Bayesian population approaches to the analysis of dose escalation studies

• Published in: Computer methods and programs in biomedicine Vol. 107; no. 2; pp. 189 - 201
• Main Authors: Russu, Alberto, Nicolao, Giuseppe De, Poggesi, Italo, Neve, Marta, Gomeni, Roberto
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Ireland Ltd 01.08.2012; Elsevier
• Subjects: Bayes Theorem; Bayesian population model; Biological and medical sciences; Clinical Trials, Phase I as Topic - methods; Clinical Trials, Phase I as Topic - statistics & numerical data; Computer Simulation; Dose escalation; Dose-Response Relationship, Drug; Endpoint Determination - methods; Humans; Internal Medicine; Likelihood Functions; Medical sciences; Mixed effects model; Models, Biological; Models, Statistical; Other; Pharmaceutical Preparations - blood; Pharmacokinetics; Phase I trials; Radiotherapy. Instrumental treatment. Physiotherapy. Reeducation. Rehabilitation, orthophony, crenotherapy. Diet therapy and various other treatments (general aspects); Technology. Biomaterials. Equipments. Material. Instrumentation; Mixed effects model; Dose escalation; Phase I trials; Bayesian population model; Biomedical engineering
• ISSN: 0169-2607; 1872-7565; 1872-7565
• DOI: 10.1016/j.cmpb.2011.05.010

In dose escalation studies cohorts of subjects are given increasing doses of a candidate drug to assess safety and tolerability, pharmacokinetics and pharmacological response. The escalation is carried on until a predefined stopping limit is achieved, often identified by a pharmacokinetic endpoint such as peak plasma concentration or area under the plasma concentration–time profile. In the present work, the application of Bayesian methodologies to Phase I dose escalation studies is explored. A Bayesian population model is devised, which provides predictions of dose–response and dose–risk curves, both for individuals already enrolled in the trial and for a new, previously untested subject. Empirical and fully Bayesian estimation algorithms are worked out. Such methods provide equivalent performances on both experimental and simulated datasets. With respect to previous work, it is quantitatively proven not only that a more general and flexible model is identifiable, but also that such flexibility is needed in real scenarios.