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A comparative analysis of Phase I dose-finding designs incorporating pharmacokinetics information

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Title: A comparative analysis of Phase I dose-finding designs incorporating pharmacokinetics information
Authors: Vuorinen, Axel, Comets, Emmanuelle, Ursino, Moreno
Contributors: Vuorinen, Axel
Source: The American Statistician. :1-22
Publisher Information: Informa UK Limited, 2025.
Publication Year: 2025
Subject Terms: [SDV] Life Sciences [q-bio], Simulation study, [STAT.ME] Statistics [stat]/Methodology [stat.ME], Adaptive trial design, Bayesian methods, Dose-toxicity relationship, [INFO.INFO-MO] Computer Science [cs]/Modeling and Simulation, Maximum tolerated dose, [MATH.MATH-ST] Mathematics [math]/Statistics [math.ST], Exposure
Description: In early clinical trials, incorporating biological mechanisms of drug action in model-based drug development may improve Phase I success rates compared to approaches neglecting established mechanisms. Our goal is to investigate how pharmacokinetics (PK) knowledge is introduced in dose-finding methods and assess the performance of Bayesian designs incorporating PK data to estimate toxicity and robustness to misspecifications. Following a literature review, three approaches to integrate PK data into toxicity estimation were selected. The first approach assumes a normal distribution for the Area Under the Curve (AUC). The second method estimates a population PK model from longitudinal concentration data to compute the AUC for each patient. The third considers latent PK profiles to measure drug exposure. Different scenarios were implemented reflecting assumptions about the maximum tolerated dose (MTD) position and misspecifications in PK exposure measures or the PK model. Dose-finding methods were compared using the probability of correct MTD selection and the estimated probability of toxicity at each dose. PK dose-finding designs performed well in terms of accurate MTD selection and were at least as effective as a method without PK. They were robust to underlying PK model misspecification and incorrect exposure measure. Additionally, these methods can assess the dose-toxicity curve.
Document Type: Article
Other literature type
Conference object
File Description: application/pdf
Language: English
ISSN: 1537-2731
0003-1305
DOI: 10.1080/00031305.2025.2560371
DOI: 10.6084/m9.figshare.30112763.v1
DOI: 10.6084/m9.figshare.30112763
Access URL: https://hal.sorbonne-universite.fr/hal-04733488v1
Rights: CC BY
CC BY NC ND
Accession Number: edsair.doi.dedup.....5630ee3ab5c2fc9f21c5f3cbfe4a3085
Database: OpenAIRE
Description
Abstract:In early clinical trials, incorporating biological mechanisms of drug action in model-based drug development may improve Phase I success rates compared to approaches neglecting established mechanisms. Our goal is to investigate how pharmacokinetics (PK) knowledge is introduced in dose-finding methods and assess the performance of Bayesian designs incorporating PK data to estimate toxicity and robustness to misspecifications. Following a literature review, three approaches to integrate PK data into toxicity estimation were selected. The first approach assumes a normal distribution for the Area Under the Curve (AUC). The second method estimates a population PK model from longitudinal concentration data to compute the AUC for each patient. The third considers latent PK profiles to measure drug exposure. Different scenarios were implemented reflecting assumptions about the maximum tolerated dose (MTD) position and misspecifications in PK exposure measures or the PK model. Dose-finding methods were compared using the probability of correct MTD selection and the estimated probability of toxicity at each dose. PK dose-finding designs performed well in terms of accurate MTD selection and were at least as effective as a method without PK. They were robust to underlying PK model misspecification and incorrect exposure measure. Additionally, these methods can assess the dose-toxicity curve.
ISSN:15372731
00031305
DOI:10.1080/00031305.2025.2560371