Abstract
Development of an HIV vaccine is essential to ending the HIV/AIDS pandemic. However, vaccines can result in the emergence and spread of vaccine-resistant strains. Indeed, analyses of breakthrough infections in the HIV vaccine trial RV144 identified HIV genotypes with differential rates of transmission in vaccine and placebo recipients. We hypothesized that, for HIV vaccination programs based on partially effective vaccines similar to RV144, HIV adaptation will diminish the expected vaccine impact. Using two HIV epidemic models, we simulated large-scale vaccination programs and, critically, included HIV strain diversity with respect to the vaccine response. We show here that rapid population-level viral adaptation can lead to decreased overall vaccine efficacy and substantially fewer infections averted by vaccination, when comparing scenarios with and without viral evolution (depending on vaccination coverage, vaccine efficacy against the sensitive allele, and the initial resistant allele frequency). Translating this to the epidemic in South Africa, a scenario with 70% vaccination coverage may result in 250,000 new infections within 10 years of vaccine rollout that are due solely to HIV adaptation, all else being equal. These findings suggest that approaches to HIV vaccine development, program implementation, and epidemic modeling may require attention to viral evolutionary responses to vaccination.
Main
Despite concerted global effort and the existence of effective methods for prevention, HIV continues to be a public health crisis. The need for an HIV vaccine remains paramount. The phase 3 RV144 HIV vaccine trial is the only trial of an HIV vaccine to show modest success in preventing infection3. RV144 resulted in an estimated 31% vaccine efficacy (VE) at 3.5 years post-vaccination (p=0.04, modified intent-to-treat analysis). The vaccine was partially protective but not therapeutic; i.e. vaccinated individuals had decreased rates of infection, but breakthrough infections were not associated with differences in early HIV plasma viral loads, post-infection CD4+ T cell counts, or HIV disease progression rates, when comparing vaccine and placebo recipients6. The RV144 results spurred the development of the recently initiated HVTN 702, a large phase 3 HIV vaccine trial in South Africa that aims to replicate the RV144 findings in a different study group, with regimen and schedule that follow from RV144 (with several modifications, including a vaccine insert specific to HIV subtype C, the most common subtype in South Africa).
Partially effective vaccines have been of enduring scientific, clinical, and theoretical interest. For HIV in particular, two decades of mathematical modeling studies suggest that partially effective vaccines, whether protective or therapeutic, can have a substantial impact on the HIV pandemic7-19. More recently, HIV epidemic models were used to predict the impact of a partially effective (protective) vaccine similar to RV144 in terms of VE and duration. Models were used to assess the impact of vaccination programs with 30% and 60% population coverage of sexually active adults, with subsequent vaccine rollouts at 1- to 5-year intervals20. Results were consistent across several model and epidemic types, e.g., multiple vaccination rounds, at 60% coverage, were predicted to prevent 5-15% of new infections over 10 years21-28. The expected impact of vaccination programs depended on vaccination coverage, VE, and the duration of vaccine protection.
However, the potential for HIV adaptation at the population-level in response to vaccination was not considered in these modeling studies. The requirements for adaptive evolution are few: there must be phenotypic variation in a population, this variation must be heritable (linked to genetic variation), and this variation must be related to fitness (differential reproduction)29. Evidence from RV144 follow-up studies suggest that, with respect to a partially effective protective vaccine, HIV meets these requirements. Namely, genetic sieve analyses of RV144 breakthrough infections showed that sequences from infected vaccine recipients differed from those isolated from infected placebo recipients. Two signatures were identified in the Env V2 region: in the vaccine recipients, K169X mutations were more frequent (34% vs. 17%) and 181I was more conserved (91% vs. 71%). VE against viruses matching the vaccine at position 169 was 48% (95% confidence interval (CI) 18% to 66%), whereas VE against viruses mismatching the vaccine at position 181 was 78% (CI 35% to 93%)4,5. Thus, heritable (genetic) variation in HIV can be associated with differential infection rates in a vaccinated population, making viral adaptation a potential outcome.
We hypothesized that HIV population-level adaptation after vaccine rollout will result from selection acting on a viral locus containing an allele that confers resistance to the vaccine response; i.e., viruses not blocked by a vaccine-elicited immune response will spread in the HIV-infected population. Our goal was to predict the public health impact of this viral evolution, under varying VE, population vaccination coverage and initial frequency of vaccine-resistant genotypes. We quantified this impact in terms of the resistant genotype frequency, the overall VE and the cumulative HIV infections averted by vaccination.
Methods
We used two stochastic, individual-based HIV epidemic models, both of which were based on existing model frameworks (described in further detail in the Methods, below; Tables S1, S2). The first model was roughly calibrated to the heterosexual epidemic of South Africa30, while the second model was parameterized using behavioral data from men who have sex with men (MSM) in the United States. Both models contained antiretroviral therapy (ART) to approximate the population-level effects of ART on background incidence and prevalence in our vaccine simulations, and we repeated all simulations under two ART population coverage levels: 30% and 70% for the heterosexual model, 40% and 70% for the MSM model (lower coverage levels reflect approximate current states; upper level is aspirational).
The vaccine in our models protected individuals from infection by decreasing the per-contact probability of transmission; it did not affect HIV disease progression, viral load, or CD4 count in vaccinated individuals who became infected (consistent with RV144). The mean duration of VE was set to three years with efficacy reduced immediately to 0% at the end of this period. Vaccination programs started in year 20 or 25 (depending on the model), with continuous rollout. Target vaccination coverage was generally reached within three to four years. We modeled viral diversity with respect to vaccine-induced host response via one locus with two alleles: sensitive and resistant. Sensitive viruses had reduced per-contact probabilities of infecting vaccinated hosts. Resistant viruses experienced no change in transmission probability, regardless of the vaccination status of possible recipients. We used a single viral genetic locus as an approximation of the complete set of putative vaccine-resistant variants identified by sieve analyses4,5; in effect, multiple independent vaccine-resistant alleles at low frequency will have a similar population-level impact as a single vaccine-resistant allele at high frequency. For each ART coverage level we evaluated two vaccine scenarios, at increasing vaccination coverage levels: 1) VE for the sensitive virus = 75% and initial resistant virus proportion = 0.25; and 2) VE for sensitive virus = 90% and initial resistant virus proportion = 0.50. We selected these scenarios because both correspond to an overall VE of roughly ~50% (56.25% and 45%, respectively, calculated as the weighted average of sensitive and resistant VE at first vaccine rollout), a VE that the HVTN 702 vaccine trial is powered to detect relative to the null hypothesis (VE≤25%).
Results and Discussion
To confirm the validity of our models, we first examined the effects of introducing a partially effective vaccine into HIV epidemics with a single, vaccine-sensitive viral strain (all viruses were equally sensitive to the vaccine response). We found, in agreement with previous models 21-28, that a vaccine similar to RV144 can indeed have a modest impact. For example, an HIV vaccine with 45% VE and 50% coverage can prevent ~20% of cumulative infections within 10 years of vaccine rollout, in comparison to populations with no vaccine (Tables S3, S4). As expected, these values increase with higher vaccination coverage and higher VE: a vaccine with 56% VE and 70% overall coverage can prevent ~40% of cumulative infections within 10 years (Tables S3, S4).
Next, we examined the impact of a partially effective vaccine in a population that contained resistant viruses (VE=0 for the resistant viruses). In all simulations, for both heterosexual and MSM models, resistant virus increased in proportion after vaccine rollout (Figure 1). In a representative scenario from the heterosexual model (Figure 1), with 30% ART coverage, 70% vaccination coverage, and 75% VE against the sensitive allele, the resistant allele population proportion increased from 0.25 to 0.38 within 10 years of vaccine rollout. With 70% ART coverage and 90% VE against the sensitive allele, the resistant allele increased from 0.51 to 0.69 within 10 years. Across all epidemic simulations, the rate at which the resistant allele frequency increased was faster with higher vaccination coverage and greater VE against the sensitive virus. As the resistant allele frequency increases, the overall VE correspondingly declines (Figure 2); e.g., with 50% vaccination coverage, overall VE declines from 56% to 50%, or from 45% to 33% in 10 years (depending on sensitive virus VE and initial resistant frequency). Thus, we see that both programmatic and vaccine-related parameters can exert evolutionary pressure on HIV and impact vaccine effectiveness.
Public health impact
Most critically, our results predict that HIV adaptation in response to vaccination may have a considerable, and detrimental, public health impact. Fewer infections were averted over time in scenarios with resistant strains, relative to counterfactual simulations with no vaccine-resistant HIV strains (Figures 3, 4). Since overall VEs at the time of first vaccine rollout were equivalent across runs, we can assign causality for the marginal differences (in the proportion of infections averted) to viral adaptation. The proportion of infections averted by vaccination decreased dramatically as resistant viruses increased in frequency and overall VE decreased. In the representative examples above, with 70% vaccination coverage and either 75% or 90% VE against the sensitive allele and 25% or 50% resistant virus at vaccine rollout, respectively, infections averted decreased from 39% to 37%, or from 32% to 26%, within 10 years (Figure 3, Table S3). These predictions can be placed in the context of the current HIV epidemic: in South Africa, with approximately 380,000 new HIV infections in 201531, scenarios of 70% vaccination coverage (30% ART coverage, Figure 3) result in approximately 100,000 to 250,000 new infections in the first decade after vaccine rollout that are due solely to the emergence and spread of vaccine-resistant strains (in scenarios of 25% resistant virus and 75% sensitive virus VE, and 50% resistant virus and 90% sensitive virus VE, respectively). These results highlight the potential public health impact of HIV adaptation in response to vaccination. They also underscore the need to understand the underlying viral determinants of partially effective vaccines: despite their similar overall VE of ~50%, the distinct sensitive virus VE and resistant frequency scenarios (25% and 50% resistant virus; 75% and 90% sensitive virus VE) differed greatly in implications for public health.
In scenarios in which a high-risk subgroup in the heterosexual model is preferentially targeted for vaccination (Figure S1, Table S5), we see equivalent declines in the proportion of infections averted, but with much lower overall vaccination coverage: up to 150,000 new infections in the first 10 years after vaccine rollout due to the emergence and spread of vaccine-resistant strains, in a scenario with 70% vaccination coverage of a high-risk subgroup but only 32% coverage of the total population. This increases to 350,000 new infections with 90% coverage of the high-risk subgroup and 35% total coverage. Vaccination efforts that use high-risk behavioral targeting must be prepared for significant resistance impacts at lower vaccination coverage levels; further studies of the impact of transmission network structure on viral adaptation in response to vaccination, guided by empirical data, are warranted.
We note that similar, but not identical, HIV population-level adaptation and diminished public health impacts were seen in the heterosexual and MSM models, despite differences between the models in population structure, transmission dynamics, HIV prevalence and incidence, and overall effect of vaccination. Additionally, the impacts of HIV adaptation were consistent across the two ART scenarios, suggesting that ART parameters will likely not substantially affect the rates or impact of viral adaptation (Figure 3).
Our HIV-specific model predictions are consistent with findings from other pathogens. These include empirical evidence of vaccine-induced strain replacement in, e.g., Streptococcus pneumoniae, Haemophilus influenza, Neisseria meningitidis, Bordetella pertussis, Plasmodium falciparum, and hepatitis B virus (reviewed in1,2). Mathematical models have evaluated patterns and processes of strain replacement in, e.g., Mycobacterium tuberculosis32, rotavirus33, and S. pneumoniae34,35, and generalized pathogens1,36 Yet, despite widespread acknowledgement and concern for the evolutionary potential of HIV—with respect to resistance to ART and PrEP37,38, vaccine design39,40, the human immune response41-44, and a potential vaccine-induced cellular immune response45,46, HIV strain replacement in response to an imperfect vaccine had not been evaluated previously.
Conclusions
Previous epidemic models that have estimated the effects of partially effective HIV vaccines have likely overestimated the benefits conferred on a population by vaccination. Perhaps more pressing, strategies for HIV vaccine development and program implementation may benefit from careful attention to the potential evolutionary consequences of vaccination. This includes continued surveillance of viral genetic diversity, accompanied by vaccine design that limits the mutational pathways available for viral adaptation and subsequent emergence of vaccine-resistant viruses40,47,48. Our analysis is particularly relevant given the recent initiation of the HVTN 702 trial, which is the critical test for licensure in South Africa of the first vaccine to prevent HIV infection. If successful, such an HIV vaccination program may necessarily evolve into a program similar to that in place for influenza, comprising an acceptable vaccine that requires periodic updating.
Author contributions
J.T.H. conceived the study and designed the experiments. J.T.H., K.P., J.T.M., G.S.G., N.A., J.E.M, and S.G.M. developed the modeling platforms. J.T.H and K.P. performed the modeling experiments. J.T.H., K.P., P.T.E., M.R., G.S.G., N.A., J.I.M, J.E.M, and S.M.G. interpreted the data and contributed to writing the manuscript.
Competing financial interests
The authors declare no competing financial interests.
Acknowledgments
We thank Jonathan Carlson, Christophe Fraser, Christian Selinger and members of the University of Washington International Clinical Research Center for input and discussion. This work was supported by grants from the U.S. National Institutes of Health (R01AI108490 to J.T.H., J.E.M., and S.M.G., and P30AI027757 to the University of Washington Center for AIDS Research) and by an Interagency Agreement with the US Army Medical Research and Material Command (Y1-AI-2642-12) and by a cooperative agreement between the Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc., and the US Department of Defense (MR). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health, the US Department of Defense or the Department of the Army. The authors declare no competing interests.