Next Article in Journal
Impact in Clinical Practice of the European Medicines Agency Health Alert About the Restriction of the Use of JAK Inhibitors
Previous Article in Journal
Aspergillus oryzae Fermented Plumula Nelumbinis Against Atopic Dermatitis Through AKT/mTOR and Jun Pathways
Previous Article in Special Issue
Spanlastic Nano-Vesicles: A Novel Approach to Improve the Dissolution, Bioavailability, and Pharmacokinetic Behavior of Famotidine
 
 
Article
Peer-Review Record

Evaluation of Prediction Models for the Capping and Breaking Force of Tablets Using Machine Learning Tools in Wet Granulation Commercial-Scale Pharmaceutical Manufacturing

Pharmaceuticals 2025, 18(1), 23; https://doi.org/10.3390/ph18010023
by Sun Ho Kim 1,*, Su Hyeon Han 2, Dong-Wan Seo 1 and Myung Joo Kang 1
Reviewer 1: Anonymous
Reviewer 2: Anonymous
Pharmaceuticals 2025, 18(1), 23; https://doi.org/10.3390/ph18010023
Submission received: 3 December 2024 / Revised: 21 December 2024 / Accepted: 24 December 2024 / Published: 27 December 2024

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

See attachment

Comments for author File: Comments.pdf

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

The article titled "Evaluation of Prediction Models for Capping and Breaking Force of Tablets Using Machine Learning Tools in Wet Granulation Commercial-Scale Pharmaceutical Manufacturing" presents an innovative approach to integrating machine learning into pharmaceutical manufacturing, specifically targeting the prediction of critical quality attributes in tablet production. This work contributes significantly to the optimization of large-scale wet granulation processes and ensures product quality with enhanced precision. The study targets a significant problem in commercial-scale pharmaceutical manufacturing, specifically optimizing CQAs such as tablet breaking force, friability, and capping occurrence. The comments are as follows that need to be addressed.

1.       Tablet breaking force? Hardness of the table is not a better word as it is widely used and official in pharmacopoeias.

2.       Introduction needs to improve the addition of machine learning tools/study in pharma manufacturing scale Pharmaceutical. There is no need for patient safety and effective use examples (it can be removed).

3.       Table 1. Composition of mixtures subjected to granulation processes. On what basis was this composition selected?

4. Figure, 2,4 and 5 should have error bars.  

5.       While the results are promising, elaborate on why specific ML models were chosen for regression and classification.

6.       Highlight or discuss any limitations, such as the applicability of the models to different tablet formulations or manufacturing conditions, to provide a balanced perspective.

7. Discuss the applicability of these models to other formulations or manufacturing processes in the healthcare sector.

8.       Page 10: The equations should be numbered.

 

 

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

The authors have addressed my minor review comments; I support publication

Back to TopTop