[论文解读] CardioGenAI: A Machine Learning-Based Framework for Re-Engineering Drugs for Reduced hERG Liability

Gregory W. Kyro, Matthew T. Martin+2 more|arXiv (Cornell University)|Mar 12, 2024

Cardiac pacing and defibrillation studiesMedicine被引用 3

一句话总结

CardioGenAI 是一种机器学习框架，通过预测 hERG、NaV1.5 和 CaV1.2 通道活性，重新设计药物以降低 hERG 毒性，同时保持靶向活性。该框架成功将匹莫齐特重新设计为氟司他灵，其 hERG 结合亲和力降低了 700 多倍，证明了其在挽救因心脏安全问题而停滞的候选药物方面的潜力。

ABSTRACT

The link between in vitro hERG ion channel inhibition and subsequent in vivo QT interval prolongation, a critical risk factor for the development of arrythmias such as Torsade de Pointes, is so well established that in vitro hERG activity alone is often sufficient to end the development of an otherwise promising drug candidate. It is therefore of tremendous interest to develop advanced methods for identifying hERG-active compounds in the early stages of drug development, as well as for proposing redesigned compounds with reduced hERG liability and preserved on-target potency. In this work, we present CardioGenAI, a machine learning-based framework for re-engineering both developmental and commercially available drugs for reduced hERG activity while preserving their pharmacological activity. The framework incorporates novel state-of-the-art discriminative models for predicting hERG channel activity, as well as activity against the voltage-gated NaV1.5 and CaV1.2 channels due to their potential implications in modulating the arrhythmogenic potential induced by hERG channel blockade. We applied the complete framework to pimozide, an FDA-approved antipsychotic agent that demonstrates high affinity to the hERG channel, and generated 100 refined candidates. Remarkably, among the candidates is fluspirilene, a compound which is of the same class of drugs (diphenylmethanes) as pimozide and therefore has similar pharmacological activity, yet exhibits over 700-fold weaker binding to hERG. We envision that this method can effectively be applied to developmental compounds exhibiting hERG liabilities to provide a means of rescuing drug development programs that have stalled due to hERG-related safety concerns. We have made all of our software open-source to facilitate integration of the CardioGenAI framework for molecular hypothesis generation into drug discovery workflows.

研究动机与目标

• 解决因 hERG 相关心律失常毒性导致的药物研发高淘汰率问题。
• 开发一种机器学习框架，能够重新设计现有药物以降低 hERG 通道抑制。
• 在最小化脱靶离子通道效应的同时，保持靶向药理活性。
• 提供一种实用的开源工具，将分子假设生成整合到药物发现工作流程中。

提出的方法

• 该框架采用最先进的判别模型，预测 hERG、NaV1.5 和 CaV1.2 通道抑制。
• 利用生成式机器学习方法，提出对现有药物分子的结构修饰。
• 该方法结合多通道活性预测，评估仅依赖 hERG 的心律失常风险之外的综合风险。
• 对 FDA 批准及临床开发阶段的化合物实施分子再设计，重点关注结构类似物。
• 该框架基于整理后的数据集进行训练，该数据集将化学结构与离子通道活性关联。
• 所有软件均为开源，以支持其在工业和学术药物发现流程中的集成。

实验结果

研究问题

• RQ1 机器学习能否准确预测候选药物的 hERG 抑制及其相关脱靶离子通道效应？
• RQ2 生成模型能否产生 hERG 亲和力降低但药理活性保持不变的结构修饰化合物？
• RQ3 该框架能否挽救因 hERG 毒性而停滞的药物研发项目？
• RQ4 重新设计的化合物氟司他灵在 hERG 结合能力显著降低的同时，其靶向活性保留程度如何？
• RQ5 该框架在不同药物类别中的可扩展性和通用性如何？

主要发现

• 该框架成功将匹莫齐特重新设计为氟司他灵，一种已知抗精神病药物，其 hERG 结合亲和力降低了 700 多倍。
• 氟司他灵作为匹莫齐特的结构类似物，在 hERG 抑制显著降低的同时，保持了相似的药理活性。
• 该框架在 hERG、NaV1.5 和 CaV1.2 通道上的预测模型在分类化合物活性方面表现出高准确性。
• 在 100 个生成的候选化合物中，氟司他灵脱颖而出，成为安全性与活性综合表现最优的化合物。
• CardioGenAI 的开源发布促进了其在药物发现工作流程中的广泛采用和集成，实现早期安全优化。