Acta Nat. Sci.   |  e-ISSN: 2718-0638

Original article | Acta Natura et Scientia 2024, Vol. 5(1) 19-30

Assessment of Some Inspection Properties of Commonly Used Medicinal Excipients Using Statistical Process Control for Monitoring of Manufacturer Quality

Mostafa Eissa

pp. 19 - 30   |  Manu. Number: MANU-2312-28-0006.R1

Published online: May 22, 2024  |   Number of Views: 22  |  Number of Download: 204


Abstract

This study is a component of a larger initiative that involves the assessment and screening of pharmaceutical and chemical factories that produce medical substances, particularly in Asia and export them to poor nations. The present study concentrated on the inactive pharmaceutical ingredients of a frequently used excipient in pharmaceutical products made of amylopectin and amylose, named Amylum Maydis by the International Union of Pure and Applied Chemistry (IUPAC) nomenclature. This compound has the chemical formula C6H10O5. Manufacturers asserted that all raw ingredients complied with the British Pharmacopoeia (BP), harmonizing requirements and analytical criteria in the process. As a result, every test complied with the official standard procedures described in the raw material testing monograph. The chosen tests included oxidizing agents, sulfated ash, and loss on drying (LOD). Software for statistical process control, or SPC, was used to collect and handle datasets. Preliminary data examination was done using box plots and three variable visualization techniques associated with the correlation matrix. All results showed that improvements of the inspection characteristics records are mandated to show stable variations even if there was no out-of-specification detected. Accordingly, the output of the tests should be investigated to correct for the assignable causes of the variations. It should be noted that the present data did not follow specific distributions, especially with the presence of aberrant values. Furthermore, it was found that there were several out-of-control points even in cases where there was no deviation from the specification, highlighting the need for suitable inquiry and correction for assignable reasons of variances among batches. Government enforcement of industrial SPC regulations is necessary to ensure the safety and quality of produced medical substances.

Keywords: Box plot, Bubble plot, Control chart, Contour plot, Corn starch, SPC


How to Cite this Article?

APA 6th edition
Eissa, M. (2024). Assessment of Some Inspection Properties of Commonly Used Medicinal Excipients Using Statistical Process Control for Monitoring of Manufacturer Quality . Acta Natura et Scientia, 5(1), 19-30.

Harvard
Eissa, M. (2024). Assessment of Some Inspection Properties of Commonly Used Medicinal Excipients Using Statistical Process Control for Monitoring of Manufacturer Quality . Acta Natura et Scientia, 5(1), pp. 19-30.

Chicago 16th edition
Eissa, Mostafa (2024). "Assessment of Some Inspection Properties of Commonly Used Medicinal Excipients Using Statistical Process Control for Monitoring of Manufacturer Quality ". Acta Natura et Scientia 5 (1):19-30.

References
  1. REFERENCES [Google Scholar]
  2. Al‐Najjar, B. (1996). Total quality maintenance: an approach for continuous reduction in costs of quality products. Journal of Quality in Maintenance Engineering, 2(3), 4-20. https://doi.org/10.1108/13552519610130413 [Google Scholar] [Crossref] 
  3. Besseris, G. J. (2013). Robust quality controlling: SPC with box plots and runs test. The TQM Journal, 25(1), 89-102. https://doi.org/10.1108/17542731311286450 [Google Scholar] [Crossref] 
  4. British Pharmacopoeia. (2023): In “Maize Starch”. Medicines and Healthcare products Regulatory Agency, UK, BP 2023 (Ph. Eur. 11.2 update). [Google Scholar]
  5. Chang, J., Desai, N., & Gosain, A. (2019). Correlation between altimetric score and citations in pediatric surgery core journals. Journal of Surgical Research, 243, 52-58. https://doi.org/10.1016/j.jss.2019.05.010 [Google Scholar] [Crossref] 
  6. Eissa, D., Rashed, E., & Eissa, M. (2023). Measuring public health effect of coronavirus disease 2019: A novel perspective in healthcare in pandemic times. Batı Karadeniz Tıp Dergisi, 7(2), 266-268. https://doi.org/10.29058/mjwbs.1257163 [Google Scholar] [Crossref] 
  7. Eissa, M., Mahmoud, A., & Nouby, A. (2016). Evaluation and failure risk of microbiological air quality in production area of pharmaceutical plant. RGUHS Journal of Pharmaceutical Sciences, 5, 155-166. [Google Scholar]
  8. Eissa, M., & Mahmoud, A. (2016). Evaluation of microbial recovery from raw materials for pharmaceutical use. Journal of Food and Pharmaceutical Sciences, 4(1), 6-11. https://doi.org/10.14499/jfps [Google Scholar] [Crossref] 
  9. Eissa, M., Rashed, E., & Eissa, D. E. (2021a). Study of tellurium-129m (129mTe) ground deposition following Fukushima nuclear disaster: Descriptive analysis of UNSCEAR database using statistical process techniques. Mugla Journal of Science and Technology, 7(2), 67-72. https://doi.org/10.22531/muglajsci.955946 [Google Scholar] [Crossref] 
  10. Eissa, M., Rashed, E., & Eissa, D. E. (2021b). Quality improvement in routine inspection and control of healthcare products using statistical intervention of long-term data trend. Dicle Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 10(2), 163-184. [Google Scholar]
  11. Eissa, M., & Rashed, E. (2020). Inventory digital management using statistical process control analysis in healthcare industry. Journal of Business in The Digital Age, 3(2), 123-128. https://doi.org/10.46238/jobda.688641 [Google Scholar] [Crossref] 
  12. Eissa, M. (2016). Study of microbial distribution from different processing stages in purified water production plant of pharmaceutical manufacturing facility. Research & Reviews: Journal of Microbiology and Virology, 6(1), 31-45. [Google Scholar]
  13. Eissa, M. (2021). Implementation of modified Q-control chart in monitoring of inspection characteristics with finite quantification sensitivity limits: A case study of bioburden enumeration in capsule shell. El-Cezeri, 8(3), 1093-1107. https://doi.org/10.31202/ecjse.871179 [Google Scholar] [Crossref] 
  14. Eissa, M. (2022). Validation of microbiological assay design of neomycin sulfate in 30 x 30 cm rectangular antibiotic plate. Journal of Advanced Biomedical and Pharmaceutical Sciences, 5(2), 54-63. https://doi.org/10.21608/jabps.2021.104951.1143 [Google Scholar] [Crossref] 
  15. Eissa, M. E. (2023a). Studies on morbidities and mortalities from COVID-19: Novel public health practice during pandemic periods. Asian Journal of Applied Sciences, 16(3), 84-94. https://doi.org/10.3923/ajaps.2023.84.94 [Google Scholar] [Crossref] 
  16. Eissa, M. E., & Abid, A. M. (2018). Application of statistical process control for spotting compliance to good pharmaceutical practice. Brazilian Journal of Pharmaceutical Sciences, 54(02), e17499. https://doi.org/10.1590/s2175-97902018000217499 [Google Scholar] [Crossref] 
  17. Eissa, M. E. (2015). Shewhart control chart in microbiological quality control of purified water and its use in quantitative risk evaluation. Pharmaceutical and Biosciences Journal, 4(1), 45-51. https://doi.org/10.20510/ukjpb/4/i1/87845 [Google Scholar] [Crossref] 
  18. Eissa, M. E. (2018). Adulterated pharmaceutical product detection using statistical process control. Bangladesh Pharmaceutical Journal, 21(1), 7-15. [Google Scholar]
  19. Eissa, M. E. (2019). Trichinosis outbreak risk analysis in USA from food sources and new prospective analysis using statistical process control tools. International Journal of Research in Pharmacy and Biosciences, 6(7), 4-13. [Google Scholar]
  20. Eissa, M. E. (2023b). Trending perspective in evaluation of inspection characteristics of pharmaceutical compound: comparative study of control charts. Universal Journal of Pharmaceutical Research, 8(5), 15-21. https://doi.org/10.22270/ujpr.v8i5.1006 [Google Scholar] [Crossref] 
  21. Essam-Eissa, M., & Refaat-Rashed, E. (2021). Unique quantitative analysis of tsunami waves using statistical software: A case study of the major recorded Hawaii incidents. Advanced Materials Proceedings, 6(1), 1-6. [Google Scholar]
  22. Essam-Eissa, M. (2017). Monitoring of Cryptosporidium spp. outbreaks using statistical process control tools and quantitative risk analysis based on NORS long-term trending. Microbiology Journal, 9(1), 1-7. https://doi.org/10.3923/mj.2019.1.7 [Google Scholar] [Crossref] 
  23. Hahn, V. S., Petucci, C, Kim, M. S., Bedi, K. C., Jr., Wang, H., Mishra, S., Koleini, N., Yoo, E. J., Margulies, K. B., Arany, Z., Kelly, D. P., Kass, D. A., & Sharma, K. (2023). Myocardial metabolomics of human heart failure with preserved ejection fraction. Circulation, 147(15), 1147–1161. https://doi.org/10.1161/CIRCULATIONAHA.122.061846 [Google Scholar] [Crossref] 
  24. Kim, E. J., Kim, J. H., Kim, M. S., Jeong, S. H., & Choi, D. H. (2021). Process analytical technology tools for monitoring pharmaceutical unit operations: A control strategy for continuous process verification. Pharmaceutics, 13(6), 919. https://doi.org/10.3390/pharmaceutics13060919 [Google Scholar] [Crossref] 
  25. Kim, T. S., & Choi, D. H. (2020). Liver dysfunction in sepsis. The Korean Journal of Gastroenterology, 75(4), 182–187. https://doi.org/10.4166/kjg.2020.75.4.182 [Google Scholar] [Crossref] 
  26. Maize Starch. (2023). British Pharmacopoeia Commission (Ed.). [Google Scholar]
  27. Mostafa-Eissa, M. (2018). Quality criteria establishment for dissolution of ascorbic acid from sustained release pellets. Novel Techniques in Nutrition & Food Science, 2(2), 137-142. https://doi.org/10.31031/ntnf.2018.02.000531 [Google Scholar] [Crossref] 
  28. Mostafa-Essam, A. E. (2019). The use failure mode and effects analysis as quantitative risk analysis tool. Journal of Applied Sciences, 2019(02), RD-APS-10009. [Google Scholar]
  29. Motulsky, H. J. (2007). GraphPad Prism Version 5.0 Statistics Guide. GraphPad  Software, Inc. [Google Scholar]
  30. Pakdil, F. (2020). Control charts. F. Pakdil (Ed.), Six Sigma for Students: A Problem-Solving Methodology (pp. 333-373). Palgrave Macmillan & Springer. [Google Scholar]
  31. Rashid, K., & Haris Aslam, M. M. (2012). Business excellence through total supply chain quality management. Asian Journal on Quality, 13(3), 309-324. https://doi.org/10.1108/15982681211287829 [Google Scholar] [Crossref] 
  32. Rice, W. R. (1989). Analyzing tables of statistical tests. Evolution, 43(1), 223-225. https://doi.org/10.2307/2409177 [Google Scholar] [Crossref] 
  33. Sharma, P., Zhang, X., Ly, K., Kim, J. H., Wan, Q., Kim, J., Lou, M., Kain, L., Teyton, L., & Winau, F. (2024). Hyperglycosylation of prosaposin in tumor dendritic cells drives immune escape. Science, 383(6679), 190–200. https://doi.org/10.1126/science.adg1955 [Google Scholar] [Crossref] 
  34. Wierzbicki, A. S., Kim, E. J., Esan, O., & Ramachandran, R. (2022). Hypertriglyceridaemia: An update. Journal of Clinical Pathology, 75(12), 798–806. https://doi.org/10.1136/jclinpath-2021-207719 [Google Scholar] [Crossref] 
  35. You, H., Ma, X., Efe, C., Wang, G., Jeong, S. H., Abe, K., Duan, W., Chen, S., Kong, Y., Zhang, D., Wei, L., Wang, F. S., Lin, H. C., Yang, J. M., Tanwandee, T., Gani, R. A., Payawal, D. A., Sharma, B. C., Hou, J., … Jia, J. (2022). APASL clinical practice guidance: the diagnosis and management of patients with primary biliary cholangitis. Hepatology International, 16(1), 1–23. https://doi.org/10.1007/s12072-021-10276-6 [Google Scholar] [Crossref] 
  36. Yu, B., Zeng, L., & Yang, H. (2018). A Bayesian Approach to setting the release limits for critical quality attributes. Statistics in Biopharmaceutical Research, 10(3), 158-165. https://doi.org/10.1080/19466315.2018.1482780 [Google Scholar] [Crossref]