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Context In patients with GH deficiency (GHD) receiving GH treatment, IGF-1 concentrations are used by physicians to monitor treatment safety and efficacy and guide dosing decisions. Somatrogon is a long-acting GH approved as a once-weekly treatment for pediatric GHD. Somatrogon administration results in characteristic changes in the IGF-1 profile, with values measured at 96 hours postdose representing mean IGF-1 concentrations that best reflect overall somatrogon exposure. Objective To develop a simple method to enable physicians to predict mean IGF-1 concentrations following somatrogon dosing, based on a single IGF-1 measurement taken at any point during the 7-day dosing interval. Methods Data from phase 2 and phase 3 somatrogon studies were used to develop a 2-compartment pharmacokinetic model with delayed first-order absorption. An indirect-response pharmacokinetic/pharmacodynamic model was applied to the predicted somatrogon concentrations, and model simulations were used to predict IGF-1 and IGF-1 SD score (SDS) levels for participants in both studies. Results A total of 16,213 dosing records (from 42 and 109 participants in the phase 2 and 3 studies, respectively) were used for the simulations, generating predicted values for IGF-1 and IGF-1 SDS. Predicted values were scaled against the respective values at 96 hours (day 4). These values were used to create a table showing the adjustments required to predict mean IGF-1 and IGF-1 SDS values depending on time after dose. Conclusion We developed a simple method enabling physicians to predict mean weekly IGF-1 values using IGF-1 values measured at any point in the dosing interval. [ABSTRACT FROM AUTHOR]

Inflammatory bowel disease (IBD) is a heterogeneic disease with a variety of treatments targeting different mechanisms. A multistate, mechanistic, mathematical model of IBD was developed in part 1 of this two‐part article series. In this paper, application of the model to predict response of key clinical biomarkers following different treatment options for Crohn's disease was explored. Five therapies, representing four different mechanisms of action, were simulated in the model and longitudinal profiles of key clinical markers, C‐reactive protein and fecal calprotectin were compared with clinical observations. Model simulations provided an accurate match with both central tendency and variability observed in biomarker profiles. We also applied the model to predict biomarker and clinical response in an experimental, combination therapy of existing therapeutic options and provide possible mechanistic basis for the increased response. Overall, we present a validated, modular, mechanistic model construct, which can be applied to explore key biomarkers and clinical outcomes in IBD. [ABSTRACT FROM AUTHOR]

A mechanistic, multistate, mathematical model of inflammatory bowel disease (IBD) was developed by including key biological mechanisms in blood and gut, including cell differentiation, cytokine production, and clinical biomarkers. The model structure is consistent between healthy volunteers and IBD disease phenotype, with 24 parameters changed between diseases. Modular nature of the model allows for easy incorporation of new mechanisms or modification of existing interactions. Model simulations for steady‐state levels of proteins and cells in the blood and gut using a population approach are consistent with published data. By simulating the response of two clinical biomarkers, C‐reactive protein and fecal calprotectin, to parameter perturbations, the model explores hypotheses for possible treatment mechanisms. With additional experimental validation and addition of drug treatments, the model provides a platform to test hypothesis on treatment effects in IBD. [ABSTRACT FROM AUTHOR]

The article focuses on the variability of biomarkers observed in healthy subjects and inflammatory bowel disease (IBD) patients. It discusses the variability from data generation methods and in data analysis reporting. The views on the challenges faced due to variability in biomarkers when constructing and parameterizing quantitative systems pharmacology (QSP) model of immunology are also presented.