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2016 ; 18
(1
): 47-63
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Pattern Recognition in Pharmacokinetic Data Analysis
#MMPMID26338231
Gabrielsson J
; Meibohm B
; Weiner D
AAPS J
2016[Jan]; 18
(1
): 47-63
PMID26338231
show ga
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.
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