{"created":"2023-06-20T13:23:17.442081+00:00","id":4076,"links":{},"metadata":{"_buckets":{"deposit":"3a257eca-b9bb-4e53-8a3f-fce99ab7b576"},"_deposit":{"created_by":21,"id":"4076","owners":[21],"pid":{"revision_id":0,"type":"depid","value":"4076"},"status":"published"},"_oai":{"id":"oai:ir.soken.ac.jp:00004076","sets":["2:429:17"]},"author_link":["2268","2269","2270"],"item_1_creator_2":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"庄子, 聡"}],"nameIdentifiers":[{"nameIdentifier":"2268","nameIdentifierScheme":"WEKO"}]}]},"item_1_creator_3":{"attribute_name":"フリガナ","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"ショウジ, サトシ"}],"nameIdentifiers":[{"nameIdentifier":"2269","nameIdentifierScheme":"WEKO"}]}]},"item_1_date_granted_11":{"attribute_name":"学位授与年月日","attribute_value_mlt":[{"subitem_dategranted":"2013-03-22"}]},"item_1_degree_grantor_5":{"attribute_name":"学位授与機関","attribute_value_mlt":[{"subitem_degreegrantor":[{"subitem_degreegrantor_name":"総合研究大学院大学"}]}]},"item_1_degree_name_6":{"attribute_name":"学位名","attribute_value_mlt":[{"subitem_degreename":"博士（統計科学）"}]},"item_1_description_12":{"attribute_name":"要旨","attribute_value_mlt":[{"subitem_description":"Clinical drug development for a new drug proceeds in a stepwise manner. As the first step,\nphase I clinical trials are conducted to investigate pharmacokinetics and safety of the drug\nin humans, usually in healthy volunteers. After the phase I clinical trials, early efficacy in\na selected patient population is confirmed in a phase IIa clinical trial. If the results are\nsatisfactory, this is followed by a larger phase II or III clinical trial to learn and confirm\nhow to use the drug in the target patient population.\nIn phase I clinical trials, pharmacokinetics (PK) following the drug administration to\nhumans, typically to healthy volunteers, is thoroughly investigated by each individual\nbased on dense PK data (e.g., tens of blood samples sequentially collected from each\nindividual). In phase II and III clinical trials, PK data are collected to comprehend PK in\nthe target population. Main objectives of the PK study in phase II and III clinical trials are\ngenerally to understand variability of PK in the target population and to identify factors\ncausing the variability rather than to capture individual PK profile in each patient.\nPopulation PK analysis captures population mean PK profile, individual variability from\nthe mean PK profile (between-individual variability), and residual variability within the\nindividual (within-individual variability), which fits in objectives of PK study in phase II\nand III clinical trials. Therefore, the population PK approach is often used for PK analysis\nin phase II and III clinical trials.\nHowever, because of ethical and medical concerns, relatively few samples per patient,\nthat is sparse PK data, are obtained from patients participating in the phase II and III\nclinical trials and it is sometimes difficult to estimate all the PK parameters with the\nsparse data alone. One effective approach to address this issue is to borrow strength from\ndense PK data typically obtained from healthy volunteers (HVs) in the past phase I\nclinical trials under the assumption of exchangeability between these two datasets.\nConventional approaches include fixing some PK parameter values to those obtained\nfrom the dense PK data or fitting a PK model to a combined dataset of these two.\nThe Bayesian approach using prior distributions elicited from dense PK data from\nHVs provides a general framework for the population PK analysis with this type of\ninformation sharing. However, the Bayesian approach can still suffer from estimation bias\nwhen the exchangeability assumption is violated or when the prior distribution is\nmisspecified. In order for improving estimation accuracy with reduction of estimation\nbias under possible violations of the exchangeability assumption, we propose to introduce\nan information weighting that allows discounting the information from dense PK data in\nspecifying the prior distributions.\nWe firstly present an application of the conventional approach, combining sparse PK\ndata with dense PK data, to real PK data of a new drug under development, pregabalin, in\norder to demonstrate usefulness of the approach and extract the issues. Data used for this\npopulation PK analysis were obtained from 14 clinical studies. The analysis provided an\naccurate and reliable estimation of the relationship between pregabalin clearance and\nrenal function (creatinine clearance) in patients. Although model validation suggested\nadequacy of the model to interpret PK characteristics in patients with sparse PK data, it\nshould be cautious of potential bias due to violation of exchangeability assumption,\nspecifically parameters which sparse data have less information to estimate.\nFollowing application of the conventional approach, we present a proposed approach,\nintroduction of an information weighting on informative prior distributions for population\npharmacokinetic studies with sparse data. In our simulation setting, the optimal value for\nthe information weighting ranged from 0.2 to 1.0 when the exchangeability assumption\nwas held. On the other hand, when the exchangeability assumption was violated,\ninformation weighting with 0.2 to 0.5 was optimal. In the real data analysis, we obtained\nreasonable estimates. We chose the estimation results when information weighting of 0.5\nfor a plausible violation of the exchangeability assumption, as our simulation exercises\nsuggested that information weighting with 0.2 to 0.5 was optimal when the\nexchangeability assumption was violated.\nIn this thesis, we present usefulness of the conventional approach for evaluating PK\nparameters of interest in patients with sparse PK data, where the approach needs caution\nof potential bias due to violation of exchangeability assumption. For the proposed\napproach, through investigating the impact of various information weights on estimation\naccuracy (bias and precision of parameter estimation), we identify a range of optimal\nvalue in information weighting to obtain accurate estimates of PK parameters. In future\nstudies, it is worthwhile to identify optimal combinations of distinct weights for\nrespective PK parameters under more complex PK models (e.g., multiple-compartment\nmodels and models with non-linear elimination) under extensive situations where the\nexchangeability assumption is violated.","subitem_description_type":"Other"}]},"item_1_description_7":{"attribute_name":"学位記番号","attribute_value_mlt":[{"subitem_description":"総研大甲第1596号 ","subitem_description_type":"Other"}]},"item_1_select_14":{"attribute_name":"所蔵","attribute_value_mlt":[{"subitem_select_item":"有"}]},"item_1_select_8":{"attribute_name":"研究科","attribute_value_mlt":[{"subitem_select_item":"複合科学研究科"}]},"item_1_select_9":{"attribute_name":"専攻","attribute_value_mlt":[{"subitem_select_item":"15 統計科学専攻"}]},"item_1_text_10":{"attribute_name":"学位授与年度","attribute_value_mlt":[{"subitem_text_value":"2012"}]},"item_creator":{"attribute_name":"著者","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"SHOJI, Satoshi ","creatorNameLang":"en"}],"nameIdentifiers":[{"nameIdentifier":"2270","nameIdentifierScheme":"WEKO"}]}]},"item_files":{"attribute_name":"ファイル情報","attribute_type":"file","attribute_value_mlt":[{"accessrole":"open_date","date":[{"dateType":"Available","dateValue":"2016-02-26"}],"displaytype":"simple","filename":"甲1596_要旨.pdf","filesize":[{"value":"310.4 kB"}],"format":"application/pdf","licensetype":"license_11","mimetype":"application/pdf","url":{"label":"要旨・審査要旨","url":"https://ir.soken.ac.jp/record/4076/files/甲1596_要旨.pdf"},"version_id":"b4be2636-3987-4151-96af-2bc3fd5949a3"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"eng"}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourcetype":"thesis","resourceuri":"http://purl.org/coar/resource_type/c_46ec"}]},"item_title":"Population Pharmacokinetic Analysis of Sparse Data; Use of Dense Data from Earlier Clinical Trials as Prior Information","item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"Population Pharmacokinetic Analysis of Sparse Data; Use of Dense Data from Earlier Clinical Trials as Prior Information"},{"subitem_title":"Population Pharmacokinetic Analysis of Sparse Data; Use of Dense Data from Earlier Clinical Trials as Prior Information","subitem_title_language":"en"}]},"item_type_id":"1","owner":"21","path":["17"],"pubdate":{"attribute_name":"公開日","attribute_value":"2013-11-19"},"publish_date":"2013-11-19","publish_status":"0","recid":"4076","relation_version_is_last":true,"title":["Population Pharmacokinetic Analysis of Sparse Data; Use of Dense Data from Earlier Clinical Trials as Prior Information"],"weko_creator_id":"21","weko_shared_id":21},"updated":"2023-06-20T15:15:51.393009+00:00"}