A Collaborative Environment Allowing Clinical Investigations on Integrated Biomedical Databases Matthias ASSEL, David van de VIJVER, Pieter LIBIN, Kristof THEYS, Daniel HAREZLAK, Breanndán Ó NUALLÁIN, Piotr NOWAKOWSKI, Marian BUBAK, Anne-Mieke VANDAMME, Stijn IMBRECHTS, Raphael SANGEDA,Tao JIANG, Dineke FRENTZ and Peter SLOOT. In Proceedings of the HealthGrid 2009, Berlin, 29 June - 1 July.

HIV decision support: from molecule to man P.M.A.SLOOT, PETER V.COVENEY,G.ERTAYLAN,V.MUELLER,C.A.BOUCHER AND M.BUBAK, Phil. Trans. R. Soc. A 2009 367, 2691-2703,doi: 10.1098/rsta.2009.0043

Automated Molecular Simulation Based Binding Affinity Calculator for Ligand-Bound HIV-1 Proteases. S. Kashif Sadiq, David Wright, Simon J. Watson, Stefan J. Zasada, Ileana Stoica, and Peter V. Coveney. Centre for Computational Science, Department of Chemistry, University College London,London, WC1H 0AJ, U.K. Received March 14, 2008.

Patient-specific simulation as a basis for clinical decision-making, S. KASHIF SADIQ, MARCO D. MAZZEO, STEFAN J. ZASADA, STEVEN MANOS, ILEANA STOICA, CATHERINE V. GALE, SIMON J. WATSON, PAUL KELLAM, STEFAN BREW AND PETER V. COVENEY. Phil. Trans. R. Soc. A (2008) 366, 3199–3219 doi:10.1098/rsta.2008.0100 Published online 23 June 2008

eStrategies Projects - Science, Technology and Innovation, British Publishers Ltd., Bristol, The UK, December 2008, no. 4, pp. 53-55

Prevalence and Risk Factors for Etravirine Resistance among Patients Failing to Non-Nucleoside Reverse Transcriptase Inhibitors. Lapadula G., Calabrese A., Castelnuovo F., Costarelli S., Quiros-Roldan E., Paraninfo G., Ceresoli F., Manca N., Carosi G., Torti C. Antiviral Therapy, in press.

Genotypic resistance to lopinavir and fosamprenavir with or without ritonavir of clinical isolates from patients failing protease inhibitors-containing HAART regimens: prevalence and predictors. Di Giambenedetto S, Bacarelli A, Pinnetti C, Colafigli M, Prosperi M, Gatti G, Cauda R, De Luca A. Scand J Infect Dis. 2007;39(9):813-8.

The aim of this study was to establish the prevalence and predictors of genotypic resistance of HIV-1 to lopinavir and fosamprenavir from patients failing protease inhibitors (PI)-based regimens. We selected 643 HIV-1-infected patients with available treatment history who underwent genotypic resistance assays for virological failure from a clinical site and from the Stanford database. According to the genotypic resistance interpretation of the Stanford algorithm, proportions of viruses showing full susceptibility to fosamprenavir and lopinavir were 32% and 34%, respectively (p =ns). Proportions of viruses fully susceptible to lopinavir and fosamprenavir according to the Agence Nationale pour la Recherche sur le SIDA (ANRS) algorithm, were 81% and 81%, respectively. According to the Rega algorithm, proportions of viruses showing full susceptibility to fosamprenavir and lopinavir were 80% and 70%, respectively

Declining prevalence of HIV-1 drug resistance in treatment-failing patients: a clinical cohort study. Di Giambenedetto S, Bracciale L, Colafigli M, Cattani P, Pinnetti C, Bacarelli A, Prosperi M, Fadda G, Cauda R, De Luca A. Antivir Ther. 2007;12(5):835-9.

OBJECTIVES: A major barrier to successful viral suppression in HIV type 1 (HIV-1)-infected individuals is the emergence of virus resistant to antiretroviral drugs. We explored the evolution of genotypic drug resistance prevalence in treatment-failing patients from 1999 to 2005 in a clinical cohort. PATIENTS AND METHODS: Prevalence of major International AIDS Society-USA HIV-1 drug resistance mutations was measured over calendar years in a population with treatment failure and undergoing resistance testing. Predictors of the presence of resistance mutations were analysed by logistic regression. RESULTS: Significant reductions of the prevalence of resistance to all three drug classes examined were observed. This was accompanied by a reduction in the proportion of treatment-failing patients. Independent predictors of drug resistance were the earlier calendar year, prior use of suboptimal nucleoside analogue therapy, male sex and higher CD4 levels at testing. CONCLUSIONS: In a single clinical cohort, we observed a decrease in the prevalence of resistance to all three examined antiretroviral drug classes over time. If this finding is confirmed in multicentre cohorts it may translate into reduced transmission of drug-resistant virus from treated patients.

Genotypic resistance profile and clinical progression of treatment-experienced HIV type 1-infected patients with virological failure. AIDS Res Hum Retroviruses. Di Giambenedetto S, Colafigli M, Pinnetti C, Bacarelli A, Cingolani A, Tamburrini E, Cauda R, de Luca A. 2008 Feb;24(2):149-54

We explored the relationship between HIV-1 drug resistance in treatment-experienced patients and disease progression in a cohort of patients undergoing resistance testing to guide treatment decisions. A total of 601 treatment-failing individuals tested for genotypic HIV-1 drug resistance between 1998 and 2004 were selected. At genotypic testing, median HIV-1 RNA levels and CD4 counts were 3.8 log copies/ml and 293 cells/mul, respectively; 84% had resistance mutations to nucleoside reverse transcriptase inhibitors (NRTIs), 42% had resistance mutations to non-NRTIs, 51% had major resistance mutations to protease inhibitors (PI), 12% had no major resistance mutations to any drug class, 22% had mutations to one class, 42% had mutations to two classes, and 23% had mutations to three classes. During a follow-up of 714.7 patients/year, 80 patients showed an AIDS-defining event or died. In multivariable models adjusting for prior AIDS, baseline CD4 counts, HIV-1 RNA, and calendar year, viral resistance variables associated with increased hazards of clinical progression were the presence of reverse transcriptase substitution T215F (p = 0.002) and the presence of three or more protease substitutions among L33F/I/V, V82A/F/L/T, I84V, and L90M (p = 0.003). Resistance to three drug classes remained independently predictive of clinical progression only when calendar year was not used as an adjustment factor. Prevention and treatment of multiple drug class resistance are clinical priorities for HIV-infected patients. In recent years, improved treatment options may have helped in reducing part of the resistance-associated clinical progression.

Updated prevalence of genotypic resistance among HIV-1 positive patients naive to antiretroviral therapy: a single center analysis. Lapadula G, Izzo I, Gargiulo F, Paraninfo G, Castelnuovo F, Quiros-Roldan, E, Cologni G, Ceresoli F, Manca N, Carosi G, Torti C. J Med Virol. 2008 May;80(5):747-53

Continuous surveillance of HIV primary resistance mutations is highly important due to their potential clinical impact. All patients naive to antiretrovirals who had >/=1 genotypic resistance testing at the Institute of Infectious Diseases (Brescia, Northern Italy) between 2001 and 2006 were analyzed. Primary resistance mutations were defined using epidemiological and clinical criteria. Mutations were interpreted using the Stanford University Algorithm. Logistic regression analysis was used to assess possible predictors of primary resistance mutations. Among 569 patients, 11% presented >/=1 mutation. Prevalence of primary resistance mutations to nucleoside reverse-transcriptase inhibitors (NRTI), non-nucleoside reverse-transcriptase inhibitors (NNRTI), and protease inhibitors (PI) was 6.3%, 6%, and 1.6%, respectively. The most frequent mutations to NRTI were substitutions at position 215 (215Y in 3 patients, and 215 revertants in 16), 41L (13), 219Q (12), and 210W (10). Among mutations to NNRTI, 103N was found in 21 patients, while 181C, 188L, and 190A/S in 8, 3, and 4 patients, respectively. Fifty-one patients (9%) had high-to-intermediate resistance to >/=1 antiretroviral drug before starting the treatment. Regarding the new generation drugs, nine patients had intermediate resistance to etravirine, five patients had intermediate resistance to tipranavir, while five, one, and seven patients had low resistance to etravirine, tipranavir, and darunavir. Homosexuals were more likely to harbor a virus with primary resistance mutations (OR:2.68; 95% CI:1.44-5.00; P = 0.002) while non-Italian nationality was protective (OR:0.38; 95% CI:0.17-0.86; P = 0.020). Prevalence of primary resistance mutations suggests that genotypic resistance testing should be performed before starting treatment in naive patients in Italy, particularly when NNRTI are prescribed.

Estimation of an in vivo fitness landscape experienced by HIV-1 under drug selective pressure useful for prediction of drug resistance evolution during treatment. Deforche K, Camacho R, Van Laethem K, Lemey P, Rambaut A, Moreau Y, Vandamme AM. 2008 ;24(1):34-41.

Abstract: HIV-1 antiviral resistance is a major cause of antiviral treatment failure. The in vivo fitness landscape experienced by the virus in presence of treatment could in principle be used to determine both the susceptibility of the virus to the treatment and the genetic barrier to resistance. We propose a method to estimate this fitness landscape from cross-sectional clinical genetic sequence data of different subtypes, by reverse engineering the required selective pressure for HIV-1 sequences obtained from treatment naive patients, to evolve towards sequences obtained from treated patients. The method was evaluated for recovering 10 random fictive selective pressures in simulation experiments, and for modeling the selective pressure under treatment with the protease inhibitor nelfinavir. RESULTS: The estimated fitness function under nelfinavir treatment considered fitness contributions of 114 mutations at 48 sites. Estimated fitness correlated significantly with the in vitro resistance phenotype in 519 matched genotype-phenotype pairs (R(2) = 0.47 (0.41 - 0.54)) and variation in predicted evolution under nelfinavir selective pressure correlated significantly with observed in vivo evolution during nelfinavir treatment for 39 mutations (with FDR = 0.05). AVAILABILITY: The software is available on request from the authors, and data sets are available from Nelfinavir fitness landscape: data sets and results page

The Epidemiology of Transmission of Drug Resistant HIV-1 pp. 17-36 in HIV Sequence Compendium 2006/2007. Van de Vijver D, Wensing A, Boucher C (2007). Edited by: Thomas Leitner T, Foley B, Hahn B, Marx P, McCutchan F, Mellors J, Wolinsky S, Korber B. Published by: Theoretical Biology and Biophysics Group, Los Alamos National Laboratory, Los Alamos, NM. LA-UR 07-4826

Introduction: The use of highly active antiretroviral therapy has dramatically reduced morbidity and mortality among patients infected with HIV-1 (1,2). But the success of antiretroviral treatment is frequently limited by the emergence of HIV drug resistance (3-5). Importantly, drug resistant viruses can be transmitted to newly infected individuals (6-8). Transmission of drug resistant HIV is a major public health concern, as it could lead to a situation in which no effective drugs are available for the treatment of HIV. (...)

Multi-Science Decision Support for HIV Drug Resistance Treatment, P.M.A. Sloot, A. Tirado-Ramos and M.T. Bubak, P. Cunningham and M. Cunningham, editors, Expanding the Knowledge Economy: Issues, Applications, Case Studies, eChallenges e-2007 Conference Proceedings, pp. 597-606. IOS Press, Amsterdam, 2007.

Abstract: The complete cascade from genome, proteome, metabolome, and physiome, to health forms multiscale, multiscience systems and crosses many orders of magnitude in temporal and spatial scales. The interactions between these systems create exquisite multitiered networks, with each component in nonlinear contact with many interaction partners. Understanding, quantifying, and handling this complexity is one of the biggest scientific challenges of our time. In this paper we argue that computer science in general, and Grid computing in particular, provide the language needed to study and understand these systems, and discuss a case study in decision support for HIV drug resistance treatment within the European ViroLab project.

Grid-based Interactive Decision Support in BioMedicine, A. Tirado-Ramos; P.M.A. Sloot and M.T. Bubak in T. El-Ghazali and A. Zomaya, editors, Grids for Bioinformatics and Computational Biology, (in press). John Wiley and Sons, USA, 2007.

Introduction: The challenges discovered when studying humans as complex systems, from a biomedical viewpoint (from cells to interacting individuals), cover the whole spec­ trum from genome to health and cross temporal and spatial scales. This includes studying biomedical issues using multiscale and multiscience models and techniques all the way from genomics to the macroscopic medical scale. This is also aggravated by the continuous increase in the amount of digital data produced by modern high­ throughput biomedical detection and analysis systems. As reported by Hey et al., it is expected that larger amounts of digital data will be generated by next generations of large scale, collaborative e­Science experiments. New experiments in science and engineering will cover the whole spectrum, from the simulation of complete biological systems, to cutting­edge research in bioinformatics. (...)

From Molecule to Man: Decision Support in Individualized E-Health, P.M.A. Sloot; A. Tirado-Ramos; I. Altintas; M.T. Bubak and C.A. Boucher, IEEE Computer, (Cover feature) vol. 39, nr 11 pp. 40-46. November 2006.

Computer science provides the language needed to study and understand complex multiscale,multiscience systems.ViroLab,a grid-based decision-support system, demonstrates how researchers can now study diseases from the DNA level all the way up to medical responses to treatment.

Equilibrium spherically curved two-dimensional Lennard-Jones systems , J.M. Voogd; P.M.A. Sloot and R. van Dantzig. J. Chem. Phys., vol. 123, nr 084105 pp. 1-5. 2005.

Abstract: To learn about the basic aspects of nanoscale spherical molecular shells during their formation, spherically curved two-dimensional N-particle Lennard-Jones systems are simulated, studying curvature evolution paths at zero temperature.

Computational e-Science: Studying complex systems in silico, A National Coordinated Initiative, P.M.A. Sloot; D. Frenkel; H.A. Van der Vorst; A. van Kampen; H.E. Bal; P. Klint; R.M.M. Mattheij; J. van Wijk; J. Schaye; H.-J. Langevelde; R.H. Bisseling; B. Smit; E. Valenteyn; H.J. Sips; J.B.T.M. Roerdink and K.G. Langedoen. White Paper, February 2007.

Summary: The vision of the National Coordinated Initiative ‘Computational e-Science’ is to advance innovative, interdisciplinary research where complex multi-scale, multi-domain problems in science and engineering are solved on distributed systems, integrating sophisticated numerical methods, computation, data, networks, and novel devices. We aim to become one of the world-leaders in the field of computational e-science. We will build on the impressive results obtained from previous NWO programs and the (new) Dutch infrastructural programs that focused on understanding through modeling and simulation of interdisciplinary, multi-domain, multi-scale processes in science and engineering. (...)

Collaboratories on the Grid, A. Tirado-Ramos, Collaborative Software Architectures for Interactive Biomedical Applications, PhD thesis, University of Amsterdam, June 2007.

Background: The most important goal in Biomedical Informatics is to advance the quality of health care and the breadth and depth of its reach in society. Working towards the purpose of covering the whole healthcare spectrum, from prevention to treatment to rehabilitation, experts in the field have been focusing in the last few years on complex issues such as large-scale data integration and resource interoperability [116,136]. These new foci of research are causing a technological revolution in the field, where unprecedented amounts of biomedical digital information produced by data-intensive applications are rapidly changing the way computer scientists think about and design sofware architectures.

Collaborative Virtual Laboratory for e-Health, M.T. Bubak; T. Gubala; M. Kasztelnik; M. Malawski; P. Nowakowski and P.M.A. Sloot. in P. Cunningham and M. Cunningham, editors, Expanding the Knowledge Economy: Issues, Applications, Case Studies, eChallenges e-2007 Conference Proceedings, pp. 537-544. IOS Press, Amsterdam, 2007.

Abstract: This paper describes the Virtual Laboratory for e-Health system which is currently being developed in the EU IST ViroLab project. The Virtual Laboratory is an environment that enables clinical researchers to prepare and execute computing experiments using a distributed Grid infrastructure, while not requiring in-depth Grid computing technologies knowledge. By virtualizing the hardware, computing infrastructure and databases, the Virtual Laboratory is a user friendly environment, with tailored workflow templates to harness and automate such diverse tasks as data archiving, data integration, data mining and analysis, modeling and simulation.

A Grid-based HIV Expert System, Journal of Clinical Monitoring and Computing, vol. 19, nr. 4-5 , October 2005. P.M.A. Sloot; A.V. Boukhanovsky; W. Keulen; A. Tirado-Ramos and C.A. Boucher

ABSTRACT: Objectives. This paper addresses Grid-based integration and access of distributed data from infectious disease patient databases, literature on in-vitro and in-vivo pharmaceutical data, mutation databases, clinical trials, simulations and medical expert knowledge. Methods. Multivariate analyses combined with rule-based fuzzy logic are applied to the integrated data to provide ranking of patient-specific drugs. In addition, cellular automata-based simulations are used to predict the drug behaviour over time. Access to and integration of data is done through existing Internet servers and emerging Grid-based frameworks like Globus. Data presentation is done by standalone PC based software, Web-access and PDA roaming WAP access. The experiments were carried out on the DAS2, a Dutch Grid testbed. Results. The output of the problem-solving environment (PSE) consists of a prediction of the drug sensitivity of the virus, generated by comparing the viral genotype to a relational database which contains a large number of phenotype-genotype pairs. Conclusions. Artificial Intelligence and Grid technology are effectively used to abstract knowledge from the data and provide the physicians with adaptive interactive advice on treatment applied to drug resistant HIV. An important aspect of our research is to use a variety of statistical and numerical methods to identify relationships between HIV genetic sequences and antiviral resistance to investigate consistency of results. KEYWORDS: computational Grids, HIV, PSE, expert system, artificial intelligence, bio-statistics.