Methods and tools for multiscale modelling in Systems Pharmacology: a review
Borella Elisa, Carrara Letizia, Lavezzi Silvia Maria, Massaiu Ilaria, Sauta Elisabetta, Tosca Elena Maria, Vitali Francesca, Zucca Susanna, Pasotti Lorenzo, De Nicolao Giuseppe, Magni Paolo
Department of Electrical, Computer and Biomedical Engineering, University of Pavia, via Ferrata 5, Pavia, I-27100, Italy.
Objectives: Systems Pharmacology aims to quantitatively study the dynamic interactions between drugs and biological systems by integrating models and data at different scales, to understand how the interrelated behaviour of individual constituents (modelled, e.g., via biological networks) and the behaviour of the whole system (modelled, e.g., via PBPK models) mutually interact[1,2]. The objective of this work is to present and discuss: how models at different scales can be coupled; the types of data that can be used; the tools supporting the implementation; the practical research and drug development studies the models were built for.
Methods: Three main methods for coupling PBPK models and biological networks were identified: i) indirect/direct coupling with dynamic Flux Balance Analysis[3], ii) combination of PBPK and networks ODEs[4-8], iii) integration of genetic information as covariate[9]. Tools and languages used[3-9] are: PK-Sim and MoBi[3,9-11], GNU MCSim[6,8,12], Matlab[9,13], Insilico discovery[4,14], COPASI[5,15], SBML[6,8,16], BioTRaNS[7,17].
Results: Method i) describes both how the PBPK model affects the network and how processes at the cellular level influence distribution of compounds at the whole body scale[3]. This approach is used to study hyperuricemia therapy, ammonia detoxification and paracetamol-induced toxication, with the support of clinical data and physiological information.
For method ii), the connection is provided by network exchange rates (often related to the liver) affecting the PBPK model concentrations. Models built with this approach are used to: predict hepatotoxicity upon treatment with acetaminophen[4], predict GSH metabolism and paracetamol toxicity[5] (both on the basis of literature and in vitro data) and study chemicals interactions[6-8].
Following method iii), in[9] genetic information is included as a covariate for tissue-specific transporter-activity. Data ranging from preclinical characterizations of enzymes and proteins to safety events rates supported the building of a PBPK model for the prediction of myopathy rates.
Conclusions: The main challenges of systems pharmacology are multiscale modelling and vertical integration of heterogeneous data, for which guidelines are still missing. Here, methods and tools to face such challenges are presented, showing promising results (and limits) in practical applications.
Acknowledgements: This work was supported by the DDMoRe project (www.ddmore.eu).
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