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Optimization of a Physiology Based P...

Watson, Shayne.

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Optimization of a Physiology Based Pharmacokinetic Model for Drug Interaction Predictions With Application to Esketamine.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Optimization of a Physiology Based Pharmacokinetic Model for Drug Interaction Predictions With Application to Esketamine./
作者:	Watson, Shayne.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2019,
面頁冊數:	74 p.
附註:	Source: Masters Abstracts International, Volume: 80-10.
Contained By:	Masters Abstracts International80-10.
標題:	Biomedical engineering. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=13812105
ISBN:	9781392033951

Optimization of a Physiology Based Pharmacokinetic Model for Drug Interaction Predictions With Application to Esketamine.
Watson, Shayne.

Optimization of a Physiology Based Pharmacokinetic Model for Drug Interaction Predictions With Application to Esketamine. - Ann Arbor : ProQuest Dissertations & Theses, 2019 - 74 p.

Source: Masters Abstracts International, Volume: 80-10.

Thesis (M.S.)--Drexel University, 2019.

This item must not be sold to any third party vendors.

Optimization of a Physiology Based Pharmacokinetic Model for Drug Interaction Predictions With Application to Esketamine Introduction: Drug-drug interactions (DDI) are commonly evaluated during development, as is recommended by health authorities. These interactions may alter the pharmacokinetics (PK), the toxicity, and efficacy of the drug. Cytochrome P450 (CYP) enzymes are commonly considered for DDIs as they play a major role in the metabolism of small molecules. However, the perpetrator drugs may interact with other proteins related to the ADME of the victim drug. A PBPK platform is ideal for exploring these interactions as it can take into account many aspects of ADME processes. Method: A PBPK model will be built utilizing an iterative approach. The model was first built for intravenous (IV) esketamine. The oral model expanded on the IV model with only oral processes being optimized. Lastly, DDI was tested for perpetrator drugs, clarithromycin and itraconazole. Results: Using the iterative process allowed for the successful development of a PBPK model describing both IV and oral esketamine. Total error for the final model reduced from 7.33 to 1.22 and from 3.52 to 2.18 for the IV and oral training datasets, respectively. DDI simulations showed an increase in AUC for clarithromycin and itraconazole, showing a lesser effect to itraconazole. AUC ratios were over predicted compared to observed values, but the simulations upheld the difference in effect of the two inhibitors. Conclusion: The modeling process described in the paper, although timely, has many benefits. It decouples the fitting of IV and oral data together, which aids identification of ADME processes and allows use of limited oral data.

ISBN: 9781392033951Subjects--Topical Terms:

535387
Biomedical engineering.

Optimization of a Physiology Based Pharmacokinetic Model for Drug Interaction Predictions With Application to Esketamine.
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520 $a Optimization of a Physiology Based Pharmacokinetic Model for Drug Interaction Predictions With Application to Esketamine Introduction: Drug-drug interactions (DDI) are commonly evaluated during development, as is recommended by health authorities. These interactions may alter the pharmacokinetics (PK), the toxicity, and efficacy of the drug. Cytochrome P450 (CYP) enzymes are commonly considered for DDIs as they play a major role in the metabolism of small molecules. However, the perpetrator drugs may interact with other proteins related to the ADME of the victim drug. A PBPK platform is ideal for exploring these interactions as it can take into account many aspects of ADME processes. Method: A PBPK model will be built utilizing an iterative approach. The model was first built for intravenous (IV) esketamine. The oral model expanded on the IV model with only oral processes being optimized. Lastly, DDI was tested for perpetrator drugs, clarithromycin and itraconazole. Results: Using the iterative process allowed for the successful development of a PBPK model describing both IV and oral esketamine. Total error for the final model reduced from 7.33 to 1.22 and from 3.52 to 2.18 for the IV and oral training datasets, respectively. DDI simulations showed an increase in AUC for clarithromycin and itraconazole, showing a lesser effect to itraconazole. AUC ratios were over predicted compared to observed values, but the simulations upheld the difference in effect of the two inhibitors. Conclusion: The modeling process described in the paper, although timely, has many benefits. It decouples the fitting of IV and oral data together, which aids identification of ADME processes and allows use of limited oral data.
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