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Published on: August 13, 2020
Modeling of Adhesion in Tablet Compression at the Molecular Level Using Thermal Analysis and Molecular Simulations.
Kaushalendra Chaturvedi1,2,3,4, Harsh S Shah1,3,4, Kenneth R Morris1,3
1Department of Pharmaceutical Sciences, Arnold and Marie Schwartz College of Pharmacy, Long Island University, 75 Dekalb Avenue, Brooklyn, New York 11201, United States.
This study links thermal analysis and molecular simulations to predict drug sticking during tablet compression. Ketoprofen exhibits the highest sticking propensity, correlating with its strong molecular interactions with metal surfaces.
Area of Science:
- Pharmaceutical Sciences
- Materials Science
- Physical Chemistry
Background:
- Tablet sticking during compression is a critical manufacturing issue.
- Understanding molecular interactions is key to predicting and preventing adhesion.
- Previous studies lack a combined thermal analysis and molecular simulation approach.
Purpose of the Study:
- To investigate the molecular basis of drug adhesion and sticking propensity.
- To correlate thermal analysis data with molecular simulation results for ibuprofen, flurbiprofen, and ketoprofen.
- To establish a predictive model for drug-surface interactions during tablet compression.
Main Methods:
- Utilized Differential Scanning Calorimetry (DSC) to determine the work of adhesion.
- Employed molecular simulations (Materials Studio) to calculate interaction energies with an iron surface.
- Investigated ibuprofen, flurbiprofen, and ketoprofen as model drug substances.
Main Results:
- Established a linear relationship between enthalpy of vaporization and sample mass for accurate thermal analysis.
- Determined threshold masses: ibuprofen (107 μg), flurbiprofen (112 μg), and ketoprofen (222 μg).
- Simulated work of adhesion rank order: ketoprofen > ibuprofen > flurbiprofen, consistent with experimental threshold masses.
Conclusions:
- Thermal analysis provides a viable method to assess drug sticking propensity at the molecular level.
- Molecular simulations accurately predict drug-surface interaction energies.
- The combined approach offers a powerful tool for optimizing tablet manufacturing processes and preventing sticking.
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