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Biomedical subjects

Teresa P Alves

Publications and source records attributed to Teresa P Alves.

2 recordsLinked to original sources

Improving drug manufacturing with process analytical technology.

Within the process analytical technology (PAT) framework, as presented in the US Food and Drug Administration guidelines, the aim is to design, develop and operate processes consistently to ensure a pre-defined level of quality at the end of the manufacturing process. Three PAT implementation scenarios can be envisaged. Firstly, PAT could be used in its most modest version (in an almost non-PAT manner) to simply replace an existing quality control protocol (eg, using near-infrared spectroscopy for an in-process quality control, such as moisture content). Secondly, the use of in-process monitoring and process analysis could be integrated to enhance process understanding and operation for an existing industrial process. Thirdly, PAT could be used extensively and exclusively throughout development, scale-up and full-scale production of a new product and process. Although the first type of implementations are well known, reports of the second and third types remain scarce. Herein, results obtained from PAT implementations of the second and third types are described for two industrial processes for preparing bulk active pharmaceutical ingredients, demonstrating the benefits in terms of increased process understanding and process control.

Calibration↗

Monitoring complex media fermentations with near-infrared spectroscopy: comparison of different variable selection methods.

Near-infrared spectroscopy (NIRS) is known to be a suitable technique for rapid fermentation monitoring. Industrial fermentation media are complex, both chemically (ill-defined composition) and physically (multiphase sample matrix), which poses an additional challenge to the development of robust NIRS calibration models. We investigated the use of NIRS for at-line monitoring of the concentration of clavulanic acid during an industrial fermentation. An industrial strain of Streptomyces clavuligerus was cultivated at 200-L scale for the production of clavulanic acid. Partial least squares (PLS) regression was used to develop calibration models between spectral and analytical data. In this work, two different variable selection methods, genetic algorithms (GA) and PLS-bootstrap, were studied and compared with models built using all the spectral variables. Calibration models for clavulanic acid concentration performed well both on internal and external validation. The two variable selection methods improved the predictive ability of the models up to 20%, relative to the calibration model built using the whole spectra.

Algorithms↗