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Cancer is a high-mortality disease (9.6 million deaths in 2018 worldwide). Given various anticancer drugs, drug selection plays a key role in patient survival in clinical trials. Drug Sensitivity Testing (DST), one of the leading drug selective systems, was widely practiced for therapeutic promotion in the clinic. Notably, DSTs assist in drug selection that benefits drug responses against cancer from 20-22% to 30-35% over the past two decades. The relationship between drug resistance in vitro and drug treatment benefits was associated with different tumor origins and subtypes. Medical theory and underlying DST mechanisms remain poorly understood until now. The study of the clinical scenario, sustainability and financial support for mechanism and technical promotions is indispensable. Despite the great technical advance, therapeutic prediction and drug selection by DST needs to be miniature, versatility and cost-effective in the clinic. Multi-parameters and automation of DST should be a future trend. Advanced biomedical knowledge and clinical approaches to translating oncologic profiles into drug selection were the main focuses of DST developments. With a great technical stride, the clinical architecture of the DST platform was entering higher levels (drug response testing at any stage of cancer patients and miniaturization of tumor samples). The cancer biology and pharmacology for drug selection mutually benefit the clinic. New proposals to reveal more therapeutic information and drug response prediction at genetic, molecular and omics levels should be estimated overall. By upholding this goal of non-invasive, versatility and automation, DST could save the life of several thousand annually worldwide. In this article, new insights into DST novelty and development are highlighted. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.

Citation

Da-Yong Lu, Ting-Ren Lu, Nagendra Sastry Yarla, Bin Xu. Drug Sensitivity Testing for Cancer Therapy, Key Areas. Reviews on recent clinical trials. 2022;17(4):291-299

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PMID: 35986532

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