Homology modeling of clinically-relevant rilpivirine-resistant HIV-RT variants identifies novel rilpivirine analogs with retained binding affinity against NNRTI-resistant HIV mutations

Abstract

Human immunodeficiency virus (HIV), which affects tens of millions of individuals worldwide, can lead to acquired immunodeficiency syndrome (AIDS). Though there is currently no cure for HIV, the development of small molecule antiretroviral agents has greatly improved the prognosis of infected individuals, especially in developed countries. In particular, compounds such as rilpivirine have been developed as non-nucleoside reverse transcriptase inhibitors (NNRTIs), which allosterically target the reverse transcriptase enzyme in the retrovirus. However, mutations to the reverse transcriptase enzyme threaten to undermine the efficacy of this class of antivirals. Recent advances in computational biophysical modeling enable the structural analyses of such variants without the complications of obtaining crystal structures. Here, we employ homology modeling and molecular docking towards the identification of novel rilpivirine analogs that retain high binding affinity to clinically relevant rilpivirine-resistant mutations of the HIV reverse transcriptase enzyme.