Pattern Recognition in Pharmacodynamic Data Analysis

The AAPS Journal, Nov 2015

Pattern recognition is a key element in pharmacodynamic analyses as a first step to identify drug action and selection of a pharmacodynamic model. The essence of this process is going from data to insight through exploratory data analysis. There are few formal strategies that scientists typically use when the experiment has been done and data collected. This report attempts to ameliorate this deficit by identifying the properties of a pharmacodynamic model via dissection of the pattern revealed in response-time data. Pattern recognition in pharmacodynamic analyses contrasts with pharmacokinetic analyses with respect to time course. Thus, the time course of drug in plasma usually differs markedly from the time course of the biomarker response, as a consequence of a myriad of interactions (transport to biophase, binding to target, activation of target and downstream mediators, physiological response, cascade and amplification of biosignals, homeostatic feedback) between the events of exposure to test compound and the occurrence of the biomarker response. Homing in on this important—but less often addressed—element, 20 datasets of varying complexity were analyzed, and from this, we summarize a set of points to consider, specifically addressing baseline behavior, number of phases in the response-time course, time delays between concentration- and response-time courses, peak shifts in response with increasing doses, saturation, and other potential nonlinearities. These strategies will hopefully give a better understanding of the complete pharmacodynamic response-time profile.

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Pattern Recognition in Pharmacodynamic Data Analysis

The AAPS Journal Pattern Recognition in Pharmacodynamic Data Analysis Johan Gabrielsson 2 Stephan Hjorth 0 1 0 PharmaLot Consulting AB, V. Bäckvägen 21B , SE-434 92, Vallda , Sweden 1 Department of Molecular and Clinical Medicine, Institute of Medicine, The Sahlgrenska Academy at Gothenburg University , SE-413 45, Gothenburg , Sweden 2 Division of Pharmacology and Toxicology, Department of Biomedical Sciences and Veterinary Public Health , SLU, Box 7028, SE-750 07, Uppsala , Sweden Pattern recognition is a key element in pharmacodynamic analyses as a first step to identify drug action and selection of a pharmacodynamic model. The essence of this process is going from data to insight through exploratory data analysis. There are few formal strategies that scientists typically use when the experiment has been done and data collected. This report attempts to ameliorate this deficit by identifying the properties of a pharmacodynamic model via dissection of the pattern revealed in responsetime data. Pattern recognition in pharmacodynamic analyses contrasts with pharmacokinetic analyses with respect to time course. Thus, the time course of drug in plasma usually differs markedly from the time course of the biomarker response, as a consequence of a myriad of interactions (transport to biophase, binding to target, activation of target and downstream mediators, physiological response, cascade and amplification of biosignals, homeostatic feedback) between the events of exposure to test compound and the occurrence of the biomarker response. Homing in on this important-but less often addressed-element, 20 datasets of varying complexity were analyzed, and from this, we summarize a set of points to consider, specifically addressing baseline behavior, number of phases in the response-time course, time delays between concentration- and response-time courses, peak shifts in response with increasing doses, saturation, and other potential nonlinearities. These strategies will hopefully give a better understanding of the complete pharmacodynamic response-time profile. duration of response; exploratory data analysis; intensity of response; mixture dynamics; modeling; onset of action; oscillatory response; physiological limit; response half-life; response-time courses; saturation; transduction; turnover INTRODUCTION During many years of project work in pharma drug research and discovery settings, we have repeatedly experienced instances where pharmacologists and kineticists/ modelers alike have utilized pharmacodynamic response data suboptimally. It is our impression that this partly resides in different terminologies and “language” used in the two disciplines, but also in differences regarding interpretation that emanates from the inherent focus on pharmacokinetics or pharmacodynamics, depending on the very inclination of the person analyzing the data. Given that important information can be lost this way, it is evident to us that an integrated view would greatly facilitate and increase power of data “Things are much more marvelous than the scientific method allows us to conceive” (Keller 1983, pp. 198–207) analysis. In turn, this is likely to have positive repercussions on speed and cost of drug discovery and development. In this tutorial article, we endeavor to enable such integration by focusing on data patterns and identifying potential underlying factors that will further analysis and interpretation. Pattern recognition is a key element in pharmacodynamic data analyses when first selecting a model to be regressed to data. We call this process going from data to insight exploratory data analysis. Despite being a key element toward further analysis and understanding, there are no formal best practices that scientists typically use. This report deals with identifying the properties of a pharmacodynamic model by dissecting the pattern that responsetime data reveal graphically. Pattern recognition is a pivotal activity when modeling pharmacodynamic data, because a rigorous strategy is essential for dissecting the determinants behind response-time courses. In the pharmacology field, pattern recognition has also been proposed for interpreting results of drug-drug interactions and pharmacokinetic data ( 1,2 ). To inspire young kineticists beyond the slavery of computers, we have practiced pattern recognition over three decades in our pharmacology teaching. The central question is how much information one can extract from the data without falling into the trap of machine-made answers. The analyst should be in charge of the knowledge extraction prior to utilizing software. A set of points to consider are proposed that specifically addresses exploratory data analyses, number of phases in the response-time course, convex or concave curvature, baseline behavior, time delays between concentration- and responsetime courses, lag time prior to drug action, peak shifts in time of the maximum response with increasing doses, satura (...truncated)


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Johan Gabrielsson, Stephan Hjorth. Pattern Recognition in Pharmacodynamic Data Analysis, The AAPS Journal, 2016, pp. 64-91, Volume 18, Issue 1, DOI: 10.1208/s12248-015-9842-5