Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata 700032, India.
J Chem Inf Model. 2024 May 27;64(10):4298-4309. doi: 10.1021/acs.jcim.4c00433. Epub 2024 May 3.
The intricate nature of the blood-brain barrier (BBB) poses a significant challenge in predicting drug permeability, which is crucial for assessing central nervous system (CNS) drug efficacy and safety. This research utilizes an innovative approach, the classification read-across structure-activity relationship (c-RASAR) framework, that leverages machine learning (ML) to enhance the accuracy of BBB permeability predictions. The c-RASAR framework seamlessly integrates principles from both read-across and QSAR methodologies, underscoring the need to consider similarity-related aspects during the development of the c-RASAR model. It is crucial to note that the primary goal of this research is not to introduce yet another model for predicting BBB permeability but rather to showcase the refinement in predicting the BBB permeability of organic compounds through the introduction of a c-RASAR approach. This groundbreaking methodology aims to elevate the accuracy of assessing neuropharmacological implications and streamline the process of drug development. In this study, an ML-based c-RASAR linear discriminant analysis (LDA) model was developed using a dataset of 7807 compounds, encompassing both BBB-permeable and -nonpermeable substances sourced from the B3DB database (freely accessible from https://github.com/theochem/B3DB), for predicting BBB permeability in lead discovery for CNS drugs. The model's predictive capability was then validated using three external sets: one containing 276,518 natural products (NPs) from the LOTUS database (accessible from https://lotus.naturalproducts.net/download) for data gap filling, another comprising 13,002 drug-like/drug compounds from the DrugBank database (available from https://go.drugbank.com/), and a third set of 56 FDA-approved drugs to assess the model's reliability. Further diversifying the predictive arsenal, various other ML-based c-RASAR models were also developed for comparison purposes. The proposed c-RASAR framework emerged as a powerful tool for predicting BBB permeability. This research not only advances the understanding of molecular determinants influencing CNS drug permeability but also provides a versatile computational platform for the rapid assessment of diverse compounds, facilitating informed decision-making in drug development and design.
血脑屏障（BBB）的复杂性质给预测药物渗透性带来了重大挑战，这对于评估中枢神经系统（CNS）药物的疗效和安全性至关重要。本研究采用了一种创新方法，即分类读跨结构-活性关系（c-RASAR）框架，利用机器学习（ML）来提高 BBB 渗透性预测的准确性。c-RASAR 框架无缝集成了读跨和 QSAR 方法的原理，强调在开发 c-RASAR 模型时需要考虑相似性相关方面。需要注意的是，本研究的主要目的不是引入另一种预测 BBB 渗透性的模型，而是通过引入 c-RASAR 方法来展示提高预测有机化合物 BBB 渗透性的方法。这种开创性的方法旨在提高评估神经药理学影响的准确性，并简化药物开发过程。在这项研究中，使用来自 B3DB 数据库（可从 https://github.com/theochem/B3DB 免费获得）的 7807 种化合物的数据集，包括 BBB 可渗透和不可渗透的物质，开发了基于 ML 的 c-RASAR 线性判别分析（LDA）模型，用于预测 CNS 药物发现中的 BBB 渗透性。然后使用三个外部数据集验证模型的预测能力：一个包含 LOTUS 数据库（可从 https://lotus.naturalproducts.net/download 获得）的 276518 种天然产物（NPs）的数据填补，另一个包含 DrugBank 数据库（可从 https://go.drugbank.com/）的 13002 种类药/药物化合物，以及第三个包含 56 种 FDA 批准药物的数据集，以评估模型的可靠性。为了进一步丰富预测武器库，还开发了其他几种基于 ML 的 c-RASAR 模型进行比较。所提出的 c-RASAR 框架是一种预测 BBB 渗透性的强大工具。这项研究不仅推进了对影响 CNS 药物渗透性的分子决定因素的理解，还为快速评估各种化合物提供了一个多功能的计算平台，为药物开发和设计中的决策提供了信息支持。
