Quantitative Systems Pharmacologist Pfizer, United States
Objectives: Several biotherapeutics modulating targets related to Th2 immunity have been investigated for the treatment of atopic dermatitis (AD) [1-3]. More recently, there has been interest in exploring novel multi-pathway combination therapies with the potential for improved efficacy either through additive or synergistic combinations of the atopic and alarmin pathways. A quantitative systems pharmacology-based approach incorporating disparate preclinical and clinical information from monotherapies can be used to streamline and accelerate the development of novel combination therapies to treat AD.
Methods: We extended a previously developed QSP model of AD [4] accounting for interactions between disease-relevant cytokines, T-cells, skin barrier integrity, pathogen infiltration and disease relevant biomarkers like CCL17. The model was updated to incorporate published information on pharmacokinetics [5, 6] and in vitro potency assessments [7, 8] for key Th2 targeting therapies (including anti-IL-4R, anti-IL-13 anti-TSLP and anti-IL-33 mAbs). To appropriately recapitulate variability in treatment response, we calibrated a robust virtual population against published reports from Phase 2 studies with dose and time-course information on EASI-75, a key AD clinical endpoint and partially validated the model against corresponding Phase 3 studies.
Results: The virtual population quantitatively matches the dynamics of key clinical endpoints and biomarkers from the placebo and treated arms of Phase 2 studies. Interestingly, to capture the clinically observed dose response from the Phase 2 studies, the model predicts the estimated clinical potency of these biotherapeutics is approximately 2-orders-of-magnitude weaker compared to in vitro potency assessments using conventional stimulation assays. The robustness of these estimates is partially validated against data from independently evaluated Phase 3 studies for both monotherapies and combination therapies when available.
Conclusion: The model enables the clinical translation of cytokine targeting biotherapeutics based on in vitro characterization of their bioactivity. Such a model, linking exposure to clinical efficacy, can be used to de-risk the clinical development of novel multi-pathway combination therapies and accelerate the design of dose ranging and pivotal clinical trials. Future work will involve incorporating clinical target engagement data for these antibody therapeutics for a more comprehensive description of their preclinical to clinical translation.
Citations: References 1. D. Thaci et al., Lancet, pp. 40-52, (2016) 2. E. Guttman-Yassky et al., JAMA Dermatol, pp. 411-420, (2020) 3. E. L. Simpson et al., J Am Acad Dermatol, pp. 1013-1021, (2019) 4. T. Miyano et al., Allergy, pp. 582-594, (2022) 5. P. Kovalenko et al., Clin Pharmacol Drug Dev, pp. 756-767, (2020) 6. R. Zhu et al., Pulm Pharmacol Ther, pp. 88-98, (2017) 7. A. Le Floc'h et al., Allergy, pp. 1188-1204, (2020) 8. A. J. Okragly et al., Dermatol Ther, pp. 1535-1547, (2023)
