Literature DB >> 22526410

Novel physiologically based pharmacokinetic modeling of patupilone for human pharmacokinetic predictions.

Binfeng Xia1, Tycho Heimbach, Tsu-han Lin, Handan He, Yanfeng Wang, Eugene Tan.   

Abstract

PURPOSE: Patupilone (EPO906) is a novel potent microtubule stabilizer, which has been evaluated for cancer treatment. A novel physiologically based pharmacokinetics (PBPK) model was developed based on nonclinical data to predict the disposition of patupilone in cancer patients.
METHODS: After a single intravenous dose (1.2 mg/kg) in male Han-Wistar rats, the tissue distribution of (14)C-patupilone was investigated by quantitative whole-body autoradiography (QWBA). The blood radioactivity and patupilone concentration were determined by LC-MS/MS and liquid scintillation counting. A novel PBPK model was developed based on rat tissue concentration data to predict blood concentration-time profiles of patupilone in cancer patients. PBPK parameters derived from the rat were applied to a human PBPK model. Phase I clinical pharmacokinetic data in Caucasian and Japanese cancer patients at various doses ranging from 0.75 to 10 mg/m(2) were successfully described using the PBPK approach.
RESULTS: Patupilone dispositions in lung, heart, muscle, spleen, liver, brain, adipose, and testes of rats were well described using the PBPK model developed assuming a perfusion rate-limited distribution between different compartments. For skin and bone marrow, concentration-time profiles were modeled assuming a permeability-limited distribution between different compartments. The simulated human pharmacokinetic profiles from the PBPK model showed good agreement with observed clinical pharmacokinetic data, where the model predicted AUC, t(1/2), V(ss), and CL values were within approximately twofold of the observed values for all dose groups.
CONCLUSIONS: The distribution of patupilone in rats was well described by a PBPK model based on measured tissue distribution profiles generated by QWBA combined with metabolism data. The human PBPK model adequately predicted blood pharmacokinetics of patupilone in cancer patients. The PBPK model based upon preclinical tissue distribution data can aid in successful prediction of pharmacokinetics in humans.

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Year:  2012        PMID: 22526410     DOI: 10.1007/s00280-012-1863-5

Source DB:  PubMed          Journal:  Cancer Chemother Pharmacol        ISSN: 0344-5704            Impact factor:   3.333


  8 in total

1.  Development of a Physiologically Based Pharmacokinetic Model for Sinogliatin, a First-in-Class Glucokinase Activator, by Integrating Allometric Scaling, In Vitro to In Vivo Exploration and Steady-State Concentration-Mean Residence Time Methods: Mechanistic Understanding of its Pharmacokinetics.

Authors:  Ling Song; Yi Zhang; Ji Jiang; Shuang Ren; Li Chen; Dongyang Liu; Xijing Chen; Pei Hu
Journal:  Clin Pharmacokinet       Date:  2018-10       Impact factor: 6.447

2.  Case studies for practical food effect assessments across BCS/BDDCS class compounds using in silico, in vitro, and preclinical in vivo data.

Authors:  Tycho Heimbach; Binfeng Xia; Tsu-han Lin; Handan He
Journal:  AAPS J       Date:  2012-11-10       Impact factor: 4.009

3.  A simplified PBPK modeling approach for prediction of pharmacokinetics of four primarily renally excreted and CYP3A metabolized compounds during pregnancy.

Authors:  Binfeng Xia; Tycho Heimbach; Rakesh Gollen; Charvi Nanavati; Handan He
Journal:  AAPS J       Date:  2013-07-09       Impact factor: 4.009

4.  Utility of physiologically based pharmacokinetic (PBPK) modeling in oncology drug development and its accuracy: a systematic review.

Authors:  Teerachat Saeheng; Kesara Na-Bangchang; Juntra Karbwang
Journal:  Eur J Clin Pharmacol       Date:  2018-07-05       Impact factor: 2.953

Review 5.  Physiologically Based Pharmacokinetic Modelling for First-In-Human Predictions: An Updated Model Building Strategy Illustrated with Challenging Industry Case Studies.

Authors:  Neil A Miller; Micaela B Reddy; Aki T Heikkinen; Viera Lukacova; Neil Parrott
Journal:  Clin Pharmacokinet       Date:  2019-06       Impact factor: 6.447

Review 6.  Challenges Associated With Applying Physiologically Based Pharmacokinetic Modeling for Public Health Decision-Making.

Authors:  Yu-Mei Tan; Rachel R Worley; Jeremy A Leonard; Jeffrey W Fisher
Journal:  Toxicol Sci       Date:  2018-04-01       Impact factor: 4.849

Review 7.  Drug Exposure to Establish Pharmacokinetic-Response Relationships in Oncology.

Authors:  Belén P Solans; María Jesús Garrido; Iñaki F Trocóniz
Journal:  Clin Pharmacokinet       Date:  2020-02       Impact factor: 6.447

8.  Interrogating the relationship between rat in vivo tissue distribution and drug property data for >200 structurally unrelated molecules.

Authors:  Andrew W Harrell; Caroline Sychterz; May Y Ho; Andrew Weber; Klara Valko; Kitaw Negash
Journal:  Pharmacol Res Perspect       Date:  2015-08-10
  8 in total

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