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. 2022 Oct 20:13:1040838.
doi: 10.3389/fphar.2022.1040838. eCollection 2022.

Development of QSAR models to predict blood-brain barrier permeability

Sadegh Faramarzi  1 , Marlene T Kim  1 , Donna A Volpe  1 , Kevin P Cross  2 , Suman Chakravarti  3 , Lidiya Stavitskaya  1
Affiliations

Development of QSAR models to predict blood-brain barrier permeability

Sadegh Faramarzi et al. Front Pharmacol. .

Abstract

Assessing drug permeability across the blood-brain barrier (BBB) is important when evaluating the abuse potential of new pharmaceuticals as well as developing novel therapeutics that target central nervous system disorders. One of the gold-standard in vivo methods for determining BBB permeability is rodent log BB; however, like most in vivo methods, it is time-consuming and expensive. In the present study, two statistical-based quantitative structure-activity relationship (QSAR) models were developed to predict BBB permeability of drugs based on their chemical structure. The in vivo BBB permeability data were harvested for 921 compounds from publicly available literature, non-proprietary drug approval packages, and University of Washington's Drug Interaction Database. The cross-validation performance statistics for the BBB models ranged from 82 to 85% in sensitivity and 80-83% in negative predictivity. Additionally, the performance of newly developed models was assessed using an external validation set comprised of 83 chemicals. Overall, performance of individual models ranged from 70 to 75% in sensitivity, 70-72% in negative predictivity, and 78-86% in coverage. The predictive performance was further improved to 93% in coverage by combining predictions across the two software programs. These new models can be rapidly deployed to predict blood brain barrier permeability of pharmaceutical candidates and reduce the use of experimental animals.

Keywords: QSAR; blood-brain barrier; in silico; log BB; permeability.

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Conflict of interest statement

Author KC was employed by the Leadscope Inc; Author SC was employed by the MultiCASE Inc. LS reports that she is the co-Principal Investigator on two Research Collaboration Agreements (RCAs) between the US Food and Drug Administration’s Center for Drug Evaluation and Research, and Leadscope Inc., and MultiCASE Inc., respectively. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
Database analysis (A) Assessment of the functional groups present in the entire BBB database. (B) Anatomical Therapeutic Chemical (ATC) level 1 classes present BBB database. (C) ACT level 2 classes of the nervous and cardiovascular systems.
FIGURE 2
FIGURE 2
Leadscope model analysis. (A) Selected chemical features with highest and lowest Z-scores. The arrow shows the order of Z-scores associated with features in the model. (B) Correlation (R 2) between pairs of physicochemical features. (C) Histogram of the predictivity (blue bars) and frequency (grey bars) as a function of probability in LS model.
FIGURE 3
FIGURE 3
Case Ultra model analysis. (A) ROC plot of the BBB model. The orange dot corresponds to the optimal classification threshold (B) Selected examples of chemical fragments with highest number of positive and negative compounds.
See this image and copyright information in PMC

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