Wee Chew

Trends in process analytical technology

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.

Self-modeling curve resolution of multi-component vibrational spectroscopic data using automatic band-target entropy minimization (AutoBTEM)

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.

Hierarchical band-target entropy minimization curve resolution and Pearson VII curve-fitting analysis of cellular protein infrared imaging spectra.

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-alpha-helix, beta-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.

Systematic Framework for Design of Environmentally Sustainable Pharmaceutical Supply Chain Network

PurposeThe current push towards sustainability has pressurized pharmaceutical companies to reduce greenhouse gas (GHG) emissions in their manufacturing supply chains (SCs). However, the heavily regulated nature of the pharmaceutical industry has necessitated decisions such as sourcing of raw materials including names and addresses of suppliers and siting of plants to be locked early during the registration of a new drug. This could result in SC inefficiencies during the drug commercial life leading to higher than necessary GHG emissions. This paper presents a systematic framework for design of a more sustainable pharmaceutical SC network at the commercial stage that can be performed during the early stages of drug development.MethodsThe framework comprises the following steps. First, basic SC information including process chemistries, outsourcing strategies, and potential supplier and manufacturer sites is consolidated. Next, an analytic hierarchy process (AHP) is performed to identify the most suitable supplier and manufacturer sites followed by mapping the entire SC network by connecting all the sites that have been identified as high priority. Subsequently, a set of indicator metrics—namely, cost, lead time, and GHG emissions—is calculated to evaluate the economic and environmental performances of the network.ResultsThe framework has been applied to an industrially motivated case study. Two network alternatives were proposed and analyzed based on their metrics together with synergies and trade-offs highlighted.ConclusionsThe findings demonstrate the efficacy of the framework in generating different network alternatives and identifying the most sustainable one on the basis of economic and environmental benefits. As such, the framework is applicable to the early stages of drug development where information is very limited.

Application of mid-infrared chemical imaging and multivariate chemometrics analyses to characterise a population of microalgae cells

A suite of multivariate chemometrics methods was applied to a mid-infrared imaging dataset of a eustigmatophyte, marine Nannochloropsis sp. microalgae strain. This includes the improved leader-follower cluster analysis (iLFCA) to interrogate spectra in an unsupervised fashion, a resonant Mie optical scatter correction algorithm (RMieS-EMSC) that improves data linearity, the band-target entropy minimization (BTEM) self-modeling curve resolution for recovering component spectra, and a multi-linear regression (MLR) for estimating relative concentrations and plotting chemical maps of component spectra. A novel Alpha-Stable probability calculation for microalgae cellular lipid-to-protein ratio Λi is introduced for estimating population characteristics.

Applications of the improved leader-follower cluster analysis (iLFCA) algorithm on large array (LA) and very large array (VLA) hyperspectral mid-infrared imaging datasets

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.

Integrated Platform at ICES Kilo-Lab for Process Quality by Design

Abstract The paradigm shift that is attempting to change the way pharmaceutical manufacturing is undertaken in the 21 st century has raised practical challenges for the adoption and implementation of the Process Analytical Technology (PAT) framework initiated by the U.S. Food and Drug Administration (FDA). The motive is to engender a science-oriented pharmaceutical manufacturing that is along FDAs pharmaceutical product quality by design (QbD) ideology. One such challenge revolves around the integration of PAT technologies such as varied process analytics (e.g. sensors, spectrometry, chromatography, etc.), multivariate analyses, knowledge management, and process control under a common information exchange and data-logging platform. Such an integrated platform was recently installed and commissioned at the Kilo-Lab in the Institute of Chemical and Engineering Sciences (ICES). Its efficacy was demonstrated through synthesizing 4-D-erythronolactone at kilo-scale using a four-phase hybrid process.

In Silico Process Optimization and Quality by Design with Business and Environmental Sustainability Considerations

PurposeIn 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.MethodsAt 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.ResultsIn 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.ConclusionsA 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.