Publications by authors named "Mattia C F Prosperi"

28 Publications

  • Page 1 of 1

Brain tissue transcriptomic analysis of SIV-infected macaques identifies several altered metabolic pathways linked to neuropathogenesis and poly (ADP-ribose) polymerases (PARPs) as potential therapeutic targets.

J Neurovirol 2021 Feb 6;27(1):101-115. Epub 2021 Jan 6.

Department of Pathology, Immunology, and Laboratory Medicine, College of Medicine, University of Florida, Gainesville, FL, USA.

Despite improvements in antiretroviral therapy, human immunodeficiency virus type 1 (HIV-1)-associated neurocognitive disorders (HAND) remain prevalent in subjects undergoing therapy. HAND significantly affects individuals' quality of life, as well as adherence to therapy, and, despite the increasing understanding of neuropathogenesis, no definitive diagnostic or prognostic marker has been identified. We investigated transcriptomic profiles in frontal cortex tissues of Simian immunodeficiency virus (SIV)-infected Rhesus macaques sacrificed at different stages of infection. Gene expression was compared among SIV-infected animals (n = 11), with or without CD8+ lymphocyte depletion, based on detectable (n = 6) or non-detectable (n = 5) presence of the virus in frontal cortex tissues. Significant enrichment in activation of monocyte and macrophage cellular pathways was found in animals with detectable brain infection, independently from CD8+ lymphocyte depletion. In addition, transcripts of four poly (ADP-ribose) polymerases (PARPs) were up-regulated in the frontal cortex, which was confirmed by real-time polymerase chain reaction. Our results shed light on involvement of PARPs in SIV infection of the brain and their role in SIV-associated neurodegenerative processes. Inhibition of PARPs may provide an effective novel therapeutic target for HIV-related neuropathology.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1007/s13365-020-00927-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7786889PMC
February 2021

Cost-Effectiveness of a Statewide Pre-Exposure Prophylaxis Program for Gay, Bisexual, and Other Men Who Have Sex with Men.

R I Med J (2013) 2019 Nov 1;102(9):36-39. Epub 2019 Nov 1.

Department of Health Services, Policy, and Practice, School of Public Health, Brown University, Providence, RI.

Pre-exposure prophylaxis (PrEP) is an effective tool for preventing HIV infection among men who have sex with men (MSM), but its cost-effectiveness has varied across settings. Using an agent-based model, we projected the cost-effectiveness of a statewide PrEP program for MSM in Rhode Island over the next decade. In the absence of PrEP, the model predicted an average of 830 new HIV infections over ten years. Scaling up the existing PrEP program to cover 15% of MSM with ten or more partners each year could reduce the number of new HIV infections by 33.1% at a cost of $184,234 per quality-adjusted life-year (QALY) gained. Expanded PrEP use among MSM at high risk for HIV infection has the potential to prevent a large number of new HIV infections but the high drug-related costs may limit the cost-effectiveness of this intervention.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7461153PMC
November 2019

Emergence of recombinant Mayaro virus strains from the Amazon basin.

Sci Rep 2017 08 18;7(1):8718. Epub 2017 Aug 18.

Emerging Pathogens Institute, University of Florida, Gainesville, FL, USA.

Mayaro virus (MAYV), causative agent of Mayaro Fever, is an arbovirus transmitted by Haemagogus mosquitoes. Despite recent attention due to the identification of several cases in South and Central America and the Caribbean, limited information on MAYV evolution and epidemiology exists and represents a barrier to prevention of further spread. We present a thorough spatiotemporal evolutionary study of MAYV full-genome sequences collected over the last sixty years within South America and Haiti, revealing recent recombination events and adaptation to a broad host and vector range, including Aedes mosquito species. We employed a Bayesian phylogeography approach to characterize the emergence of recombinants in Brazil and Haiti and report evidence in favor of the putative role of human mobility in facilitating recombination among MAYV strains from geographically distinct regions. Spatiotemporal characteristics of recombination events and the emergence of this previously neglected virus in Haiti, a known hub for pathogen spread to the Americas, warrants close monitoring of MAYV infection in the immediate future.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1038/s41598-017-07152-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5562835PMC
August 2017

Computer-Aided Optimization of Combined Anti-Retroviral Therapy for HIV: New Drugs, New Drug Targets and Drug Resistance.

Curr HIV Res 2016 ;14(2):101-9

Department of Epidemiology, College of Public Health and Health Professions & College of Medicine, University of Florida, Gainesville, US.

Background: Resistance to antiretroviral drugs is a complex and evolving area with relevant implications in the treatment of human immunodeficiency virus (HIV) infection. Several rules, algorithms and full-fledged computer programs have been developed to assist the HIV specialist in the choice of the best patient-tailored therapy.

Methods: Experts' rules and statistical/machine learning algorithms for interpreting HIV drug resistance, along with their program implementations, were retrieved from PubMed and other on-line resources to be critically reviewed in terms of technical approach, performance, usability, update, and evolution (i.e. inclusion of novel drugs or expansion to other viral agents).

Results: Several drug resistance prediction algorithms for the nucleotide/nucleoside/non-nucleoside reverse transcriptase, protease and integrase inhibitors as well as coreceptor antagonists are currently available, routinely used, and have been validated thoroughly in independent studies. Computer tools that combine single-drug genotypic/phenotypic resistance interpretation and optimize combination antiretroviral therapy have been also developed and implemented as web applications. Most of the systems have been updated timely to incorporate new drugs and few have recently been expanded to meet a similar need in the Hepatitis C area. Prototype systems aiming at predicting virological response from both virus and patient indicators have been recently developed but they are not yet being routinely used.

Conclusion: Computing HIV genotype to predict drug susceptibility in vitro or response to combination antiretroviral therapy in vivo is a continuous and productive research field, translating into successful treatment decision support tools, an essential component of the management of HIV patients.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.2174/1570162x13666151029102254DOI Listing
October 2016

Evolution pathways of IgE responses to grass and mite allergens throughout childhood.

J Allergy Clin Immunol 2015 Dec 8;136(6):1645-1652.e8. Epub 2015 May 8.

Centre for Respiratory Medicine and Allergy, Institute of Inflammation and Repair, University of Manchester & University Hospital of South Manchester, Manchester, United Kingdom; Centre for Health Informatics, Institute of Population Health, University of Manchester, Manchester, United Kingdom. Electronic address:

Background: Little is known about longitudinal patterns of the development of IgE to distinct allergen components.

Objective: We sought to investigate the evolution of IgE responses to allergenic components of timothy grass and dust mite during childhood.

Methods: In a population-based birth cohort (n = 1184) we measured IgE responses to 15 components from timothy grass and dust mite in children with available samples at 3 time points (ages 5, 8, and 11 years; n = 235). We designed a nested, 2-stage latent class analysis to identify cross-sectional sensitization patterns at each follow-up and their longitudinal trajectories. We then ascertained the association of longitudinal trajectories with asthma, rhinitis, eczema, and lung function in children with component data for at least 2 time points (n = 534).

Results: Longitudinal latent class analysis revealed 3 grass sensitization trajectories: (1) no/low sensitization; (2) early onset; and (3) late onset. The early-onset trajectory was associated with asthma and diminished lung function, and the late-onset trajectory was associated with rhinitis. Four longitudinal trajectories emerged for mite: (1) no/low sensitization; (2) group 1 allergens; (3) group 2 allergens; and (3) complete mite sensitization. Children in the complete mite sensitization trajectory had the highest odds ratios (ORs) for asthma (OR, 7.15; 95% CI, 3.80-13.44) and were the only group significantly associated with comorbid asthma, rhinitis, and eczema (OR, 5.91; 95% CI, 2.01-17.37). Among children with wheezing, those in the complete mite sensitization trajectory (but not other longitudinal mite trajectories) had significantly higher risk of severe exacerbations (OR, 3.39; 95% CI, 1.62-6.67).

Conclusions: The nature of developmental longitudinal trajectories of IgE responses differed between grass and mite allergen components, with temporal differences (early vs late onset) dominant in grass and diverging patterns of IgE responses (group 1 allergens, group 2 allergens, or both) in mite. Different longitudinal patterns bear different associations with clinical outcomes, which varied by allergen.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.jaci.2015.03.041DOI Listing
December 2015

Can multiple SNP testing in BRCA2 and BRCA1 female carriers be used to improve risk prediction models in conjunction with clinical assessment?

BMC Med Inform Decis Mak 2014 Oct 1;14:87. Epub 2014 Oct 1.

Institute of Population Health, Centre for Health Informatics, University of Manchester, Manchester, UK.

Background: Several single nucleotide polymorphisms (SNPs) at different loci have been associated with breast cancer susceptibility, accounting for around 10% of the familial component. Recent studies have found direct associations between specific SNPs and breast cancer in BRCA1/2 mutation carriers. Our aim was to determine whether validated susceptibility SNP scores improve the predictive ability of risk models in comparison/conjunction to other clinical/demographic information.

Methods: Female BRCA1/2 carriers were identified from the Manchester genetic database, and included in the study regardless of breast cancer status or age. DNA was extracted from blood samples provided by these women and used for gene and SNP profiling. Estimates of survival were examined with Kaplan-Meier curves. Multivariable Cox proportional hazards models were fit in the separate BRCA datasets and in menopausal stages screening different combinations of clinical/demographic/genetic variables. Nonlinear random survival forests were also fit to identify relevant interactions. Models were compared using Harrell's concordance index (1 - c-index).

Results: 548 female BRCA1 mutation carriers and 523 BRCA2 carriers were identified from the database. Median Kaplan-Meier estimate of survival was 46.0 years (44.9-48.1) for BRCA1 carriers and 48.9 (47.3-50.4) for BRCA2. By fitting Cox models and random survival forests, including both a genetic SNP score and clinical/demographic variables, average 1 - c-index values were 0.221 (st.dev. 0.019) for BRCA1 carriers and 0.215 (st.dev. 0.018) for BRCA2 carriers.

Conclusions: Random survival forests did not yield higher performance compared to Cox proportional hazards. We found improvement in prediction performance when coupling the genetic SNP score with clinical/demographic markers, which warrants further investigation.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1186/1472-6947-14-87DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4197237PMC
October 2014

Spatiotemporal dynamics of simian immunodeficiency virus brain infection in CD8+ lymphocyte-depleted rhesus macaques with neuroAIDS.

J Gen Virol 2014 Dec 9;95(Pt 12):2784-2795. Epub 2014 Sep 9.

Emerging Pathogens Institute, University of Florida, Gainesville, FL, USA.

Despite the success of combined antiretroviral therapy in controlling viral replication in human immunodeficiency virus (HIV)-infected individuals, HIV-associated neurocognitive disorders, commonly referred to as neuroAIDS, remain a frequent and poorly understood complication. Infection of CD8(+) lymphocyte-depleted rhesus macaques with the SIVmac251 viral swarm is a well-established rapid disease model of neuroAIDS that has provided critical insight into HIV-1-associated neurocognitive disorder onset and progression. However, no studies so far have characterized in depth the relationship between intra-host viral evolution and pathogenesis in this model. Simian immunodeficiency virus (SIV) env gp120 sequences were obtained from six infected animals. Sequences were sampled longitudinally from several lymphoid and non-lymphoid tissues, including individual lobes within the brain at necropsy, for four macaques; two animals were sacrificed at 21 days post-infection (p.i.) to evaluate early viral seeding of the brain. Bayesian phylodynamic and phylogeographic analyses of the sequence data were used to ascertain viral population dynamics and gene flow between peripheral and brain tissues, respectively. A steady increase in viral effective population size, with a peak occurring at ~50-80 days p.i., was observed across all longitudinally monitored macaques. Phylogeographic analysis indicated continual viral seeding of the brain from several peripheral tissues throughout infection, with the last migration event before terminal illness occurring in all macaques from cells within the bone marrow. The results strongly supported the role of infected bone marrow cells in HIV/SIV neuropathogenesis. In addition, our work demonstrated the applicability of Bayesian phylogeography to intra-host studies in order to assess the interplay between viral evolution and pathogenesis.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1099/vir.0.070318-0DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4233634PMC
December 2014

Challenges in identifying asthma subgroups using unsupervised statistical learning techniques.

Am J Respir Crit Care Med 2013 Dec;188(11):1303-12

1 Centre for Health Informatics, Institute of Population Health, and.

Rationale: Unsupervised statistical learning techniques, such as exploratory factor analysis (EFA) and hierarchical clustering (HC), have been used to identify asthma phenotypes, with partly consistent results. Some of the inconsistency is caused by the variable selection and demographic and clinical differences among study populations.

Objectives: To investigate the effects of the choice of statistical method and different preparations of data on the clustering results; and to relate these to disease severity.

Methods: Several variants of EFA and HC were applied and compared using various sets of variables and different encodings and transformations within a dataset of 383 children with asthma. Variables included lung function, inflammatory and allergy markers, family history, environmental exposures, and medications. Clusters and original variables were related to asthma severity (logistic regression and Bayesian network analysis).

Measurements And Main Results: EFA identified five components (eigenvalues ≥ 1) explaining 35% of the overall variance. Variations of the HC (as linkage-distance functions) did not affect the cluster inference; however, using different variable encodings and transformations did. The derived clusters predicted asthma severity less than the original variables. Prognostic factors of severity were medication usage, current symptoms, lung function, paternal asthma, body mass index, and age of asthma onset. Bayesian networks indicated conditional dependence among variables.

Conclusions: The use of different unsupervised statistical learning methods and different variable sets and encodings can lead to multiple and inconsistent subgroupings of asthma, not necessarily correlated with severity. The search for asthma phenotypes needs more careful selection of markers, consistent across different study populations, and more cautious interpretation of results from unsupervised learning.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1164/rccm.201304-0694OCDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3919072PMC
December 2013

Challenges in interpreting allergen microarrays in relation to clinical symptoms: a machine learning approach.

Pediatr Allergy Immunol 2014 Feb 16;25(1):71-9. Epub 2013 Oct 16.

Centre for Health Informatics, Institute of Population Health, University of Manchester, Manchester, UK; Centre for Respiratory Medicine and Allergy, Institute of Inflammation and Repair, University of Manchester, Manchester, UK.

Background: Identifying different patterns of allergens and understanding their predictive ability in relation to asthma and other allergic diseases is crucial for the design of personalized diagnostic tools.

Methods: Allergen-IgE screening using ImmunoCAP ISAC(®) assay was performed at age 11 yrs in children participating a population-based birth cohort. Logistic regression (LR) and nonlinear statistical learning models, including random forests (RF) and Bayesian networks (BN), coupled with feature selection approaches, were used to identify patterns of allergen responses associated with asthma, rhino-conjunctivitis, wheeze, eczema and airway hyper-reactivity (AHR, positive methacholine challenge). Sensitivity/specificity and area under the receiver operating characteristic (AUROC) were used to assess model performance via repeated validation.

Results: Serum sample for IgE measurement was obtained from 461 of 822 (56.1%) participants. Two hundred and thirty-eight of 461 (51.6%) children had at least one of 112 allergen components IgE > 0 ISU. The binary threshold >0.3 ISU performed less well than using continuous IgE values, discretizing data or using other data transformations, but not significantly (p = 0.1). With the exception of eczema (AUROC~0.5), LR, RF and BN achieved comparable AUROC, ranging from 0.76 to 0.82. Dust mite, pollens and pet allergens were highly associated with asthma, whilst pollens and dust mite with rhino-conjunctivitis. Egg/bovine allergens were associated with eczema.

Conclusions: After validation, LR, RF and BN demonstrated reasonable discrimination ability for asthma, rhino-conjunctivitis, wheeze and AHR, but not for eczema. However, further improvements in threshold ascertainment and/or value transformation for different components, and better interpretation algorithms are needed to fully capitalize on the potential of the technology.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1111/pai.12139DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4282342PMC
February 2014

Empirical validation of viral quasispecies assembly algorithms: state-of-the-art and challenges.

Sci Rep 2013 Oct 3;3:2837. Epub 2013 Oct 3.

1] University of Manchester, Faculty of Medical and Human Sciences, Northwest Institute of Bio-Health Informatics, Centre for Health Informatics, Institute of Population Health, Manchester, UK [2] University of Florida, College of Medicine, Department of Pathology, Immunology and Laboratory Medicine, Gainesville, Florida, USA.

Next generation sequencing (NGS) is superseding Sanger technology for analysing intra-host viral populations, in terms of genome length and resolution. We introduce two new empirical validation data sets and test the available viral population assembly software. Two intra-host viral population 'quasispecies' samples (type-1 human immunodeficiency and hepatitis C virus) were Sanger-sequenced, and plasmid clone mixtures at controlled proportions were shotgun-sequenced using Roche's 454 sequencing platform. The performance of different assemblers was compared in terms of phylogenetic clustering and recombination with the Sanger clones. Phylogenetic clustering showed that all assemblers captured a proportion of the most divergent lineages, but none were able to provide a high precision/recall tradeoff. Estimated variant frequencies mildly correlated with the original. Given the limitations of currently available algorithms identified by our empirical validation, the development and exploitation of additional data sets is needed, in order to establish an efficient framework for viral population reconstruction using NGS.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1038/srep02837DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3789152PMC
October 2013

Declining prevalence of HIV-1 drug resistance in antiretroviral treatment-exposed individuals in Western Europe.

J Infect Dis 2013 Apr 11;207(8):1216-20. Epub 2013 Jan 11.

Catholic University of the Sacred Heart, Rome, Italy.

HIV-1 drug resistance represents a major obstacle to infection and disease control. This retrospective study analyzes trends and determinants of resistance in antiretroviral treatment (ART)-exposed individuals across 7 countries in Europe. Of 20 323 cases, 80% carried at least one resistance mutation: these declined from 81% in 1997 to 71% in 2008. Predicted extensive 3-class resistance was rare (3.2% considering the cumulative genotype) and peaked at 4.5% in 2005, decreasing thereafter. The proportion of cases exhausting available drug options dropped from 32% in 2000 to 1% in 2008. Reduced risk of resistance over calendar years was confirmed by multivariable analysis.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1093/infdis/jit017DOI Listing
April 2013

Predictors of first-line antiretroviral therapy discontinuation due to drug-related adverse events in HIV-infected patients: a retrospective cohort study.

BMC Infect Dis 2012 Nov 12;12:296. Epub 2012 Nov 12.

Viral Immunodeficiency Unit, National Institute for Infectious Diseases Lazzaro Spallanzani, Rome, Italy.

Background: Drug-related toxicity has been one of the main causes of antiretroviral treatment discontinuation. However, its determinants are not fully understood. Aim of this study was to investigate predictors of first-line antiretroviral therapy discontinuation due to adverse events and their evolution in recent years.

Methods: Patients starting first-line antiretroviral therapy were retrospectively selected. Primary end-point was the time to discontinuation of therapy due to adverse events, estimating incidence, fitting Kaplan-Meier and multivariable Cox regression models upon clinical/demographic/chemical baseline patients' markers.

Results: 1,096 patients were included: 302 discontinuations for adverse events were observed over 1,861 person years of follow-up between 1988 and 2010, corresponding to an incidence (95% CI) of 0.16 (0.14-0.18). By Kaplan-Meier estimation, the probabilities (95% CI) of being free from an adverse event at 90 days, 180 days, one year, two years, and five years were 0.88 (0.86-0.90), 0.85 (0.83-0.87), 0.79 (0.76-0.81), 0.70 (0.67-0.74), 0.55 (0.50-0.61), respectively. The most represented adverse events were gastrointestinal symptoms (28.5%), hematological (13.2%) or metabolic (lipid and glucose metabolism, lipodystrophy) (11.3%) toxicities and hypersensitivity reactions (9.3%). Factors associated with an increased hazard of adverse events were: older age, CDC stage C, female gender, homo/bisexual risk group (vs. heterosexual), HBsAg-positivity. Among drugs, zidovudine, stavudine, zalcitabine, didanosine, full-dose ritonavir, indinavir but also efavirenz (actually recommended for first-line regimens) were associated to an increased hazard of toxicity. Moreover, patients infected by HIV genotype F1 showed a trend for a higher risk of adverse events.

Conclusions: After starting antiretroviral therapy, the probability of remaining free from adverse events seems to decrease over time. Among drugs associated with increased toxicity, only one is currently recommended for first-line regimens but with improved drug formulation. Older age, CDC stage, MSM risk factor and gender are also associated with an increased hazard of toxicity and should be considered when designing a first-line regimen.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1186/1471-2334-12-296DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3519703PMC
November 2012

PhyloTempo: A Set of R Scripts for Assessing and Visualizing Temporal Clustering in Genealogies Inferred from Serially Sampled Viral Sequences.

Evol Bioinform Online 2012 11;8:261-9. Epub 2012 Jun 11.

Department of Laboratory Medicine, Division of Clinical Microbiology, Karolinska University Hospital, Karolinska Institutet, Stockholm, Sweden.

Serially-sampled nucleotide sequences can be used to infer demographic history of evolving viral populations. The shape of a phylogenetic tree often reflects the interplay between evolutionary and ecological processes. Several approaches exist to analyze the topology and traits of a phylogenetic tree, by means of tree balance, branching patterns and comparative properties. The temporal clustering (TC) statistic is a new topological measure, based on ancestral character reconstruction, which characterizes the temporal structure of a phylogeny. Here, PhyloTempo is the first implementation of the TC in the R language, integrating several other topological measures in a user-friendly graphical framework. The comparison of the TC statistic with other measures provides multifaceted insights on the dynamic processes shaping the evolution of pathogenic viruses. The features and applicability of PhyloTempo were tested on serially-sampled intra-host human and simian immunodeficiency virus population data sets. PhyloTempo is distributed under the GNU general public license at https://sourceforge.net/projects/phylotempo/.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.4137/EBO.S9738DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3382462PMC
August 2012

Computational models for prediction of response to antiretroviral therapies.

AIDS Rev 2012 Apr-Jun;14(2):145-53

Department of Pathology, Immunology and Laboratory Medicine, College of Medicine, Emerging Pathogens Institute, University of Florida, Gainesville, FL 32610, USA.

This review describes the state-of-the-art in statistical, machine learning, and expert-advised computational methods for the evaluation and optimization of combination antiretroviral therapy, with respect to the virologic outcomes in HIV-1-infected patients. Currently employed methodologies are based on the paradigm for which mutations present in patient viral genotypes, selected either by treatment or already transmitted to the patient as resistant mutants, are the major drivers of virologic outcomes. Genotypic interpretation systems have been designed with the prime objective of characterizing the resistance to individual drugs, deriving scores from the association of viral genotypes with in vitro phenotypic drug susceptibility or in vivo response to treatment. Nevertheless, the very large range of possible drug combinations and of viral mutational patterns leads to an extremely complex scenario, making prediction of in vivo treatment response extremely challenging. To deal with such complexity, machine learning methods are being increasingly explored, thanks to the availability of exponentially growing HIV data bases in recent years. The combination of genotypic interpretation systems with other laboratory markers, treatment history, past clinical events, and the usage of data-driven techniques has dramatically raised the confidence in predicting virologic outcomes. A few of these systems have been implemented as free web-services, indicating ranks of suitable combination antiretroviral therapy regimens given a patient's clinical background. Future perspectives in the field foresee the extension of therapy optimization systems to newly approved antiretroviral drug targets and the prediction of other clinical outcomes, rather than the sole virologic response.
View Article and Find Full Text PDF

Download full-text PDF

Source
August 2012

Can linear regression modeling help clinicians in the interpretation of genotypic resistance data? An application to derive a lopinavir-score.

PLoS One 2011 16;6(11):e25665. Epub 2011 Nov 16.

Department of Infection and Population Health, Division of Population Health, UCL Medical School, Royal Free Campus, London, United Kingdom.

Background: The question of whether a score for a specific antiretroviral (e.g. lopinavir/r in this analysis) that improves prediction of viral load response given by existing expert-based interpretation systems (IS) could be derived from analyzing the correlation between genotypic data and virological response using statistical methods remains largely unanswered.

Methods And Findings: We used the data of the patients from the UK Collaborative HIV Cohort (UK CHIC) Study for whom genotypic data were stored in the UK HIV Drug Resistance Database (UK HDRD) to construct a training/validation dataset of treatment change episodes (TCE). We used the average square error (ASE) on a 10-fold cross-validation and on a test dataset (the EuroSIDA TCE database) to compare the performance of a newly derived lopinavir/r score with that of the 3 most widely used expert-based interpretation rules (ANRS, HIVDB and Rega). Our analysis identified mutations V82A, I54V, K20I and I62V, which were associated with reduced viral response and mutations I15V and V91S which determined lopinavir/r hypersensitivity. All models performed equally well (ASE on test ranging between 1.1 and 1.3, p = 0.34).

Conclusions: We fully explored the potential of linear regression to construct a simple predictive model for lopinavir/r-based TCE. Although, the performance of our proposed score was similar to that of already existing IS, previously unrecognized lopinavir/r-associated mutations were identified. The analysis illustrates an approach of validation of expert-based IS that could be used in the future for other antiretrovirals and in other settings outside HIV research.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0025665PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3217925PMC
March 2012

QuRe: software for viral quasispecies reconstruction from next-generation sequencing data.

Bioinformatics 2012 Jan 15;28(1):132-3. Epub 2011 Nov 15.

Department of Pathology, Immunology and Laboratory Medicine, College of Medicine, Emerging Pathogens Institute, University of Florida, Gainesville, FL 32610-3633, USA.

Summary: Next-generation sequencing (NGS) is an ideal framework for the characterization of highly variable pathogens, with a deep resolution able to capture minority variants. However, the reconstruction of all variants of a viral population infecting a host is a challenging task for genome regions larger than the average NGS read length. QuRe is a program for viral quasispecies reconstruction, specifically developed to analyze long read (>100 bp) NGS data. The software performs alignments of sequence fragments against a reference genome, finds an optimal division of the genome into sliding windows based on coverage and diversity and attempts to reconstruct all the individual sequences of the viral quasispecies--along with their prevalence--using a heuristic algorithm, which matches multinomial distributions of distinct viral variants overlapping across the genome division. QuRe comes with a built-in Poisson error correction method and a post-reconstruction probabilistic clustering, both parameterized on given error rates in homopolymeric and non-homopolymeric regions.

Availability: QuRe is platform-independent, multi-threaded software implemented in Java. It is distributed under the GNU General Public License, available at https://sourceforge.net/projects/qure/.

Contact: ahnven@yahoo.it; ahnven@gmail.com

Supplementary Information: Supplementary data are available at Bioinformatics online.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1093/bioinformatics/btr627DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3244773PMC
January 2012

Interpretation of genotypic HIV-1 resistance to darunavir and virological response: validation of available systems and of a new score.

Antivir Ther 2011 ;16(4):489-97

Institute of Clinical Infectious Diseases, Catholic University, Rome, Italy.

Background: There is not yet consensus on interpretation of genotypic HIV-1 resistance to darunavir (DRV). We validated existing rules and a newly derived score.

Methods: Protease inhibitor (PI)-failing patients starting a DRV/ritonavir-based regimen, with available baseline resistance genotypes, were extracted from three Italian databases. Virological response (VR) was analysed between 4 and 32 follow-up weeks, defined as a drop from baseline HIV RNA of ≥2 log(10) or a value <50 copies/ml if the last measurement had been obtained at ≤12 weeks and as HIV RNA<50 copies/ml if it had been obtained at >12 weeks of follow-up. DRV/ritonavir resistance was interpreted by seven algorithms. A new weighted score (DRV-2009) was derived and validated, analysing associations of protease mutations with VR.

Results: A total of 217 patients were analysed, with a mean (±sd) follow-up time of 17 (±9) weeks. At baseline, median HIV RNA was 4.26 log(10) copies/ml (IQR 3.11-5.03); VR was achieved in 135/217 (62%) patients. Adjusting for use of a new drug class, number of previous PIs experienced, CD4(+) T-cell count and HIV RNA, only the Rega DRV/ritonavir interpretation was significantly associated with VR (per increase in susceptibility category, OR 1.94, 95% CI 1.32-2.86; P<0.001). The DRV-2009 score V11I+L33F+R41K+I47V+2*I50V+2*I54M+K55R+D60E+L74P+L76V+N88D+2*L89V-L10I/V-I13V-G16E-G48V-F53I/L-I62V-I66F-V77I (<0 indicating susceptibility, 0-1 intermediate resistance and ≥2 resistance) correlated with VR in the derivation set (n=132, R=0.395; P<0.001). In the validation set (n=85), after adjusting for mutual interpretation and new use of enfuvirtide, DRV-2009 (P=0.017) and Rega (P=0.013) were both independently associated with VR.

Conclusions: In contrast to the other algorithms, both the DRV-2009 score and Rega interpretation showed a robust predictive capacity of VR to DRV/ritonavir-containing regimens.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.3851/IMP1799DOI Listing
October 2011

A prognostic model for estimating the time to virologic failure in HIV-1 infected patients undergoing a new combination antiretroviral therapy regimen.

BMC Med Inform Decis Mak 2011 Jun 14;11:40. Epub 2011 Jun 14.

Infectious Diseases Clinic, Catholic University of the Sacred Heart, Rome, Italy.

Background: HIV-1 genotypic susceptibility scores (GSSs) were proven to be significant prognostic factors of fixed time-point virologic outcomes after combination antiretroviral therapy (cART) switch/initiation. However, their relative-hazard for the time to virologic failure has not been thoroughly investigated, and an expert system that is able to predict how long a new cART regimen will remain effective has never been designed.

Methods: We analyzed patients of the Italian ARCA cohort starting a new cART from 1999 onwards either after virologic failure or as treatment-naïve. The time to virologic failure was the endpoint, from the 90th day after treatment start, defined as the first HIV-1 RNA > 400 copies/ml, censoring at last available HIV-1 RNA before treatment discontinuation. We assessed the relative hazard/importance of GSSs according to distinct interpretation systems (Rega, ANRS and HIVdb) and other covariates by means of Cox regression and random survival forests (RSF). Prediction models were validated via the bootstrap and c-index measure.

Results: The dataset included 2337 regimens from 2182 patients, of which 733 were previously treatment-naïve. We observed 1067 virologic failures over 2820 persons-years. Multivariable analysis revealed that low GSSs of cART were independently associated with the hazard of a virologic failure, along with several other covariates. Evaluation of predictive performance yielded a modest ability of the Cox regression to predict the virologic endpoint (c-index≈0.70), while RSF showed a better performance (c-index≈0.73, p < 0.0001 vs. Cox regression). Variable importance according to RSF was concordant with the Cox hazards.

Conclusions: GSSs of cART and several other covariates were investigated using linear and non-linear survival analysis. RSF models are a promising approach for the development of a reliable system that predicts time to virologic failure better than Cox regression. Such models might represent a significant improvement over the current methods for monitoring and optimization of cART.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1186/1472-6947-11-40DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3144446PMC
June 2011

Detection of drug resistance mutations at low plasma HIV-1 RNA load in a European multicentre cohort study.

J Antimicrob Chemother 2011 Aug 30;66(8):1886-96. Epub 2011 May 30.

Clinic of Infectious Diseases, Catholic University of Sacred Heart, Rome, Italy.

Background And Objectives: Guidelines indicate a plasma HIV-1 RNA load of 500-1000 copies/mL as the minimal threshold for antiretroviral drug resistance testing. Resistance testing at lower viral load levels may be useful to guide timely treatment switches, although data on the clinical utility of this remain limited. We report here the influence of viral load levels on the probability of detecting drug resistance mutations (DRMs) and other mutations by routine genotypic testing in a large multicentre European cohort, with a focus on tests performed at a viral load <1000 copies/mL.

Methods: A total of 16 511 HIV-1 reverse transcriptase and protease sequences from 11 492 treatment-experienced patients were identified, and linked to clinical data on viral load, CD4 T cell counts and antiretroviral treatment history. Test results from 3162 treatment-naive patients served as controls. Multivariable analysis was employed to identify predictors of reverse transcriptase and protease DRMs.

Results: Overall, 2500/16 511 (15.14%) test results were obtained at a viral load <1000 copies/mL. Individuals with viral load levels of 1000-10000 copies/mL showed the highest probability of drug resistance to any drug class. Independently from other measurable confounders, treatment-experienced patients showed a trend for DRMs and other mutations to decrease at viral load levels <500 copies/mL.

Conclusions: Genotypic testing at low viral load may identify emerging antiretroviral drug resistance at an early stage, and thus might be successfully employed in guiding prompt management strategies that may reduce the accumulation of resistance and cross-resistance, viral adaptive changes, and larger viral load increases.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1093/jac/dkr171DOI Listing
August 2011

A novel methodology for large-scale phylogeny partition.

Nat Commun 2011 24;2:321. Epub 2011 May 24.

Clinic of Infectious Diseases, Catholic University of the Sacred Heart, Rome, Italy.

Understanding the determinants of virus transmission is a fundamental step for effective design of screening and intervention strategies to control viral epidemics. Phylogenetic analysis can be a valid approach for the identification of transmission chains, and very-large data sets can be analysed through parallel computation. Here we propose and validate a new methodology for the partition of large-scale phylogenies and the inference of transmission clusters. This approach, on the basis of a depth-first search algorithm, conjugates the evaluation of node reliability, tree topology and patristic distance analysis. The method has been applied to identify transmission clusters of a phylogeny of 11,541 human immunodeficiency virus-1 subtype B pol gene sequences from a large Italian cohort. Molecular transmission chains were characterized by means of different clinical/demographic factors, such as the interaction between male homosexuals and male heterosexuals. Our method takes an advantage of a flexible notion of transmission cluster and can become a general framework to analyse other epidemics.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1038/ncomms1325DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6045912PMC
August 2011

Combinatorial analysis and algorithms for quasispecies reconstruction using next-generation sequencing.

BMC Bioinformatics 2011 Jan 5;12. Epub 2011 Jan 5.

Clinic of Infectious Diseases, Catholic University of Sacred Heart, Rome, Italy.

Background: Next-generation sequencing (NGS) offers a unique opportunity for high-throughput genomics and has potential to replace Sanger sequencing in many fields, including de-novo sequencing, re-sequencing, meta-genomics, and characterisation of infectious pathogens, such as viral quasispecies. Although methodologies and software for whole genome assembly and genome variation analysis have been developed and refined for NGS data, reconstructing a viral quasispecies using NGS data remains a challenge. This application would be useful for analysing intra-host evolutionary pathways in relation to immune responses and antiretroviral therapy exposures. Here we introduce a set of formulae for the combinatorial analysis of a quasispecies, given a NGS re-sequencing experiment and an algorithm for quasispecies reconstruction. We require that sequenced fragments are aligned against a reference genome, and that the reference genome is partitioned into a set of sliding windows (amplicons). The reconstruction algorithm is based on combinations of multinomial distributions and is designed to minimise the reconstruction of false variants, called in-silico recombinants.

Results: The reconstruction algorithm was applied to error-free simulated data and reconstructed a high percentage of true variants, even at a low genetic diversity, where the chance to obtain in-silico recombinants is high. Results on empirical NGS data from patients infected with hepatitis B virus, confirmed its ability to characterise different viral variants from distinct patients.

Conclusions: The combinatorial analysis provided a description of the difficulty to reconstruct a quasispecies, given a determined amplicon partition and a measure of population diversity. The reconstruction algorithm showed good performance both considering simulated data and real data, even in presence of sequencing errors.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1186/1471-2105-12-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3022557PMC
January 2011

Antiretroviral therapy optimisation without genotype resistance testing: a perspective on treatment history based models.

PLoS One 2010 Oct 29;5(10):e13753. Epub 2010 Oct 29.

Clinic of Infectious Diseases, Catholic University of Sacred Heart, Rome, Italy.

Background: Although genotypic resistance testing (GRT) is recommended to guide combination antiretroviral therapy (cART), funding and/or facilities to perform GRT may not be available in low to middle income countries. Since treatment history (TH) impacts response to subsequent therapy, we investigated a set of statistical learning models to optimise cART in the absence of GRT information.

Methods And Findings: The EuResist database was used to extract 8-week and 24-week treatment change episodes (TCE) with GRT and additional clinical, demographic and TH information. Random Forest (RF) classification was used to predict 8- and 24-week success, defined as undetectable HIV-1 RNA, comparing nested models including (i) GRT+TH and (ii) TH without GRT, using multiple cross-validation and area under the receiver operating characteristic curve (AUC). Virological success was achieved in 68.2% and 68.0% of TCE at 8- and 24-weeks (n = 2,831 and 2,579), respectively. RF (i) and (ii) showed comparable performances, with an average (st.dev.) AUC 0.77 (0.031) vs. 0.757 (0.035) at 8-weeks, 0.834 (0.027) vs. 0.821 (0.025) at 24-weeks. Sensitivity analyses, carried out on a data subset that included antiretroviral regimens commonly used in low to middle income countries, confirmed our findings. Training on subtype B and validation on non-B isolates resulted in a decline of performance for models (i) and (ii).

Conclusions: Treatment history-based RF prediction models are comparable to GRT-based for classification of virological outcome. These results may be relevant for therapy optimisation in areas where availability of GRT is limited. Further investigations are required in order to account for different demographics, subtypes and different therapy switching strategies.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0013753PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2966424PMC
October 2010

The threshold bootstrap clustering: a new approach to find families or transmission clusters within molecular quasispecies.

PLoS One 2010 Oct 25;5(10):e13619. Epub 2010 Oct 25.

Infectious Diseases Clinic, Catholic University of the Sacred Heart, Rome, Italy.

Background: Phylogenetic methods produce hierarchies of molecular species, inferring knowledge about taxonomy and evolution. However, there is not yet a consensus methodology that provides a crisp partition of taxa, desirable when considering the problem of intra/inter-patient quasispecies classification or infection transmission event identification. We introduce the threshold bootstrap clustering (TBC), a new methodology for partitioning molecular sequences, that does not require a phylogenetic tree estimation.

Methodology/principal Findings: The TBC is an incremental partition algorithm, inspired by the stochastic Chinese restaurant process, and takes advantage of resampling techniques and models of sequence evolution. TBC uses as input a multiple alignment of molecular sequences and its output is a crisp partition of the taxa into an automatically determined number of clusters. By varying initial conditions, the algorithm can produce different partitions. We describe a procedure that selects a prime partition among a set of candidate ones and calculates a measure of cluster reliability. TBC was successfully tested for the identification of type-1 human immunodeficiency and hepatitis C virus subtypes, and compared with previously established methodologies. It was also evaluated in the problem of HIV-1 intra-patient quasispecies clustering, and for transmission cluster identification, using a set of sequences from patients with known transmission event histories.

Conclusion: TBC has been shown to be effective for the subtyping of HIV and HCV, and for identifying intra-patient quasispecies. To some extent, the algorithm was able also to infer clusters corresponding to events of infection transmission. The computational complexity of TBC is quadratic in the number of taxa, lower than other established methods; in addition, TBC has been enhanced with a measure of cluster reliability. The TBC can be useful to characterise molecular quasipecies in a broad context.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0013619PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2963616PMC
October 2010

Low rate of virological failure and maintenance of susceptibility to HIV-1 protease inhibitors with first-line lopinavir/ritonavir-based antiretroviral treatment in clinical practice.

J Med Virol 2010 Dec;82(12):1996-2003

Clinic of Infectious Diseases, Catholic University of Sacred Heart, Rome, Italy.

Protease inhibitor (PI)-resistant HIV-1 has hardly ever been detected at failed boosted PI-based first-line antiretroviral regimens in clinical trials. However, this phenomenon has not been investigated in clinical practice. To address this gap, data from patients starting a first-line lopinavir/ritonavir (LPV/rtv)-based therapy with available baseline HIV-1 RNA load, a viral genotype and follow-up viral load after 3 and 6 months of treatment were extracted from the Italian Antiretroviral Resistance Cohort Analysis (ARCA) observational database. Based on survival analysis, 39 (7.1%) and 43 (7.8%) of the 548 examined patient cases had an HIV-1 RNA >500 and >50 copies/ml, respectively, after 6 months of treatment. Cox proportional hazard models detected baseline HIV-1 RNA (RH 1.79, 95%CI 1.10-2.92 per 1-log(10) increase, P=0.02) and resistance to the nucleoside backbone (RH 1.04, 95%CI 1.02-1.06 per 10-point increase using the Stanford HIVdb algorithm, P<0.001) as independent predictors of HIV-1 RNA at >500 copies/ml, but not at the >50 copies/ml cutoff criteria. Higher baseline viral load, older patient age, heterosexual route of infection and use of tenofovir/emtricitabine were predictors of failure at month 3 using the 50-copy and/or 500-copy threshold. Resistance to LPV/rtv did not occur or increase in any of the available 36 follow-up HIV-1 genotypes. Resistance to the nucleoside backbone (M184V) developed in four cases. Despite the likely differences in patient population and adherence, both the low rate of virological failure and the lack of development of LPV/rtv resistance documented in clinical trials are thus confirmed in clinical practice.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1002/jmv.21927DOI Listing
December 2010

Comparative determination of HIV-1 co-receptor tropism by Enhanced Sensitivity Trofile, gp120 V3-loop RNA and DNA genotyping.

Retrovirology 2010 Jun 30;7:56. Epub 2010 Jun 30.

Infectious Diseases Clinic, Catholic University of Sacred Heart, Rome, Italy.

Background: Trofile is the prospectively validated HIV-1 tropism assay. Its use is limited by high costs, long turn-around time, and inability to test patients with very low or undetectable viremia. We aimed at assessing the efficiency of population genotypic assays based on gp120 V3-loop sequencing for the determination of tropism in plasma viral RNA and in whole-blood viral DNA. Contemporary and follow-up plasma and whole-blood samples from patients undergoing tropism testing via the enhanced sensitivity Trofile (ESTA) were collected. Clinical and clonal geno2pheno[coreceptor] (G2P) models at 10% and at optimised 5.7% false positive rate cutoff were evaluated using viral DNA and RNA samples, compared against each other and ESTA, using Cohen's kappa, phylogenetic analysis, and area under the receiver operating characteristic (AUROC).

Results: Both clinical and clonal G2P (with different false positive rates) showed good performances in predicting the ESTA outcome (for V3 RNA-based clinical G2P at 10% false positive rate AUROC = 0.83, sensitivity = 90%, specificity = 75%). The rate of agreement between DNA- and RNA-based clinical G2P was fair (kappa = 0.74, p < 0.0001), and DNA-based clinical G2P accurately predicted the plasma ESTA (AUROC = 0.86). Significant differences in the viral populations were detected when comparing inter/intra patient diversity of viral DNA with RNA sequences.

Conclusions: Plasma HIV RNA or whole-blood HIV DNA V3-loop sequencing interpreted with clinical G2P is cheap and can be a good surrogate for ESTA. Although there may be differences among viral RNA and DNA populations in the same host, DNA-based G2P may be used as an indication of viral tropism in patients with undetectable plasma viremia.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1186/1742-4690-7-56DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2907304PMC
June 2010

Investigation of expert rule bases, logistic regression, and non-linear machine learning techniques for predicting response to antiretroviral treatment.

Antivir Ther 2009 ;14(3):433-42

Computer Science and Automation Department, Roma Tre University, Rome, Italy.

Background: The extreme flexibility of the HIV type-1 (HIV-1) genome makes it challenging to build the ideal antiretroviral treatment regimen. Interpretation of HIV-1 genotypic drug resistance is evolving from rule-based systems guided by expert opinion to data-driven engines developed through machine learning methods.

Methods: The aim of the study was to investigate linear and non-linear statistical learning models for classifying short-term virological outcome of antiretroviral treatment. To optimize the model, different feature selection methods were considered. Robust extra-sample error estimation and different loss functions were used to assess model performance. The results were compared with widely used rule-based genotypic interpretation systems (Stanford HIVdb, Rega and ANRS).

Results: A set of 3,143 treatment change episodes were extracted from the EuResist database. The dataset included patient demographics, treatment history and viral genotypes. A logistic regression model using high order interaction variables performed better than rule-based genotypic interpretation systems (accuracy 75.63% versus 71.74-73.89%, area under the receiver operating characteristic curve [AUC] 0.76 versus 0.68-0.70) and was equivalent to a random forest model (accuracy 76.16%, AUC 0.77). However, when rule-based genotypic interpretation systems were coupled with additional patient attributes, and the combination was provided as input to the logistic regression model, the performance increased significantly, becoming comparable to the fully data-driven methods.

Conclusions: Patient-derived supplementary features significantly improved the accuracy of the prediction of response to treatment, both with rule-based and data-driven interpretation systems. Fully data-driven models derived from large-scale data sources show promise as antiretroviral treatment decision support tools.
View Article and Find Full Text PDF

Download full-text PDF

Source
July 2009

Robust supervised and unsupervised statistical learning for HIV type 1 coreceptor usage analysis.

AIDS Res Hum Retroviruses 2009 Mar;25(3):305-14

Department of Virology, National Institute for Infectious Diseases L. Spallanzani, 00149 Rome, Italy.

Human immunodeficiency virus type 1 (HIV-1) isolates differ in their use of coreceptors to enter target cells. This has important implications for both viral pathogenicity and susceptibility to entry inhibitors, recently approved or under development. Predicting HIV-1 coreceptor usage on the basis of sequence information is a challenging task, due to the high variability of the envelope. The associations of the whole HIV-1 envelope genetic features (subtype, mutations, insertions-deletions, physicochemical properties) and clinical markers (viral RNA load, CD8(+), CD4(+) T cell counts) with viral tropism were investigated, using a set of 2896 (659 after filter, 593 patients) sequence-tropism pairs available at the Los Alamos HIV database. Bootstrapped hierarchical clustering was used to assess mutational covariation. Univariate and multivariate analysis was performed to assess the relative importance of different features. Different machine learning (logistic regression, support vector machines, decision trees, rule bases, instance based reasoning) and feature selection (filter and embedded) methods, along with loss functions (accuracy, AUC of ROC curves, sensitivity, specificity, f-measure), were applied and compared for the classification of X4 variants. Extra-sample error estimation was assessed via multiple cross-validation and adjustments for multiple testing. A high-performing, compact, and interpretable logistic regression model was derived to infer HIV-1 coreceptor tropism for a given patient [accuracy = 92.76 (SD 3.07); AUC = 0.93 (SD 0.04)].
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1089/aid.2008.0039DOI Listing
March 2009

Stochastic modelling of genotypic drug-resistance for human immunodeficiency virus towards long-term combination therapy optimization.

Bioinformatics 2009 Apr 31;25(8):1040-7. Epub 2008 Oct 31.

Department of Computer Science and Automation, University of Roma TRE, Informa Contract Research Organisation, Infectious Disease Clinic, Catholic University of Sacred Heart, Rome, Italy.

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).
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1093/bioinformatics/btn568DOI Listing
April 2009