518. Model-informed Dose Selection of Dual Toll-like Receptor 7/8 Inhibitor Enpatoran (M5049) for the Treatment of COVID-19 Pneumonia

Background Enpatoran, formerly known as M5049, is a potential first-in-class small molecule antagonist of toll-like receptors (TLR) 7 and 8, which may prevent viral-associated hyperinflammatory response and progression to ‘cytokine storm’ in coronavirus disease 2019 (COVID-19) patients. The objective of this study was to leverage existing population pharmacokinetic/pharmacodynamic (popPK/PD) models for enpatoran to inform dose selection for an accelerated Phase II study in COVID-19 patients with pneumonia. Methods The popPK/PD models were based on plasma PK and PD biomarker (ex vivo-stimulated interleukin [IL]6 and interferon α [IFNα] secretion) data from the enpatoran first-in-human Phase I study in healthy participants (Port A, et al. Lupus Sci Med 2020;7(Suppl. 1): Abstract P135). A two-compartment model describing PK used a sigmoidal E max model with proportional decrease from baseline characterizing the PD response across the investigated single and multiple daily dose range of 1–200 mg (N=72). Concentrations that inhibited 50% and 90% (IC 50 /IC 90 ) of cytokine secretion were estimated and stochastic simulations were performed to assess target coverage under different dosing regimens. Results Simulations suggested that, to achieve maximal inhibition of IL-6 over time, enpatoran PK concentrations would be maintained above the IC 90 throughout the dosing interval with doses of 100 mg and 50 mg twice daily in 90% and 30% of participants, respectively. In comparison, IFNα inhibition was predicted to be lower, with IC 90 coverage in 60% and 8% of participants with twice daily doses of 100 mg and 50 mg enpatoran, respectively. Conclusion Utilization of existing popPK/PD models allowed for the accelerated development of enpatoran in COVID-19 to address an unprecedented global pandemic. Rational model-informed dose selection was supported by data from a Phase I study in which there were no safety concerns. Disclosures Lena Klopp-Schulze, PhD , Merck KGaA, Darmstadt, Germany (Employee) Jamie Shaw, BS , EMD Serono Research & Development Institute, Inc., Billerica, MA, USA (an affiliate of Merck KGaA, Darmstadt, Germany) (Employee) Jennifer Dong, PhD , EMD Serono Research & Development Institute, Inc., Billerica, MA, USA (an affiliate of Merck KGaA, Darmstadt, Germany) (Employee) Akash Khandelwal, PhD , Merck KGaA, Darmstadt, Germany (Employee, Shareholder) Elizabeth Adams, MD , BioNTech SE, Germany (Employee)EMD Serono Research & Development Institute, Inc., Billerica, MA, USA (an affiliate of Merck KGaA, Darmstadt, Germany)(employer at the time of study) (Employee) Dongzi Yu, MD , EMD Serono Research & Development Institute, Inc., Billerica, MA, USA (an affiliate of Merck KGaA, Darmstadt, Germany) (Employee) Kosalaram Goteti, PhD , EMD Serono Research & Development Institute, Inc., Billerica, MA, USA (an affiliate of Merck KGaA, Darmstadt, Germany) (Employee) Pfizer (Shareholder)