Environmental Engineering Laboratory, Departament d' Enginyeria Quimica, Universitat Rovira i Virgili, Av. Països Catalans 26, 43007 Tarragona, Spain.
Institut d'Investigació Sanitària Pere Virgili (IISPV), Hospital Universitari Sant Joan de Reus, Universitat Rovira I Virgili, 43201 Reus, Spain.
Int J Environ Res Public Health. 2022 Oct 18;19(20):13471. doi: 10.3390/ijerph192013471.
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.
每天接触的外源性化学物质会影响人类健康，尤其是神经系统，导致神经退行性疾病。神经系统由血脑屏障 (BBB) 中的紧密连接保护，但只有具有理想物理化学特性的分子才能穿透它。这就是为什么渗透是避免不必要的脑毒性和开发神经元药物的决定性步骤。计算方法被用作减少动物测试和体外筛选过程时间复杂度的初始步骤。然而，大多数计算方法都是基于配体的，只考虑配体的物理化学性质。然而，这些基于配体的方法有其自身的局限性，有时无法预测外源性化学物质的 BBB 渗透性。本工作的目的是研究蛋白-配体相互作用的药效特征对 BBB 渗透性的影响。为此，使用对接和 Rdkit 分别开发了基于受体的药效团和基于配体的药效团指纹。然后，将这些指纹训练到经典机器学习模型上，并与经典指纹进行比较。在所测试的指纹中，基于配体的药效团指纹的性能略好（准确率为 77%），优于经典指纹方法。相比之下，基于受体的药效团与经典描述符相比并没有带来太大的改进。通过考虑外排蛋白（如乳腺癌耐药蛋白 BCRP 和 P-糖蛋白 P-gp），可以进一步提高性能。然而，其他蛋白的药效团相互作用数据有限，这是提高性能的一个瓶颈。尽管如此，所开发的模型和探索性分析为扩展相同框架以用于环境化学物质提供了一条途径，这些化学物质与药物一样，也是外源性化学物质。这项研究可以通过神经毒性物质的预先筛选来帮助进行人类健康风险评估。
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