Tang Haifeng, Jensen Kristian, Houang Evelyne, McRobb Fiona M, Bhat Sathesh, Svensson Mats, Bochevarov Art, Day Tyler, Dahlgren Markus K, Bell Jeffery A, Frye Leah, Skene Robert J, Lewis James H, Osborne James D, Tierney Jason P, Gordon James A, Palomero Maria A, Gallati Caroline, Chapman Robert S L, Jones Daniel R, Hirst Kim L, Sephton Mark, Chauhan Alka, Sharpe Andrew, Tardia Piero, Dechaux Elsa A, Taylor Andrea, Waddell Ross D, Valentine Andrea, Janssens Holden B, Aziz Omar, Bloomfield Dawn E, Ladha Sandeep, Fraser Ian J, Ellard John M
Schrödinger Inc., New York, New York 10036, United States.
Takeda Development Center Americas, Inc., San Diego, California 92121, United States.
J Med Chem. 2022 May 12;65(9):6775-6802. doi: 10.1021/acs.jmedchem.2c00118. Epub 2022 Apr 28.
d-Serine is a coagonist of the -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). Many studies have linked decreased d-serine concentration and/or increased DAO expression and enzyme activity to NMDA dysfunction and schizophrenia. Thus, it is feasible to employ 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 the best-in-class properties. The program execution leveraged an hDAO FEP+ model to prospectively predict compound potency. A new class of DAO inhibitors with desirable properties has been discovered from this endeavor. Our modeling technology on this program has not only enhanced the efficiency of structure-activity relationship development but also helped to identify a previously unexplored subpocket for further optimization.
D-丝氨酸是N-甲基-D-天冬氨酸（NMDA）受体的协同激动剂，NMDA受体是一种关键的兴奋性神经递质受体。在大脑中，D-丝氨酸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。许多研究已将D-丝氨酸浓度降低和/或DAO表达及酶活性增加与NMDA功能障碍和精神分裂症联系起来。因此，使用DAO抑制剂治疗精神分裂症和其他适应症是可行的。在薛定谔计算建模平台的支持下，我们启动了一项研究计划，以鉴定具有同类最佳特性的新型DAO抑制剂。该计划的实施利用了人DAO的自由能微扰（FEP+）模型来前瞻性地预测化合物的效力。通过这一努力，已发现了一类具有理想特性的新型DAO抑制剂。我们在该计划中的建模技术不仅提高了构效关系研究的效率，还帮助确定了一个以前未被探索的亚口袋，以便进一步优化。
