What do mathematical models tell us about the emergence and spread of drug-resistant HIV?

Drug Resistance During HIV Pre-Exposure Prophylaxis

Clinical studies have demonstrated that use of tenofovir disoproxil fumarate with or without emtricitabine as antiretroviral pre-exposure prophylaxis (PrEP) can decrease the risk of human immunodeficiency virus (HIV) acquisition when medication adherence is high. However, the potential for PrEP to promote antiretroviral resistance remains an important public health consideration. We performed a search of the medical literature to identify studies that address HIV drug resistance during PrEP use. In this review, we summarize findings about emergent drug resistance during clinical trials of PrEP, case reports of seroconversions in patients adherent to PrEP, and animal studies of PrEP effectiveness against drug-resistant viral strains. We also discuss the potential utility of novel PrEP formulations for protection against drug-resistant HIV, the impact of drug resistance on HIV treatment options, and mathematical models that estimate the potential contribution of PrEP to population-level drug resistance. Evidence suggests that selection for HIV drug resistance with PrEP use is infrequent and most likely to occur when PrEP is used during undiagnosed acute HIV infection. Breakthrough infections during PrEP use with high adherence are possible, but appear to be rare. The prevalence of drug-resistant HIV strains needs to be monitored as PrEP is scaled up. However, the benefit of a decreased HIV incidence with wider PrEP use is likely to outweigh the risk of harms from possible increases in the prevalence of HIV drug resistance.

Fuzzy-multidimensional deep learning for efficient prediction of patient response to antiretroviral therapy

Drug component interactions are most likely to trigger unexpected pharmacological effects with unknown causal mechanisms, hence, demanding the discovery of patterns to establish suitable and effective regimens. This paper proposes a novel framework that embeds machine learning (ML) and multidimensional scaling (MDS) techniques, for efficient prediction of patient response to antiretroviral therapy (ART). To achieve this, experiment databases were created from two independent sources: a publicly available HIV domain datasets of patients with failed treatment – hosted by the Stanford University, hereinafter referred to as the Stanford HIV database, and locally sourced datasets gathered from 13 prominent healthcare facilities treating HIV patients in Akwa Ibom State of Nigeria, hereinafter referred to as the Akwa-Ibom HIV database: with 5,780 and 3,168 individual treatment change episodes (TCEs) of HIV treatment indicators (baseline CD4 count (BCD4), followup CD4 count (FCD4), baseline viral load (BRNA), followup viral load (FRNA), and drug type combination (DType)), observed from 1,521 and 1,301 unique patient records, respectively. A hybridised (two-stage) classification system consuming the Interval Type-2 Fuzzy Logic (IT2FL) and Deep Neural Network (DNN) was employed to model and optimise patients’ response to ART with appreciable error pruning achieved through MDS. Visualisation of the experiment databases showed remarkable immunological changes in the Akwa-Ibom HIV database, as the FCD4 of TCEs clustered far above the BCD4, compared to the Stanford HIV database, where over 40% of FCD4 clustered below the BCD4. Similar changes were noticed for the RNA, as more FRNA copies clustered below the BRNA for the Akwa-Ibom datasets, compared to the Stamford datasets. DNN classification results for both databases showed best performance metrics for the Levenberg-Marquardt algorithm when compared with the resilient backpropagation algorithm, with improved drug pattern predictions for experiment with MDS. This paper is most likely to evolve an avenue that triggers interesting combination(s) for optimum patient response, while ensuring minimal side effects, as further findings revealed the superiority of the proposed approach over existing approaches.

Population-level mathematical modeling of antimicrobial resistance: a systematic review

BACKGROUND

Mathematical transmission models are increasingly used to guide public health interventions for infectious diseases, particularly in the context of emerging pathogens; however, the contribution of modeling to the growing issue of antimicrobial resistance (AMR) remains unclear. Here, we systematically evaluate publications on population-level transmission models of AMR over a recent period (2006-2016) to gauge the state of research and identify gaps warranting further work.

METHODS

We performed a systematic literature search of relevant databases to identify transmission studies of AMR in viral, bacterial, and parasitic disease systems. We analyzed the temporal, geographic, and subject matter trends, described the predominant medical and behavioral interventions studied, and identified central findings relating to key pathogens.

RESULTS

We identified 273 modeling studies; the majority of which (> 70%) focused on 5 infectious diseases (human immunodeficiency virus (HIV), influenza virus, Plasmodium falciparum (malaria), Mycobacterium tuberculosis (TB), and methicillin-resistant Staphylococcus aureus (MRSA)). AMR studies of influenza and nosocomial pathogens were mainly set in industrialized nations, while HIV, TB, and malaria studies were heavily skewed towards developing countries. The majority of articles focused on AMR exclusively in humans (89%), either in community (58%) or healthcare (27%) settings. Model systems were largely compartmental (76%) and deterministic (66%). Only 43% of models were calibrated against epidemiological data, and few were validated against out-of-sample datasets (14%). The interventions considered were primarily the impact of different drug regimens, hygiene and infection control measures, screening, and diagnostics, while few studies addressed de novo resistance, vaccination strategies, economic, or behavioral changes to reduce antibiotic use in humans and animals.

CONCLUSIONS

The AMR modeling literature concentrates on disease systems where resistance has been long-established, while few studies pro-actively address recent rise in resistance in new pathogens or explore upstream strategies to reduce overall antibiotic consumption. Notable gaps include research on emerging resistance in Enterobacteriaceae and Neisseria gonorrhoeae; AMR transmission at the animal-human interface, particularly in agricultural and veterinary settings; transmission between hospitals and the community; the role of environmental factors in AMR transmission; and the potential of vaccines to combat AMR.

The impact of the 2013 WHO antiretroviral therapy guidelines on the feasibility of HIV population prevention trials

BACKGROUND

Several cluster-randomized HIV prevention trials aim to demonstrate the population-level preventive impact of antiretroviral therapy (ART). 2013 World Health Organization (WHO) guidelines raising the ART initiation threshold to CD4 <500/µL could attenuate these trials' effect size by increasing ART usage in control clusters.

METHODS

We used a computational model to simulate strategies from a hypothetical cluster-randomized HIV prevention trial. The primary model outcome was the relative reduction in 24-month HIV incidence between control (ART offered with CD4 below threshold) and intervention (ART offered to all) strategies. We assessed this incidence reduction using the revised (CD4 <500/µL) and prior (CD4 <350/µL) control ART initiation thresholds. Additionally, we evaluated changes to trial characteristics that could bolster the incidence reduction.

RESULTS

With a control ART initiation threshold of CD4 <350/µL, 24-month HIV incidence under control and intervention strategies was 2.46/100 person-years (PY) and 1.96/100 PY, a 21% reduction. Raising the threshold to CD4 <500/µL decreased the incidence reduction by more than one-third, to 12%. Using this higher threshold, moving to a 36-month horizon (vs 24-month), yearly control-strategy HIV screening (vs bian-nual), and intervention-strategy screening every 2 months (vs biannual), resulted in a 31% incidence reduction that was similar to effect size projections for ongoing trials. Alternate assumptions regarding cross-cluster contamination had the greatest influence on the incidence reduction.

CONCLUSIONS

Implementing the 2013 WHO HIV treatment threshold could substantially diminish the incidence reduction in HIV population prevention trials. Alternative HIV testing frequencies and trial horizons can bolster this incidence reduction, but they could be logistically and ethically challenging. The feasibility of HIV population prevention trials should be reassessed as the implementation of treatment guidelines evolves.

Tenofovir-based HIV pre-exposure prophylaxis

HIV pre-exposure prophylaxis (PrEP) with daily oral tenofovir disoproxil fumarate (TDF) or TDF–emtricitabine (FTC) has been shown to be effective against sexual and injection-drug related HIV acquisition in four out of six large clinical trials. This article reviews the pharmacology of TDF and FTC as it relates both to PrEP efficacy and the emergence of viral resistance, summarizes the six trials and the inherent challenges to PrEP they highlighted, and proposes some future areas for further exploration.

Analytic review of modeling studies of ARV Based PrEP interventions reveals strong influence of drug-resistance assumptions on the population-level effectiveness

Background

Four clinical trials have shown that oral and topical pre-exposure prophylaxis (PrEP) based on tenofovir may be effective in preventing HIV transmission. The expected reduction in HIV transmission and the projected prevalence of drug resistance due to PrEP use vary significantly across modeling studies as a result of the broad spectrum of assumptions employed. Our goal is to quantify the influence of drug resistance assumptions on the predicted population-level impact of PrEP.

Methods

All modeling studies which evaluate the impact of oral or topical PrEP are reviewed and key assumptions regarding mechanisms of generation and spread of drug-resistant HIV are identified. A dynamic model of the HIV epidemic is developed to assess and compare the impact of oral PrEP using resistance assumptions extracted from published studies. The benefits and risks associated with ten years of PrEP use are evaluated under identical epidemic, behavioral and intervention conditions in terms of cumulative fractions of new HIV infections prevented, resistance prevalence among those infected with HIV, and fractions of infections in which resistance is transmitted.

Results

Published models demonstrate enormous variability in resistance-generating assumptions and uncertainty in parameter values. Depending on which resistance parameterization is used, a resistance prevalence between 2% and 44% may be expected if 50% efficacious oral PrEP is used consistently by 50% of the population over ten years. We estimated that resistance may be responsible for up to a 10% reduction or up to a 30% contribution to the fraction of prevented infections predicted in different studies.

Conclusions

Resistance assumptions used in published studies have a strong influence on the projected impact of PrEP. Modelers and virologists should collaborate toward clarifying the set of resistance assumptions biologically relevant to the PrEP products which are already in use or soon to be added to the arsenal against HIV.

A model of HIV drug resistance driven by heterogeneities in host immunity and adherence patterns

Background

Population transmission models of antiretroviral therapy (ART) and pre-exposure prophylaxis (PrEP) use simplistic assumptions – typically constant, homogeneous rates – to represent the short-term risk and long-term effects of drug resistance. In contrast, within-host models of drug resistance allow for more detailed dynamics of host immunity, latent reservoirs of virus, and drug PK/PD. Bridging these two levels of modeling detail requires an understanding of the “levers” – model parameters or combinations thereof – that change only one independent observable at a time. Using the example of accidental tenofovir-based pre-exposure prophyaxis (PrEP) use during HIV infection, we will explore methods of implementing host heterogeneities and their long-term effects on drug resistance.

Results

We combined and extended existing models of virus dynamics by incorporating pharmacokinetics, pharmacodynamics, and adherence behavior. We identified two “levers” associated with the host immune pressure against the virus, which can be used to independently modify the setpoint viral load and the shape of the acute phase viral load peak. We propose parameter relationships that can explain differences in acute and setpoint viral load among hosts, and demonstrate their influence on the rates of emergence and reversion of drug resistance. The importance of these dynamics is illustrated by modeling long-lived latent reservoirs of virus, through which past intervals of drug resistance can lead to failure of suppressive drug regimens. Finally, we analyze assumptions about temporal patterns of drug adherence and their impact on resistance dynamics, finding that with the same overall level of adherence, the dwell times in drug-adherent versus not-adherent states can alter the levels of drug-resistant virus incorporated into latent reservoirs.

Conclusions

We have shown how a diverse range of observable viral load trajectories can be produced from a basic model of virus dynamics using immunity-related “levers”. Immune pressure, in turn, influences the dynamics of drug resistance, with increased immune activity delaying drug resistance and driving more rapid return to dominance of drug-susceptible virus after drug cessation. Both immune pressure and patterns of drug adherence influence the long-term risk of drug resistance. In the case of accidental PrEP use during infection, rapid transitions between adherence states and/or weak immunity fortifies the “memory” of previous PrEP exposure, increasing the risk of future drug resistance. This model framework provides a means for analyzing individual-level risks of drug resistance and implementing heterogeneities among hosts, thereby achieving a crucial prerequisite for improving population-level models of drug resistance.

A literature review of reporting standards of HIV progression models

OBJECTIVE

We conducted a systematic review of HIV progression models to identify the mathematical structures used, the main research questions and key model aspects in terms of quality and robustness.

METHODS

We searched for articles published before February 2009 that described models of HIV progression in humans. We included two strategies of search with and without MeSH terms. We classified the models by their mathematical structure and research question. We created a checklist of desirable features of the models, reviewed and classified the articles to inform our conclusions.

RESULTS

Among 3491 articles found, 93 met the inclusion criteria. Among the selected articles, 60 used transition models, 25 applied differential equations, and eight had other structures. We did not find a relation between the type of question explored and the modeling method used. None of the studies complied with the complete set of items in the checklist, but 6.5% cover at least 90% of them.

CONCLUSION

There is an enormous heterogeneity of HIV modeling exercises in terms of methods used and topics addressed, as well as in the presentation of key aspects of the articles in terms of quality and robustness.

Pre-exposure prophylaxis and antiretroviral resistance: HIV prevention at a cost?

Pre-exposure prophylaxis (PrEP), the use of antiretrovirals (ARVs) by human immunodeficiency virus (HIV)-uninfected individuals to prevent acquisition of the virus during high-risk sexual encounters, enjoyed its first 2 major successes with the Centre for the AIDS Programme of Research in South Africa (CAPRISA) 004 and the Pre-Exposure Prophylaxis Initiative (iPrEx). These successes were buoyed by additional positive results from the TDF2 and Partners PrEP trials. Although no seroconverters in either arm of CAPRISA developed resistance to tenofovir, 2 participants in iPrEx with undetected, seronegative acute HIV infection were randomized to receive daily oral tenofovir-emtricitabine and resistance to emtricitabine was later discovered in both men. A similar case in the TDF2 study resulted in resistance to both ARVs. These cases prompted us to examine existing literature on the nature of resistance mutations elicited by ARVs used for PrEP. Here, we discuss the impact of signature mutations selected by PrEP, how rapidly these emerge with daily ARV exposure, and the individual-level and public health consequences of ARV resistance.