Predicting Resistance To Investigational Microbicides Dapivirine and MIV-150 Using Structure-based methods and Fluorescence Enzyme Inhibition Microbicides may be an alternative option in I-11V prevention for women affected in sub-Saharan Africa. Many microbicides are non-nucleoside reverse transcriptase inhibitors (NNRTIs) that target HIV reverse transcriptase (RT), Resistance mutations result in NNRTI treatment failure. These mutations may also affect the efficacy of chemically similar NNRTIs used as microbicides. The objective of this study was to predict mutations in RT that may cause resistance to investigational microbicide NNRTIs Dapivirine (DPV) and MIV-150. A computational, structure-based approach was used to predict resistance mutations for both NNRTIs. This method calculates changes in affinity (DeltaA) and stability (DeltaS) imparted by the mutation. The DeltaA + DeltaS values for K 101 P predict that this mutation confers high-level resistance to DPV and M1V-150. Based on this prediction, we analyzed the models and used Molecular Dynamics (MD) to compare the interactions of DPV and MIV-150 with RT wild type (WT) and with RT (K101P). From MD, we found key interactions were lost with K 101 P. To experimentally validate our findings, we conducted a fluorescence-based reverse transcription assay for DPV and MIV-150 with RT (WT) and RT (K101P). Our IC,, values showed 58-fold and 170-fold changes in potency for DPV and MIV-150, respectively. The assays results validate that the K 101 P mutation confers resistance to DPV and MIV-150. Our results are consistent with antiviral data reported in the literature. From these results, we believe that this approach is effective for predicting resistance to microbicide NNRTIs.