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A multivariate calibration-minimum framework for efficient estimation of quality attributes in pharmaceutical manufacturing
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Baranwal, Yukteshwar. A multivariate calibration-minimum framework for efficient estimation of quality attributes in pharmaceutical manufacturing. Retrieved from https://doi.org/doi:10.7282/t3-x8hf-0d32

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TitleA multivariate calibration-minimum framework for efficient estimation of quality attributes in pharmaceutical manufacturing
NameBaranwal, Yukteshwar (author); Ramachandran, Rohit (chair); Romañach, Rodolfo J. (member); Tsilomilekis, George (member); Callegari, Gerardo (member); Rutgers University; School of Graduate Studies
Date Created2021
Other Date2021-10 (degree)
SubjectChemical engineering, calibration-minimum, partial least squares (PLS), pharmaceutical manufacturing, Process analytical technology (PAT)
Extent1 online resource (xxiii, 180 pages)
DescriptionProcess analytical technology (PAT) in pharmaceutical process development often requires quantitative measurements through partial least squares (PLS) models. These calibrated models are resource and time intensive. Correlation between each ingredient needs to be minimized to generate standards that represent the variation induced by the process and composition. The selection of latent variables is critical to avoid overfitting, but often this process requires additional layers of validation. Traditional chemometrics approaches are appropriate and accurate for many simple mixtures; however, they do not account for mixture constraints. Without a mixture constraint, the sum of mixture component ratios would not necessarily be equal to 100\%. Therefore, the accuracy of predicted concentration ratios, especially of very small or large component concentrations may be less than ideal (e.g., negative percentage or greater than 100\%). This mixture constraint is critical when quantitatively monitoring multiple components in a mixture. Hence, a novel optimized approach of developing a model with minimum calibration efforts is required. Such tools should require limited model maintenance and are flexible in incorporating factors such as change of spectroscopic instruments, variations in raw materials, environmental conditions, and methods of tablet compaction. In this work, a robust framework based on mathematical optimization is developed. This framework is capable of model development using experimental data and predicting the performance of new samples. Unlike PLS, which needs tedious development of standards to incorporate variations in the process, the proposed calibration-minimum methodology minimizes significant calibration effort by developing a mathematical model that uses only one standard and spectral information of pure powders present in the tablet. It is rapid and efficient, and model maintenance is significantly reduced when there is variation in raw materials and changes in the selection of the spectroscopic instrument. A comparison of results from two approaches (PLS and calibration-minimum) was established. Both approaches were applied to Near-Infrared spectroscopy data, acquired for blends and tablets. In the case study of tablets with varying formulation (chemical) and thickness (physical), the calibration-minimum approach was able to predict the entire formulations of tablets instead of the only API.
NotePh.D.
NoteIncludes bibliographical references
Genretheses
Persistent URLhttps://doi.org/doi:10.7282/t3-x8hf-0d32
LanguageEnglish
CollectionSchool of Graduate Studies Electronic Theses and Dissertations
Organization NameRutgers, The State University of New Jersey
RightsThe author owns the copyright to this work.
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