Date Published: May 3, 2012
Publisher: BioMed Central
Author(s): Soo-Yon Rhee, Jose Luis Blanco, Tommy F Liu, Iñaki Pere, Rolf Kaiser, Maurizio Zazzi, Francesca Incardona, William Towner, Josep Maria Gatell, Andrea De Luca, W Jeffrey Fessel, Robert W Shafer.
To identify the determinants of successful antiretroviral (ARV) therapy, researchers study the virological responses to treatment-change episodes (TCEs) accompanied by baseline plasma HIV-1 RNA levels, CD4+ T lymphocyte counts, and genotypic resistance data. Such studies, however, often differ in their inclusion and virological response criteria making direct comparisons of study results problematic. Moreover, the absence of a standard method for representing the data comprising a TCE makes it difficult to apply uniform criteria in the analysis of published studies of TCEs.
To facilitate data sharing for TCE analyses, we developed an XML (Extensible Markup Language) Schema that represents the temporal relationship between plasma HIV-1 RNA levels, CD4 counts and genotypic drug resistance data surrounding an ARV treatment change. To demonstrate the adaptability of the TCE XML Schema to different clinical environments, we collaborate with four clinics to create a public repository of about 1,500 TCEs. Despite the nascent state of this TCE XML Repository, we were able to perform an analysis that generated a novel hypothesis pertaining to the optimal use of second-line therapies in resource-limited settings. We also developed an online program (TCE Finder) for searching the TCE XML Repository and another program (TCE Viewer) for generating a graphical depiction of a TCE from a TCE XML Schema document.
The TCE Suite of applications – the XML Schema, Viewer, Finder, and Repository – addresses several major needs in the analysis of the predictors of virological response to ARV therapy. The TCE XML Schema and Viewer facilitate sharing data comprising a TCE. The TCE Repository, the only publicly available collection of TCEs, and the TCE Finder can be used for testing the predictive value of genotypic resistance interpretation systems and potentially for generating and testing novel hypotheses pertaining to the optimal use of salvage ARV therapy.
To identify determinants of successful antiretroviral (ARV) therapy in HIV-1-infected patients for whom a previous ARV treatment regimen has failed, researchers study clinical data associated with treatment-change episodes (TCEs) . These studies characterize the relationship between past ARV treatments, plasma HIV-1 RNA levels, HIV-1 drug resistance genotype results, and the subsequent virological response to a salvage therapy regimen [2-8]. Such studies, however, often differ in their inclusion criteria, salvage therapy requirements, and definition of virological response.
The Department of Human Health Services (DHHS) and the WHO  have guidelines on which ARV regimens to use for initial and second-line therapy of HIV-1-infected patients. However, many clinical scenarios are not addressed by these guidelines including the management of (i) patients who began ARV therapy with suboptimal regimens – a problem particularly common in the U.S., Europe in the past, and many middle income countries where previously available ARVs were considerably less potent and drugs were used as they became available rather than as part of a national treatment program, (ii) patients with transmitted resistance, and (iii) heavily treated patients and patients whose viruses have complex patterns of drug-resistance mutations.
The TCE Suite of applications – the XML Schema, Viewer, Finder, and Repository – addresses several major needs in the analysis of predictors of virological response to ARV therapy. The TCE XML Schema facilitates data sharing for generating and testing new hypotheses. The TCE Viewer helps users validate the temporal relationship between different data elements and it can be a useful teaching tool. The TCE Finder is an application designed for researchers who do not want to download the entire TCE repository but who would rather examine the solely the clinical data of patients sharing similar ARV treatment and genotypic resistance characteristics. The TCE Repository is the largest collection of publicly available TCEs. It is already useful for comparing the predictive value of genotypic resistance interpretation systems. As it increases in size it will become an increasingly useful resource for hypothesis generation and knowledge discovery.
SYR and RWS designed the study and wrote the manuscript. JLB, RK, MZ, WT, JG, ADL and WJF collected and annotated the clinical data. SYR, TFL, IP and FI created and implemented procedures for transferring data from clinical databases to the TCE XML Schema documents. MZ, ADL and WJF also contributed to drafting the manuscript. All authors read and approved the final manuscript.
The authors do not have a commercial or other association that might pose a competing interest.