Tim Waterhouse, Ph.D.

Group Leader Statistics, Principal Scientist II

Tim joined Metrum in 2019, bringing 12 years of experience in the pharmaceutical industry. He attained his Ph.D. in Statistics from the University of Queensland, where he applied optimal design methods to PK/PD models. He then worked at Eli Lilly and Company in the PK/PD & Pharmacometrics department, using modeling and simulation to inform decision making in all phases of drug development across a range of therapeutic areas.

Recent publications by this scientist

Integrated Two‑Analyte Population Pharmacokinetics Model of Patritumab Deruxtecan (HER3‑DXd) Monotherapy in Patients with Solid Tumors

June 17, 2025

This work illustrates how integrated population PK modeling of antibody-conjugated payload and free payload analytes for an ADC can inform development strategies for targeted therapies—particularly in complex oncology settings.

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Exposure–Response Relationships in Patients with Non-Small-Cell Lung Cancer and Other Solid Tumors Treated with Patritumab Deruxtecan (HER3-DXd)

April 14, 2025

This paper contributes to the oncology MIDD field by using robust exposure-response modeling to identify the optimal dosing regimen for HER3-DXd in EGFR-mutated NSCLC. Analyzing data from over 700 patients across four studies, it demonstrates that 5.6 mg/kg Q3W offers a favorable balance of efficacy and safety. The analysis incorporates patient covariates and compares fixed and up-titration regimens, supporting data-driven selection. These methods align closely with the goals of Project Optimus, emphasizing the importance of modeling and simulation in selecting doses that are both effective and tolerable, rather than defaulting to the maximum tolerated dose.

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Population Pharmacokinetic and Exposure-Response Modeling of Patritumab Deruxtecan (HER3-DXd)

March 25, 2025

Presented at the Indiana CTSI Pharmacometrics Modeling and Simulation Symposium 2025. Dr. Tim Waterhouse explores the complexities of modeling an antibody-drug conjugate (ADC) for targeted cancer treatment, covering pharmacokinetics models with multiple clearance pathways and exposure-response models in a Bayesian framework.

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