From Drying Kinetics, Solvate Structure, Particle Morphology, and Modeling to Optimal Drying Protocol

Abstract

The mechanistic understanding of three key active pharmaceutical ingredient (API) drying elements: drying kinetics, physical property control, and stability control, is critical for downstream formulation development and manufacture. The objective of this study was to collect drying kinetic data through the use of a laboratory integrated sorption chamber to elucidate the drying mechanism and thereby establish a drying model. In addition, the collection of drying kinetic data would provide a greater understanding of the solvent to API ratio as well as the API stability to help develop a large-scale drying protocol. The study material is an ethanol solvate (ethanolate) of the API. There is no fundamental crystal structure change upon drying of the solvate in the temperature range from 50 to 90 °C, and the drying kinetic data show that the molar ratio of ethanol to API is 1:1. This is confirmed by X-ray crystallography. An analysis of the drying kinetics showed that the diffusion of ethanol through the crystal lattice provided the primary mass transfer resistance, which can be simulated using the Fickian principle of diffusion through a slab. The diffusion coefficients from 50 to 90 °C at 20 mmHg were found to be strongly temperature-dependent and fit well using an Arrhenius type relationship. The activation energy for diffusion was then determined from this relationship. By using these diffusion parameters, the drying curves at various temperatures were generated to predict the drying time to meet the final ethanol specification. The predicted drying time was in good agreement with experimental drying data at high temperatures (80 and 90 °C) but shorter than that observed when drying at lower temperatures (between 50 and 70 °C). Thermal stability data showed that the compound is stable at drying temperatures equal or below 70 °C. Drying data from this study are generated from a static balance. Hence, the effect of shear forces imposed from cake depth and dryer agitation on particle morphology, physical stability of the desolvated API, and drying time needs to be studied to generate the optimal drying protocol for large-scale operation.

COMF: Comprehensive Model-Fitting Method for Simulating Isothermal and Single-Step Solid-State Reactions

“…We are interested in the CMF method, mainly due to its mathematical simplicity and straightforwardness for the determination of the kinetic triplet. Particularly, we are interested in using CMF for analyzing single-step isothermal solid-state reactions (ISSRs) [22], which usually take place during the drying process of pharmaceutical hydrates/solvates [1][2][3][4]21,[23][24][25][26]. The most crucial step when performing CMF is to identify the 'best' candidate model (BCM) for a particular ISSR and then the complete kinetic triplet is calculated.…”

Robust Process Scale-Up Leveraging Design of Experiments to Map Active Pharmaceutical Ingredient Humid Drying Parameter Space

“…Values of all drying parameters were recorded continuously throughout each experiment. Process analytical technology (PAT) tools were used to monitor the volatile components in the exiting gas stream by mass spectrometry, while the crystalline form of the drying solids was tracked by Raman spectroscopy. , Alternatively, the Raman probe could be replaced with a thermocouple to monitor the temperature of the cake during drying.…”

Mechanistic Elucidation of Hard Agglomerate Formation from Drying Kinetics in the Integrated Sorption Chamber

“…The partial differential equation given in eq can be solved by the method of separation of variables, and its solution is available in the literature. , The solution of eq in terms of concentration as a function of time and location can be integrated from – l to + l to provide a measurable quantity in terms of weight loss as a function of drying time as follows: where M 0 is the weight of water before the diffusion process, M t is the weight of water at drying time t , and n is an integer.…”

“…Although eq provides the relationship between the weight change and drying time, it does not offer a clear analytical relationship between the weight loss and the drying time at the beginning of drying when t is small. This kind of relationship can be obtained from the solution of eq via the Laplace transformation, as described in the literature. , The solution from this transformation is provided as follows: Equation offers two useful features of Fickian diffusion. First, the plot of dimensionless weight loss M t / M 0 versus the square root of drying time is linear at small t .…”

“…The development of the optimal drying protocol , requires a fundamental mechanistic understanding of the drying process and the impact of shear forces on the particle morphology. The formation of hard agglomerates is closely related to the interaction between the API and the solvent and the shear force during drying.…”