Deciphering Nonbioavailable Substructures Improves the Bioavailability of Antidepressants by Serotonin Transporter

Inadequate bioavailability is one of the most critical reasons for the failure of oral drug development. However, the way that substructures affect bioavailability remains largely unknown. Serotonin transporter (SERT) inhibitors are first-line drugs for major depression disorder, and improving their bioavailability may be able to decrease side-effects by reducing daily dose. Thus, it is an excellent model to probe the relationship between substructures and bioavailability. Here, we proposed the concept of “nonbioavailable substructures”, referring to substructures that are unfavorable to bioavailability. A machine learning model was developed to identify nonbioavailable substructures based on their molecular properties and shows the accuracy of 83.5%. A more potent SERT inhibitor DH4 was discovered with a bioavailability of 83.28% in rats by replacing the nonbioavailable substructure of approved drug vilazodone. DH4 exhibits promising anti-depression efficacy in animal experiments. The concept of nonbioavailable substructures may open up a new venue for the improvement of drug bioavailability.生物利用度不足是口服药物研发失败的最关键原因之一。然而，子结构如何影响生物利用度在很大程度上仍未明确。5-羟色胺转运体（SERT）抑制剂是治疗重度抑郁症的一线用药，提升其生物利用度可通过降低每日给药剂量来减少不良反应，因此该类药物是探究子结构与生物利用度之间关联的优秀模型。在此，我们提出“非生物利用度友好子结构”的概念，即对生物利用度不利的子结构。我们开发了一款基于分子性质的机器学习模型，用于识别此类非生物利用度友好子结构，其准确率可达83.5%。通过替换获批药物维拉唑酮（vilazodone）的非生物利用度友好子结构，我们发现了活性更强的SERT抑制剂DH4，该化合物在大鼠体内的生物利用度为83.28%。动物实验结果显示，DH4具备颇具前景的抗抑郁功效。非生物利用度友好子结构的概念或许能为提升药物生物利用度开辟全新的研究方向。