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Why don’t all HIV infections become resistant to antivirals after implementing universal test-and-treat? - NU/LSHTM project

Supervisory team

LSHTM

Nagasaki University

Project

Background

Antimicrobial resistance (AMR) is the ability of any microbe to evade the control of treatments such as antivirals and antibiotics.  AMR remains one of the greatest challenges to human and animal health globally, with many common gastrointestinal diseases, urinary tract infections, tuberculosis, gonorrhoea, and HIV being resistant against first-line treatment; some infections have even developed resistance against second- and third-line treatment. 

Amongst these issues, there is considerable variation in the observed frequency of resistance across microbes. For example, penicillin-resistant gonorrhoea reached 100% prevalence quickly after the 1940s in Europe, while in the same regions, penicillin-resistant pneumococcal pneumonia has been maintained at low levels, often less than 15%. These differences likely arise through a complex interplay between the epidemiology, the pathogen biology and its genetic constraints. Moreover, for many pathogens, it is unclear whether an observed intermediate resistance frequency is part of a temporal trend toward all isolates being resistant, or whether resistance has stabilised at this intermediate frequency. 

Determining the long-term stable resistance frequency - that is, the probability that an infection is resistant to at least one drug - predicts the worst-case public health scenario. It is clear from our empirical data that many pathogens do not all evolve to be resistant to all drugs contrary to the intuition of the 'doomsday' scenario in which all currently treatable infections will eventually be resistant.

Explaining this phenomenon of a stable intermediate 'coexistence' between resistant and sensitive strains has been a long-standing problem in evolutionary biology. That is, we lack validated models that explain resistance frequency. Importantly, this failure leads to a substantial gap in our clinical knowledge because we lack tools to explain why some people are infected with drug resistant infections. Moreover, we cannot accurately predict the long-term effect of interventions, leading to a gap in our public health knowledge.

For commensal bacteria at least, we are now beginning to understand the mechanisms underlying pathogen evolution that give rise to the empirical relationship observed between increasing antibiotic use and increasing stable frequencies of resistance.  However, due to the recency of these developments, there has been no concerted effort both to determine the long-term stable equilibrium resistance frequency and to explain the underlying mechanisms for the many other types of pathogens for which drug resistance is a growing public health threat, such as HIV.

For HIV, without a mechanistic understanding of whether resistance stabilises at intermediate frequencies, we will not be able to predict the long-term sustainability of the new integrase inhibitor regimens nor understand who is at most risk of drug-resistant infections. Furthermore, our experience with bacterial resistance teaches us that it is also impossible to confidently predict the impact of control strategies without empirically validated models.

Proposed project

This project will seek to develop mathematical models of HIV that capture historical trends in population drug resistance to identify the predicted long-term frequency of resistance to integrase inhibitors and the differences in individual risk. The project will model the country-level resistance frequencies of three main classes of drugs (NNRTI, NRTI and INSTI) and understand how these frequencies are driven by population treatment adherence. For this, the student will use publicly available data on long-term drug resistance frequencies in both high and low middle income countries (collated via reviews and meta-analysis e.g. Gupta et al. 2017,  Bertagnolio et al. 2022) and recent data on Dolutegravir resistance emergence and transmission via study collaborators (Loosli et al. 2025).

References

  1. Gupta, R. K. et al. HIV-1 drug resistance before initiation or re-initiation of first-line antiretroviral therapy in low-income and middle-income countries: a systematic review and meta-regression analysis. Lancet Infect. Dis. 18, 346–355 (2017).
  2. Bertagnolio, S et al.  Epidemiology of HIV drug resistance in low- and middle-income countries and WHO global strategy to monitor its emergence. Current Opinion in HIV and AIDS 17(4):229-239 (2022)
  3. Loosli, T et al. HIV-1 drug resistance in people on dolutegravir-based antiretroviral therapy: a collaborative cohort analysis The Lancet HIV 10 (11): e733-e741 (2023)
  4. Davies, N. G. et al. Within-host dynamics shape antibiotic resistance in commensal bacteria. Nat. Ecol. Evol. 3(3):440-449(2019)
  5. Hauser A et al. Bridging the gap between HIV epidemiology and antiretroviral resistance evolution: Modelling the spread of resistance in South Africa. PLOS Comp Biol 15(6): e1007083 (2019).

The role of LSHTM and NU in this collaborative project

  • Katie Atkins is an expert in evolutionary ecology and the modelling of drug resistance. The project will build on her work modelling the emergence and transmission of bacterial and HIV drug resistance. She will lead on the development of the HIV drug resistance models.
  • Laura Skrip is an expert on modelling programme implementation and will contribute to the scaling up of the within-host drug resistance model to a national level to understand how different treatment adherence patterns can explain the observed frequency of drug resistance across countries.

Particular prior educational requirements for a student undertaking this project

Proven experience in one or more of the following is desirable:

  • Infectious disease epidemiology
  • Mathematical modelling
  • One scientific programming language (e.g. R, Python).

Skills we expect a student to develop/acquire whilst pursuing this project

The candidate will develop core skills in infectious disease epidemiology (with a particular emphasis on antimicrobial resistance), mathematical modelling, programming (e.g. Python, R, Julia), data analysis, scientific writing, critical literature review, and scientific presentation. The student will be expected to use and develop AI-based approaches as needed within their workflows.