Resistance Database Initiative

HIV Resistance Response Database (RDI) is a non -profit-making organization established in 2002 with the mission of improving the clinical management of HIV infection through the application of bioinformatics to HIV drug resistance and treatment outcome data. The RDI has the following specific goals:

  1. To be an independent repository of HIV resistance and treatment outcome data
  2. To use bioinformatics to explore the relationships between resistance, other clinical and laboratory factors, and HIV treatment outcome
  3. To develop and make available a system of preventive treatment, as an aid to optimizing and individualizing the clinical management of HIV infection

The RDI consists of a small executive group based in the UK, an international advisory group of leading HIV / AIDS scientists and clinicians, and an extensive global network of collaborators and data contributors.

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 25 HIV ‘ antiretroviral ‘ drugs that have been approved for the treatment of HIV infection, from six different classes, based on the point in the HIV.

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 suppression of the virus to very low, undetectable Ideally, 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, During drug therapy, low-level viral replication occurs, especially when a patient has a dose. HIV Makes errors in copying genetic material icts and, if a mutation Makes the virus resistant to one or more of the drugs, it May begin to replicate more successfully in the presence of That Undermine drug and the effect of the treatment. If this happens then the treatment needs to be changed to re-establish control over the virus.

In this article, we describe the patient’s health and well-being. The kind of test in common use is the MOST genotype test, qui Detects mutations in the viral genetic code . This information is then typically interpreted using rules equating individual mutations with resistance against individual drugs. However, there are a number of factors that may affect the accuracy of the results.

RDI Overview

The RDI was established in 2002 to pioneer a new approach: to develop computational models using the genotype and a wide range of other clinically relevant HAART all over the world and to use these models to predict how an individual Patient will respond to different combinations of drugs. The RDI’s goal was to make a free treatment-response prediction tool over the Internet.

Key to the success of this approach is the collection of large quantities of data with which the models and predictions. In order to achieve this, the RDI is an integral part of the duplication of effort and competition.

As of October 2013, the RDI has collected data from approximately 110,000 patients from dozens of clinics in more than 30 countries. It is probably the largest database of its kind in the world. The data includes demographic information for the patient, and multiple determinations of the amount of virus in the patient’s bloodstream, CD4 cells counts (a white blood cell critical to the function of the immune system That HIV targets and destroys), genetic code of the patient Virus, and details of the drugs that have been used to treat the patient.

The RDI has used this data to conduct the most accurate and accurate results. This research involved the development and comparison of different computational modeling methods including artificial neural networks , vector machine support , random forests and logistical regression. [1]

The predictions of the RDI’s models have historically correlated well with the actual changes in viruses load of patients in the clinic, typically achieving a co-efficient correlation of 0.8 or more. [2]

HIV-TRePS

In October 2010, the RDI made its experimental HIV testing treatments in two multinational studies, Prevention System, HIV-TRePS available over the Internet. In January 2011, two clinical studies were published indicating that the HIV-TRePS system could lead to clinical and economic benefits. [3] The studies, conducted by expert HIV physicians in the USA, Canada and Italy, which were predicted to result in better virological responses, suggesting That used the system could potentially improve patient outcomes and reduce the overall number of drugs used.

[2] [1] [1] [1] [1] Anatomy and Methods of Pregnancy,

Genotyping, the RDI has developed models that have a predicted effect on the genotype. [5] In July 2011, the RDI made these models available as part of the HIV-TRePS system. This version is especially suitable for use in situations where genotyping is often not routinely available. The most recent of these models, trained with the largest dataset so far, achieved 80% accuracy, which is comparable to models that use a genotype in their predictions and more accurate than genotyping with rules-based interpretation itself. [6] [7]

HIV-TRePS is now used in 70 countries as a tool to predict virological response to treatment and avoid treatment failure.

The system has been expanded to enable physicians to include their local drug costs in the modeling. A recent study of data from an Indian cohort demonstrated that the system was capable of identifying the combinations of three locally available drugs with a higher probability of successive regimen prescribed in the clinic, including those cases where the treatment used in the clinic failed. Moreover, in all these cases some of the alternatives were less costly than the regimen used in the clinic, suggesting that the system could not only help avoid treatment failure but also reduce costs. [8]

RDI Staff

RDI Executive

  • Dr. Brendan Larder – Scientific Chair
  • Dr. Andrew Revell – Executive Director
  • Dr Dechao Wang – Director Bioinformatics
  • Daniel Coe – Director of Software Development

International Advisory Group

  • Dr. Julio Montaner (BC Center for Excellence in HIV / AIDS, Vancouver , Canada)
  • Dr. Carlo Torti (University of Brescia , Italy)
  • Dr. John Baxter ( Cooper University Hospital , Camden, NJ, United States)
  • Dr. Sean Emery (National Center for HIV Epidemiology and Clinical Research, Sydney , Australia)
  • Dr. Jose Gatell (Hospital Clinic of Barcelona , Spain)
  • Dr. Brian Gazzard ( Chelsea and Westminster Hospital , London , United Kingdom)
  • Dr. Anna-Maria Geretti ( Royal Free Hospital , London , United Kingdom)
  • Dr. Richard Harrigan (BC Center for Excellence in HIV / AIDS, Vancouver , Canada)

RDI data and study group

Cohorts: Peter Reiss and Ard van Sighem (ATHENA, the Netherlands); Julio Montaner and Richard Harrigan (BC Center for Excellence in HIV & AIDS, Canada); Tobias Rinke of Wit, Raph Hamers and Kim Sigaloff (PASER-M cohort, The Netherlands); Brian Agan, Vincent Marconi and Scott Wegner (US Department of Defense); Wataru Sugiura (National Institute of Health, Japan); Maurizio Zazzi (MASTER, Italy); Adrian Streinu-Cercel National Institute of Infectious Diseases Prof.Dr. Matei Balş, Bucharest, Romania; Gerardo Alvarez-Uria (VFHCS, India). Clinics: Jose Gatell and Elisa Lazzari (University Hospital, Barcelona, ​​Spain); Brian Gazzard, Mark Nelson, Anton Pozniak and Sundhiya Mandalia (Chelsea and Westminster Hospital, London, UK); Lidia Ruiz and Bonaventura Clotet (Fundacion Irsi Caixa, Badelona, ​​Spain); Schlomo Staszewski (Hospital of the Johann Wolfgang Goethe-University, Frankfurt, Germany); Carlo Torti (University of Brescia); Cliff Lane and Julie Metcalf (National Institutes of Health Clinic, Rockville, USA); Maria-Jesus Perez-Elias (Instituto Ramón y Cajal of Investigación Sanitaria, Madrid, Spain); Andrew Carr, Richard Norris and Karl Hesse (Immunology B Ambulatory Care Service, St. Vincent’s Hospital, Sydney, NSW, Australia); Dr. Emanuel Vlahakis (Taylor’s Square Private Clinic, Darlinghurst, NSW, Australia); Hugo Tempelman and Roos Barth (Ndlovu Care Group, Elandsdoorn, South Africa), Carl Morrow and Robin Wood (Desmond Tutu HIV Center, University of Cape Town, South Africa); Luminita Ene (“Dr. Victor Babes” Hospital for Infectious and Tropical Diseases, Bucharest, Romania); Gordana Dragovic (University of Belgrade, Belgrade, Serbia). Clinical Trials: Sean Emery and David Cooper (CREST); Carlo Torti (GenPherex); John Baxter (GART, MDR); Laura Monno and Carlo Torti (PhenGen); Jose Gatell and Bonventura Clotet (HAVANA); Gaston Picchio and Marie-Pierre deBethune (DUET 1 & 2 and POWER 3); Maria-Jesus Perez-Elias (RealVirfen).

References

  1. Jump up^ Wang, Dechao (2009). “A comparison of three computational models for the prediction of virological response to combination HIV therapy”. Artificial Intelligence in Medicine . 47 (1): 6374. doi : 10.1016 / j.artmed.2009.05.002 .
  2. Jump up^ Larder, Brendan (2007). “The development of artificial neural networks to predict virological response to combination HIV therapy”. Antiviral Therapy . 12 (12): 15-24.
  3. Jump up^ Larder, Brendan (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): 29-36. Doi : 10.1089 / apc.2010.0254 .
  4. Jump up^ Revell, Andrew (2011). “The development of an expert system to predict virological response to HIV therapy as part of an online treatment support tool.” AIDS . 25 (15): 1855-1863. Doi : 10.1097 / QAD.0b013e328349a9c2 .
  5. Jump up^ Revell, Andrew (2010). “Modeling 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 .
  6. Jump up^ Revell, Andrew; Wang, D; Wood R; et al. (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. Doi : 10.1093 / jac / dkt041 .
  7. Jump up^ Larder, Brendan; Revell AD; Hamers R; Tempelman H; et al. (2013). “Accurate prediction of response to HIV therapy without a genotype a potential tool for therapy optimization in resource-limited settings”. Antiviral Therapy .
  8. Jump up^ Revell, Andrew; Alvarez-Uria G; Wang D; Pozniak A; Montaner JSG; Lane HC; Larder BA; et al. (2013). “Potential Impact of 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 . Doi : 10.1155 / 2013/579741 .

Leave a Comment

Your email address will not be published. Required fields are marked *