Biomedical subjects

James K Drennen

Publications and source records attributed to James K Drennen.

6 recordsLinked to original sources

PubMed2006

A Process Analytical Technology approach to near-infrared process control of pharmaceutical powder blending. Part III: Quantitative near-infrared calibration for prediction of blend homogeneity and characterization of powder mixing kinetics.

The Process Analytical Technology (PAT) initiative, undertaken by the Food and Drug Administration (FDA), paves the way for improvement of drug manufacturing through real-time measurements that allow better process understanding. This study is the third and final Part in a series of studies that represent an integrated approach for real-time blend uniformity assessment using near-infrared (NIR) technology. In this study, the development of a quantitative NIR model for prediction of blending end point is presented. Process signature was built into NIR calibration models by using blend samples that were collected from actual blend experiments under different processing conditions. Evaluation of various calibration algorithms including principal component regression (PCR), partial least squares (PLS), and multi-term linear regression (MLR) was performed. It was found that linear regression, using a single wavelength, yielded optimum calibration and prediction results. The blending profiles predicted by the NIR quantitative model correlated well to those determined by the UV reference analytical method. Characterization of intra-shell versus inter-shell powder mixing kinetics and its implication in sensor positioning was also performed and will be discussed.

Calibration↗

PubMed2006

A Process Analytical Technology approach to near-infrared process control of pharmaceutical powder blending: Part II: Qualitative near-infrared models for prediction of blend homogeneity.

The successful implementation of near-infrared spectroscopy (NIRS) in process control of powder blending requires constructing an inclusive spectral database that reflects the anticipated voluntary or involuntary changes in processing conditions, thereby minimizing bias in prediction of blending behavior. In this study, experimental design was utilized as an efficient way of generating blend experiments conducted under varying processing conditions such as humidity, blender speed and component concentration. NIR spectral data, collected from different blending experiments, was used to build qualitative models for prediction of blend homogeneity. Two pattern recognition algorithms: Soft Independent Modeling of Class Analogies (SIMCA) and Principal Component Modified Bootstrap Error-adjusted Single-sample Technique (PC-MBEST) were evaluated for qualitative analysis of NIR blending data. Optimization of NIR models, for the two algorithms, was achieved by proper selection of spectral processing, and training set samples. The models developed were successful in predicting blend homogeneity of independent blend samples under different processing conditions.

Algorithms↗

PubMed2006

A Process Analytical Technology approach to near-infrared process control of pharmaceutical powder blending. Part I: D-optimal design for characterization of powder mixing and preliminary spectral data evaluation.

Experimental design, multivariate data acquisition, and analysis in addition to real time monitoring and control through process analyzers, represent an integrated approach for implementation of Process Analytical Technology (PAT) in the pharmaceutical industry. This study, which is the first in a series of three parts, uses an experimental design approach to identify critical factors affecting powder blending. Powder mixtures composed of salicylic acid and lactose were mixed in an 8 qt. V-blender. D-optimal design was employed to characterize the blending process, by studying the effect of humidity, component concentration, and blender speed on mixing end point. Additionally, changes in particle size and density of powder mixtures were examined. A near-infrared (NIR) fiber-optic probe was used to monitor mixing, through multiple optical ports on the blender. Humidity, component concentration, and blender speed were shown to have a significant impact on the blending process. Furthermore, humidity and concentration had a significant effect on particle size and density of powder mixtures. NIRS was sensitive to changes in physicochemical properties of the mixtures, resulting from process variables. Proper selection of NIR spectral preprocessing is of ultimate importance for successful implementation of this technology in the monitoring and control of powder blending and is discussed.

Chemistry, Pharmaceutical↗

PubMed2005

Process analytical technology case study part I: feasibility studies for quantitative near-infrared method development.

This article is the first of a series of articles detailing the development of near-infrared (NIR) methods for solid-dosage form analysis. Experiments were conducted at the Duquesne University Center for Pharmaceutical Technology to qualify the capabilities of instrumentation and sample handling systems, evaluate the potential effect of one source of a process signature on calibration development, and compare the utility of reflection and transmission data collection methods. A database of 572 production-scale sample spectra was used to evaluate the interbatch spectral variability of samples produced under routine manufacturing conditions. A second database of 540 spectra from samples produced under various compression conditions was analyzed to determine the feasibility of pooling spectral data acquired from samples produced at diverse scales. Instrument qualification tests were performed, and appropriate limits for instrument performance were established. To evaluate the repeatability of the sample positioning system, multiple measurements of a single tablet were collected. With the application of appropriate spectral preprocessing techniques, sample repositioning error was found to be insignificant with respect to NIR analyses of product quality attributes. Sample shielding was demonstrated to be unnecessary for transmission analyses. A process signature was identified in the reflection data. Additional tests demonstrated that the process signature was largely orthogonal to spectral variation because of hardness. Principal component analysis of the compression sample set data demonstrated the potential for quantitative model development. For the data sets studied, reflection analysis was demonstrated to be more robust than transmission analysis.

Feasibility Studies↗

PubMed2005

Process analytical technology case study: part II. Development and validation of quantitative near-infrared calibrations in support of a process analytical technology application for real-time release.

This article is the second of a series of articles detailing the development of near-infrared (NIR) methods for solid dosage-form analysis. Experiments were conducted at the Duquesne University Center for Pharmaceutical Technology to demonstrate a method for developing and validating NIR models for the analysis of active pharmaceutical ingredient (API) content and hardness of a solid dosage form. Robustness and cross-validation testing were used to optimize the API content and hardness models. For the API content calibration, the optimal model was determined as multiplicative scatter correction with Savitsky-Golay first-derivative preprocessing followed by partial least-squares (PLS) regression including 4 latent variables. API content calibration achieved root mean squared error (RMSE) and root mean square error of cross validation (RMSECV) of 1.48 and 1.80 mg, respectively. PLS regression and baseline-fit calibration models were compared for the prediction of tablet hardness. Based on robustness testing, PLS regression was selected for the final hardness model, with RMSE and RMSECV of 8.1 and 8.8 N, respectively. Validation testing indicated that API content and hardness of production-scale tablets is predicted with root mean square error of prediction of 1.04 mg and 8.5 N, respectively. Explicit robustness testing for high-flux noise and wavelength uncertainty demonstrated the robustness of the API concentration calibration model with respect to normal instrument operating conditions.

Computer Systems↗

PubMed2005

Process analytical technology case study, part III: calibration monitoring and transfer.

This is the third of a series of articles detailing the development of near-infrared spectroscopy methods for solid dosage form analysis. Experiments were conducted at the Duquesne University Center for Pharmaceutical Technology to develop a system for continuous calibration monitoring and formulate an appropriate strategy for calibration transfer. Indicators of high-flux noise (noise factor level) and wavelength uncertainty were developed. These measurements, in combination with Hotelling's T(2) and Q residual, are used to continuously monitor instrument performance and model relevance. Four calibration transfer techniques were compared. Three established techniques, finite impulse response filtering, generalized least squares weighting, and piecewise direct standardization were evaluated. A fourth technique, baseline subtraction, was the most effective for calibration transfer. Using as few as 15 transfer samples, predictive capability of the analytical method was maintained across multiple instruments and major instrument maintenance.

Calibration↗