Dataset Information

Predictive Performance of Physiologically Based Pharmacokinetic Models for the Effect of Food on Oral Drug Absorption: Current Status.

ABSTRACT: A comprehensive search in literature and published US Food and Drug Administration reviews was conducted to assess whether physiologically based pharmacokinetic (PBPK) modeling could be prospectively used to predict clinical food effect on oral drug absorption. Among the 48 resulted food effect predictions, ?50% were predicted within 1.25-fold of observed, and 75% within 2-fold. Dissolution rate and precipitation time were commonly optimized parameters when PBPK modeling was not able to capture the food effect. The current work presents a knowledgebase for documenting PBPK experience to predict food effect.

SUBMITTER: Li M

PROVIDER: S-EPMC5824104 | biostudies-literature | 2018 Feb

REPOSITORIES: biostudies-literature

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Predictive Performance of Physiologically Based Pharmacokinetic Models for the Effect of Food on Oral Drug Absorption: Current Status.

Li Mengyao M Zhao Ping P Pan Yuzhuo Y Wagner Christian C

CPT: pharmacometrics & systems pharmacology 20171123 2

A comprehensive search in literature and published US Food and Drug Administration reviews was conducted to assess whether physiologically based pharmacokinetic (PBPK) modeling could be prospectively used to predict clinical food effect on oral drug absorption. Among the 48 resulted food effect predictions, ∼50% were predicted within 1.25-fold of observed, and 75% within 2-fold. Dissolution rate and precipitation time were commonly optimized parameters when PBPK modeling was not able to capture ...[more]

PMID: 29168611

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Project description:Background and objectivesPhysiologically based pharmacokinetic (PBPK) modelling has evolved to accommodate different routes of drug administration and enables prediction of drug concentrations in tissues as well as plasma. The inhalation route of administration has proven successful in treating respiratory diseases but can also be used for rapid systemic delivery, holding great promise for treatment of diseases requiring systemic exposure. The objective of this work was to develop a PBPK model that predicts plasma and tissue concentrations following inhalation administration of the PI3Kδ inhibitor nemiralisib.MethodsA PBPK model was built in GastroPlus® that includes a complete mechanistic description of pulmonary absorption, systemic distribution and oral absorption following inhalation administration of nemiralisib. The availability of clinical data obtained after intravenous, oral and inhalation administration enabled validation of the model with observed data and accurate assessment of pulmonary drug absorption. The PBPK model described in this study incorporates novel use of key parameters such as lung systemic absorption rate constants derived from human physiological lung blood flows, and implementation of the specific permeability-surface area product per millilitre of tissue cell volume (SpecPStc) to predict tissue distribution.ResultsThe inhaled PBPK model was verified using plasma and bronchoalveolar lavage fluid concentration data obtained in human subjects. Prediction of tissue concentrations using the permeability-limited systemic disposition tissue model was further validated using tissue concentration data obtained in the rat following intravenous infusion administration to steady state.ConclusionsFully mechanistic inhaled PBPK models such as the model described herein could be applied for cross molecule assessments with respect to lung retention and systemic exposure, both in terms of pharmacology and toxicology, and may facilitate clinical indication selection.

Project description:Background and objectiveAmikacin is preferred in treating Gram-negative infections in neonates and it has a narrow therapeutic window. The population pharmacokinetic modeling approach can aid in designing optimal dosage regimens for amikacin in neonates. In this study, we attempted to identify the suitable population pharmacokinetic model from the published reports for the study population from an Indian setting.MethodsPublished population pharmacokinetic studies for amikacin in neonates were identified. Data on structural models and typical pharmacokinetic parameters were extracted from the studies. For the clinical study, neonates who met the inclusion criteria were enrolled in the study from the NICU, Kasturba Medical College, Manipal, during Jan 2020 to March 2022. Drug concentrations were estimated, and demographic and clinical data were collected. Identified population pharmacokinetic models were used to predict the amikacin concentrations in neonates. Predicted concentrations were compared against the observed concentrations. Differences between predicted and observed concentrations were quantified using statistical measures. The population pharmacokinetic model, which was able to predict the data well, is considered a suitable model for the study population. Dosing regimens were suggested for neonates using the pharmacometric simulation approach generated by the selected model.ResultsA total of 43 plasma samples were collected from 31 neonates. Twelve population pharmacokinetic models were found for amikacin in neonates. The predictive performance of the 12 studies was performed using clinical data. A two-compartment model reported by Illamola et al. predicted the amikacin concentrations better than other models. Illamola et al. reported creatinine clearance and body weight as the significant covariates impacting the pharmacokinetic parameters of amikacin. This model was able to predict the clinical data with 29.97% and 0.686 of relative median absolute prediction error and relative root mean square error, respectively, which is the best among the published models. The Illamola et al. model was selected as the final model to perform pharmacometric simulations for the subjects with different combinations of creatinine clearance and body weight. Dosage regimens were designed to attain target therapeutic concentrations for the virtual subjects and a nomogram was developed.ConclusionsThe population pharmacokinetic model reported by the Illamola et al. model was selected as the final model to explain the clinical data with the lowest relative median absolute prediction error and relative root mean square error when compared with other models. An amikacin nomogram was developed for the neonates whose creatinine clearance and body weight ranged between 10 and 90 mL/min and between 2 and 4 kg, respectively. A developed nomogram can assist clinicians to design an optimal dosage regimen of amikacin for term neonates.

Dataset Information

Predictive Performance of Physiologically Based Pharmacokinetic Models for the Effect of Food on Oral Drug Absorption: Current Status.

Publications

Predictive Performance of Physiologically Based Pharmacokinetic Models for the Effect of Food on Oral Drug Absorption: Current Status.

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