Application of in vitro PAMPA technique and in silico computational methods for blood-brain barrier permeability prediction of novel CNS drug candidates

生物信息学 数量结构-活动关系 血脑屏障 支持向量机 分子描述符 磁导率 体内 化学 计算生物学 中枢神经系统 机器学习 计算机科学 生物 生物化学 神经科学 生物技术 基因
作者
Milica Radan,Teodora Djikić,Darija Obradović,Katarina Nikolić
出处
期刊:European Journal of Pharmaceutical Sciences [Elsevier BV]
卷期号:168: 106056-106056 被引量:29
标识
DOI:10.1016/j.ejps.2021.106056
摘要

Permeability assessment of small molecules through the blood-brain barrier (BBB) plays a significant role in the development of effective central nervous system (CNS) drug candidates. Since in vivo methods for BBB permeability estimation require a lot of time and resources, in silico and in vitro approaches are becoming increasingly popular nowadays for faster and more economical predictions in early phases of drug discovery. In this work, through application of in vitro parallel artificial membrane permeability assay (PAMPA-BBB) and in silico computational methods we aimed to examine the passive permeability of eighteen compounds, which affect serotonin and dopamine levels in the CNS. The data set was consisted of novel six human dopamine transporter (hDAT) substrates that were previously identified as the most promising lead compounds for further optimisation to achieve neuroprotective effect, twelve approved CNS drugs, and their related compounds. Firstly, PAMPA methods was used to experimentally determine effective BBB permeability (Pe) for all studied compounds and obtained results were further submitted for quantitative structure permeability relationship (QSPR) analysis. QSPR models were built by using three different statistical methods: stepwise multiple linear regression (MLR), partial least square (PLS), and support-vector machine (SVM), while their predictive capability was tested through internal and external validation. Obtained statistical parameters (MLR- R2pred=-0.10; PLS- R2pred=0.64, r2m=0.69, r/2m=0.44; SVM- R2pred=0.57, r2m=0.72, r/2m=0.55) indicated that the SVM model is superior over others. The most important molecular descriptors (H0p and SolvEMt_3D) were identified and used to propose structural modifications of the examined compounds in order to improve their BBB permeability. Moreover, steered molecular dynamics (SMD) simulation was employed to comprehensively investigate the permeability pathway of compounds through a lipid bilayer. Taken together, the created QSPR model could be used as a reliable and fast pre-screening tool for BBB permeability prediction of structurally related CNS compounds, while performed MD simulations provide a good foundation for future in silico examination.

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