HIV decision support: from molecule to man

Abstract

Human immunodeficiency virus (HIV) is recognized to be one of the most destructive pandemics in recorded history. Effective highly active antiretroviral therapy and the availability of genetic screening of patient virus data have led to sustained viral suppression and higher life expectancy in patients who have been infected with HIV. The sheer complexity of the disease stems from the multiscale and highly dynamic nature of the system under study. The complete cascade from genome, proteome, metabolome and physiome to health forms a multidimensional system that crosses many orders of magnitude in temporal and spatial scales. Understanding, quantifying and handling this complexity is one of the biggest challenges of our time, which requires a highly multidisciplinary approach. In order to supply researchers with an interactive framework and to provide the medical professional with appropriate tools and information for making a balanced and reliable clinical decision, we have developed 'ViroLab', a collaborative decision-support system (http://www.virolab.org/). ViroLab contains computational models that cover various spatial and temporal scales from atomic-level interactions in nanoseconds up to sociological interactions on the epidemiological level, spanning years of disease progression. ViroLab allows for personalized drug ranking. It is on trial in six hospitals and various virology and epidemiology laboratories across Europe.

Rapid and accurate ranking of binding affinities of epidermal growth factor receptor sequences with selected lung cancer drugs

“…Personalized drug ranking has been included in a prototype clinical decision support system for HIV treatment [11]. An automated workflow supplements existing inductive ('Baconian') decision support systems with deductive ('Popperian') predictive modelling and drug ranking [12].…”

“…They can distort the ATP-binding cleft and introduce potential new interactions between EGFR and inhibitor/ATP [10], which in turn influence the relative strengths of inhibitor-and ATP-binding. A better understanding of the reasons for the success or failure of a therapeutic intervention will help in the selection of subgroups of patients who are most likely to respond to specific drugs and paves the way for individualized treatment, building on similar approaches already developed to handle infectious disease treatment [11].…”

“…Our simulations reveal that mutations in the tyrosine kinase domain may enhance or lessen the inhibitory effects of TKIs; they may thus be important for evaluating the cause of treatment success/failure, the selection of targeted drugs and the identification of more responsive patients. Therefore, it seems that it would probably be beneficial for cancer patients to have genotypic assays performed prior to treatment in a similar way to that which is routine for HIV/AIDS patients today [11].…”

Advances in dynamic temporal networks: Understanding the temporal dynamics of complex adaptive networks

“…[19,24,26,33]) into a new focus of investigation. Several important problems ranging from virology [4], to contact networks [34,35] and ecological theory [23] similarly invite a treatment of dynamic temporal networks on their own. A list of early works is given in [10].…”

RegaDB: community-driven data management and analysis for infectious diseases

“…Researchers need access to large amounts of data to test hypotheses or extract valuable information through data mining (Sloot et al, 2008(Sloot et al, , 2009. For this purpose, RegaDB was developed as a free and open source data management and analysis environment for infectious diseases (Libin et al, 2007).…”