Pattern Recognition in Pharmacokinetic Data Analysis

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• 作者：Johan Gabrielsson ; Bernd Meibohm ; Daniel Weiner
• 关键词：absorption ; area under the curve ; bi ; exponential ; half ; life ; induction ; intravenous and extravascular dosing ; lag time ; mono ; exponential ; multi ; compartment ; nonlinear elimination ; plasma concentration ; time courses ; target ; mediated drug disposition ; transporters
• 刊名：The AAPS Journal
• 出版年：2016
• 出版时间：January 2016
• 年：2016
• 卷：18
• 期：1
• 页码：47-63
• 全文大小：2,887 KB
• 参考文献：1.Duan JZ. Drug-drug interaction pattern recognition. Drugs R D. 2010;10(1):9–24.PubMedCentral CrossRef PubMed
  2.Gabrielsson J, Weiner D. Pharmacokinetic and pharmacodynamic data analysis: concepts and applications. 1, 2, 3, 4th ed. Stockholm: Swedish Pharmaceutical Press; 1994–2010.
  3.Jusko WJ. Guidelines for collection and analysis of pharmacokinetic data. In: Burton ME, Shaw LM, Schentag JJ, Evans WE, editors. Applied pharmacokinetics: principles of therapeutic drug monitoring. 4th ed. Philadelphia: Lippincott Williams and Wilkins; 2006.
  4.Cobelli C, DiStefano III JJ. Parameters and structural identifiability concepts and ambiguities: a critical review and analysis. Am J Physiol. 1980;239:R7–24.PubMed
  5.Norberg Å, Gabrielsson J, Jones AW, Hahn RG. Within-and between-subject variations in pharmacokinetic parameters of ethanol by analysis of breath, venous blood and urine. Br J Clin Pharmacol. 2000;49:399–408.PubMedCentral CrossRef PubMed
  6.Norberg Å, Jones AW, Hahn RG, Gabrielsson J. Role of variability in ethanol kinetics—review. Clin Pharmacokinet. 2003;42:1–31.CrossRef PubMed
  7.Peletier LA, Gabrielsson J. Dynamics of target-mediated drug disposition: characteristic profiles and parameter identification. J Pharmacokinet Pharmacodyn. 2012;39:429–51.PubMedCentral CrossRef PubMed
  8.Chirmule N, Jawa V, Meibohm B. Immunogenicity to therapeutic proteins: impact on PK/PD and efficacy. AAPS J. 2012;14:296–302.PubMedCentral CrossRef PubMed
  9.von Bahr C, Steiner E, Koike Y, Gabrielsson J. Time course of enzyme induction in humans: effect of pentobarbital on nortriptyline metabolism. Clin Pharmacol Ther. 1998;64:18–26.CrossRef
  10.Lockard JS, Levy RH, Uhlir V, Farquhar JA. Pharmacokinetic evaluation of anticonvulsants prior to efficacy testing exemplified by carbamazepine in epileptic monkey model. Epilepsia. 1974;15(3):351–59.CrossRef PubMed
  11.Yang J, Jamei M, Yeo KR, Rostami-Hodjegan A, Tucker GT. Misuse of the wellstirred model of hepatic drug clearance. Drug Metab Dispos. 2007;35:501–2.CrossRef PubMed
  
• 作者单位：Johan Gabrielsson (1)
  Bernd Meibohm (2)
  Daniel Weiner (3)
  
  1. Department of Biomedical Sciences and Veterinary Public Health, SLU, Division of Pharmacology and Toxicology, Box 7028, SE-750 07, Uppsala, Sweden
  2. College of Pharmacy, University of Tennessee Health Science Center, 881 Madison Avenue, Rm. 444, Memphis, Tennessee, 38163, USA
  3. 709 Cambridge Hall Loop, Apex, North Carolina, 27539, USA
  
• 刊物主题：Pharmacology/Toxicology; Biochemistry, general; Biotechnology; Pharmacy;
• 出版者：Springer US
• ISSN：1550-7416

文摘

Pattern recognition is a key element in pharmacokinetic data analyses when first selecting a model to be regressed to data. We call this process going from data to insight and it is an important aspect of exploratory data analysis (EDA). But there are very few formal ways or strategies that scientists typically use when the experiment has been done and data collected. This report deals with identifying the properties of a kinetic model by dissecting the pattern that concentration-time data reveal. Pattern recognition is a pivotal activity when modeling kinetic data, because a rigorous strategy is essential for dissecting the determinants behind concentration-time courses. First, we extend a commonly used relationship for calculation of the number of potential model parameters by simultaneously utilizing all concentration-time courses. Then, a set of points to consider are proposed that specifically addresses exploratory data analyses, number of phases in the concentration-time course, baseline behavior, time delays, peak shifts with increasing doses, flip-flop phenomena, saturation, and other potential nonlinearities that an experienced eye catches in the data. Finally, we set up a series of equations related to the patterns. In other words, we look at what causes the shapes that make up the concentration-time course and propose a strategy to construct a model. By practicing pattern recognition, one can significantly improve the quality and timeliness of data analysis and model building. A consequence of this is a better understanding of the complete concentration-time profile. KEY WORDS absorption area under the curve bi-exponential half-life induction intravenous and extravascular dosing lag time mono-exponential multi-compartment nonlinear elimination plasma concentration-time courses target-mediated drug disposition transporters