HIV Resistance Response Database Initiative

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HIV Resistance Response Database Initiative (RDI) was formed in 2002 to use artificial intelligence (AI) to predict how patients will respond to HIV drugs using data from more 250,000 patients from around 50 countries around the world.

The RDI used its models to power its HIV Treatment Response Prediction System (HIV-TRePS). Launched in 2010, this free online tool enabled healthcare professionals to upload their patient’s data and obtain highly accurate predictions of how they would respond to different combinations of the 30 or more drugs available. The tool enabled physicians to individualize their patients’ treatment, using these predictions based on more than a million patient-years of treatment experience.

HIV-TRePS was possibly the first ever AI-based system for medical decision-making to be developed, successfully tested, and used in clinical practice. It has since been used by thousands of healthcare professionals to optimise the treatment of tens of thousands of patients.

Since the RDI’s inception the treatment of HIV infection has progressed enormously, with more effective and better tolerated drugs available in ever more convenient combination formulations. In most countries HIV is now considered a chronic, manageable condition. Moreover, the success of the drugs in reducing the amount of virus is substantially reducing the onward transmission of the virus and cases of new infections are falling in many settings.

This improvement in HIV treatment means the need for sophisticated AI to support HIV treatment decisions has significantly reduced. In response, the RDI ceased development of further models and, in March 2024, withdrew its HIV-TRePS system.

Background

Human immunodeficiency virus (HIV) is the virus that causes acquired immunodeficiency syndrome (AIDS), a condition in which the immune system begins to fail, leading to life-threatening opportunistic infections.

There are approximately 30 HIV antiretroviral drugs that have been approved for the treatment of HIV infection, from six different classes, based on the point in the HIV life-cycle at which they act.

They are used in combination; typically 3 or more drugs from 2 or more different classes, a form of therapy known as highly active antiretroviral therapy or HAART. The aim of therapy is to suppress the virus to very low, ideally undetectable, levels in the blood. This prevents the virus from depleting the immune cells that it preferentially attacks (CD4 cells) and prevents or delays illness and death.

Despite the expanding availability of these drugs and the impact of their use, treatments continue to fail, often involving to the development of resistance. During drug therapy, low-level virus replication may still occur, particularly when a patient misses a dose. HIV makes errors in copying its genetic material and, if a mutation makes the virus resistant to one or more of the drugs in the patient's treatment, it may begin to replicate more successfully in the presence of that drug and undermine the effect of the treatment. If this happens, the treatment needs to be changed to re-establish control over the virus.

RDI's Approach

The RDI’s approach was to use artificial intelligence (including neural network and random forest models), trained with data from hundreds of thousands of patients, treated with different drugs in a variety of clinical settings all over the world, to predict how an individual patient will respond to any new combination of HIV drugs. The models were tested with independent data sets and consistently achieved accuracy of approximately 80%.[1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15]

References

  1. ^ Revell, A. D., Wang, D., Perez-Elias, M.-J., Wood, R., Cogill, D., Tempelman, H., et al. (18 June 2021). "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". Journal_of_Acquired_Immune_Deficiency_Syndromes. 76 (7). Oxford Academic: 1898–1906. doi:10.1093/JAC/DKAB078. PMC 8212763. PMID 33792714.
  2. ^ Revell, A. D., Wang, D., Perez-Elias, M.-J., Wood, R., Tempelman, H., Clotet, B., et al. (1 June 2019). "Predicting Virological Response to HIV Treatment Over Time: A Tool for Settings With Different Definitions of Virological Response". Journal_of_Acquired_Immune_Deficiency_Syndromes. 81 (2). Lippincott Williams and Wilkins: 207–215. doi:10.1097/QAI.0000000000001989. PMID 30865186.
  3. ^ Revell, A. D., Wang, D., Perez-Elias, M.-J., Wood, R., Cogill, D., Tempelman, H., et al. (1 August 2018). "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". Journal of Antimicrobial Chemotherapy. 73 (8). Oxford University Press: 2186–2196. doi:10.1093/JAC/DKY179. PMC 6054173. PMID 29889249.
  4. ^ Revell, A. D., Khabo, P., Ledwaba, L., Emery, S., Wang, D., Wood, R., Morrow, C., Tempelman, H., Hamers, R. L., Reiss, P., van Sighem, A., Pozniak, A., Montaner, J. S. G., Lane, H. C., Larder, B. (30 June 2016). "Computational models as predictors of HIV treatment outcomes for the Phidisa cohort in South Africa". Southern African Journal of HIV Medicine. 17 (1). AOSIS: 450. doi:10.4102/SAJHIVMED.V17I1.450. PMC 5843195. PMID 29568609.
  5. ^ Revell, A. D., Wang, D., Wood, R., Morrow, C. A., Tempelman, H., Hamers, R. L., Reiss, P., van Sighem, A., Nelson, M., Montaner, J. S. G., Lane, H. C., Larder, B. (1 October 2016). "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". Journal of Antimicrobial Chemotherapy. 71 (10). Oxford University Press: 2928–2937. doi:10.1093/JAC/DKW217. PMC 5031919. PMID 27330070.
  6. ^ Revell, A. D., Boyd, M. A., Wang, D., Emery, S., Gazzard, B., Reiss, P., van Sighem, A. I., Montaner, J. S. G., Lane, H. C., Larder, B. A. (15 April 2014). "A comparison of computational models with and without genotyping for prediction of response to second-line HIV therapy". HIV_Medicine. 15 (7). HIV Med: 442–448. doi:10.1111/HIV.12156. PMID 24735474.
  7. ^ Revell, A. D., Wang, D., Wood, R., et al. (1 April 2014). "An update to the HIV-TRePS system: the development of new computational models that do not require a genotype to predict HIV treatment outcomes". Journal of Antimicrobial Chemotherapy. 69 (4). Oxford University Press: 1104–1110. doi:10.1093/JAC/DKT447. PMC 3956369. PMID 24275116.
  8. ^ Revell, A. D., Alvarez-Uria, G., Wang, D., Pozniak, A., Montaner, J. S. G., Lane, H. C., Larder, B. A., et al., et al. (2013). "Potential Impact of a Free Online HIV Treatment Response Prediction System for Reducing Virological Failures and Drug Costs after Antiretroviral Therapy Failure in a Resource-Limited Setting". BioMed_Research_International. 2013: 1–6. doi:10.1155/2013/579741. PMC 3794568. PMID 24175292.
  9. ^ Revell, A. D., Wang, D., Wood, R., Morrow, C., Tempelman, H., Hamers, R. L., et al. (1 June 2013). "Computational models can predict response to HIV therapy without a genotype and may reduce treatment failure in different resource-limited settings". Journal of Antimicrobial Chemotherapy. 68 (6). Oxford University Press: 1406–1414. doi:10.1093/JAC/DKT041. PMC 3654223. PMID 23485767.
  10. ^ Revell, A. D., Ene, L., Duiculescu, D., Wang, D., Youle, M., Pozniak, A., et al. (1 March 2012). "The use of computational models to predict response to HIV therapy for clinical cases in Romania". GERMS. 2 (1). European Academy of HIV/AIDS and Infectious Diseases: 6–11. doi:10.11599/GERMS.2012.1007. PMC 3882835. PMID 24432257.
  11. ^ Larder, B., Revell, A. D., Mican, J. M., Agan, B. K., Harris, M., Torti, C., et al. (1 January 2011). "Clinical evaluation of the potential utility of computational modeling as an HIV treatment selection tool by physicians with considerable HIV experience". AIDS_Patient_Care_and_STDs. 25 (1). Mary Ann Liebert, Inc. 140 Huguenot Street, 3rd Floor New Rochelle, NY 10801 USA: 29–36. doi:10.1089/APC.2010.0254. PMC 3030912. PMID 21214377.
  12. ^ Revell, A. D., Wang, D., Boyd, M. A., Emery, S., Pozniak, A., de Wolf, F., et al. (1 September 2011). "The development of an expert system to predict virological response to HIV therapy as part of an online treatment support tool". AIDS_(journal). 25 (15). AIDS: 1855–1863. doi:10.1097/QAD.0B013E328349A9C2. PMID 21785323.
  13. ^ Revell, A. D., Wang, D., Harrigan, R., Hamers, R. L., Wensing A. M. J., de Wolf, F., et al. (2010). "Modelling response to HIV therapy without a genotype: an argument for viral load monitoring in resource-limited settings". Journal of Antimicrobial Chemotherapy. 65 (4): 605–607. doi:10.1093/jac/dkq032. PMC 2837552. PMID 20154024.
  14. ^ Wang, D., Larder B. A., Revell, A. D., Montaner J, Harrigan, R., de Wolf, F., et al. (2009). "A comparison of three computational modelling methods for the prediction of virological response to combination HIV therapy". Artificial Intelligence in Medicine. 47 (1): 63–74. doi:10.1016/j.artmed.2009.05.002. PMID 19524413.
  15. ^ Larder B. A., Wang, D., Revell, A. D., Montaner J, Harrigan, R., Hamers, R. L., de Wolf, F., et al. (2007). "The development of artificial neural networks to predict virological response to combination HIV therapy". Antiviral Therapy. 12 (12): 15–24. doi:10.1177/135965350701200112. PMID 17503743. S2CID 6947981.

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