In-silico simulated prototype-patients using TPMS technology to study a potential adverse effect of sacubitril and valsartan

Jorba Argemí, Guillem; Aguirre Plans, Joaquim, 1993-; Junet, Valentin; Segú-Vergés, Cristina; Ruiz, José Luis; Pujol, Albert; Fernández Fuentes, Narcís; Mas, José Manuel; Oliva Miguel, Baldomero

doi:http://dx.doi.org/10.1371/journal.pone.0228926

In-silico simulated prototype-patients using TPMS technology to study a potential adverse effect of sacubitril and valsartan

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Jorba G, Aguirre-Plans J, Junet V, Segú-Vergés C, Ruiz JL, Pujol A, Fernández-Fuentes N, Mas JM, Oliva B. In-silico simulated prototype-patients using TPMS technology to study a potential adverse effect of sacubitril and valsartan. PLoS One. 2020; 15(2):e0228926. DOI: 10.1371/journal.pone.0228926

Abstract

Unveiling the mechanism of action of a drug is key to understand the benefits and adverse reactions of a medication in an organism. However, in complex diseases such as heart diseases there is not a unique mechanism of action but a wide range of different responses depending on the patient. Exploring this collection of mechanisms is one of the clues for a future personalized medicine. The Therapeutic Performance Mapping System (TPMS) is a Systems Biology approach that generates multiple models of the mechanism of action of a drug. Each molecular mechanism generated could be associated to particular individuals, here defined as prototype-patients, hence the generation of models using TPMS technology may be used for detecting adverse effects to specific patients. TPMS operates by (1) modelling the responses in humans with an accurate description of a protein network and (2) applying a Multilayer Perceptron-like and sampling strategy to find all plausible solutions. In the present study, TPMS is applied to explore the diversity of mechanisms of action of the drug combination sacubitril/valsartan. We use TPMS to generate a wide range of models explaining the relationship between sacubitril/valsartan and heart failure (the indication), as well as evaluating their association with macular degeneration (a potential adverse effect). Among the models generated, we identify a set of mechanisms of action associated to a better response in terms of heart failure treatment, which could also be associated to macular degeneration development. Finally, a set of 30 potential biomarkers are proposed to identify mechanisms (or prototype-patients) more prone of suffering macular degeneration when presenting good heart failure response. All prototype-patients models generated are completely theoretical and therefore they do not necessarily involve clinical effects in real patients. Data and accession to software are available at http://sbi.upf.edu/data/tpms/.