Matches in SemOpenAlex for { <https://semopenalex.org/work/W4313328092> ?p ?o ?g. }
Showing items 1 to 84 of
84
with 100 items per page.
- W4313328092 endingPage "418" @default.
- W4313328092 startingPage "418" @default.
- W4313328092 abstract "In this study, the modern tool of machine learning is used to address an old problem from a new perspective. Traditionally, the scientific basis for determining bioequivalence is based on a pharmacokinetic comparison, specifically the rate and extent of absorption between two products. Even though it is generally agreed that the peak plasma concentration (Cmax) should be used to measure the rate of absorption, several studies have raised concerns. Thus, alternative pharmacokinetic metrics have been proposed to address Cmax shortcomings. The aim of this study is to utilize unsupervised (principal component analysis) and supervised (random forest) machine learning algorithms to uncover the relationships among the pharmacokinetic parameters and identify the most suitable metric for absorption rate. One actual and three simulated donepezil bioequivalence datasets were utilized. For the needs of this study, a population pharmacokinetic model of donepezil was also developed and further used for the simulation of BE datasets with different absorption kinetics. Among the pharmacokinetic metrics explored, the newly proposed Cmax/Tmax ratio is also investigated. The latter was found to better reflect the absorption rate, regardless of the kinetic properties of absorption. This is one of the first studies utilizing machine learning in the field of bioequivalence." @default.
- W4313328092 created "2023-01-06" @default.
- W4313328092 creator A5016554880 @default.
- W4313328092 date "2022-12-28" @default.
- W4313328092 modified "2023-09-25" @default.
- W4313328092 title "Machine Learning in Bioequivalence: Towards Identifying an Appropriate Measure of Absorption Rate" @default.
- W4313328092 cites W1540398233 @default.
- W4313328092 cites W1563687119 @default.
- W4313328092 cites W1583179025 @default.
- W4313328092 cites W15934772 @default.
- W4313328092 cites W1965140853 @default.
- W4313328092 cites W1972829697 @default.
- W4313328092 cites W2014405952 @default.
- W4313328092 cites W2034238518 @default.
- W4313328092 cites W2034993989 @default.
- W4313328092 cites W2065090825 @default.
- W4313328092 cites W2066860556 @default.
- W4313328092 cites W2110928488 @default.
- W4313328092 cites W2117756735 @default.
- W4313328092 cites W212698896 @default.
- W4313328092 cites W2254997984 @default.
- W4313328092 cites W3040868855 @default.
- W4313328092 cites W306015106 @default.
- W4313328092 cites W36508194 @default.
- W4313328092 cites W4250237542 @default.
- W4313328092 cites W595896130 @default.
- W4313328092 doi "https://doi.org/10.3390/app13010418" @default.
- W4313328092 hasPublicationYear "2022" @default.
- W4313328092 type Work @default.
- W4313328092 citedByCount "1" @default.
- W4313328092 countsByYear W43133280922023 @default.
- W4313328092 crossrefType "journal-article" @default.
- W4313328092 hasAuthorship W4313328092A5016554880 @default.
- W4313328092 hasBestOaLocation W43133280921 @default.
- W4313328092 hasConcept C112705442 @default.
- W4313328092 hasConcept C119857082 @default.
- W4313328092 hasConcept C125287762 @default.
- W4313328092 hasConcept C127413603 @default.
- W4313328092 hasConcept C154945302 @default.
- W4313328092 hasConcept C159985019 @default.
- W4313328092 hasConcept C176217482 @default.
- W4313328092 hasConcept C192562407 @default.
- W4313328092 hasConcept C21547014 @default.
- W4313328092 hasConcept C22979827 @default.
- W4313328092 hasConcept C33923547 @default.
- W4313328092 hasConcept C41008148 @default.
- W4313328092 hasConcept C42404028 @default.
- W4313328092 hasConcept C71924100 @default.
- W4313328092 hasConcept C98274493 @default.
- W4313328092 hasConceptScore W4313328092C112705442 @default.
- W4313328092 hasConceptScore W4313328092C119857082 @default.
- W4313328092 hasConceptScore W4313328092C125287762 @default.
- W4313328092 hasConceptScore W4313328092C127413603 @default.
- W4313328092 hasConceptScore W4313328092C154945302 @default.
- W4313328092 hasConceptScore W4313328092C159985019 @default.
- W4313328092 hasConceptScore W4313328092C176217482 @default.
- W4313328092 hasConceptScore W4313328092C192562407 @default.
- W4313328092 hasConceptScore W4313328092C21547014 @default.
- W4313328092 hasConceptScore W4313328092C22979827 @default.
- W4313328092 hasConceptScore W4313328092C33923547 @default.
- W4313328092 hasConceptScore W4313328092C41008148 @default.
- W4313328092 hasConceptScore W4313328092C42404028 @default.
- W4313328092 hasConceptScore W4313328092C71924100 @default.
- W4313328092 hasConceptScore W4313328092C98274493 @default.
- W4313328092 hasIssue "1" @default.
- W4313328092 hasLocation W43133280921 @default.
- W4313328092 hasOpenAccess W4313328092 @default.
- W4313328092 hasPrimaryLocation W43133280921 @default.
- W4313328092 hasRelatedWork W2087796686 @default.
- W4313328092 hasRelatedWork W2367728005 @default.
- W4313328092 hasRelatedWork W2371169609 @default.
- W4313328092 hasRelatedWork W2374069256 @default.
- W4313328092 hasRelatedWork W2381431760 @default.
- W4313328092 hasRelatedWork W2388991600 @default.
- W4313328092 hasRelatedWork W2401080248 @default.
- W4313328092 hasRelatedWork W2409052002 @default.
- W4313328092 hasRelatedWork W2787516851 @default.
- W4313328092 hasRelatedWork W3203385489 @default.
- W4313328092 hasVolume "13" @default.
- W4313328092 isParatext "false" @default.
- W4313328092 isRetracted "false" @default.
- W4313328092 workType "article" @default.