MOTIVATION: In predicting HIV therapy outcomes, a critical clinical question is whether using historical information can enhance predictive capabilities compared with current or latest available data analysis. This study analyses whether historical knowledge, which includes viral mutations detected in all genotypic tests before therapy, their temporal occurrence, and concomitant viral load measurements, can bring improvements. We introduce a method to weigh mutations, considering the previously enumerated factors and the reference mutation-drug Stanford resistance tables. We compare a model encompassing history (H) with one not using this information (NH). RESULTS: The H-model demonstrates superior discriminative ability, with a higher ROC-AUC score (76.34%) than the NH-model (74.98%). Wilcoxon test results confirm significant improvement of predictive accuracy for treatment outcomes through incorporating historical information. The increased performance of the H-model might be attributed to its consideration of latent HIV reservoirs, probably obtained when leveraging historical information. The findings emphasize the importance of temporal dynamics in acquiring mutations. However, our result also shows that prediction accuracy remains relatively high even when no historical information is available. AVAILABILITY AND IMPLEMENTATION: This analysis was conducted using the Euresist Integrated DataBase (EIDB). For further validation, we encourage reproducing this study with the latest release of the EIDB, which can be accessed upon request through the Euresist Network.

Combined antiretroviral therapies can successfully suppress HIV in the serum and bring its viral load below detection rate. However, drug resistance remains a major challenge. As resistance patterns vary between patients, therapy personalization is required. Automatic systems for therapy personalization exist and were shown to better predict therapy outcome than HIV experts in some settings. However, these systems focus only on selecting the therapy most likely to suppress the virus for several weeks, a choice that may be suboptimal over the longer term due to evolution of drug resistance. We present a novel generative model for HIV drug resistance evolution. This model is based on factorial HMMs, applying a novel collapsed Gibbs Sampling algorithm for approximate learning. Using the suggested model, we obtain better therapy outcome predictions than existing methods and recommend therapies that may be more effective in the long term. We demonstrate our results using simulated data and using real data from the EuResist dataset.

MOTIVATION: Clinicians, health officials and researchers are interested in the epidemic spread of pathogens in both space and time to support the optimization of intervention measures and public health policies. Large sequence databases of virus sequences provide an interesting opportunity to study this spread through phylogenetic analysis. To infer knowledge from large phylogenetic trees, potentially encompassing tens of thousands of virus strains, an efficient method for data exploration is required. The clades that are visited during this exploration should be annotated with strain characteristics (e.g. transmission risk group, tropism, drug resistance profile) and their geographic context. RESULTS: PhyloGeoTool implements a visual method to explore large phylogenetic trees and to depict characteristics of strains and clades, including their geographic context, in an interactive way. PhyloGeoTool also provides the possibility to position new virus strains relative to the existing phylogenetic tree, allowing users to gain insight in the placement of such new strains without the need to perform a de novo reconstruction of the phylogeny. AVAILABILITY AND IMPLEMENTATION: https://github.com/rega-cev/phylogeotool (Freely available: open source software project). CONTACT: [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Several mathematical models have been investigated for the description of viral dynamics in the human body: HIV-1 infection is a particular and interesting scenario, because the virus attacks cells of the immune system that have a role in the antibody production and its high mutation rate permits to escape both the immune response and, in some cases, the drug pressure. The viral genetic evolution is intrinsically a stochastic process, eventually driven by the drug pressure, dependent on the drug combinations and concentration: in this article the viral genotypic drug resistance onset is the main focus addressed. The theoretical basis is the modelling of HIV-1 population dynamics as a predator-prey system of differential equations with a time-dependent therapy efficacy term, while the viral genome mutation evolution follows a Poisson distribution. The instant probabilities of drug resistance are estimated by means of functions trained from in vitro phenotypes, with a roulette-wheel-based mechanisms of resistant selection. Simulations have been designed for treatments made of one and two drugs as well as for combination antiretroviral therapies. The effect of limited adherence to therapy was also analyzed. Sequential treatment change episodes were also exploited with the aim to evaluate optimal synoptic treatment scenarios. RESULTS: The stochastic predator-prey modelling usefully predicted long-term virologic outcomes of evolved HIV-1 strains for selected antiretroviral therapy combinations. For a set of widely used combination therapies, results were consistent with findings reported in literature and with estimates coming from analysis on a large retrospective data base (EuResist).

MOTIVATION: Optimizing HIV therapies is crucial since the virus rapidly develops mutations to evade drug pressure. Recent studies have shown that genotypic information might not be sufficient for the design of therapies and that other clinical and demographical factors may play a role in therapy failure. This study is designed to assess the improvement in prediction achieved when such information is taken into account. We use these factors to generate a prediction engine using a variety of machine learning methods and to determine which clinical conditions are most misleading in terms of predicting the outcome of a therapy. RESULTS: Three different machine learning techniques were used: generative-discriminative method, regression with derived evolutionary features, and regression with a mixture of effects. All three methods had similar performances with an area under the receiver operating characteristic curve (AUC) of 0.77. A set of three similar engines limited to genotypic information only achieved an AUC of 0.75. A straightforward combination of the three engines consistently improves the prediction, with significantly better prediction when the full set of features is employed. The combined engine improves on predictions obtained from an online state-of-the-art resistance interpretation system. Moreover, engines tend to disagree more on the outcome of failure therapies than regarding successful ones. Careful analysis of the differences between the engines revealed those mutations and drugs most closely associated with uncertainty of the therapy outcome. AVAILABILITY: The combined prediction engine will be available from July 2008, see http://engine.euresist.org.