HIV-PDI: A protein drug interaction resource for structural analyses of HIV drug resistance: 2. Examples of use and proof-of-concept
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Ghemtio, L., Souchet, M., Djikeng, A., Keminse, L., Kelbert, P., Ritchie, D., Maigret, B. and Ouwe-Missi-Oukem-Boyer, O. 2011. HIV-PDI: A protein drug interaction resource for structural analyses of HIV drug resistance: 2. Examples of use and proof-of-concept. Journal of Health and Medical Informatics 2 (1): 1-12.
Permanent link to cite or share this item: http://hdl.handle.net/10568/68349
The HIV-PDI resource was designed and implemented to address the problems of drug resistance with a central focus on the 3D structure of the target-drug interaction. Clinical and biological data, structural and physico-chemical information and 3D interaction data concerning the targets (HIV protease) and the drugs (ARVs) were meticulously integrated and combined with tools dedicated to study HIV mutations and their consequences on the efficacy of drugs. Here, the capabilities of the HIV-PDI resource are demonstrated for several different scenarios ranging from retrieving information associated with patients to analyzing structural data relating cognate proteins and ligands. HIV-PDI allows such diverse data to be correlated, especially data linking antiretroviral drug (ARV) resistance to a given treatment with changes in three-dimensional interactions between a drug molecule and the mutated protease. Our work is based on the assumption that ARV resistance results from a loss of affinity between the mutated HIV protease and a drug molecule due to subtle changes in the nature of the protein-ligand interaction. Therefore, a set of patients whose resistance to first line treatment was corrected by a second line treatment was selected from the HIV-PDI database for detailed study, and several queries regarding these patients are processed via its graphical user interface. Considering the protease mutations found in the selected set of patients, our retrospective analysis was able to establish in most cases that the first line treatment was not suitable, and it predicted a second line treatment which agreed perfectly with the clincian’s prescription. The present study demonstrates the capabilities of HIV-PDI. We anticipate that this decision support tool will help clinicians and researchers find suitable HIV treatments for individual patients. The HIVPDI database is thereby useful as a system of data collection allowing interpretation on the basis of all available information, thus helping in possible decision-makings.
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