British Journal of Pharmaceutical Research, ISSN: 2231-2919,Vol.: 7, Issue.: 6
Diverse Data Sets Can Yield Reliable Information through Mechanistic Modeling: Salicylic Acid Clearance
G. M. Raymond1 and J. B. Bassingthwaighte1* 1Department of Bioengineering, University of Washington, Seattle, USA.
G. M. Raymond1 and J. B. Bassingthwaighte1*
1Department of Bioengineering, University of Washington, Seattle, USA.
(1) Rafik Karaman, Bioorganic Chemistry, College of Pharmacy, Al-Quds University, USA.
(1) Atef Mahmoud Mahmoud Attia, Biochemistry Department, National Research Centre, Egypt.
(2) Xing Li, Department of Health Sciences Research, Mayo Clinic College of Medicine, USA.
Complete Peer review History: http://sciencedomain.org/review-history/10289
This is a practical example of a powerful research strategy: putting together data from studies covering a diversity of conditions can yield a scientifically sound grasp of the phenomenon when the individual observations failed to provide definitive understanding. The rationale is that defining a realistic, quantitative, explanatory hypothesis for the whole set of studies, brings about a “consilience” of the often competing hypotheses considered for individual data sets. An internally consistent conjecture linking multiple data sets simultaneously provides stronger evidence on the characteristics of a system than does analysis of individual data sets limited to narrow ranges of conditions. Our example examines three very different data sets on the clearance of salicylic acid from humans: a high concentration set from aspirin overdoses; a set with medium concentrations from a research study on the influences of the route of administration and of sex on the clearance kinetics, and a set on low dose aspirin for cardiovascular health. Three models were tested: (1) a first order reaction, (2) a Michaelis-Menten (M-M) approach, and (3) an enzyme kinetic model with forward and backward reactions. The reaction rates found from model 1 were distinctly different for the three data sets, having no commonality. The M-M model 2 fitted each of the three data sets but gave a reliable estimates of the Michaelis constant only for the medium level data (Km = 24±5.4 mg/L); analyzing the three data sets together with model 2 gave Km = 18±2.6 mg/L. (Estimating parameters using larger numbers of data points in an optimization increases the degrees of freedom, constraining the range of the estimates). Using the enzyme kinetic model (3) increased the number of free parameters but nevertheless improved the goodness of fit to the combined data sets, giving tighter constraints, and a lower estimated Km = 14.6±2.9 mg/L, demonstrating that fitting diverse data sets with a single model improves confidence in the results. This modeling effort is also an example of reproducible science available at html://www.physiome.org/jsim/models/ webmodel/NSR/SalicylicAcidClearance
Briggs-Haldane; clearance; enzyme; half-life; JSim; Michaelis-Menten; model; reproducible; salicylic acid; salicylurate; CoA; aspirin; multilevel systems; confidence ranges.
DOI : 10.9734/BJPR/2015/19156Review History Comments
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