A bio-predictive release assay for liposomal prednisolone phosphate.

Predictive performance assays are crucial for the development and approval of nanomedicines and their bioequivalent successors. At present, there are no established compendial methods that provide a reliable standard for comparing and selecting these formulation prototypes, and our understanding of the in vivo release remains still incomplete. Consequently, extensive animal studies, with enhanced analytical resolution for both, released and encapsulated drug, are necessary to assess bioequivalence.

Liquid-Liquid Dispersion Performance Prediction and Uncertainty Quantification Using Recurrent Neural Networks.

We demonstrate the application of a recurrent neural network (RNN) to perform multistep and multivariate time-series performance predictions for stirred and static mixers as exemplars of complex multiphase systems. We employ two network architectures in this study, fitted with either long short-term memory and gated recurrent unit cells, which are trained on high-fidelity, three-dimensional, computational fluid dynamics simulations of the mixer performance, in the presence and absence of surfactants, in terms of drop size distributions and interfacial areas as a function of system parameters; these include physicochemical properties, mixer geometry, and operating conditions. Our results demonstrate that while it is possible to train RNNs with a single fully connected layer more efficiently than with an encoder-decoder structure, the latter is shown to be more capable of learning long-term dynamics underlying dispersion metrics.

Simulation of immiscible liquid-liquid flows in complex microchannel geometries using a front-tracking scheme.

The three-dimensional two-phase flow dynamics inside a microfluidic device of complex geometry is simulated using a parallel, hybrid front-tracking/level-set solver. The numerical framework employed circumvents numerous meshing issues normally associated with constructing complex geometries within typical computational fluid dynamics packages. The device considered in the present work is constructed via a module that defines solid objects by means of a static distance function.