Parameter estimation and sensitivity analysis for dynamic modelling and simulation of beer fermentation

Kinetic models towards an enhanced understanding of diverse ADC conjugation reactions

The conjugation reaction is the central step in the manufacturing process of antibody-drug conjugates (ADCs). This reaction generates a heterogeneous and complex mixture of differently conjugated sub-species depending on the chosen conjugation chemistry. The parametrization of the conjugation reaction through mechanistic kinetic models offers a chance to enhance valuable reaction knowledge and ensure process robustness. This study introduces a versatile modeling framework for the conjugation reaction of cysteine-conjugated ADC modalities-site-specific and interchain disulfide conjugation. Various conjugation kinetics involving different maleimide-functionalized payloads were performed, while controlled gradual payload feeding was employed to decelerate the conjugation, facilitating a more detailed investigation of the reaction mechanism. The kinetic data were analyzed with a reducing reversed phase (RP) chromatography method, that can readily be implemented for the accurate characterization of ADCs with diverse drug-to-antibody ratios, providing the conjugation trajectories of the single chains of the monoclonal antibody (mAb). Possible kinetic models for the conjugation mechanism were then developed and selected based on multiple criteria. When calibrating the established model to kinetics involving different payloads, conjugation rates were determined to be payload-specific. Further conclusions regarding the kinetic comparability across the two modalities could also be derived. One calibrated model was used for an exemplary in silico screening of the initial concentrations offering valuable insights for profound understanding of the conjugation process in ADC development.

Bringing a scalable adaptive hybrid modeling framework closer to industrial use: Application on a multiscale fungal fermentation

Digitalization has paved the way for new paradigms such as digital shadows and digital twins for fermentation processes, opening the door for real-time process monitoring, control, and optimization. With a digital shadow, real-time model adaptation to accommodate complex metabolic phenomena such as metabolic shifts of a process can be monitored. Despite the many benefits of digitalization, the potential has not been fully reached in the industry. This study investigates the development of a digital shadow for a very complex fungal fermentation process in terms of microbial physiology and fermentation operation on pilot-scale at Novonesis and the challenges thereof. The process has historically been difficult to optimize and control due to a lack of offline measurements and an absence of biomass measurements. Pilot-scale and lab-scale fermentations were conducted for model development and validation. With all available pilot-scale data, a data-driven soft sensor was developed to estimate the main substrate concentration (glucose) with a normalized root mean squared error (N-RMSE) of 2%. This robust data-driven soft sensor was able to estimate accurately in lab-scale (volume < 20× pilot) with a N-RMSE of 7.8%. A hybrid soft sensor was developed by combining the data-driven soft sensor with a mass balance to estimate the glycerol and biomass concentrations on pilot-scale data with N-RMSEs of 11% and 21%, respectively. A digital shadow modeling framework was developed by coupling a mechanistic model (MM) with the hybrid soft sensor. The digital shadow modeling framework significantly improved the predictability compared with the MM. The contribution of this study brings the application of digital shadows closer to industrial implementation. It demonstrates the high potential of using this type of modeling framework for scale-up and leads the way to a new generation of in silico-based process development.

Systematic Parameter Estimation and Dynamic Simulation of Cold Contact Fermentation for Alcohol-Free Beer Production

Global demand for Low-Alcohol Beer (LAB) and Alcohol-Free Beer (AFB) has surged due to flavor attributes, health benefits, and lifestyle changes, prompting efforts for process intensification. This paper aims to offer a detailed modelling basis for LAB manufacturing study and optimisation. A first-principles dynamic model for conventional beer manufacturing has been re-parameterized and used for dynamic simulation of Cold Contact Fermentation (CCF), an effective LAB and AFB production method, with concentrations tracked along plausible temperature manipulation profiles. Parameter estimation is pursued using industrial production data, with a detailed local sensitivity analysis portraying the effect of key parameter variation on sugar consumption, ethanol production, and key flavor component (ethyl acetate and diacetyl) evolution during (and final values after) CCF. Ethyl acetate (esters in general) affecting fruity flavors emerge as most sensitive to CCF conditions.