Literature DB >> 23225350

A physiologically based pharmacokinetic (PBPK) approach to evaluate pharmacokinetics in patients with cancer.

Sravanthi Cheeti1, Nageshwar R Budha, Sharmila Rajan, Mark J Dresser, Jin Y Jin.   

Abstract

Potential differences in pharmacokinetics (PK) between healthy subjects and patients with cancer were investigated using a physiologically based pharmacokinetic approach integrating demographic and physiological data from patients with cancer. Demographic data such as age, sex and body weight, and clinical laboratory measurements such as albumin, alpha-1 acid glycoprotein (AAG) and hematocrit were collected in ~2500 patients with cancer. A custom oncology population profile was built using the observed relationships among demographic variables and laboratory measurements in Simcyp® software, a population based ADME simulator. Patients with cancer were older compared with the age distribution in a built-in healthy volunteer profile in Simcyp. Hematocrit and albumin levels were lower and AAG levels were higher in patients with cancer. The custom population profile was used to investigate the disease effect on the pharmacokinetics of two probe substrates, saquinavir and midazolam. Higher saquinavir exposure was predicted in patients relative to healthy subjects, which was explained by the altered drug binding due to elevated AAG levels in patients with cancer. Consistent with historical clinical data, similar midazolam exposure was predicted in patients and healthy subjects, supporting the hypothesis that the CYP3A activity is not altered in patients with cancer. These results suggest that the custom oncology population profile is a promising tool for the prediction of PK in patients with cancer. Further evaluation and extension of this population profile with more compounds and more data will be needed.
Copyright © 2012 John Wiley & Sons, Ltd.

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Year:  2013        PMID: 23225350     DOI: 10.1002/bdd.1830

Source DB:  PubMed          Journal:  Biopharm Drug Dispos        ISSN: 0142-2782            Impact factor:   1.627


  28 in total

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4.  Assessment of DCE-MRI parameters for brain tumors through implementation of physiologically-based pharmacokinetic model approaches for Gd-DOTA.

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6.  A physiologically based pharmacokinetic model for voriconazole disposition predicts intestinal first-pass metabolism in children.

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Journal:  Clin Pharmacokinet       Date:  2014-12       Impact factor: 6.447

7.  Population pharmacokinetic analysis of abiraterone in chemotherapy-naïve and docetaxel-treated patients with metastatic castration-resistant prostate cancer.

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Journal:  Clin Pharmacokinet       Date:  2014-12       Impact factor: 6.447

8.  Physiologically based absorption modelling to predict the impact of drug properties on pharmacokinetics of bitopertin.

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9.  A Novel Integrated Pharmacokinetic-Pharmacodynamic Model to Evaluate Combination Therapy and Determine In Vivo Synergism.

Authors:  Young Hee Choi; Chao Zhang; Zhenzhen Liu; Mei-Juan Tu; Ai-Xi Yu; Ai-Ming Yu
Journal:  J Pharmacol Exp Ther       Date:  2021-03-12       Impact factor: 4.030

10.  Comprehensive PBPK model to predict drug interaction potential of Zanubrutinib as a victim or perpetrator.

Authors:  Kun Wang; Xueting Yao; Miao Zhang; Dongyang Liu; Yuying Gao; Srikumar Sahasranaman; Ying C Ou
Journal:  CPT Pharmacometrics Syst Pharmacol       Date:  2021-05-02
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