Persistent Link:
http://hdl.handle.net/10150/290158
Title:
Allometric scaling for predicting human drug clearance
Author:
Tang, Huadong
Issue Date:
2005
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:
Various modified methods have been proposed in response to criticisms regarding the practical applicability of allometric scaling, which is one of the most widely used approaches in predicting human drug clearance based on data from animal species. The major problems encountered among allometric methods in predicting human drug clearance are addressed in this dissertation. In chapter 2, a large data set for allometric scaling (n = 138) was collected from the literature and was categorized according to the following criteria: oral or systemic clearance; elimination routes; protein or non-protein chemicals; low, intermediate, or high metabolic clearance. Some significant observations have been made regarding the applicability of allometric scaling according to the pharmacokinetic and physical-chemical properties of the drugs examined. Of special note, several potential rules were developed for when one could expect large vertical allometry. In chapter 3, a new model for predicting human clearance was developed. The new model was shown to provide better predictability than any other current approach. In particular, the new model for the first time predicts the occurrence of large vertical allometry noted in humans. In chapter 4, a general equation was derived, which directly describes the mathematical relationship between predicted pharmacokinetic (PK) parameters in humans and the body weights of animals and the values of their corresponding measured PK parameters. This relationship clearly illustrates the species or body weight-dependency of the prediction performance by allometric scaling. Finally, real data from the literature demonstrated the species-dependency predicted from the equation. In chapter 5, the functionality of the correction factors, maximum life-span potential (MLP) and brain weight (BrW) in allometry is mathematically described for the first time. It was found that corrections by MLP or BrW are equivalent to a multiplication of certain constants by the predicted values in humans from simple allometry and has nothing to do with any measured values of PK parameters in any animal species. The role of correction factors (MLP and BrW) or "rule of exponents" in species scaling was evaluated.
Type:
text; Dissertation-Reproduction (electronic)
Keywords:
Health Sciences, Pharmacology.; Biology, Animal Physiology.; Health Sciences, Pharmacy.
Degree Name:
Ph.D.
Degree Level:
doctoral
Degree Program:
Graduate College; Pharmaceutical Sciences
Degree Grantor:
University of Arizona
Advisor:
Mayersohn, Michael

Full metadata record

DC FieldValue Language
dc.language.isoen_USen_US
dc.titleAllometric scaling for predicting human drug clearanceen_US
dc.creatorTang, Huadongen_US
dc.contributor.authorTang, Huadongen_US
dc.date.issued2005en_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.abstractVarious modified methods have been proposed in response to criticisms regarding the practical applicability of allometric scaling, which is one of the most widely used approaches in predicting human drug clearance based on data from animal species. The major problems encountered among allometric methods in predicting human drug clearance are addressed in this dissertation. In chapter 2, a large data set for allometric scaling (n = 138) was collected from the literature and was categorized according to the following criteria: oral or systemic clearance; elimination routes; protein or non-protein chemicals; low, intermediate, or high metabolic clearance. Some significant observations have been made regarding the applicability of allometric scaling according to the pharmacokinetic and physical-chemical properties of the drugs examined. Of special note, several potential rules were developed for when one could expect large vertical allometry. In chapter 3, a new model for predicting human clearance was developed. The new model was shown to provide better predictability than any other current approach. In particular, the new model for the first time predicts the occurrence of large vertical allometry noted in humans. In chapter 4, a general equation was derived, which directly describes the mathematical relationship between predicted pharmacokinetic (PK) parameters in humans and the body weights of animals and the values of their corresponding measured PK parameters. This relationship clearly illustrates the species or body weight-dependency of the prediction performance by allometric scaling. Finally, real data from the literature demonstrated the species-dependency predicted from the equation. In chapter 5, the functionality of the correction factors, maximum life-span potential (MLP) and brain weight (BrW) in allometry is mathematically described for the first time. It was found that corrections by MLP or BrW are equivalent to a multiplication of certain constants by the predicted values in humans from simple allometry and has nothing to do with any measured values of PK parameters in any animal species. The role of correction factors (MLP and BrW) or "rule of exponents" in species scaling was evaluated.en_US
dc.typetexten_US
dc.typeDissertation-Reproduction (electronic)en_US
dc.subjectHealth Sciences, Pharmacology.en_US
dc.subjectBiology, Animal Physiology.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.disciplinePharmaceutical Sciencesen_US
thesis.degree.grantorUniversity of Arizonaen_US
dc.contributor.advisorMayersohn, Michaelen_US
dc.identifier.proquest3177534en_US
dc.identifier.bibrecord.b49001309en_US
All Items in UA Campus Repository are protected by copyright, with all rights reserved, unless otherwise indicated.