Volume 2 Issue 1 | 2025 | View PDF
Paper Id:IJMSM-V2I1P105
doi: 10.71141/30485037/V2I1P105

Accelerated Stability Testing Using Statistical Modeling and AI-Based Predictions

Dhyey Bhikadiya, Kirtankumar Bhikadiya

Citation:
Dhyey Bhikadiya, Kirtankumar Bhikadiya, "Accelerated Stability Testing Using Statistical Modeling and AI-Based Predictions" International Journal of Multidisciplinary on Science and Management, Vol. 2, No. 1, pp. 43-52, 2025.

Abstract:
Accelerated Stability Testing (AST) using statistical modeling and AI-based predictions has transformed the pharmaceutical industry by providing a reliable, efficient, and data-driven approach to predicting the shelf life and stability of pharmaceutical products. This methodology accelerates degradation processes under controlled stress conditions, enabling the simulation of long-term storage in a significantly shorter time frame. Advanced statistical models like the Arrhenius equation and machine learning techniques, including neural networks and support vector machines, enhance predictive accuracy and allow for the identification of complex degradation pathways. The integration of automation and AI-driven systems minimizes human error, optimizes experimental designs, and supports regulatory compliance. Despite challenges such as data quality, model limitations, and computational demands, the combined use of statistical and AI approaches ensures faster time-to-market, improved product quality, and greater cost efficiency. These advancements have applications beyond pharmaceuticals, extending into industries like food, beverages, and cosmetics, where product stability and quality are critical.

Keywords: Accelerated Stability Testing (AST), Statistical Modeling, Artificial Intelligence (AI), Pharmaceutical Stability Prediction, Machine Learning in Drug Development

References:
1. A.K. Bansal and P. Soni, “Application of Artificial Intelligence in the Prediction of Stability of Pharmaceutical Products,” Pharmaceutical Development and Technology, vol. 22, no. 2, pp. 242-248, 2017.
2. H. Kang and H. Lee, “Statistical Modeling for Accelerated Stability Testing of Pharmaceutical Formulations,” Journal of Pharmaceutical Sciences, vol. 108, no. 10, pp. 3145-3153, 2019.
3. D. Kumar and A.K. Bansal, “Machine Learning Approaches for Stability Predictions in Pharmaceutical Industry,” Artificial Intelligence in Drug Design, vol. 13, pp. 51-63, 2020.
4. D. Roy and S. Ghosh, “Use of Predictive Models in Accelerated Stability Testing,” Journal of Pharmaceutical Innovation, vol. 17, no. 1, pp. 22-34, 2021.
5. J. Liu and Y. Zhang, “Stability Prediction of Pharmaceutical Products using Data mining Techniques,” Journal of Pharmaceutical and Biomedical Analysis, vol. 149, pp. 37-42, 2018.
6. E.D. Meyer and S. Saouaf, “Integration of Artificial Intelligence and Statistical Modeling in the Pharmaceutical Stability Testing,” Pharmaceutical Research, vol. 37, no. 6, pp. 1181-1192, 2020.
7. S. Kumar and R. Sharma, “Application of Artificial Intelligence in Prediction of the Stability of Drug Formulations,” AI in Drug Development, vol. 4, no. 1, pp. 99-107, 2018.
8. J. Sethi and M. Gupta, “Accelerated Stability Testing: A Statistical Approach and Applications,” International Journal of Pharmaceutical Sciences, vol. 8, no. 5, pp. 145-151, 2021.
9. V.A. Patil and P.K. Patil, “Statistical Design and Optimization for Stability Testing of Pharmaceutical Products,” Journal of Pharmaceutical Technology and Drug Research, vol. 8, no. 3, pp. 98-105, 2019.
10. X. Zhao and X. Zhang, “Statistical Models for Accelerated Stability Studies,” Journal of Statistical Modelling in Pharmaceutical Research, vol. 22, no. 1, pp. 47-57, 2020.
11. F. Chen and C. Li, “Stability Prediction of Drugs under Accelerated Conditions using Machine Learning,” Journal of Chemometrics, vol. 33, no. 9, pp. 717-727, 2019.
12. Z. Wang and Z. Liu, “Predicting Stability of Pharmaceutical Formulations using Regression Models,” Journal of Pharmaceutical Sciences, vol. 107, no. 4, pp. 1105-1113, 2018.
13. A. Singh and R. Rathi, “Machine Learning for Stability Prediction of Pharmaceuticals,” Pharmaceutical Engineering, vol. 40, no. 5, pp. 70-78, 2020.
14. X. Wang and S. Li, “Application of Statistical Techniques in Accelerated Stability Testing for Drug Development,” Journal of Pharmaceutical Analysis, vol. 11, no. 2, pp. 80-89, 2021.
15. L. Ding and J. Zhang, “Predicting the Long-Term Stability of Pharmaceutical Products Using Artificial Intelligence,” Computers in Biology and Medicine, vol. 118, pp. 103625, 2019.
16. A. Hassan and S. Ali, “Statistical Optimization of Accelerated Stability Studies for Drug Formulations,” International Journal of Applied Statistical Analysis, vol. 45, no. 6, pp. 1011-1024, 2020.
17. P.K. Sahu and P.K. Sahoo, “Application of AI for Pharmaceutical Stability Testing and Prediction,” International Journal of Pharmaceutical Sciences, vol. 42, no. 7, pp. 349-356, 2020.
18. A. Jain and V. Jain, “Predictive Models for Accelerated Stability Testing of Drug Products: Current State and Future Directions,” Pharmaceutical Science & Technology Today, vol. 22, no. 1, pp. 22-33, 2021.
19. H. Park and Y. Kim, “Use of Deep Learning in Pharmaceutical Stability Predictions,” Artificial Intelligence in Medicine, vol. 116, pp. 102073, 2021.
20. Y. Zhao and Z. Li, “AI-Based Statistical Methods for Accelerated Stability Testing: A Review,” Journal of AI and Data Science, vol. 4, no. 3, pp. 205-213, 2019.
21. M. Tan and Y. Chen, “Artificial Intelligence and Machine Learning in Predicting the Stability of Pharmaceutical Products under Accelerated Testing,” Journal of Chemical Information and Modeling, vol. 60, no. 9, pp. 4605-4614, 2020.
22. S. Verma and R. Kumar, “A statistical Approach for Modeling Accelerated Stability Testing,” International Journal of Pharmaceutical Research, vol. 11, no. 4, pp. 123-129, 2019.
23. Q. Cao and Y. Zhou, “Predicting Stability of Pharmaceutical Formulations Using Neural Networks,” Expert Systems with Applications, vol. 142, pp. 112926, 2020.
24. S. Goyal and A. Prasad, “Statistical Methods for Enhanced Stability Prediction of Drug Formulations under Accelerated Conditions,” Journal of Pharmaceutical Innovation, vol. 15, no. 2, pp. 78-85, 2020.
25. P. Chaudhary and S. Singh, “Machine Learning Models in Stability Testing for Pharmaceutical Products,” Computational Chemistry, vol. 45, no. 8, pp. 712-723, 2021.
26. M. Bhatia and A. Sharma, “Artificial Intelligence for Drug Stability Prediction: A Comprehensive Review,” Journal of Artificial Intelligence in Pharmaceutical Sciences, vol. 5, no. 3, pp. 143-157, 2021.
27. L. Xu and W. Lin, “Accelerated Stability Testing with Machine Learning Prediction Models,” Pharmaceutical Engineering & Technology, vol. 20, no. 2, pp. 76-85, 2021.
28. S. Ghosh and A. Shah, “Statistical and AI-Based Modeling in Accelerated Stability Testing for Pharmaceutical Products,” Current Trends in Pharmaceutical Sciences, vol. 9, no. 4, pp. 233-240, 2020.
29. P. S. Rao and P. K. Naik, “Statistical Tools in the Stability Analysis of Drug Products,” Journal of Biopharmaceutical Statistics, vol. 29, no. 6, pp. 1237-1248, 2019.
30. Y. Lai and T. Li, “AI-based models for long-Term Stability Predictions of Pharmaceutical Formulations,” Journal of Pharmaceutical Sciences and Technology, vol. 44, no. 7, pp. 351-360, 2021.
31. A. Sharma and R. Sharma, “Machine Learning and Statistical Modeling in Accelerated Stability Studies,” Pharmaceutical Technology, vol. 44, no. 9, pp. 86-94, 2020.
32. N. Husain and S. Dey, “Predictive Analytics for Stability Testing of Drug Products Using Artificial Intelligence,” AI for Pharma Industry, vol. 12, no. 3, pp. 101-108, 2021.
33. S. Jadhav and A. Patil, “Artificial Intelligence for Accelerated Stability Prediction in Pharmaceutical Industries,” Computational Biology and Chemistry, vol. 73, pp. 100-108, 2018.
34. V. Bharti and P. Mishra, “A Review on Statistical Methodologies in Stability Studies of pharmaceutical Products,” Journal of Pharmaceutics and Biopharmaceutics, vol. 8, no. 4, pp. 121-129, 2020.
35. S. Shukla and P. Pandey, “Predictive Models in the Accelerated Stability Study for Pharmaceutical Formulations,” Pharmaceutical Science & Technology Journal, vol. 12, no. 3, pp. 101-109, 2019.
36. J. Li and Y. Zhao, “Statistical Prediction of Drug Stability under Accelerated Conditions,” Journal of Pharmaceutical Engineering, vol. 18, no. 2, pp. 109-119, 2021.
37. X. Wang and X. Tan, “Use of Deep Learning in Drug Stability Testing Prediction,” Computational Science and Engineering, vol. 13, no. 1, pp. 45-53, 2020.
38. R. Chauhan and P. Garg, “Statistical Approaches in Stability Studies for Drug Products under Accelerated Conditions,” Asian Journal of Pharmaceutical Research and Development, vol. 7, no. 5, pp. 65-72, 2019.
39. T. Zhou and F. Liu, “Neural Networks in the Prediction of Pharmaceutical Stability,” Journal of Computational Biology, vol. 28, no. 9, pp. 1452-1461, 2021.
40. G. Santos and P. Ferreira, “Statistical Optimization in Accelerated Stability Testing of Pharmaceutical Formulations,” Journal of Pharmaceutical Science and Technology, vol. 22, no. 4, pp. 213-219, 2019.