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Design evaluation for pharmacokineti...

Liu, Jin.

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Design evaluation for pharmacokinetic studies in patients with renal impairment.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Design evaluation for pharmacokinetic studies in patients with renal impairment./
作者:	Liu, Jin.
面頁冊數:	87 p.
附註:	Adviser: Michael E. Brier.
Contained By:	Dissertation Abstracts International68-05B.
標題:	Health Sciences, Pharmacology. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3267094
ISBN:	9780549052784

Design evaluation for pharmacokinetic studies in patients with renal impairment.
Liu, Jin.

Design evaluation for pharmacokinetic studies in patients with renal impairment. - 87 p.

Adviser: Michael E. Brier.

Thesis (Ph.D.)--University of Louisville, 2007.

Objective. The common belief in pharmacokinetics is that drug clearance is linearly related to renal function. The pharmacokinetics of individual drugs excreted by the kidney in patients with impaired renal function have been well studied using this assumption. However, recent studies revealed the fact of a nonlinear relationship between drug clearance and renal function. Our objective was to evaluate pharmacokinetic study design in renal impairment to detect a true relationship between total body drug clearance and renal function and estimate the risk of missing a true nonlinear relationship. Methods. Clinical trials using traditional pharmacokinetic study design in patients with renal insufficiency were simulated for both real drugs and hypothetical drugs. Both published and hypothetical linear relationships were examined in the study to evaluate the power of the study design to detect a true linear relationship giving different sample size and random error. Nonlinear relationships with different degree of nonlinearity were examined in the study to estimate the risk of concluding a linear or no relationship between drug clearance and renal function when the true one is nonlinear. A linear regression approach using computer program Matlab and a nonlinear regression approach using another computer program NONMEM were applied in the power studies. Traditional study design was optimized for those studies without sufficient power by either increasing sample size or changing creatinine clearance intervals. Drug dosing and dose recommendations were simulated using the computer programs Matlab and NONMEM to investigate the outcome of a nonlinear relationship interpreted as linear. Average plasma concentrations at steady state were used as a criterion for dosage adjustment. Results. For drugs primarily excreted in the urine, a sample size of 6 subjects per study group was adequate for detecting a true linear relationship between renal function and drug clearance. For drugs with high nonrenal clearance (>100ml/min), a sample size of more than 10 or 12 patients per group was necessary in order for the study to have adequate power. The risk of a nonlinear relationship interpreted as linear existed for drugs with urinary excretion percentage greater than 75%. When a relationship was highly nonlinear and inter-subject variability was high, no relationship between renal function and drug clearance may be concluded. Either increasing sample size or changing creatinine clearance interval improved the power to detect a true nonlinear relationship using nonlinear regression approach. Dosage adjustment based on a linear assumption when the true relationship is nonlinear resulted in over or under-dosing compared with dosage adjustment based on total drug clearance. After dosage adjustment based on total drug clearance, average steady state concentrations among renal dysfunction groups were consistently at the same level as normal renal function group. However, after dosage was adjusted based on linear regression, fluctuation of average steady state concentration among renal dysfunction groups was observed, especially in the most severely impaired renal function group. Conclusions . Traditional Pharmacokinetic study design is adequate for detecting a true linear relationship given sufficient sample size. If the actual relationship is nonlinear, simply doing linear regression may result in a false linear or no relationship. If a nonlinear relationship between drug clearance and renal function is suspected, using a nonlinear regression approach and increasing sample size or changing creatinine clearance intervals helps detect a true relationship. For drugs with a narrow therapeutic range, dosage recommendation based on linear assumption may lead to overdosing in patients with severe renal dysfunction, depending on the shape of the true nonlinear relationship.

ISBN: 9780549052784Subjects--Topical Terms:

1017717
Health Sciences, Pharmacology.

Design evaluation for pharmacokinetic studies in patients with renal impairment.
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300 $a 87 p.
500 $a Adviser: Michael E. Brier.
500 $a Source: Dissertation Abstracts International, Volume: 68-05, Section: B, page: 2982.
502 $a Thesis (Ph.D.)--University of Louisville, 2007.
520 $a Objective. The common belief in pharmacokinetics is that drug clearance is linearly related to renal function. The pharmacokinetics of individual drugs excreted by the kidney in patients with impaired renal function have been well studied using this assumption. However, recent studies revealed the fact of a nonlinear relationship between drug clearance and renal function. Our objective was to evaluate pharmacokinetic study design in renal impairment to detect a true relationship between total body drug clearance and renal function and estimate the risk of missing a true nonlinear relationship. Methods. Clinical trials using traditional pharmacokinetic study design in patients with renal insufficiency were simulated for both real drugs and hypothetical drugs. Both published and hypothetical linear relationships were examined in the study to evaluate the power of the study design to detect a true linear relationship giving different sample size and random error. Nonlinear relationships with different degree of nonlinearity were examined in the study to estimate the risk of concluding a linear or no relationship between drug clearance and renal function when the true one is nonlinear. A linear regression approach using computer program Matlab and a nonlinear regression approach using another computer program NONMEM were applied in the power studies. Traditional study design was optimized for those studies without sufficient power by either increasing sample size or changing creatinine clearance intervals. Drug dosing and dose recommendations were simulated using the computer programs Matlab and NONMEM to investigate the outcome of a nonlinear relationship interpreted as linear. Average plasma concentrations at steady state were used as a criterion for dosage adjustment. Results. For drugs primarily excreted in the urine, a sample size of 6 subjects per study group was adequate for detecting a true linear relationship between renal function and drug clearance. For drugs with high nonrenal clearance (>100ml/min), a sample size of more than 10 or 12 patients per group was necessary in order for the study to have adequate power. The risk of a nonlinear relationship interpreted as linear existed for drugs with urinary excretion percentage greater than 75%. When a relationship was highly nonlinear and inter-subject variability was high, no relationship between renal function and drug clearance may be concluded. Either increasing sample size or changing creatinine clearance interval improved the power to detect a true nonlinear relationship using nonlinear regression approach. Dosage adjustment based on a linear assumption when the true relationship is nonlinear resulted in over or under-dosing compared with dosage adjustment based on total drug clearance. After dosage adjustment based on total drug clearance, average steady state concentrations among renal dysfunction groups were consistently at the same level as normal renal function group. However, after dosage was adjusted based on linear regression, fluctuation of average steady state concentration among renal dysfunction groups was observed, especially in the most severely impaired renal function group. Conclusions . Traditional Pharmacokinetic study design is adequate for detecting a true linear relationship given sufficient sample size. If the actual relationship is nonlinear, simply doing linear regression may result in a false linear or no relationship. If a nonlinear relationship between drug clearance and renal function is suspected, using a nonlinear regression approach and increasing sample size or changing creatinine clearance intervals helps detect a true relationship. For drugs with a narrow therapeutic range, dosage recommendation based on linear assumption may lead to overdosing in patients with severe renal dysfunction, depending on the shape of the true nonlinear relationship.
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