      Chemoavailability of Organic Electrophiles: Impact of Hydrophobicity and Reactivity on Their Aquatic Excess Toxicity
      Bohme Alexander; Laqua Anja; Schuurmann Gerrit; Laqua Anja; Schuurmann Gerrit
      Chemical research in toxicology (2016), 29 (6), 952-62 ISSN:.
      Organic electrophiles have been recognized as important components of the exposome that can be characterized as cumulative totality of exposure in the organism in response to environmental perturbation. For such compounds, chemical reactivity may contribute significantly to the toxicological profile through covalent attacks at nucleophilic sites of peptides such as glutathione (GSH), proteins, lipid components, and the DNA and RNA. Employing a Michael acceptor set of 58 α,β-unsaturated carbonyls with 15 ketones, 18 aldehydes, and 25 esters, the hydrophobicity and reactivity contributions to their toxicity enhancement Te over baseline narcosis with the ciliates Tetrahymena pyriformis is analyzed through a conceptual model, featuring toxicokinetic phase transfer steps and the reactive molecular initiating event (MIE) at endogenous target sites exposed to water-rich or water-poor compartments. To this end, hydrophobicity was quantified by the octanol/water partition coefficient, Kow, electrophilic reactivity through second-order rate constants of reaction with GSH in a kinetic chemoassay, kGSH, and Te as the ratio of narcosis-level vs experimental concentration yielding 50% growth inhibition of the ciliates within 48 h of exposure. The observed decrease of log Te with increasing log Kow can be traced back to a rate-determining impact of the toxicant transfer from the membrane to the intracellular cytosol. Moreover, the recently introduced concept of chemoavailability is shown to enable, from knowledge of log Kow and log kGSH alone, a screening-level discrimination between reactive and hydrophobic MIEs triggering predominantly alone or in parallel respective adverse outcome pathways (AOPs) including the diffusion-control limit of reactive MIE saturation. As such, chemoavailability may aid in evaluating prevalent MIEs expected for a given organic electrophile and in assessing its toxicological profile within AOP schemes addressing aquatic toxicity.
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    21. 21
      Schwöbel, J. A. H.; Madden, J. C.; Cronin, M. T. D. Examination of Michael Addition Reactivity towards Glutathione by Transition-State Calculations. SAR QSAR Environ. Res. 2010, 21, 693– 710,  DOI: 10.1080/1062936X.2010.528943
      21
      Examination of Michael addition reactivity towards glutathione by transition-state calculations
      Schwobel, J. A. H.; Madden, J. C.; Cronin, M. T. D.
      SAR and QSAR in Environmental Research (2010), 21 (7-8), 693-710CODEN: SQERED; ISSN:1062-936X. (Taylor & Francis Ltd.)
      Kinetic rate consts. (kGSH) for the reaction of compds. acting as Michael acceptors with glutathione (GSH) were modelled by quantum chem. transition-state calcns. at the B3LYP/6-31G** and B3LYP/TZVP level. The data set included α,β-unsatd. aldehydes, ketones and esters, with double bonds and triple bonds, linear and cyclic systems, both with and without substituents in the α-position. Predicted values for kGSH were found to be in good agreement with exptl. kGSH values. Factors affecting rate consts. have been elucidated, esp. solvent effects and the influence of steric hindrance. Solvent effects were examd. by adding explicit solvent mols. to the system and by using a polarizable continuum solvent model. Detailed anal. of transition-state energies shows that the reaction is reversible. The reactive enolic intermediate plays an important role in Michael addn. to GSH, while the subsequent keto-enol-tautomerism is not rate limiting.
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    22. 22
      Schwöbel, J. A. H.; Wondrousch, D.; Koleva, Y. K.; Madden, J. C.; Cronin, M. T. D.; Schürmann, G. Prediction of Michael-Type Acceptor Reactivity toward Glutathione. Chem. Res. Toxicol. 2010, 23, 1576– 1585,  DOI: 10.1021/tx100172x
      22
      Prediction of Michael-Type Acceptor Reactivity toward Glutathione
      Schwobel, Johannes A. H.; Wondrousch, Dominik; Koleva, Yana K.; Madden, Judith C.; Cronin, Mark T. D.; Schuurmann, Gerrit
      Chemical Research in Toxicology (2010), 23 (10), 1576-1585CODEN: CRTOEC; ISSN:0893-228X. (American Chemical Society)
      A model has been developed to predict the kinetic rate consts. (kGSH) of α,β-unsatd. Michael acceptor compds. for their reaction with glutathione (GSH). The model uses the local charge-limited electrophilicity index ωq at the β-carbon atom as a descriptor of reactivity, a descriptor for resonance stabilization of the transition state, and one for steric hindrance at the reaction sites involved. Overall, the Michael addn. model performs well (r2 = 0.91; rms = 0.34). It includes various classes of compds. with double and triple bonds, linear and cyclic systems, and compds. with and without substituents in the α-position. Comparison of exptl. and predicted rate consts. demonstrates even better performance of the model for individual classes of compds. (e.g., for aldehydes, r2 = 0.97 and rms = 0.15; for ketones, r2 = 0.95 and rms = 0.35). The model also allows for the prediction of the RC50 values from the Schultz chemoassay, the accuracy being close to the interlab. exptl. error. Furthermore, kGSH and assocd. RC50 values can be predicted in cases where exptl. measurements are not possible or restricted, for example, because of low soly. or high volatility. The model has the potential to provide information to assist in the assessment and categorization of toxicants and in the application of integrated testing strategies.
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    23. 23
      Mulliner, D.; Wondrousch, D.; Schürmann, G. Predicting Michael-Acceptor Reactivity and Toxicity through Quantum Chemical Transition-State Calculations. Org. Biomol. Chem. 2011, 9, 8400– 8412,  DOI: 10.1039/c1ob06065a
      23
      Predicting Michael-acceptor reactivity and toxicity through quantum chemical transition-state calculations
      Mulliner, Denis; Wondrousch, Dominik; Schueuermann, Gerrit
      Organic & Biomolecular Chemistry (2011), 9 (24), 8400-8412CODEN: OBCRAK; ISSN:1477-0520. (Royal Society of Chemistry)
      The electrophilic reactivity of Michael acceptors is an important determinant of their toxicity. For a set of 35 α,β-unsatd. aldehydes, ketones and esters with exptl. rate consts. of their reaction with glutathione (GSH), kGSH, quantum chem. transition-state calcns. of the corresponding Michael addn. of the model nucleophile methane thiol (CH3SH) have been performed at the B3LYP/6-31G** level, focusing on the 1,2-olefin addn. pathway without and with initial protonation. Inclusion of Boltzmann-weighting of conformational flexibility yields intrinsic reaction barriers ΔE‡ that for the case of initial protonation correctly reflect the structural variation of kGSH across all three compd. classes, except that they fail to account for a systematic (essentially incremental) decrease in reactivity upon α-substitution. By contrast, the redn. in kGSH through β-substitution is well captured by ΔE‡. Empirical correction for the α-substitution effect yields a high squared correlation coeff. (r2 = 0.96) for the quantum chem. prediction of log kGSH, thus enabling an in silico screening of the toxicity-relevant electrophilicity of α,β-unsatd. carbonyls. The latter is demonstrated through application of the calcn. scheme for a larger set of 46 Michael-acceptor aldehydes, ketones and esters with exptl. values for their toxicity toward the ciliates Tetrahymena pyriformis in terms of 50% growth inhibition values after 48 h exposure (EC50). The developed approach may add in the predictive hazard evaluation of α,β-unsatd. carbonyls such as for the European REACH (Registration, Evaluation, Authorization and Restriction of Chems.) Directive, enabling in particular an early identification of toxicity-relevant Michael-acceptor reactivity.
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    24. 24
      Townsend, P. A.; Grayson, M. N. Reactivity Prediction in Aza-Michael Additions without Transition State Calculations: The Ames Test for Mutagenicity. Chem. Commun. 2020, 56, 13661– 13664,  DOI: 10.1039/d0cc05681b
      24
      Reactivity prediction in aza-Michael additions without transition state calculations: the Ames test for mutagenicity
      Townsend, Piers A.; Grayson, Matthew N.
      Chemical Communications (Cambridge, United Kingdom) (2020), 56 (88), 13661-13664CODEN: CHCOFS; ISSN:1359-7345. (Royal Society of Chemistry)
      Animal testing remains a contentious ethical issue in predictive toxicol. Thus, a fast, versatile, low-cost quantum chem. model is presented for predicting the risk of Ames mutagenicity in a series of 1,4 Michael acceptor type compds. This framework eliminates the need for transition state calcns., and uses an intermediate structure to probe the reactivity of aza-Michael acceptors. This model can be used in a variety of settings e.g., the design of targeted covalent inhibitors and polyketide biosyntheses.
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    25. 25
      Enoch, S. J.; Roberts, D. W. Predicting Skin Sensitization Potency for Michael Acceptors in the LLNA Using Quantum Mechanics Calculations. Chem. Res. Toxicol. 2013, 26, 767– 774,  DOI: 10.1021/tx4000655
      25
      Predicting Skin Sensitization Potency for Michael Acceptors in the LLNA Using Quantum Mechanics Calculations
      Enoch, S. J.; Roberts, D. W.
      Chemical Research in Toxicology (2013), 26 (5), 767-774CODEN: CRTOEC; ISSN:0893-228X. (American Chemical Society)
      This study outlines the development of a series of quant. mechanistic models enabling skin sensitization potency in the LLNA to be predicted for direct acting Michael acceptors. These models utilized several computational descriptors based on knowledge of the Michael addn. reaction mechanism. The key descriptor was calcd. using d. functional theory and modeled the stability of the reaction intermediate. A second descriptor relating to the available surface area at the site of the reaction was also found to be important. Several poorly predicted compds. were identified, and in all cases, these could be rationalized mechanistically. The anal. of these compds. allowed a well-defined mechanistically driven applicability domain to be developed. The study showed that in silico quant. mechanistic models, with a well-defined applicability domain, can be used to predict skin sensitization potency in the LLNA. The approach presented has the potential to be of use as part of a wt. of evidence approach for predicting skin sensitization without the use of animals in risk assessment.
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    26. 26
      Ebbrell, D. J.; Madden, J. C.; Cronin, M. T. D.; Schultz, T. W.; Enoch, S. J. Validation of a Fragment-Based Profiler for Thiol Reactivity for the Prediction of Toxicity: Skin Sensitization and Tetrahymena Pyriformis. Chem. Res. Toxicol. 2017, 30, 604– 613,  DOI: 10.1021/acs.chemrestox.6b00361
      26
      Validation of a Fragment-Based Profiler for Thiol Reactivity for the Prediction of Toxicity: Skin Sensitization and Tetrahymena pyriformis
      Ebbrell, David J.; Madden, Judith C.; Cronin, Mark T. D.; Schultz, Terry W.; Enoch, Steven J.
      Chemical Research in Toxicology (2017), 30 (2), 604-613CODEN: CRTOEC; ISSN:0893-228X. (American Chemical Society)
      This study outlines the use of a recently developed fragment-based thiol reactivity profiler for Michael acceptors to predict toxicity towards Tetrahymena pyriformis and skin sensitization potency as detd. in the Local Lymph Node Assay (LLNA). The results showed that the calcd. reactivity parameter from the profiler, -log RC50(calc), was capable of predicting toxicity for both endpoints with excellent statistics. However, the study highlighted the importance of a well-defined applicability domain for each endpoint. In terms of Tetrahymena pyriformis this domain was defined in terms of how fast or slowly a given Michael acceptor reacts with thiol leading to two sep. quant. structure-activity models. The first, for fast reacting chems. required only 1Log RC50(calc) as a descriptor, while the second required the addn. of a descriptor for hydrophobicity. Modeling of the LLNA required only a single descriptor, -log RC50(calc), enabling potency to be predicted. The applicability domain excluded chems. capable of undergoing polymn. and those that were predicted to be volatile. The modeling results for both endpoints, using the 1log RC50(calc) value from the profiler, were in keeping with previously published studies that have utilized exptl. detd. measurements of reactivity. This results demonstrate the output from the fragment-based thiol reactivity profiler can be used to develop quant. structure-activity relationship models where reactivity towards thiol is a driver of toxicity.
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    27. 27
      Ayala, P.; Schlegel, H. A Combined Method for Determining Reaction Paths, Minima and Transition State Geometries. J. Chem. Phys. 1997, 107, 375– 384,  DOI: 10.1063/1.474398
      27
      A combined method for determining reaction paths, minima, and transition state geometries
      Ayala, Philippe Y.; Schlegel, H. Bernhard
      Journal of Chemical Physics (1997), 107 (2), 375-384CODEN: JCPSA6; ISSN:0021-9606. (American Institute of Physics)
      Mapping out a reaction mechanism involves optimizing the reactants and products, finding the transition state and following the reaction path connecting them. Transition states can be difficult to locate and reaction paths can be expensive to follow. We describe an efficient algorithm for detg. the transition state, min. and reaction path in a single procedure. Starting with an approx. path represented by N points, the path is iteratively relaxed until one of the N points reached the transition state, the end points optimize to min. and the remaining points converged to a second order approxn. of the steepest descent path. The method appears to be more reliable than conventional transition state optimization algorithms, and requires only energies and gradients, but not second deriv. calcns. The procedure is illustrated by application to a no. of model reactions. In most cases, the reaction mechanism can be described well using 5 to 7 points to represent the transition state, the min. and the path. The computational cost of relaxing the path is less than or comparable to the cost of std. techniques for finding the transition state and the min., detg. the transition vector and following the reaction path on both sides of the transition state.
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    28. 28
      Frisch, M. J.; Trucks, G. W.; Schlegel, H. B.; Scuseria, G. E.; Robb, M. A.; Cheeseman, J. R.; Scalmani, G.; Barone, V.; Mennucci, B.; Petersson, G. A.; Nakatsuji, H.; Caricato, M.; Li, X.; Hratchian, H. P.; Izmaylov, A. F.; Bloino, J.; Zheng, J.; Sonnenberg, J. L.; Hada, M.; Ehara, M.; Toyota, K.; Fukuda, R.; Hasegawa, J.; Ishida, M.; Nakajima, T.; Honda, Y.; Kitao, O.; Nakai, H.; Vreven, T.; Montgomery, J. A.; Peralta, J. E.; Ogliaro, F.; Bearpark, M.; Heyd, J. J.; Brothers, E.; Kudin, K. N.; Staroverov, V. N.; Kobayashi, R.; Normand, J.; Raghavachari, K.; Rendell, J. C. A.; Burant, S.; Iyengar, S.; Tomasi, J.; Cossi, M.; Rega, N.; Millam, J. M.; Klene, M.; Knox, J. E.; Cross, J. B.; Bakken, V.; Adamo, C.; Jaramillo, J.; Gomperts, R.; Stratmann, R. E.; Yazyev, O.; Austin, A. J.; Cammi, R.; Pomelli, C.; Ochterski, J. W.; Martin, R. L.; Morokuma, K.; Zakrzewski, V. G.; Voth, G. A.; Salvador, P.; Dannenberg, J. J.; Dapprich, S.; Daniels, A. D.; Farkas, O.; Foresman, J. B.; Ortiz, J. V.; Cioslowski, J.; Fox, D. J. Gaussian 16, Revision A.03; Gaussian, Inc.: Wallingford, CT, 2016.
      There is no corresponding record for this reference.
    29. 29
      Lam, Y. H.; Grayson, M. N.; Holland, M. C.; Simon, A.; Houk, K. N. Theory and Modeling of Asymmetric Catalytic Reactions. Acc. Chem. Res. 2016, 49, 750– 762,  DOI: 10.1021/acs.accounts.6b00006
      29
      Theory and Modeling of Asymmetric Catalytic Reactions
      Lam, Yu-hong; Grayson, Matthew N.; Holland, Mareike C.; Simon, Adam; Houk, K. N.
      Accounts of Chemical Research (2016), 49 (4), 750-762CODEN: ACHRE4; ISSN:0001-4842. (American Chemical Society)
      Modern d. functional theory and powerful contemporary computers have made it possible to explore complex reactions of value in org. synthesis. We describe recent explorations of mechanisms and origins of stereoselectivities with d. functional theory calcns. The specific functionals and basis sets that are routinely used in computational studies of stereoselectivities of org. and organometallic reactions in our group are described, followed by our recent studies that uncovered the origins of stereocontrol in reactions catalyzed by (1) vicinal diamines, including cinchona alkaloid-derived primary amines, (2) vicinal amidophosphines, and (3) organo-transition-metal complexes. Two common cyclic models account for the stereoselectivity of aldol reactions of metal enolates (Zimmerman-Traxler) or those catalyzed by the organocatalyst proline (Houk-List). Three other models were derived from computational studies described in this Account.Cinchona alkaloid-derived primary amines and other vicinal diamines are venerable asym. organocatalysts. For α-fluorinations and a variety of aldol reactions, vicinal diamines form enamines at one terminal amine and activate electrophilically with NH+ or NF+ at the other. We found that the stereocontrolling transition states are cyclic and that their conformational preferences are responsible for the obsd. stereoselectivity. In fluorinations, the chair seven-membered cyclic transition states is highly favored, just as the Zimmerman-Traxler chair six-membered aldol transition state controls stereoselectivity. In aldol reactions with vicinal diamine catalysts, the crown transition states are favored, both in the prototype and in an exptl. example, shown in the graphic. We found that low-energy conformations of cyclic transition states occur and control stereoselectivities in these reactions. Another class of bifunctional organocatalysts, the vicinal amidophosphines, catalyzes the (3 + 2) annulation reaction of allenes with activated olefins. Stereocontrol here is due to an intermol. hydrogen bond that activates the electrophilic partner in this reaction. We have also studied complex organometallic catalysts. Krische's ruthenium-catalyzed asym. hydrohydroxyalkylation of butadiene involves two chiral ligands at Ru, a chiral diphosphine and a chiral phosphate. The size of this combination strains the limits of modern computations with over 160 atoms, multiple significant steps, and a variety of ligand coordinations and conformations possible. We found that carbon-carbon bond formation occurs via a chair Zimmerman-Traxler-type transition structure and that a formyl CH···O hydrogen bond from aldehyde CH to phosphate oxygen, as well as steric interactions of the two chiral ligands, control the stereoselectivity.
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    30. 30
      Fordham, J. M.; Grayson, M. N.; Aggarwal, V. K. Vinylidene Homologation of Boronic Esters and Its Application to the Synthesis of the Proposed Structure of Machillene. Angew. Chem. Int. Ed. 2019, 131, 15412– 15416,  DOI: 10.1002/ange.201907617
      There is no corresponding record for this reference.
    31. 31
      Falcone, B. N.; Grayson, M. N.; Rodriguez, J. B. Mechanistic Insights into a Chiral Phosphoric Acid-Catalyzed Asymmetric Pinacol Rearrangement. J. Org. Chem. 2018, 83, 14683– 14687,  DOI: 10.1021/acs.joc.8b02812
      31
      Mechanistic Insights into a Chiral Phosphoric Acid-Catalyzed Asymmetric Pinacol Rearrangement
      Falcone, Bruno N.; Grayson, Matthew N.; Rodriguez, Juan B.
      Journal of Organic Chemistry (2018), 83 (23), 14683-14687CODEN: JOCEAH; ISSN:0022-3263. (American Chemical Society)
      The first catalytic enantioselective pinacol rearrangement was reported by Antilla and co-workers in 2010. The reaction was catalyzed by a chiral phosphoric acid and resulted in high levels of enantioselectivity (up to 96% ee). The present study uses d. functional theory to investigate the mechanism and origins of stereoselectivity of this important reaction and to explain the difference in selectivity between different catalysts. An OH···O hydrogen bond between the intermediate indolyl alc. and the phosphate group from the catalyst together with a CH···O hydrogen bond between the indole and the phosphate group were obsd. in the preferred activation mode for the stereodetermining [1,2]-aryl shift. A stronger CH···O interaction in the major transition state was found to contribute to the high levels of enantioselectivity. A more bulky catalyst (TRIP) was found to impede the formation of the key CH···O interaction, leading to lower levels of enantioselectivity.
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    32. 32
      Townsend, P. A.; Grayson, M. N. Density Functional Theory Transition-State Modeling for the Prediction of Ames Mutagenicity in 1,4 Michael Acceptors. J. Chem. Inf. Model. 2019, 59, 5099– 5103,  DOI: 10.1021/acs.jcim.9b00966
      32
      Density Functional Theory Transition-State Modeling for the Prediction of Ames Mutagenicity in 1,4 Michael Acceptors
      Townsend, Piers A.; Grayson, Matthew N.
      Journal of Chemical Information and Modeling (2019), 59 (12), 5099-5103CODEN: JCISD8; ISSN:1549-9596. (American Chemical Society)
      Assessing the safety of new chems., without introducing the need for animal testing, is a task of great importance. The Ames test, a widely used bioassay to assess mutagenicity, can be an expensive, wasteful process with animal-derived reagents. Existing in silico methods for the prediction of Ames test results are traditionally based on chem. category formation and can lead to false pos. predictions. Category formation also neglects the intrinsic chem. assocd. with DNA reactivity. Activation energies and HOMO/LUMO energies for thirty 1,4 Michael acceptors were calcd. using a model nucleobase and were further used to predict the Ames test result of these compds. The proposed model builds upon existing work and examines the fundamental toxicant-target interactions using d. functional theory transition-state modeling. The results show that Michael acceptors with activation energies <20.7 kcal/mol and LUMO energies < -1.85 eV are likely to act as direct mutagens upon exposure to DNA.
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    33. 33
      Pedregosa, F.; Varoquaux, G.; Gramfort, A.; Michel, V.; Thirion, B.; Grisel, O.; Blondel, M.; Prettenhofer, P.; Weiss, R.; Dubourg, V.; Vanderplas, J.; Passos, A.; Cournapeau, D.; Brucher, M.; Perrot, M.; Duchesnay, É. Scikit-Learn: Machine Learning in Python. J. Mach. Learn. Res. 2011, 12, 2825– 2830,  DOI: 10.5555/1953048.2078195
      There is no corresponding record for this reference.
    34. 34
      Pasha, F. A.; Srivastava, H. K.; Singh, P. P. Comparative QSAR Study of Phenol Derivatives with the Help of Density Functional Theory. Bioorg. Med. Chem. 2005, 13, 6823– 6829,  DOI: 10.1016/j.bmc.2005.07.064
      34
      Comparative QSAR study of phenol derivatives with the help of density functional theory
      Pasha, F. A.; Srivastava, H. K.; Singh, P. P.
      Bioorganic & Medicinal Chemistry (2005), 13 (24), 6823-6829CODEN: BMECEP; ISSN:0968-0896. (Elsevier B.V.)
      Quantum chem. reactivity descriptors based QSAR study of 50 phenol derivs. is presented in this paper. Four different methods have been employed to certify the reliability of QSAR study. The mol. wt., hardness, chem. potential, total energy, and electrophilicity index provide valuable information and have a significant role in the assessment of the toxicity of phenols. The first model has been drawn up with the help of AM1 calcns. and in this model the correlation coeff. r2 is 0.88 and the cross-validation coeff. r2cv is 0.78. Second and third models have been designed with the PM3 and PM5 calcns., resp. The values of correlation coeff. r2 and cross-validation coeff. r2cv in the second case are 0.85 and .070, while in the third case they are 0.85 and 0.71. Finally, the DFT calcns. have been made for the same series of compds. by using a B88-PW91 GGA energy functional with the DZVP basis set. The DFT models have a higher predictive power than AM1, PM3, and PM5 methods, and the reliability of this model is clear from its correlation coeff. r2 0.91 and cross-validation coeff. r2cv 0.88. This study is also helpful in detg. the effect of any particular phenol deriv. of this series over Tetrahymena pyriformis.
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      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD2MXhtFyks77P&md5=e4f38e1539f9d7c4c1b9de7fe991f7bc
    35. 35
      Zhu, M.; Ge, F.; Zhu, R.; Wang, X.; Zheng, X. A DFT-Based QSAR Study of the Toxicity of Quaternary Ammonium Compounds on Chlorella Vulgaris. Chemosphere 2010, 80, 46– 52,  DOI: 10.1016/j.chemosphere.2010.03.044
      35
      A DFT-based QSAR study of the toxicity of quaternary ammonium compounds on Chlorella vulgaris
      Zhu, Men-Jun; Ge, Fei; Zhu, Run-Liang; Wang, Xue-Ye; Zheng, Xiao-Yan
      Chemosphere (2010), 80 (1), 46-52CODEN: CMSHAF; ISSN:0045-6535. (Elsevier Ltd.)
      The DFT-based descriptors were used to derive the quant. structure-activity relationship (QSAR) models enabling the calcd. quantum chem. parameters to be correlated to the toxicity of quaternary ammonium compds. (QACs) on green alga Chlorella vulgaris. DFT/B3LYP level of theory with the 6-31G(d) basis set was applied to calc. a set of quantum chem. descriptors for 11 QACs. The partial least squares (PLS) anal. implemented in Simca-P was employed to obtain the QSAR models. The optimal PLS model with the cumulative cross-validated regression coeff. (Q2cum = 0.893) and the correlation coeff. between obsd. values and fitted values (R = 0.975) explained 95.3% of the variance of the independent variables and 92.8% of the variance of the dependent variable. The results of this investigation show that alkyl chain lengths (CL), polarizability tense (αzz), the most pos. net at. charges on a hydrogen atom (q+H) and entropy (So) are the major descriptors in governing the log(1/EC50) values of the QACs.
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    36. 36
      Trohalaki, S.; Pachter, R. Quantum Descriptors for Predictive Toxicology of Halogenated Aliphatic Hydrocarbons. SAR QSAR Environ. Res. 2003, 14, 131– 143,  DOI: 10.1080/1062936031000073153
      36
      Quantum Descriptors for Predictive Toxicology of Halogenated Aliphatic Hydrocarbons
      Trohalaki, S.; Pachter, R.
      SAR and QSAR in Environmental Research (2003), 14 (2), 131-143CODEN: SQERED; ISSN:1062-936X. (Taylor & Francis Ltd.)
      In order to improve Quant. Structure-Activity Relationships (QSARs) for halogenated aliphatics (HA) and to better understand the biophys. mechanism of toxic response to these ubiquitous chems., we employ improved quantum-mech. descriptors to account for HA electrophilicity. We demonstrate that, unlike the LUMO energy, ELUMO, which was previously used as a descriptor, the electron affinity can be systematically improved by application of higher levels of theory. We also show that employing the reciprocal of ELUMO, which is more consistent with frontier MO (FMO) theory, improves the correlations with in vitro toxicity data. We offer explanations based on FMO theory for a result from our previous work, in which the LUMO energies of HA anions correlated surprisingly well with in vitro toxicity data. Addnl. descriptors are also suggested and interpreted in terms of the accepted biophys. mechanism of toxic response to HAs and new QSARs are derived for various chem. categories that compose the data set employed. These alternate descriptors provide important insight and could benefit other classes of compds. where the biophys. mechanism of toxic response involves dissociative attachment.
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    37. 37
      Jorner, K.; Tomberg, A.; Bauer, C.; Sköld, C.; Norrby, P. O. Organic Reactivity from Mechanism to Machine Learning. Nat. Rev. 2021, 5, 240,  DOI: 10.1038/s41570-021-00260-x
      37
      Organic reactivity from mechanism to machine learning
      Jorner, Kjell; Tomberg, Anna; Bauer, Christoph; Skold, Christian; Norrby, Per-ola
      Nature Reviews Chemistry (2021), 5 (4), 240-255CODEN: NRCAF7; ISSN:2397-3358. (Nature Portfolio)
      A review. As more data are introduced in the building of models of chem. reactivity, the mechanistic component can be reduced until 'big data' applications are reached. These methods no longer depend on underlying mechanistic hypotheses, potentially learning them implicitly through extensive data training. Reactivity models often focus on reaction barribers, but can also be trained to directly predict lab-relevant properties, such as yields or conditions. Calcns. with a quantum-mech. component are still preferred for quant. predictions of reactivity. Although big data applications tend to be more qual., they have the advantage to be broadly applied to different kinds of reactions. There is a continuum of methods in between these extremes, such as methods that use quantum-derived data or descriptors in machine learning models. Here, we present an overview of the recent machine learning applications in the field of chem. reactivity from a mechanistic perspective. Starting with a summary of how reactivity questions are addressed by quantum-mech. methods, we discuss methods that augment or replace quantum-based modeling with faster alternatives relying on machine learning.
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    38. 38
      Bondi, A. Van Der Waals Volumes and Radii. J. Phys. Chem. 1964, 68, 441– 451,  DOI: 10.1021/j100785a001
      38
      van der Waals volumes and radii
      Bondi, A.
      Journal of Physical Chemistry (1964), 68 (3), 441-51CODEN: JPCHAX; ISSN:0022-3654.
      Intermol. van der Waals radii of the nonmetallic elements were assembled into a list of recommended values for vol. calcns. These values were arrived at by selecting from the most reliable x-ray diffraction data those which could be reconciled with crystal d. at 0°K. (to give reasonable packing d.), gas kinetic collision cross section, crit. d., and with liquid state properties. A qual. understanding of the nature of van der Waals radii is provided by correlation with the de Broglie wavelength of the outermost valence electron. Tentative values for the van der Waals radii of metallic elements in organometallic compds. are proposed. A list of increments for the vol. of mols. impenetrable to thermal collision, the so-called van der Waals vol., and of the corresponding increments in area per mol. is given.
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    39. 39
      Gwee, E. S. H.; Seeger, Z. L.; Appadoo, D. R. T.; Wood, B. R.; Izgorodina, E. I. Influence of DFT Functionals and Solvation Models on the Prediction of Far-Infrared Spectra of Pt-Based Anticancer Drugs: Why Do Different Complexes Require Different Levels of Theory?. ACS Omega 2019, 4, 5254– 5269,  DOI: 10.1021/acsomega.8b03455
      39
      Influence of DFT Functionals and Solvation Models on the Prediction of Far-Infrared Spectra of Pt-Based Anticancer Drugs: Why Do Different Complexes Require Different Levels of Theory?
      Gwee, Eunice S. H.; Seeger, Zoe L.; Appadoo, Dominique R. T.; Wood, Bayden R.; Izgorodina, Ekaterina I.
      ACS Omega (2019), 4 (3), 5254-5269CODEN: ACSODF; ISSN:2470-1343. (American Chemical Society)
      Computational modeling was applied to Far IR (FIR) spectra of Pt-based anti-cancer drugs to study the hydrolysis of these important mols. Here we present a study that investigates the influence of different factors - basis sets, effective core potentials (ECPs), D. Functional Theory (DFT) functionals and solvation models - on the prediction of FIR spectra of two Pt-based anticancer drugs, cisplatin and carboplatin. Geometry optimisations and frequency calcns. were performed with a range of functionals (PBE, PBE0, M06-L and M06-2X), basis sets (VDZ, VTZ, aVDZ and aVTZ), effect core potentials (VDZ-pp, VTZ-pp, aVDZ-pp and aVTZ-pp) and solvation models (PCM, CPCM and SMD). The best combination of basis set/DFT functional/solvation model was identified for each anti-cancer drug by comparing with exptl. available FIR spectra. Different combinations were established for cisplatin and carboplatin, which was rationalised by means of the partial at. charge scheme, ChelpG, that was utilized to study the charge transfer between the Pt ion and ligands in both cisplatin and carboplatin.
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    40. 40
      Hou, G.; Zhu, X.; Cui, Q. An Implicit Solvent Model for SCC-DFTB with Charge-Dependent Radii. J. Chem. 2010, 6, 2303– 2314,  DOI: 10.1021/ct1001818
      40
      An Implicit Solvent Model for SCC-DFTB with Charge-Dependent Radii
      Hou, Guanhua; Zhu, Xiao; Cui, Qiang
      Journal of Chemical Theory and Computation (2010), 6 (8), 2303-2314CODEN: JCTCCE; ISSN:1549-9618. (American Chemical Society)
      Motivated by the need to rapidly explore the potential energy surface of chem. reactions that involve highly charged species, we have developed an implicit solvent model for approx. d. functional theory, SCC-DFTB. The solvation free energy is calcd. using a popular model that employs Poisson-Boltzmann for electrostatics and a surface-area term for nonpolar contributions. To balance the treatment of species with different charge distributions, we make the at. radii that define the dielec. boundary and solute cavity depend on the solute charge distribution. Specifically, the at. radii are assumed to be linearly dependent on the Mulliken charges and solved self-consistently together with the solute electronic structure. Benchmark calcns. indicate that the model leads to solvation free energies of comparable accuracy to the SM6 model (esp. for ions), which requires much more expensive DFT calcns. With anal. first derivs. and favorable computational speed, the SCC-DFTB-based solvation model can be effectively used, in conjunction with high-level QM calcns., to explore the mechanism of soln. reactions. This is illustrated with a brief anal. of the hydrolysis of monomethyl monophosphate ester (MMP) and tri-Me monophosphate ester (TMP). Possible future improvements are also briefly discussed.
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    41. 41
      Agmon, N. Quantitative Hammond Postulate. J. Chem. Soc. 1978, 74, 388– 404,  DOI: 10.1039/F29787400388
      41
      Quantitative Hammond postulate
      Agmon, Noam
      Journal of the Chemical Society, Faraday Transactions 2: Molecular and Chemical Physics (1978), 74 (2), 388-404CODEN: JCFTBS; ISSN:0300-9238.
      A quant. Hammond postulate is presented in which 4 assumptions are made: Pauling's relation (L. Pauling, 1947); bond order conservation; an approxn. for the potential energy along the reaction coordinate; and a symmetry principle. An expression for the bond order of the transition state was obtained and compared with results from potential energy surfaces (A.; 1977), bond energy-bond order (Johnston, H. S.; Parr, C. A., 1963), and the theory of R. A. Marcus (1975). A theor. fit was obtained for nonlinear Broensted plots over a moderate range of ΔpK (K = equil. const.). Several other theories were analyzed which relate kinetics to thermodn. Two empirical relations of M. H. Mok and J. C. Polanyi (1969), and the relation of M. G. Evans and M. Polanyi (1938) was derived by using the symmetry principle. Marcus' theory, which may be related to a weak symmetry principle, was given a new proof by using the model of the inverted parabola, of E. R. Thornton (1967). These theories have much in common, and may all follow from 1 general theory.
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    42. 42
      Northrop, B. H.; Frayne, S. H.; Choudhary, U. Thiol-Maleimide “Click” Chemistry: Evaluating the Influence of Solvent, Initiator, and Thiol on the Reaction Mechanism, Kinetics, and Selectivity. Polym. Chem. 2015, 6, 3415– 3430,  DOI: 10.1039/c5py00168d
      42
      Thiol-maleimide "click" chemistry: evaluating the influence of solvent, initiator, and thiol on the reaction mechanism, kinetics, and selectivity
      Northrop, Brian H.; Frayne, Stephen H.; Choudhary, Umesh
      Polymer Chemistry (2015), 6 (18), 3415-3430CODEN: PCOHC2; ISSN:1759-9962. (Royal Society of Chemistry)
      The mechanism and kinetics of thiol-maleimide "click" reactions carried out under a variety of conditions were investigated computationally and using exptl. competition reactions. The influence of three different solvents (chloroform, ethane thiol, and N,N-dimethylformamide), five different initiators (ethylamine, diethylamine, triethylamine, diazabicyclo[2.2.2]octane, and dimethylphenylphosphine), and seven different thiols (Me mercaptan, β-mercaptoethanol, thioacetic acid, Me thioglycolate, Me 3-mercaptopropionate, cysteine Me ester, and thiophenol) on the energetics and kinetics of thiol-maleimide reactions were examd. using d. functional methods. Computational and kinetic modeling indicate that the choice of solvent, initiator, and thiol directly influences whether product formation follows a base-, nucleophile-, or ion pair-initiated mechanism (or some combination thereof). The type of mechanism followed dets. the overall thiol-maleimide reaction kinetics. Insights from computational studies are then used to understand the selectivity of ternary thiol-maleimide reactions between N-Me maleimide, thiophenol, and 1-hexanethiol in different combinations of solvents and initiators. The results provide considerable insight into the interplay between reaction conditions, kinetics, and selectivity in thiol-maleimide reactions in particular and thiol-Michael reactions in general, with implications ranging from small mol. synthesis to bioconjugation chem. and multifunctional materials.
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    43. 43
      Grüber, R.; Fleurat-Lessard, P. Performance of Recent Density Functionals to Discriminate between Olefin and Nitrogen Binding to Palladium. Theor. Chem. Acc. 2014, 133, 1– 10,  DOI: 10.1007/s00214-014-1533-2
      There is no corresponding record for this reference.
    44. 44
      Dewar, M. J. S.; Zoebisch, E. G.; Healy, E. F.; Stewart, J. J. P. AM1: A New General Purpose Quantum Mechanical Molecular Model. J. Am. Chem. Soc. 1985, 107, 3902– 3909,  DOI: 10.1021/ja00299a024
      44
      Development and use of quantum mechanical molecular models. 76. AM1: a new general purpose quantum mechanical molecular model
      Dewar, Michael J. S.; Zoebisch, Eve G.; Healy, Eamonn F.; Stewart, James J. P.
      Journal of the American Chemical Society (1985), 107 (13), 3902-9CODEN: JACSAT; ISSN:0002-7863.
      A new parametric quantum mech. mol. model, AM1 (Austin Model 1), based on the NDDO approxn., is described. In it the major weaknesses of MNDO, in particular failure to reproduce H bonds, are overcome without any increase in computing time. Results for 167 mols. are reported. Parameters are currently available for C, H, O, and N.
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    45. 45
      Spencer, S. R.; Xue, L.; Klenz, E. M.; Talalay, P. The potency of inducers of NAD(P)H:(quinone-acceptor) oxidoreductase parallels their efficiency as substrates for glutathione transferases. Structural and electronic correlations. Biochem. J. 1991, 273, 711– 717,  DOI: 10.1042/bj2730711
      45
      The potency of inducers of NAD(P)H:(quinone-acceptor) oxidoreductase parallels their efficiency as substrates for glutathione transferases. Structural and electronic correlations
      Spencer, Sharon R.; Xue, Liang; Klenz, Elizabeth M.; Talalay, Paul
      Biochemical Journal (1991), 273 (3), 711-17CODEN: BIJOAK; ISSN:0264-6021.
      Induction of glutathione transferases (EC.2.5.1.18), NAD(P)H:(quinone-acceptor) oxidoreductase (EC 1.6.99.2;quinone reductase) and other detoxification enzymes is a major mechanism for protecting cells against the toxicities of electrophiles, including many carcinogens. Although inducers of these two enzymes belong to many different chem. classes, they nevertheless contain (or acquire by metab.) electrophilic centers that appear to be essential for inductive activity, and many inducers are Michael reaction acceptors (Talalay, P. et al., 1988). The inducers therefore share structural and electronic features with glutathione transferase substrates. To define these features more precisely, the inductive potencies were examd. (by measuring quinone reductase in murine hepatoma cells) of two types of glutathione transferase substrates: a series of 1-chloro-2-nitrobenzenes bearing para-oriented electron-donating or -withdrawing substituents and a wide variety of other commonly used and structurally unrelated glutathione transferase substrates. It was concluded that virtually all gltuathione transferase substrates are inducers, and their potencies in the nitrobenzene series correlate linearly with the Hammett σ or σ- values of the arom. substituents, precisely as previously reported for their efficiencies as glutathione transferase substrates. More detailed information on the electronic requirements for inductive activity was obtained with a series of Me trans-cinnamates bearing electron-withdrawing or -donating substituents on the arom. ring, and in which the electronic densities at the olefinic and adjacent carbon atoms were measured by 13C NMR. Electron-withdrawing meta-substituents markedly enhance inductive potency in parallel with their increased non-enzymic reactivity with GSH. Thus, Me 3-bromo-, 3-nitro-, and 3-chloro-cinnamates are 21, 14 and 8 times more potent inducers than the parent Me cinnamate. This finding permits the design of more potent inducers, which are important for elucidation of the mol. mechanisms of induction.
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