Bioinformatical Assistance of Selecting Anti-HIV Therapies: Where Do We Stand?

Lengauer, Thomas

doi:10.1159/000332000

Cited by 8 publications

(4 citation statements)

References 36 publications

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“…Rules-based systems or systems that predict the phenotypic resistance factor for each drug, like geno2pheno [resistance] (virtual phenotype), are most frequently used. Also discussed by Lengauer [33] in this issue, one speculated reason for this ambiguous acceptance is that the mechanisms of how the bioinformatic systems choose the optimal treatment are sometimes not comprehensible. In many cases this could be prevented by an optimized presentation of the results.…”

Section: Discussionmentioning

confidence: 99%

HIV-GRADE: A Publicly Available, Rules-Based Drug Resistance Interpretation Algorithm Integrating Bioinformatic Knowledge

Obermeier¹,

Pironti

Berg³

et al. 2012

Intervirology

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tion system (geno2pheno [resistance] ). HIV-GRADE presents the option of seeing the rules and results of other drug resistance algorithms for a given sequence simultaneously. Methods: The HIV-GRADE rules-based interpretation system was developed by the members of the HIV-GRADE registered society. For continuous updates, this expert committee meets twice a year to analyze data from various sources. Besides data from clinical studies and the centers involved, published correlations for mutations with drug resistance and genotype-phenotype correlation data information from the bioinformatic models of geno2pheno are used to generate the rules for the HIV-GRADE interpretation system. A freely available online tool was developed on the basis of the Stanford HIVdb rules interpretation tool using the algorithm specification interface. Clinical validation of the interpretation system was performed on the data of treatment episodes consisting of sequence information, antiretroviral treatment and viral load, before and 3 months after treatment change. Data were analyzed using multiple linear regression. Results: As the developed online tool allows easy Key Words HIV-GRADE ؒ Genotypic drug resistance ؒ Clinical validation Abstract Background: Genotypic drug resistance testing provides essential information for guiding treatment in HIV-infected patients. It may either be used for identifying patients with transmitted drug resistance or to clarify reasons for treatment failure and to check for remaining treatment options. While different approaches for the interpretation of HIV sequence information are already available, no other available rules-based systems specifically have looked into the effects of combinations of drugs. HIV-GRADE (Genotypischer Resistenz Algorithmus Deutschland) was planned as a countrywide approach to establish standardized drug resistance interpretation in Germany and also to introduce rules for estimating the influence of mutations on drug combinations. The rules for HIV-GRADE are taken from the literature, clinical follow-up data and from a bioinformatics-driven interpreta- comparison of different drug resistance interpretation systems, coefficients of determination (R 2 ) were compared for the freely available rules-based systems. HIV-GRADE (R 2 = 0.40), Stanford HIVdb (R 2 = 0.40), REGA algorithm (R 2 = 0.36) and ANRS (R 2 = 0.35) had a very similar performance using this multiple linear regression model. Conclusion: The performance of HIV-GRADE is comparable to alternative rulesbased interpretation systems. While there is still room for improvement, HIV-GRADE has been made publicly available to allow access to our approach regarding the interpretation of resistance against single drugs and drug combinations.

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Section: Discussionmentioning

confidence: 99%

HIV-GRADE: A Publicly Available, Rules-Based Drug Resistance Interpretation Algorithm Integrating Bioinformatic Knowledge

Obermeier¹,

Pironti

Berg³

et al. 2012

Intervirology

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“…Therefore, one aim of this study was to explore the perspectives of virtualizing the phenotype vector by providing a computational procedure that estimates the phenotype vector based on the V3 loop sequence of the virus (see also [47]). We developed a method for predicting the phenotype vector that is based on the V3 loop sequence.…”

Section: Discussionmentioning

confidence: 99%

An expanded model of HIV cell entry phenotype based on multi-parameter single-cell data

et al. 2012

Self Cite

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BackgroundEntry of human immunodeficiency virus type 1 (HIV-1) into the host cell involves interactions between the viral envelope glycoproteins (Env) and the cellular receptor CD4 as well as a coreceptor molecule (most importantly CCR5 or CXCR4). Viral preference for a specific coreceptor (tropism) is in particular determined by the third variable loop (V3) of the Env glycoprotein gp120. The approval and use of a coreceptor antagonist for antiretroviral therapy make detailed understanding of tropism and its accurate prediction from patient derived virus isolates essential. The aim of the present study is the development of an extended description of the HIV entry phenotype reflecting its co-dependence on several key determinants as the basis for a more accurate prediction of HIV-1 entry phenotype from genotypic data.ResultsHere, we established a new protocol of quantitation and computational analysis of the dependence of HIV entry efficiency on receptor and coreceptor cell surface levels as well as viral V3 loop sequence and the presence of two prototypic coreceptor antagonists in varying concentrations. Based on data collected at the single-cell level, we constructed regression models of the HIV-1 entry phenotype integrating the measured determinants. We developed a multivariate phenotype descriptor, termed phenotype vector, which facilitates a more detailed characterization of HIV entry phenotypes than currently used binary tropism classifications. For some of the tested virus variants, the multivariant phenotype vector revealed substantial divergences from existing tropism predictions. We also developed methods for computational prediction of the entry phenotypes based on the V3 sequence and performed an extrapolating calculation of the effectiveness of this computational procedure.ConclusionsOur study of the HIV cell entry phenotype and the novel multivariate representation developed here contributes to a more detailed understanding of this phenotype and offers potential for future application in the effective administration of entry inhibitors in antiretroviral therapies.

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“…Recently, this approach has been supplemented with machine learning techniques 70. To influence patient treatment molecular modeling will have to work alongside these and other novel bioinformatics approaches71 and eventually be incorporated into existing clinical tools. A comparison of decision support systems has found that machine learning techniques outperform traditional rule based systems unless additional patient attributes are considered 72.…”

Section: The Future Now: Genotypic Assays and Hivmentioning

confidence: 99%

From base pair to bedside: molecular simulation and the translation of genomics to personalized medicine

Wright

Wan

Shublaq

et al. 2012

WIREs Mechanisms of Disease

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Despite the promises made that genomic sequencing would transform therapy by introducing a new era of personalized medicine, relatively few tangible breakthroughs have been made. This has led to the recognition that complex interactions at multiple spatial, temporal, and organizational levels may often combine to produce disease. Understanding this complexity requires that existing and future models are used and interpreted within a framework that incorporates knowledge derived from investigations at multiple levels of biological function. It also requires a computational infrastructure capable of dealing with the vast quantities of data generated by genomic approaches. In this review, we discuss the use of molecular modeling to generate quantitative and qualitative insights at the smallest scales of the systems biology hierarchy, how it can play an important role in the development of a systems understanding of disease and in the application of such knowledge to help discover new therapies and target existing ones on a personal level.

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Bioinformatical Assistance of Selecting Anti-HIV Therapies: Where Do We Stand?

Abstract: In this opinion statement, we give a critical synopsis of the state-of-the-art of bioinformatic HIV resistance analysis and point out what we consider to be challenges and perspectives.

Cited by 8 publications

References 36 publications

HIV-GRADE: A Publicly Available, Rules-Based Drug Resistance Interpretation Algorithm Integrating Bioinformatic Knowledge

HIV-GRADE: A Publicly Available, Rules-Based Drug Resistance Interpretation Algorithm Integrating Bioinformatic Knowledge

An expanded model of HIV cell entry phenotype based on multi-parameter single-cell data

From base pair to bedside: molecular simulation and the translation of genomics to personalized medicine

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