Generative PAT Fingerprint Approach for Verification of the Scale-Up of Pharmaceutical Processes

Dijkmans, Jan; Chau, Joris; Maes, Tor; Khamiakova, Tatsiana; Laps, Stijn; Vandervoort, Niels

doi:10.1021/acs.oprd.3c00500

Cited by 3 publications

(2 citation statements)

References 19 publications

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“…Key factors influencing the reliability of the temperature as an indicator are the exothermicity of the reaction, the reaction rate, and the heat capacity of the solvent. However, as an alternative to reactor temperature, other recorded data such as total pressure, composition, or mid-infrared spectroscopy of functional groups, can also serve as reaction progress indicators, ensuring the applicability across different industrial processes …”

Section: Model Construction and Methodology Developmentmentioning

confidence: 99%

Advancing Nitrobenzene Hydrogenation Process Understanding: A Multiscale Modeling Approach using Industrial Pharmaceutical Production Data

Callewaert,

Pessanha,

Lauwaert

et al. 2024

Ind. Eng. Chem. Res.

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The pharmaceutical industry is under increasing pressure to reduce production costs and operate within agile supply chains by leveraging the capabilities that come with integrated data infrastructures and increasing access to various forms of modeling. In contrast to bulk chemical production, pharmaceutical productions typically operate at significantly lower volumes and allow for narrower variability in critical parameters due to tightly defined operating ranges. This makes the data acquired from the process challenging to analyze with statistical methods, but the application of engineering and scientific relations as multiscale models offers a more effective way of leveraging the historical data that are available. In this work, a multiscale reaction model is developed for an exothermic liquid phase hydrogenation of a nitrobenzene functionality in the synthesis of an active pharmaceutical ingredient (API) by using available production data. The developed model successfully described the interplay between reaction kinetics, gas−liquid mass transfer, and heat removal present in the process data, as evident from the simulated versus observed temperature evolution with batch time, which was used as an indirect measurement of the reaction conversion. Moreover, the model was also able to reproduce the temperature profiles in the case of a 30% scale-up. Simulated concentration profiles indicate that the end of the reaction occurs within a much shorter time frame than that prescribed by the process recipe, suggesting that the batch time can be reduced by more than 50%. The results demonstrate the flexibility and predictive power of this type of modeling approach because this model was developed using passive data collection of standard process parameters rather than through a dedicated Design of Experiments (DoE).

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Section: Model Construction and Methodology Developmentmentioning

confidence: 99%

Advancing Nitrobenzene Hydrogenation Process Understanding: A Multiscale Modeling Approach using Industrial Pharmaceutical Production Data

Callewaert,

Pessanha,

Lauwaert

et al. 2024

Ind. Eng. Chem. Res.

View full text Add to dashboard Cite

show abstract

“…Automation and data-rich experimentation (DRE) have revolutionized process development by offering enhanced capabilities at the lab benchtop. 1 The integration of process analytical tools, such as on-line HPLC 2,3 and in situ spectroscopy, 4–7 increases the data density in each experiment, providing information on both the reaction kinetics and the overall reaction performance. Enhancements to throughput can be achieved through the utilization of parallel reactor technology, integrated with automated sampling strategies, allowing for collection of comprehensive reaction profiles.…”

Section: Introductionmentioning

confidence: 99%