In silico ADME modelling: prediction models for blood–brain barrier permeation using a systematic variable selection method

Abstract

Quantitative Structure–Property Relationship models (QSPR) based on in vivo blood–brain permeation data (logBB) of 88 diverse compounds, 324 descriptors and a systematic variable selection method, namely ‘Variable Selection and Modeling method based on the prediction (VSMP)’, are reported. Of all the models developed using VSMP, the best three-descriptors model is based on Atomic type E-state index (SsssN), AlogP98 and Van der Waal’s surface area (r = 0.8425, q = 0.8239, F = 68.49 and SE = 0.4165); the best four-descriptors model is based on Kappa shape index of order 1, Atomic type E-state index (SsssN), Atomic level based AI topological descriptor (AIssssC) and AlogP98 (r = 0.8638, q = 0.8472, F = 60.982 and SE = 0.3919). The performance of the models on three test sets taken from the literature is illustrated and compared with the results from other reported computational approaches. Test set III constitutes 91 compounds from the literature with known qualitative BBB indication and is used for virtual screening studies. The success rate of the reported models is 82% in the case of BBB+ compounds and a similar success rate is observed with BBB− compounds. Finally, as the models reported herein are based on computed properties, they appear as a valuable tool in virtual screening, where selection and prioritization of candidates is required.

Introduction

The escalating cost of drug discovery and development¹ is due to the liabilities associated with the drugs that are often not identified until a compound reaches the clinic, the most expensive phase of pharma R&D. The liabilities in question include non-optimum values of absorption, distribution, metabolism and excretion, as well as toxicity, usually referred to as ADMET.2, 3, 4, 5, 6, 7, 8, 9, 10 Blood–Brain Barrier (BBB) Permeation of new chemical entities (NCEs) is one of the most important ADMET properties considered in drug discovery and development. The BBB, a complex cellular system consisting of endothelial cells of the brain capillaries, plays the role of maintaining the homeostasis of the central nervous system (CNS) by separating the brain from the systemic blood circulation.¹¹ The distribution of potential drugs between the blood and the brain depends on the ability of compounds to penetrate the BBB. Lipophilic drugs can easily cross the BBB by passive diffusion; however, polar molecules normally do not cross the BBB, but sometimes active transport process facilitates their permeation.

In the search of new drugs targeted at CNS disease, the ideal drug candidates must be able to penetrate BBB effectively. On the other hand, peripherally acting drugs must have limited ability to cross BBB to avoid adverse CNS effects. In experiments, the relative affinity of a drug to the blood or brain tissue can be expressed in terms of the blood–brain partition coefficient, logBB = log(C_brain/C_blood), where C_brain and C_blood are the equilibrium concentrations of the drug in the brain and the blood, respectively. Experimental determination of BBB permeation is time consuming, expensive and requires a sufficient quantity of the pure compounds, often in radiolabelled form. This stringent criterion makes it not suitable for high-throughput screening of large compound libraries.3, 4 A reliable and easily applicable computational model12, 13 for predicting BBB permeation of drug candidates can help in early identification of compounds with poor BBB penetration profile, prior even to chemical synthesis and will therefore have a significant impact on drug discovery and development.

Various authors14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28 have attempted to predict BBB permeation with molecular properties such as lipophilicity (logP), topological indices, polar surface area, quantum chemical descriptors, etc., some of the reported models suffer from the use of computationally intensive calculations, making them not amenable for virtual screening of large libraries of compounds.15, 18 The most common statistical methods applied in these studies are multiple linear regression14, 15, 16, 18, 19, 22, 23, 24, 25, 26, 27 (MLR), principal component analysis²⁰ (PCA) and partial least squares regression¹⁷ (PLS). While the above statistical methods are successfully employed to develop predictive ADMET models, surprisingly, automated variable selection methods have received less attention, except for few scattered reports.5, 25, 29 We believe that automated variable selection approaches30, 31, 32, 33 provide the following advantages over the variable reduction methods such as PLS, PCA, etc., (a) they can provide multiple models, based on different combinations of properties, thus providing the end user with multiple solutions and (b) they help in better understanding the mechanism of the modelled phenomenon.⁵ These advantages prompted us to study the applications of the variable selection methods to generate predictive ADMET models and also to understand the molecular properties that influence the various ADMET properties.

In this paper, we describe the derivation of novel QSPR models for BBB permeation using VSMP,³² an efficient systematic variable selection method along with MLR. Significantly, this is the first report as of date describing the application of VSMP for predictive ADME model generation to the best of our knowledge. Further, we report the performance of the QSPR models based on internal and external validations using three datasets taken from the literature and compared them with other published computational approaches. The application of the models for the whole medicinal chemical space is demonstrated by performing virtual screening experiments, on structurally new and diverse datasets. As the models reported herein are based on computed properties, they appear as valuable tools for virtual screening, where selection and prioritization of candidates is required.