Literature DB >> 18336055

Evaluation of a generic physiologically based pharmacokinetic model for lineshape analysis.

Sheila Annie Peters1.   

Abstract

BACKGROUND AND
OBJECTIVE: The mechanistic framework of physiologically based pharmacokinetic (PBPK) models makes them uniquely suited to hypothesis testing and lineshape analysis, which help provide valuable insights into mechanisms that contribute to the observed concentration-time profiles. The aim of this article is to evaluate the utility of PBPK models for simulating oral lineshapes by optimizing clearance and distribution parameters through fitting observed intravenous pharmacokinetic profiles.
METHODS: A generic PBPK model, built in-house using MATLAB software and incorporating absorption, metabolism, distribution, biliary and renal elimination models, was employed for simulation of the concentration-time profiles of nine marketed drugs with diverse physicochemical and pharmacokinetic profiles and absorption rates determined solely by transcellular or paracellular permeability and solubility. The model is based on easily available physicochemical properties of compounds such as the log P, acid dissociation constant and solubility, and in vitro pharmacokinetic data such as Caco-2 permeability, the fraction of the compound unbound in plasma, and microsomal or hepatocyte intrinsic clearance. Clearance and distribution parameters optimized through simulation of intravenous profiles were used to simulate their corresponding oral profiles, which are determined by a multitude of parameters, both compound-dependent and physiological. Comparison of the simulated and observed oral profiles was done using goodness-of-fit parameters such as the reduced chi(2) statistic. Fold errors were calculated for the area under the plasma concentration-time curve (AUC), maximum plasma concentration (C(max)) and time to reach the C(max) (t(max)), to assess the accuracy of predictions.
RESULTS: The approach of predicting the oral profiles by optimizing the clearance and distribution parameters using the observed intravenous profile seemed to perform well for the nine compounds chosen for the study. The mean fold error for oral pharmacokinetic parameters, such as the C(max), t(max) and AUC, and for lineshape simulation was within 2-fold.
CONCLUSIONS: The validation of the generic PBPK model built in-house demonstrated that as long as the absorption profile of a compound is determined solely by solubility and paracellular or transcellular permeability, the PBPK simulations of oral profiles using optimized parameters from intravenous simulations provide reasonably good agreement with the observed profile with respect to both the lineshape fit and prediction of pharmacokinetic parameters. Therefore, any lineshape mismatch between PBPK simulated and observed oral profiles can be interpreted suitably to gain mechanistic insights into the pharmacokinetic processes that have resulted in the observed lineshape. A strategy has been proposed to identify involvement of carrier-mediated transport; clearance saturation; enterohepatic recirculation of the parent compound; extra-hepatic, extra-gut elimination; higher in vivo solubility than predicted in vitro; drug-induced gastric emptying delays; gut loss and regional variation in gut absorption.

Mesh:

Year:  2008        PMID: 18336055     DOI: 10.2165/00003088-200847040-00004

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


  51 in total

Review 1.  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

2.  A physiologic model for simulating gastrointestinal flow and drug absorption in rats.

Authors:  Stefan Willmann; Walter Schmitt; Jörg Keldenich; Jennifer B Dressman
Journal:  Pharm Res       Date:  2003-11       Impact factor: 4.200

3.  SOLUBILITY STUDIES ON CERTAIN BARBITURATES.

Authors:  R L SEDAM; A R GENNARO; A SOSOL
Journal:  J Pharm Sci       Date:  1965-02       Impact factor: 3.534

4.  Evaluation of an integrated in vitro-in silico PBPK (physiologically based pharmacokinetic) model to provide estimates of human bioavailability.

Authors:  Hongliang Cai; Chad Stoner; Anita Reddy; Sascha Freiwald; Danielle Smith; Roger Winters; Charles Stankovic; Narayanan Surendran
Journal:  Int J Pharm       Date:  2005-12-13       Impact factor: 5.875

5.  Application of a generic physiologically based pharmacokinetic model to the estimation of xenobiotic levels in human plasma.

Authors:  F A Brightman; D E Leahy; G E Searle; S Thomas
Journal:  Drug Metab Dispos       Date:  2005-10-12       Impact factor: 3.922

6.  Correlation of human jejunal permeability (in vivo) of drugs with experimentally and theoretically derived parameters. A multivariate data analysis approach.

Authors:  S Winiwarter; N M Bonham; F Ax; A Hallberg; H Lennernäs; A Karlén
Journal:  J Med Chem       Date:  1998-12-03       Impact factor: 7.446

Review 7.  Clinical pharmacokinetics of cimetidine.

Authors:  A Somogyi; R Gugler
Journal:  Clin Pharmacokinet       Date:  1983 Nov-Dec       Impact factor: 6.447

8.  Protein binding and ivermectin estimations in patients with onchocerciasis.

Authors:  P O Okonkwo; J E Ogbuokiri; E Ofoegbu; U Klotz
Journal:  Clin Pharmacol Ther       Date:  1993-04       Impact factor: 6.875

9.  Factors affecting diazepam infusion: solubility, administration-set composition, and flow rate.

Authors:  N A Mason; S Cline; M L Hyneck; R R Berardi; N F Ho; G L Flynn
Journal:  Am J Hosp Pharm       Date:  1981-10

10.  DrugBank: a comprehensive resource for in silico drug discovery and exploration.

Authors:  David S Wishart; Craig Knox; An Chi Guo; Savita Shrivastava; Murtaza Hassanali; Paul Stothard; Zhan Chang; Jennifer Woolsey
Journal:  Nucleic Acids Res       Date:  2006-01-01       Impact factor: 16.971

View more
  37 in total

1.  Comment on: "A Physiologically Based Pharmacokinetic Drug-Disease Model to Predict Carvedilol Exposure in Adult and Paediatric Heart Failure Patients by Incorporating Pathophysiological Changes in Hepatic and Renal Blood".

Authors:  Guo-Fu Li; Xiao Gu; Guo Yu; Shui-Yu Zhao; Qing-Shan Zheng
Journal:  Clin Pharmacokinet       Date:  2016-01       Impact factor: 6.447

2.  Predicting the Disposition of the Antimalarial Drug Artesunate and Its Active Metabolite Dihydroartemisinin Using Physiologically Based Pharmacokinetic Modeling.

Authors:  Ryan Arey; Brad Reisfeld
Journal:  Antimicrob Agents Chemother       Date:  2021-02-17       Impact factor: 5.191

Review 3.  To scale or not to scale: the principles of dose extrapolation.

Authors:  Vijay Sharma; John H McNeill
Journal:  Br J Pharmacol       Date:  2009-06-05       Impact factor: 8.739

4.  Predicting Drug-Drug Interactions Between Rifampicin and Long-Acting Cabotegravir and Rilpivirine Using Physiologically Based Pharmacokinetic Modeling.

Authors:  Rajith K R Rajoli; Paul Curley; Justin Chiong; David Back; Charles Flexner; Andrew Owen; Marco Siccardi
Journal:  J Infect Dis       Date:  2019-05-05       Impact factor: 5.226

5.  Simulation of human intravenous and oral pharmacokinetics of 21 diverse compounds using physiologically based pharmacokinetic modelling.

Authors:  Hannah M Jones; Iain B Gardner; Wendy T Collard; Phil J Stanley; Penny Oxley; Natilie A Hosea; David Plowchalk; Steve Gernhardt; Jing Lin; Maurice Dickins; S Ravi Rahavendran; Barry C Jones; Kenny J Watson; Henry Pertinez; Vikas Kumar; Susan Cole
Journal:  Clin Pharmacokinet       Date:  2011-05       Impact factor: 6.447

Review 6.  Dose selection based on physiologically based pharmacokinetic (PBPK) approaches.

Authors:  Hannah M Jones; Kapil Mayawala; Patrick Poulin
Journal:  AAPS J       Date:  2012-12-27       Impact factor: 4.009

7.  Evaluation of the GastroPlus™ Advanced Compartmental and Transit (ACAT) Model in Early Discovery.

Authors:  N Gobeau; R Stringer; S De Buck; T Tuntland; B Faller
Journal:  Pharm Res       Date:  2016-06-08       Impact factor: 4.200

8.  Predicting Pharmacokinetics of a Tenofovir Alafenamide Subcutaneous Implant Using Physiologically Based Pharmacokinetic Modelling.

Authors:  Rajith K R Rajoli; Zach R Demkovich; Charles Flexner; Andrew Owen; Marco Siccardi
Journal:  Antimicrob Agents Chemother       Date:  2020-07-22       Impact factor: 5.191

9.  Modeling Drug Disposition and Drug-Drug Interactions Through Hypothesis-Driven Physiologically Based Pharmacokinetics: a Reversal Translation Perspective.

Authors:  Guo-Fu Li; Qing-Shan Zheng
Journal:  Eur J Drug Metab Pharmacokinet       Date:  2018-06       Impact factor: 2.441

10.  Modelling and PBPK simulation in drug discovery.

Authors:  Hannah M Jones; Iain B Gardner; Kenny J Watson
Journal:  AAPS J       Date:  2009-03-12       Impact factor: 4.009

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.