Exploring evolutionary trajectories of drug resistance

Abstract

Drug resistance poses a major global health challenge, necessitating the development of effective therapeutic strategies. The main challenge is to predict drug-resistant mutations and design drugs that retain efficacy against such evolving targets. Our previous effort in computing the vitality value has provided a framework in assessing drug resistance. While promising, the approach lacked accuracy due to insufficient information about mutation tendencies and protein stability. In this study, we used the Stanford University HIV Drug Resistance Database and observed that drug resistance, usually quantified as K i mutant / K i WT , exhibits a positive correlation with the Maximum Entropy energy, E M a x E n t . However, both drug resistance and vitality are also correlated with the number of mutations, indicating that the virus cannot easily gain resistance through specific mutational pathways and must sacrifice stability and function to escape inhibition. To overcome this number dependence, we looked for a system with less extensive mutagenesis and chose HCV protease. In this case, resistance substitutions cluster at low E M a x E n t values, reflecting a limited mutational space. This restricted landscape enables E M a x E n t to predict evolutionary pathways of resistance and to guide the identification of robust therapeutic candidates.