Prediction of pharmacokinetics and pharmacodynamics of trelagliptin and omarigliptin in healthy humans and in patients with renal impairment using physiologically based pharmacokinetic combined DPP-4 occupancy modeling

Wu, Chunnuan; Liu, Huining; Yu, Shuang; Ren, Chao; Zhang, Jie; Wang, Guopeng; Li, Bole; Liu, Yang

doi:10.1016/j.biopha.2022.113509

Cited by 5 publications

(2 citation statements)

References 27 publications

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“…If some data was unavailable, the mean value built-in PK-Sim was used as a surrogate. The final parameters of the model are summarized in Table . − …”

Section: Methodsmentioning

confidence: 99%

Prediction for Plasma Trough Concentration and Optimal Dosing of Imatinib under Multiple Clinical Situations Using Physiologically Based Pharmacokinetic Modeling

Gao

Wang²,

et al. 2023

ACS Omega

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(1) Purpose: This study aimed to develop a physiologically based pharmacokinetic (PBPK) model to predict the trough concentration (C trough) of imatinib (IMA) at steady state in patients and to explore the role of free concentration (f up), α1-acid glycoprotein (AGP) level, and organic cation transporter 1 (OCT1) activity/expression in clinical efficacy. (2) Methods: The population PBPK model was built using physicochemical and biochemical properties, metabolizing and transporting kinetics, tissue distribution, and human physiological parameters. (3) Results: The PBPK model successfully predicted the C trough of IMA administered alone in chronic phase (CP) and accelerated phase (AP) patients, the C trough of IMA co-administered with six modulators, and C trough in CP patients with hepatic impairment. Most of the ratios between predicted and observed data are within 0.70–1.30. Additionally, the recommendations for dosing adjustments for IMA have been given under multiple clinical uses. The sensitivity analysis showed that exploring the f up and AGP level had a significant influence on the plasma C trough of IMA. Meanwhile, the simulations also revealed that OCT1 activity and expression had a significant impact on the intracellular C trough of IMA. (4) Conclusion: The current PBPK model can accurately predict the IMA C trough and provide appropriate dosing adjustment recommendations in a variety of clinical situations.

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“…If some data was unavailable, the mean value built-in PK-Sim was used as a surrogate. The final parameters of the model are summarized in Table . − …”

Section: Methodsmentioning

confidence: 99%

Prediction for Plasma Trough Concentration and Optimal Dosing of Imatinib under Multiple Clinical Situations Using Physiologically Based Pharmacokinetic Modeling

Gao

Wang²,

et al. 2023

ACS Omega

Self Cite

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“…These studies used a variety of simulation platforms/programs. For the renal impairment population three of the studies were conducted using PK-Sim platform (C Wu et al, 2022;Dubinsky et al, 2022;Alsmadi and Alzughoul, 2023), three were conducted using Simcyp population simulator (Miao et al, 2022;Wang and Chan, 2022b;a) and one using Gastroplus . For hepatic impairment populations three of the studies were conducted using Simcyp (Watanabe et al, 2022;X Wu et al, 2022;Ladumor et al, 2023) and one using PK-Sim (L Xu et al, 2022).…”

Section: Model Development Applications and Verificationmentioning

confidence: 99%

Physiologically Based Pharmacokinetic (PBPK) Modeling of small molecules: How Much Progress Have We Made?

Isoherranen

2024

Drug Metab Dispos

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Physiologically based pharmacokinetic (PBPK) models of small molecules have become mainstream in drug development and in academic research. The use of PBPK models is continuously expanding with the majority of work now focusing on predictions of drug-drug interactions, drug-disease interactions, and changes in drug disposition across lifespan. Recently, publications that use PBPK modeling to predict drug disposition during pregnancy and in organ impairment have increased reflecting the advances in incorporating diverse physiological changes into the models. Due to the expanding computational power and diversity of modeling platforms available, the complexity of PBPK models has also increased. Academic efforts have provided clear advances in better capturing human physiology in PBPK models and incorporating more complex mathematical concepts into PBPK models. Examples of such advances include the segregated gut model with a series of gut compartments allowing modeling of physiological blood flow distribution within an organ and zonation of metabolic enzymes, and series compartment liver models allowing simulations of hepatic clearance for high extraction drugs. Despite these advances in academic research, the progress in assessing model quality and defining model acceptance criteria based on the intended use of the models has not kept pace. This review suggests that awareness of the need for predefined criteria for model acceptance has increased but many manuscripts still lack description of scientific justification and/or rationale for chosen acceptance criteria. As artificial intelligence and machine learning approaches become more broadly accepted, these tools offer promise for development of comprehensive assessment for existing observed data and analysis of model performance.

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Regulatory Requirements and Applications of Physiologically Based Pharmacokinetic Models

Cuquerella-Gilabert,

Merino-Sanjuán,

García-Arieta

et al. 2024

ADME Processes in Pharmaceutical Sciences

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Prediction of pharmacokinetics and pharmacodynamics of trelagliptin and omarigliptin in healthy humans and in patients with renal impairment using physiologically based pharmacokinetic combined DPP-4 occupancy modeling

Cited by 5 publications

References 27 publications

Prediction for Plasma Trough Concentration and Optimal Dosing of Imatinib under Multiple Clinical Situations Using Physiologically Based Pharmacokinetic Modeling

Prediction for Plasma Trough Concentration and Optimal Dosing of Imatinib under Multiple Clinical Situations Using Physiologically Based Pharmacokinetic Modeling

Physiologically Based Pharmacokinetic (PBPK) Modeling of small molecules: How Much Progress Have We Made?

Regulatory Requirements and Applications of Physiologically Based Pharmacokinetic Models

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