Dose optimization for cancer treatments with considerations for late-onset toxicities.

Dose optimization cumulative toxicities delayed toxicities dose-finding oncology toxicity efficacy designs

Journal

Clinical trials (London, England)
ISSN: 1740-7753
Titre abrégé: Clin Trials
Pays: England
ID NLM: 101197451

Informations de publication

Date de publication:
09 Apr 2024
Historique:
medline: 9 4 2024
pubmed: 9 4 2024
entrez: 9 4 2024
Statut: aheadofprint

Résumé

Given that novel anticancer therapies have different toxicity profiles and mechanisms of action, it is important to reconsider the current approaches for dose selection. In an effort to move away from considering the maximum tolerated dose as the optimal dose, the Food and Drug Administration Project Optimus points to the need of incorporating long-term toxicity evaluation, given that many of these novel agents lead to late-onset or cumulative toxicities and there are no guidelines on how to handle them. Numerous methods have been proposed to handle late-onset toxicities in dose-finding clinical trials. A summary and comparison of these methods are provided. Moreover, using PI3K inhibitors as a case study, we show how late-onset toxicity can be integrated into the dose-optimization strategy using current available approaches. We illustrate a re-design of this trial to compare the approach to those that only consider early toxicity outcomes and disregard late-onset toxicities. We also provide proposals going forward for dose optimization in early development of novel anticancer agents with considerations for late-onset toxicities.

Identifiants

pubmed: 38591582
doi: 10.1177/17407745231221152
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

17407745231221152

Déclaration de conflit d'intérêts

Declaration of conflicting interestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Auteurs

Lucie Biard (L)

INSERM U1153 Team ECSTRRA, Université Paris Cité, Paris, France.

Anaïs Andrillon (A)

INSERM U1153 Team ECSTRRA, Université Paris Cité, Paris, France.
Department of Statistical Methodology, Saryga, Tournus, France.

Rebecca B Silva (RB)

Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, NY, USA.

Shing M Lee (SM)

Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, NY, USA.

Classifications MeSH