Monitoring Blending of Pharmaceutical Powders with Multipoint NIR Spectroscopy

Abstract

Abstract. Blending of powders is a crucial step in the production of pharmaceutical solid dosage forms. The active pharmaceutical ingredient (API) is often a powder that is blended with other powders (excipients) in order to produce tablets. The blending efficiency is influenced by several external factors, such as the desired degree of homogeneity and the required blending time, which mainly depend on the properties of the blended materials and on the geometry of the blender. This experimental study investigates the mixing behavior of acetyl salicylic acid as an API and α-lactose monohydrate as an excipient for different filling orders and filling levels in a blender. A multiple near-infrared probe setup on a laboratoryscale blender is used to observe the powder composition quasi-simultaneously and in-line in up to six different positions of the blender. Partial least squares regression modeling was used for a quantitative analysis of the powder compositions in the different measurement positions. The end point for the investigated mixtures and measurement positions was determined via moving block standard deviation. Observing blending in different positions helped to detect good and poor mixing positions inside the blender that are affected by convective and diffusive mixing.

Process Simulation and Data Modeling in Solid Oral Drug Development and Manufacture

“…It has been proposed in the literature that development of a robust fluid bed process depends on control of the moisture profile [23,24,[31][32][33][34][35][36][37][38][39][40]. Moisture profile control of a fluid bed granulation process requires an operator or controller to manipulate three interdependent control "levers" available during processing.…”

“…A list of the various aggregation kernels available in literature has been shown in a tabular form by Cameron et al [12]. The approach based on [26] and [50,51] was implemented by Immanuel and Doyle [37] in order to obtain a mechanistic kernel, which takes into account various system parameters, such as particle diameter and temperature, resulting in a more generalized kernel which requires fewer estimated parameters. A modified physical-based kernel has also been proposed recently, which considers variable binder thickness for obtaining the aggregation kernel [52].…”

“…The permeability of a granular material is influenced by particle properties such as size, shape, surface texture, and stiffness as well as bulk properties such as cohesion and bulk density. Permeability is typically measured as the pressure drop across a powder bed over a range of consolidation states, as achieved by the FT4 from Freeman Technology [37]. A schematic of the test and sample raw data that can be obtained are shown below in Fig.…”

“…Owen and Cleary [37] also simulated a screw conveyor using DEM, analyzing the flow rates and power consumption under different operating conditions, such as inclination angle, fill level, and rotational speed.…”

Monitoring of a Hot Melt Coating Process via a Novel Multipoint Near-Infrared Spectrometer

“…A single probe may fail or may become useless due to several reasons; very common is the occurrence of fouling. Thus, a multipoint system provides a better monitoring quality and control of the coating process, for example, with regard to coating homogeneity and endpoint control of a process (21)(22)(23). Different positions inside the coating vessel were evaluated regarding their susceptibility for fouling and their reliability.…”