Megan Cala Pane, Ph.D.

Research Scientist II

Megan joined Metrum Research Group in August of 2022. She earned her PhD in Chemical Engineering from the University of Pittsburgh in 2020 where her dissertation research was focused on developing a multiphase and multiscale computational model of blood coagulation. She then completed a postdoctoral fellowship in pharmacometrics with the Quantitative Medicine program at the Critical Path Institute and contributed to the development of their Rare Disease Cures Accelerator Data and Analytics Platform.

Recent publications by this scientist

Population pharmacokinetics of nerandomilast in patients with idiopathic pulmonary fibrosis and progressive pulmonary fibrosis

March 18, 2026

Population pharmacokinetics of nerandomilast in patients with idiopathic pulmonary fibrosis and progressive pulmonary fibrosis

Megan Pane1, Curtis Johnston1∗, Rena Eudy-Byrne1, Tyler Dunlap1, Nikolas Onufrak2∗, Sonja Hartmann2∗, Steve Choy21Metrum Research Group, Boston, MA, U.S.A.,2Boehringer Ingelheim Pharmaceuticals, Inc., Ridgefield, CT, U.S.A.

∗Affiliation during time of analysis

Mechanism of Action
• Nerandomilast (JASCAYD®) is an orally administered, preferential inhibitor of the
phosphodiesterase-4B (PDE4B) isoenzyme
• PDE4B hydrolyzes and inactivates cyclic adenosine monophosphate
Therapeutic Indication
• Approved by the U.S. FDA and China’s CDE for the treatment of idiopathic pul-
monary fibrosis (IPF) and progressive pulmonary fibrosis (PPF)
• IPF is a specific type of interstitial lung disease (ILD) and PPF is associated with a
subset of ILDs distinct from IPF
• Both IPF and PPF lead to lung scarring that progresses over time, with a median
survival time of 3-5 years
Chemical and Metabolic Properties
• Nerandomilast (R-enantiomer) contains a chiral sulfoxide group and undergoes a
minor level of metabolic chiral inversion following oral administration
• The resulting S-enantiomer is pharmacologically inactive
• Both nerandomilast and the S-enantiomer were characterized in this analysis

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A MODEL INFORMED DRUG DEVELOPMENT (MIDD)-BASED QUANTITATIVE DECISION FRAMEWORK (QDF) FOR IMPROVING R&D PRODUCTIVITY: PROOF OF CONCEPT FOR ATOPIC DERMATITIS (AD)

March 18, 2026

A MODEL INFORMED DRUG DEVELOPMENT (MIDD)-BASED QUANTITATIVE DECISION FRAMEWORK (QDF) FOR IMPROVING R&D PRODUCTIVITY: PROOF OF CONCEPT FOR ATOPIC DERMATITIS (AD)
E. Anderson¹, BW. Corrigan¹, M. Cala Pane¹, A. Tredennick¹, T. Dunlap¹, L. Lomeli¹, B. Davis¹, MR.Gastonguay¹1Metrum Research Group, Boston, MA

Project Rationale

QDF Components QDF Components
Competitive Landscape
MIDD Enhanced Valuations

Rising costs, uncertain reimbursement, competition, and declining success rates have
reduced drug R&D productivity and investment over the last decade.
Proposed strategies to improve R&D productivity include four key factors: 1) leveraging all
data sources; 2) utilizing quantitative models; 3) elimination of information silos across R&D
and commercial organizations; and 4) application of decision frameworks to reduce
cognitive bias and improve decision making.1

A QDF for a drug development program in atopic dermatitis (AD) was developed to: 1) link
MIDD models aligned with a target product profile (TPP) to risk-adjusted net present value
(rNPV); and 2) integrate context-sensitive large language models (LLMs) to incorporate
non-structured data from novel sources into the decision-making framework in a responsible
manner.

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Reinforcement Learning for Pharmacometrics: A Proof of Concept and Future Directions.

December 6, 2024

Presented at ACoP 2024. A proof-of-concept was conducted using deep reinforcement learning to optimize vancomycin dosing based on predicted PK profiles. The reinforcement learner was able to produce dose recommendations that (on average) exceeded the standard-of-care recommended dose, and future opportunities for using reinforcement learning within pharmacometrics are vast with a large potential for impact through personalized dosing.

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