Lecture
Reimagining the Analytical Lab: Data-Driven, In Silico, AI-Powered, and Scaled for Sustainability
- at -
- ICM Saal 3
- Type: Lecture
Lecture description
K.J. Vanhoutte, Beerse/BE, M. Hellings, Beerse/BE, P. Breugelmans, Beerse/BE, J.J. Moes, Beerse/BE
Pharmaceutical development increasingly demands rapid, reliable analytical strategies to shorten cycle times and reduce costs, with sustainability emerging as an intrinsic benefit rather than a primary objective, both for R&D as well as for commercial testing. The ultimate vision is closed-loop manufacturing that eliminates end-point quality control, with rapid and real-time release testing serving as critical transitional steps. These approaches enhance operational efficiency and significantly reduce environmental impact. Development laboratories play a pivotal role in establishing frameworks that enable deployment in commercial QC environments. This transformation is driven by data-centric decision-making, in silico modeling, and AI-powered workflows that minimize reliance on empirical testing and conventional analytical methods. Integration of new technology, automation, digital data threads, and miniaturization accelerates development, reduces material and solvent consumption, and improves scientific precision. [1] Illustrative examples include near-infrared spectroscopy (NIR) as a robust alternative to ultra-performance liquid chromatography (UPLC) for content uniformity and assay determination, alternative microbiological methods replacing animal-based pyrogen testing (i.e., monocyte activation testing) and reducing animal-derived reagents for endotoxin testing (i.e., recombinant alternatives), and surrogate modeling approaches that predict dissolution behavior without conventional wet-lab experimentation. These methodologies leverage multivariate chemometric algorithms, in-process analytical technologies, complete product/process understanding, and predictive modeling to ensure regulatory compliance while minimizing experimental burden. Collectively, these innovations are redefining analytical laboratories by accelerating development timelines, reducing reliance on animal testing, and embedding sustainability principles, all while preserving the rigor and reproducibility essential for scientific and regulatory integrity. [2] [3] [4]
Literature:
[1] Application of Artificial Neural Networks in the Process Analytical Technology of Pharmaceutical Manufacturing—a Review. Brigitta Nagy, AAPS J 2022, 24, 74
[2] Near-Infrared Spectroscopy in the Pharmaceutical Industry. Benoît Igne. In Y. Ozaki & C. Huck (Eds.), Near-Infrared Spectroscopy: Theory, Spectral Analysis, Instrumentation, and Applications. 2021, 391
[3] Validation of the Monocyte Activation Test with three therapeutic monoclonal antibodies. Ruth Daniels, Alternatives to animal experimentation (ALTEX) 2022, 39, 4
[4] First-Principles and Empirical Approaches to Predicting In Vitro Dissolution for Pharmaceutical Formulation and Process Development and for Product Release Testing. Nikolay Zaborenko, AAPS J 2019, 21, 32
Pharmaceutical development increasingly demands rapid, reliable analytical strategies to shorten cycle times and reduce costs, with sustainability emerging as an intrinsic benefit rather than a primary objective, both for R&D as well as for commercial testing. The ultimate vision is closed-loop manufacturing that eliminates end-point quality control, with rapid and real-time release testing serving as critical transitional steps. These approaches enhance operational efficiency and significantly reduce environmental impact. Development laboratories play a pivotal role in establishing frameworks that enable deployment in commercial QC environments. This transformation is driven by data-centric decision-making, in silico modeling, and AI-powered workflows that minimize reliance on empirical testing and conventional analytical methods. Integration of new technology, automation, digital data threads, and miniaturization accelerates development, reduces material and solvent consumption, and improves scientific precision. [1] Illustrative examples include near-infrared spectroscopy (NIR) as a robust alternative to ultra-performance liquid chromatography (UPLC) for content uniformity and assay determination, alternative microbiological methods replacing animal-based pyrogen testing (i.e., monocyte activation testing) and reducing animal-derived reagents for endotoxin testing (i.e., recombinant alternatives), and surrogate modeling approaches that predict dissolution behavior without conventional wet-lab experimentation. These methodologies leverage multivariate chemometric algorithms, in-process analytical technologies, complete product/process understanding, and predictive modeling to ensure regulatory compliance while minimizing experimental burden. Collectively, these innovations are redefining analytical laboratories by accelerating development timelines, reducing reliance on animal testing, and embedding sustainability principles, all while preserving the rigor and reproducibility essential for scientific and regulatory integrity. [2] [3] [4]
Literature:
[1] Application of Artificial Neural Networks in the Process Analytical Technology of Pharmaceutical Manufacturing—a Review. Brigitta Nagy, AAPS J 2022, 24, 74
[2] Near-Infrared Spectroscopy in the Pharmaceutical Industry. Benoît Igne. In Y. Ozaki & C. Huck (Eds.), Near-Infrared Spectroscopy: Theory, Spectral Analysis, Instrumentation, and Applications. 2021, 391
[3] Validation of the Monocyte Activation Test with three therapeutic monoclonal antibodies. Ruth Daniels, Alternatives to animal experimentation (ALTEX) 2022, 39, 4
[4] First-Principles and Empirical Approaches to Predicting In Vitro Dissolution for Pharmaceutical Formulation and Process Development and for Product Release Testing. Nikolay Zaborenko, AAPS J 2019, 21, 32
