278种制剂生物等效性研究的个体差异性预测:使用物理化学和药代动力学参数的综合分析。

IF 1.5 4区 医学 Q4 CHEMISTRY, MEDICINAL
Masaki Higashino
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引用次数: 0

摘要

本研究的目的是利用观察到的各种制剂的理化和药代动力学参数来预测Cmax的个体内变异性(%CVintra)值。采用数据库对最高剂量片剂、口腔崩解片(ODT)和胶囊(278个剂型[238个化合物])等口服药物的临床生物等效性(BE)研究参数进行汇总。作为解释变量,选择了%CVintra、个体间变异性(%CVinter)、绝对生物利用度(BA)、Tmax、t1/2、剂量数(Do)和溶出率(D%)。通过多变量分析确定与%CVintra相关的解释变量,并通过k均值聚类分析进行定量分组。%CVintra的预测比较了多元回归、增强树和神经网络三种模型。在神经网络中,决定系数(R2)和均方根误差(RMSE)最好,试验数据的预测值与实测值具有良好的相关性(R2 = 0.69)。本研究中使用的解释变量可以从参考制剂和体外测量的文献中获得。因此,在不进行试点研究的情况下预测Cmax的%CVintra对仿制药开发早期阶段的临床规划是有用的。我们认为,我们可以进一步为加快和降低仿制药开发的成本作出贡献。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Prediction of Intra-individual Variability in Bioequivalence Studies of 278 Formulations: Comprehensive Analysis Using Physicochemical and Pharmacokinetic Parameters.

The purpose of the present study was to predict the intra-individual variability (%CVintra) values of Cmax using observed parameters of physicochemical and pharmacokinetic for a variety of formulations. A database was used to summarize the parameters of clinical bioequivalence (BE) studies of oral drugs, including the highest dose tablets, orally disintegrating tablets (ODT), and capsules (278 formulations [238 compounds]). As explanatory variables, %CVintra, inter-individual variability (%CVinter), absolute bioavailability (BA), Tmax, t1/2, dose number (Do), and dissolution rate (D%) were selected. Explanatory variables correlated with %CVintra were identified by multivariate analysis and grouped quantitatively by K-means clustering analysis. The %CVintra predictions compared three models of multiple regression, boosting tree, and neural network. In the neural network, the coefficient of determination (R2) and the root mean square error (RMSE) were the best, with good correlation between the predicted and observed values of the test data (R2 = 0.69). The explanatory variables used in this study are readily available from the literature of reference formulation and in vitro measurement. Therefore, predicting %CVintra for Cmax without conducting pilot studies is useful for clinical planning in the early stages of generic drug development. We believe that we could further contribute to speeding up and reducing the cost of generic drug development.

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来源期刊
CiteScore
3.20
自引率
5.90%
发文量
132
审稿时长
1.7 months
期刊介绍: The CPB covers various chemical topics in the pharmaceutical and health sciences fields dealing with biologically active compounds, natural products, and medicines, while BPB deals with a wide range of biological topics in the pharmaceutical and health sciences fields including scientific research from basic to clinical studies. For details of their respective scopes, please refer to the submission topic categories below. Topics: Organic chemistry In silico science Inorganic chemistry Pharmacognosy Health statistics Forensic science Biochemistry Pharmacology Pharmaceutical care and science Medicinal chemistry Analytical chemistry Physical pharmacy Natural product chemistry Toxicology Environmental science Molecular and cellular biology Biopharmacy and pharmacokinetics Pharmaceutical education Chemical biology Physical chemistry Pharmaceutical engineering Epidemiology Hygiene Regulatory science Immunology and microbiology Clinical pharmacy Miscellaneous.
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