Pharmacophore Modeling Using Machine Learning for Screening the Blood–Brain Barrier Permeation of Xenobiotics

Abstract

Daily exposure to xenobiotics affects human health, especially the nervous system, causing neurodegenerative diseases. The nervous system is protected by tight junctions present at the blood–brain barrier (BBB), but only molecules with desirable physicochemical properties can permeate it. This is why permeation is a decisive step in avoiding unwanted brain toxicity and also in developing neuronal drugs. In silico methods are being implemented as an initial step to reduce animal testing and the time complexity of the in vitro screening process. However, most in silico methods are ligand based, and consider only the physiochemical properties of ligands. However, these ligand-based methods have their own limitations and sometimes fail to predict the BBB permeation of xenobiotics. The objective of this work was to investigate the influence of the pharmacophoric features of protein–ligand interactions on BBB permeation. For these purposes, receptor-based pharmacophore and ligand-based pharmacophore fingerprints were developed using docking and Rdkit, respectively. Then, these fingerprints were trained on classical machine-learning models and compared with classical fingerprints. Among the tested footprints, the ligand-based pharmacophore fingerprint achieved slightly better (77% accuracy) performance compared to the classical fingerprint method. In contrast, receptor-based pharmacophores did not lead to much improvement compared to classical descriptors. The performance can be further improved by considering efflux proteins such as BCRP (breast cancer resistance protein), as well as P-gp (P-glycoprotein). However, the limited data availability for other proteins regarding their pharmacophoric interactions is a bottleneck to its improvement. Nonetheless, the developed models and exploratory analysis provide a path to extend the same framework for environmental chemicals, which, like drugs, are also xenobiotics. This research can help in human health risk assessment by a priori screening for neurotoxicity-causing agents.

Pharmacophore-Modeling-Based Optimal Placement and Sizing of Large-Scale Energy Storage Stations in a Power System including Wind Farms

“…The optimization was applied using pharmacophore modeling, which is used in the medical sector in many applications [26], but this is the first time it has been applied in solving an engineering problem. PM has better performance than state-of-the-art techniques because it has parallel control on features and radius that pushes the search area toward a near global minima solution.…”

Advances in Modeling Approaches for Oral Drug Delivery: Artificial Intelligence, Physiologically Based Pharmacokinetic, and First-Principal Models

“…In recent years, non-linear QSAR models, based on ML and DL techniques, have become the most popular strategy to develop QSAR models for the prediction of ADME properties of drug candidates [84], and formulation design [85]. Similarly to conventional QSAR, non-linear QSAR models were developed to estimate the permeability [49,82,86], physio-chemical properties [58,59], distribution [49,82], affinity to P-gp [87,88], hepatic clearance [49,82], metabolism by the CYP family [89][90][91][92], and F in fasted [59] and fed states [93]. For reviews on using AI for ADME properties, and other usage in the pharmaceutical industry see Refs.…”

The Role of “Physiologically Based Pharmacokinetic Model (PBPK)” New Approach Methodology (NAM) in Pharmaceuticals and Environmental Chemical Risk Assessment

“…The development of PBPK models is sometimes considered time- and resource-intensive and complicated due to multiple parameters, with many having unknown values, which are often fitted. Therefore, utilizing machine learning and artificial intelligence approaches can help in predicting some of the PK parameters of new substances, which can speed up the process of developing robust NAM-based PBPK models with limited experimental data [ 13 , 14 ].…”