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Biology subjects

Leighow, S.

Publications and source records attributed to Leighow, S..

2 recordsLinked to original sources

Characterizing Variants of Uncertain Drug Resistance (VUDRs) Using Quantitative Measurements at Clinical Exposures

Somatic missense mutations in oncogenes drive resistance to anticancer drugs, yet many variants remain clinically uncharacterized. Analogous to Variants of Uncertain Significance (VUS) in genetic disorders, these Variants of Uncertain Drug Resistance (VUDRs) in cancer lack the functional annotation needed to guide clinical management. Here, we applied a standards-driven deep mutational scanning platform that connects quantitative concentration-response measurements to human pharmacokinetics across 4922 missense variants (>96% coverage) at approved (400 mg QD) and investigational (400 mg and 500 mg BID) human doses of imatinib. Resistance phenotypes for 18 standards spanning 2 orders of magnitude of drug sensitivity showed strong quantitative performance and clinical concordance. Analyzing 257 clinical VUDRs, >10% conferred modest levels of resistance that might be overcome by dose escalation with generic imatinib instead of a branded alternative, potentially alleviating financial toxicity. Integration with global germline data also revealed ancestry-specific variants with the potential to create private VUDRs. These preclinical data establish the first generalizable framework for high throughput resistance variant classification directly tied to known human doses.

bioengineering↗

Exploiting the 'survival of the likeliest' to enable evolution-guided drug design

Theoretical treatments of evolutionary dynamics tend to model the probability that a single "resistant" species will arise in a population. However, experimental studies have identified a diversity of mutations that can lead to genetic resistance. By quantitatively predicting mutations that occur across an entire drug target during treatment, we identify and bridge a fundamental gap in drug resistance theory: that nucleotide/codon substitution biases can dictate which resistant variants arise in the clinic. We find that the likeliest mutation can beat the most resistant mutation. This creates a new paradigm in drug resistance that we term "survival of the likeliest". We use epidemiological evidence in leukemia, isogenic experiments, stochastic dynamics, and large-scale simulations to support this theory. In addition, this work has strong implications for drug design because not all resistance liabilities are created equal. In pathogenic populations that exhibit survival of the likeliest, exploiting the least likely evolutionary path can minimize resistance across a population during widespread drug use, even when a vulnerability-free molecule or combination cannot be made. Data and Code Availabilityhttps://github.com/pritchardlabatpsu/SurvivalOfTheLikeliest/

evolutionary biology↗