{"title":"Evaluating the prediction power and accuracy of two smart response surface experimental designs after revisiting repaglinide floating tablets","authors":"Tarek Elsayed, Rania M. Hathout","doi":"10.1186/s43094-024-00611-7","DOIUrl":null,"url":null,"abstract":"<div><h3>Background</h3><p>There is a soar in the figure of companies aiming to achieve efficiency in undergoing experimental processes. Therefore, instead of deploying one-factor-at-a-time, design of experiments is becoming rampantly utilized in order to reduce the resources outflow. There are a copious of different smart designs which could be employed as design of experiments tools. Central composite and d-optimal designs were investigated in this paper. The purpose of this investigation was to compare the two designs and identify the most accurate design at analyzing, interpreting and making predictions with regards to the data offered. The aforementioned purpose was achieved by applying both designs to a preexisting study which sought to prolong the gastrointestinal retention of repaglinide tablets through deploying a full factorial design. Further optimization was performed using Design-Expert software after inducing an outlier point.</p><h3>Results</h3><p><i>R</i>-squared, adjusted <i>R</i>-squared, predicted <i>R</i>-squared and adequate precision were computed in addition to acquiring diagnostics figures such as predicted versus actual, residual versus run, Box–Cox, contour plot and 3D surface plots. Model equations were also produced for each design. Results showed that both designs were successful at modeling the data both scoring <i>r</i>-squared values > 0.7 and adequate precision > 4 implying high fitting, prediction power and ability to navigate the experimental space using a reduced number of experimental runs. The d-optimal design obtained the least relative error of only 3.81%.</p><h3>Conclusions</h3><p>In conclusion, the d-optimal design provides a great tool for reduction of experimental testing which in turn diminishes resources consumption. Therefore, this design is favored to be enforced in the pharmaceutical sector.</p></div>","PeriodicalId":577,"journal":{"name":"Future Journal of Pharmaceutical Sciences","volume":null,"pages":null},"PeriodicalIF":3.4000,"publicationDate":"2024-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://fjps.springeropen.com/counter/pdf/10.1186/s43094-024-00611-7","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Future Journal of Pharmaceutical Sciences","FirstCategoryId":"1085","ListUrlMain":"https://link.springer.com/article/10.1186/s43094-024-00611-7","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"PHARMACOLOGY & PHARMACY","Score":null,"Total":0}
引用次数: 0
Abstract
Background
There is a soar in the figure of companies aiming to achieve efficiency in undergoing experimental processes. Therefore, instead of deploying one-factor-at-a-time, design of experiments is becoming rampantly utilized in order to reduce the resources outflow. There are a copious of different smart designs which could be employed as design of experiments tools. Central composite and d-optimal designs were investigated in this paper. The purpose of this investigation was to compare the two designs and identify the most accurate design at analyzing, interpreting and making predictions with regards to the data offered. The aforementioned purpose was achieved by applying both designs to a preexisting study which sought to prolong the gastrointestinal retention of repaglinide tablets through deploying a full factorial design. Further optimization was performed using Design-Expert software after inducing an outlier point.
Results
R-squared, adjusted R-squared, predicted R-squared and adequate precision were computed in addition to acquiring diagnostics figures such as predicted versus actual, residual versus run, Box–Cox, contour plot and 3D surface plots. Model equations were also produced for each design. Results showed that both designs were successful at modeling the data both scoring r-squared values > 0.7 and adequate precision > 4 implying high fitting, prediction power and ability to navigate the experimental space using a reduced number of experimental runs. The d-optimal design obtained the least relative error of only 3.81%.
Conclusions
In conclusion, the d-optimal design provides a great tool for reduction of experimental testing which in turn diminishes resources consumption. Therefore, this design is favored to be enforced in the pharmaceutical sector.
期刊介绍:
Future Journal of Pharmaceutical Sciences (FJPS) is the official journal of the Future University in Egypt. It is a peer-reviewed, open access journal which publishes original research articles, review articles and case studies on all aspects of pharmaceutical sciences and technologies, pharmacy practice and related clinical aspects, and pharmacy education. The journal publishes articles covering developments in drug absorption and metabolism, pharmacokinetics and dynamics, drug delivery systems, drug targeting and nano-technology. It also covers development of new systems, methods and techniques in pharmacy education and practice. The scope of the journal also extends to cover advancements in toxicology, cell and molecular biology, biomedical research, clinical and pharmaceutical microbiology, pharmaceutical biotechnology, medicinal chemistry, phytochemistry and nutraceuticals.