Dose Optimization in Kidney Disease: Opportunities for PBPK Modeling and Simulation

Abstract

Kidney disease affects pharmacokinetic (PK) profiles of not only renally cleared drugs but also nonrenally cleared drugs. The impact of kidney disease on drug disposition has not been fully elucidated, but describing the extent of such impact is essential for conducting dose optimization in kidney disease. Accurate evaluation of kidney function has been a clinical interest for dose optimization, and more scientists pay attention and conduct research for clarifying the role of drug transporters, metabolic enzymes, and their interplay in drug disposition as kidney disease progresses. Physiologically based pharmacokinetic (PBPK) modeling and simulation can provide valuable insights for dose optimization in kidney disease. It is a powerful tool to integrate discrete knowledge from preclinical and clinical research and mechanistically investigate system-and drug-dependent factors that may contribute to the changes in PK profiles. PBPK-based prediction of drug exposures may be used a priori to adjust dosing regimens and thereby minimize the likelihood of drug-related toxicity. With real-time clinical studies, parameter estimation may be performed with PBPK approaches that can facilitate identification of sources of interindividual variability. PBPK modeling may also facilitate biomarker research that aids dose optimization in kidney disease. U.S. Food and Drug Administration guidances related to conduction of PK studies in kidney impairment and PBPK documentation provide the foundation for facilitating model-based dose-finding research in kidney disease.

Physiologically Based Pharmacokinetic Model To Predict Metoprolol Disposition in Healthy and Disease Populations

“…On the other hand, in moderate renal impairment, the dosage was tapered to about 12% of the doses (IV and oral), respectively, for bound AUC, i.e., (AUC 0– t ) that depicted analogous values in comparison with those of healthy ones (population). The whole pathway of dosage modification was followed by already published research papers . Apparently, no differences were noticed between AUC 0– t and AUC 0– t (unbound) in the optimization of doses as exemplified below (Figures and ).…”

Physiologically-based pharmacokinetic modelling and dosing evaluation of gentamicin in neonates using PhysPK

“…Apart from the important usefulness and potential applications of the gentamicin minimal PBPK model presented in this work, some limitations must be acknowledged such as: 1) The model has been validate with a small sample size, just 2 concentration-time points and retrospective data which might limit precise assessments and additional evaluations of the model; 2) No correlations between the physiological variables simulated were considered in the Monte Carlo simulations ( Franchetti and Nolin, 2020 ). However, the impact of these correlations is not expected to be relevant as most of the parameters were expressed per kg of TBW and specific values observed in the literature were considered for preterm and term neonates.…”

The emerging role of physiologically-based pharmacokinetic/biopharmaceutics modeling in formulation development

“…The authors tested the scenario when a patient switches from fast-release to sustained-release tacrolimus formulations, and based on the modeling outputs, they were able to provide personalized recommendations on drug dosing (35). PBPK modeling orientated toward providing proper dosing recommendations was also reported for geriatric patients (36)(37)(38) and certain disease populations (39)(40)(41).…”