Streamlining tablet lubrication design via model-based design of experiments.

In oral solid dosage production through direct compression powder lubrication must be carefully selected to facilitate the manufacturing of tablets without degrading product manufacturability and quality (e.g. dissolution). To do so, several semi-empirical models relating compression performance to process operating conditions have been developed. Among them, we consider an extension of the Kushner and Moore model (Kushner and Moore, 2010, International Journal Pharmaceutics, 399:19) that is useful for the purpose, but requires an extensive experimental campaign for parameters identification. This implies the preparation and compression of multiple powder blends, each one with a different lubrication extent. In turn, this translates into a considerable consumption of Active Pharmaceutical Ingredient (API), and into time-consuming experiments. We tackled this issue by proposing a novel model-based design of experiments (MBDoE) approach, which minimizes the number of optimal blends for model calibration, while obtaining statistically sound parameters estimates and model predictions. Both sequential and parallel MBDoE configurations were compared. Experimental results involving two placebo blends with different lubrication sensitivity showed that this methodology is able to reduce the experimental effort by 60-70% with respect to the standard industrial practice independently of the formulation considered and configuration (i.e. parallel vs. sequential) adopted.

Data analytics on raw material properties to accelerate pharmaceutical drug development.

Manufacturability of active pharmaceutical ingredients (APIs) is often evaluated by an empirical approach during development due to limited material availability. This brings challenges in designing flexible yet robust manufacturing processes under highly accelerated timelines. Hence, good utilisation of a limited material dataset is key to accelerate the delivery of high quality final drug product into the market at minimum cost and maximum process capacity. In this study, we present a data-driven method to investigate a raw materials database where the integration of multivariate analysis and machine learning modelling aids the selection of new incoming materials based on their manufacturability. The procedure was applied to an industrial representative database of thirty-four APIs and seven excipients where eight measurements relevant to flow properties for each of those forty-one materials were collected. The models identified four clusters of materials with different flow properties. These models can serve as a risk assessment tool for new API in early product development phases based on the nearest surrogate material which behave similarly, as well as to identify targeted and material sparring experiments to address key risks during secondary process selection.

Multivariate monitoring for the industrialisation of a continuous wet granulation tableting process.

The pharmaceutical industry is undergoing a significant change in product development and manufacturing strategies with the progressive shift from batch to continuous processes. These typically feature vast volumes of data generated by the numerous sensors connected to several unit operations running over the period of several hours or even days and that demand the application of increasingly efficient tools for process understanding, monitoring and control. This paper describes the use of multivariate statistical process modeling by means of chemometric methods to monitor the continuous wet granulation tableting process for a drug product currently under development. Models are tailored to the different units that make up the continuous tableting line, from material feeding and granulation up to tablet compression, where the solutions devised reflect the different dynamics of each unit and are used as maintenance and intervention tools to optimise manufacturing and associated operations retrospectively as well as in real-time, as part of the product industrialisation programme.

Knowledge management in secondary pharmaceutical manufacturing by mining of data historians-A proof-of-concept study.

In this proof-of-concept study, a methodology is proposed to systematically analyze large data historians of secondary pharmaceutical manufacturing systems using data mining techniques. The objective is to develop an approach enabling to automatically retrieve operation-relevant information that can assist the management in the periodic review of a manufactory system. The proposed methodology allows one to automatically perform three tasks: the identification of single batches within the entire data-sequence of the historical dataset, the identification of distinct operating phases within each batch, and the characterization of a batch with respect to an assigned multivariate set of operating characteristics. The approach is tested on a six-month dataset of a commercial-scale granulation/drying system, where several millions of data entries are recorded. The quality of results and the generality of the approach indicate that there is a strong potential for extending the method to even larger historical datasets and to different operations, thus making it an advanced PAT tool that can assist the implementation of continual improvement paradigms within a quality-by-design framework.

General procedure to aid the development of continuous pharmaceutical processes using multivariate statistical modeling - an industrial case study.

Streamlining the manufacturing process has been recognized as a key issue to reduce production costs and improve safety in pharmaceutical manufacturing. Although data available from earlier developmental stages are often sparse and unstructured, they can be very useful to improve the understanding about the process under development. In this paper, a general procedure is proposed for the application of latent variable statistical methods to support the development of new continuous processes in the presence of limited experimental data. The proposed procedure is tested on an industrial case study concerning the development of a continuous line for the manufacturing of paracetamol tablets. The main driving forces acting on the process are identified and ranked according to their importance in explaining the variability in the available data. This improves the understanding about the process by elucidating how different active pharmaceutical ingredient pretreatments, different formulation modes and different settings on the processing units affect the overall operation as well as the properties of the intermediate and final products. The results can be used as a starting point to perform a comprehensive and science-based quality risk assessment that help to define a robust control strategy, possibly enhanced with the integration of a design space for the continuous process at a later stage.

Consensus multivariate methods in gas chromatography mass spectrometry and denaturing gradient gel electrophoresis: MHC-congenic and other strains of mice can be classified according to the profiles of volatiles and microflora in their scent-marks.

House mice (Mus domesticus) communicate using scent-marks, and the chemical and microbial composition of these 'extended phenotypes' are both influenced by genetics. This study examined how the genes of the major histocompatibility complex (MHC) and background genes influence the volatile compounds (analysed with Gas Chromatography Mass Spectrometry or GC/MS) and microbial communities (analysed using Denaturating Gradient Gel Electrophoresis or DGGE) in scent-marks produced by congenic strains of mice. The use of Consensus Principal Components Analysis is described and shows relationships between the two types of fingerprints (GC/MS and DGGE profiles). Classification methods including Support Vector Machines and Discriminant Partial Least Squares suggest that mice can be classified according to both background strain and MHC-haplotype. As expected, the differences among the mice were much greater between strains that vary at both MHC and background loci than the congenics, which differ only at the MHC. These results indicate that the volatiles in scent-marks provide information about genetic similarity of the mice, and support the idea that the production of these genetically determined volatiles is influenced by commensal microflora. This paper describes the application of consensus methods to relate two blocks of analytical data.

Detection and assessment of co-association in inhalable drug particles using aerosol time-of-flight mass spectrometry.

Aerosol Time-of-Flight Mass Spectrometry (AToFMS) was used to examine co-association between two inhaled drugs, fluticasone propionate (FP) and salmeterol xinofoate (SX), in fine aerosolised particles emitted from Seretide(R)/Advair(R) inhaled combination products. Principal Component Analysis (PCA) was used to identify fragmentation patterns indicative of either pure or co-associated particles (particles containing both drugs). A third component of the particles emitted from dry powder inhalers (DPIs), lactose, gave only a very weak mass spectral signal and no interpretable data was acquired for this compound; however, it was not found to interfere with the detection of the two drug substances. High levels of co-association were found in the emitted doses from both pressurised metered dose inhaler (pMDI) and dry powder inhaler (DPI) products.

Pattern recognition for the analysis of polymeric materials.

A new method of polymer classification is described involving dynamic mechanical analysis of polymer properties as temperature is changed. The method is based on the chemometric analysis of the damping factor (tan delta) as a function of temperature. In this study four polymer groups, namely, polypropylene, low density polyethylene, polystyrene and acrylonitrile-butadiene-styrene, each characterised by different grades, were studied. The aim is to distinguish polymer groups from each other. The polymers were studied over a temperature range of -50 degrees C until the minimum stiffness was reached, tan delta values were recorded approximately every 1.5 degrees . Principal components analysis was performed to visualise groupings and also for feature reduction prior to classification and clustering. Several clustering and classification methods were compared including k-means clustering, hierarchical cluster analysis, linear discriminant analysis, k-nearest neighbours, and class distances using both Euclidean and Mahalanobis measures. It is demonstrated that thermal analysis together with chemometrics provides excellent discrimination, representing a new approach for characterisation of polymers.

Component detection weighted index of analogy: similarity recognition on liquid chromatographic mass spectral data for the characterization of route/process specific impurities in pharmaceutical tablets.

Detection and identification of impurities in pharmaceuticals is an essential task for determining the possible infringement of a patent. This article reports a multivariate analysis method to distinguish between tablets of the same substance on the basis of their origin, by characterizing route/process specific impurities via diagnostic ion chromatograms, using liquid chromatography/mass spectrometry (LC/MS). The approach is based on the formulation of a novel index that quantifies the similarity between LC/MS samples, named the component detection weighted index of analogy. The index estimates similarity by fully exploiting the two-dimensional nature of the data, where the relative contribution of chromatograms relates to their quality and noise level. Results show that well-defined clusters are formed according to the origin of tablets; a series of ions are identified as characterizing each class and can be used to predict the origin of unknown tablet samples. The method presented is designed for analysis of larger data sets and can be suitable for exploratory analysis where any a priori knowledge on the data is scarce or absent, hence requiring the acquisition of chromatograms in a broad m/z range.

Multivariate analysis and classification of two-dimensional angular optical scattering patterns from aggregates.

Two-dimensional light-scattering patterns from aggregates have undergone feature extraction followed by multivariate statistical analysis. The aggregates are comprised of primary particles of varying shape and size. Morphological descriptors (features) were extracted by a nonlinear filtering algorithm (spectrum enhancement) and then processed by principal component analysis and discriminant function analysis. The analysis was performed on two data sets, one in which the aggregates had a fixed primary particle size but varied in overall dimension and another in which the aggregate size was fixed but the primary particle size varied. Classification of the samples was performed adequately, providing some distinction among the limited classes that were analyzed.

Toxicological classification of urine samples using pattern recognition techniques and capillary electrophoresis.

In toxicology, hazardous substances detected in organisms may often lead to different pathological conditions depending on the type of exposure and level of dosage; hence, further analysis on this can suggest the best cure. Urine profiling may serve the purpose because samples typically contain hundreds of compounds representing an effective metabolic fingerprint. This paper proposes a pattern recognition procedure for determining the type of cadmium dosage, acute or chronic, administrated to laboratory rats, where urinary profiles are detected using capillary electrophoresis. The procedure is based on the composition of a sample data matrix consisting of areas of common peaks, with appropriate pre-processing aimed at reducing the lack of reproducibility and enhancing the potential contribution of low-level metabolites in discrimination. The matrix is then used for pattern recognition including principal components analysis, cluster analysis, discriminant analysis and support vector machines. Attention is particularly focussed on the last of these techniques, because of its novelty and some attractive features such as its suitability to work with datasets that are small and/or have low samples/variable ratios. The type of cadmium administration is detected as a relevant feature that contributes to the structure of the sample matrix, and samples are classified according to the class membership, with discriminant analysis and support vector machines performing complementarily on a training and on a test set.