A physiologically based pharmacokinetic and pharmacodynamic (PBPK/PD) model of the histone deacetylase (HDAC) inhibitor vorinostat for pediatric and adult patients and its application for dose specification

Abstract

Purpose: This study aimed at recommending pediatric dosages of the histone deacetylase (HDAC) inhibitor vorinostat and potentially more effective adult dosing regimens than the approved standard dosing regimen of 400 mg/day, using a comprehensive physiologically-based pharmacokinetic/pharmacodynamic (PBPK/PD) modeling approach. Methods: A PBPK/PD model for vorinostat was developed for predictions in adults and children. It includes the maturation of relevant metabolizing enzymes. The PBPK model was expanded by (i) effect compartments to describe vorinostat concentration-time profiles in peripheral blood mononuclear cells (PBMCs), (ii) an indirect response model to predict the HDAC inhibition, and (iii) a thrombocyte model to predict the dose-limiting thrombocytopenia. Parameterization of drug and system-specific processes was based on published and unpublished in silico, in vivo, and in vitro data. The PBPK modeling software used was PK-Sim and MoBi. Results: The PBPK/PD model suggests dosages of 80 and 230 mg/m2 for children of 0-1 and 1-17 years of age, respectively. In comparison to the approved standard treatment, in silico trials reveal 11 dosing regimens (9 oral, 2 intravenous infusion rates) increasing the HDAC inhibition by an average of 31%, prolonging the HDAC inhibition by 181%, while only decreasing the circulating thrombocytes to a tolerable 53%. The most promising dosing regimen prolongs the HDAC inhibition by 509%. Conclusions: Thoroughly developed PBPK models enable dosage recommendations in pediatric patients and integrated PBPK/PD models, considering PD biomarkers (e.g., HDAC activity and platelet count), are well suited to guide future efficacy trials by identifying dosing regimens potentially superior to standard dosing regimens.

Predicting the correct dose in children: Role of computational Pediatric Physiological‐based pharmacokinetics modeling tools

“…The P‐PBPK dose prediction articles included in the study, the name of the drug, the platform, and the study's funding are described in Table 1. Verified PBPK models of drugs are presented in support of pediatric dose prediction (Figure 4): 13 articles reported results from P‐PBPK models, which are presented in support of dose selection, 30–42 five articles were on the optimization of dosage in the design of future pediatric clinical studies, 3,29,43–45 and six articles reported findings from studies on determining dose regimens 46–51 . Fourteen publications were on some special pediatric populations and uncommon diseases with an assessment of renal impairment, 52–56 children with obesity, 57–60 and pediatric patients with severe coronavirus disease 2019 (COVID‐19) disease 61–65 .…”

Phenoconversion of Cytochrome P450 Metabolism: A Systematic Review

“…Secondly, phenoconversion for some conditions (e.g., inflammation) may only develop after prolonged or repeated exposure, which requires in-vitro models, such as 3D liver spheroids, in which the long-term consequences of these factors for drug metabolism can be adequately studied [ 68 , 69 ]. In-silico models, such as physiologically based pharmacokinetic (PBPK) models, have also been suggested as an excellent approach to study phenoconversion [ 70 , 71 , 72 , 73 , 74 , 75 ]. These models are particularly suitable to investigate the effects of complex interactions between multiple drugs and disease states.…”

Guiding dose adjustment of amlodipine after co-administration with ritonavir containing regimens using a physiologically-based pharmacokinetic/pharmacodynamic model

“…These models have been used frequently to simulate drug pharmacokinetics [ 13 – 15 ] and especially in elucidating complex DDI with various co-medications, all of which may be impossible to study through dedicated clinical trials [ 16 – 18 ]. PBPK models are often linked with PD models in order to predict changes in drug effect due to extrinsic or intrinsic factors that affect the drug PK, for which a recent example being the work of Moj et al [ 19 ]. Blei [ 20 ] developed a PBPK model for amlodipine, but the model did not include CYP3A4-mediated clearance, which is essential in order to model mechanistic DDI with perpetrator drugs.…”