Nandkishor K. Nere

Drying process optimization for an API solvate using heat transfer model of an agitated filter dryer

Drying an early stage active pharmaceutical ingredient candidate required excessively long cycle times in a pilot plant agitated filter dryer. The key to faster drying is to ensure sufficient heat transfer and minimize mass transfer limitations. Designing the right mixing protocol is of utmost importance to achieve efficient heat transfer. To this order, a composite model was developed for the removal of bound solvent that incorporates models for heat transfer and desolvation kinetics. The proposed heat transfer model differs from previously reported models in two respects: it accounts for the effects of a gas gap between the vessel wall and solids on the overall heat transfer coefficient, and headspace pressure on the mean free path length of the inert gas and thereby on the heat transfer between the vessel wall and the first layer of solids. A computational methodology was developed incorporating the effects of mixing and headspace pressure to simulate the drying profile using a modified model framework within the Dynochem software. A dryer operational protocol was designed based on the desolvation kinetics, thermal stability studies of wet and dry cake, and the understanding gained through model simulations, resulting in a multifold reduction in drying time.

Nanocrystal Dissolution Kinetics and Solubility Increase Prediction from Molecular Dynamics: The Case of α-, β-, and γ-Glycine

Nanocrystals are receiving increased attention for pharmaceutical applications due to their enhanced solubility relative to their micron-sized counterpart and, in turn, potentially increased bioavailability. In this work, a computational method is proposed to predict the following: (1) polymorph specific dissolution kinetics and (2) the multiplicative increase in the polymorph specific nanocrystal solubility relative to the bulk solubility. The method uses a combination of molecular dynamics and a parametric particle size dependent mass transfer model. The method is demonstrated using a case study of α-, β-, and γ-glycine. It is shown that only the α-glycine form is predicted to have an increasing dissolution rate with decreasing particle size over the range of particle sizes simulated. On the contrary, γ-glycine shows a monotonically increasing dissolution rate with increasing particle size and dissolves at a rate 1.5 to 2 times larger than α- or β-glycine. The accelerated dissolution rate of γ-glycine relative to the other two polymorphs correlates directly with the interfacial energy ranking of γ > β > α obtained from the dissolution simulations, where γ- is predicted to have an interfacial energy roughly four times larger than either α- or β-glycine. From the interfacial energies, α- and β-glycine nanoparticles were predicted to experience modest solubility increases of up to 1.4 and 1.8 times the bulk solubility, where as γ-glycine showed upward of an 8 times amplification in the solubility. These MD simulations represent a first attempt at a computational (pre)screening method for the rational design of experiments for future engineering of nanocrystal API formulations.