Data-smart machine learning methods for predicting composition-dependent Young’s modulus of pharmaceutical compacts
The ability to predict mechanical properties of compacted powder blends of Active Pharmaceutical Ingredients (API) and excipients solely from component properties can reduce the amount of ‘trial-and-error’ involved in formulation design. Machine Learning (ML) can reduce model development time and effort with the imperative of adequate historical data.
This work describes the utility of linear and nonlinear ML models for predicting Young’s modulus (YM) of directly compressed blends of known excipients and unknown API mixed at arbitrary compositions given only the true density of the API. The models were trained with data from compacts of three BCS Class I APIs and two excipients blended at four drug loadings, three excipient compositions, and compacted to five nominal solid fractions. The prediction accuracy of the models was measured using three cross-validation (CV) schemes.
Finally, we demonstrate an application of the model to enable Quality-by-Design in formulation design. Limitations of the models and future work have been also discussed.
Article Information: Author links open overlay panelStephen Thomas, Hannah Palahnuk, Hossein Amini, Ilgaz Akseli. International Journal of Pharmaceutics. https://doi.org/10.1016/j.ijpharm.2020.120049.
Keywords: Powder compaction, Young’s modulus, Machine learning, Non-destructive, Material Profiling, Microcrystalline Cellulose PH102 (Avicel PH 102), Lactose FastFlo 316, Croscarmellose Sodium (Ac-Di-Sol), Sodium Stearyl Fumarate (Pruv)