Development of a stochastic model for the efficacy of NRTIs using known mechanisms of action

We analyze the mechanisms by which nucleoside-analogue reverse transcriptase inhibitors, the most common class of drugs used in the treatment of HIV-1, exert their antiviral effects. We then seek to identify ways in which those known mechanisms can be employed to generate mathematical models for drug efficacy in terms of measurable physical values. We demonstrate that the probability a NRTI instead of a natural nucleotide is included can be expressed in terms of intracellular drug concentrations, natural nucleotide concentrations, and relevant rate constants derived from reverse transcriptase's mechanism of nucleotide addition. In order to determine the ultimate effect, the resistance of the NRTI to removal from the genome must be considered, which is achieved via stochastic modeling. We employ this model to determine the relationship between efficacy and drug concentration, as well as other drug characteristics like half life. We also investigate the effect of drug administration time on the overall efficacy. The model is employed for four different drugs and a sensitivity analysis on mutation and resistance is performed.