Patil Sachin P, Fattakhova Elena, Hofer Jeremy, Oravic Michael, Bender Autumn, Brearey Jason, Parker Daniel, Radnoff Madison, Smith Zackary
NanoBio Lab, School of Engineering, Widener University, Chester, PA 19013, USA.
Department of Chemical Engineering, Widener University, Chester, PA 19013, USA.
Pharmaceuticals (Basel). 2022 May 16;15(5):613. doi: 10.3390/ph15050613.
The selective activation of the innate immune system through blockade of immune checkpoint PD1-PDL1 interaction has proven effective against a variety of cancers. In contrast to six antibody therapies approved and several under clinical investigation, the development of small-molecule PD1-PDL1 inhibitors is still in its infancy with no such drugs approved yet. Nevertheless, a promising series of small molecules inducing PDL1 dimerization has revealed important spatio-chemical features required for effective PD1-PDL1 inhibition through PDL1 sequestration. In the present study, we utilized these features for developing machine-learning (ML) classifiers by fitting Random Forest models to six 2D fingerprint descriptors. A focused database of ~16 K bioactive molecules, including approved and experimental drugs, was screened using these ML models, leading to classification of 361 molecules as potentially active. These ML hits were subjected to molecular docking studies to further shortlist them based on their binding interactions within the PDL1 dimer pocket. The top 20 molecules with favorable interactions were experimentally tested using HTRF human PD1-PDL1 binding assays, leading to the identification of two active molecules, CRT5 and P053, with the IC values of 22.35 and 33.65 µM, respectively. Owing to their bioactive nature, our newly discovered molecules may prove suitable for further medicinal chemistry optimization, leading to more potent and selective PD1-PDL1 inhibitors. Finally, our ML models and the integrated screening protocol may prove useful for screening larger libraries for novel PD1-PDL1 inhibitors.
通过阻断免疫检查点PD1-PDL1相互作用来选择性激活先天免疫系统已被证明对多种癌症有效。与已批准的六种抗体疗法和几种正在临床研究的疗法不同，小分子PD1-PDL1抑制剂的开发仍处于起步阶段，尚无此类药物获批。尽管如此，一系列有前景的诱导PDL1二聚化的小分子揭示了通过隔离PDL1有效抑制PD1-PDL1所需的重要空间化学特征。在本研究中，我们利用这些特征，通过将随机森林模型拟合到六个二维指纹描述符来开发机器学习（ML）分类器。使用这些ML模型筛选了一个包含约16K个生物活性分子的聚焦数据库，其中包括已批准和实验性药物，从而将361个分子分类为具有潜在活性。对这些ML命中的分子进行分子对接研究，以根据它们在PDL1二聚体口袋内的结合相互作用进一步筛选。使用HTRF人PD1-PDL1结合试验对具有良好相互作用的前20个分子进行实验测试，从而鉴定出两个活性分子CRT5和P053，其IC值分别为22.35和33.65μM。由于它们的生物活性性质，我们新发现的分子可能证明适用于进一步的药物化学优化，从而产生更有效和选择性更高的PD1-PDL1抑制剂。最后，我们的ML模型和综合筛选方案可能证明对筛选更大的文库以寻找新型PD1-PDL1抑制剂有用。
