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AAPS PharmSciTech

, 20:76 | Cite as

Optimal Selection of Incoming Materials from the Inventory for Achieving the Target Drug Release Profile of High Drug Load Sustained-Release Matrix Tablet

  • Yi Zhang
  • Bing XuEmail author
  • Xin Wang
  • Shengyun Dai
  • Xinyuan Shi
  • Yanjiang QiaoEmail author
Research Article
  • 40 Downloads

Abstract

In the pharmaceutical process, raw material (including APIs and excipients) variability can be delivered to the final product, and lead to batch-to-batch and lot-to-lot variances in its quality, finally impacting the efficacy of the drug. In this paper, the Panax notoginseng saponins (PNS) sustained-release matrix tablet was taken as the model formulation. Hydroxypropyl methylcellulose with the viscosity of 4000 mPa·s (HPMCK4M) from different vendors and batches were collected and their physical properties were characterized by the SeDeM methodology. The in-vitro dissolution profiles of active pharmaceutical ingredients (APIs) from matrix tablets made up of different batches HPMC K4M displayed significant variations. Multi-block partial least squares (MB-PLS) modeling results further demonstrated that physical properties of excipients played dominant roles in the drug release. In order to achieve the target drug release profile with respect to those far from the criteria, the optimal selection method of incoming materials from the available was established and validated. This study provided novel insights into the control of the input variability of the process and amplified the application of the SeDeM expert system, emphasizing the importance of the physical information of the raw materials in the drug manufacturing process.

KEY WORDS

excipient variability SeDeM latent variable modeling sustained-release matrix tablet formulation optimization 

Notes

Funding Information

Project of National Standardization of Traditional Chinese Medicine (No. ZYBZH-C-QIN-45) and National Natural Science Foundation of China (No. 81403112) provided generous financial supports.

Compliance with Ethical Standards

Conflict of Interest

The authors declare that there is no conflict of interests.

Supplementary material

12249_2018_1268_MOESM1_ESM.xlsx (13 kb)
ESM 1 (XLSX 12 kb)

References

  1. 1.
    Moreton C. Functionality and performance of excipients in a quality-bydesign world, part 4: obtaining information on excipient variability for formulation design space. Am Pharm Rev. 2009;12:28–33.Google Scholar
  2. 2.
    Alekya T, Narendar D, Mahipal D, Arjun N, Nagaraj B. Design and evaluation of chronomodulated drug delivery of tramadol hydrochloride. Drug Res. 2018;68:174–80.CrossRefGoogle Scholar
  3. 3.
    International Conference on Harmonization of Technical Requirements for Registration of Pharmaceuticals for Human Use (ICH). Quality guideline Q8 pharmaceutical development Q8. Center for Drug Evaluation and Research; 2006.Google Scholar
  4. 4.
    U.S. Food Drug Administration. Pharmaceutical cGMPs for the 21st century. Rockville; 2004.Google Scholar
  5. 5.
    Lawrence XY. Pharmaceutical quality by design: product and process development, understanding, and control. Pharm Res. 2008;25:781–91.CrossRefGoogle Scholar
  6. 6.
    Fonteyne M, Wickström H, Peeters E, Vercruysse J, Ehlers H, Peters BH, et al. Influence of raw material properties upon critical quality attributes of continuously produced granules and tablets. Eur J Pharm Biopharm. 2014;87:252–63.CrossRefGoogle Scholar
  7. 7.
    Zacour BM, Drennen JK, Anderson CA. Development of a fluid bed granulation design space using critical quality attribute weighted tolerance intervals. J Pharm Sci. 2012;101:2917–29.CrossRefGoogle Scholar
  8. 8.
    Piriyaprasarth S, Sriamornsak P. Effect of source variation on drug release from HPMC tablets: linear regression modeling for prediction of drug release. Int J Pharm. 2011;411:36–42.CrossRefGoogle Scholar
  9. 9.
    Zhou D, Law D, Reynolds J, Davis L, Smith C, Torres JL, et al. Understanding and managing the impact of HPMC variability on drug release from controlled release formulations. J Pharm Sci. 2014;103:1664–72.CrossRefGoogle Scholar
  10. 10.
    Jain AK, Söderlind E, Viridén A, Schug B, Abrahamsson B, Knopke C, et al. The influence of hydroxypropyl methylcellulose (HPMC) molecular weight, concentration and effect of food on in vivo erosion behavior of HPMC matrix tablets. J Control Release. 2014;187:50–8.CrossRefGoogle Scholar
  11. 11.
    Paul S, Sun CC. Modulating sticking propensity of pharmaceuticals through excipient selection in a direct compression tablet formulation. Pharm Res. 2018;35:113.CrossRefGoogle Scholar
  12. 12.
    Aguilardíaz JE, Garcíamontoya E, Suñenegre JM, Pérez-Lozano P, Miñarro M, Ticó JR. Predicting orally disintegrating tablets formulations of ibuprophen tablets: an application of the new SeDeM-ODT expert system. Eur J Pharm Biopharm. 2012;80:638–48.CrossRefGoogle Scholar
  13. 13.
    Willecke N, Szepes A, Wunderlich M, Remon JP, Vervaet C, De Beer T. A novel approach to support formulation design on twin screw wet granulation technology: understanding the impact of overarching excipient properties on drug product quality attributes. Int J Pharm. 2018;545:128–43.CrossRefGoogle Scholar
  14. 14.
    Mercuri A, Pagliari M, Baxevanis F, Fares R, Fotaki N. Understanding and predicting the impact of critical dissolution variables for nifedipine immediate release capsules by multivariate data analysis. Int J Pharm. 2017;518:41–9.CrossRefGoogle Scholar
  15. 15.
    Tomba E, Facco P, Bezzo F, Barolo M. Latent variable modeling to assist the implementation of quality-by-design paradigms in pharmaceutical development and manufacturing: a review. Int J Pharm. 2013;457:283–97.CrossRefGoogle Scholar
  16. 16.
    MacGregor JF, Liu Z, Bruwer MJ, Polsky B, Visscher G. Setting simultaneous specifications on multiple raw materials to ensure product quality and minimize risk. Chemom Intell Lab Syst. 2016;157:96–103.CrossRefGoogle Scholar
  17. 17.
    Sun F, Xu B, Zhang Y, Dai S, Shi X, Qiao Y. Latent variable modeling to analyze the effects of process parameters on the dissolution of paracetamol tablet. Bioengineered. 2017;8:61–70.CrossRefGoogle Scholar
  18. 18.
    Garcı́a-Muñoz S, Mercado J. Optimal selection of raw materials for pharmaceutical drug product design and manufacture using mixed integer nonlinear programming and multivariate latent variable regression models. Ind Eng Chem Res. 2013;52:5934–42.CrossRefGoogle Scholar
  19. 19.
    Escotetespinoza MS, Vadodaria S, Muzzio FJ, Ierapetritou MG. Modeling the effects of material properties on tablet compaction: a building block for controlling both batch and continuous pharmaceutical manufacturing processes. Int J Pharm. 2018;543:274–87.CrossRefGoogle Scholar
  20. 20.
    Zhang Y, Xu B, Wang X, Dai S, Sun F, Ma Q, et al. Setting up multivariate specifications on critical raw material attributes to ensure consistent drug dissolution from high drug-load sustained-release matrix tablet. Drug Dev Ind Pharm. 2018;44:1733–43.CrossRefGoogle Scholar
  21. 21.
    Suñé-Negre JM, Pérez-Lozano P, Miñarro M, Roig M, Fuster R, Hernández C, et al. Application of the SeDeM diagram and a new mathematical equation in the design of direct compression tablet formulation. Eur J Pharm Biopharm. 2008;69:1029–39.CrossRefGoogle Scholar
  22. 22.
    Negre JMS, Montoya EG, Díaz JEA, Díaz JEA, Carreras MR, García RF, et al. SeDeM diagram: a new expert system for the formulation of drugs in solid form. In: Balik M, editor. Expert systems for human, materials and automation: InTech INTECH open access Publisher; 2011. p. 17–34.Google Scholar
  23. 23.
    Suñé-Negre JM, Pérez-Lozano P, Roig M, Fuster R, Hernández C, Ruhí R, et al. Optimization of parameters of the SeDeM diagram expert system: Hausner index (IH) and relative humidity (%RH). Eur J Pharm Biopharm. 2011;79:464–72.CrossRefGoogle Scholar
  24. 24.
    Font Quer P. Medicamenta: guía teórico práctica para farmacéuticos y médicos. Labor Ed.; 1962. p. 340–341.Google Scholar
  25. 25.
    Council of Europe. Section 2.9.34. Bulk density and tapped density of powders. European Pharmacopeia 9.0; 2018.Google Scholar
  26. 26.
    Sun F, Xu B, Zhang Y, Dai S, Yang C, Cui X, et al. Statistical modeling methods to analyze the impacts of multiunit process variability on critical quality attributes of Chinese herbal medicine tablets. Drug Des Devel Ther. 2016;10:3909–24.CrossRefGoogle Scholar
  27. 27.
    Dai S, Xu B, Zhang Y, Sun F, Li J, Shi X, et al. Robust design space development for HPLC analysis of five chemical components in Saponins. J Liq Chromatogr Relat Technol. 2016;39:504–12.CrossRefGoogle Scholar
  28. 28.
    Saurí J, Millán D, Suñé-Negre JM, Pérez-Lozano P, Sarrate R, Fàbregas A, et al. The use of the SeDeM diagram expert system for the formulation of captopril SR matrix tablets by direct compression. Int J Pharm. 2014;461:38–45.CrossRefGoogle Scholar
  29. 29.
    The United States Pharmacopeial Convention. United States Pharmacopeia 35 - National Formulary 29 (USP 35- NF 30). United states pharmacopeia; 2012.Google Scholar
  30. 30.
    Bonferoni MC, Rossi S, Ferrari F, Bertoni M, Sinistri R, Caramella C. Characterization of three hydroxypropylmethylcellulose substitution types: rheological properties and dissolution behaviour. Eur J Pharm Biopharm. 1995;41:242–6.Google Scholar
  31. 31.
    Narayan P, Hancock BC. The relationship between the particle properties, mechanical behavior, and surface roughness of some pharmaceutical excipient compacts. Mater Sci Eng A. 2003;355:24–36.CrossRefGoogle Scholar

Copyright information

© American Association of Pharmaceutical Scientists 2019

Authors and Affiliations

  1. 1.Research Center of TCM Information EngineeringBeijing University of Chinese MedicineBeijingChina
  2. 2.Beijing Key Laboratory for Production Process Control and Quality Evaluation of Traditional Chinese MedicineBeijingChina
  3. 3.College of Chinese Materia MedicaBeijing University of Chinese MedicineBeijing CityPeople’s Republic of China

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