首页|Data on Cancer Reported by Christian Macedonia and Colleagues (Can Machine Learn ing Overcome the 95% Failure Rate and Reality that Only 30% of Approved Cancer Drugs Meaningfully Extend Patient Survival?)
Data on Cancer Reported by Christian Macedonia and Colleagues (Can Machine Learn ing Overcome the 95% Failure Rate and Reality that Only 30% of Approved Cancer Drugs Meaningfully Extend Patient Survival?)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Cancer is the subject of a report. According to news reporting out of Lancaster, Pennsylvania, by News Rx editors, research stated, “Despite implementing hundreds of strategies, cance r 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.” Our news journalists obtained a quote from the research, “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 interdependen t factors: potency/specificity to the on/off-targets determining efficacy in tum ors at clinical doses, on/off-targetdriven tissue/cell selectivity influencing adverse effects in the normal organs at clinical doses, and optimal clinical dos es balancing efficacy/safety as determined by potency/specificity and tissue/cel l selectivity.”
LancasterPennsylvaniaUnited StatesNorth and Central AmericaCancerCyborgsDrugs and TherapiesEmerging Techno logiesHealth and MedicineMachine LearningOncology
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