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


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.

This is a preview of subscription content, log in to check access.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9


  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.

    CAS  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.

    CAS  Article  Google 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.

  4. 4.

    U.S. Food Drug Administration. Pharmaceutical cGMPs for the 21st century. Rockville; 2004.

  5. 5.

    Lawrence XY. Pharmaceutical quality by design: product and process development, understanding, and control. Pharm Res. 2008;25:781–91.

    Article  Google 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.

    CAS  Article  Google 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.

    CAS  Article  Google 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.

    CAS  Article  Google 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.

    CAS  Article  Google 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.

    CAS  Article  Google 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.

    Article  Google 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.

    Article  Google 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.

    CAS  Article  Google 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.

    CAS  Article  Google 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.

    CAS  Article  Google 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.

    CAS  Article  Google 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.

    CAS  Article  Google 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.

    Article  Google 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.

    CAS  Article  Google 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.

    CAS  Article  Google 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.

    Article  Google 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.

  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.

    Article  Google Scholar 

  24. 24.

    Font Quer P. Medicamenta: guía teórico práctica para farmacéuticos y médicos. Labor Ed.; 1962. p. 340–341.

  25. 25.

    Council of Europe. Section 2.9.34. Bulk density and tapped density of powders. European Pharmacopeia 9.0; 2018.

  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.

    Article  Google 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.

    CAS  Article  Google 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.

    Article  Google Scholar 

  29. 29.

    The United States Pharmacopeial Convention. United States Pharmacopeia 35 - National Formulary 29 (USP 35- NF 30). United states pharmacopeia; 2012.

  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.

    CAS  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.

    Article  Google Scholar 

Download references


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.

Author information



Corresponding authors

Correspondence to Bing Xu or Yanjiang Qiao.

Ethics declarations

Conflict of Interest

The authors declare that there is no conflict of interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic Supplementary Material


(XLSX 12 kb)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Zhang, Y., Xu, B., Wang, X. et al. Optimal Selection of Incoming Materials from the Inventory for Achieving the Target Drug Release Profile of High Drug Load Sustained-Release Matrix Tablet. AAPS PharmSciTech 20, 76 (2019). https://doi.org/10.1208/s12249-018-1268-9

Download citation


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