Objectives: Tablet manufacturing development is costly, laborious, and time-consuming. Technologies related to artificial intelligence like ,predictive model ,can be used in the control process to facilitate and accelerate the tablet manufacturing process. predictive models have become popular recently. However, predictive models need a comprehensive dataset of related data in the field, due to the lack of a dataset of tablet formulations, the aim of this study is to aggregate and integrate fast disintegration tablet's formulation into a comprehensive dataset.
Data description: The search strategy has been prepared between the years of 2010 to 2020, consisting of the keyword's 'formulation' ,'disintegrating' and 'Tablet', as well as their synonyms. By searching four databases, 1503 articles were retrieved, from these articles only 232 articles met all of the study's criteria. By reviewing 232 articles, 1982 formulations have been extracted, afterward pre-processing and cleaning data, contain steps of unifying the name and units, removing inappropriate formulations by an expert, and finally, data tidying was done on data. The developed dataset contains valuable information from various FDT's formulations, which can be used in pharmaceutical studies that are critical to the discovery and development of new drugs. this method can be applied to aggregate datasets from the other dosage forms.
Keywords: Data extraction; Dataset development; Direct compression; Fast disintegrating tablets; Pre-formulation; Predictive model.
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