The development and validation of a quality by design based process analytical tool for the inline quantification of Ramipril during hot-melt extrusion.

Continuous processing is superseding conventional batch processing as a means of manufacturing within the pharmaceutical research/industry. This paradigm shift has led to the implementation of Process Analytical Technology (PAT) as a semi-automatic, predictive tool offering real-time quality control that can be built into the production line. However, PAT tools have been mainly utilised to monitor a single process (e.g. powder blending, synthesis of biopharmaceuticals and small molecules) rather than a full continuous manufacturing process. In addition, there is a paucity of guidance documents that consider the continuous and dynamic conditions of real-time measurements for validation purposes. In this study, the feasibility of developing and validating a predictive and reliable Raman method based on quality by design (QbD) and PAT frameworks for the real-time quantification of Ramipril (RMP) during hot-melt extrusion (HME) were investigated. Through QbD, a design space elucidating the quality attributes of RMP stability was successfully identified based on offline HPLC measurements. Process temperature and powder feeding rate were the main quality attributes to affect the stability of RMP during HME. The optimum combination of process and formulation variables were extracted from the validated design space and used to extrude RMP at a concentration range of 2.5-12.5 %w/w. Three calibration models were established using PLS regression analysis. The developed PLS calibration models showed excellent linearity (R2 = 0.989, 0.995, 0.992), accuracy (RMSEcv = 0.31, 0.26, 0.30%) and specificity (PC1 = 81, 85, 89%) for models 1, 2 and 3, respectively. Furthermore, the developed QbD-PAT model was able to predict the quantity of RMP at varied process feed rate (10, 35 rpm) operating under long processing time (60 min). The output of this study allows in-process optimisation of formulation and process variables to control the quality and quantity of RMP during HME. Furthermore, it allows the implementation of PAT tools as routine methods of analysis within the laboratory.

Metformin Hydrochloride and Sitagliptin Phosphate Fixed-Dose Combination Product Prepared Using Melt Granulation Continuous Processing Technology.

The development of oral solid dosage forms, such as tablets that contain a high dose of drug(s), requires polymers and other additives to be incorporated at low levels as possible, to keep the final tablet weight low, and, correspondingly, the dosage form size small enough to be acceptable from a patient perspective. Additionally, a multi-step batch-based manufacturing process is usually required for production of solid dosage forms. This study presents the development and production, by twin-screw melt granulation technology, of a high-dose immediate-release fixed-dose combination (FDC) product of metformin hydrochloride (MET) and sitagliptin phosphate (SIT), with drug loads of 80% w/w and 6% w/w, respectively. For an 850/63 mg dose of MET/SIT, the final weight of the caplets was approximately 1063 mg compared with 1143 mg for the equivalent dose in Janumet®, the marketed product. Mixtures of the two drugs and polymers were melt-granulated at temperatures below the individual melting temperatures of MET and SIT (231.65 and 213.89°C, respectively) but above the glass transition temperature or melting temperature of the binder(s) used. By careful selection of binders, and processing conditions, direct compressed immediate-release caplets with desired product profiles were successfully produced. The melt granule formulations before compression showed good flow properties, were larger in particle size than individual starting API materials and were easily compressible. Melt granulation is a suitable platform for developing direct compressible high-dose immediate-release solid dosage forms of FDC products.

A predictive integrated framework based on the radial basis function for the modelling of the flow of pharmaceutical powders.

This study presents a modelling framework to predict the flowability of various commonly used pharmaceutical powders and their blends. The flowability models were trained and validated on 86 samples including single components and binary mixtures. Two modelling paradigms based on artificial intelligence (AI) namely, a radial basis function (RBF) and an integrated network were employed to model the flowability represented by the flow function coefficient (FFC) and the bulk density (RHOB). Both approaches were utilized to map the input parameters (i.e. particle size, shape descriptors and material type) to the flow properties. The input parameters of the blends were determined from the particle size, shape and material type properties of the single components. The results clearly indicated that the integrated network outperformed the single RBF network in terms of the predictive performance and the generalization capabilities. For the integrated network, the coefficient of determination of the testing data set (not used for training the model) for FFC was R2=0.93, reflecting an acceptable predictive power of this model. Since the flowability of the blends can be predicted from single component size and shape descriptors, the integrated network can assist formulators in selecting excipients and their blend concentrations to improve flowability with minimal experimental effort and material resulting in the (i) minimization of the time required, (ii) exploration and examination of the design space, and (iii) minimization of material waste.