Effective prevention strategies for controlling the HIV pandemic are urgently needed. One potential strategy, currently being investigated in phase III clinical trials, is pre-exposure prophylaxis (PrEP)1
. PrEP is the administration of low levels of antiretrovirals (ARVs), specifically Tenofovir (TDF) or Truvada (TDF in combination with emtricitabine (FTC)) prior to HIV exposure2,3
. Results from the first Phase III clinical trial of oral PrEP, the iPrEx trial, have recently been published4
. The study involved 2,499 men who have sex with men (MSM) and transgender women who have sex with men from six countries in the Americas, Africa and Asia. Once-daily oral Truvada was found to reduce the risk of acquiring HIV infection by 44% in the study population overall. Recent results (currently unpublished) from two other clinical trials provide additional evidence PrEP can reduce risk. The TDF2 trial investigated the use of once-daily oral Truvada in 1,219 heterosexual men and women in Botswana; the Partners PrEP trial evaluated both TDF and Truvada in 4,758 HIV serodiscordant couples in Kenya and Uganda. Both studies showed significant reductions in risk of infection ranging from 62% for TDF (in the Partners PrEP study) to between 63% and 73% for Truvada (in the TDF2 and Partners PrEP studies, respectively)5,6
. Based on the results from the clinical trials, PrEP may soon be rolled out in resource-constrained countries as an intervention to reduce heterosexual transmission of HIV. However there is concern this could generate drug resistance7
, because HIV-infected individuals may inadvertently use PrEP. Drug resistance has already arisen in many resource-constrained countries as a consequence of their HIV treatment programs8,9
. Here we model the dynamic interactions that will occur between treatment programs and PrEP interventions in resource-constrained countries. We predict the consequences of these interactions for HIV transmission and drug resistance. We evaluate both TDF-based and Truvada-based PrEP. The implications of our results for the rollout of PrEP interventions in Sub-Saharan Africa are discussed.
For user-dependent prevention interventions (e.g., PrEP), phase III clinical trials measure the effectiveness of the product rather than efficacy10,11
. Effectiveness is a function of the biological efficacy of the product and participants' adherence. Effectiveness is a reasonable measure of biological efficacy if adherence is ~100%11
. The Phase III clinical trials of PrEP (iPrEx, TDF2, and Partners PrEP) all found significant differences in effectiveness depending on participants' adherence to the study protocol. In the IPrEx trial, the overall effectiveness of Truvada-based PrEP was 44% (95% confidence interval (CI): 15 to 63%), but was extremely dependent upon adherence. PrEP adherence was defined in terms of the percentage of the daily doses of PrEP that were taken. Specifically, incidence was reduced by 73% if adherence was high (≥ 90% of doses), 50% if adherence was intermediate (≥50% of doses) and 32% if adherence was low (< 50% of doses)4
. Notably, PrEP was found to reduce incidence by 92% (95% CI: 40 to 99%) if the regimen was taken exactly as prescribed4
. No resistance mutations for TDF were found among iPrEx participants, although three cases of resistance for FTC were found: one in the placebo arm and two in the Truvada arm. The case in the placebo arm appears to reflect transmitted resistance, and the two individuals who developed mutations in the Truvada arm appear to have begun PrEP before it was known they were infected with HIV4
. Based on these results it remains unknown whether individuals who begin PrEP when they are uninfected, then fail PrEP and remain on PrEP are likely to develop resistance.
In the TDF2 trial, the effectiveness of Truvada-based PrEP was 63% (95% CI: 22 to 83%)6
. However, among participants known to have a supply of study drugs, protection was even greater, with an effectiveness of 78% (95% CI: 41 to 94%). Although some gender differences were noted, the study was not large enough to draw definitive conclusions about the effectiveness of Truvada based on gender. Consistent with the IPrEx trial, there were no cases of drug resistance among participants taking Truvada who became infected after enrollment. One case of TDF and FTC resistance occurred in a participant who had unrecognized HIV infection at the time of enrollment. In Partners PrEP, the study found 62% (95% CI: 34 to 78%) effectiveness for TDF-based PrEP and 73% (95% CI: 49 to 85%) for Truvada-based PrEP5
. Both PrEP regimens had similar effectiveness in men and women. Although it appeared Truvada provided more protection than TDF alone, this difference was not statistically significant. Adherence to the daily PrEP medication was very high – more than 97% of dispensed doses of the study medications were taken. Twelve participants who had tested HIV-negative at screening were found to have acute HIV infection; however information on drug resistance has not been released yet. Notably, the results of the Partners PrEP study and TDF2 contrast with those of the FEM-PrEP study, which failed to demonstrate that Truvada-based PrEP was effective in protecting against HIV acquisition among at-risk women in Kenya, Tanzania and South Africa12
. In addition, the VOICE trial of PrEP in heterosexual African women recently discontinued the daily oral TDF arm and the TDF gel arm, although the oral Truvada arm of the study is still under way. Currently there are no publicly available data to explain why Truvada was not found to be effective in FEM-PrEP, nor TDF in the VOICE study.
Prior to the clinical trial results, the available data concerning PrEP efficacy were mainly from pre-clinical studies of PrEP in the rhesus macaque model of SHIV/SIV infection13,14,15,16,17
. These studies investigated daily PrEP with TDF or Truvada13,14,17
. Results showed the risk of simian human immunodeficiency virus (SIV) infection in macaques receiving daily PrEP with TDF or Truvada was 3.8- and 7.8-fold, respectively, lower than in untreated macaques13
; indicating PrEP might significant reduce transmission. Results from another macaque study15
suggest PrEP may be less effective in protecting against infection with drug-resistant viruses than against infection with wild-type viruses. In that study loss of protection to infection by TDF-based PrEP was observed in macaques exposed to a SIV isolate carrying the TDF resistance mutation, K65R15
. Other studies of macaques have shown resistance can emerge while on PrEP13
, as can occur when an individual is receiving ARVs for therapeutic purposes18
. Since mutations to TDF and FTC acquired during treatment have been observed to rapidly revert when treatment has stopped19,20,21
, it is likely that mutations selected while on PrEP will also revert once the pressure of PrEP is removed. We use the results from these empirical observations and studies, as well as the recent results from the Phase III trials, to inform our modeling of PrEP-based interventions.
Previously we (VS & SB) designed a mathematical model to predict the effect of PrEP interventions in a “high-risk” community in a resource-rich country; specifically the MSM community in San Francisco22
. Our new model is designed to investigate the dynamic interactions between HIV treatment programs and potential PrEP interventions in resource-constrained countries. The model tracks the transmission dynamics of wild-type and resistant strains of HIV in a generalized epidemic driven by heterosexual transmission. Generalized epidemics are characterized by a high prevalence of HIV in the general population and occur in many African countries. In our analyses, PrEP interventions are implemented when treatment programs are in place, resistant strains are evolving in treated individuals, and resistant strains are being transmitted. Previous models of PrEP have been based on unrealistic assumptions. Specifically, treatment will be unavailable in resource-constrained countries when PrEP is rolled out23,24,25,26
, all infected individuals are eligible for PrEP24,26
or neither treatment programs nor PrEP interventions can generate drug resistance27
We use our model to investigate the effect of the “quality” of the PrEP interventions and the effect of the “quality” of the treatment programs on transmission and resistance by using uncertainty and sensitivity analyses (see Methods). We characterize the “quality” of PrEP interventions in terms of four of the models' parameters: (i) coverage (specified by the proportion of sexually active individuals adopting PrEP each year), (ii) the effectiveness of PrEP for individuals who are highly adherent to the regimen (defined as taking ≥90% of daily doses), (iii) the proportion of individuals who are highly adherent to PrEP and (iv) the average level of adherence in individuals who have low/moderate adherence (defined as taking < 90% of daily doses). We characterize the “quality” of treatment programs in terms of two of the models' parameters: (i) the proportion of treated individuals who achieve complete viral suppression (<400 copies/ml) and (ii) the rate of developing resistance in treated individuals who only achieve partial viral suppression. Parameter values used to define the “quality” of PrEP interventions and the “quality” of treatment programs are given in Table S6
and Table S7
in the Supplementary Material (SM).
Our model includes behavioral heterogeneity with respect to adherence to PrEP regimens. We define adherence, as in the clinical trials, in terms of the number of daily doses that are taken. We model adherence for each gender independently. We assume a certain proportion of individuals on PrEP are highly adherent (i.e., take ≥90% of daily doses) and the remaining proportion on PrEP are low to moderately adherent (i.e., take less than 90% of daily doses). We vary the degree of behavioral heterogeneity in adherence to PrEP regimens by letting: (i) the size of the high adherence group vary from 0% to 100% of the individuals taking PrEP, (ii) the average level of adherence (in the high adherence group) vary from 90% to 100% and (iii) the average level of the adherence (in the low/moderately adherent group) vary from zero to 89%. Our model is designed to use data from clinical trials; consequently, we model effectiveness of PrEP rather than efficacy. We model effectiveness as a function of adherence and whether the strain is wild-type or resistant; see Section 1e and Figure S2
in the SM for technical details. Based on data from the macaque studies15
we assume PrEP is less effective against resistant strains than against wild-type. We also assume that below a certain level of adherence there are not enough ARVs present to protect against infection or to select for resistance. Therefore, when modeling the effectiveness of PrEP and the risk of developing a DRM on PrEP, we include an adherence threshold below which effectiveness is very low and resistance unlikely. Our modeling of this threshold is described in detail in Sections 1e and 1f in the SM, and shown in Figures S2
in the SM.
We assume to begin PrEP and/or to renew a prescription an individual has to test negative for HIV. However, none of the currently available HIV antibody tests can detect infection during the first few weeks after infection (i.e., during the “window period”)28,29
. In Botswana, HIV testing is by parallel rapid tests or parallel ELISAs: in the first case, Uni-Gold Recombigen HIV (Trinity Biotech, Bray, Ireland) and Determine HIV 1/2 (Abbott Diagnostics, Abbott Park, IL) tests are used. If the results are discordant, then parallel tests are repeated. If the rapid tests are still discordant, the OraQuick (OraSure Technologies, Bethlehem, PA) test is used as a tie-breaker30
. These tests have shown very high sensitivity and specificity (~100%) in Botswana in detecting HIV in individuals whose infection is outside the “window period”31,32
. Consequently, when modeling PrEP interventions we assume tests are 100% accurate in detecting HIV when testing occurs after the “window period” and do not detect HIV when testing occurs during the “window period”. Hence in our analyses, recently infected individuals tested during the “window period” could inadvertently be prescribed PrEP, but infected individuals tested after the “window period” would not be prescribed PrEP. The Center for Disease Control and Prevention in the United States recommends that individuals who take PrEP should be tested every three months to check whether they have become infected. Testing frequency in resource-constrained countries is unlikely to be more frequent than in the US. Therefore in our analyses we explored a range of testing frequencies varying from every three months to every six months.
Our model includes two mechanisms for selecting for a drug-resistant mutation (DRM) on PrEP. A DRM can be selected if (i) an HIV-infected individual in the “window period” of infection inadvertently begins taking PrEP or (ii) an individual acquires infection when they are on PrEP and then remains on the regimen. We model the risk of an individual developing a DRM on PrEP as a function of: their level of adherence to the regimen, their stage of HIV infection (primary or chronic), the specific PrEP regimen they take (TDF-based or Truvada-based) and the time they spend on PrEP once infected with HIV; see Section 1f and Figure S3
in the SM for technical details. As well as modeling the emergence of resistant strains due to the selective pressure of PrEP, we model the emergence of resistant strains in treated individuals taking first-line therapies. We also model reversion of resistant strains to wild-type in infected individuals who: i) develop resistance while on PrEP and then come off PrEP or ii) acquire transmitted resistance. Reversion occurs because wild-type strains out-compete the resistant strains in the absence of ARVs. In addition, we model (after reversion has occurred) the reemergence of resistant strains under the selective pressure of treatment. Resistance can reemerge quickly as resistant strains are maintained in reservoirs as minority strains within the individual. For the technical details of our modeling of resistance see SM.
We investigate the two PrEP regimens that are currently being investigated in clinical trials: TDF and Truvada. A full listing of PrEP trials is given in . In our modeling of the evolution of resistance on TDF-based PrEP, we model the DRM that has been observed to be selected for by TDF, K56R. In our analysis of Truvada-based PrEP we model the risk of developing M184V. In infected humans and non-human primates taking Truvada, the first DRM that has generally been observed is M184V; K65R has been observed to arise subsequently, if the infected primate or human remains on Truvada7
. We do not model the possibility of further selection for K65R because we include frequent testing in our model. If individuals on PrEP, in our model, are found to be infected with HIV they will not be given further PrEP regimens. If testing is frequent, it is unlikely that an HIV-infected individual would remain on PrEP long enough to select both M184V and K65R. We note that in the iPrEx trial (where trial participants were tested approximately monthly) it was found that among HIV-infected individuals on Truvada-based PrEP only M184V was selected4
; the virus did not evolve further and acquire K65R. In TDF2, there was one case of a participant who started taking Truvada while having acute HIV infection and had several false negative HIV tests in the months following enrollment6
. The individual tested positive for K65R and M184V, and also had a broad-spectrum NNRTI mutation A62V, which suggests that the virus they had contracted was not wild-type. One seroconverter in the placebo arm was also found to have low levels of K65R. We note that the complexity of our model could be increased to include the sequential evolution of multiple DRMs.
Table 1 Ongoing and Planned daily oral PrEP Trials1
The Government of Botswana is considering implementing public health interventions based on PrEP if several of the Phase III trials demonstrate effectiveness, PrEP is shown to be cost effective and the health system is able to deliver such services. Botswana has one of the highest levels of HIV in the world. The most recent World Health Organization report33
and the Botswana AIDS Impact Survey34
indicate that: (i) ~30% of women (aged 15–49 years) and ~20% of men (aged 15–49 years) are infected with HIV, (ii) HIV incidence is high, ~4.4% in women and ~2.5% in men34
and (iii) transmitted drug resistance has reached ~4%35
. Botswana is a relatively rich country with one of the best healthcare systems in Africa and, potentially, has the resources available to provide PrEP to the general population. In addition, the population size is small, only ~1 million adults aged between 15 and 49 years old, live in Botswana. Therefore, it is a feasible strategy for the entire population to be offered PrEP. In addition, since it has the highest HIV treatment coverage of any African country it may now be able to afford to concentrate on prevention. In 2002 it was the first African country to offer free ARVs to everyone in need of treatment; treatment was rapidly scaled up and now 70–80% of those in need are receiving ARVs36,37
Treatment programs in Botswana have been very successful; a study of the first 5 years of treatment found the percentage of patients with viral loads less than 400 copies/ml at one, three and five years was 91%, 90% and 98%, respectively38
. However some patients on first-line regimens are now virologically failing treatment and developing resistance to TDF39
, although the number of patients needing second-line therapies (SLT) is currently low35
. In Botswana, as well as in many other Sub-Saharan African countries, the potential problem of PrEP increasing resistance is of particular concern since their first-line treatment regimens are based on TDF40
. For example, Atripla (efavirenz/FTC/TDF) has been used, since 2008, as the first-line treatment regimen in Botswana. A rise in TDF-resistance could challenge future treatment options and potentially increase the need for SLT regimens in Botswana, as well as could occur in other countries in Sub-Saharan Africa. We use our model to investigate the consequences, for HIV transmission and drug resistance, of the dynamic interactions between potential PrEP interventions and current treatment programs in Botswana.