Table 1.
Key mechanisms surveyed to inform modeling assumptions about PrEP-associated resistance.
Fig 1.
Diagram of emergence and transmission of PrEP-associated drug-resistance.
The diagram shows how resistance can occur in the presence of PrEP. The boxes represent different states in which individuals can be divided. These states include the use of PrEP by uninfected and infected individuals, as well as infected and HIV-infected individuals who do not use PrEP and HIV-infected individuals who carry PrEP-associated drug resistance. The processes contributing to PrEP-associated resistance include 1) transmission of resistance in which individuals become exposed to and infected with drug-resistant HIV (transmitted drug resistance, TDR), and 2) resistance that occur when individuals continue the use of PrEP after they become infected (acquired drug resistance, ADR). PrEP-associated resistance may drop below detectable level in individuals not exposed to PrEP for long period of time. They may lose the ability to transmit resistance but remain at elevated risk to fail ART when initiated.
Table 2.
Resistance parameters sets based on the point estimates (ranges) provided by the virologists.
Fig 2.
Emergence and transmission of PrEP-associated resistance predicted by the virologists.
A) rate of resistance emergence in infected PrEP users; B) relative infectiousness of the resistance carriers compared to infected with wild-type HIV; C) relative chance to transmit drug-resistant over wild-type HIV if resistance is acquired on PrEP (ADR); D) relative chance to transmit drug-resistant over wild-type HIV if resistance is acquired through transmission (TDR); E) PrEP protection against drug-resistant HIV and F) relative chance for viral suppression of the resistance carriers when ART is initiated. The bars (whiskers) represent the mean estimate (range) predicted by each respondent. Complete description of the survey results is provided in the Table 2.
Fig 3.
Projections on the expected drug-resistance after 10 years of PrEP use based on the virologists’ opinion.
A) resistance prevalence due to PrEP; B) cumulative fraction of infections in which resistance is transmitted (TRF) and C) the fraction of infected individuals with elevated risk to fail ART. The model is parameterized with the responses to the survey, assuming different levels of adherence to PrEP. The bars represent the mean metrics estimates based on 1,000 epidemics simulated. Intervention parameters are fixed on their baseline values from Table A, part 3 in S1 Text.
Fig 4.
Results from multivariate sensitivity analysis.
Partial rank correlation coefficients (PRCC) between resistance parameters and intervention metrics: A) resistance prevalence due to PrEP (RP), B) cumulative fraction of infections in which resistance is transmitted (TRF), C) the fraction of infected individuals with elevated risk to fail ART and D) cumulative fraction of prevented infections (10-year CPF) and. Resistance parameters are sampled from their pooled ranges based on the responses to the virologists survey (Table 2). Linear increase of the ADR emergence rate with adherence to PrEP and PrEP efficacy per act proportional to adherence are assumed. Variation in the probabilities of ADR and TDR with respect of who is using PrEP when transmission occurs is considered. For instance (ADR transm: on->off) denotes the probability that ADR is transmitted from a PrEP user to a non-user.
Table 3.
Dependence of the intervention outcomes on the resistance parameters.