New global models outperform genotyping and local African models
London, UK; Thursday 23rd June 2016. New computer models published this week in the Journal of Antimicrobial Chemotherapy (JAC) predict how patients whose HIV therapy is failing will respond to any new combination of drugs, with the highest accuracy to date. The models, which were developed with data from tens of thousands of patients around the world were significantly better at identifying potentially effective drug combinations than 'local' models trained with data from South Africa when tested with HIV patients from that country.
The publication describes three new sets of models: Global models trained with around 30,000 cases from around the world that predict response to HIV treatment without the need for a genotype (a test that reads the genetic code of the virus) and similar 'local' South African models, trained with around 3,000 cases from South Africa. These two sets of 'no-genotype' models were developed specifically for low-income settings where genotyping is unaffordable. Finally a set of global models that use a genotype in their predictions were developed.
The global and local 'no-genotype' models predicted responses to HIV treatment for South African patients with a similar level of accuracy (around 80%) but the global models were significantly better at identifying alternative combinations of drugs that were predicted to work for South African patients who failed the new treatment given in the clinic.
The global genotype models were marginally but not statistically significantly more accurate than the no-genotype models and all three sets of models were substantially more accurate predictors of treatment response than the genotype test itself (55-58% accuracy).
"The performance of the global models that do not require a genotype was very encouraging and provides further evidence that this could be a very helpful approach for selecting effective therapy in resource-constrained settings, such as South Africa," commented Andrew Revell, Executive Director of the RDI and lead author on the paper.
The paper concludes "the global models have the potential to reduce virological failure and improve patient outcomes in all parts of the world, with particular utility in resource-limited settings. The models can provide clinicians with a practical tool to support optimized treatment decision-making in the absence of resistance tests and where expertise may be lacking in the context of a public health approach to antiretroviral roll-out and management."
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 https://www.hivrdi.org/treps/
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 10 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 100,000 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.
The paper
An update to the HIV-TRePS system: the development and evaluation of new global and local computational models to predict HIV treatment outcomes, with or without a genotype
Andrew D. Revell; Dechao Wang; Robin Wood; Carl Morrow; Hugo Tempelman; Raph L. Hamers; Peter Reiss; Ard I. van Sighem; Mark Nelson; Julio S. G. Montaner; H. Clifford Lane; Brendan A. Larder;
Journal of Antimicrobial Chemotherapy 2016; doi: 10.1093/jac/dkw217
Available at: http://jac.oxfordjournals.org/content/early/2016/06/20/jac.dkw217.full.pdf+html
For further information contact:
Andrew Revell (Executive Director, RDI) on +44 207 226 7314, +44 7967 126498 (mobile) or andrewrevell@hivrdi.org or visit : http://www.hivrdi.org
Date published: 23rd June 2016