Can Machine Learning Overcome the 95% Failure Rate and Reality that Only 30% of Approved Cancer Drugs Meaningfully Extend Patient Survival?,Journal of Medicinal Chemistry

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Can Machine Learning Overcome the 95% Failure Rate and Reality that Only 30% of Approved Cancer Drugs Meaningfully Extend Patient Survival?
Journal of Medicinal Chemistry ( IF 6.8 ) Pub Date : 2024-09-10 , DOI: 10.1021/acs.jmedchem.4c01684
Duxin Sun , Christian Macedonia ₁ , Zhigang Chen ₂ , Sriram Chandrasekaran , Kayvan Najarian , Simon Zhou ₃ , Tim Cernak , Vicki L Ellingrod , H V Jagadish , Bernard Marini , Manjunath Pai , Angela Violi , Jason C Rech , Shaomeng Wang , Yan Li ₄ , Brian Athey , Gilbert S Omenn

Affiliation

Despite implementing hundreds of strategies, cancer drug development suffers from a 95% failure rate over 30 years, with only 30% of approved cancer drugs extending patient survival beyond 2.5 months. Adding more criteria without eliminating nonessential ones is impractical and may fall into the “survivorship bias” trap. Machine learning (ML) models may enhance efficiency by saving time and cost. Yet, they may not improve success rate without identifying the root causes of failure. We propose a “STAR-guided ML system” (structure–tissue/cell selectivity–activity relationship) to enhance success rate and efficiency by addressing three overlooked interdependent factors: potency/specificity to the on/off-targets determining efficacy in tumors at clinical doses, on/off-target-driven tissue/cell selectivity influencing adverse effects in the normal organs at clinical doses, and optimal clinical doses balancing efficacy/safety as determined by potency/specificity and tissue/cell selectivity. STAR-guided ML models can directly predict clinical dose/efficacy/safety from five features to design/select the best drugs, enhancing success and efficiency of cancer drug development.

中文翻译：

机器学习能否克服 95% 的失败率和只有 30% 的获批癌症药物有意义地延长患者生存期的现实？

尽管实施了数百种策略，但癌症药物开发的失败率在 30 年中仍高达 95%，只有 30% 的获批癌症药物将患者生存期延长至 2.5 个月以上。在不消除非必要标准的情况下添加更多标准是不切实际的，并且可能会落入“幸存者偏差”陷阱。机器学习（ML）模型可以通过节省时间和成本来提高效率。然而，如果不确定失败的根本原因，他们可能无法提高成功率。我们提出了一种“STAR 引导的 ML 系统”（结构-组织/细胞选择性-活性关系），通过解决三个被忽视的相互依赖因素来提高成功率和效率：在临床剂量下对开/脱靶的效力/特异性决定肿瘤的疗效，开/脱靶驱动的组织/细胞选择性影响临床剂量下正常器官的不良反应，以及由效价/特异性和组织/细胞决定的最佳临床剂量平衡疗效/安全性选择性。STAR 引导的 ML 模型可以从五个特征直接预测临床剂量/疗效/安全性，以设计/选择最佳药物，从而提高癌症药物开发的成功率和效率。

更新日期：2024-09-10

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