Tuesday, January 21, 2025
1/21/2025
Project Summary Defining and Avoiding Molecular Mechanisms of Drug Resistance Drug resistance is a major challenge in modern medicine. Resistance impacts the lives of millions, limiting the effectiveness of many of our most potent drugs. This often happens under the selective pressure of therapy in bacterial, viral, and fungal infections and in cancer due to rapid evolution. Instead of considering resistance only after a drug fails, we need a paradigm shift to incorporate preemptive strategies into drug design to avoid resistance. Research in my laboratory combines a variety of experimental and computational techniques to elucidate the molecular mechanisms of drug resistance and to lay the foundation for developing strategies to avoid resistance in drug design. The rapid evolution of viruses continues to challenge our therapies, highlighting the significance of deciphering the intersection of evolution and resistance. Insights from my years of research on HIV-1 protease and subsequently other viral proteases led to fundamental concepts including that primary resistance mutations occur where inhibitors physically contact regions of the active site beyond the substrate envelope. I hypothesized that inhibitors that stay within the substrate envelope are less susceptible to drug resistance. Validating this hypothesis, my strategy of adding the constraint that inhibitors must stay within the substrate envelope in structure-based drug design (SBDD) yielded extremely potent inhibitors that are effective against resistant variants. Beyond the substrate envelope, I have demonstrated mutations distal from the active site also significantly contribute to resistance. My ongoing research is 1) characterizing the molecular mechanisms of these distal changes in conferring resistance and 2) developing strategies to counter their impact. The complex and dynamic interplay between such mutations necessitates integrated approaches. Thus, we are developing a novel strategy combining parallel molecular dynamics (pMD) with machine learning and demonstrated the feasibility and power of this approach in identifying key physical interactions that are indicators of resistance. We are now assessing whether a similar approaches, optimizing various linear and non-linear machine learning algorithms, can be used to evaluate a set of inhibitors, with various functional groups. In the future we will leverage lessons learned, specifically the substrate envelope concept and pMD coupled with machine learning, to a diverse set of drug targets in quickly evolving diseases. My long-term goal is to develop broadly applicable approaches leveraging machine learning in SBDD to guide inhibitor selection, to avoid resistance and enhance potency for diverse enzyme targets . Overall, I expect to decipher principles and mechanisms that underlie drug resistance by developing and optimizing tools to capture the essential dynamics of a given drug-target system toward devising broadly applicable strategies to identify robust inhibitors that retain potency in the face of evolution.
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