New predictive models accurately predict HIV treatment response despite missing data

A major step forward in utility for settings with limited resources and monitoring

London, UK; April 29th, 2021. New models that can accurately predict how patients will respond to different combinations of HIV drugs in settings with limited resources and monitoring are published in the Journal of Antimicrobial Chemotherapy (JAC). The models, which were developed by the RDI with data from tens of thousands of patients around the world, predict response without the need for a viral genotype, CD4 count or a record of the time on therapy.


The publication describes twelve sets of new models, with various combinations of baseline patient data missing. Those models with all the data available achieved greater than 80% accuracy. Those with various combinations of missing data achieved accuracy that was reduced by between 0 and 10%. All the models were able to identify combinations of readily available drugs that were predicted to produce a response in the great majority of the cases that failed the new combination introduced in the clinic.


"These models make it much more feasible to individualise HIV therapy in countries where genotyping is unavailable and laboratory monitoring and treatment options are limited", commented Dr Brendan Larder, Scientific Chair of the RDI and senior author on the paper.


The models were developed as part of a research project funded by the National Cancer Institute, U.S. National Institutes of Health via a subcontract with the Frederick National Laboratory for Cancer Research, currently operated by Leidos Biomedical Research, Inc.


Currently, drug changes in resource-limited settings are not generally individualized but made according to set protocols, which can lead to sub-optimal treatments being introduced that can enable the development of drug resistance. Resistance is on the increase in many low to middle income countries, which poses a threat not only to the individual but to whole populations through the increased risk of onward transmission of drug-resistant virus.


The new models are now available to be used free-of-charge by healthcare professionals as part of the RDI's HIV Treatment Response Prediction System (HIV-TRePS), which is available online at https://www.hivrdi.org/treps


"The main factor limiting the global benefits of these new models is their uptake by healthcare professionals who may be reluctant or unable to deviate from their institutional, regional or national treatment protocols", said Dr Andrew Revell, Executive Director of the RDI and lead author on the paper.



The RDI is an independent, not-for-profit international research collaboration set-up in 2002 with the mission to improve the clinical management of HIV infection through the application of bioinformatics to HIV drug resistance and treatment outcome data. Over the 19 years since its inception, the RDI has worked with many of the leading clinicians and scientists in the world to develop the world's largest database of HIV drug resistance and treatment outcome data, containing information from approximately 260,000 patients in more than 40 countries.


HIV-TRePS is an experimental system intended for research use only. The predictions of the system are not intended to replace professional medical care and attention by a qualified medical practitioner and consequently the RDI does not accept any responsibility for the selection of drugs, the patient's response to treatment or differences between the predictions and patients' responses.


The paper


Revell AD, Wang D, Perez-Elias M-J et al. 2021 update to HIV-TRePS: a highly flexible and accurate system for the prediction of treatment response from incomplete baseline information in different healthcare settings. J Antimicrob Chemother 2021; doi: 10.1093/jac/dkab078


https://academic.oup.com/jac/advance-article-abstract/doi/10.1093/jac/dkab078/6207542


For further information contact:


Andrew Revell (Executive Director, RDI) on +44 207 226 7314, +44 7967 126498 (mobile) or andrewrevell@hivrdi.org or visit : https://www.hivrdi.org



Date published: 29th April 2021

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