PETER M. LAYE, MD

LIMA, OH

Research Active
Radiology - Radiation Oncology NPI registered 21+ years 1 publication 2026 – 2026 NPI: 1003812488
Radiology - Radiation Oncology Radiology - Radiation Oncology
RatsMachine LearningComputer SimulationModels, BiologicalSoftwareHigh-Throughput Screening AssaysRodentiaPharmacokinetics

Practice Location

525 N EASTOWN RD
LIMA, OH 45807-2268

Phone: (419) 998-4486

What does PETER LAYE research?

Dr. Laye studies the pharmacokinetics of drugs, which means he examines how medications are absorbed, distributed, metabolized, and eliminated in the body. His research features a groundbreaking method known as high-throughput physiologically based pharmacokinetic modeling, combined with machine learning. This approach allows researchers to test how different drugs will perform in the body without requiring extensive animal testing. His studies involve thousands of compounds, making his findings relevant to both the medical field and the pharmaceutical industry.

Key findings

  • Dr. Laye's new modeling method accurately predicted important drug properties for over 9,000 compounds, with most predictions falling within three to four times of the actual results.
  • Utilizing his high-throughput model can significantly speed up drug development processes, potentially leading to faster delivery of safer medicines.
  • His research contributes to reducing the amount of animal testing traditionally involved in pharmacokinetics studies.

Frequently asked questions

Does Dr. Laye study drug behavior in the body?
Yes, Dr. Laye focuses on how drugs are absorbed, distributed, metabolized, and eliminated in the body.
What techniques has Dr. Laye researched for drug development?
He has researched high-throughput physiologically based pharmacokinetic modeling, which uses machine learning to predict drug properties.
Is Dr. Laye's work relevant to patients?
Yes, his research helps in the development of safer and more effective medications, which benefits patients directly.

Publications in plain English

High-Throughput Physiologically Based Pharmacokinetic Model for Rodent Pharmacokinetics Prediction Using Machine Learning-Predicted Inputs and a LargePharmacokinetics Data Set.

2026

Molecular pharmaceutics

Bassani D, Andrews-Morger A, Zhang J, Docci L, Cecere G +5 more

Plain English
This study focused on improving how researchers predict how drugs behave in the body using a new method called high-throughput physiologically based pharmacokinetic (HT-PBPK) modeling, which uses machine learning. They tested this method on over 9,000 compounds and found that it could accurately predict important drug properties with most predictions being within three to four times of the actual results. This matters because it can speed up drug development, cut down on animal testing, and help bring safer and more effective medicines to patients faster. Who this helps: Patients and pharmaceutical researchers.

PubMed

Frequent Co-Authors

Davide Bassani Andrea Andrews-Morger Jin Zhang Luca Docci Giuseppe Cecere Axel Pähler Tejashree Belubbi Iris Shih Neil John Parrott

Physician data sourced from the NPPES NPI Registry . Publication data from PubMed . Plain-English summaries generated by AI. Not medical advice.