Pattern Recognition in Pharmacokinetic Data Analysis.
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.
Department of Biomedical Sciences and Veterinary Public Health, SLU, Division of Pharmacology and Toxicology, Box 7028, SE-750 07, Uppsala, Sweden. johan.gabrielsson@slu.se.
Johan
http://orcid.org/0000-0001-7270-2106
J
Gabrielsson
College of Pharmacy, University of Tennessee Health Science Center, 881 Madison Avenue, Rm. 444, Memphis, Tennessee, 38163, USA.
Bernd
B
Meibohm
, 709 Cambridge Hall Loop, Apex, North Carolina, 27539, USA.
Daniel
D
Weiner
D000465:Algorithms; D019540:Area Under Curve; D003627:Data Interpretation, Statistical; D006207:Half-Life; D006801:Humans; D015233:Models, Statistical; D017711:Nonlinear Dynamics; D010363:Pattern Recognition, Automated / Q000379:methods*; D010599:Pharmacokinetics
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