{"entity":{"id":"idea-bio1-reverse-translation-resistance-models","kind":"idea","name":"Every resistance mechanism found in a patient must be rebuilt in the laboratory","aka":[],"tldr":"When doctors discover how a tumour escaped a drug, that finding usually stops at a paper. Recreating it in a model gives everyone a system to test the next drug against.","summary":"Clinically observed resistance mechanisms (mutations, bypass activation, lineage switch) are frequently reported but rarely converted into a distributed, isogenic model. A standing reverse-translation facility would engineer each reported mechanism into relevant backgrounds, verify the resistance phenotype, and distribute the lines openly, creating a growing panel that drug developers must test new candidates against.","asOf":"2026-09-08","links":[{"label":"Bottleneck evidence (Lab models that fail to predict what happens in patients): Wong, Siah & Lo, Estimation of clinical trial success rates (Biostatistics 2019)","url":"https://doi.org/10.1093/biostatistics/kxx069"}],"tags":[],"related":[],"cancers":[],"sections":[],"technologies":["crispr-screens","functional-drug-testing"],"targets":[],"drugs":[],"companies":[],"institutions":[],"pathways":[],"terms":["resistance"],"trials":[],"people":[],"bottlenecks":["b-preclinical-models","b-resistance"],"keyPapers":["paper-wong-biostatistics"],"journals":[],"dependsOn":[],"notes":[],"hypothesis":"A public panel of clinically derived resistance models identifies cross-resistance for a substantial fraction of next-generation agents before they enter trials, and correctly anticipates their clinical failure settings.","rationale":"Next-generation inhibitors are often developed against laboratory-derived resistance that does not match what patients actually develop; a clinically anchored panel aligns discovery with reality.","test":"Build 50 clinically derived resistance models for three drug classes, profile ten clinical-stage successor agents against them blinded, and compare with subsequent trial results.","maturity":"speculative","actor":"research","cost":"medium","horizonYears":5},"route":"/ideas/idea-bio1-reverse-translation-resistance-models/","neighbours":{"technology":[{"id":"crispr-screens","kind":"technology","name":"CRISPR functional genomics","route":"/technologies/crispr-screens/"},{"id":"functional-drug-testing","kind":"technology","name":"Functional (ex vivo) drug testing","route":"/technologies/functional-drug-testing/"}],"term":[{"id":"resistance","kind":"term","name":"Drug resistance (primary and acquired)","route":"/terms/resistance/"}],"bottleneck":[{"id":"b-resistance","kind":"bottleneck","name":"Acquired resistance to every therapy","route":"/bottlenecks/b-resistance/"},{"id":"b-preclinical-models","kind":"bottleneck","name":"Lab models that fail to predict what happens in patients","route":"/bottlenecks/b-preclinical-models/"}],"paper":[{"id":"paper-wong-biostatistics","kind":"paper","name":"Estimation of clinical trial success rates and related parameters","route":"/key-papers/paper-wong-biostatistics/"}]}}