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Understanding the Influence of API Conformations on Amorphous Dispersion Formation Potential Predictions using the R3m Molecular Descriptor
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Understanding the Influence of API Conformations on Amorphous Dispersion Formation Potential Predictions using the R3m Molecular Descriptor
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  • Kevin DeBoyace
    Kevin DeBoyace
    School of Pharmacy and Graduate School of Pharmaceutical Sciences, Duquesne University, 600 Forbes Ave, Pittsburgh, Pennsylvania 15282, United States
    Pfizer Worldwide R&D, Eastern Point Road, Groton, Connecticut 06340, United States
    More by Kevin DeBoyace
  • Mustafa Bookwala
    Mustafa Bookwala
    School of Pharmacy and Graduate School of Pharmaceutical Sciences, Duquesne University, 600 Forbes Ave, Pittsburgh, Pennsylvania 15282, United States
    More by Mustafa Bookwala
  • Deliang Zhou
    Deliang Zhou
    Drug Product Development, Research and Development, AbbVie, 1 North Waukegan Road, North Chicago, Illinois 60064, United States
    Small Molecules Drug Product Development, BeiGene USA, Inc., 55 Cambridge Parkway, Cambridge, Massachusetts 02142, United States
    More by Deliang Zhou
    Orcidhttps://orcid.org/0000-0003-0689-890X
  • Ira S. Buckner
    Ira S. Buckner
    School of Pharmacy and Graduate School of Pharmaceutical Sciences, Duquesne University, 600 Forbes Ave, Pittsburgh, Pennsylvania 15282, United States
    More by Ira S. Buckner
  • Peter L.D. Wildfong*
    Peter L.D. Wildfong
    School of Pharmacy and Graduate School of Pharmaceutical Sciences, Duquesne University, 600 Forbes Ave, Pittsburgh, Pennsylvania 15282, United States
    *Email: [email protected]
    More by Peter L.D. Wildfong
    Orcidhttps://orcid.org/0000-0001-6346-1806
Open PDFSupporting Information (1)

Molecular Pharmaceutics

Cite this: Mol. Pharmaceutics 2024, 21, 2, 770–780
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https://doi.org/10.1021/acs.molpharmaceut.3c00909
Published January 5, 2024

Copyright © 2024 The Authors. Published by American Chemical Society. This publication is licensed under

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Abstract

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The R3m molecular descriptor (R-GETAWAY third-order autocorrelation index weighted by the atomic mass) has previously been shown to encode molecular attributes that appear to be physically and chemically relevant to grouping diverse active pharmaceutical ingredients (API) according to their potential to form persistent amorphous solid dispersions (ASDs) with polyvinylpyrrolidone–vinyl acetate copolymer (PVPVA). The initial R3m dispersibility model was built by using a single three-dimensional (3D) conformation for each drug molecule. Since molecules in the amorphous state will adopt a distribution of conformations, molecular dynamics simulations were performed to sample conformations that are probable in the amorphous form, which resulted in a distribution of R3m values for each API. Although different conformations displayed R3m values that differed by as much as 0.4, the median of each R3m distribution and the value predicted from the single 3D conformation were very similar for most structures studied. The variability in R3m resulting from the distribution of conformations was incorporated into a logistic regression model for the prediction of ASD formation in PVPVA, which resulted in a refinement of the classification boundary relative to the model that only incorporated a single conformation of each API.

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1. Introduction

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Amorphous solid dispersions (ASDs), which are molecular mixtures of an active pharmaceutical ingredient (API) in a carrier polymer, represent a key formulation development strategy reflected by their employment in several marketed products. (1−4) The amorphous form of a drug often improves its apparent aqueous solubility relative to the thermodynamically stable crystalline form, (5) while achieving a single-phase mixture with a polymer adds the potential benefits of improved storage stability relative to the amorphous API and prolonged supersaturation during dissolution. (6) Although a number of methods have been proposed for the prediction of ASD formation and stability, (7) a priori selection of the appropriate API–polymer mixture has proven difficult and the result has been that the majority of ASD formulations have been developed using a trial-and-error approach.
With the goal of improving over empirical formulation, and thereby reducing development costs and time to market, our research group has pursued molecular descriptors as a materials-sparing tool for the prediction of ASD formation. A single molecular descriptor known as the R-GETAWAY third-order autocorrelation index weighted by the atomic mass (R3m) was previously used to group an 18-molecule library of structurally diverse API according to their ability to form an ASD with polyvinylpyrrolidone–vinyl acetate copolymer (PVPVA), prepared using various methods. (7−9) When applied to the subset of 15 API that could be prepared using the melt-quench method (Figure 1), it was found that molecules having R3m >0.65 all formed persistent ASDs in PVPVA, at both 15 and 75% w/w drug loading, as confirmed using a suite of analytical characterization techniques. (7,8)

Figure 1

Figure 1. Molecular structures of the 15 API in the compound library generated using the CORINA algorithm. R3m values based on this conformation are given parenthetically. Hydrogen atoms have been excluded for the sake of clarity, except in the case of hydrogen bond donors.

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R3m encodes information about the size and shape of an API molecule, its topological connectivity, and the number and relative positions of its atoms in 3D space (with increasing significance for molecules that contain heavier peripheral atoms). The relationship between the physicochemical meaning of R3m and how it groups molecules according to their ability to form dispersions with PVPVA was thoroughly investigated in a recent publication. (9) In our previous work, each R3m value was calculated based on a single molecular conformation, either taken from the reported crystal structure (8) or generated using the COoRdINAtes (CORINA) algorithm (7) for each library API. In the present work, it is proposed that, in the amorphous state, each API is likely to adopt multiple conformations, resulting in a distribution of R3m values, as opposed to one absolute value for each molecule. It was observed that the respective values of R3m were larger when heavy atoms, capable of forming specific noncovalent interactions with the PVPVA, were peripherally located. (9) This suggested that conformations in which the 3D coordinates of these key atoms were shifted further from the geometric centers of the API molecules would have correspondingly higher R3m values. Likewise, conformations where these heavier atoms were positioned closer to the molecular geometric center would have lower R3m values.
To evaluate the potential range of R3m that an API might have, molecular dynamics (MD) was used to explore the likely conformations that the library API might adopt in amorphous systems. In this paper, it was hypothesized that the distribution of R3m values captured by MD-simulated API conformations will be better predictors of dispersibility than a single conformation dictated by either that adopted in a crystal structure or gaseous state minimizations. Although a distribution of R3m for each molecule was expected to refine the classification boundary relative to its initial value, (7−10) this approach was expected to result in better estimations of the probability of dispersion formation in PVPVA. To address this hypothesis, the overall impact of 3D conformation on the value of R3m was investigated. MD simulations were used to obtain a reasonable distribution of conformations expected for the amorphous forms for each library API molecule. Distributions of R3m were evaluated to see if they contained the original value (calculated from the conformation in the crystal structure). Finally, the model for the prediction of ASD formation was updated to include the distribution in 3D conformations.

2. Experimental Section

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2.1. Preparation of Co-Solidified Mixtures

The preparation of co-solidified mixtures by melt-quenching has been previously described. (7) Briefly, the polymer and API were mixed at a specific weight ratio and transferred to a crucible, where the mixture was then heated to 10 °C above the API melting point, where it was maintained isothermally for ∼30 min with intermittent stirring. The molten mass was then quenched by immersion of the crucible in ice water under a dry N2 purge to minimize exposure to water condensation and plasticization. The resulting solids were stored overnight in a P2O5 desiccator and characterized the next day by using powder X-ray diffraction (PXRD), differential scanning calorimetry (DSC), and hot-stage polarized light microscopy. Additional details concerning the categorization of dispersibility can be found in the original reference. (7)

2.2. Crystal Structure Data

To explore conformational diversity due to different chemical structures beyond the original library shown in Figure 1, 80 individual molecules were identified, consisting of primarily biopharmaceutical classification system (BCS) Class II API. Since the application of this work is highly relevant to ASDs, molecules having poor aqueous solubility were considered as the primary focus to the applicability of the work herein. Crystal structure files were obtained from the Cambridge Crystallographic Data Centre (CCDC) (11) in order to compare predicted 3D structures to experimental (crystal) data. A total of 130 CCDC files were used, composed of 33 molecules with multiple polymorphic forms and 47 molecules with a single reported crystal structure. The CCDC reference codes for these files can be found in the Supporting Information Table S1. For cases in which multiple crystal structure files existed for a single polymorph, the file with the lowest reported R-factor was selected.

2.3. Molecular Dynamics

MD simulations were performed in silico using Materials Studio 7.0 (Biovia, San Diego, California) to sample multiple 3D structural conformations in the amorphous form. The COMPASS II force field was applied for all of the simulations, since it has been parametrized for drug-like molecules, includes functional groups of interest, and has been optimized using experimental data. (12) The MD process is illustrated in Figure 2. First, the API structure was drawn and its geometry was optimized using the Forcite module. The Conformers module was then applied using the systematic grid scan method to identify the conformation of the lowest total energy in a vacuum (Figure 2a). This was performed to ensure that the initial structure for MD simulations was not sterically trapped in an unlikely conformation. Next, 40 molecules derived in the previously identified lowest energy conformation of the API were placed into a virtual cubic “box” having a density of 1 g/cm3 using the Amorphous Cell module. The construction of the amorphous cell was replicated to generate a total of 10 unique cells, and the three cells having the lowest total energy following geometry optimization were selected for subsequent MD simulations (Figure 2b). Finally, the Forcite module was used to complete MD simulations for a total of 500 ps by using the NPT ensemble to permit the cell volume to fluctuate (Figure 2c). The virtual temperature was held at 298 K using the Nosé–Hoover thermostat, and the virtual pressure was held at 1 atm using the Berendsen barostat. The amorphous density for each API was determined by taking the average of the density over the final 300 ps of the simulation, which was previously shown to result in accurate estimations of this parameter. (13) Seven equally spaced frames were selected (Figure 2d) throughout the final 300 ps of the simulations (illustrated by the red boxes in Figure 2c). Individual molecular conformations were then extracted from these frames, and an R3m value was calculated for each conformation (Figure 2e).

Figure 2

Figure 2. Schematic illustrating the MD simulation for bicalutamide. (a) The Conformer tool was used to identify the lowest energy conformation (black dashed line). (b) This conformation allowed construction of 10 unique amorphous cells, where the three having the lowest energy (indicated in orange) were selected for MD simulations. (c) MD simulations using the NPT ensemble allowed selection of multiple frames after equilibration (indicated by the red boxes). (d) Coordinates from the selected frames were extracted using MATLAB allowing calculation of the R3m values for each individual molecule.

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MATLAB was used to extract the 3D molecule conformations from the Materials Studio MD simulations. Seven frames per simulation were saved as as.xsd files, where relevant data, such as the atom types, atom coordinates, and connectivity, were obtained using an additional MATLAB code, developed in-house. Coordinates for each molecule were centered, and the R3m descriptor was calculated for each conformation using another in-house MATLAB function. The MATLAB functions for this process can be obtained via the links provided in the Supporting Information.

3. Results and Discussion

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R3m belongs to the geometry, topology, and atom weights assembly (GETAWAY) class of descriptors, whose calculation is described in greater detail elsewhere. (9,14,15) R3m is calculated using eq 1 (15):
R3m=∑i=1A−1∑i>jhii×hjjrij×mi×mj×δ(3;dij)
(1)
where A is the number of atoms in the molecule, m is the atomic mass normalized to carbon, i and j refer to two separate atoms, rjj is the Euclidean distance between them, hii and hjj are the leverages, and δ(k:dij) is a delta-Dirac function, where k = 3 restricts the calculation to atoms separated by a topological distance of 3. Calculation of R3m uses several matrices. The first is the 3 × A molecular matrix, which contains the atomic coordinates. The A × A symmetric geometry matrix contains the Euclidean distance between each atom in the molecule and is calculated in the same manner as the leverage matrix for linear regression, (14) where its diagonal contains the leverage values (i.e., hii, hjj) used in eq 1.
In the present work, probable 3D conformations of a molecule in the amorphous state were used to evaluate their impact on the matrices comprising eq 1. Changing the conformation of a molecule results in a change to the values of the molecular matrix for an API and subsequently its geometry matrix. While the Euclidean distance of atoms having a topological distance of k = 1 (i.e., atoms connected by a covalent bond) will essentially remain the same, owing to the consistency of these bond lengths, the Euclidean distances of atoms separated by larger topological distances will vary when conformational differences result in altered torsion and bond angles. Likewise, leverage values, which measure the distance of atoms from the geometric center of a molecule, will also change when torsion angles cause significant changes in the location of the geometric center of a molecule. This reinforces that changes in 3D conformation should result in changes to R3m; however, the extent and significance of these changes depend on the structure. It was, therefore, necessary to first investigate whether conformational changes could result in any significant changes to R3m, and by extension, have a potential impact on future predictions of ASD formation in PVPVA made by the original R3m model. (9) It is also noted that previous investigations of R3m in the context of dispersions formed via solvent-based cosolidification (e.g., spray-drying) have revealed that residual solvent can affect drug–polymer miscibility and, therefore, is likely to affect the conformations of API in the amorphous state. (7,8,10) For simplicity, therefore, only binary drug–PVPVA systems prepared via melt-quenching were considered here and extrapolation of the present findings to ASDs prepared using solvent-based methods should be done with caution.

3.1. Investigating the Impact of 3D Conformation

Since the 3D coordinate system of an API impacts the magnitude of its R3m, it was important to consider the source of atomic coordinates. The 3D coordinates generated by pseudoforce field predictions and MD simulations were compared against experimental data to investigate the variability in R3m caused by variations in 3D coordinates.

3.1.1. CORINA vs Crystal Structure Data

Initially, R3m was calculated using coordinates from API conformers extracted from reported crystal structures of marketed forms; however, to make the original molecular descriptor model more generally applicable, 1D data from the Simplified Molecular Input Line Entry Specification (SMILES) files were used to predict 3D conformations. The 3D conformations of the 15 library API shown in Figure 1 were predicted using the CORINA algorithm, (16) which sets bond angles and lengths based on the types of atoms, hybridization states, and bonds. Additionally, atom positions are adjusted to avoid nonbonded atom overlap and a pseudoforce field is applied to minimize the sum of stretching, bending, out-of-plane, and torsional energies. (16) CORINA has been shown to make reasonable predictions of molecular conformations in crystals, (16) where a data set composed of 2443 small organic molecules resulted in an average root-mean-square deviation between actual and predicted atomic coordinates of 0.95 Å. The shift from crystal structure conformations to CORINA-predicted conformations was made so that the R3m model could be applied to new chemical entities without solved crystal structures. This change in approach also provided an initial evaluation of how sensitive R3m values were to conformational changes, in general.
An initial comparison was undertaken using coordinates from both the reported crystallographic structures and from CORINA predictions. To investigate how the value of R3m varies with conformation for a diverse set of molecular structures, a data set of primarily BCS Class II crystalline structures was assembled (see Table S1 in the Supporting Information for a list of API and their respective CCDC reference codes). Of these 80 API, 33 molecules had reported more than one polymorphic form (ranging from two to eight forms), allowing R3m to be determined from more than one conformation of these molecules. Inclusion of these data allowed for the assessment of the effect of 3D conformational variability on R3m within individual API. Histograms reporting the average absolute difference between calculated R3m values are shown in Figure 3a, which includes a total of 130 molecules (50 polymorphs from 33/80 original structures). A low average absolute difference between R3m values was observed for most molecules, where the difference in R3m between CORINA and CCDC 3D coordinates was ≤0.05 for 67% (87/130) of the 130 structures (API and polymorphs), although 4% (5/130) differed by almost 0.3. Figure 3b regresses the 130 structures that included polymorphs and have respective slopes of 0.91 and 1.04. In either case, 81% of the variation in R3m values calculated from CORINA conformations was explained by variation in R3m values from CCDC conformations.

Figure 3

Figure 3. Histograms showing the average absolute difference between R3m values calculated from CORINA-predicted and CCDC obtained structures. (a) One hundred thirty API, including R3m from 50 polymorphic structures. Linear relationship between (b) R3m values for 80 API (blue diamonds) and 50 polymorphs totaling 130 structures (orange circles). The orange regression line reflects the fit for all 130 structures. Points enclosed by the red box specifically highlight the different R3m values calculated for the polymorphs of aripiprazole. Gray dashes indicate the parity line.

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While the R3m difference between the CORINA and CCDC conformations was generally small, there were some notable exceptions. One such example was aripiprazole (see red box in Figure 3b), which had the largest observed difference between the calculations of R3m from CCDC and CORINA conformations (ΔR3m = 0.28). For comparison, aripiprazole has several polymorphs in the CCDC with different R3m values. Figure 4a shows the aripiprazole molecular structure, while Figure 4b shows the 3D coordinates from CCDC crystal structures for its polymorphs having the largest difference in their calculated R3m values. Polymorphs I and VII, respectively, had dihedral angles of 174.7 and 58.34°, representing significant torsional differences between their linear butyl carbon chains. Correspondingly, the different geometric centers for each conformation resulted in changes to the contribution of the matrices for leverage, specific atomic positions, and, ultimately, the Euclidean distances between atoms, which resulted in R3m = 0.951 (form I) and R3m = 1.204 (form VII).

Figure 4

Figure 4. (a) Molecular structure of aripiprazole. (b) CCDC structures for two aripiprazole polymorphs, overlaid on the dichlorophenyl groups. The torsional differences changed the 3D coordinates, resulting in a relatively significant difference in R3m values: aripiprazole I (CCDC ID: MELFIT01, (17) orange) and aripiprazole VII (CCDC ID: MELFIT07, (18) teal).

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Although the average absolute difference in R3m for all molecules studied was 0.06, the potential for variation in R3m with conformation was clear: any calculated value of R3m may vary significantly from the “true” R3m value. Theoretically, the “true” value would reflect the real 3D conformation of the API in the environment of interest, yet the conformation of molecules in an amorphous system is likely more variable than in vacuum or a crystal. This was especially important for the original R3m model for ASD formation in PVPVA, which was established by using molecular conformations from API crystal structures. Since dispersion behavior was predicted based on a classification boundary value of R3m = 0.65, conformational variability of API having an R3m close to 0.65 could potentially allow the range of calculated R3m values to fall on either side of this boundary, resulting in different dispersibility predictions. In these cases, it seems that the distribution of possible conformations could be particularly important.

3.2. Simulation of Amorphous 3D Conformations Using Molecular Dynamics

Capturing information about the conformations of molecules in the amorphous state is experimentally challenging (if not impossible). The use of MD simulations, therefore, provides an in silico means of investigating the effects of probable API conformations on the formation of dispersions. Ideally, since the value of R3m can be used to predict the formation of ASDs in PVPVA, the calculated values of R3m should reflect the distribution of conformational possibilities for the API that are most likely in the amorphous form. By determining the breadth of R3m distributions for each API in our library, it was possible to refine the dispersion prediction model and comment on the likelihood of misclassified dispersion behavior for molecules close to the decision boundary.
Since experimental data for the 3D conformations likely in the amorphous form were not available, MD simulations were applied to predict these structures, as described in Section 2.3. Triplicate 500 ps simulations were run for each API, and individual simulation frames were extracted from seven frames equally spaced throughout the final 300 ps of the simulation (i.e., after 200 ps, where the system appeared to reach equilibrium). A sampling of possible 3D conformations allowed extraction of the 3D coordinates from individual molecules, and R3m was calculated for each. The distribution of R3m values for molecules in the amorphous state was then constructed from a total of 840 conformations for each API. To ensure that the output of MD simulations was meaningful, the amorphous density of simulated materials was compared with the experimental amorphous density values (Table 1). (13)
Table 1. Predicted and Actual (where available) Amorphous Densities for Library API
APICORINA R3mexperimental amorphous density(g/cm3)predicted amorphous density(g/cm3)95% crystalline densitycrystallographic density(g/cm3)CCDC refcode
propranolol0.342N/A1.08 ± 0.0041.1061.164IMITON (19)
cimetidine0.403N/A1.20 ± 0.0011.2461.312CIMETD (20)
melatonin0.407N/A1.16 ± 0.0081.2121.276MELATN01 (21)
terfenadine0.561N/A1.04 ± 0.0161.071.13EWEMIF (22)
cloperastine0.562N/A1.11 ± 0.003N/AN/AN/A
nifedipine0.5681.20a1.23 ± 0.0041.3131.382BICCIZ03 (23)
quinidine0.5931.17 ± 0.009b1.14 ± 0.0021.1721.234BOMDUC (24)
sulfanilamide0.595N/A1.44 ± 0.0161.4381.514SULAMD03 (25)
tolbutamide0.687N/A1.21 ± 0.0071.1891.252ZZZPUS18 (26)
indomethacin0.7371.31c1.29 ± 0.0021.3031.372INDMET (27)
ketoconazole0.8141.27 ± 0.006b1.30 ± 0.0031.3301.4KCONAZ (28)
itraconazole0.8721.27d1.26 ± 0.0151.2921.36TEHZIP (29)
chlorpropamide0.927N/A1.36 ± 0.0031.3781.45BEDMIG10 (30)
felodipine0.9641.28a1.26 ± 0.0051.3781.451DONTIJ (31)
bicalutamide1.001N/A1.43 ± 0.0091.4761.554JAYCES (32)
a

Marsac et al. (33)

b

Bookwala et al. (13)

c

Tong and Zografi (34)

d

Six et al. (35)

Previously, it was shown that the experimentally determined amorphous densities for 10 small-molecule organic solids were very comparable with MD-simulated amorphous densities, having an average percent error of −0.7%. When compared with estimates of amorphous density using a previous suggestion that takes 95% of the crystallographic density, (33,36,37) the values differed by an average percent error of +3.7%. (13) This suggested that the MD simulations captured molecular arrangements in a volume close to that of the real amorphous form. This result helped to indicate that MD-simulated amorphous densities were reasonable approximations for materials that could not be experimentally rendered amorphous. Consequently, simulations of molecular conformations in realistic amorphous “cells” lent realism to their use in this work and all amorphous densities used were confirmed to be always lower than the corresponding crystallographic density obtained from crystal structures where available.

3.3. Calculating the Distribution of R3m

The box-and-whisker plot shown in Figure 5 reports the 840 R3m values calculated by sampling conformations of 15 API molecules simulated by MD. Median R3m values determined using MD simulations for each molecule were generally similar to CORINA-predicted values and R3m calculated from crystal structure conformations used to build the original dispersibility model. (7−9) The absolute difference between the single R3m calculated from CORINA structures and the medians of R3m distributions resulting from MD was taken, resulting in an average absolute difference of 0.03 for the library. The largest of these absolute differences was for cimetidine (0.11), where the variability observed for this molecule specifically resulted from the flexibility of the chain extending from the imidazole group, which caused variations in the Euclidean distances between the intramolecular S and the two nearest N atoms (see Figure 1).

Figure 5

Figure 5. A box-and-whisker plot for R3m values calculated from MD simulations for each API. Red lines toward the center of each box are median values, while the black diamonds indicate the mean. Each box boundary represents the interquartile range, with whiskers indicating the range of observed values. Outliers are shown using red “+” symbols, and the blue dashed lines indicate R3m values calculated from SMILES files and the CORINA algorithm. The horizontal black dotted line indicates the R3m = 0.65 classification boundary published previously, (8,9) and the horizontal black dot-dashed line indicates the boundary updated using MD simulation results (R3m = 0.632, see Figure 8). Green boxes indicate API experimentally observed to successfully form ASDs in PVPVA, while those in red indicate compounds that failed to form ASDs. (7−10)

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Values of R3m from CORINA-predicted conformations all fell within the range of MD simulations with the exception of propranolol (Figure 5). The CORINA algorithm predicted a linear conformation for propranolol, shown by the blue molecule (Figure 6b,c). The red conformation, also shown in Figure 6b, was taken from the MD simulation and had an R3m value matching the median for the distribution. The green conformation in Figure 6c was also extracted from the MD simulation, based on its similarity to the R3m value calculated using the CORINA algorithm (difference in R3m = 0.0014). A linear conformation was less commonly observed in the MD simulations owing to the impact of the surrounding environment. In contrast, rotation about the O8, C7, C6, and C5 dihedral was more common and resulted in an increase in R3m relative to that calculated for a linear conformation. Numerically, the R3m value changes due to the resulting shift in the geometric center of the molecule and subsequent change in leverage of each atom.

Figure 6

Figure 6. Propranolol conformations and their effect on R3m. (a) Propranolol structure with atoms labeled. Propranolol molecules in blue correspond to the CORINA calculated conformation ((b) and (c)). (b) The propranolol molecule in red was extracted from MD simulations and has an R3m value equal to the median (0.4342). (c) The green propranolol molecule shows a conformation from the MD simulation having an R3m value (0.3406) most similar to the CORINA conformation (0.342) (difference in R3m = −0.0014).

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These results increased the confidence that the R3m values determined using the conformations predicted from the CORINA algorithm were sufficiently representative of molecules in the amorphous state. The R3m values applied to build the original dispersibility model all fell within the distributions calculated using conformations obtained from the MD simulations. This indicated that the R3m values used to build the original model captured relevant and realistic structural information for the API molecules, relative to how they might appear during ASD formation with PVPVA. (9) Importantly, the MD-determined R3m values allow dispersibility predictions to shift away from a phenomenological dichotomy and embrace the fact that molecules may adopt conformations that make dispersion in PVPVA more or less probable. As such, the distribution of R3m for a molecule can help incorporate the relative risk of an incorrect prediction based on probabilities that deviate from 1 or 0. A bar plot that compares the R3m values calculated from CORINA-predicted structures, conformations extracted from CCDC structures, and MD simulated structures is shown in Figure S1of the Supporting Information.

3.4. Updating the R3m Model

To account for the large number of potential molecule conformations extracted from the present MD simulations, their respective R3m values were modeled against experimental observations of dispersion formation in PVPVA, using logistic regression, resulting in eq 2.
logitP(Y)=−46.79+74.01(R3m)
(2)
All R3m values associated with API that were observed to disperse in PVPVA were assigned a probability of 1 and all API that did not disperse were assigned probability of 0, and the model remained highly significant having p < 0.0001, a pseudo R2 = 0.91, and χ2 = 1.52 × 104. As Figure 7 shows, this leads to some overlap between the R3m values associated with successful or unsuccessful dispersion formation. Applying logistic regression to these data predicted that structures having an R3m = 0.632 had a 50% probability of successfully forming a dispersion in PVPVA. Furthermore, according to this MD-based model, R3m values below 0.570 had a probability <1% and values greater than 0.693 had a probability >99% of forming a homogeneous amorphous dispersion with PVPVA. Expansion of the R3m model in this way affords some evaluation of the risk associated with attempting to disperse an API in PVPVA, enabling its potential use as a formulation tool. While a priori application would be difficult, especially for API having a median R3m < 0.632, but some conformers with R3m above this threshold, it is anticipated that the R3m model could serve as the first step in a materials-sparing process that also included experimental determination of dispersibility behavior. Take for example the cases of melatonin and quinidine. The values in the distribution of R3m for melatonin all fall well below the 0.632 categorical boundary (Figure 5). Combined with the probabilities shown in Figure 7, melatonin is very unlikely to ever form a dispersion in PVPVA, consistent with experimental observations under various conditions. (7−9) A new API having similar data would, therefore, make a poor candidate for ASD formation using this polymer. In contrast, the data for quinidine show that a small portion of its conformer distribution has values of R3m > 0.632. A new API having MD-simulated data similar to quinidine might merit experimental confirmation of dispersibility in PVPVA, especially if the therapeutic implications of the drug were promising, and aqueous solubility was its main limitation. Although experimentally, quinidine was never found to disperse in PVPVA, (7−9) use of a materials-sparing tool such as the R3m model could additionally point toward the inclusion of a ternary dispersion-enabling component, should the molecular descriptor suggest that there are conformations that make dispersion in the desired polymer feasible.

Figure 7

Figure 7. Logistic regression model logit P(Y) = −46.79 + 74.01(R3m) (blue line) using the expanded MD data set, resulting in an updated classification boundary value of R3m = 0.632. This model contains 12,600 data points for R3m (jittered to more clearly show the amount of data). Red circles correspond to API that failed to form ASDs in PVPVA, while green circles correspond to API that successfully formed ASDs in PVPVA by melt-quenching. The red dashed line indicates that molecules having R3m <0.570 have <1% probability to disperse in PVPVA, while the green dashed line indicates that molecules having R3m >0.693 have >99% probability to disperse in PVPVA.

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In the original model, all API having R3m <0.6 failed to form ASDs while those having R3m >0.68 successfully formed ASDs in PVPVA. (38) When each molecule was constrained to a single conformation, the completely separated data made logistic regression unnecessary and inappropriate. (39) Instead, the classification boundary (R3m = 0.65) that grouped molecules according to dispersibility in this copolymer was defined as the midpoint between the values at the edge of each phenomenological category. As shown, the new R3m model boundary value shifted to 0.632 from the previously reported value of 0.65, which is illustrated by the horizontal dotted (R3m = 0.65) and dot–dash (R3m = 0.632) lines in Figure 5. When classifications of the dispersibility of the 15 library molecules in PVPVA were made using the updated model boundary using either the CORINA-predicted R3m or the median from the R3m distributions, they remained unchanged relative to the original model.
In the present case where R3m adopts a distribution of values for each API, there is uncertainty in the true boundary because of the gap between R3m values of 0.595 (sulfanilamide) and 0.687 (tolbutamide). Although the updated boundary (R3m = 0.632) is based on a larger data set that includes the distribution of 12,600 3D conformations, the number of unique API remains relatively small (n = 15). Incorporation of additional molecules (and their probable conformers) whose R3m distributions are close to 0.632 is expected to improve the uncertainty around the value of the categorical boundary; nonetheless, the classification of dispersion behaviors for each API using the original model remains unchanged. The similarity between the models further supports the conclusion that the R3m descriptor is able to capture relevant 3D information about the API. Incorporation of these additional data into the model resulted in improved confidence in the model boundary by ensuring that the model incorporates conformational flexibility. As a result, future predictions will likely improve, particularly for API with R3m values near the model boundary.

3.5. Marketed API Misclassification by the R3m Model: Ritonavir and Lopinavir

It was previously identified that ritonavir and lopinavir had CORINA-based R3m values <0.632, respectively, at R3m = 0.611 and R3m = 0.588. These values indicate that ritonavir and lopinavir group with other APIs whose molecular attributes make the formation of persistent dispersions with PVPVA less likely. (9) Since both ritonavir and lopinavir are marketed ASD products, it has already been demonstrated that these molecules have an ability to persist as an ASD with this copolymer (9); however, it is important to note that these products also contain surfactants, albeit at relatively low concentrations. Additionally, the marketed ASD drug products of ritonavir and lopinavir have very low drug loadings. (40) Although the applicability of the R3m model to dispersions having more than two components and drug loadings less than 15% have not been studied, MD simulations of these molecules were also performed to (1) see what percentage (if any) of likely conformations for either molecule in the amorphous phase resulted in R3m >0.632 and (2) check the energetic feasibility of those conformations. It is acknowledged that conformations of molecules in their pure amorphous form may not be the same as those adopted in a dispersion where the drug–polymer interactions may affect the API conformation. Based on the comparability of the R3m values determined from conformations observed in the crystal structures to those determined from CORINA predictions and MD simulations, the conformations in the amorphous API were assumed to not be dramatically different from those in amorphous dispersions with PVPVA. Future studies will investigate the influence of formulation composition on the API conformation and R3m values. Both ritonavir and lopinavir are known to be dispersible in PVPVA; an understanding of the R3m descriptor showcases that higher R3m values for molecules are representative of their interaction capability with the polymer, thus the conformations representing higher R3m values are of importance. (9)
Figures 8 and 9 show the R3m distributions calculated from MD-predicted conformations for ritonavir and lopinavir. Both distributions of R3m are broad and range from 0.54 to 0.7 (ritonavir) and 0.54 to 0.68 (lopinavir). Notably, >30% of the ritonavir conformations and >15% of the lopinavir conformations result in R3m >0.632. On comparison of the highest R3m (R3m max) vs CORINA (R3m CORINA) vs the lowest R3m (R3m min) conformations, it was observed that the heavier atoms were farther away and directed outward from the geometric center of the molecule for the R3m max conformation. On further investigation of individual atomic contributions to R3m, it was observed that the values for heavier atoms were different, based on the molecular conformation. For example, for the ritonavir conformation that resulted in the R3m max, the heavier N, O, and S atoms contributed 54.2% to the value of R3m whereas these same atoms contributed only 37.2% in the conformation that resulted in R3m min. Both ritonavir and lopinavir adopted conformations where heavier atoms increased R3m values above 0.632.

Figure 8

Figure 8. R3m distribution for 840 MD conformations of ritonavir likely in the amorphous state. The structures in conformations reflective of R3m min, R3m CORINA, and R3m max are shown (hydrogen atoms are removed for clarity except in the cases of hydrogen bond donors).

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Figure 9

Figure 9. R3m distribution for 840 MD conformations of lopinavir likely in the amorphous state. The structures in conformations reflective of R3m min, R3m CORINA, and R3m max are shown (hydrogen atoms are removed for clarity, except in the cases of hydrogen bond donors).

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Consistent with previous work in this area, evaluation of the distributions of R3m for both ritonavir and lopinavir suggests that these molecules can adopt conformations that position atoms in such a way that the probability of adhesive, noncovalent interactions with the polymer PVPVA is increased. (9) More broadly, this evaluation suggests that molecules closer to the R3m classification boundary may adopt conformations that are more or less conducive to dispersion in PVPVA. The CORINA R3m value can act as an excellent, scientifically driven starting point for predicting dispersion formation potential, but does not represent the only conformation that may be important for rationalizing the outcomes. Molecules whose R3m distributions are very close to the classification boundary enable more informed formulation decisions, by estimating the probability that they will disperse in PVPVA. This case is clear with ritonavir and lopinavir, by evaluating the threshold R3m that estimates >80% probability to successfully disperse in PVPVA. Based on Figure 7, this value occurs when R3m ≥0.654, where ritonavir and lopinavir respectively have 12.5 and 3% conformations whose R3m exceeds this value. Ultimately, it is envisioned that the R3m model could be used as a materials-sparing formulation tool that can be used to suggest the risk of proceeding with an API as a candidate for dispersion in PVPVA. Had the model based exclusively on single molecular conformations been used, both ritonavir and lopinavir would have been classified as unable to disperse in PVPVA. In contrast, the probability estimations given by the logistic regression model indicate that both API can adopt conformations that have a very good likelihood of forming a dispersion in this copolymer. In the case of a new chemical entity, this model could be used to evaluate the distribution of the R3m values for conformations in the amorphous state. If a reasonable percentage of the distribution indicated conformations that enable ASD formation, then experimental confirmation of its dispersibility in PVPVA would be warranted. Further evaluation of the accuracy of probabilities for API having median R3m values close to the classification boundary (gray region in Figure 7), should be next steps.

4. Conclusions

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The 3D conformations that an API molecule can adopt have an impact on its R3m value because molecular shape changes influence the matrices used to calculate this molecular descriptor. Comparisons between 3D conformations obtained using the CORINA algorithm, crystal structure data, and MD simulations showed that the resulting variations in R3m were generally low for each API. Nevertheless, there are cases where changes in conformation can result in significant changes in the subsequent R3m value, as shown with aripiprazole. A large majority (86%) of an expanded data set of API had a difference in R3m ≤0.1, with a maximum difference in R3m equal to 0.28. The average absolute difference between CORINA and the median MD R3m values was 0.03, and the largest difference between the median MD R3m and CORINA R3m values was 0.11.
Ultimately, these results support the hypothesis that using MD to predict the distribution of 3D conformations library molecules can adopt results in an R3m model that better predicts dispersibility in PVPVA. In addition to greater confidence in the R3m classification boundary, by incorporating the potential for large conformational changes to impact R3m, the regression model is now able to incorporate probabilities of successful dispersion formation, which is particularly important for API having an R3m value near the model classification boundary. These findings further support the idea that R3m captures nuanced information about the API relevant to its dispersibility in PVPVA. Inclusion of conformational flexibility in future applications of R3m captures a more realistic description of the molecules and, subsequently, can allow for improved formulation decisions.

Supporting Information

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The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acs.molpharmaceut.3c00909.

  • List of all 80 APIs used to examine the relationship between R3m calculated by CORINA-generated 3D structures and 3D structures obtained from the CCDC; a comparison of R3m values calculated from 3D conformations generated from the CORINA algorithm, obtained from crystal structure data, and generated by molecular dynamic simulations; and links to the MATLAB code for the calculation of R3m and extraction of molecule coordinates from Materials Studio .xsd files (PDF)

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Author Information

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  • Corresponding Author
  • Authors
    • Kevin DeBoyace - School of Pharmacy and Graduate School of Pharmaceutical Sciences, Duquesne University, 600 Forbes Ave, Pittsburgh, Pennsylvania 15282, United States;  Pfizer Worldwide R&D, Eastern Point Road, Groton, Connecticut 06340, United States
    • Mustafa Bookwala - School of Pharmacy and Graduate School of Pharmaceutical Sciences, Duquesne University, 600 Forbes Ave, Pittsburgh, Pennsylvania 15282, United States
    • Deliang Zhou - Drug Product Development, Research and Development, AbbVie, 1 North Waukegan Road, North Chicago, Illinois 60064, United States;  Small Molecules Drug Product Development, BeiGene USA, Inc., 55 Cambridge Parkway, Cambridge, Massachusetts 02142, United States;  Orcidhttps://orcid.org/0000-0003-0689-890X
    • Ira S. Buckner - School of Pharmacy and Graduate School of Pharmaceutical Sciences, Duquesne University, 600 Forbes Ave, Pittsburgh, Pennsylvania 15282, United States
  • Author Contributions

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

  • Funding

    Funding for this research was provided by AbbVie, Inc.

  • Notes
    The authors declare the following competing financial interest(s): Duquesne University and AbbVie jointly participated in study design, research, data collection, analysis and interpretation of data, writing, reviewing, and approving the publication. KD was and MB is a graduate student at Duquesne University. ISB and PLDW are professors at Duquesne University. They all have no competing financial interest. DZ was a former employee of AbbVie and may own AbbVie stock. KD is a current employee of Pfizer. This work was not funded by Pfizer and may not reflect the views of Pfizer.

Acknowledgments

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The authors would like to thank AbbVie, Inc., for financial support and for their invaluable scientific input. The authors acknowledge the Minnesota Supercomputing Institute (MSI) at the University of Minnesota for providing resources that contributed to the research results reported within this paper. URL: http://www.msi.umn.edu. The authors are grateful to Dr. Calvin Sun and Gerrit Vreeman for performing MD simulations for ritonavir and lopinavir.

References

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    Gumireddy, Ashwini; Bookwala, Mustafa; Zhou, Deliang; Wildfong, Peter L. D.; Buckner, Ira S.
    Journal of Pharmaceutical Sciences (Philadelphia, PA, United States) (2023), 112 (1), 318-327CODEN: JPMSAE; ISSN:0022-3549. (Elsevier Inc.)
    Evaluation of different amorphous solid dispersion carrier matrixes is enabled by active pharmaceutical ingredient (API) structure-based predictions. This study compares the utility of Hansen Soly. Parameters with the R3m mol. descriptor for identifying dispersion polymers based on the structure of the drug mol. Twelve API-polymer combinations (4 APIs and 3 interrelated polymers) were used to test each approach. Co-solidified mixts. contg. 75% API were prepd. by melt-quenching. Phase behavior was evaluated and classified using differential scanning calorimetry, powder X-ray diffraction, polarized light microscopy, and hot stage microscopy. Observations of dispersion behavior were compared to predictions made using the Hansen Soly. Parameter and R3m. The soly. parameter approach misclassified the dispersion behavior of 1 API-polymer combination and also did not produce definite predictions in 3 out of 12 of the API-polymer combinations. In contrast, R3m classifications of dispersion behavior were correct in all but two cases, with one misclassification and one ambiguous prediction. The soly. parameters best classify dispersion behavior when specific drug-polymer intermol. interactions are present, but may be less useful otherwise. Ultimately, these two methods are most effectively used together, as they are based on distinct features of the same mol. structure.
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  11. 11
    Groom, C. R.; Bruno, I. J.; Lightfoot, M. P.; Ward, S. C. The Cambridge Structural Database. Acta Cryst. B 2016, 72 (2), 171– 179,  DOI: 10.1107/S2052520616003954
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    The Cambridge Structural Database
    Groom, Colin R.; Bruno, Ian J.; Lightfoot, Matthew P.; Ward, Suzanna C.
    Acta Crystallographica, Section B: Structural Science, Crystal Engineering and Materials (2016), 72 (2), 171-179CODEN: ACSBDA; ISSN:2052-5206. (International Union of Crystallography)
    The Cambridge Structural Database (CSD) contains a complete record of all published org. and metal-org. small-mol. crystal structures. The database has been in operation for over 50 years and continues to be the primary means of sharing structural chem. data and knowledge across disciplines. As well as structures that are made public to support scientific articles, it includes many structures published directly as CSD Communications. All structures are processed both computationally and by expert structural chem. editors prior to entering the database. A key component of this processing is the reliable assocn. of the chem. identity of the structure studied with the exptl. data. This important step helps ensure that data is widely discoverable and readily reusable. Content is further enriched through selective inclusion of addnl. exptl. data. Entries are available to anyone through free CSD community web services. Linking services developed and maintained by the CCDC, combined with the use of std. identifiers, facilitate discovery from other resources. Data can also be accessed through CCDC and third party software applications and through an application programming interface.
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  12. 12
    Sun, H.; Jin, Z.; Yang, C.; Akkermans, R. L. C.; Robertson, S. H.; Spenley, N. A.; Miller, S.; Todd, S. M. COMPASS II: Extended Coverage for Polymer and Drug-like Molecule Databases. J. Mol. Model. 2016, 22 (2), 47,  DOI: 10.1007/s00894-016-2909-0
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    COMPASS II: extended coverage for polymer and drug-like molecule databases
    Sun Huai; Jin Zhao; Yang Chunwei; Akkermans Reinier L C; Robertson Struan H; Spenley Neil A; Miller Simon; Todd Stephen M
    Journal of molecular modeling (2016), 22 (2), 47 ISSN:.
    The COMPASS II force field has been developed by extending the coverage of the COMPASS force field (J Phys Chem B 102(38):7338-7364, 1998) to polymer and drug-like molecules found in popular databases. Using a fragmentation method to systematically construct small molecules that exhibit key functional groups found in these databases, parameters applicable to database compounds were efficiently obtained. Based on the same parameterization paradigm as used in the development of the COMPASS force field, new parameters were derived by a combination of fits to quantum mechanical data for valence parameters and experimental liquid and crystal data for nonbond parameters. To preserve the quality of the original COMPASS parameters, a quality assurance suite was used and updated to ensure that additional atom-types and parameters do not interfere with the existing ones. Validation against molecular properties, liquid and crystal densities, and enthalpies, demonstrates that the quality of COMPASS is preserved and the same quality of prediction is achieved for the additional coverage.
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    Bookwala, M.; DeBoyace, K.; Buckner, I. S.; Wildfong, P. L. D. Predicting Density of Amorphous Solid Materials using Molecular Dynamics Simulation. AAPS PharmSciTech 2020, 21 (3), 1– 11,  DOI: 10.1208/s12249-020-1632-4
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    Consonni, V.; Todeschini, R.; Pavan, M. Structure/Response Correlations and Similarity/Diversity Analysis by GETAWAY Descriptors. 1. Theory of the Novel 3D Molecular Descriptors. J. Chem. Inf. Model 2002, 42 (3), 682– 692,  DOI: 10.1021/ci015504a
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    Todeschini, R.; Consonni, V. Molecular Descriptors for Chemoinformatics; John Wiley & Sons, 2009.
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    Gasteiger, J.; Rudolph, C.; Sadowski, J. Automatic Generation of 3D-atomic Coordinates for Organic Molecules. Tetrahedron Comput. Methodol. 1990, 3 (6, Part C), 537– 547,  DOI: 10.1016/0898-5529(90)90156-3
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    Automatic generation of 3D atomic coordinates for organic molecules
    Gasteiger, J.; Rudolph, C.; Sadowski, J.
    Tetrahedron Computer Methodology (1990), 3 (6c), 537-47CODEN: TCMTE6; ISSN:0898-5529.
    A system has been developed that can automatically generate 3-dimensional at. coordinates from the constitution of a mol. as expressed by a connection table. The program, CORINA, is applicable to the entire range of org. chem. It can also handle structures that are beyond the scope of some other programs, e.g., macrocyclic and polymacrocyclic mols. Computation times are short and the results compare favorably with data from x-ray crystallog. and with those of mol. mechanics calcns.
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  17. 17
    Braun, D. E.; Gelbrich, T.; Kahlenberg, V.; Tessadri, R.; Wieser, J.; Griesser, U. J. Conformational Polymorphism in Aripiprazole: Preparation, Stability and Structure of Five Modifications. J. Pharm. Sci. 2009, 98 (6), 2010– 2026,  DOI: 10.1002/jps.21574
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    Conformational polymorphism in aripiprazole: Preparation, stability and structure of five modifications
    Braun, Doris E.; Gelbrich, Thomas; Kahlenberg, Volker; Tessadri, Richard; Wieser, Josef; Griesser, Ulrich J.
    Journal of Pharmaceutical Sciences (2009), 98 (6), 2010-2026CODEN: JPMSAE; ISSN:0022-3549. (Wiley-Liss, Inc.)
    Five phase-pure modifications of the antipsychotic drug aripiprazole were prepd. and characterized by thermal anal., vibrational spectroscopy and X-ray diffractometry. All modifications can be produced from solvents, form I addnl. by heating of form X° to ∼120°C (solid-solid transformation) and form III by crystn. from the melt. Thermodn. relationships between the polymorphs were evaluated on the basis of thermochem. data and visualized in a semi-schematic energy/temp. diagram. At least six of the ten polymorphic pairs are enantiotropically and two monotropically related. Form X° is the thermodynamically stable modification at 20°C, form II is stable in a window from about 62-77°C, and form I above 80°C (high-temp. form). Forms III and IV are triclinic (P\overline 1), I and X° are monoclinic (P21) and form II orthorhombic (Pna21). Each polymorph exhibits a distinct mol. conformation, and there are two fundamental N-H\···O hydrogen bond synthons (catemers and dimers). Hirshfeld surface anal. was employed to display differences in intermol. short contacts. A high kinetic stability was obsd. for three metastable polymorphs which can be categorized as suitable candidates for the development of solid dosage forms. © 2008 Wiley-Liss, Inc. and the American Pharmacists Assocn. J Pharm Sci 98:2010-2026, 2009.
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  18. 18
    Delaney, S. P.; Pan, D.; Yin, S. X.; Smith, T. M.; Korter, T. M. Evaluating the Roles of Conformational Strain and Cohesive Binding in Crystalline polymorphs of Aripiprazole. Cryst. Growth Des. 2013, 13 (7), 2943– 2952,  DOI: 10.1021/cg400358e
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    Evaluating the Roles of Conformational Strain and Cohesive Binding in Crystalline Polymorphs of Aripiprazole
    Delaney, Sean P.; Pan, Duohai; Yin, Shawn X.; Smith, Tiffany M.; Korter, Timothy M.
    Crystal Growth & Design (2013), 13 (7), 2943-2952CODEN: CGDEFU; ISSN:1528-7483. (American Chemical Society)
    The relative stabilities of cryst. polymorphs are an important aspect of the manufg. and effective utilization of pharmaceuticals. These stabilities are driven by both mol. conformational energy within the solid-state components and cohesive binding energy of the cryst. arrangement. The combined approach of exptl. vibrational terahertz spectroscopy with solid-state d. functional theory provides a powerful tool to study such properties and is applied here in the anal. of conformational polymorphism in cryst. aripiprazole. The low-frequency (<95 cm-1) terahertz vibrations of several aripiprazole polymorphs were measured, revealing distinct spectral features that uniquely identify each form. Solid-state d. functional theory was employed to interpret the exptl. terahertz spectra, correlating the obsd. spectral features to specific at. motions within the cryst. lattice. The computational anal. provides insight into the formation and stability of the polymorphs by revealing the balance between the external binding forces and internal mol. forces that is ultimately responsible for the phys. characteristics of the numerous cryst. polymorphs of aripiprazole.
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  19. 19
    Bredikhin, A. A.; Savel"ev, D. V.; Bredikhina, Z. A.; Gubaidullin, A. T.; Litvinov, I. A. Crystallization of Chiral Compounds. 2. Propranolol: Free Base and Hydrochloride. Russ. Chem. Bull. 2003, 52 (4), 853– 861,  DOI: 10.1023/A:1024435906421
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    Crystallization of chiral compounds. 2. Propranolol: free base and hydrochloride
    Bredikhin, A. A.; Savel ev, D. V.; Bredikhina, Z. A.; Gubaidullin, A. T.; Litvinov, I. A.
    Russian Chemical Bulletin (Translation of Izvestiya Akademii Nauk, Seriya Khimicheskaya) (2003), 52 (4), 853-861CODEN: RCBUEY; ISSN:1066-5285. (Kluwer Academic/Consultants Bureau)
    The data from IR spectroscopy, DSC, and x-ray diffraction anal. are compared for cryst. specimens of homochiral and racemic propranolol and its hydrochloride. The stabilities of the racemates were quant. characterized and the factors responsible for the order of their stability are revealed.
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    Hädicke, E.; Frickel, F.; Franke, A. Die Struktur von Cimetidin (N″-Cyan-N-Methyl-N′-[2-[(5-methyl-1H-imidazol-4-yl)methylthio]ethyl]guanidin), einem Histamin H2-Rezeptor-Antagonist. Chem. Ber. 1978, 111 (9), 3222– 3232,  DOI: 10.1002/cber.19781110926
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    Quarles, W. G.; Templeton, D. H.; Zalkin, A. The Crystal and Molecular Structure of Melatonin. Acta Cryst. B 1974, 30 (1), 99– 103,  DOI: 10.1107/S0567740874002287
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    Crystal and molecular structure of melatonin
    Quarles, William G.; Templeton, David H.; Zalkin, Allan
    Acta Crystallographica, Section B: Structural Crystallography and Crystal Chemistry (1974), 30 (1), 99-103CODEN: ACBCAR; ISSN:0567-7408.
    Crystals of melatonin (C13H16N2O2) are monoclinic, space group P21/c, with lattice parameters a 7.707 (2), b 9.252 (2), c 17.077 (4) Å, β 96.78 (3)°, Z = 4, d.(obsd.) = 1.272, and d.(x-ray) = 1.276. The structural model was refined to R = 0.036 for 900 independent x-ray reflections measured on an automatic diffractometer. The mols. are nearly flat with the side chain in the fully extended (trans) conformation. Mols. are connected in sheets by weak H bonds from N to O, with N-O = 2.90 and 2.96 Å.
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    Bookwala, M.; Gumireddy, A.; Aitken, J. A.; Wildfong, P. L. D. Single Crystal Structure of Terfenadine Form I. J. Chem. Crystallogr. 2022, 52 (1), 81– 88,  DOI: 10.1007/s10870-021-00892-3
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    Single Crystal Structure of Terfenadine Form I
    Bookwala, Mustafa; Gumireddy, Ashwini; Aitken, Jennifer A.; Wildfong, Peter L. D.
    Journal of Chemical Crystallography (2022), 52 (1), 81-88CODEN: JCCYEV; ISSN:1074-1542. (Springer)
    Terfenadine, C32H41NO2, 1, contains an α,α-diphenyl-4-piperidinomethanol moiety, which is related to the H1-receptor blocking activity, facilitating its prior use as an antihistamine drug. In addn. to its bioactivity, terfenadine is useful as a model, small-mol. cryst. solid for studying several material properties. Despite a history of therapeutic use, the absence of a crystal structure has limited current studies of the physicochem. behavior of this material. In the present manuscript, the elusive X-ray crystal structure of 1 was solved and refined at 296 K using single crystals grown from a co-solvent mixt. of acetonitrile:methanol:ethanol (0.50:0.25:0.25). Terfenadine crystallizes in the monoclinic space group P21/n, and exhibits a chair conformation of the piperidine ring and a gauche conformation of the Bu chain. Hydrogen bonds between O-H···O and O-H···N, along with weak van der Waals interactions between C16-H16B···H16B'-C16'; and C2-H2···H16A-C16A were confirmed using Hirshfeld-Surface anal. Differential scanning calorimetry and X-ray powder diffraction confirmed that the crystal structure reported herein was that of the most thermodynamically stable monotropic polymorph of terfenadine (form I).
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    Gunn, E.; Guzei, I. A.; Cai, T.; Yu, L. Polymorphism of Nifedipine: Crystal Structure and Reversible Transition of the Metastable β polymorph. Cryst. Growth Des. 2012, 12 (4), 2037– 2043,  DOI: 10.1021/cg3000075
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    Polymorphism of Nifedipine: Crystal Structure and Reversible Transition of the Metastable β Polymorph
    Gunn, Erica; Guzei, Ilia A.; Cai, Ting; Yu, Lian
    Crystal Growth & Design (2012), 12 (4), 2037-2043CODEN: CGDEFU; ISSN:1528-7483. (American Chemical Society)
    The authors report the 1st structural detn. of the metastable β polymorph of nifedipine (NIF) by single-crystal x-ray diffraction. Stable, high-quality crystals were grown from the melt in the presence of a polymer dopant. The authors' β NIF structure was characterized by a unit cell similar to that of the structure recently proposed from powder diffraction, but significantly different mol. conformations. Unlike the stable α polymorph, β NIF undergoes a reversible solid-state transformation near 60°. The now available β NIF structure clarifies some confusion concerning NIF polymorphs and enables inquiries into the structural basis for the selective crystn. of β NIF from glasses. The authors report that another polymorph crystallizes concomitantly with β NIF from the supercooled melt and transforms to β NIF at room temp.; this polymorph also undergoes reversible solid-state transformation. Crystallog. data and at. coordinates are given.
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    Kashino, S.; Haisa, M. Structure of Quinidine, C20H24N2O2. Acta Cryst. C 1983, 39 (2), 310– 312,  DOI: 10.1107/S0108270183004515
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    O’Connell, A. M.; Maslen, E. N. X-ray and Neutron Diffraction Studies of β-Sulphanilamide. Acta Crystallogr. 1967, 22 (1), 134– 145,  DOI: 10.1107/S0365110X67000210
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    X-ray and neutron diffraction studies of β-sulfanilamide
    O'Connell, A. M.; Maslen, Edward N.
    Acta Crystallographica (1967), 22 (1), 134-45CODEN: ACCRA9; ISSN:0365-110X.
    The crystal structure has been refined from 3-dimensional photographic x-ray data and 2-dimensional neutron diffraction data. Positional and anisotropic thermal parameters of the non-H atoms and positional and isotrophic thermal parameters of the H atoms were refined in the x-ray analysis to give a final R index of 4.9%. Positional and isotropic thermal parameters of the H atoms were refined in the neutron study. The final residual factors were R(h01) 8.0%, and R(hk0) 9.1%. The mean standard deviation in the C-C bonds is 0.0028 A. and the estd. standard in the H atom positional parameters are ∼0.035 A. The bond lengths suggest that there is a small but significant contribution of a quinonoid resonance form to the structure of the mol. The distribution of residual electron d. within the benzene ring and in the tetrahedral sulfamide group is explained in terms of effects resulting from electron redistribution at bonding. The H bond system closely resembles that found in α-sulfanilamide and N-H...O bonds are ∼0.25 A. longer than in related zwitterion compds.
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    Drebushchak, T. N.; Drebushchak, V. A.; Pankrushina, N. A.; Boldyreva, E. V. Single-crystal to Single-crystal Conformational Polymorphic Transformation in Tolbutamide at 313 K. Relation to Other Polymorphic Transformations in Tolbutamide and Chlorpropamide. CrystEngComm 2016, 18 (30), 5736– 5743,  DOI: 10.1039/C6CE00764C
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    Single-crystal to single-crystal conformational polymorphic transformation in tolbutamide at 313 K. Relation to other polymorphic transformations in tolbutamide and chlorpropamide
    Drebushchak, T. N.; Drebushchak, V. A.; Pankrushina, N. A.; Boldyreva, E. V.
    CrystEngComm (2016), 18 (30), 5736-5743CODEN: CRECF4; ISSN:1466-8033. (Royal Society of Chemistry)
    Three polymorphs of tolbutamide (IL, II, and III) were studied using single-crystal X-ray diffraction, from 100 K to room temp. (forms II and III) and to 350 K (form I), and differential scanning calorimetry. The reversible transformation, IL ↔ IH, was found to be of the single-crystal to single-crystal type and the structure of the high-temp. form (IH) was solved and refined. The structure of IH differs from that of IL only in the conformation of the mol., with mol. arrangements being practically unchanged (isostructural conformational transformation). The transition takes place at 313 K with no sign of hysteresis. The vol. change, ΔV/V, across the reversible transformation IL ↔ IH was calcd. and compared to those for the other conformational transformations. Two types of conformational polymorphic transformations (irreversible reconstructive and reversible isostructural) in tolbutamide and chlorpropamide were compared.
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    Cox, P. J.; Manson, P. L. γ-Indomethacin at 120 K. Acta Crystallogr., Sect. E: Struct. Rep. Online 2003, 59 (7), o986– 988,  DOI: 10.1107/S160053680301290X
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    γ-Indomethacin at 120 K
    Cox, Philip J.; Manson, Pamela L.
    Acta Crystallographica, Section E: Structure Reports Online (2003), 59 (7), o986-o988CODEN: ACSEBH; ISSN:1600-5368. (International Union of Crystallography)
    The crystal structure of γ-indomethacin, C19H16ClNO4, is supported, not only by O-H···O interactions, but also by C-H··· π and π-π interactions. Crystallog. data are given.
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    Peeters, O. M.; Blaton, N. M.; De Ranter, C. J. Cis-1-Acetyl-4-(4-{[2-(2,4-dichlorophenyl)-2-(1H-1-imidazolylmethyl)-1,3-dioxolan-4-yl]methoxy}phenyl)piperazine: Ketoconazole. A Crystal Structure with Disorder. Acta Cryst. B 1979, 35 (10), 2461– 2464,  DOI: 10.1107/S0567740879009651
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    Peeters, O. M.; Blaton, N. M.; De Ranter, C. J. Cis-2-sec-Butyl-4-{4-[4-(4-{[2-(2,4-dichlorophenyl)-2-(1H-1,2,4-triazol-1-ylmethyl)-1,3-dioxolan-4-yl]methoxy}phenyl)-1-piperazinyl]phenyl}-2,4-dihydro-3H-1,2,4-triazol-3-one (Itraconazole). Acta Cryst. C 1996, 52 (9), 2225– 2229,  DOI: 10.1107/S0108270196004180
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    cis-2-sec-Butyl-4-{4-[4-(4-{[2-(2,4-dichlorophenyl)-2-(1H-1,2,4-triazol-1-ylmethyl)-1,3-dioxolan-4-yl]methoxy}phenyl)-1-piperazinyl]phenyl}-2,4-dihydro-3H-1,2,4-triazol-3-one (itraconazole)
    Peeters, Oswald M.; Blaton, Norbert M.; De Ranter, Camiel J.
    Acta Crystallographica, Section C: Crystal Structure Communications (1996), C52 (9), 2225-2229CODEN: ACSCEE; ISSN:0108-2701. (Munksgaard)
    The mol. structure of itraconazole, C35H38Cl2N8O4, was detd. from single-crystal x-ray diffraction data. The two mols. in the asym. unit differ mainly in the conformation of the methoxyphenylpiperazine moiety. Apart from a 180° rotation of the triazole ring, the geometry of the dichlorophenylethoxytriazole moiety is almost the same as the dichlorophenylethoxyimidazole geometry found in miconazole, econazole and ketoconazole. Crystallog. data and at. coordinates are given.
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    Drebushchak, T. N.; Chesalov, Y. A.; Boldyreva, E. V. A Conformational Polymorphic Transition in the High-temperature ε-form of Chlorpropamide on Cooling: A New ε’-form. Acta Cryst. B 2009, 65 (6), 770– 781,  DOI: 10.1107/S010876810903290X
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    A conformational polymorphic transition in the high-temperature .vepsiln.-form of chlorpropamide on cooling: a new .vepsiln.'-form
    Drebushchak, Tatiana N.; Chesalov, Yury A.; Boldyreva, Elena V.
    Acta Crystallographica, Section B: Structural Science (2009), 65 (6), 770-781CODEN: ASBSDK; ISSN:0108-7681. (International Union of Crystallography)
    Structural changes in the high-temp. ε-polymorph of chlorpropamide, 4-chloro-N-(propylaminocarbonyl)benzenesulfonamide, C10H13ClN2O3S, on cooling down to 100 K and on reverse heating were followed by single-crystal X-ray diffraction. At temps. below 200 K the phase transition into a new polymorph (termed the ε'-form) has been obsd. for the first time. The polymorphic transition preserves the space group Pna21, is reversible and is accompanied by discontinuous changes in the cell vol. and parameters, resulting from changes in mol. conformation. As shown by IR spectroscopy and X-ray powder diffraction, the phase transition in a powder sample is inhomogeneous throughout the bulk, and the two phases co-exist in a wide temp. range. The cell parameters and the mol. conformation in the new polymorph are close to those in the previously known α-polymorph, but the packing of the z-shaped mol. ribbons linked by hydrogen bonds inherits that of the ε-form and is different from the packing in the α-polymorph. A structural study of the α-polymorph in the same temp. range has revealed no phase transitions.
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    Fossheim, R. Crystal Structure of the Dihydropyridine Calcium Antagonist Felodipine. Dihydropyridine Binding Prerequisites Assessed from Crystallographic Data. J. Med. Chem. 1986, 29 (2), 305– 307,  DOI: 10.1021/jm00152a023
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    Crystal structure of the dihydropyridine calcium antagonist felodipine. Dihydropyridine binding prerequisites assessed from crystallographic data
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  • Abstract

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    Figure 1

    Figure 1. Molecular structures of the 15 API in the compound library generated using the CORINA algorithm. R3m values based on this conformation are given parenthetically. Hydrogen atoms have been excluded for the sake of clarity, except in the case of hydrogen bond donors.

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    Figure 2

    Figure 2. Schematic illustrating the MD simulation for bicalutamide. (a) The Conformer tool was used to identify the lowest energy conformation (black dashed line). (b) This conformation allowed construction of 10 unique amorphous cells, where the three having the lowest energy (indicated in orange) were selected for MD simulations. (c) MD simulations using the NPT ensemble allowed selection of multiple frames after equilibration (indicated by the red boxes). (d) Coordinates from the selected frames were extracted using MATLAB allowing calculation of the R3m values for each individual molecule.

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    Figure 3

    Figure 3. Histograms showing the average absolute difference between R3m values calculated from CORINA-predicted and CCDC obtained structures. (a) One hundred thirty API, including R3m from 50 polymorphic structures. Linear relationship between (b) R3m values for 80 API (blue diamonds) and 50 polymorphs totaling 130 structures (orange circles). The orange regression line reflects the fit for all 130 structures. Points enclosed by the red box specifically highlight the different R3m values calculated for the polymorphs of aripiprazole. Gray dashes indicate the parity line.

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    Figure 4

    Figure 4. (a) Molecular structure of aripiprazole. (b) CCDC structures for two aripiprazole polymorphs, overlaid on the dichlorophenyl groups. The torsional differences changed the 3D coordinates, resulting in a relatively significant difference in R3m values: aripiprazole I (CCDC ID: MELFIT01, (17) orange) and aripiprazole VII (CCDC ID: MELFIT07, (18) teal).

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    Figure 5

    Figure 5. A box-and-whisker plot for R3m values calculated from MD simulations for each API. Red lines toward the center of each box are median values, while the black diamonds indicate the mean. Each box boundary represents the interquartile range, with whiskers indicating the range of observed values. Outliers are shown using red “+” symbols, and the blue dashed lines indicate R3m values calculated from SMILES files and the CORINA algorithm. The horizontal black dotted line indicates the R3m = 0.65 classification boundary published previously, (8,9) and the horizontal black dot-dashed line indicates the boundary updated using MD simulation results (R3m = 0.632, see Figure 8). Green boxes indicate API experimentally observed to successfully form ASDs in PVPVA, while those in red indicate compounds that failed to form ASDs. (7−10)

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    Figure 6

    Figure 6. Propranolol conformations and their effect on R3m. (a) Propranolol structure with atoms labeled. Propranolol molecules in blue correspond to the CORINA calculated conformation ((b) and (c)). (b) The propranolol molecule in red was extracted from MD simulations and has an R3m value equal to the median (0.4342). (c) The green propranolol molecule shows a conformation from the MD simulation having an R3m value (0.3406) most similar to the CORINA conformation (0.342) (difference in R3m = −0.0014).

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    Figure 7

    Figure 7. Logistic regression model logit P(Y) = −46.79 + 74.01(R3m) (blue line) using the expanded MD data set, resulting in an updated classification boundary value of R3m = 0.632. This model contains 12,600 data points for R3m (jittered to more clearly show the amount of data). Red circles correspond to API that failed to form ASDs in PVPVA, while green circles correspond to API that successfully formed ASDs in PVPVA by melt-quenching. The red dashed line indicates that molecules having R3m <0.570 have <1% probability to disperse in PVPVA, while the green dashed line indicates that molecules having R3m >0.693 have >99% probability to disperse in PVPVA.

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    Figure 8

    Figure 8. R3m distribution for 840 MD conformations of ritonavir likely in the amorphous state. The structures in conformations reflective of R3m min, R3m CORINA, and R3m max are shown (hydrogen atoms are removed for clarity except in the cases of hydrogen bond donors).

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    Figure 9

    Figure 9. R3m distribution for 840 MD conformations of lopinavir likely in the amorphous state. The structures in conformations reflective of R3m min, R3m CORINA, and R3m max are shown (hydrogen atoms are removed for clarity, except in the cases of hydrogen bond donors).

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      DeBoyace Kevin; Buckner Ira S; Gong Yuchuan; Ju Tzu-Chi Rob; Wildfong Peter L D
      Journal of pharmaceutical sciences (2018), 107 (1), 334-343 ISSN:.
      The expansion of a novel in silico model for the prediction of the dispersability of 18 model compounds with polyvinylpyrrolidone-vinyl acetate copolymer is described. The molecular descriptor R3m (atomic mass weighted 3rd-order autocorrelation index) is shown to be predictive of the formation of amorphous solid dispersions at 2 drug loadings (15% and 75% w/w) using 2 preparation methods (melt quenching and solvent evaporation using a rotary evaporator). Cosolidified samples were characterized using a suite of analytical techniques, which included differential scanning calorimetry, powder X-ray diffraction, pair distribution function analysis, polarized light microscopy, and hot stage microscopy. Logistic regression was applied, where appropriate, to model the success and failure of compound dispersability in polyvinylpyrrolidone-vinyl acetate copolymer. R3m had combined prediction accuracy greater than 90% for tested samples. The usefulness of this descriptor appears to be associated with the presence of heavy atoms in the molecular structure of the active pharmaceutical ingredient, and their location with respect to the geometric center of the molecule. Given the higher electronegativity and atomic volume of these types of atoms, it is hypothesized that they may impact the molecular mobility of the active pharmaceutical ingredient, or increase the likelihood of forming nonbonding interactions with the carrier polymer.
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    8. 8
      Moore, M. D.; Wildfong, P. L. D. Informatics Calibration of a Molecular Descriptors Database to Predict Solid Dispersion Potential of Small Molecule Organic Solids. Int. J. Pharm. 2011, 418 (2), 217– 226,  DOI: 10.1016/j.ijpharm.2011.06.003
      8
      Informatics calibration of a molecular descriptors database to predict solid dispersion potential of small molecule organic solids
      Moore, Michael D.; Wildfong, Peter L. D.
      International Journal of Pharmaceutics (2011), 418 (2), 217-226CODEN: IJPHDE; ISSN:0378-5173. (Elsevier B.V.)
      The use of a novel, in silico method for making an intelligent polymer selection to phys. stabilize small mol. org. (SMO) solid compds. formulated as amorphous mol. solid dispersions is reported. 12 compds. (75%, wt./wt.) were individually co-solidified with polyvinyl pyrrolidone:vinyl acetate (PVPva) copolymer by melt-quenching. Co-solidified products were analyzed intact using differential scanning calorimetry (DSC) and the pair distribution function (PDF) transform of powder X-ray diffraction (PXRD) data to assess miscibility. Mol. descriptor indexes were calcd. for all twelve compds. using their reported crystallog. structures. Logistic regression was used to assess correlation between mol. descriptors and amorphous mol. solid dispersion potential. The final model was challenged with three compds. Of the 12 compds., 6 were miscible with PVPva (i.e. successful formation) and 6 were phase sepd. (i.e. unsuccessful formation). 2 of the 6 unsuccessful compds. exhibited detectable phase-sepn. using the PDF method, where DSC indicated miscibility. Logistic regression identified 7 mol. descriptors correlated to solid dispersion potential (α = 0.001). The at. mass-weighted third-order R autocorrelation index (R3m) was the only significant descriptor to provide completely accurate predictions of dispersion potential. The three compds. used to challenge the R3m model were also successfully predicted.
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    9. 9
      DeBoyace, K.; Bookwala, M.; Buckner, I. S.; Zhou, D.; Wildfong, P. L. D. Interpreting the Physicochemical Meaning of a Molecular Descriptor which is Predictive of Amorphous Solid Dispersion Formation in Polyvinylpyrrolidone Vinyl Acetate. Mol. Pharmaceutics 2022, 19 (1), 303– 317,  DOI: 10.1021/acs.molpharmaceut.1c00783
      9
      Interpreting the Physicochemical Meaning of a Molecular Descriptor Which Is Predictive of Amorphous Solid Dispersion Formation in Polyvinylpyrrolidone Vinyl Acetate
      DeBoyace, Kevin; Bookwala, Mustafa; Buckner, Ira S.; Zhou, Deliang; Wildfong, Peter L. D.
      Molecular Pharmaceutics (2022), 19 (1), 303-317CODEN: MPOHBP; ISSN:1543-8384. (American Chemical Society)
      A mol. descriptor known as R3m (the R-GETAWAY third-order autocorrelation index weighted by the at. mass) was previously identified as capable of grouping members of an 18-compd. library of org. mols. that successfully formed amorphous solid dispersions (ASDs) when co-solidified with the co-polymer polyvinylpyrrolidone vinyl acetate (PVPva) at two concns. using two prepn. methods. To clarify the phys. meaning of this descriptor, the R3m calcn. is examd. in the context of the physicochem. mechanisms of dispersion formation. The R3m equation explicitly captures information about mol. topol., at. leverage, and mol. geometry, features which might be expected to affect the formation of stabilizing non-covalent interactions with a carrier polymer, as well as the mol. mobility of the active pharmaceutical ingredient (API) mol. Mols. with larger R3m values tend to have more atoms, esp. the heavier ones that form stronger non-covalent interactions, generally, more irregular shapes, and more complicated topol. Accordingly, these mols. are more likely to remain dispersed within PVPva. Furthermore, multiple linear regression modeling of R3m and more interpretable descriptors supported these conclusions. Finally, the utility of the R3m descriptor for predicting the formation of ASDs in PVPva was tested by analyzing the com. available products that contain amorphous APIs dispersed in the same polymer. All of these analyses support the conclusion that the information about the API geometry, size, shape, and topol. connectivity captured by R3m relates to the ability of a mol. to interact with and remain dispersed within an amorphous PVPva matrix.
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    10. 10
      Gumireddy, A.; Bookwala, M.; Zhou, D.; Wildfong, P. L. D.; Buckner, I. S. Investigating and Comparing the Applicability of the R3m Molecular Descriptor and Solubility Parameter Estimation Approaches in Predicting Dispersion Formation Potential of APIs in a Random Co-polymer Polyvinylpyrrolidone Vinyl Acetate and its Homopolymer. J. Pharm. Sci. 2023, 112 (1), 318– 327,  DOI: 10.1016/j.xphs.2022.11.004
      10
      Investigating and applicability of R3m molecular descriptor and solubility parameter estimation approaches in predicting dispersion of APIs in PVP vinyl acetate random polymer
      Gumireddy, Ashwini; Bookwala, Mustafa; Zhou, Deliang; Wildfong, Peter L. D.; Buckner, Ira S.
      Journal of Pharmaceutical Sciences (Philadelphia, PA, United States) (2023), 112 (1), 318-327CODEN: JPMSAE; ISSN:0022-3549. (Elsevier Inc.)
      Evaluation of different amorphous solid dispersion carrier matrixes is enabled by active pharmaceutical ingredient (API) structure-based predictions. This study compares the utility of Hansen Soly. Parameters with the R3m mol. descriptor for identifying dispersion polymers based on the structure of the drug mol. Twelve API-polymer combinations (4 APIs and 3 interrelated polymers) were used to test each approach. Co-solidified mixts. contg. 75% API were prepd. by melt-quenching. Phase behavior was evaluated and classified using differential scanning calorimetry, powder X-ray diffraction, polarized light microscopy, and hot stage microscopy. Observations of dispersion behavior were compared to predictions made using the Hansen Soly. Parameter and R3m. The soly. parameter approach misclassified the dispersion behavior of 1 API-polymer combination and also did not produce definite predictions in 3 out of 12 of the API-polymer combinations. In contrast, R3m classifications of dispersion behavior were correct in all but two cases, with one misclassification and one ambiguous prediction. The soly. parameters best classify dispersion behavior when specific drug-polymer intermol. interactions are present, but may be less useful otherwise. Ultimately, these two methods are most effectively used together, as they are based on distinct features of the same mol. structure.
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    11. 11
      Groom, C. R.; Bruno, I. J.; Lightfoot, M. P.; Ward, S. C. The Cambridge Structural Database. Acta Cryst. B 2016, 72 (2), 171– 179,  DOI: 10.1107/S2052520616003954
      11
      The Cambridge Structural Database
      Groom, Colin R.; Bruno, Ian J.; Lightfoot, Matthew P.; Ward, Suzanna C.
      Acta Crystallographica, Section B: Structural Science, Crystal Engineering and Materials (2016), 72 (2), 171-179CODEN: ACSBDA; ISSN:2052-5206. (International Union of Crystallography)
      The Cambridge Structural Database (CSD) contains a complete record of all published org. and metal-org. small-mol. crystal structures. The database has been in operation for over 50 years and continues to be the primary means of sharing structural chem. data and knowledge across disciplines. As well as structures that are made public to support scientific articles, it includes many structures published directly as CSD Communications. All structures are processed both computationally and by expert structural chem. editors prior to entering the database. A key component of this processing is the reliable assocn. of the chem. identity of the structure studied with the exptl. data. This important step helps ensure that data is widely discoverable and readily reusable. Content is further enriched through selective inclusion of addnl. exptl. data. Entries are available to anyone through free CSD community web services. Linking services developed and maintained by the CCDC, combined with the use of std. identifiers, facilitate discovery from other resources. Data can also be accessed through CCDC and third party software applications and through an application programming interface.
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    12. 12
      Sun, H.; Jin, Z.; Yang, C.; Akkermans, R. L. C.; Robertson, S. H.; Spenley, N. A.; Miller, S.; Todd, S. M. COMPASS II: Extended Coverage for Polymer and Drug-like Molecule Databases. J. Mol. Model. 2016, 22 (2), 47,  DOI: 10.1007/s00894-016-2909-0
      12
      COMPASS II: extended coverage for polymer and drug-like molecule databases
      Sun Huai; Jin Zhao; Yang Chunwei; Akkermans Reinier L C; Robertson Struan H; Spenley Neil A; Miller Simon; Todd Stephen M
      Journal of molecular modeling (2016), 22 (2), 47 ISSN:.
      The COMPASS II force field has been developed by extending the coverage of the COMPASS force field (J Phys Chem B 102(38):7338-7364, 1998) to polymer and drug-like molecules found in popular databases. Using a fragmentation method to systematically construct small molecules that exhibit key functional groups found in these databases, parameters applicable to database compounds were efficiently obtained. Based on the same parameterization paradigm as used in the development of the COMPASS force field, new parameters were derived by a combination of fits to quantum mechanical data for valence parameters and experimental liquid and crystal data for nonbond parameters. To preserve the quality of the original COMPASS parameters, a quality assurance suite was used and updated to ensure that additional atom-types and parameters do not interfere with the existing ones. Validation against molecular properties, liquid and crystal densities, and enthalpies, demonstrates that the quality of COMPASS is preserved and the same quality of prediction is achieved for the additional coverage.
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    13. 13
      Bookwala, M.; DeBoyace, K.; Buckner, I. S.; Wildfong, P. L. D. Predicting Density of Amorphous Solid Materials using Molecular Dynamics Simulation. AAPS PharmSciTech 2020, 21 (3), 1– 11,  DOI: 10.1208/s12249-020-1632-4
      There is no corresponding record for this reference.
    14. 14
      Consonni, V.; Todeschini, R.; Pavan, M. Structure/Response Correlations and Similarity/Diversity Analysis by GETAWAY Descriptors. 1. Theory of the Novel 3D Molecular Descriptors. J. Chem. Inf. Model 2002, 42 (3), 682– 692,  DOI: 10.1021/ci015504a
      There is no corresponding record for this reference.
    15. 15
      Todeschini, R.; Consonni, V. Molecular Descriptors for Chemoinformatics; John Wiley & Sons, 2009.
      There is no corresponding record for this reference.
    16. 16
      Gasteiger, J.; Rudolph, C.; Sadowski, J. Automatic Generation of 3D-atomic Coordinates for Organic Molecules. Tetrahedron Comput. Methodol. 1990, 3 (6, Part C), 537– 547,  DOI: 10.1016/0898-5529(90)90156-3
      16
      Automatic generation of 3D atomic coordinates for organic molecules
      Gasteiger, J.; Rudolph, C.; Sadowski, J.
      Tetrahedron Computer Methodology (1990), 3 (6c), 537-47CODEN: TCMTE6; ISSN:0898-5529.
      A system has been developed that can automatically generate 3-dimensional at. coordinates from the constitution of a mol. as expressed by a connection table. The program, CORINA, is applicable to the entire range of org. chem. It can also handle structures that are beyond the scope of some other programs, e.g., macrocyclic and polymacrocyclic mols. Computation times are short and the results compare favorably with data from x-ray crystallog. and with those of mol. mechanics calcns.
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    17. 17
      Braun, D. E.; Gelbrich, T.; Kahlenberg, V.; Tessadri, R.; Wieser, J.; Griesser, U. J. Conformational Polymorphism in Aripiprazole: Preparation, Stability and Structure of Five Modifications. J. Pharm. Sci. 2009, 98 (6), 2010– 2026,  DOI: 10.1002/jps.21574
      17
      Conformational polymorphism in aripiprazole: Preparation, stability and structure of five modifications
      Braun, Doris E.; Gelbrich, Thomas; Kahlenberg, Volker; Tessadri, Richard; Wieser, Josef; Griesser, Ulrich J.
      Journal of Pharmaceutical Sciences (2009), 98 (6), 2010-2026CODEN: JPMSAE; ISSN:0022-3549. (Wiley-Liss, Inc.)
      Five phase-pure modifications of the antipsychotic drug aripiprazole were prepd. and characterized by thermal anal., vibrational spectroscopy and X-ray diffractometry. All modifications can be produced from solvents, form I addnl. by heating of form X° to ∼120°C (solid-solid transformation) and form III by crystn. from the melt. Thermodn. relationships between the polymorphs were evaluated on the basis of thermochem. data and visualized in a semi-schematic energy/temp. diagram. At least six of the ten polymorphic pairs are enantiotropically and two monotropically related. Form X° is the thermodynamically stable modification at 20°C, form II is stable in a window from about 62-77°C, and form I above 80°C (high-temp. form). Forms III and IV are triclinic (P\overline 1), I and X° are monoclinic (P21) and form II orthorhombic (Pna21). Each polymorph exhibits a distinct mol. conformation, and there are two fundamental N-H\···O hydrogen bond synthons (catemers and dimers). Hirshfeld surface anal. was employed to display differences in intermol. short contacts. A high kinetic stability was obsd. for three metastable polymorphs which can be categorized as suitable candidates for the development of solid dosage forms. © 2008 Wiley-Liss, Inc. and the American Pharmacists Assocn. J Pharm Sci 98:2010-2026, 2009.
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    18. 18
      Delaney, S. P.; Pan, D.; Yin, S. X.; Smith, T. M.; Korter, T. M. Evaluating the Roles of Conformational Strain and Cohesive Binding in Crystalline polymorphs of Aripiprazole. Cryst. Growth Des. 2013, 13 (7), 2943– 2952,  DOI: 10.1021/cg400358e
      18
      Evaluating the Roles of Conformational Strain and Cohesive Binding in Crystalline Polymorphs of Aripiprazole
      Delaney, Sean P.; Pan, Duohai; Yin, Shawn X.; Smith, Tiffany M.; Korter, Timothy M.
      Crystal Growth & Design (2013), 13 (7), 2943-2952CODEN: CGDEFU; ISSN:1528-7483. (American Chemical Society)
      The relative stabilities of cryst. polymorphs are an important aspect of the manufg. and effective utilization of pharmaceuticals. These stabilities are driven by both mol. conformational energy within the solid-state components and cohesive binding energy of the cryst. arrangement. The combined approach of exptl. vibrational terahertz spectroscopy with solid-state d. functional theory provides a powerful tool to study such properties and is applied here in the anal. of conformational polymorphism in cryst. aripiprazole. The low-frequency (<95 cm-1) terahertz vibrations of several aripiprazole polymorphs were measured, revealing distinct spectral features that uniquely identify each form. Solid-state d. functional theory was employed to interpret the exptl. terahertz spectra, correlating the obsd. spectral features to specific at. motions within the cryst. lattice. The computational anal. provides insight into the formation and stability of the polymorphs by revealing the balance between the external binding forces and internal mol. forces that is ultimately responsible for the phys. characteristics of the numerous cryst. polymorphs of aripiprazole.
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    19. 19
      Bredikhin, A. A.; Savel"ev, D. V.; Bredikhina, Z. A.; Gubaidullin, A. T.; Litvinov, I. A. Crystallization of Chiral Compounds. 2. Propranolol: Free Base and Hydrochloride. Russ. Chem. Bull. 2003, 52 (4), 853– 861,  DOI: 10.1023/A:1024435906421
      19
      Crystallization of chiral compounds. 2. Propranolol: free base and hydrochloride
      Bredikhin, A. A.; Savel ev, D. V.; Bredikhina, Z. A.; Gubaidullin, A. T.; Litvinov, I. A.
      Russian Chemical Bulletin (Translation of Izvestiya Akademii Nauk, Seriya Khimicheskaya) (2003), 52 (4), 853-861CODEN: RCBUEY; ISSN:1066-5285. (Kluwer Academic/Consultants Bureau)
      The data from IR spectroscopy, DSC, and x-ray diffraction anal. are compared for cryst. specimens of homochiral and racemic propranolol and its hydrochloride. The stabilities of the racemates were quant. characterized and the factors responsible for the order of their stability are revealed.
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    20. 20
      Hädicke, E.; Frickel, F.; Franke, A. Die Struktur von Cimetidin (N″-Cyan-N-Methyl-N′-[2-[(5-methyl-1H-imidazol-4-yl)methylthio]ethyl]guanidin), einem Histamin H2-Rezeptor-Antagonist. Chem. Ber. 1978, 111 (9), 3222– 3232,  DOI: 10.1002/cber.19781110926
      There is no corresponding record for this reference.
    21. 21
      Quarles, W. G.; Templeton, D. H.; Zalkin, A. The Crystal and Molecular Structure of Melatonin. Acta Cryst. B 1974, 30 (1), 99– 103,  DOI: 10.1107/S0567740874002287
      21
      Crystal and molecular structure of melatonin
      Quarles, William G.; Templeton, David H.; Zalkin, Allan
      Acta Crystallographica, Section B: Structural Crystallography and Crystal Chemistry (1974), 30 (1), 99-103CODEN: ACBCAR; ISSN:0567-7408.
      Crystals of melatonin (C13H16N2O2) are monoclinic, space group P21/c, with lattice parameters a 7.707 (2), b 9.252 (2), c 17.077 (4) Å, β 96.78 (3)°, Z = 4, d.(obsd.) = 1.272, and d.(x-ray) = 1.276. The structural model was refined to R = 0.036 for 900 independent x-ray reflections measured on an automatic diffractometer. The mols. are nearly flat with the side chain in the fully extended (trans) conformation. Mols. are connected in sheets by weak H bonds from N to O, with N-O = 2.90 and 2.96 Å.
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    22. 22
      Bookwala, M.; Gumireddy, A.; Aitken, J. A.; Wildfong, P. L. D. Single Crystal Structure of Terfenadine Form I. J. Chem. Crystallogr. 2022, 52 (1), 81– 88,  DOI: 10.1007/s10870-021-00892-3
      22
      Single Crystal Structure of Terfenadine Form I
      Bookwala, Mustafa; Gumireddy, Ashwini; Aitken, Jennifer A.; Wildfong, Peter L. D.
      Journal of Chemical Crystallography (2022), 52 (1), 81-88CODEN: JCCYEV; ISSN:1074-1542. (Springer)
      Terfenadine, C32H41NO2, 1, contains an α,α-diphenyl-4-piperidinomethanol moiety, which is related to the H1-receptor blocking activity, facilitating its prior use as an antihistamine drug. In addn. to its bioactivity, terfenadine is useful as a model, small-mol. cryst. solid for studying several material properties. Despite a history of therapeutic use, the absence of a crystal structure has limited current studies of the physicochem. behavior of this material. In the present manuscript, the elusive X-ray crystal structure of 1 was solved and refined at 296 K using single crystals grown from a co-solvent mixt. of acetonitrile:methanol:ethanol (0.50:0.25:0.25). Terfenadine crystallizes in the monoclinic space group P21/n, and exhibits a chair conformation of the piperidine ring and a gauche conformation of the Bu chain. Hydrogen bonds between O-H···O and O-H···N, along with weak van der Waals interactions between C16-H16B···H16B'-C16'; and C2-H2···H16A-C16A were confirmed using Hirshfeld-Surface anal. Differential scanning calorimetry and X-ray powder diffraction confirmed that the crystal structure reported herein was that of the most thermodynamically stable monotropic polymorph of terfenadine (form I).
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    23. 23
      Gunn, E.; Guzei, I. A.; Cai, T.; Yu, L. Polymorphism of Nifedipine: Crystal Structure and Reversible Transition of the Metastable β polymorph. Cryst. Growth Des. 2012, 12 (4), 2037– 2043,  DOI: 10.1021/cg3000075
      23
      Polymorphism of Nifedipine: Crystal Structure and Reversible Transition of the Metastable β Polymorph
      Gunn, Erica; Guzei, Ilia A.; Cai, Ting; Yu, Lian
      Crystal Growth & Design (2012), 12 (4), 2037-2043CODEN: CGDEFU; ISSN:1528-7483. (American Chemical Society)
      The authors report the 1st structural detn. of the metastable β polymorph of nifedipine (NIF) by single-crystal x-ray diffraction. Stable, high-quality crystals were grown from the melt in the presence of a polymer dopant. The authors' β NIF structure was characterized by a unit cell similar to that of the structure recently proposed from powder diffraction, but significantly different mol. conformations. Unlike the stable α polymorph, β NIF undergoes a reversible solid-state transformation near 60°. The now available β NIF structure clarifies some confusion concerning NIF polymorphs and enables inquiries into the structural basis for the selective crystn. of β NIF from glasses. The authors report that another polymorph crystallizes concomitantly with β NIF from the supercooled melt and transforms to β NIF at room temp.; this polymorph also undergoes reversible solid-state transformation. Crystallog. data and at. coordinates are given.
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    24. 24
      Kashino, S.; Haisa, M. Structure of Quinidine, C20H24N2O2. Acta Cryst. C 1983, 39 (2), 310– 312,  DOI: 10.1107/S0108270183004515
      There is no corresponding record for this reference.
    25. 25
      O’Connell, A. M.; Maslen, E. N. X-ray and Neutron Diffraction Studies of β-Sulphanilamide. Acta Crystallogr. 1967, 22 (1), 134– 145,  DOI: 10.1107/S0365110X67000210
      25
      X-ray and neutron diffraction studies of β-sulfanilamide
      O'Connell, A. M.; Maslen, Edward N.
      Acta Crystallographica (1967), 22 (1), 134-45CODEN: ACCRA9; ISSN:0365-110X.
      The crystal structure has been refined from 3-dimensional photographic x-ray data and 2-dimensional neutron diffraction data. Positional and anisotropic thermal parameters of the non-H atoms and positional and isotrophic thermal parameters of the H atoms were refined in the x-ray analysis to give a final R index of 4.9%. Positional and isotropic thermal parameters of the H atoms were refined in the neutron study. The final residual factors were R(h01) 8.0%, and R(hk0) 9.1%. The mean standard deviation in the C-C bonds is 0.0028 A. and the estd. standard in the H atom positional parameters are ∼0.035 A. The bond lengths suggest that there is a small but significant contribution of a quinonoid resonance form to the structure of the mol. The distribution of residual electron d. within the benzene ring and in the tetrahedral sulfamide group is explained in terms of effects resulting from electron redistribution at bonding. The H bond system closely resembles that found in α-sulfanilamide and N-H...O bonds are ∼0.25 A. longer than in related zwitterion compds.
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    26. 26
      Drebushchak, T. N.; Drebushchak, V. A.; Pankrushina, N. A.; Boldyreva, E. V. Single-crystal to Single-crystal Conformational Polymorphic Transformation in Tolbutamide at 313 K. Relation to Other Polymorphic Transformations in Tolbutamide and Chlorpropamide. CrystEngComm 2016, 18 (30), 5736– 5743,  DOI: 10.1039/C6CE00764C
      26
      Single-crystal to single-crystal conformational polymorphic transformation in tolbutamide at 313 K. Relation to other polymorphic transformations in tolbutamide and chlorpropamide
      Drebushchak, T. N.; Drebushchak, V. A.; Pankrushina, N. A.; Boldyreva, E. V.
      CrystEngComm (2016), 18 (30), 5736-5743CODEN: CRECF4; ISSN:1466-8033. (Royal Society of Chemistry)
      Three polymorphs of tolbutamide (IL, II, and III) were studied using single-crystal X-ray diffraction, from 100 K to room temp. (forms II and III) and to 350 K (form I), and differential scanning calorimetry. The reversible transformation, IL ↔ IH, was found to be of the single-crystal to single-crystal type and the structure of the high-temp. form (IH) was solved and refined. The structure of IH differs from that of IL only in the conformation of the mol., with mol. arrangements being practically unchanged (isostructural conformational transformation). The transition takes place at 313 K with no sign of hysteresis. The vol. change, ΔV/V, across the reversible transformation IL ↔ IH was calcd. and compared to those for the other conformational transformations. Two types of conformational polymorphic transformations (irreversible reconstructive and reversible isostructural) in tolbutamide and chlorpropamide were compared.
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    27. 27
      Cox, P. J.; Manson, P. L. γ-Indomethacin at 120 K. Acta Crystallogr., Sect. E: Struct. Rep. Online 2003, 59 (7), o986– 988,  DOI: 10.1107/S160053680301290X
      27
      γ-Indomethacin at 120 K
      Cox, Philip J.; Manson, Pamela L.
      Acta Crystallographica, Section E: Structure Reports Online (2003), 59 (7), o986-o988CODEN: ACSEBH; ISSN:1600-5368. (International Union of Crystallography)
      The crystal structure of γ-indomethacin, C19H16ClNO4, is supported, not only by O-H···O interactions, but also by C-H··· π and π-π interactions. Crystallog. data are given.
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    28. 28
      Peeters, O. M.; Blaton, N. M.; De Ranter, C. J. Cis-1-Acetyl-4-(4-{[2-(2,4-dichlorophenyl)-2-(1H-1-imidazolylmethyl)-1,3-dioxolan-4-yl]methoxy}phenyl)piperazine: Ketoconazole. A Crystal Structure with Disorder. Acta Cryst. B 1979, 35 (10), 2461– 2464,  DOI: 10.1107/S0567740879009651
      There is no corresponding record for this reference.
    29. 29
      Peeters, O. M.; Blaton, N. M.; De Ranter, C. J. Cis-2-sec-Butyl-4-{4-[4-(4-{[2-(2,4-dichlorophenyl)-2-(1H-1,2,4-triazol-1-ylmethyl)-1,3-dioxolan-4-yl]methoxy}phenyl)-1-piperazinyl]phenyl}-2,4-dihydro-3H-1,2,4-triazol-3-one (Itraconazole). Acta Cryst. C 1996, 52 (9), 2225– 2229,  DOI: 10.1107/S0108270196004180
      29
      cis-2-sec-Butyl-4-{4-[4-(4-{[2-(2,4-dichlorophenyl)-2-(1H-1,2,4-triazol-1-ylmethyl)-1,3-dioxolan-4-yl]methoxy}phenyl)-1-piperazinyl]phenyl}-2,4-dihydro-3H-1,2,4-triazol-3-one (itraconazole)
      Peeters, Oswald M.; Blaton, Norbert M.; De Ranter, Camiel J.
      Acta Crystallographica, Section C: Crystal Structure Communications (1996), C52 (9), 2225-2229CODEN: ACSCEE; ISSN:0108-2701. (Munksgaard)
      The mol. structure of itraconazole, C35H38Cl2N8O4, was detd. from single-crystal x-ray diffraction data. The two mols. in the asym. unit differ mainly in the conformation of the methoxyphenylpiperazine moiety. Apart from a 180° rotation of the triazole ring, the geometry of the dichlorophenylethoxytriazole moiety is almost the same as the dichlorophenylethoxyimidazole geometry found in miconazole, econazole and ketoconazole. Crystallog. data and at. coordinates are given.
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    30. 30
      Drebushchak, T. N.; Chesalov, Y. A.; Boldyreva, E. V. A Conformational Polymorphic Transition in the High-temperature ε-form of Chlorpropamide on Cooling: A New ε’-form. Acta Cryst. B 2009, 65 (6), 770– 781,  DOI: 10.1107/S010876810903290X
      30
      A conformational polymorphic transition in the high-temperature .vepsiln.-form of chlorpropamide on cooling: a new .vepsiln.'-form
      Drebushchak, Tatiana N.; Chesalov, Yury A.; Boldyreva, Elena V.
      Acta Crystallographica, Section B: Structural Science (2009), 65 (6), 770-781CODEN: ASBSDK; ISSN:0108-7681. (International Union of Crystallography)
      Structural changes in the high-temp. ε-polymorph of chlorpropamide, 4-chloro-N-(propylaminocarbonyl)benzenesulfonamide, C10H13ClN2O3S, on cooling down to 100 K and on reverse heating were followed by single-crystal X-ray diffraction. At temps. below 200 K the phase transition into a new polymorph (termed the ε'-form) has been obsd. for the first time. The polymorphic transition preserves the space group Pna21, is reversible and is accompanied by discontinuous changes in the cell vol. and parameters, resulting from changes in mol. conformation. As shown by IR spectroscopy and X-ray powder diffraction, the phase transition in a powder sample is inhomogeneous throughout the bulk, and the two phases co-exist in a wide temp. range. The cell parameters and the mol. conformation in the new polymorph are close to those in the previously known α-polymorph, but the packing of the z-shaped mol. ribbons linked by hydrogen bonds inherits that of the ε-form and is different from the packing in the α-polymorph. A structural study of the α-polymorph in the same temp. range has revealed no phase transitions.
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    31. 31
      Fossheim, R. Crystal Structure of the Dihydropyridine Calcium Antagonist Felodipine. Dihydropyridine Binding Prerequisites Assessed from Crystallographic Data. J. Med. Chem. 1986, 29 (2), 305– 307,  DOI: 10.1021/jm00152a023
      31
      Crystal structure of the dihydropyridine calcium antagonist felodipine. Dihydropyridine binding prerequisites assessed from crystallographic data
      Fossheim, Rune
      Journal of Medicinal Chemistry (1986), 29 (2), 305-7CODEN: JMCMAR; ISSN:0022-2623.
      The mol. structure of the dihydropyridine Ca2+ antagonist felodipine (I; R = Me, R1 = Et, R2 =2-NO2) [72509-76-3] was detd. by X-ray crystallog. methods. The dihydropyridine ring in this potent smooth-muscle relaxant is among the flattest found in such structures. This is in qual. agreement with previous investigations of dihydropyridine Ca2+ antagonists; deviations from planarity in the dihydropyridine ring are generally smallest in the most active compds. H-bonding patterns obsd. in the crystal lattices of several dihydropyridine Ca2+ antagonists (I; R and R1 = Me, Et, neopentyl, or (trimethylsilyl)methyl and R2 = H, 3- or 4-Me, 3- or 4-NO2, 4-Me2N, F5, 2,3-Cl2, etc.) are compared. Antiperiplanar carbonyl groups are partly shielded from forming H bonds in compds. with relatively bulky ortho Ph substituents. Conformational prerequisites for a favorable H-bonding geometry toward a receptor site may thus involve synperiplanar carbonyl groups.
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    32. 32
      Hu, X. R.; Gu, J. M. N-[4-Cyano-3-(trifluoromethyl)phenyl]-3-(4-fluorophenylsulfonyl)-2-hydroxy-2-methylpropionamide. Acta Crystallogr., Sect. E: Struct. Rep. Online 2005, 61 (11), o3897– 3898,  DOI: 10.1107/S1600536805034501
      32
      N-[4-Cyano-3-(trifluoromethyl)phenyl]-3-(4-fluorophenylsulfonyl)-2-hydroxy-2-methylpropionamide
      Hu, Xiu Rong; Gu, Jian Ming
      Acta Crystallographica, Section E: Structure Reports Online (2005), 61 (11), o3897-o3898CODEN: ACSEBH; ISSN:1600-5368. (International Union of Crystallography)
      The structure of the title compd., C18H14F4N2O2S, consists of mols. that pack in a linear H-bonded chain along the c axis. This H-bonding arrangement involves the hydroxy group and one of the sulfonyl O atoms. Crystallog. data are given.
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    33. 33
      Marsac, P. J.; Li, T.; Taylor, L. S. Estimation of Drug-Polymer Miscibility and Solubility in Amorphous Solid Dispersions using Experimentally Determined Interaction Parameters. Pharm. Res. 2009, 26 (1), 139– 151,  DOI: 10.1007/s11095-008-9721-1
      33
      Estimation of Drug-Polymer Miscibility and Solubility in Amorphous Solid Dispersions Using Experimentally Determined Interaction Parameters
      Marsac, Patrick J.; Li, Tonglei; Taylor, Lynne S.
      Pharmaceutical Research (2009), 26 (1), 139-151CODEN: PHREEB; ISSN:0724-8741. (Springer)
      The amorphous form of a drug may provide enhanced soly., dissoln. rate, and bioavailability but will also potentially crystallize over time. Miscible polymeric additives provide a means to increase phys. stability. Understanding the miscibility of drug-polymer systems is of interest to optimize the formulation of such systems. The purpose of this work was to develop exptl. models which allow for more quant. ests. of the thermodn. of mixing amorphous drugs with glassy polymers. The thermodn. of mixing several amorphous drugs with amorphous polymers was estd. by coupling soln. theory with exptl. data. The entropy of mixing was estd. using Flory-Huggins lattice theory. The enthalpy of mixing and any deviations from the entropy as predicted by Flory-Huggins lattice theory were estd. using 2 sep. exptl. techniques; (1) m.p. depression of the cryst. drug in the presence of the amorphous polymer was measured using differential scanning calorimetry and (2) detn. of the soly. of the drug in 1-ethyl-2-pyrrolidone. The estd. activity coeff. was used to calc. the free energy of mixing of the drugs in the polymers and the corresponding soly. Mixts. previously reported as miscible showed various degrees of m.p. depression while systems reported as immiscible or partially miscible showed little or no m.p. depression. The soly. of several compds. in 1-ethyl-2-pyrrolidone predicts that most drugs have a rather low soly. in poly(vinylpyrrolidone). Miscibility of various drugs with polymers can be explored by coupling soln. theories with exptl. data. These approxns. provide insight into the phys. stability of drug-polymer mixts. and the thermodn. driving force for crystn.
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    34. 34
      Tong, P.; Zografi, G. A Study of Amorphous Molecular Dispersions of Indomethacin and its Sodium Salt. J. Pharm. Sci. 2001, 90 (12), 1991– 2004,  DOI: 10.1002/jps.1150.abs
      34
      A study of amorphous molecular dispersions of indomethacin and its sodium salt
      Tong, Ping; Zografi, George
      Journal of Pharmaceutical Sciences (2001), 90 (12), 1991-2004CODEN: JPMSAE; ISSN:0022-3549. (Wiley-Liss, Inc.)
      Amorphous solid dispersions of indomethacin (IMC) and sodium indomethacin (NaIMC) over a range of compns. were prepd. by phys. mixing amorphous IMC and amorphous NaIMC, as well as by copptn. from methanol soln. Measurement of glass transition temps., Tg, for the phys. mixts. revealed 2 values indicating, as expected, phase sepn. In contrast, all samples of copptd. materials exhibited one value of Tg, which was greater than that predicted for ideal miscibility in the formation of a mol. dispersion. Such nonideality suggests a stronger acid-salt interaction in the amorphous state than that between acid-acid and salt-salt. FTIR spectroscopic anal. provides evidence for interactions between NaIMC and IMC through a combination of hydrogen bonding and ion-dipole interactions between the carboxylic group of the acid and the carboxylate anion of the salt. The inhibition of isothermal crystn. of IMC by NaIMC only when in mol. dispersion is believed to result from the interaction between the acid and the salt, which prevents the formation of hydrogen-bonded carboxylic acid dimers for IMC, required for the formation of crystal nuclei and crystn.
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    35. 35
      Six, K.; Berghmans, H.; Leuner, C.; Dressman, J.; Van Werde, K.; Mullens, J.; Benoist, L.; Thimon, M.; Meublat, L.; Verreck, G. Characterization of Solid Dispersions of Itraconazole and Hydroxypropylmethylcellulose Prepared by Melt Extrusion. Part II. Pharm. Res. 2003, 20 (7), 1047– 1054,  DOI: 10.1023/A:1024414423779
      There is no corresponding record for this reference.
    36. 36
      Mathioudakis, C.; Kelires, P. C. Softening of Elastic Moduli of Amorphous Semiconductors. J. Non-Cryst. Solids 2000, 266, 161– 165,  DOI: 10.1016/S0022-3093(99)00796-6
      36
      Softening of elastic moduli of amorphous semiconductors
      Mathioudakis, C.; Kelires, P. C.
      Journal of Non-Crystalline Solids (2000), 266-269 (Pt. A), 161-165CODEN: JNCSBJ; ISSN:0022-3093. (Elsevier Science B.V.)
      We study the rigidity problem of amorphous semiconductors using Monte Carlo (MC) simulations and empirical potentials. We find that networks of tetrahedral a-C, a-Si, and a-Ge consistently have smaller elastic moduli than their cryst. counterparts. The redn. of rigidity seems to be assocd. with the reduced d. and the random orientation of sp3 hybrids in the fully tetrahedral amorphous networks and, in addn., with the presence of sp2 sites in tetrahedral a-C.
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    37. 37
      Hancock, B. C.; Carlson, G. T.; Ladipo, D. D.; Langdon, B. A.; Mullarney, M. P. Comparison of the Mechanical Properties of the Crystalline and Amorphous Forms of a Drug Substance. Int. J. Pharm. 2002, 241 (1), 73– 85,  DOI: 10.1016/S0378-5173(02)00133-3
      37
      Comparison of the mechanical properties of the crystalline and amorphous forms of a drug substance
      Hancock, Bruno C.; Carlson, Glenn T.; Ladipo, Dauda D.; Langdon, Beth A.; Mullarney, Matthew P.
      International Journal of Pharmaceutics (2002), 241 (1), 73-85CODEN: IJPHDE; ISSN:0378-5173. (Elsevier Science B.V.)
      Purpose: To better understand the influence of long-range mol. order on the processing characteristics of an active pharmaceutical ingredient (API). Methods: Cryst. and amorphous samples of a model drug substance were isolated and their "true" d., crystallinity, m.p., glass transition temp., particle size distribution, and powder flow characteristics detd. Compacts of a std. porosity were manufd. from each form and their dynamic indentation hardness, quasi-static indentation hardness, tensile strength and "compromised tensile strength" detd. X-ray powder diffraction was used to confirm that no changes were induced by compact formation or testing. Results: The cryst. and amorphous forms of the drug substance (a spirostanone cholesterol absorption inhibitor) had relatively high melting and glass transition temps. (approx. 271 and 142 °C, resp.) and were phys. and chem. stable under the conditions of the testing lab. Consistent with this there was no evidence of crystallinity in the amorphous samples or vice versa before, during or after testing. The two API lots were effectively equiv. in their particulate properties (e.g. particle size distribution), although differences in their particle morphologies were obsd. which influenced powder flow behavior. The compacts of the bulk drug samples exhibited moderate ductility, elasticity, and strength, and high brittleness, in keeping with many other drug substance samples. A significantly greater compression stress was required to form the compacts of the cryst. material, and these sample materials were more ductile, less brittle and less elastic than those made from the amorphous API. There were no major differences in the tensile strength or the viscoelasticity of the compacts made from the cryst. and amorphous samples. Conclusions: The mech. properties of compacted amorphous and cryst. samples of a drug substance have been measured and the contributions due to the mol. ordering of the cryst. form proposed. Small but significant differences in the mech. properties were noted which could potentially affect the processing performance of API.
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    38. 38
      Cannon, A. R.; Cobb, G. W.; Hartlaub, B. A.; Legler, J. M.; Lock, R. H.; Moore, T. L.; Rossman, A. J.; Witmer, J. STAT2: Building Models for a World of Data. Freeman 2013.
      There is no corresponding record for this reference.
    39. 39
      Kleinbaum, D. G.; Klein, M. Logistic Regression: A Self-Learning Text; Springer, 2010.
      There is no corresponding record for this reference.
    40. 40
      Newman, A.; Zografi, G. Considerations in the Development of Physically Stable High Drug Load API-Polymer Amorphous Solid Dispersions in the Glassy State. J. Pharm. Sci. 2023, 112 (1), 8– 18,  DOI: 10.1016/j.xphs.2022.08.007
      40
      Considerations in the Development of Physically Stable High Drug Load API- Polymer Amorphous Solid Dispersions in the Glassy State
      Newman, Ann; Zografi, George
      Journal of Pharmaceutical Sciences (Philadelphia, PA, United States) (2023), 112 (1), 8-18CODEN: JPMSAE; ISSN:0022-3549. (Elsevier Inc.)
      In this Commentary, the authors expand on their earlier studies of the solid-state long-term isothermal crystn. of amorphous API from the glassy state in amorphous solid dispersions, and focus on the effects of polymer concn., and its implications for producing high load API doses with min. polymer concn. After presenting an overview of the various mechanistic factors which influence the ability of polymers to inhibit API crystn., including the chem. structure of the polymer relative to the API, the nature and strength of API-polymer noncovalent interactions, polymer mol. wt., impact on primary diffusive mol. mobility, as well as on secondary motions in the bulk and surface phases of the glass, we consider in more detail, the effects of polymer concn. Here, we examine the factors that appear to allow relatively low polymer concns., i.e., less than 10%wt./wt. polymer, to greatly reduce crystn., including a focus on the heterogeneous structure of the glassy state, and the possible spatial distribution and concn. of polymer in certain key regions of the glass. This is followed by a review and anal. of examples in the recent literature focused on detg. the min. polymer concn. in an amorphous solid dispersion, capable of producing optimally stable high drug load amorphous dispersions.
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  • Supporting Information

    Supporting Information

    The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acs.molpharmaceut.3c00909.

    • List of all 80 APIs used to examine the relationship between R3m calculated by CORINA-generated 3D structures and 3D structures obtained from the CCDC; a comparison of R3m values calculated from 3D conformations generated from the CORINA algorithm, obtained from crystal structure data, and generated by molecular dynamic simulations; and links to the MATLAB code for the calculation of R3m and extraction of molecule coordinates from Materials Studio .xsd files (PDF)


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