AI-driven pharmaceutical manufacturing : Revolutionizing quality control and process optimization
N. R. Jadhavnrjadhav18@rediffmail.comKrishna Vishwa Vidyapeeth (Deemed to be University)Krishna Institute of PharmacyKarad, Maharashtra, IndiaView full profile → , *Sunil BhutadaCorresponding authorsunilb@sreenidhi.edu.inDepartment of Information TechnologySreenidhi Institute of Science and TechnologyHyderabad, Telangana, IndiaView full profile → , S. R. Sagavkarsandhyaranikanunje@gmail.comDepartment of PharmaceuticsKrishna Vishwa Vidyapeeth (Deemed to be University)Krishna Institute of PharmacyKarad, Maharashtra, IndiaView full profile → , Rohit Pawarpawarohit@rknec.eduDepartment of Computer Science and Engineering (Data Science)Shri Ramdeobaba College of Engineering and ManagementNagpur, Maharashtra, IndiaView full profile → , Archana Bajirao Kanwadearchana_kanwade@yahoo.comDepartment of Electronics & Telecommunication EngineeringMarathwada Mitra Mandal College of EngineeringPune, Maharashtra, IndiaView full profile → , Purva Mangepurva.mange@gmail.comSymbiosis School of Planning Architecture and DesignSymbiosis International UniversityNagpur, Maharashtra, IndiaView full profile →
* Corresponding author · click or hover a name for details
- Published Online:
- 30 Mar 2024
- Article type:
- Research Article
- Language:
- EN
- Article no.:
- JSMS-1265
- Pages:
- 405–416
Abstract
Keywords
Subject Classifications
References
[1] I. Siradjuddin, I. N. Agiska, B. C. Ningrum, A. D. W. Sumari, I. A. Siradjuddin and Y. W. Syaifudin, “A Non-Pharmaceutical Intervention Policy for Mitigating COVID-19 Pandemic Using Predictive Control Scheme and SEIR Compartmental Model,” 2023 10th International Conference on Information Technology, Computer, and Electrical Engineering (ICITACEE), Semarang, Indonesia, pp. 242-247 (2023), doi: 10.1109/ICITACEE58587.2023.10277389.
[2] D. Copot, C. F. Caruntu, C. Portier, R. De Keyser and C. M. Ionescu, “An open source benchmark simulator for sustainable and flexible pharmaceutical manufacturing,” 2023 IEEE 28th International Conference on Emerging Technologies and Factory Automation (ETFA), Sinaia, Romania, pp. 1-7 (2023),
[3] P. H. Pathirannahalage et al., “Setpoint Determination Method for Pharmaceutical Continuous Manufacturing: Proactive-RTD-based Approach *,” 2023 IEEE Conference on Control Technology and Applications (CCTA), Bridgetown, Barbados, pp. 502-507 (2023), doi: 10.1109/CCTA54093.2023.10253250.
[4] Z. Wang et al., “Adaptive Quality Control With Uncertainty for a Pharmaceutical Cyber-Physical System Based on Data and Knowledge Integration,” in IEEE Transactions on Industrial Informatics, doi: 10.1109/TII.2023.3306355.
[5] F. Stauffer, V. Vanhoorne, G. Pilcer, P.-F. Chavez, C. Vervaet and T. De, “Managing API raw material variability in a continuous manufacturing line – Prediction of process robustness”, International Journal of Pharmaceutics, vol. 569, pp. 118525, Oct. (2019).
[6] H. Liu, S. Galbraith, B. Ricart, C. Stanton, B. Smith-Goettler, L. Verdi, et al., “Optimization of critical quality attributes in continuous twin-screw wet granulation via design space validated with pilot scale experimental data”, International Journal of Pharmaceutics, vol. 525, no. 1, pp. 249-263, June (2017).
[7] H. Liu, B. Ricart, C. Stanton, B. Smith-Goettler, L. Verdi, T. O’Connor, et al., “Design space determination and process optimization in at-scale continuous twin screw wet granulation”, Computers & Chemical Engineering, vol. 125, pp. 271-286, June (2019).
[8] K. Agnihotri, P. Chilbule, S. Prashant, P. Jain and P. Khobragade, “Generating Image Description Using Machine Learning Algorithms,” 2023 11th International Conference on Emerging Trends in Engineering & Technology - Signal and Information Processing (ICETET - SIP), Nagpur, India (2023).
[9] H. Arai, T. Nagato, T. Koide, E. Yonemochi, H. Yamamoto and H. Sugiyama, “Tablet Quality-Prediction Model Using Quality Material Attributes: Toward Flexible Switching Between Batch and Continuous Granulation”, Journal of Pharmaceutical Innovation, vol. 16, pp. 588-602, July (2020).
[10] E. Mäki-Lohiluoma, N. Säkkinen, M. Palomäki, O. Winberg, H. X. Ta, T. Heikkinen, et al., “Use of machine learning in prediction of granule particle size distribution and tablet tensile strength in commercial pharmaceutical manufacturing”, International Journal of Pharmaceutics, vol. 609, pp. 121146, Nov. (2021).
[11] T. Oishi, Y. Hayashi, M. Noguchi, F. Yano, S. Kumada, K. Takayama, et al., “Creation of novel large dataset comprising several granulation methods and the prediction of tablet properties from critical material attributes and critical process parameters using regularized linear regression models including interaction terms”, International Journal of Pharmaceutics, vol. 577, no. 2, pp. 119083, March (2020).
