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      <ArticleTitle>Development and validation of a system for the prediction of excipient-excipient incompatibility using machine learning tools</ArticleTitle>
      <Abstract>The importance of pharmaceutical excipients in the creation of any dosage form is critical. These excipients are occasionally to blame for product underperformance and dosage form deterioration. Product deterioration and underperformance could be attributed to incompatibilities between drug and excipient or sometimes excipient and excipient either due to the presence of reactive impurities in the excipients or a reaction between the functional groups present on the excipients. Though, the drug and excipient incompatibilities are monitored and reported, excipient-excipient incompatibilities are overlooked due to a paucity of literature. Pharmaceutical companies used to work in a controlled environment (compatibility tests between excipients to determine the best excipients for dosage form creation) and utilize mitigation measures to suppress any incompatibilities between excipients when necessary. In the current paper we propose a system to predict excipient-excipient incompatibilities that can occur during dosage form development, possible incompatibility reactions, as well as a potential ways to counter these incompatibilities by suggesting alternative excipients based on the descriptor knowledge of the excipients. The system was developed and validated using SOM and Random forest algorithms. Two systems were developed based on incompatibility and property relationship, this approach provides a wide scope of our system to select compatible excipients in place of incompatible ones. Two systems predicted top 10 key incompatibilities and properties which cover the maximum range of the pharmaceutical research and development. After the validation using random forest, our system was able to find the structural components responsible for these top incompatibilities and properties. The prediction accuracy was found to be &gt;85%. This was confirmed by preparing confusion matrix of the top incompatibilities and properties and assigning values of 0.5 to predict the outcome. The excipients with probability of &gt;=0.5 were considered active (possessing incompatibility or property) and the excipients with probability</Abstract>
      <Keywords>Machine learning, excipient-excipient incompatibility, SOM, Random forest, Structural incompatibility relationship, Structural property relationship</Keywords>
        <Abstract>https://isfcppharmaspire.com/ubijournal-v1copy/journals/abstract.php?article_id=14168&amp;title=Development and validation of a system for the prediction of excipient-excipient incompatibility using machine learning tools</Abstract>