Elias Clark, Ph.D.

Research Scientist I

Eli joined Metrum Research Group in 2023 after obtaining his Ph.D. in Mathematics from the University of Utah. His dissertation research focused on mathematical modeling of medication nonadherence and the effect it has on PK/PD models. Additionally, he has held internships at the Air Force Research Laboratory and Los Alamos National Laboratory where he worked on problems related to satellite tracking, shockwave physics, image processing, and fluid dynamics.

Recent publications by this scientist

Simulated efficacy of nerandomilast on forced vital capacity decline in idiopathic pulmonary fibrosis and progressive pulmonary fibrosis across background antifibrotic therapies

March 18, 2026

Presented at the ASCPT 2026 Annual Meeting. An exposure-response model was developed to evaluate the effect of nerandomilast on forced vital capacity (FVC) in patients with idiopathic pulmonary fibrosis (IPF) and progressive pulmonary fibrosis (PPF) , capturing both an initial “offset effect” and a long-term “disease-modifying effect” on the rate of FVC decline. The analysis confirmed a positive exposure-response relationship that is maintained regardless of the underlying diagnosis. Furthermore, simulations support using an 18 mg twice-daily dose to mitigate the reduced drug exposure associated with background pirfenidone use, ensuring a robust treatment response for patients on multi-drug regimens.

Simulated efficacy of nerandomilast on forced vital capacity decline in idiopathic pulmonary fibrosis and progressive pulmonary fibrosis across background antifibrotic therapies Samuel P. Callisto1, Kyle Baron1, Elias Clark1, Curtis Johnston1∗, Nikolas Onufrak2∗, Sonja Hartmann2∗, Steve Choy2 1Metrum Research Group, Boston, MA, U.S.A., 2Boehringer Ingelheim Pharmaceuticals, Inc., Ridgefield, CT, U.S.A. ∗Affiliation during time of analysis Introduction.   

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Losing the Forest: Causal Shapley Values for interpretation of Population-Pharmacometric Models.

December 6, 2024

Presented at ACoP 2024. SHAP analyis, an interpretable ML technique, was applied to PopPK models with two examples: saturable PK and causal dependence in covariates. This analysis yieled insights beyond forest plots and clairified differences in different types of forest plots typically presented.

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