Dear subscriber / HIV-TRePS user,
The development of our latest sets of models have been published online in the Journal of Antimicrobial Therapy. The models, which are available via HIV-TRePS, represent a step forward in terms of accuracy and utility for resource-limited settings, bringing the individualisation of HIV therapy through the power of Big Data a step closer for all.
We would like to thank all of you who have contributed data for making this research possible and encourage you to continue to do so!
The press release for this publication is reproduced below.
New predictive models for individualising HIV therapy in countries with limited resources
Models out-perform genotyping and identify potentially effective alternative regimens
London, UK; June 8, 2018. New predictive computer models designed to optimize HIV therapy in countries with limited healthcare resources are published online this week in the Journal of Antimicrobial Chemotherapy (JAC). The models, which were developed with data from tens of thousands of patients around the world, accurately predict how an individual on failing therapy will respond to any new combination of HIV drugs.
The publication describes two new sets of models: one that does not require the genetic code of the virus, for use settings where HIV genotyping tests are unavailable, and another that includes this information for use in well-resourced settings. Both sets of models were developed with relaxed requirements for input data, again to suit low to middle income countries.
Both sets of models predicted the responses to the new regimen introduced in the clinic with approximately 80% accuracy. They were significantly more accurate than using genotyping, with state of the art interpretation, to predict responses. Both sets of models were able to identify combinations of locally available drugs that were predicted to produce a response in 90% or more of the cases that failed the new combination introduced in the clinic.
"These models represent a significant step forward towards the individualisation of HIV therapy in countries where genotyping is unavailable, treatment options are limited, and the selection of the best combination is particularly critical," commented Dr Brendan Larder, Scientific Chair of the RDI and an author on the paper.
Currently, drug changes 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 by healthcare professionals as part of the RDI's HIV Treatment Response Prediction System (HIV-TRePS), which is freely available online at www.hivrdi.org/treps.
The RDI’s participation in this project is through a subcontract with Leidos Biomedical Research, the prime contractor for the Frederick National Laboratory for Cancer Research, sponsored by the National Cancer Institute.
Background
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 14 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 240,000 patients in more than 30 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.
Funding
This project has been funded in whole or in part with federal funds from the National Cancer Institute, National Institutes of Health, under Contract No. HHSN261200800001E. This research was supported by the National Institute of Allergy and Infectious Diseases. The content of this publication does not necessarily reflect the views or policies of the Department of Health and Human Services, nor does mention of trade names, commercial products, or organizations imply endorsement by the U.S. Government.
The paper
Revell AD, Wang D, Perez-Elias M-J et al. 2018 update to the HIV-TRePS system: the development of new computational models to predict HIV treatment outcomes, with or without a genotype, with enhanced usability for low-income settings. J Antimicrob Chemother 2018; doi: 10.1093/jac/dky179
Available at: https://doi.org/10.1093/jac/dky179
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: 8th June 2018