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Computational modeling of the pharmacokinetics and pharmacodynamics of selected xenobiotics- [electronic resource]
Computational modeling of the pharmacokinetics and pharmacodynamics of selected xenobiotics- [electronic resource]
상세정보
- 자료유형
- 학위논문(국외)
- 자관 청구기호
- 기본표목-개인명
- 표제와 책임표시사항
- Computational modeling of the pharmacokinetics and pharmacodynamics of selected xenobiotics - [electronic resource] / Zurlinden, Todd J.
- 발행, 배포, 간사 사항
- 형태사항
- 1 online resource(228 p)
- 일반주기
- Source: Dissertation Abstracts International, Volume: 78-05(E), Section: B.
- 일반주기
- Adviser: Brad Reisfeld.
- 학위논문주기
- Thesis (Ph.D.)--Colorado State University, 2016.
- 요약 등 주기
- 요약The determination of important endpoints in toxicology and pharmacology continues to involve the acquisition of large amounts of data through resource-intensive experimental studies involving a large number of resources. Because of this, only a small fraction of chemicals in the environment and marketplace can reasonably be evaluated for safety, and many promising drug candidates must be eliminated from consideration based on inadequate evaluation. Promisingly, advances in biologically-based computational models are beginning to allow researchers to estimate these endpoints and make useful extrapolations using a limited set of experimental data.
- 요약 등 주기
- 요약The work described in this dissertation examined how computational models can provide meaningful insight and quantitation of important pharmacological and toxicological endpoints related to toxicity and pharmacological efficacy. To this end, physiologically-based pharmacokinetic and pharmacodynamic models were developed and applied for several pharmaceutical agents and environmental toxicants to predict significant, and diverse, biological endpoints. First, physiologically-based modeling allowed for the evaluation of various dosing regimens of rifapentine, a drug that is showing great promise for the treatment of tuberculosis, by comparing lung-specific concentration predictions to experimentally-derived thresholds for antibacterial activity. Second, physiologically-based pharmacokinetic modeling, coupled with Bayesian inference, was used as part of a methodology to characterize genetic differences in acetaminophen pharmacokinetics and also to help clinicians predict an ingested dose of this drug under overdose conditions. Third, a methodology for using physiologically-based pharmacokinetic modeling to predict health-based cognitive endpoints was demonstrated for chronic exposure to chlorpyrifos, an organophosphorus insecticide. The environmental public health indicators derived from this work allowed for biomarkers of exposure to be used to predict neurobehavioral changes following long-term exposure to this chemical. Finally, computational modeling was used to develop a mechanistically-plausible pharmacodynamic model for hepatoprotective and pro-inflammatory events to relate trichloroethylene dosing conditions to observed pathologies associated with auto-immune hepatitis.
- 주제명부출표목-일반주제명
- 주제명부출표목-일반주제명
- 주제명부출표목-일반주제명
- 부출표목-단체명
- 기본자료저록
- Dissertation Abstracts International. 78-05B(E).
- 기본자료저록
- Dissertation Abstract International
- 전자적 위치 및 접속
- 원문정보보기
- 소장사항
-
20170404 2017
MARC
008170601s2016 us esm 001c eng■001MOKWON01254388
■00520170418120357
■007cr
■020 ▼a9781369460131
■035 ▼a(MiAaPQ)AAI10240444
■040 ▼aMiAaPQ▼cMiAaPQ
■090 ▼a전자도서(박사논문)
■1001 ▼aZurlinden, Todd J.
■24510▼aComputational modeling of the pharmacokinetics and pharmacodynamics of selected xenobiotics▼h[electronic resource]▼cZurlinden, Todd J.
■260 ▼a[Sl]▼bColorado State University▼c2016
■300 ▼a1 online resource(228 p)
■500 ▼aSource: Dissertation Abstracts International, Volume: 78-05(E), Section: B.
■500 ▼aAdviser: Brad Reisfeld.
■5021 ▼aThesis (Ph.D.)--Colorado State University, 2016.
■520 ▼aThe determination of important endpoints in toxicology and pharmacology continues to involve the acquisition of large amounts of data through resource-intensive experimental studies involving a large number of resources. Because of this, only a small fraction of chemicals in the environment and marketplace can reasonably be evaluated for safety, and many promising drug candidates must be eliminated from consideration based on inadequate evaluation. Promisingly, advances in biologically-based computational models are beginning to allow researchers to estimate these endpoints and make useful extrapolations using a limited set of experimental data.
■520 ▼aThe work described in this dissertation examined how computational models can provide meaningful insight and quantitation of important pharmacological and toxicological endpoints related to toxicity and pharmacological efficacy. To this end, physiologically-based pharmacokinetic and pharmacodynamic models were developed and applied for several pharmaceutical agents and environmental toxicants to predict significant, and diverse, biological endpoints. First, physiologically-based modeling allowed for the evaluation of various dosing regimens of rifapentine, a drug that is showing great promise for the treatment of tuberculosis, by comparing lung-specific concentration predictions to experimentally-derived thresholds for antibacterial activity. Second, physiologically-based pharmacokinetic modeling, coupled with Bayesian inference, was used as part of a methodology to characterize genetic differences in acetaminophen pharmacokinetics and also to help clinicians predict an ingested dose of this drug under overdose conditions. Third, a methodology for using physiologically-based pharmacokinetic modeling to predict health-based cognitive endpoints was demonstrated for chronic exposure to chlorpyrifos, an organophosphorus insecticide. The environmental public health indicators derived from this work allowed for biomarkers of exposure to be used to predict neurobehavioral changes following long-term exposure to this chemical. Finally, computational modeling was used to develop a mechanistically-plausible pharmacodynamic model for hepatoprotective and pro-inflammatory events to relate trichloroethylene dosing conditions to observed pathologies associated with auto-immune hepatitis.
■590 ▼aSchool code: 0053.
■650 4▼aChemical engineering
■650 4▼aPharmacology
■650 4▼aToxicology
■690 ▼a0542
■690 ▼a0419
■690 ▼a0383
■71020▼aColorado State University▼bChemical and Biological Engineering.
■7730 ▼tDissertation Abstracts International▼g78-05B(E).
■773 ▼tDissertation Abstract International
■790 ▼a0053
■791 ▼aPh.D.
■792 ▼a2016
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T14490138▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
■980 ▼a20170404▼f2017
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