Towards Patient Stratification and Treatment in the Autoimmune Disease Lupus Erythematosus using a Systems Pharmacology approach.
José David Gómez-Mantilla(1), Itziar Irurzun-Arana(1), Leire Ruiz-Cerdá(1), Ignacio González-Garcia(1)(2), Chuanpu Hu(3) , Honghui Zhou(3) , An Vermeulen(4), Iñaki F. Trocóniz(1).
(1) Pharmacometrics & Systems Pharmacology, Department of Pharmacy and Pharmaceutical Technology, School of Pharmacy, University of Navarra, Pamplona 310890, Spain. (2) Pharmacy and Pharmaceutical Technology Department. University of Valencia. Valencia, Spain. (3) Model Based Drug Development, Janssen Research and Development, LLC, Spring House, PA 19477, USA (4)Janssen Research and Development, a division of Janssen Pharmaceutica NV, Beerse B-2340, Belgium
Objectives: Systemic lupus erythematosus (SLE) exhibits very heterogeneous manifestations among patients [1], consequently, all the patients may not share the same molecular alterations in their immune response. Therefore, specific patient subpopulations may respond differently to the same therapeutic agent. This project aims to: 1) identify plausible altered pathways of the immune response that may explain the different and heterogeneous alterations in SLE patients, 2) classify patients according to their alterations, and 3) identify an optimal therapy for each patient subpopulation.
Methods: Due to the complexity associated with SLE, together with the lack of in vivo quantitative longitudinal data, the described aims were approached through developing a systems pharmacology framework. The immune response after production of autoantigens was modeled by Boolean networks [2,3]. Networks were built based on a rigorous bibliographic review, focused on the components of the immune response that have been reported to be altered in autoimmune diseases patients. Simulations of the immune response were performed perturbing the network by simulated upregulation or downregulation of different nodes in the network in order to identify which ones, if perturbed may trigger alterations similar to those observed in SLE patients. Clustering analysis was performed to group the network nodes according to the alterations these nodes may trigger after being up or downregulated. Network implementation and all simulations and analyses were performed in R.
Results: Different clinical manifestations were linked to different altered pathways of the immune response. Virtual lupus patients were classified into five major categories, according to common manifestations reported in the literature and five group-specific therapies were identified. Manipulation of the PD1-PD1L, CD45, IL23, GMCSF and CD40-CD40L pathways were able to reduce the disease alterations for each patient subpopulation. No single treatment was able to reduce the manifestations in all patient subpopulation, advocating the need of personalized therapies.
Conclusions: Heterogeneity of SLE manifestations can be modeled by different underlying altered pathways of the immune system. Patients can be classified into different categories according to their alterations and optimal treatments can be identified for each patient subpopulation.
References:
[1] Marion TN, Postlethwaite AE (2014) Chance, genetics, and the heterogeneity of disease and pathogenesis in systemic lupus erythematosus. Semin Immunopathol 36: 5: 495-5172.
[2] Thakar J, Pilione M, Kirimanjeswara G, Harvill ET, Albert R (2007) Modeling systems-level regulation of host immune responses. PLoS Comput Biol 3: e109.
[3] Saadatpour A, Albert R (2013) Boolean modeling of biological regulatory networks: a methodology tutorial. Methods 62: 3–12.