AAPS PharmSciTech

, Volume 4, Issue 4, pp 461–468

A study on moisture isotherms of formulations: the Use of polynomial equations to predict the moisture isotherms of tablet products


DOI: 10.1208/pt040459

Cite this article as:
Li, Y., Sanzgiri, Y.D. & Chen, Y. AAPS PharmSciTech (2003) 4: 461. doi:10.1208/pt040459


The objectives of this study were to investigate the effects of manufacturing parameters on the moisture sorption isotherms of some tablet formulations and to predict the moisture isotherms of the final formulations using polynomial equations. Three tablet formulations including a placebo and 2 drug products were prepared through wet granulation, drying, compression, and coating processes. Equilibrium moisture content of excipients and granules at 25°C with different relative humidities were determined using a dynamic moisture sorption microbalance, while such data for tablets were determined using desiccators. Moisture sorption isotherms were expressed in polynomial equations. Excipient isotherms were used to predict the moisture sorption isotherms of the 3 tablet products. Results showed that different physical properties of granules and tablets, such as particle size distribution, density, and porosity resulting from different granulation and compression conditions did not have significant effect on the moisture isotherms of the materials. Changing coating materials from a powder mixture to a film also did not change the moisture sorption characteristics significantly. The predicted moisture sorption isotherms of the formulations agreed well with the experimental results. These results show that moisture isotherms of solid pharmaceutical products manufactured with conventional processes may be predicted using the isotherms of excipients, and polynomial equations may be used as a tool for the prediction of moisture isotherms.


prediction moisture sorption isotherm excipients formulations 

Copyright information

© American Association of Pharmaceutical Scientists 2003

Authors and Affiliations

  • Yanxia Li
    • 1
  • Yeshwant D. Sanzgiri
    • 1
  • Yisheng Chen
    • 1
  1. 1.Abbott LaboratoriesGlobal Pharmaceutical Research and DevelopmentNorth Chicago

Personalised recommendations