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Predictive Pharmacokinetic Modeling of Orally Administered Drugs

dash.author.emailchristopherbruno14@gmail.com
dash.depositing.authorBruno, Christopher D.
dash.identifier.vireohttp://etds.lib.harvard.edu/college/admin/view/148
dash.licenseLAA
dc.contributor.authorBruno, Christopher D.
dc.date.accessioned2019-03-26T10:41:32Z
dc.date.available2019-03-26T10:41:32Z
dc.date.created2016-05
dc.date.issued2016-06-22
dc.date.submitted2016
dc.description.abstractDrug discovery and development is a long, costly process that could become more efficient if pre-clinical exploration of drugs was more thorough and accurate. In order to provide pre-clinical insight into drug absorption, distribution, metabolism and elimination (ADME), as well as other behavioral patterns and dosing, we explore a physiologically-based pharmacokinetic model that incorporates an algorithm for predicting tissue:blood partition coefficients. Methods for predicting necessary parameters using in vitro and other laboratory techniques are also explored. The model was then used to simulate 150 person single-dose trials for five drugs (3 bases, 1 acid, 1 neutral) and assessed based on its ability to recreate observed C_max and AUC data. The model's accuracy ranged from predicting observed C_max within a standard deviation to only predicting 2.9% of the reported C_max, with similar results for AUC data. Overall, this model seems useful seems limited but useful for understanding general trends in drug ADME and dosing.
dc.format.mimetypeapplication/pdf
dc.identifier.orcid0000-0003-2533-2960
dc.identifier.urihttp://nrs.harvard.edu/urn-3:HUL.InstRepos:38811449*
dc.language.isoen
dc.subjectBiology, Physiology
dc.subjectBiology, Molecular
dc.titlePredictive Pharmacokinetic Modeling of Orally Administered Drugs
dc.typeThesis or Dissertation
dc.type.materialtext
dspace.entity.typePublication
oaire.licenseConditionLAA
thesis.degree.date2016
thesis.degree.grantorHarvard College
thesis.degree.levelUndergraduate
thesis.degree.nameAB

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