Bayesian sequential integration within a preclinical pharmacokinetic and pharmacodynamic modeling framework: Lessons learned

Abstract

The present manuscript aims to discuss the implications of sequential knowledge integration of small preclinical trials in a Bayesian pharmacokinetic and pharmacodynamic (PK‐PD) framework. While, at first sight, a Bayesian PK‐PD framework seems to be a natural framework to allow for sequential knowledge integration, the scope of this paper is to highlight some often‐overlooked challenges while at the same time providing some guidances in the many and overwhelming choices that need to be made. Challenges as well as opportunities will be discussed that are related to the impact of (1) the prior specification, (2) the choice of random effects, (3) the type of sequential integration method. In addition, it will be shown how the success of a sequential integration strategy is highly dependent on a carefully chosen experimental design when small trials are analyzed.

Bayesian framework for multi-source data integration-Application to human extrapolation from preclinical studies

“…The Food and Drug Administration (FDA) 5 provides guidelines regarding the use of preclinical knowledge to compute the maximum recommended starting dose (MRSD) of the first-in-human (FIH) trial, with healthy volunteers and drug products for which systemic exposure is intended. These guidelines outline an empirical algorithmic approach to compute the FIH dose in four steps; (1) for each animal species used in the in vivo studies, the human equivalent dose (HED) is computed based on the no-observedadverse-effect level (NOAEL) and on the body surface area; (2) the HED corresponding to the most sensitive animal species is selected and (3) used for the calculation of the MRSD by applying a safety factor accounting for the expected variability coming from animal-to-human toxicity extrapolation; and (4) the MRSD is then adjusted based on the predicted pharmacological mechanism. Although this approach is a valuable starting point, it shows several drawbacks.…”

“…For example, in the preclinical and clinical context, La Gamba et al. 1 sequentially introduced knowledge in preclinical investigations within a Bayesian PK/PD setting, using the posterior distributions resulting from one trial to build the prior distributions for the following trial. More widely, the Bayesian approach has also been used to use information obtained from one population for the analysis of another population.…”

“…When studies are performed sequentially and the endpoints can be linked (via mathematical transformations, for example), the inference can be easily placed under a Bayesian framework that can update posterior knowledge each time new data becomes available. For example, in the preclinical and clinical context, La Gamba et al 1 sequentially introduced knowledge in preclinical investigations within a Bayesian PK/PD setting, using the posterior distributions resulting from one trial to build the prior distributions for the following trial. More widely, the Bayesian approach has also been used to use information obtained from one population for the analysis of another population.…”

Incorporation of healthy volunteers data on receptor occupancy into a phase II proof‐of‐concept trial using a Bayesian dynamic borrowing design

“…Other researchers have examined bridging models to identify differences between healthy volunteers and patients (Jonsson et al., 2005; Willmann et al., 2021). In the statistical literature, several publications have focused on extrapolation from pharmacokinetic models to clinical data (La Gamba et al., 2019), and increasing attention has been given to the incorporation of historical data (Natanegara et al., 2013; Ghadessi et al., 2020; Burger et al., 2021). The main advantage of these methods is the ability to estimate the treatment effect with precision and increase in study power while limiting, or even reducing (Viele et al., 2014), type I error in cases of consistency between historical and concurrent data.…”

The Use of Translational Modelling and Simulation to Develop Immunomodulatory Therapy as an Adjunct to Antibiotic Treatment in the Context of Pneumonia

“…A recent study describing a Bayesian meta-analytic approach applied by Zheng et al suggests how to use preclinical data to inform the design and prior distributions [ 53 ]. Indeed, the Bayesian approach has also been recognized as a powerful and flexible method in the pharmacometric field [ 54 , 55 ] and has been applied in sequential preclinical trials [ 56 ] to build informative prior distributions in human PK analysis using preclinical information [ 57 ] or using information from adult clinical studies to design pediatric trials [ 58 ].…”