Prediction of Intra-individual Variability in Bioequivalence Studies of 278 Formulations: Comprehensive Analysis Using Physicochemical and Pharmacokinetic Parameters.
{"title":"Prediction of Intra-individual Variability in Bioequivalence Studies of 278 Formulations: Comprehensive Analysis Using Physicochemical and Pharmacokinetic Parameters.","authors":"Masaki Higashino","doi":"10.1248/cpb.c24-00806","DOIUrl":null,"url":null,"abstract":"<p><p>The purpose of the present study was to predict the intra-individual variability (%CV<sub>intra</sub>) values of C<sub>max</sub> 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, %CV<sub>intra</sub>, inter-individual variability (%CV<sub>inter</sub>), absolute bioavailability (BA), T<sub>max</sub>, t<sub>1/2</sub>, dose number (Do), and dissolution rate (D%) were selected. Explanatory variables correlated with %CV<sub>intra</sub> were identified by multivariate analysis and grouped quantitatively by K-means clustering analysis. The %CV<sub>intra</sub> predictions compared three models of multiple regression, boosting tree, and neural network. In the neural network, the coefficient of determination (R<sup>2</sup>) and the root mean square error (RMSE) were the best, with good correlation between the predicted and observed values of the test data (R<sup>2</sup> = 0.69). The explanatory variables used in this study are readily available from the literature of reference formulation and in vitro measurement. Therefore, predicting %CV<sub>intra</sub> for C<sub>max</sub> 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.</p>","PeriodicalId":9773,"journal":{"name":"Chemical & pharmaceutical bulletin","volume":"73 4","pages":"349-354"},"PeriodicalIF":1.5000,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Chemical & pharmaceutical bulletin","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1248/cpb.c24-00806","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"CHEMISTRY, MEDICINAL","Score":null,"Total":0}
引用次数: 0
Abstract
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.
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.