Evaluation of the impact of sodium lauryl sulfate source variability on solid oral dosage form development.

OBJECTIVE: The objective of this study was to investigate the effects of sodium lauryl sulfate (SLS) from different sources on solubilization/wetting, granulation process, and tablet dissolution of BILR 355 and the potential causes. METHODS: The particle size distribution, morphology, and thermal behaviors of two pharmaceutical grades of SLS from Spectrum and Cognis were characterized. The surface tension and drug solubility in SLS solutions were measured. The BILR 355 tablets were prepared by a wet granulation process and the dissolution was evaluated. RESULTS: The critical micelle concentration was lower for Spectrum SLS, which resulted in a higher BILR 355 solubility. During wet granulation, less water was required to reach the same end point using Spectrum than Cognis SLS. In general, BILR 355 tablets prepared with Spectrum SLS showed a higher dissolution than the tablets containing Cognis SLS. Micronization of SLS achieved the same improved tablet dissolution as micronized active pharmaceutical ingredient. CONCLUSIONS: The observed differences in wetting and solubilization were likely due to the different impurity levels in SLS from two sources. This study demonstrated that SLS from different sources could have significant impact on wet granulation process and dissolution. Therefore, it is critical to evaluate SLS properties from different suppliers, and then identify optimal formulation and process parameters to ensure robustness of drug product manufacture process and performance.

A semi-theoretical model for simulating the temporal evolution of moisture-temperature during industrial fluidized bed granulation.

Moisture plays a major role in determining the attributes of granules prepared by fluidized bed granulation (FBG). Here, a semi-theoretical droplet-based evaporation rate model was developed and incorporated into moisture mass-enthalpy balances to simulate the temporal evolution of bed moisture-temperature. Experimental data from a GPCG30 unit were used to fit the model parameters. With only two fitting parameters, the model demonstrated excellent capability to describe the moisture-temperature evolution for a wide range of operating conditions. Then, in a global process model (GPM) approach, the evaporation parameters were fitted to multi-linear functions of inlet air temperature, binder concentration, and spray rate. The GPM was validated successfully by simulating a different data set which was not used in its calibration. As the GPM demonstrated a good predictive capability, it was further used to investigate the impacts of process parameters. Numerical simulations suggest that the proposed GPM predicts the experimentally well-established trends of moisture-temperature profiles in previously published data, proving the applicability of the GPM approach. This study has demonstrated the capabilities of simple process models as a practical approach to predict time-wise evolution of bed moisture-temperature profiles in industrial FBG modeling, while also pointing out their limitations.

A Practical Framework Toward Prediction of Breaking Force and Disintegration of Tablet Formulations Using Machine Learning Tools.

Enabling the paradigm of quality by design requires the ability to quantitatively correlate material properties and process variables to measureable product performance attributes. Conventional, quality-by-test methods for determining tablet breaking force and disintegration time usually involve destructive tests, which consume significant amount of time and labor and provide limited information. Recent advances in material characterization, statistical analysis, and machine learning have provided multiple tools that have the potential to develop nondestructive, fast, and accurate approaches in drug product development. In this work, a methodology to predict the breaking force and disintegration time of tablet formulations using nondestructive ultrasonics and machine learning tools was developed. The input variables to the model include intrinsic properties of formulation and extrinsic process variables influencing the tablet during manufacturing. The model has been applied to predict breaking force and disintegration time using small quantities of active pharmaceutical ingredient and prototype formulation designs. The novel approach presented is a step forward toward rational design of a robust drug product based on insight into the performance of common materials during formulation and process development. It may also help expedite drug product development timeline and reduce active pharmaceutical ingredient usage while improving efficiency of the overall process.

Modeling powder encapsulation in dosator-based machines: II. Experimental evaluation.

A theoretical model was previously derived to predict powder encapsulation in dosator-based machines. The theoretical basis of the model was discussed earlier. In this part; the model was evaluated experimentally using two powder formulations with substantially different flow behavior. Encapsulation experiments were performed using a Zanasi encapsulation machine under two sets of experimental conditions. Model predicted outcomes such as encapsulation fill weight and plug height were compared to those experimentally obtained. Results showed a high correlation between predicted and actual outcomes demonstrating the model's success in predicting the encapsulation of both formulations. The model is a potentially useful in silico analysis tool that can be used for capsule dosage form development in accordance to quality by design (QbD) principles.