George L. Reid

De-risking Scale-up of a High Shear Wet Granulation Process Using Latent Variable Modeling and Near-Infrared Spectroscopy

In the development of wet granulated drug products, two primary sources of variance (disturbance) include the operational scale of the high shear wet granulation (HSWG) process and active pharmaceutical ingredient (API) lot-to-lot variability, particularly for formulations containing a high drug load. This paper presents a novel Process Analytical Technology strategy using latent variable modeling with near-infrared spectroscopy (NIRS) to reduce risk in scale-up operations of the HSWG process while simultaneously accounting for API lot-to-lot variability, even with limited manufacturing history. The process involves building a partial least square (PLS) model among the API material properties, the HSWG process parameters, and the NIRS end points of the HSWG process based on small-scale design of experiment batches. The PLS model is then used in an optimization framework to find suitable small-scale mechanical process parameters (impeller/chopper speed) that approximate a previous large-scale operation so as to keep the NIRS end point of large-scale operation constant. Prior to making additional large-scale batches with a new lot of API, NIRS end points of large-scale HSWG with the new API lot are predicted based on the PLS model developed from the small-scale operation. If the predicted NIRS end point for the HSWG using the new API lot is not within the target region, the risks associated with the scale-up operation can then be significantly reduced by modifying other HSWG process parameters such as the total amount of water added or total granulation time to achieve the target region. A case study is presented that demonstrates the effectiveness of this methodology during development and scale-up of a drug product manufactured using a HSWG process.

De-risking Pharmaceutical Tablet Manufacture Through Process Understanding, Latent Variable Modeling, and Optimization Technologies

In pharmaceutical tablet manufacturing processes, a major source of disturbance affecting drug product quality is the (lot-to-lot) variability of the incoming raw materials. A novel modeling and process optimization strategy that compensates for raw material variability is presented. The approach involves building partial least squares models that combine raw material attributes and tablet process parameters and relate these to final tablet attributes. The resulting models are used in an optimization framework to then find optimal process parameters which can satisfy all the desired requirements for the final tablet attributes, subject to the incoming raw material lots. In order to de-risk the potential (lot-to-lot) variability of raw materials on the drug product quality, the effect of raw material lot variability on the final tablet attributes was investigated using a raw material database containing a large number of lots. In this way, the raw material variability, optimal process parameter space and tablet attributes are correlated with each other and offer the opportunity of simulating a variety of changes in silico without actually performing experiments. The connectivity obtained between the three sources of variability (materials, parameters, attributes) can be considered a design space consistent with Quality by Design principles, which is defined by the ICH-Q8 guidance (USDA 2006). The effectiveness of the methodologies is illustrated through a common industrial tablet manufacturing case study.

Purification of pharmaceutical excipients with supercritical fluid extraction

Supercritical fluid extraction (SFE), with carbon dioxide as the solvent, was tested for its ability to remove common reactive impurities from several pharmaceutical excipient powders including starch, microcrystalline cellulose (MCC), hydroxypropylcellulose (HPC), polyethylene oxide (PEO), and polyvinylpyrrolidone (PVP). Extraction of the small molecule impurities, formic acid and formaldehyde, was conducted using SFE methods under conditions that did not result in visible physical changes to polymeric excipient powders. It could be shown that spiked, largely surface-bound, impurities could be removed effectively; however, SFE could only remove embedded impurities in the excipient particles after significant exposure times due to slow diffusion of the impurities to the particle surfaces. Attempts at hydrogen peroxide extraction were hindered by its low solubility in CO2, thereby effectively precluding SFE for removal of hydrogen peroxide from excipients. This work suggests that SFE will only be commercially useful for removal of low molecular weight impurities in polymeric excipients when migration of the impurities to the particle surfaces is sufficiently rapid for extraction to be completed in a reasonable time frame.

REVERSED-PHASE LIQUID CHROMATOGRAPHIC METHOD DEVELOPMENT IN AN ANALYTICAL QUALITY BY DESIGN FRAMEWORK

The Analytical Quality by Design (AQbD) concept is demonstrated in the development of a stability-indicating HPLC method for an immediate release dosage form. The AQbD workflow is discussed and demonstrated with a systematic three stage liquid chromatograph method development (wave 1 through wave 3), risk assessment (RA), design of experiments (DOEs), and assessment of the data to provide a method operable design region (MODR) and center point for the method. The use of AQbD workflows streamlines the development of methods as compared to traditional approaches. With the addition of systematic RAs and DOEs, robust and rugged analytical methods result. These methods will have fewer issues and failures throughout the lifecycle due to the knowledge gained via the AQbD process and defining chromatographic set points away from the edges of failure.

Feed-Forward Process Control Strategy for Pharmaceutical Tablet Manufacture Using Latent Variable Modeling and Optimization Technologies

Abstract In pharmaceutical tablet manufacturing processes, a major source of disturbance affecting drug product quality is the (lot to lot) variability of the incoming raw materials. A Feed-Forward process control strategy that compensates for raw material variability is presented. The approach involves building PLS (partial least squares) models that combine raw material attributes and tablet process parameters and relate these to final tablet attributes. The resulting models are used in an optimization framework to then find optimal process parameters which can satisfy all the desired requirements for the final tablet attributes, subject to the incoming raw material lots, prior to performing a batch. The connectivity obtained between the three sources of variability (materials, parameters, attributes) can be considered a design space consistent with Quality by Design (QbD) principles, which is defined by the ICH-Q8 guidance [1]. To implement the FF control, an in-house process simulator is presented. The effectiveness of the methodologies is illustrated through a common industrial tablet manufacturing case study.

De-risking Scale-up of a High Shear Wet Granulation Process Using Latent Variable Modeling and Near Infrared Spectroscopy

In the development of wet granulated drug products, two primary sources of variance (disturbance) include the operational scale of the high shear wet granulation (HSWG) process and active pharmaceutical ingredient (API) lot-to-lot variability, particularly for formulations containing a high drug load. This paper presents a novel Process Analytical Technology (PAT) strategy using latent variable modeling (LVM) with near infrared spectroscopy (NIRS) to reduce risk in scale-up operations of the HSWG process while simultaneously accounting for API lot-to-lot variability, even with limited manufacturing history. The process involves building a partial least square (PLS) model among the API material properties, the HSWG process parameters and the NIRS end points of the HSWG process based on small scale design of experiment (DOE) batches. The PLS model is then used in an optimization framework to find suitable small scale mechanical process parameters (impeller/chopper speed) that approximate a previous large scale operation so as to keep the NIRS end point of large scale operation constant. Prior to making additional large scale batches with a new lot of API, NIRS end points of large-scale HSWG with the new API lot are predicted based on the PLS model developed from the small-scale operation. If the predicted NIRS end point for the HSWG using the new API lot is not within the target region, the risks associated with the scale-up operation can then be significantly reduced by modifying other HSWG process parameters such as the total amount of water added or total granulation time to achieve the target region. A case study is presented that demonstrates the effectiveness of this methodology during development and scale-up of a drug product manufactured using a HSWG process. The detailed full paper of this study has been published in (Muteki et al., 2011).

No Sample Preparation

Sample preparation for pharmaceutical dosage forms can be a time-consuming and labor-intensive task. One option to reduce or eliminate this work is to use an analysis method that requires no or minimal sample preparation. This chapter discusses uses of vibrational spectroscopy (e.g., infrared, Raman) and mass spectrometry techniques to analyze dosage forms with no or minimal sample preparation to obtain identification, polymorph, water content, potency, and purity information. A high-level description of these techniques will be presented along with example applications.