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Author Topic: interactions betw PrEP/treatment: predicting HIV transmission/resistance  (Read 792 times)

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Offline coolstone25

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http://www.nature.com/srep/2011/111207/srep00185/full/srep00185.html

Modeling dynamic interactions between pre-exposure prophylaxis interventions & treatment programs: predicting HIV transmission & resistance

Virginie Supervie,   et al & Sally Blower

Clinical trials have recently demonstrated the effectiveness of Pre-Exposure Prophylaxis (PrEP) in preventing HIV infection. Consequently, PrEP may soon be used for epidemic control. We model the dynamic interactions that will occur between treatment programs and potential PrEP interventions in resource-constrained countries. We determine the consequences for HIV transmission and drug resistance. We use response hypersurface modeling to predict the effect of PrEP on decreasing transmission as a function of effectiveness, adherence and coverage. We predict PrEP will increase need for second-line therapies (SLT) for treatment-naïve individuals, but could significantly decrease need for SLT for treatment-experienced individuals. If the rollout of PrEP is carefully planned it could increase the sustainability of treatment programs. If not, need for SLT could increase and the sustainability of treatment programs could be compromised. Our results show the optimal strategy for rolling out PrEP in resource-constrained countries is to begin around the “worst” treatment programs.

.....
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.

.......

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 Table 1. 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.

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.

...

The model predicts that even without PrEP interventions, incidence and prevalence will decrease slightly over the next decade due to the effect of the current treatment programs on reducing infectivity of treated individuals (Figure 1)....
Our modeling shows that the introduction of Truvada-based PrEP interventions, over a decade, could prevent 39% (median; IQR 29%–49%) of new infections in women and 40% (median; IQR 30%–50%) of new infections in men in Botswana. Predictions for the number of infections prevented are not significantly different for TDF-based PrEP versus Truvada-based PrEP.

Key parameters in reducing transmission
.......

Change in need for Second-Line Therapies (SLT) for treatment-naïve individuals
.......
In contrast to our results for treatment-naïve individuals, our results show the number of treatment-experienced individuals in need of SLT is very likely to decrease in the decade after implementing PrEP interventions. This occurred in 927 of the 937 simulations conducted for the two uncertainty analyses where PrEP interventions were introduced when treatment programs were in place. The result is regardless of whether the regimen was based on TDF or Truvada. Our sensitivity analyses identified five key parameters that determined the decrease in need for SLT by treatment-experienced individuals; three parameters characterize the “quality” of PrEP interventions and two parameters characterize the “quality” of the treatment programs (Table 2 and S10 in the SM).

....

Taken together our modeling results show a dynamic interaction between treatment programs and PrEP interventions will determine the magnitude of decrease in the number of treatment-experienced individuals in need of SLT. The effects of this interaction between the “quality” of the PrEP intervention and the “quality” of the treatment program is shown in the form of a response hypersurface in Figure 4B; this hypersurface shows the interaction between treatment programs and Truvada-based PrEP interventions on decreasing the need for SLT for treatment-experienced women. Corresponding results for men and Truvada-based PrEP are shown in Figure S7D in the SM; results for TDF-based PrEP in Figure S7E (for women) and Figure S7F (for men). The color-coded response hypersurface in Figure 4B shows the ratio of need decreases as the proportion of women on PrEP who are highly adherent (shown on the Y-axis) increases and/or the proportion of individuals who are virally suppressed on treatment decreases (shown on the X-axis). The interaction effect between these two key parameters is shown by the curvature of the hypersurface; the regression equation used to construct the hypersurface is given in the Figure Legend. These results show the greatest decrease in number of treatment-experienced individuals in need of SLT will occur if high “quality” PrEP interventions are rolled out around the “worst” treatment programs.

Notably, we find that there is no correlation between changing the need for SLT for treatment-naïve individuals and changing the need for treatment-experienced individuals (PCC = 0.20).

Discussion
Introduction Results Discussion    Methods References Acknowledgements Author information Supplementary information Comments
In our comparison of TDF and Truvada-based PrEP we found both regimens would lead to an increase in the number of treatment-naïve individuals infected with resistant strains. Regardless of the level of adherence to the regimen, we found the increase would be greater if TDF-based PrEP was used than if Truvada-based PrEP was used. Our results indicate that Truvada-based PrEP would be a more optimal regimen. In addition, the specific mutations that arise will influence subsequent treatment and therefore need to be considered when choosing between regimens; K65R will be selected by TDF and M184V by Truvada. K65R may limit the utility of TDF in combination therapy for a newly infected individual. This may lead to greater use of alternative agents such as zidovudine with variable costs, toxicities and effectiveness. If K65R limits TDF effectiveness then the convenience of TDF co-formulated products may be lost and the use of more complex regimens with more pills or multiple daily administrations may be required; this might influence medication adherence. In addition, a widely spread K65R mutation would limit TDF as an effective agent for PrEP. M184V may have less subsequent clinical impact on the use of co-formulated pills for the treatment of infected individuals or for the need for alternative treatment regimens. Thus the implications of our modeling suggest that Truvada-based PrEP if well tolerated and affordable would be the more optimal regimen, as it would cause less clinical complexities than TDF-based PrEP.

In a previous modeling study, we (VS & SB) found that if PrEP is widely used in a “high-risk” community in San Francisco (i.e., in a resource-rich country) the number of treatment-naïve individuals infected with resistant strains is likely to decrease (if risk behavior does not increase). In contrast, in this study we have found that after the introduction of PrEP interventions in Botswana, the number of treatment-naïve individuals infected with resistant strains is likely to increase. This occurs because the level of ambient resistance is higher in San Francisco than in Botswana due to a longer treatment history. This comparison of results indicates that the impact of PrEP on transmitted resistance will be highly dependent on the number of years since treatment was first made available, as well as the current success of treatment programs. Consequently the impact of PrEP interventions on transmitted resistance may be beneficial in resource-rich countries, but detrimental in resource-constrained countries.

In this study, we have presented a novel mathematical model designed to predict the impact of PrEP interventions introduced into resource-constrained countries with generalized HIV epidemics and treatment programs already in place. We have parameterized our model using country-specific data in order to make predictions for the impact of PrEP interventions on transmission and resistance in Botswana. The response hypersurfaces that we have constructed can be used for policy and planning purposes by health officials in Botswana to predict the effect of TDF-based or Truvada-based PrEP interventions on decreasing transmission for specified levels of effectiveness, adherence and coverage. Health officials can also use the model predictions to determine the number of SLT that will be needed by specific treatment programs. Our model can be reparameterized and used to make predictions for other countries in sub-Saharan Africa that have generalized HIV epidemics and treatment programs. Reparameterization of the model will enable country-specific response hypersurfaces to be constructed and for country-specific predictions to be made regarding changing needs for SLT. Therefore our model could be used as an important policy and planning tool in many resource-constrained countries. Although quantitative results will be country-specific, the qualitative insights we have gained regarding the impact of interactions between treatment programs and PrEP interventions will hold for other resource-constrained countries with generalized epidemics.

Our modeling shows it is essential to consider the dynamic interaction that will occur between treatment programs and PrEP interventions. The outcome of this interaction has significant implications for the success of PrEP interventions and the sustainability of treatment programs. We have found “high quality” PrEP interventions will substantially reduce the number of treatment-naïve individuals in need of first-line therapies and could also substantially reduce the number of treatment-experienced individuals in need of SLT. Hence “high quality” PrEP interventions are likely to reduce treatment costs which would contribute to the sustainability of treatment programs. However, if PrEP interventions are not “high quality” (for example, if - on average - individuals on PrEP only take between 40% and 89% of daily doses) the number of treatment-naïve individuals in need of SLT could significantly increase; even if individuals taking PrEP are frequently tested. Consequently poor “quality” PrEP interventions could reduce the success of current treatment programs. Our response hypersurface modeling shows PrEP interventions could prevent the same number of HIV infections whether behavior is very heterogeneous with respect to adherence (i.e., the majority of individuals are extremely adherent and the minority have very low adherence) or fairly homogeneous (i.e., all individuals are moderately adherent; none have extremely high, or low, adherence). These results indicate it will be difficult to assess the “quality” of PrEP interventions in terms of their effectiveness in reducing transmission by monitoring adherence.

Notably, our results indicate the most beneficial rollout strategy would be to begin introducing high “quality” PrEP interventions around poor “quality” treatment programs (i.e., programs with low success in viral suppression and high rates of acquired resistance). This rollout strategy would maximize the reduction in the number of treatment-experienced individuals in need of SLT. In summary our analysis shows that if the rollout of PrEP is carefully planned it could decrease the need for SLT and increase the sustainability of treatment programs. If it is not, the need for SLT could increase and the sustainability of treatment programs in resource-constrained countries could be compromised.


 


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