WEKO3
アイテム
Population Pharmacokinetic Analysis of Sparse Data; Use of Dense Data from Earlier Clinical Trials as Prior Information
https://ir.soken.ac.jp/records/4076
https://ir.soken.ac.jp/records/40765568ea83-1dc9-41af-ad34-3defdfc933f4
| 名前 / ファイル | ライセンス | アクション |
|---|---|---|
要旨・審査要旨 (310.4 kB)
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| Item type | 学位論文 / Thesis or Dissertation(1) | |||||
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| 公開日 | 2013-11-19 | |||||
| タイトル | ||||||
| タイトル | Population Pharmacokinetic Analysis of Sparse Data; Use of Dense Data from Earlier Clinical Trials as Prior Information | |||||
| タイトル | ||||||
| タイトル | Population Pharmacokinetic Analysis of Sparse Data; Use of Dense Data from Earlier Clinical Trials as Prior Information | |||||
| 言語 | en | |||||
| 言語 | ||||||
| 言語 | eng | |||||
| 資源タイプ | ||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_46ec | |||||
| 資源タイプ | thesis | |||||
| 著者名 |
庄子, 聡
× 庄子, 聡 |
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| フリガナ |
ショウジ, サトシ
× ショウジ, サトシ |
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| 著者 |
SHOJI, Satoshi
× SHOJI, Satoshi |
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| 学位授与機関 | ||||||
| 学位授与機関名 | 総合研究大学院大学 | |||||
| 学位名 | ||||||
| 学位名 | 博士（統計科学） | |||||
| 学位記番号 | ||||||
| 内容記述タイプ | Other | |||||
| 内容記述 | 総研大甲第1596号 | |||||
| 研究科 | ||||||
| 値 | 複合科学研究科 | |||||
| 専攻 | ||||||
| 値 | 15 統計科学専攻 | |||||
| 学位授与年月日 | ||||||
| 学位授与年月日 | 2013-03-22 | |||||
| 学位授与年度 | ||||||
| 値 | 2012 | |||||
| 要旨 | ||||||
| 内容記述タイプ | Other | |||||
| 内容記述 | Clinical drug development for a new drug proceeds in a stepwise manner. As the first step, phase I clinical trials are conducted to investigate pharmacokinetics and safety of the drug in humans, usually in healthy volunteers. After the phase I clinical trials, early efficacy in a selected patient population is confirmed in a phase IIa clinical trial. If the results are satisfactory, this is followed by a larger phase II or III clinical trial to learn and confirm how to use the drug in the target patient population. In phase I clinical trials, pharmacokinetics (PK) following the drug administration to humans, typically to healthy volunteers, is thoroughly investigated by each individual based on dense PK data (e.g., tens of blood samples sequentially collected from each individual). In phase II and III clinical trials, PK data are collected to comprehend PK in the target population. Main objectives of the PK study in phase II and III clinical trials are generally to understand variability of PK in the target population and to identify factors causing the variability rather than to capture individual PK profile in each patient. Population PK analysis captures population mean PK profile, individual variability from the mean PK profile (between-individual variability), and residual variability within the individual (within-individual variability), which fits in objectives of PK study in phase II and III clinical trials. Therefore, the population PK approach is often used for PK analysis in phase II and III clinical trials. However, because of ethical and medical concerns, relatively few samples per patient, that is sparse PK data, are obtained from patients participating in the phase II and III clinical trials and it is sometimes difficult to estimate all the PK parameters with the sparse data alone. One effective approach to address this issue is to borrow strength from dense PK data typically obtained from healthy volunteers (HVs) in the past phase I clinical trials under the assumption of exchangeability between these two datasets. Conventional approaches include fixing some PK parameter values to those obtained from the dense PK data or fitting a PK model to a combined dataset of these two. The Bayesian approach using prior distributions elicited from dense PK data from HVs provides a general framework for the population PK analysis with this type of information sharing. However, the Bayesian approach can still suffer from estimation bias when the exchangeability assumption is violated or when the prior distribution is misspecified. In order for improving estimation accuracy with reduction of estimation bias under possible violations of the exchangeability assumption, we propose to introduce an information weighting that allows discounting the information from dense PK data in specifying the prior distributions. We firstly present an application of the conventional approach, combining sparse PK data with dense PK data, to real PK data of a new drug under development, pregabalin, in order to demonstrate usefulness of the approach and extract the issues. Data used for this population PK analysis were obtained from 14 clinical studies. The analysis provided an accurate and reliable estimation of the relationship between pregabalin clearance and renal function (creatinine clearance) in patients. Although model validation suggested adequacy of the model to interpret PK characteristics in patients with sparse PK data, it should be cautious of potential bias due to violation of exchangeability assumption, specifically parameters which sparse data have less information to estimate. Following application of the conventional approach, we present a proposed approach, introduction of an information weighting on informative prior distributions for population pharmacokinetic studies with sparse data. In our simulation setting, the optimal value for the information weighting ranged from 0.2 to 1.0 when the exchangeability assumption was held. On the other hand, when the exchangeability assumption was violated, information weighting with 0.2 to 0.5 was optimal. In the real data analysis, we obtained reasonable estimates. We chose the estimation results when information weighting of 0.5 for a plausible violation of the exchangeability assumption, as our simulation exercises suggested that information weighting with 0.2 to 0.5 was optimal when the exchangeability assumption was violated. In this thesis, we present usefulness of the conventional approach for evaluating PK parameters of interest in patients with sparse PK data, where the approach needs caution of potential bias due to violation of exchangeability assumption. For the proposed approach, through investigating the impact of various information weights on estimation accuracy (bias and precision of parameter estimation), we identify a range of optimal value in information weighting to obtain accurate estimates of PK parameters. In future studies, it is worthwhile to identify optimal combinations of distinct weights for respective PK parameters under more complex PK models (e.g., multiple-compartment models and models with non-linear elimination) under extensive situations where the exchangeability assumption is violated. |
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| 所蔵 | ||||||
| 値 | 有 | |||||
