Literature DB >> 30729397

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

Neil A Miller1, Micaela B Reddy2, Aki T Heikkinen3, Viera Lukacova4, Neil Parrott5.   

Abstract

Physiologically based pharmacokinetic modelling is well established in the pharmaceutical industry and is accepted by regulatory agencies for the prediction of drug-drug interactions. However, physiologically based pharmacokinetic modelling is valuable to address a much wider range of pharmaceutical applications, and new regulatory impact is expected as its full power is leveraged. As one example, physiologically based pharmacokinetic modelling is already routinely used during drug discovery for in-vitro to in-vivo translation and pharmacokinetic modelling in preclinical species, and this leads to the application of verified models for first-in-human pharmacokinetic predictions. A consistent cross-industry strategy in this application area would increase confidence in the approach and facilitate further learning. With this in mind, this article aims to enhance a previously published first-in-human physiologically based pharmacokinetic model-building strategy. Based on the experience of scientists from multiple companies participating in the GastroPlus™ User Group Steering Committee, new Absorption, Distribution, Metabolism and Excretion knowledge is integrated and decision trees proposed for each essential component of a first-in-human prediction. We have reviewed many relevant scientific publications to identify new findings and highlight gaps that need to be addressed. Finally, four industry case studies for more challenging compounds illustrate and highlight key components of the strategy.

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Year:  2019        PMID: 30729397     DOI: 10.1007/s40262-019-00741-9

Source DB:  PubMed          Journal:  Clin Pharmacokinet        ISSN: 0312-5963            Impact factor:   6.447


  83 in total

1.  Prediction of human clearance of twenty-nine drugs from hepatic microsomal intrinsic clearance data: An examination of in vitro half-life approach and nonspecific binding to microsomes.

Authors:  R S Obach
Journal:  Drug Metab Dispos       Date:  1999-11       Impact factor: 3.922

2.  Rapid-gradient HPLC method for measuring drug interactions with immobilized artificial membrane: comparison with other lipophilicity measures.

Authors:  K Valko; C M Du; C D Bevan; D P Reynolds; M H Abraham
Journal:  J Pharm Sci       Date:  2000-08       Impact factor: 3.534

3.  Prediction of pharmacokinetics prior to in vivo studies. 1. Mechanism-based prediction of volume of distribution.

Authors:  Patrick Poulin; Frank-Peter Theil
Journal:  J Pharm Sci       Date:  2002-01       Impact factor: 3.534

Review 4.  Predicting the impact of physiological and biochemical processes on oral drug bioavailability.

Authors:  B Agoram; W S Woltosz; M B Bolger
Journal:  Adv Drug Deliv Rev       Date:  2001-10-01       Impact factor: 15.470

Review 5.  Predicting drug disposition via application of BCS: transport/absorption/ elimination interplay and development of a biopharmaceutics drug disposition classification system.

Authors:  Chi-Yuan Wu; Leslie Z Benet
Journal:  Pharm Res       Date:  2005-01       Impact factor: 4.200

Review 6.  Rapid method for the estimation of octanol/water partition coefficient (log P(oct)) from gradient RP-HPLC retention and a hydrogen bond acidity term (zetaalpha(2)(H)).

Authors:  K Valko; C My Du; C Bevan; D P Reynolds; M H Abraham
Journal:  Curr Med Chem       Date:  2001-07       Impact factor: 4.530

7.  Prediction of human hepatic clearance from in vivo animal experiments and in vitro metabolic studies with liver microsomes from animals and humans.

Authors:  Y Naritomi; S Terashita; S Kimura; A Suzuki; A Kagayama; Y Sugiyama
Journal:  Drug Metab Dispos       Date:  2001-10       Impact factor: 3.922

8.  Correlation of biliary excretion in sandwich-cultured rat hepatocytes and in vivo in rats.

Authors:  X Liu; J P Chism; E L LeCluyse; K R Brouwer; K L Brouwer
Journal:  Drug Metab Dispos       Date:  1999-06       Impact factor: 3.922

9.  A comprehensive analysis of the role of correction factors in the allometric predictivity of clearance from rat, dog, and monkey to humans.

Authors:  Rakesh Nagilla; Keith W Ward
Journal:  J Pharm Sci       Date:  2004-10       Impact factor: 3.534

10.  Physiologically based pharmacokinetic model for terbinafine in rats and humans.

Authors:  Mahboubeh Hosseini-Yeganeh; Andrew J McLachlan
Journal:  Antimicrob Agents Chemother       Date:  2002-07       Impact factor: 5.191

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  17 in total

1.  Sampling Site Has a Critical Impact on Physiologically Based Pharmacokinetic Modeling.

Authors:  Weize Huang; Nina Isoherranen
Journal:  J Pharmacol Exp Ther       Date:  2019-10-11       Impact factor: 4.030

2.  Characterization of Preclinical Pharmacokinetic Properties and Prediction of Human PK Using a Physiologically Based Pharmacokinetic Model for a Novel Anti-Arrhythmic Agent Sulcardine Sulfate.

Authors:  Chang Ren; Yao Wang; Mei Zhang; Dexuan Kong; Chen Ning; Yujie Cheng; Yueying Bian; Mengqi Sun; Shengdi Su; Yucong Wang; Yongjie Zhang; Yang Lu; Ning Li; Di Zhao; Xijing Chen
Journal:  Pharm Res       Date:  2021-11-12       Impact factor: 4.200

Review 3.  Harnessing the predictive power of preclinical models for oncology drug development.

Authors:  Alexander Honkala; Sanjay V Malhotra; Shivaani Kummar; Melissa R Junttila
Journal:  Nat Rev Drug Discov       Date:  2021-10-26       Impact factor: 84.694

4.  Clinical Ocular Exposure Extrapolation for Ophthalmic Solutions Using PBPK Modeling and Simulation.

Authors:  Maxime Le Merdy; Farah AlQaraghuli; Ming-Liang Tan; Ross Walenga; Andrew Babiskin; Liang Zhao; Viera Lukacova
Journal:  Pharm Res       Date:  2022-09-23       Impact factor: 4.580

Review 5.  Current State and Challenges of Physiologically Based Biopharmaceutics Modeling (PBBM) in Oral Drug Product Development.

Authors:  Di Wu; Min Li
Journal:  Pharm Res       Date:  2022-09-08       Impact factor: 4.580

Review 6.  Applications, Challenges, and Outlook for PBPK Modeling and Simulation: A Regulatory, Industrial and Academic Perspective.

Authors:  Wen Lin; Yuan Chen; Jashvant D Unadkat; Xinyuan Zhang; Di Wu; Tycho Heimbach
Journal:  Pharm Res       Date:  2022-05-13       Impact factor: 4.580

7.  Physiologically based pharmacokinetic modeling (PBPK's) prediction potential in clinical pharmacology decision making during pregnancy.

Authors:  Ahizechukwu C Eke; Rahel D Gebreyohannes
Journal:  Int J Gynaecol Obstet       Date:  2020-04-21       Impact factor: 3.561

8.  Evaluation of the Success of High-Throughput Physiologically Based Pharmacokinetic (HT-PBPK) Modeling Predictions to Inform Early Drug Discovery.

Authors:  Doha Naga; Neil Parrott; Gerhard F Ecker; Andrés Olivares-Morales
Journal:  Mol Pharm       Date:  2022-04-27       Impact factor: 5.364

Review 9.  Prescription drugs and mitochondrial metabolism.

Authors:  Cameron A Schmidt
Journal:  Biosci Rep       Date:  2022-04-29       Impact factor: 3.976

10.  Internationalization of read-across as a validated new approach method (NAM) for regulatory toxicology.

Authors:  Costanza Rovida; Tara Barton-Maclaren; Emilio Benfenati; Francesca Caloni; P. Charukeshi Chandrasekera; Christophe Chesné; Mark T D Cronin; Joop De Knecht; Daniel R Dietrich; Sylvia E Escher; Suzanne Fitzpatrick; Brenna Flannery; Matthias Herzler; Susanne Hougaard Bennekou; Bruno Hubesch; Hennicke Kamp; Jaffar Kisitu; Nicole Kleinstreuer; Simona Kovarich; Marcel Leist; Alexandra Maertens; Kerry Nugent; Giorgia Pallocca; Manuel Pastor; Grace Patlewicz; Manuela Pavan; Octavio Presgrave; Lena Smirnova; Michael Schwarz; Takashi Yamada; Thomas Hartung
Journal:  ALTEX       Date:  2020-04-30       Impact factor: 6.250

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