Severity: Warning
Message: file_get_contents(https://...@gmail.com&api_key=61f08fa0b96a73de8c900d749fcb997acc09&a=1): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests
Filename: helpers/my_audit_helper.php
Line Number: 197
Backtrace:
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 197
Function: file_get_contents
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 271
Function: simplexml_load_file_from_url
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3165
Function: getPubMedXML
File: /var/www/html/application/controllers/Detail.php
Line: 597
Function: pubMedSearch_Global
File: /var/www/html/application/controllers/Detail.php
Line: 511
Function: pubMedGetRelatedKeyword
File: /var/www/html/index.php
Line: 317
Function: require_once
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Artificial Intelligence (AI) is emerging as a valuable tool in pharmaceutical formulations, including the development of effervescent tablets (ETs). This review highlights how AI techniques are being explored to support ET formulation designs, optimize component ratios, predict disintegration and dissolution behavior, and control reactions through artificial neural networks, support vector machines, and machine learning. These techniques have been applied in recent studies to enhance stability, improve disintegration times, and flavor masking. Computational fluid dynamics simulations of effervescence and dissolution are underexplored for ETs. Data-driven approaches, like response surface modeling, require ingredient concentrations, tablet properties, consumer preferences, and predictive analytics for optimization. However, limited comprehensive datasets, complex reactions, environmental sensitivities, and ethical/regulatory considerations pose challenges. Overcoming these obstacles, as identified in the current literature, could enable AI to innovate ET development and personalization.
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Source |
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http://dx.doi.org/10.2174/0113895575402013250821121137 | DOI Listing |