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Use of normalized prediction distribution errors for assessing population physiologically-based pharmacokinetic model adequacy.

ABSTRACT: Currently employed methods for qualifying population physiologically-based pharmacokinetic (Pop-PBPK) model predictions of continuous outcomes (e.g., concentration-time data) fail to account for within-subject correlations and the presence of residual error. In this study, we propose a new method for evaluating Pop-PBPK model predictions that account for such features. The approach focuses on deriving Pop-PBPK-specific normalized prediction distribution errors (NPDE), a metric that is commonly used for population pharmacokinetic model validation. We describe specific methodological steps for computing NPDE for Pop-PBPK models and define three measures for evaluating model performance: mean of NPDE, goodness-of-fit plots, and the magnitude of residual error. Utility of the proposed evaluation approach was demonstrated using two simulation-based study designs (positive and negative control studies) as well as pharmacokinetic data from a real-world clinical trial. For the positive-control simulation study, where observations and model simulations were generated under the same Pop-PBPK model, the NPDE-based approach denoted a congruency between model predictions and observed data (mean of NPDE = - 0.01). In contrast, for the negative-control simulation study, where model simulations and observed data were generated under different Pop-PBPK models, the NPDE-based method asserted that model simulations and observed data were incongruent (mean of NPDE = - 0.29). When employed to evaluate a previously developed clindamycin PBPK model against prospectively collected plasma concentration data from 29 children, the NPDE-based method qualified the model predictions as successful (mean of NPDE = 0). However, when pediatric subpopulations (e.g., infants) were evaluated, the approach revealed potential biases that should be explored.

SUBMITTER: Maharaj AR

PROVIDER: S-EPMC7293575 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Introduction: Niclosamide (Nc) is an FDA-approved anthelmintic drug that was recently identified in a drug repurposing screening to possess antiviral activity against SARS-CoV-2. However, due to the low solubility and permeability of Nc, its in vivo efficacy was limited by its poor oral absorption. Method: The current study evaluated a novel prodrug of Nc (PDN; NCATS-SM4705) in improving in vivo exposure of Nc and predicted pharmacokinetic profiles of PDN and Nc across different species. ADME properties of the prodrug were determined in humans, hamsters, and mice, while the pharmacokinetics (PK) of PDN were obtained in mice and hamsters. Concentrations of PDN and Nc in plasma and tissue homogenates were measured by UPLC-MS/MS. A physiologically based pharmacokinetic (PBPK) model was developed based on physicochemical properties, pharmacokinetic and tissue distribution data in mice, validated by the PK profiles in hamsters and applied to predict pharmacokinetic profiles in humans. Results: Following intravenous and oral administration of PDN in mice, the total plasma clearance (CLp) and volume of distribution at steady-state (Vdss) were 0.061–0.063 L/h and 0.28–0.31 L, respectively. PDN was converted to Nc in both liver and blood, improving the systemic exposure of Nc in mice and hamsters after oral administration. The PBPK model developed for PDN and in vivo formed Nc could adequately simulate plasma and tissue concentration-time profiles in mice and plasma profiles in hamsters. The predicted human CLp/F and Vdss/F after an oral dose were 2.1 L/h/kg and 15 L/kg for the prodrug respectively. The predicted Nc concentrations in human plasma and lung suggest that a TID dose of 300 mg PDN would provide Nc lung concentrations at 8- to 60-fold higher than in vitro IC50 against SARS-CoV-2 reported in cell assays. Conclusion: In conclusion, the novel prodrug PDN can be efficiently converted to Nc in vivo and improves the systemic exposure of Nc in mice after oral administration. The developed PBPK model adequately depicts the mouse and hamster pharmacokinetic and tissue distribution profiles and highlights its potential application in the prediction of human pharmacokinetic profiles.

Project description:We developed a detailed, whole-body physiologically based pharmacokinetic (PBPK) modeling tool for calculating the distribution of pharmaceutical agents in the various tissues and organs of a human or animal as a function of time. Ordinary differential equations (ODEs) represent the circulation of body fluids through organs and tissues at the macroscopic level, and the biological transport mechanisms and biotransformations within cells and their organelles at the molecular scale. Each major organ in the body is modeled as composed of one or more tissues. Tissues are made up of cells and fluid spaces. The model accounts for the circulation of arterial and venous blood as well as lymph. Since its development was fueled by the need to accurately predict the pharmacokinetic properties of imaging agents, BioDMET is more complex than most PBPK models. The anatomical details of the model are important for the imaging simulation endpoints. Model complexity has also been crucial for quickly adapting the tool to different problems without the need to generate a new model for every problem. When simpler models are preferred, the non-critical compartments can be dynamically collapsed to reduce unnecessary complexity. BioDMET has been used for imaging feasibility calculations in oncology, neurology, cardiology, and diabetes. For this purpose, the time concentration data generated by the model is inputted into a physics-based image simulator to establish imageability criteria. These are then used to define agent and physiology property ranges required for successful imaging. BioDMET has lately been adapted to aid the development of antimicrobial therapeutics. Given a range of built-in features and its inherent flexibility to customization, the model can be used to study a variety of pharmacokinetic and pharmacodynamic problems such as the effects of inter-individual differences and disease-states on drug pharmacokinetics and pharmacodynamics, dosing optimization, and inter-species scaling. While developing a tool to aid imaging agent and drug development, we aimed at accelerating the acceptance and broad use of PBPK modeling by providing a free mechanistic PBPK software that is user friendly, easy to adapt to a wide range of problems even by non-programmers, provided with ready-to-use parameterized models and benchmarking data collected from the peer-reviewed literature.

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Use of normalized prediction distribution errors for assessing population physiologically-based pharmacokinetic model adequacy.

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