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    Research Article

    The Brain Exposure Efficiency (BEE) Score
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    • Mayuri Gupta
      Mayuri Gupta
      Krembil Research Institute, University Health Network, 60 Leonard Avenue, Toronto, Ontario M5T 2S8, Canada
      More by Mayuri Gupta
      Orcidhttp://orcid.org/0000-0001-8588-1262
    • Thomas Bogdanowicz
      Thomas Bogdanowicz
      Krembil Research Institute, University Health Network, 60 Leonard Avenue, Toronto, Ontario M5T 2S8, Canada
      More by Thomas Bogdanowicz
    • Mark A. Reed
      Mark A. Reed
      Krembil Research Institute, University Health Network, 60 Leonard Avenue, Toronto, Ontario M5T 2S8, Canada
      More by Mark A. Reed
    • Christopher J. Barden
      Christopher J. Barden
      Krembil Research Institute, University Health Network, 60 Leonard Avenue, Toronto, Ontario M5T 2S8, Canada
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    • Donald F. Weaver*
      Donald F. Weaver
      Krembil Research Institute, University Health Network, 60 Leonard Avenue, Toronto, Ontario M5T 2S8, Canada
      Department of Medicine, University of Toronto, Toronto, Ontario M5G 2C4, Canada
      Department of Chemistry, University of Toronto, Toronto, Ontario M5S 3H6, Canada
      Department of Pharmaceutical Sciences, University of Toronto, Toronto, Ontario M5S 3M2 Canada
      *Email: [email protected]
      More by Donald F. Weaver
      Orcidhttp://orcid.org/0000-0003-2042-6220
    Other Access OptionsSupporting Information (3)

    ACS Chemical Neuroscience

    Cite this: ACS Chem. Neurosci. 2020, 11, 2, 205–224
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    https://doi.org/10.1021/acschemneuro.9b00650
    Published December 9, 2019
    Copyright © 2019 American Chemical Society
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    Abstract

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    The blood-brain barrier (BBB), composed of microvascular tight junctions and glial cell sheathing, selectively controls drug permeation into the central nervous system (CNS) by either passive diffusion or active transport. Computational techniques capable of predicting molecular brain penetration are important to neurological drug design. A novel prediction algorithm, termed the Brain Exposure Efficiency Score (BEE), is presented. BEE addresses the need to incorporate the role of trans-BBB influx and efflux active transporters by considering key brain penetrance parameters, namely, steady state unbound brain to plasma ratio of drug (Kp,uu) and dose normalized unbound concentration of drug in brain (Cu,b). BEE was devised using quantitative structure–activity relationships (QSARs) and molecular modeling studies on known transporter proteins and their ligands. The developed algorithms are provided as a user-friendly open source calculator to assist in optimizing a brain penetrance strategy during the early phases of small molecule molecular therapeutic design.

    Copyright © 2019 American Chemical Society

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    Supporting Information

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    The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acschemneuro.9b00650.

    • Statistics for Kp,uu QSAR model results (without transporter information) evaluated as binary classification model, statistics for Kp,uu QSAR model results (with transporter information) evaluated as binary classification model, statistics for Kp,uu QSAR model results for brain slice method (with transporter information) evaluated as binary classification model, results of BCRP Docking E_score2 for scenarios A–C, list of extracellular targets, influx transporters, efflux transporters, intracellular, and other targets for drugs listed for Kp,uu experimental data given in Table 1, QSAR models for Kp,uu brain slice (Fridén et al. data set), including various efflux and influx transporter descriptors, by randomly varying training and test set molecules to ensure quality of statistics, and final QSAR model equation for log(Kp,uu) incorporating various transporters (PDF)

    • Active brain exposure efficiency (BEE) calculator (XLSX)

    • Database of Kp,uu, [Cu,b]std, P-glycoprotein (P-gp), breast cancer resistance protein (BCRP), organic cation transporter (OCT1), organic cation transporter 2 (OCT2), organic anion transporting polypeptides (OATPs) (XLSX)

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    This article is cited by 14 publications.

    1. Isaac M. Jackson, E. William Webb, Peter J.H. Scott, Michelle L. James. In Silico Approaches for Addressing Challenges in CNS Radiopharmaceutical Design. ACS Chemical Neuroscience 2022, 13 (12) , 1675-1683. https://doi.org/10.1021/acschemneuro.2c00269
    2. Baichen Xiong, Yuanyuan Wang, Ying Chen, Shuaishuai Xing, Qinghong Liao, Yao Chen, Qi Li, Wei Li, Haopeng Sun. Strategies for Structural Modification of Small Molecules to Improve Blood–Brain Barrier Penetration: A Recent Perspective. Journal of Medicinal Chemistry 2021, 64 (18) , 13152-13173. https://doi.org/10.1021/acs.jmedchem.1c00910
    3. Giang H. Ta, Max K. Leong. A novel in silico approach for predicting unbound brain-to-plasma ratio using machine learning-based support vector regression. Computers in Biology and Medicine 2025, 192 , 110366. https://doi.org/10.1016/j.compbiomed.2025.110366
    4. Berfin Gülave, Helle W. van den Maagdenberg, Luke van Boven, Gerard J. P. van Westen, Elizabeth C. M. de Lange, J. G. Coen van Hasselt. Prediction of the Extent of Blood–Brain Barrier Transport Using Machine Learning and Integration into the LeiCNS-PK3.0 Model. Pharmaceutical Research 2025, 42 (2) , 281-289. https://doi.org/10.1007/s11095-025-03828-0
    5. Maria Dichiara, Giuseppe Cosentino, Giorgia Giordano, Lorella Pasquinucci, Agostino Marrazzo, Giuliana Costanzo, Emanuele Amata. Designing drugs optimized for both blood–brain barrier permeation and intra-cerebral partition. Expert Opinion on Drug Discovery 2024, 19 (3) , 317-329. https://doi.org/10.1080/17460441.2023.2294118
    6. Mayuri Gupta, Jun Feng, Govinda Bhisetti. Experimental and Computational Methods to Assess Central Nervous System Penetration of Small Molecules. Molecules 2024, 29 (6) , 1264. https://doi.org/10.3390/molecules29061264
    7. Mohammed A. A. Saleh, Berfin Gülave, Olivia Campagne, Clinton F. Stewart, Jeroen Elassaiss-Schaap, Elizabeth C. M. de Lange. Using the LeiCNS-PK3.0 Physiologically-Based Pharmacokinetic Model to Predict Brain Extracellular Fluid Pharmacokinetics in Mice. Pharmaceutical Research 2023, 40 (11) , 2555-2566. https://doi.org/10.1007/s11095-023-03554-5
    8. Mohammed A.A. Saleh, Makoto Hirasawa, Ming Sun, Berfin Gülave, Jeroen Elassaiss-Schaap, Elizabeth C.M. de Lange. The PBPK LeiCNS-PK3.0 framework predicts Nirmatrelvir (but not Remdesivir or Molnupiravir) to achieve effective concentrations against SARS-CoV-2 in human brain cells. European Journal of Pharmaceutical Sciences 2023, 181 , 106345. https://doi.org/10.1016/j.ejps.2022.106345
    9. Brendan J. Kelly, Elena Diez-Cecilia, Luzhe Pan, Braden Sweeting, Laura Villar, Alexander Kreft, Mayuri Gupta, Shea L. Johnson, Donald F. Weaver. A privileged dual action Alzheimer's disease therapeutic platform targeting immunopathic and proteopathic mechanisms: ( E )-3-styrylindoles as inhibitors of indoleamine 2,3-dioxygenase-mediated tryptophan metabolism and β-amyloid aggregation. Canadian Journal of Chemistry 2022, 100 (9) , 660-675. https://doi.org/10.1139/cjc-2021-0324
    10. Elizabeth C. M. de Lange, Margareta Hammarlund Udenaes. Understanding the Blood‐Brain Barrier and Beyond: Challenges and Opportunities for Novel CNS Therapeutics. Clinical Pharmacology & Therapeutics 2022, 111 (4) , 758-773. https://doi.org/10.1002/cpt.2545
    11. Mayuri Gupta, Donald F. Weaver. Axonal plasma membrane-mediated toxicity of cholesterol in Alzheimer's disease: A microsecond molecular dynamics study. Biophysical Chemistry 2022, 281 , 106718. https://doi.org/10.1016/j.bpc.2021.106718
    12. Mayuri Gupta, Donald F. Weaver. Microsecond molecular dynamics studies of cholesterol-mediated myelin sheath degeneration in early Alzheimer's disease. Physical Chemistry Chemical Physics 2021, 24 (1) , 222-239. https://doi.org/10.1039/D1CP03844C
    13. Yong Zheng, Paul M. Stafford, Kurt R. Stover, Darapaneni Chandra Mohan, Mayuri Gupta, Eric C. Keske, Paolo Schiavini, Laura Villar, Fan Wu, Alexander Kreft, Kiersten Thomas, Elana Raaphorst, Jagadeesh P. Pasangulapati, Siva R. Alla, Simmi Sharma, Ramana R. Mittapalli, Irina Sagamanova, Shea L. Johnson, Mark A. Reed, Donald F. Weaver. A Series of 2‐((1‐Phenyl‐1H‐imidazol‐5‐yl)methyl)‐1H‐indoles as Indoleamine 2,3‐Dioxygenase 1 (IDO1) Inhibitors. ChemMedChem 2021, 16 (14) , 2195-2205. https://doi.org/10.1002/cmdc.202100107
    14. Zhiyu Wang, Yanfei Wang, Prachi Vilekar, Seung-Pil Yang, Mayuri Gupta, Myong In Oh, Autumn Meek, Lisa Doyle, Laura Villar, Anja Brennecke, Imindu Liyanage, Mark Reed, Christopher Barden, Donald F. Weaver. Small molecule therapeutics for COVID-19: repurposing of inhaled furosemide. PeerJ 2020, 8 , e9533. https://doi.org/10.7717/peerj.9533
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    ACS Chemical Neuroscience

    Cite this: ACS Chem. Neurosci. 2020, 11, 2, 205–224
    Click to copy citationCitation copied!
    https://doi.org/10.1021/acschemneuro.9b00650
    Published December 9, 2019
    Copyright © 2019 American Chemical Society
    Request reuse permissions

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