2022薛定谔秋季中文生命科学网络讲座 | 薛定谔计算模拟技术助力新型 D-氨基酸氧化酶抑制剂的发现
Dr. Zhe Nie
D-Serine是N-甲基d-天冬氨酸 (NMDA) 受体的共激动剂，而NMDA受体是一种关键的兴奋性神经递质受体。在大脑中，D-Serine由丝氨酸消旋酶从其L-异构体合成，并由 D-氨基酸氧化酶 (DAO, DAAO) 代谢，其中DAO是一种催化D-氨基酸(包括D-Serine)氧化降解的黄素酶, 其产物是相应的α-酮酸。许多研究已经证实了低D-Serine浓度和/或DAO高度表达以及增强酶活性与NMDA功能障碍和精神分裂症之间的关联。至此，许多公司开始探索使用DAO抑制剂治疗精神分裂症和其他适应症的可能性。我们的研究项目基于薛定谔计算建模平台的支持，以开发具有best-in-class性质的新型 DAO 抑制剂。这项研究使用hDAO FEP+模型前瞻性地预测了化合物对hDAO的抑制效力，并通过我们的AutoDesigner算法对人工设计和计算机列举的设计构思进行排序。最后，我们发现了一类具有理想药代动力学和脑渗透特性的新型DAO抑制剂。在体内小鼠 PK/PD 模型中，工具化合物37证明了通过抑制DAO功能对血浆和大脑中D-Serine浓度的调节。持续的SAR工作使DAO在生化和细胞实验中体现的效力得到了显著提高。在项目过程中，我们的建模技术不仅提高了药物化学研发的效率，还有助于识别未曾探索过的子口袋，进一步开发 SAR。
D-Serine is a co-agonist of the N-methyl D-aspartate (NMDA) receptor, a key excitatory neurotransmitter receptor. In the brain, D-Serine is synthesized from its L-isomer by serine racemase and is metabolized by the D-amino acid oxidase (DAO, DAAO), a flavoenzyme that catalyzes the oxidative degradation of D-amino acids including D-serine to the corresponding α-keto acids. Many studies have linked decreased D-serine concentration and/or increased DAO expression and enzyme activity to NMDA dysfunction and schizophrenia. Thus, many companies have explored the possibility of employing DAO inhibitors for the treatment of schizophrenia and other indications. Powered by the Schrödinger computational modeling platform, we initiated a research program to identify novel DAO inhibitors with best-in-class properties. The program execution leveraged an hDAO FEP+ model to prospectively predict compound hDAO inhibitory potency and prioritize design ideas from both human design and computer enumeration by our AutoDesigner algorithm. A novel class of DAO inhibitors with desirable pharmacokinetic and brain penetration properties were discovered from this effort. In an in vivo mouse PK/PD model, tool compound 37 demonstrated modulation of D-serine concentrations in the plasma and brain through inhibition of DAO function. Continued SAR work has led to significant potency improvement in both DAO biochemical and cell assays. Our modeling technology on this program has not only enhanced the efficiency of medicinal chemistry execution, it has also helped to identify a previously unexplored subpocket for further SAR development.