Know Your Variability: Challenges in Mechanistic Modeling of Inflammatory Response in Inflammatory Bowel Disease (IBD)

The development of quantitative systems pharmacology (QSP) models for understanding diseases and therapeutics requires integration of data from multiple sources. Since QSP models are heavily dependent on literature‐derived biomarker information, uncertainty and variability in biomarker data can have a significant effect on predictive capabilities of the model and its overall applicability to answer research questions. The percentage of Th17 cells in the same CD patients differs from ∼8% vs. 62% of CD4+ T cells, when defined by a intracellular cytokine, interleukin (IL)‐17, or a phosphoprotein, RORγt+, respectively. [...]differences in tags used to define cell types can create confusion when publications are read without understanding the impact of labeling. Even with large‐scale “omic” data the LOD needs to be considered in study design, e.g., when using proteomics some important inflammation markers IL‐2, IL‐4, and tumor necrosis factor alpha (TNFα) are often under the LOD. [...]in experiments studying the dynamics of cytokine treatment where most samples are under the LOD, the conclusions derived from such data may not be robust. [...]occurring variations between individuals occur due to a variety of factors such as age, seasonal variations, gender heritable influences, microbiota, viruses, and the environment.