Use of a stochastic model to develop understanding of the impact of different patterns of antiretroviral drug use on resistance development

ObjectiveTo use a stochastic model to gain insights into the consequence for resistance development of different drug use patterns. MethodsWe consider use of three drugs (A, B and C) where for each drug one and only one viral mutation is associated with ability to replicate (effective reproductive ratio, R > 1) in the presence of that drug as monotherapy. For drug A mutation is a, etc. We define eight populations of short-lived infected cells that live 1 day: Vo with no mutations a, b, c; Va with mutation a only, Vab with mutations a and b, etc. A random number generator was used to determine whether mutations occur in any one round of replication and to sample from a Poisson distribution to determine for each cell the number of cells of the same population created in the next generation, using the R operative at that time. Values of R depended on drug exposure, cost of resistance and availability of target cells. ResultsTreatment strategies and the resulting percentage (over 100 runs) developing full ‘resistance’ in 1500 days (Vabc ≠ 0) were: (i) ABC 1500 days 0%; (ii) A 300 days, AB 300 days, ABC 900 days 100%; (iii) AB 300 days, ABC 1200 days 33%; (iv) ABC 2/3 1500 days 15%; (v) ABC 1/2 1500 days 100%; (vi) ABC 50 days, no drugs 50 days, for 1500 days 1%, where ABC 2/3 means on-drug for 2 days in every 3, ABC 1/2 represents on-drug for 1 day in every 2, and represents suboptimal adherence. ConclusionsThis model helps to develop understanding of key principles concerning development of resistance under different patterns of treatment use.