A Quantitative Systems Pharmacology Model for the Key Interleukins Involved in Crohn's Disease

Abstract

Crohn's disease (CD) is a complex inflammatory bowel disease whose pathogenesis appears to involve several immunologic defects causing functional impairment of the gut. Its complexity and the reported loss of effectiveness over time of standard of care together with the increase in its worldwide incidence require the application of techniques aiming to find new therapeutic strategies. Currently, systems pharmacology modeling has been gaining importance as it integrates the available knowledge of the system into a single computational model. In this work, the following workflow for robust application of systems pharmacology modeling was followed: 1) scope definition; 2) species selection and circulating plasma levels based on a search in the literature; 3) representation of model topology and parametrization of the interactions, after literature data extraction and curation, and the implementation of ordinary differential equations in SimBiology (MATLAB version R2018b); and 4) model curation and evaluation by visual comparison of simulated interleukin (IL) concentrations with the reported levels in plasma, and sensitivity analysis performed to confirm model robustness and identify the most influential parameters. Finally, 5) exposure to two dose levels of recombinant human IL10 was evaluated by simulation and comparison with reported clinical study results. In summary, we present a quantitative systems pharmacology model for the main ILs involved in CD developed using a standardized methodology and supported by a comprehensive repository summarizing the most relevant literature in the field. However, it has to be taken into account that external validation is still pending as available clinical data were primarily used for model training. SIGNIFICANCE STATEMENTCrohn's disease (CD) is a complex heterogeneous inflammatory bowel disorder. Systems pharmacology modeling offers a great opportunity for integration of the available knowledge on the disease using a computational framework. As a result of this work, a comprehensive repository along with a quantitative systems pharmacology model for the main interleukins involved in CD is provided. This model is useful for the in silico evaluation of biomarkers and potential therapeutic targets and can be adapted to address research gaps regarding CD.

CYTOCON: The manually curated database of human in vivo cell and molecule concentrations

“…After developing a QSP model, the next step is to calibrate it, meaning to identify parameter values based on available experimental data. 12,13 A typical calibration workflow consists of several consecutive steps, including calibration against in vitro data, 13 baseline in vivo data, 7,9,14 and clinical data describing the response of a patient subpopulation to a particular treatment. 7,9,12,15 To proceed with calibration against baseline in vivo data, you need to collect baseline values of cells and cytokines from the literature and implement them into the QSP model in terms of units introduced in your F I G U R E 2 Query and output interfaces.…”

“…QSP modeling is a tool that addresses questions arising in the drug development framework in a mechanistic manner 3–5 . To build, calibrate, and validate such types of models, different types of data are utilized 6–8 . For example, to calibrate QSP models of diseases associated with immune response, data on baseline concentrations of immune cells and cytokines located in different tissues is required 9 .…”

A quantitative systems pharmacology model for acute viral hepatitis B

“…Similarly, other authors used a value for the infection rate constant within the same range (4 × 10 10 mL/[virion*day]) when fitting data from patients under treatment and modelling uninfected and infected hepatocytes, viruses, and effector cells [31] . Parameter estimation from literature experimental data: When parameterization of the biological processes was not directly available, but experimental data quantitatively characterizing the individual process (commonly through in vitro designs) was identified, data from the original publication was extracted or digitalized using WebPlotDigitizer 3.8 and fitted to a model, as previously shown [32] . For example, this approach was used to identify the IFNγ liver concentration inhibiting 50% of HBV synthesis (IFNγ 50_HBV ) based on experimental data from 2 publications [33] , [34] , where the inhibition of HBV replication in a liver cell line was explored in vitro at different IFNγ levels and under different conditions.…”

Application of different approaches to generate virtual patient populations for the quantitative systems pharmacology model of erythropoiesis

“…Quantitative systems pharmacology (QSP) modeling [2][3][4] is one such technique allowing the accumulation of both publicly available and proprietary data measured in vitro, ex vivo, and in vivo. QSP modeling enables us to understand more about the pathogenesis of diseases and mechanism of action of drugs, thereby increasing the efficacy of drug development [5][6][7].…”