For oral drug delivery, spray drying is often used to address formulation challenges associated with bioavailability-limited drugs, such as slow dissolution rate or low solubility in the gastrointestinal tract. Spray drying is employed in the pharmaceutical industry as a single continuous unit operation in which a solution containing dissolved active pharmaceutical ingredient (API) and excipients is atomized into a drying chamber in conjunction with a high temperature gas, resulting in a dried powder. These approaches have demonstrated the utility of these techniques to reduce experimental burden and codify prior knowledge by using a broad range of routinely collected data. Machine learning models have recently been evaluated for use in pharmaceutical development across a broad range of areas, including drug discovery and drug formulation and to a lesser extent manufacturing processes. Additionally, this approach is synergistic with regulatory guidance including the quality by design (QbD) paradigm specified by ICH Q8 and the more recent Pharma 4.0™ developed by ISPE. The results of this study illustrate how trained regression models can reduce the experimental effort required to create an in-silico design space for new molecules during early-stage process development and subsequent scale-up.Īpplication of model-based approaches during pharmaceutical drug development has been successful at reducing time, cost and raw materials required to obtain the required product quality. The optimization strategy was employed to estimate process parameters in the hold-out evaluation set and to illustrate selection of process parameters during scale-up. Additionally, an optimization strategy used the predictive model to determine initial estimates for process parameter values that best achieve a target particle size for a provided formulation. Shapley additive explanations identified how changes in formulation and process parameters drove variations in model predictions of dried particle size and were found to be consistent with mechanistic understanding of the particle formation process. An ensemble machine learning model was created to predict dried particle size across pilot and production scale spray dryers, with prediction errors between −7.7% and 18.6% (25th/75th percentiles) for a hold-out evaluation set. Additionally we developed a strategy with formulation and target particle size as inputs to define a set of “first to try” process parameters. This is the first study that demonstrates prediction of particle size independent of API for a wide range of formulation and process parameters at pilot and commercial scale. Substantial experimentation has been required to relate formulation and process parameters to particle size with the results limited to a single active pharmaceutical ingredient (API). Spray dried dispersion particle size is a critical quality attribute that impacts bioavailability and manufacturability of the spray drying process and final dosage form.
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