Validation of a Network Systems Pharmacology model for Inflammatory Bowel
Violeta Balbás-Martínez(1,2), Leire Ruiz-Cerdá(1,2), Ignacio González-García(1,3), Itziar Irurzun-Arana(1,2), José David Gómez-Mantilla(1,4), Iñaki F. Trocóniz(1,2).
(1) Pharmacometrics & Systems Pharmacology; Department of Pharmacy and Pharmaceutical Technology; School of Pharmacy and Nutrition; University of Navarra, Pamplona, Spain. (2) IdiSNA, Navarra Institute for Health Research; Pamplona, Spain (3) Current affiliation: Pharma Mar, Madrid, Spain (4) Current affiliation: Translational Medicine & Clinical Pharmacology, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach an der Riss, Germany
Objectives: To validate a robust Systems Pharmacology (SP) model for Inflammatory Bowel Disease (IBD) characterizing qualitatively the main IBD components and their dynamics.
Methods: A Boolean Network model[1] for IBD, comprised mostly of immune components, was implemented in the SP package SPIDDOR[2]. Simulations of the immune response were performed assuming chronic response to three different types of gut-bacterial antigens and impairment in antigen elimination. Relative expression of nodes was calculated by obtaining the activation probability of each node after the network reached the attractor state. The network was validated as follows: (i) comparing the simulation results with the reported alterations for IBD patients for each node, and (ii) comparing simulation vs reported outcomes from clinical trials for four investigated molecules: anti-TNFα, a monoclonal antibody (mAb) approved for IBD disease, and the failed treatments anti-IFNγ, anti-IL17, or human recombinant IL10(rhuIL-10). In addition a new promising therapy, Granulocyte and Monocyte Apheresis (GMA), was tested. Reported CDAI (Crohn Disease Activity Index) was compared with the average expression of the Metalloproteinases (MMPs), our output response-related node, in the attractor state. MMPs was selected as output node because this group of proteins are directly associated to intestinal fibrosis and tissue damage in IBD and have been recently proposed as a relevant biomarker[3]. Before model validation a network perturbation analysis was performed to know the model robustness.
Results: The network perturbation analysis indicates that our SP model for IBD is robust. SP model has satisfactorily been validated with data from the literature. Additionally, the SP model for IBD replicates the outcome of the current approved anti-TNFα therapy and the promising therapy GMA with a substantial decrease in MMPs (close to 30%). For the failed treatments (anti-IL17, rhuIL-10 and anti-IFNγ) only a slight decrease (6% for anti-IL17 and 3% for rhuIL-10) and an increase (15% for anti-IFNγ) of MMPs simulated expression was obtained, which is in line with CDAI score in clinical trials.
Conclusions: The proposed SP model represents a robust modelling tool for target and therapy identification, as well as an in silico platform to test a variety of therapy combinations. The model can be used to better understand and interpret, from a most molecular point of view, clinical trials results.
References:
[1] Balbás-Martínez .V PAGE 25 (2016) Abstr 5992 [www.page-meeting.org/?abstract=5992]
[2] Irurzun-Arana I, Pastor JM, Trocóniz IF, Gómez-Mantilla JD. Advanced Boolean modeling of biological networks applied to systems pharmacology. Bioinformatics. 2017; doi:10.1093/bioinformatics/btw747.
[3] O’Sullivan S, Gilmer JF, Medina C. Matrix metalloproteinases in inflammatory bowel disease: an update. Mediators Inflamm. 2015;2015: 964131.
