PBPK Modeling as a Tool for Predicting and Understanding Intestinal Metabolism of Uridine 5′-Diphospho-glucuronosyltransferase Substrates

Reddy, Micaela B.; Bolger, Michael B.; Fraczkiewicz, Grazyna; Frari, Laurence Del; Luo, Lan; Lukáčová, Viera; Mitra, Amitava; Macwan, Joyce S.; Mullin, Jim; Parrott, Neil; Heikkinen, Aki T.

doi:10.3390/pharmaceutics13091325

Cited by 15 publications

(12 citation statements)

References 100 publications

(184 reference statements)

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“…Next, MATE2-K concentration in human kidney organ was calculated by formula (transporter expression × 26.2 mg/g × kidney weight) from the literature ( Scotcher et al, 2017 ). The final inputting parameters used in PBPK model for PAZ are listed in Table 2 ( Drozdzik et al, 2019 ; Li et al, 2019 ; Britz et al, 2020 ; Reddy et al, 2021 ; Krens et al, 2022 ; PMDA, 2022 ). The generic workflow of the PBPK model is represented in Figure 1 .…”

Section: Methodsmentioning

confidence: 99%

Prediction for optimal dosage of pazopanib under various clinical situations using physiologically based pharmacokinetic modeling

Meng

et al. 2022

Front. Pharmacol.

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This study aimed to apply a physiologically based pharmacokinetic (PBPK) model to predict optimal dosing regimens of pazopanib (PAZ) for safe and effective administration when co-administered with CYP3A4 inhibitors, acid-reducing agents, food, and administered in patients with hepatic impairment. Here, we have successfully developed the population PBPK model and the predicted PK variables by this model matched well with the clinically observed data. Most ratios of prediction to observation were between 0.5 and 2.0. Suitable dosage modifications of PAZ have been identified using the PBPK simulations in various situations, i.e., 200 mg once daily (OD) or 100 mg twice daily (BID) when co-administered with the two CYP3A4 inhibitors, 200 mg BID when simultaneously administered with food or 800 mg OD when avoiding food uptake simultaneously. Additionally, the PBPK model also suggested that dosing does not need to be adjusted when co-administered with esomeprazole and administration in patients with wild hepatic impairment. Furthermore, the PBPK model also suggested that PAZ is not recommended to be administered in patients with severe hepatic impairment. In summary, the present PBPK model can determine the optimal dosing adjustment recommendations in multiple clinical uses, which cannot be achieved by only focusing on AUC linear change of PK.

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Section: Methodsmentioning

confidence: 99%

Prediction for optimal dosage of pazopanib under various clinical situations using physiologically based pharmacokinetic modeling

Meng

et al. 2022

Front. Pharmacol.

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“…Apart from this, some drug transporters such as ATP binding cassettes (ABCs) and solute carrier transporters (SLCs) are responsible for influx and efflux of compounds and metabolites contributing to phases 0 (absorption) and III (elimination) [ 39 ]. All these metabolic kinetics can be incorporated in PBPK model with specific parameters such as enzymatic expression in organs, maximum rate of metabolism (Vmax) and Michaelis constant (Km), especially for non-linear metabolism (Equation (8) [ 40 ].

where p is the product and S is the substrate.…”

Section: Unravelling the Art Of Developing Pbpk Modelsmentioning

confidence: 99%

The Role of “Physiologically Based Pharmacokinetic Model (PBPK)” New Approach Methodology (NAM) in Pharmaceuticals and Environmental Chemical Risk Assessment

Deepika

Kumar

2023

IJERPH

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Physiologically Based Pharmacokinetic (PBPK) models are mechanistic tools generally employed in the pharmaceutical industry and environmental health risk assessment. These models are recognized by regulatory authorities for predicting organ concentration–time profiles, pharmacokinetics and daily intake dose of xenobiotics. The extension of PBPK models to capture sensitive populations such as pediatric, geriatric, pregnant females, fetus, etc., and diseased populations such as those with renal impairment, liver cirrhosis, etc., is a must. However, the current modelling practices and existing models are not mature enough to confidently predict the risk in these populations. A multidisciplinary collaboration between clinicians, experimental and modeler scientist is vital to improve the physiology and calculation of biochemical parameters for integrating knowledge and refining existing PBPK models. Specific PBPK covering compartments such as cerebrospinal fluid and the hippocampus are required to gain mechanistic understanding about xenobiotic disposition in these sub-parts. The PBPK model assists in building quantitative adverse outcome pathways (qAOPs) for several endpoints such as developmental neurotoxicity (DNT), hepatotoxicity and cardiotoxicity. Machine learning algorithms can predict physicochemical parameters required to develop in silico models where experimental data are unavailable. Integrating machine learning with PBPK carries the potential to revolutionize the field of drug discovery and development and environmental risk. Overall, this review tried to summarize the recent developments in the in-silico models, building of qAOPs and use of machine learning for improving existing models, along with a regulatory perspective. This review can act as a guide for toxicologists who wish to build their careers in kinetic modeling.

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“…All these metabolic kinetics can be incorporated in PBPK model with specific parameters like enzymatic expression in organs, maximum rate of metabolism (Vmax) and Michaelis constant (Km) especially for non-linear metabolism (eq. 8) (Reddy et al, 2021).…”

Section: Distribution and Fraction Unboundmentioning

confidence: 99%

The Role of “Physiologically Based Pharmacokinetic Model (PBPK)” New Approach Methodology (NAMs) in Pharmaceuticals and Environmental Chemical Risk Assessment

Deepika¹,

Kumar²

2022

Preprint

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Physiologically Based Pharmacokinetic Models (PBPK) are mechanistical tools generally employed in the pharmaceutical industry and environmental health risk assessment. These models are recognised by regulatory authorities for predicting organ concentration-time profile, pharmacokinetic and daily intake dose of xenobiotics. Extension of PBPK models to capture sensitive populations like pediatric, geriatric, pregnant females, fetus etc. and diseased population like renal impairment, liver cirrhosis etc. is a must. However, the current modelling practice and existing models are not mature enough to confidently predict the risk in these populations. A multidisciplinary collaboration between clinicians, experimental and modeler scientist is vital to improve the physiology, and calculation of biochemical parameters for integrating the knowledge and refining existing PBPK models. Specific PBPK covering compartments like cerebrospinal fluid, and hippocampus are required to gain mechanistic understanding about xenobiotic disposition in these sub-parts. The PBPK model assists in building quantitative adverse outcome pathways (qAOPs) for several endpoints like developmental neurotoxicity (DNT), hepatotoxicity and cardiotoxicity. Machine learning algorithms can predict physicochemical parameters required to develop in-silico models where experimental data is unavailable. Integrating machine learning with PBPK carries the potential to revolutionize the field of drug discovery and development and environmental risk. Overall, this review tried to summarize the recent developments in the in-silico models, building qAOPs, use of machine learning for improving existing models along with a regulatory perspective. This review can act as a guide for toxicologists who wish to build their careers in kinetic modeling.

show abstract

PBPK Modeling as a Tool for Predicting and Understanding Intestinal Metabolism of Uridine 5′-Diphospho-glucuronosyltransferase Substrates

Cited by 15 publications

References 100 publications

Prediction for optimal dosage of pazopanib under various clinical situations using physiologically based pharmacokinetic modeling

Prediction for optimal dosage of pazopanib under various clinical situations using physiologically based pharmacokinetic modeling

The Role of “Physiologically Based Pharmacokinetic Model (PBPK)” New Approach Methodology (NAM) in Pharmaceuticals and Environmental Chemical Risk Assessment

The Role of “Physiologically Based Pharmacokinetic Model (PBPK)” New Approach Methodology (NAMs) in Pharmaceuticals and Environmental Chemical Risk Assessment

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