化学
抗癌药物
癌症
故障率
药理学
重症监护医学
内科学
可靠性工程
医学
工程类
作者
Duxin Sun,Christian Macedonia,Zhigang Chen,Sriram Chandrasekaran,Kayvan Najarian,Simon Zhou,Tim Cernak,Vicki L. Ellingrod,H. V. Jagadish,Bernard L. Marini,Manjunath P. Pai,Angela Violi,Jason C. Rech,Shaomeng Wang,Yan Li,Brian D. Athey,Gilbert S. Omenn
标识
DOI:10.1021/acs.jmedchem.4c01684
摘要
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.
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