Breaking the Barriers: Machine-Learning-Based c-RASAR Approach for Accurate Blood–Brain Barrier Permeability Prediction

Abstract

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 readacross 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.

Cheminformatic Read-Across Approach Revealed Ultraviolet Filter Cinoxate as an Obesogenic Peroxisome Proliferator-Activated Receptor γ Agonist

“…Another promising strategy could include utilizing chemical similarity values against active and inactive compounds for various assay end points. For example, approaches like read-across structure–activity relationship (RASAR) and classification-based RASAR (c-RASAR) have been proposed. ,, These approaches have significantly improved the predictive performance of machine learning models by using chemical similarity values instead of binary chemical fingerprints as inputs for model training. Future studies should explore the application of these frameworks to enhance the accuracy of our cheminformatic read-across approach.…”

ARKA: a framework of dimensionality reduction for machine-learning classification modeling, risk assessment, and data gap-filling of sparse environmental toxicity data

“…Additionally, it is also possible to identify the chemical nature and possible adverse outcome pathways (AOPs) of the close congeners using the concepts of Read-Across 51,52 and quantitative Read-Across structure-activity relationship (q-RASAR). 27,53,54 Furthermore, this approach can not only be used in assessing environmental/ecotoxicity endpoints but can also be extended to other elds like drug discovery. 55…”

Innovative strategies for the quantitative modeling of blood–brain barrier (BBB) permeability: harnessing the power of machine learning-based q-RASAR approach

“…Over the past decade, a multitude of computational studies have emerged focusing on predicting BBB permeability. Noteworthy contributions include works by Bujak et al (2015), 22 Zhang et al (2015), 23 Brito-S. Y. et al (2015), 24 Wang et al (2015), 25 Toropov et al (2017), 26 Radchenko et al (2020), 27 Shin et al (2021), 28 Wu et al (2021), 29 Kim et al (2021), 30 Radan et al (2022), 31 Tang et al (2022), 32 Shaker et al (2023), 33 35 These studies employ diverse algorithms, such as multiple linear regression (MLR), genetic algorithm-support vector machine (GA-SVM), 36 random forest (RF), 37 Monte Carlo (MC-SMILES), 38 artificial neural network (ANN), 39 Deep-B3, 32 support vector regression (SVR), 40 light gradient-boosting machine (LightGBM), 41 and linear discriminant analysis (LDA). 42 The datasets used in these investigations range from 18 to 7807 compounds, showcasing the breadth of approaches applied to understanding BBB permeability prediction.…”