Violeta Balbas-Martinez (1,2), Eduardo Asin-Prieto (1,2), Zinnia Parra-Guillen (1,2), Iñaki F. Troconiz (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.
Introduction:
Crohn’s disease (CD) is a complex inflammatory bowel disease, which causes a functional impairment of the gut wall leading to abdominal pain, severe diarrhoea, fatigue, weight loss and malnutrition(1). The reported lack of effectiveness in the standard of care(2) together with the worldwide increase in CD incidence(3) require the application of techniques aiming to find new targets and therapeutic strategies. At this point, systems pharmacology (SP) modelling gains importance as the available knowledge can be integrated into a single computational model.
Objectives:
To develop a SP model in humans characterizing the dynamics of the main interleukins (ILs) involved in CD using previous modelling efforts as starting point(4–7), and incorporating new relevant molecular pathways in CD.
Methods:
We followed the six‐stage workflow for robust application of SP modelling(8) to standardize the quantitative SP (QSP) model building: (i) identify main project goals; (ii) selection of species and literature search for blood levels to define their average profile in healthy subjects (HS) and CD patients; (iii) representation of model topology and parametrization of the interactions using data extraction and curation. ILs’ kinetics were characterized by zero-or first-order synthesis (ksyn) and first-order degradation (kdeg). Constant levels for ILs and cells at the steady state (SS) of HS and CD were assumed for synthesis rate constant derivation. To parametrize the IL interactions (stimulation/inhibition of synthesis), different sub-models were tested using nls in R. Model selection was based on the akaike information criterion. As an example, IL12 parametrization and ODE building would be explained in detail, including the assumptions made. Ordinary differential equations (ODEs) were implemented in SimBiology®(MATLAB®vR2018b)(9) to mathematically describe the time course of the system components’ levels in blood. Afterwards, (iv) 1000 stochastic simulations, where the ILs and cells initial conditions were randomly fixed from uniform distributions (between the literature reported ranges in CD) were run and evaluated by visual comparison of ILs concentration with their reported levels. Then, (v) the exposure to two doses of recombinant human IL10 (rhuIL10) from a clinical study was simulated(10). Finally, (vi) the next steps were defined.
Results:
A total of 21 species representative of the innate and adaptive immune response in CD were included. Those species were (i) activated macrophages, dendritic cells and CD4+ T cells, (ii) CD4+ T cells subtypes (Th2, Th1, Th17 and Treg), (iii) pro-inflammatory ILs (IFNg, TNFa, IL12, IL23, IL6, IL1b, IL17, IL22, IL18, IL4, IL2 and IL15) and (iv) regulatory ILs (IL10 and TGFb1). Individual graphical representations were generated per IL, and subsequently, integrated into one single figure of the whole model providing a big picture of model structure.
The developed QSP model included 14 ODEs and 98 parameters. Generally, each IL kinetics is ruled by three key parameters: ‘kdeg, ‘ksyn’ in the basal healthy state and ‘ksyn’ drived by antigen presenting cells stimulated in CD, which were modulated by other model components. ILs degradation parameters were obtained from the literature(11–13), whereas those referring to synthesis modulation were estimated using in vitro data. IL10, TGFb1 and IL6 presented larger differences in their levels when comparing HS with CD patients. TNFa, IFNg and IL12 were the most complex interleukins, with up to 8 interleukins regulating their synthesis.
We obtained a quantitative reproduction of CD showing the model performance accuracy. Radom values between the physiological range of the model components did not produce non-physiological SS levels for any of the ILs. Furthermore, the simulated exposure to rhuIL10 showed a reduction of IFNg, IL18 and TNFa towards HS at the lower dose which was in agreement with the general outcome of the clinical study.
Conclusions:
We present a QSP model for the main ILs involved in Crohn’s disease. Not only is supported by a comprehensive repository summarizing the most relevant literature in the field, but also by a standardized methodology for QSP model building. This model proved to be promising for the in silico evaluation of potential therapeutic targets and the search for specific biomarkers. Finally, it can be expanded or reduced as demanded, leading to different quantitative model/s to address research gaps regarding CD.
References:
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Reference: PAGE 28 (2019) Abstr 8817 [www.page-meeting.org/?abstract=8817]
Poster: Drug/Disease Modelling - Other Topics