Coventry University Featured PhD Programmes
University of Leeds Featured PhD Programmes
University of Reading Featured PhD Programmes

Precision Medicine DTP - Understanding the long term evolution of HIV drug resistance


College of Medicine and Veterinary Medicine

This project is no longer listed on FindAPhD.com and may not be available.

Click here to search FindAPhD.com for PhD studentship opportunities
Dr K Atkins No more applications being accepted Competition Funded PhD Project (Students Worldwide)

About the 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 from K. pneumoniae, urinary tract infections from E. coli, tuberculosis, gonorrhoea, and HIV being resistant against first-line treatment, with some infections having developed resistance against second- and third-line treatment.

Amongst these warnings, 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 while each pathogen-drug combination is different, many pathogens do evolve an intermediate frequency of resistance for a given treatment rate, contrary to the intuition of the ‘doomsday’ scenario in which all currently treatable infections will eventually be resistant.

Explaining this phenomenon of stable ‘coexistence’ between resistant and sensitive strains has been a long-standing problem. 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. Without a mechanistic understanding of whether resistance stabilises at intermediate frequencies, we will not be able to predict whether we are likely to enter an era of untreatable HIV infections. Furthermore, our experience with bacterial resistance teaches us that it is also impossible to confidently predict the impact of control strategies.

Aims
1: Develop a suite of ‘coexistence’ mathematical models that each allows a stable intermediate level of resistance HIV strains.
2: Evaluate whether each model within this suite of ‘coexistence’ and ‘no-coexistence’ models can capture southern African country-level longitudinal data on resistance frequency and non-nucleoside reverse transcriptase inhibitor (NNRTI) uptake.
3: Compare the predictions made by the calibrated coexistence and the no-coexistence models.

Training outcomes
The student will develop or extend competencies within the areas of: HIV and drug resistance epidemiology, mathematical modelling of infectious disease, Bayesian modelling fitting algorithms, computer programming (e.g. in R, Python, C++), data analysis for infectious disease epidemiology, and scientific writing and presentation. Emphasis will be placed on developing and sharing code for the wider scientific community through platforms such as GitHub.
The student will learn to communicate their research through publication in peer-reviewed journals and presentation in scientific conferences. By working closely with experts in HIV epidemiology and mathematical modelling, the student will become comfortable working within an interdisciplinary environment and interacting with a diverse scientific team.

This MRC programme is joint between the Universities of Edinburgh and Glasgow. You will be registered at the host institution of the primary supervisor detailed in your project selection.

All applications should be made via the University of Edinburgh, irrespective of project location. For those applying to a University of Glasgow project, your application along with any supporting documents will be shared with University of Glasgow.

http://www.ed.ac.uk/studying/postgraduate/degrees/index.php?r=site/view&id=919

Please note, you must apply to one of the projects and you must contact the primary supervisor prior to making your application. Additional information on the application process is available from the link above.

For more information about Precision Medicine visit:
http://www.ed.ac.uk/usher/precision-medicine

Funding Notes

Start: September 2021

Qualifications criteria: Applicants applying for an MRC DTP in Precision Medicine studentship must have obtained, or will soon obtain, a first or upper-second class UK honours degree or equivalent non-UK qualification, in an appropriate science/technology area. The MRC DTP in Precision Medicine grant provides tuition fees and stipend of at least £15,285 (UKRI rate 2020/21).

Full eligibility details are available: http://www.mrc.ac.uk/skills-careers/studentships/studentship-guidance/student-eligibility-requirements/

Enquiries regarding programme: [Email Address Removed]

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. Davies, N. G. et al. Within-host dynamics shape antibiotic resistance in commensal bacteria. Nat. Ecol. Evol. 3(3):440-449 (2019)

3. 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).
Search Suggestions

Search Suggestions

Based on your current searches we recommend the following search filters.



FindAPhD. Copyright 2005-2021
All rights reserved.