Join us   Log in  

PHARMASPIRE - Volume 14,Issue 1 ,2022 , January-March 2022

Pages: 18-27
Print Article   Download XML  Download PDF

Development and validation of a system for the prediction of excipient-excipient incompatibility using machine learning tools

Author: Junaid Ul Hamid, Sunil Gupta

Category: Pharmaceutics


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. Although, 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 way 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 randomforest, our system was able to find the structural components responsible for these top incompatibilities and properties. The prediction accuracy was found to be >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 ?0.5 were considered active (possessing incompatibility or property) and the excipients with probability <0.5 were considered inactive (not possessing the incompatibility or property).

Keywords: Excipient-excipient incompatibility, Machine learning, Random forest, SOM, Structural incompatibility relationship, Structural-property relationship

DOI: 10.56933/Pharmaspire.2022.14103



1. Zhang K, Pellet JD, Narang AS, Wang YJ, Zhang YT. Reactive impurities in large and small molecule pharmaceutical excipients a review. TrAC Trends Anal Chem 2018;101:34-42.
2. Bindra DS, Stein D, Pandey P, Barbour N. Incompatibility of croscarmellose sodium with alkaline excipients in a tablet formulation. Pharm Dev Technol 2014;19:285-9.
3. Wang N, Sun H, Dong J, Ouyang D. Pharm DE: A new expert system for drug-excipient compatibility evaluation. Int J Pharm 2021;607:120962.
4. Reker D, Shi Y, Kirtane AR, Hess K, Zhong GJ, Crane E, et al. Machine learning uncovers food-and excipient-drug interactions. Cell Rep 2020;30:3710-
5. Mayr A, Klambauer G, Unterthiner T, Steijaert M, Wegner JK, Ceulemans H, et al. Large-scale comparison of machine learning methods for drug target prediction on ChEMBL. Chem Sci 2018;9:5441-51.
6. Schwaller P, Laino T, Gaudin T, Bolgar P, Hunter CA, Bekas C, et al. Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS Central Sci 2019;5:1572-83