Persistent Link:
http://hdl.handle.net/10150/289017
Title:
Theoretical studies on enzyme inhibition kinetics
Author:
Kakkar, Tarundeep Singh
Issue Date:
1999
Publisher:
The University of Arizona.
Rights:
Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
Abstract:
Enzyme inhibition studies are conducted to characterize enzymes and to examine drug-drug interactions. To characterize the inhibitory process (competitive, non-competitive and uncompetitive) and to determine the inhibitory constant (Kᵢ), data analysis techniques (e.g., Dixon, Lineweaver-Burk, etc.) are used to linearize the inherently non-linear rate of substrate metabolism vs. substrate concentration data. These techniques were developed before the general use of computers. However, many investigators still rely on these techniques in spite of the easy availability of non-linear regression fitting programs. In Chapter 2, three methods (simultaneous nonlinear regression fit (SNLR); Dixon; non-simultaneous, nonlinear fit [K(m,app)]) were compared for estimating Ki from simulated data sets generated from a competitive inhibition model equation with 10% CV added random error to the data values. Of the three methods, the SNLR method was found to be the most robust, the fastest and easiest to implement. The K(m,app) method also gave good estimates but was more time-consuming. The Dixon method failed to give accurate and precise estimates of Kᵢ. The purpose of the study in Chapter 3 was to examine the minimal experimental design needed to obtain reliable and robust estimates of Kᵢ (as well as V(max) and K(m)). Four cases were examined. In the experimental design that relied upon the least amount of data, a control data set was fit simultaneously with one of the substrate-inhibitor pairs (25-10 or 250-100 μM). A total of 4 rate values were analyzed per fit (i.e., 3 control + 1 inhibitor value). A total of 100 data sets were fit per substrate-inhibitor pair. The preceding was repeated for a random error of 20 %CV. Thus, the total number of experiments was reduced from 108 (in Chapter 2) to 12 (in Chapter 3) (Case IV). Good estimates of the enzyme kinetic parameters were obtained. In Chapter 4, the ability of the SNLR method to identify the correct mechanism of inhibition was evaluated; competitive or noncompetitive enzyme-inhibition. Two experimental designs were examined ("conventional, non-optimal" and "semi-minimal"). The semi-minimal design was successful in discriminating between the two enzyme-inhibition mechanisms even for data with 30 %CV added random error.
Type:
text; Dissertation-Reproduction (electronic)
Keywords:
Chemistry, Pharmaceutical.; Health Sciences, Pharmacy.
Degree Name:
Ph.D.
Degree Level:
doctoral
Degree Program:
Graduate College; Pharmacy Practice and Science
Degree Grantor:
University of Arizona
Advisor:
Mayersohn, Michael

Full metadata record

DC FieldValue Language
dc.language.isoen_USen_US
dc.titleTheoretical studies on enzyme inhibition kineticsen_US
dc.creatorKakkar, Tarundeep Singhen_US
dc.contributor.authorKakkar, Tarundeep Singhen_US
dc.date.issued1999en_US
dc.publisherThe University of Arizona.en_US
dc.rightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.en_US
dc.description.abstractEnzyme inhibition studies are conducted to characterize enzymes and to examine drug-drug interactions. To characterize the inhibitory process (competitive, non-competitive and uncompetitive) and to determine the inhibitory constant (Kᵢ), data analysis techniques (e.g., Dixon, Lineweaver-Burk, etc.) are used to linearize the inherently non-linear rate of substrate metabolism vs. substrate concentration data. These techniques were developed before the general use of computers. However, many investigators still rely on these techniques in spite of the easy availability of non-linear regression fitting programs. In Chapter 2, three methods (simultaneous nonlinear regression fit (SNLR); Dixon; non-simultaneous, nonlinear fit [K(m,app)]) were compared for estimating Ki from simulated data sets generated from a competitive inhibition model equation with 10% CV added random error to the data values. Of the three methods, the SNLR method was found to be the most robust, the fastest and easiest to implement. The K(m,app) method also gave good estimates but was more time-consuming. The Dixon method failed to give accurate and precise estimates of Kᵢ. The purpose of the study in Chapter 3 was to examine the minimal experimental design needed to obtain reliable and robust estimates of Kᵢ (as well as V(max) and K(m)). Four cases were examined. In the experimental design that relied upon the least amount of data, a control data set was fit simultaneously with one of the substrate-inhibitor pairs (25-10 or 250-100 μM). A total of 4 rate values were analyzed per fit (i.e., 3 control + 1 inhibitor value). A total of 100 data sets were fit per substrate-inhibitor pair. The preceding was repeated for a random error of 20 %CV. Thus, the total number of experiments was reduced from 108 (in Chapter 2) to 12 (in Chapter 3) (Case IV). Good estimates of the enzyme kinetic parameters were obtained. In Chapter 4, the ability of the SNLR method to identify the correct mechanism of inhibition was evaluated; competitive or noncompetitive enzyme-inhibition. Two experimental designs were examined ("conventional, non-optimal" and "semi-minimal"). The semi-minimal design was successful in discriminating between the two enzyme-inhibition mechanisms even for data with 30 %CV added random error.en_US
dc.typetexten_US
dc.typeDissertation-Reproduction (electronic)en_US
dc.subjectChemistry, Pharmaceutical.en_US
dc.subjectHealth Sciences, Pharmacy.en_US
thesis.degree.namePh.D.en_US
thesis.degree.leveldoctoralen_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.disciplinePharmacy Practice and Scienceen_US
thesis.degree.grantorUniversity of Arizonaen_US
dc.contributor.advisorMayersohn, Michaelen_US
dc.identifier.proquest9946808en_US
dc.identifier.bibrecord.b3991446xen_US
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