NNRTI Resistance Model Aids Treatment of HIV in Africa

The World Health Organization (WHO) has a new way to identify cost-effective measures to address the increasing prevalence of drug-resistant HIV in sub-Saharan Africa through the use of a computer model of AIDS-afflicted populations.

Developed by Andrew Phillips, PhD, and colleagues at the Institute for Global Health, University College London, in the United Kingdom, the HIV Synthesis Model is an individual-based simulation model of HIV transmission, progression, and treatment response. It incorporates extensive data from published sources, accounting for demographics and behaviors, as well as specific drug effects and HIV resistance mutations.

The computer model generates simulated populations of individual adults along with quarterly interval status throughout a lifespan—or a designated period, such as 20 years—for a range of factors, including HIV testing, condomless sex, and risk of HIV acquisition.

Although the model was initially created to analyze HIV patterns in the United Kingdom, Dr. Phillips and colleagues were tasked with it to sub-Saharan Africa, which is struggling with insufficient resources to provide antiretroviral treatment (ART) to more than 18 million HIV-infected patients.

The daunting challenges of high affliction rates across large geographic regions that are under-resourced have been compounded by the emergence of treatment-resistant HIV. In fact, according to the WHO’s most-recent guidelines on the public health response to pretreatment HIV drug resistance, in studies sampling individuals from 2014 to 2016, investigators found the prevalence of resistance to the nonnucleoside reverse-transcriptase inhibitor (NNRTI) component of recommended first-line ART was “close to or above 10% in eastern Africa, southern Africa and Latin America.”

In their report on the application of the modeling program to the growing problem of ART-resistant HIV in sub-Saharan Africa, Dr. Phillips and colleagues note that approaches were analyzed for both affordability and effectiveness.

The HIV Synthesis Model “is probably one of the models that contains the most detail on drug resistance to specific drugs, so it is quite well suited to study questions relating to choice of drug regimen,” Dr. Phillips told Contagion^®’s sister publication, Healthcare Analytics News^TM.

Simulating Populations of Infected Individuals

The model analyzed 2 options. One is to test for NNRTI resistance at time of treatment initiation, with the WHO alternative dolutegravir (Tivicay, ViiV Healthcare)-based regimen provided in lieu of the first-line efavirenz (Sustiva, Bristol-Myers)-based regimen for those with treatment resistance. The other option is to designate the alternative as the new first-line regimen for all patients and obviate the need for, and cost of, testing and the logistics of reestablishing contact and treatment after obtaining results.

Parameters tracked in the individuals modeled for acquiring HIV included viral load, CD4 cell count, occurrence of WHO stage 3 and stage 4 HIV disease condition, clinic attendance and drop-out, use of specific antiretroviral drugs, presence of specific resistance mutations, adherence to ART, and toxic effects of particular regimens of ART.

Among the assumptions incorporated into the model for this application was that dolutegravir would exhibit a similar rate of resistance to that of the protease inhibitor, atazanavir (Reyataz, Bristol-Myers Squibb) boosted with ritonavir (Norvir, Abbvie). The investigators inferred this would be approximately 27 times lower than the resistance rate for efavirenz.

Dr. Phillips and colleagues reported the model predicted that a transition to a dolutegravir-based first-line regimen in regions with >10% NNRTI drug resistance would be the most cost-effective approach and achieve the most health benefits, with a reduction of about 1 death per year per 100 individuals on ART over the next 20 years.

The results were considered by the WHO Guidelines Group, which subsequently issued a consensus statement recommending that regions with >10% NNRTI drug resistance “urgently consider” a first-time ART regimen that does not contain an NNRTI.

“The urgency of the transition will depend largely on the country-specific prevalence of NNRTI resistance,” Dr. Phillips and colleagues noted.

Other Purpose-Built HIV Models

The HIV Synthesis Model is one of several models that have been brought into the HIV Modelling Consortium in the Department of Infectious Disease Epidemiology at the Imperial College London, which is funded by a grant from the Bill & Melinda Gates Foundation. The Consortium leads coordinated interaction between modeling groups and policy makers, in conjunction with targeted and responsive commissioning of new work.

“Its central objective is to help improve scientific support for decision making by coordinating a wide range of research activities in the mathematical modeling of the HIV epidemic,” according to a statement.

In addition to the HIV Synthesis Model, examples of other models in the Consortium and their uses include:

• ASSA: Assess effects of prevention and treatment programs, demographic impact of HIV, and understanding of epidemic drivers.
• BBH: Investigate resource allocation issues, including cost-effectiveness of combination interventions and optimal allocation of a fixed budget across different interventions.
• Birger Saigon IDU Model: Consider HIV-HCV co-infection, prevention, mortality.
• Eaton, Hallett, Garnett 2011: Explore interaction between concurrent sexual partnerships and elevated infectiousness during primary HIV infection.
• ICRC HIV Transmission Model: Evaluate ART as treatment and as PrEP (pre-exposure prophylaxis).
• Menzies TB-HIV: Weigh burden of this comorbidity and evaluate resource utilization of interventions for both diseases
• PopART: Examine effects of universal testing and universal “test and treat.”

The HIV Synthesis Model, described as an individual-based stochastic simulation model, is calibrated with Approximate Bayesian Computation to input parameter values that enable modeled outcomes that are similar to what would be observed if there were sufficient surveillance data available in the real-world setting.

“However, it is nowhere close to being able to replace the need for actual clinical studies, of course, in discerning differences in outcomes between where such studies are feasible,” Dr. Phillips told HCA.

“We have used the model now in around 20 papers on different questions, and I have been quite surprised that new questions continue to emerge for which our model can provide some useful insights in relation to potential effectiveness and cost-effectiveness,” he concluded.

A previous version of this article was published on Healthcare Analytics News.