Articolul precedent |
Articolul urmator |
295 1 |
Ultima descărcare din IBN: 2022-11-20 15:29 |
SM ISO690:2012 DONICI, Elena, CREŢU, Dionisie. Application of design of experiments in pharmaceutical analysis. In: Cercetarea în biomedicină și sănătate: calitate, excelență și performanță, Ed. 1, 20-22 octombrie 2021, Chişinău. Chișinău, Republica Moldova: 2021, p. 438. ISBN 978-9975-82-223-7 (PDF).. |
EXPORT metadate: Google Scholar Crossref CERIF DataCite Dublin Core |
Cercetarea în biomedicină și sănătate: calitate, excelență și performanță 2021 | ||||||
Conferința "Cercetarea în biomedicină și sănătate: calitate, excelență și performanță" 1, Chişinău, Moldova, 20-22 octombrie 2021 | ||||||
|
||||||
Pag. 438-438 | ||||||
|
||||||
Descarcă PDF | ||||||
Rezumat | ||||||
Background. The quality of a drug should be design during the analytical development. Design of experiments are widely used to determine the influence of factors on the output responses of analytical methods. There are two types of experimental designs: screening and optimization. Objective of the study. Determination of principles of implementation of experimental models: screening and optimization in development of methods of pharmaceutical analysis. Material and Methods. To identify relevant studies, it was used the following academic search engines: Medline, PubMed, the Cochrane, Scopus, IET Digital Library, Google Scholar and Science Direct. The last search was june 2021. It was also used supplementary search techniques and sources: “similar articles” function in PubMed, conference abstracts and reference lists. Results. The most well-known screening designs are: two-level full factorial, fractionate factorial and Placket-Burman, being usually used to select the most important factors that affect the responses and to remove the insignificant ones. The most well-known optimization designs are: three-level full factorial, central composite and Box-Behnken. The screening designs allow modeling only first order response surface, while optimization designs allow a second order responce surface. The model should be selected based on the application of Analysis of Variance, which compares the variability due to the level of factors with the variability due to residual error. Conclusion. Design of experiment help to identify how the independent variables affect the analytical method performance characteristics. |
||||||
Cuvinte-cheie design of experiments, pharmaceutical analysis, factorial design, proiectarea experimentelor, analiza farmaceutică, design factorial |
||||||
|