In designing pharmaceutical manufacturing alongside the principles of quality by design (QbD) and process analytical technology (PAT), unit operations process development and corporate decision-making processes such as business profits and environmental impact assessments are often undertaken separately. This paper presents a strategic three-tier framework linking the process, operations, and business/enterprise levels for process development and improvement alongside an in silico optimization approach.At the process level, first principles reaction kinetics and semi-batch process parameters are used for process simulations. Data exchanges between the process and operations levels were achieved through the OLE for process control (OPC) protocol with real-time chemometrics modeling of in situ spectroscopic measurements. At the operations level, multivariate statistical models were utilized for continuous process improvement in conjunction with profit maximization and environmental waste considerations at the business/enterprise level.In silico optimization of consecutive semi-batch epoxidation reactions was performed using MATLAB/Simulink. Pareto-optimal operating parameters within the design space that considers product quality and process efficiency, profitability, and environmental impact were arrived through systematic simulations conducted using design of experiments (DoE) and partial least squares (PLS) modeling.A simple three-tier methodological framework was proposed to bridge process development, profitability, and environmental assessment. Through such a framework, the links between process, operations, and business/enterprise levels toward sustainable development and product value chain become more transparent. The Pareto-optimal solutions generated demonstrate how process development choices could impact business/enterprise decision-making.

With the potential and advantages of infrared (IR) spectroscopic applications in biological studies, and the introduction of multi-channel focal plane array (FPA) mid-IR detectors, efficient unsupervised clustering algorithms are required to identify and group similar useful spectra from background or outlier spectra within large hyperspectral datasets. Such classification algorithms are crucial for enabling further multivariate analysis. In this paper, a clustering method coined as the improved leader-follower cluster analysis (iLFCA) algorithm is expounded and demonstrated on two mid-IR imaging datasets of exfoliated oral mucosa cells: a Large Array (LA) 64 × 64 pixels image and a Very Large Array (VLA) simulated 128 × 128 pixels image created as a montage of the original LA data. By concatenating the normalized vector form of each spectrum and its integrated areas of characteristic spectral bands, such as Amide I and II, the specificity and efficacy of the clustering algorithm is enhanced. Human intervention for selecting appropriate user-specified parameters and thresholds is also minimized through the development of an automated bisection search algorithm. This resulted in better computational efficiency for iLFCA compared to its predecessor LFCA algorithm. A comparison of iLFCA and LFCA with a common unsupervised classification method based on Principal Component Analysis (PCA) shows iLFCA achieving better clustering results at shorter computational time. In particular, iLFCA has the capability to process larger datasets, namely VLA datasets, which caused both LFCA and PCA-based methods to fail because of computer memory space limitations. iLFCA can potentially be applied to analyze vibrational microspectroscopic data for diagnosis/screening of biological tissue and cells samples, cell culture growth monitoring, and examination of active pharmaceutical ingredients (APIs) distribution and real-time release of pharmaceutical tablets.

The deployment of in situ analytics for monitoring chemical reactions in process chemistry development and scale-up is facilitated by advanced instrumentation such as Raman spectrometry. Furthermore, greater process understanding can be engendered by coupling in situ Raman data with multivariate chemometrics analyses and kinetics modeling. Such information is important for devising science-based process control strategies along the concept of quality by design (QbD) initiated through the U.S. FDA process analytical technology (PAT) framework. A series of experiments using varied glass reactors, stirring speeds, and isothermal reaction temperatures were designed with acetic anhydride hydrolysis as the model reaction to successfully demonstrate the efficacy of combining in situ Raman spectroscopy, multivariate analyses, and kinetics modeling. Two different Raman measurement methods, using immersion and noncontact probe optics, were tested through a process Raman spectrometer with multiplexing capability. Information-theoretic multivariate chemometrics were applied to elicit pure component spectra and transient concentrations of chemical species, and two differential-algebraic equations modeling approaches were adopted for elucidating chemical and dissolution kinetics information. The variations in reactor vessel type and sizes, stirring speeds, Raman measurements, and kinetics models were compared in this study.

Since the promotion of Process Analytical Technology (PAT) by the U.S. Food and Drug Administration (FDA), there has been a flurry of activities happening across related fields. This excitement permeates regulatory agencies, professional societies, academia and industry worldwide. This review surveys the PAT related developments that have taken place in the period 2004–2009. It serves as an introduction to PAT, with highlights on the parallel advances and convergence points across various fields and applications. From this review, five common threads are identified from the underlying trends of the recent global PAT endeavor, namely, organisational objectives, enabling sciences, economic outlook, collaborative efforts and emerging trends. There are also six potential gaps that require further efforts to bridge. The overall PAT venture is promising for delivering an integrated systems approach for quality design, process analyses, understanding and control, continuous improvement, knowledge and risk-based management within the FDA 21st century pharmaceuticalcGMP initiative.

Vibrational spectroscopy is being used routinely to measure multi-component samples and often times these data possess spectroscopic non-idealities such as highly overlapping spectral bands, presence of spectral non-linearities, etc. A multivariate curve resolution algorithm coined as automatic band-target entropy minimization (AutoBTEM) was developed to achieve self-modeling curve resolution of pure component spectra from multi-component vibrational spectroscopic data. This AutoBTEM is a variant extension of the band-target entropy minimization (BTEM) that combines a novel automatic band-targeting numerical strategy with exhaustive BTEM curve resolutions and unsupervised hierarchical clustering analysis in an overall blind search approach. It is also found that the number of components or significant factors and the extent of spectral band shifts can be inferred via the automatic band-targeting computations. The AutoBTEM algorithm is demonstrated herein to be successful when tested on two challenging mixture spectral datasets that are ill-conditioned. One is a two-component mid-infrared FTIR dataset containing spectral non-linearities, and the other is a 10-component Raman dataset with highly overlapping bands from its 10 chemical constituent spectra. The resolved pure component spectra correspond well with reference spectra and have an excellent normalized inner product of above 0.95 upon quantitative comparison.

A soft-modeling multivariate numerical approach that combines self-modeling curve resolution (SMCR) and mixed Lorentzian–Gaussian curve fitting was successfully implemented for the first time to elucidate spatially and spectroscopically resolved spectral information from infrared imaging data of oral mucosa cells. A novel variant form of the robust band-target entropy minimization (BTEM) SMCR technique, coined as hierarchical BTEM (hBTEM), was introduced to first cluster similar cellular infrared spectra using the unsupervised hierarchical leader–follower cluster analysis (LFCA) and subsequently apply BTEM to clustered subsets of data to reconstruct three protein secondary structure (PSS) pure component spectra—α-helix, β-sheet, and ambiguous structures—that associate with spatially differentiated regions of the cell infrared image. The Pearson VII curve-fitting procedure, which approximates a mixed Lorentzian–Gaussian model for spectral band shape, was used to optimally curve fit the resolved amide I and II bands of various hBTEM reconstructed PSS pure component spectra. The optimized Pearson VII band-shape parameters and peak center positions serve as means to characterize amide bands of PSS spectra found in various cell locations and for approximating their actual amide I/II intensity ratios. The new hBTEM methodology can also be potentially applied to vibrational spectroscopic datasets with dynamic or spatial variations arising from chemical reactions, physical perturbations, pathological states, and the like.

A suite of numerical techniques was utilized in a concerted fashion for the efficacious multivariate chemometrics analysis of hyperspectral infrared imaging data of exfoliated oral mucosa cells. Based on the vector representation of infrared spectrum1×ν, spectral vector properties (SVP) are demonstrated to possess underpinning spectral information that was exploited in crucial chemometrics analyses; which include outlier spectra identification, selection for a subset of imaged mid-infrared spectra that contain good oral mucosa cell signals, and, for the first time, obtain major biochemical constituent spectravia the band-target entropy minimization (BTEM) curve resolution algorithm. The relative concentration spatial distribution of the major biochemical constituents observed, namely membrane lipids and various cellular protein structures (α-helix, β-sheet, turns and bends), were subsequently acquired through multi-linear regression and were displayed as chemical contour maps. Amongst the set of numerical algorithms employed, two novel unsupervised clustering algorithms were developed and tested. One is useful for outlier spectra detection, and the other aids the selection of pertinent spatially distributed spectra that possess oral mucosa cell mid-infrared spectra with good signal-to-noise ratio. It is anticipated that this developed numerical suite will serve as an effective multivariate chemometrics protocol for cellular studies and biomedical diagnostics viainfrared imaging.

The band-target entropy minimization (BTEM) curve resolution technique has been used to analyze in situ reflection–absorption infrared spectroscopy (RAIRS) data of CO chemisorption on Ni(1 1 1) single crystal surfaces. The bilinearity assumption for pRAIRS data, that is, negative logarithm to the base 10 of raw reflectance RAIRS data, was found to be sufficiently valid for the test data. A total of 11 real pure component pRAIRS spectra were elucidated via BTEM in tandem with an iterative residual spectral data analysis. Furthermore, 2 abstract pure component right singular vectors were found to account for all the pRAIRS non-linearities, baseline drifts and other spectral noise. In total, 100.2% of the pRAIRS signals were accounted for by these 13 spectral components. The 11 real pure component pRAIRS spectra and their corresponding relative concentration kinetic sequences correlate with 6 well-known adsorbed CO domain structures. Moreover, amongst the BTEM resolved spectra were five new bands that were not previously observed using conventional visual identification methods adopted by surface chemists. These new bands engendered new understanding to the mechanism of CO chemisorption on Ni(1 1 1). The combination of BTEM with residual spectral analysis was thus demonstrated to be efficacious for curve resolution of in situ RAIRS data obtained from surface chemistry studies.