PO.CH01.03 · 化学
利用整合生成式化学与机制性PK模拟的AI驱动平台加速发现新型RORgammaT调节剂
Accelerated discovery of novel RORgammaT modulators using an AI-driven platform integrating generative chemistry and mechanistic PK simulation
作者与单位 Authors & Affiliations
摘要 Abstract
中文摘要
人工智能（AI）在肿瘤学药物发现中的应用有望显著加速并降低新型治疗药物鉴定的风险。在本研究中，我们展示了一个AI驱动药物设计（AIDD）平台在发现新型RORyT配体（包括激动剂和反向激动剂）中的成功应用。RORyT是一种对Th17细胞分化和IL-17信号转导至关重要的核受体，已成为自身免疫性疾病以及肿瘤免疫学的一个有前景的靶点，两类配体均具有治疗潜力。
我们的AIDD平台在初始化合物设计阶段即整合了定量构效关系（QSAR）建模、ADMET性质预测、高通量机制性PK模拟、3D体积/药效团相似性评分以及合成可及性评估。重要的是，化合物优先排序由多准则决策分析多参数优化（MPO）算法指导，该算法纳入这些特征以平衡效力、ADMET/PK和化学可行性，从而优化候选分子的选择。这一方法实现了跨多个参数的系统性决策，减少了对试错式筛选的依赖。
在我们首轮设计/合成/测试循环中，合成并测试了27个新型化合物。值得注意的是，70%的化合物在基于细胞的检测中显示出对RORyT活性>25%的抑制。最优候选化合物的IC₅₀为1.51 uM，具有良好的体外ADME特性，预测值与实测值高度一致。基于这些结果，设计了第二组反向激动剂。14/19（74%）具有活性，其中4个效力有所提升。体外和体内ADMET及PK测试正在进行中。在同期工作中，我们鉴定出一类新型RORyT激动剂，其可能应用于癌症免疫治疗；该类化合物的进一步开发也在进行中。
这项工作展示了一个整合生成式化学与机制性PK模拟及MPO的平台如何被用于加速可行药物候选分子的开发，本例中针对的是一个重要的临床靶点RORyT。
查看英文原文 English abstract
The application of artificial intelligence (AI) in oncology drug discovery offers the potential to significantly accelerate and de-risk the identification of novel therapeutic agents. In this study, we present the successful application of an AI-driven drug design (AIDD) platform to discover new classes of RORyT ligands, both agonists and inverse agonists. RORyT, a nuclear receptor central to Th17 cell differentiation and IL-17 signaling, has emerged as a promising target for auto-immune diseases as well as cancer immunology, and both classes of ligands have therapeutic potential.
Our AIDD platform integrates quantitative structure-activity relationship (QSAR) modeling, ADMET property predictions, high-throughput mechanistic PK simulations, 3D volumetric/pharmacophore similarity scoring, and synthetic accessibility assessments at the point of initial compound design. Importantly, compound prioritization is guided by a multi-criteria decision analysis multi-parameter optimization (MPO) algorithm, which incorporates these features to balance potency, ADMET/PK, and chemical tractability to optimize candidate selection. This approach enables systematic decision-making across multiple parameters, reducing reliance on trial-and-error screening.
In our initial Design/Make/Test cycle, 27 novel compounds were synthesized and tested. Remarkably, 70% demonstrated >25% inhibition of RORyT activity in cell-based assays. The top candidate had an IC₅₀ of 1.51 uM and favorable in vitro ADME, with high concordance between predicted and measured values. Based on these results, a second set of inverse agonists was designed. 14/19 (74%) were active, with 4 having improved potency. In vitro and in vivo ADMET and PK testing is ongoing. In parallel work, we identified a novel class of RORyT agonists, which could find application in cancer immunotherapy; further development of this class is ongoing as well.
This work demonstrates how a platform integrating generative chemistry with mechanistic PK simulations and MPO can be used to accelerate the development of viable drug candidates, in this case, against an important clinical target, RORyT
利益披露 Disclosure
J. Jones, None..
R. Bachorz, None..
M. Lawless, None..
J. Pastwinska, None..
A. Salkowska, None..
M. Ratajewski, None.