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PEOPLE@HES-SO – Annuaire et Répertoire des compétences
PEOPLE@HES-SO – Annuaire et Répertoire des compétences

PEOPLE@HES-SO
Annuaire et Répertoire des compétences

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Rüdt Matthias

Rüdt Matthias

Professeur-e HES Associé-e

Compétences principales

Chemical and bioprocess engineering

Process Analytical Technology (PAT)

Data Analysis

Spectroscopy

Downstream processing

Machine Learning

Biopharmaceutical manufacturing

  • Contact

  • Enseignement

  • Publications

Contrat principal

Professeur-e HES Associé-e

Bureau: ENP.19.N316

HES-SO Valais-Wallis - Haute Ecole d'Ingénierie
Rue de l'Industrie 23, 1950 Sion, CH
HEI - VS
Domaine
Chimie et sciences de la vie
Filière principale
Ingénierie des Sciences du vivant

Dr.-Ing. Matthias Rüdt studied chemistry and biotechnology at ETH Zurich with an exchange at Stanford University for his Master thesis. After a two year interlude in biopharmaceutical downstream process development at Novartis, he returned to academia for his PhD (Doktor der Ingenieurswissenschaften, completed with summa cum laude) with a subsequent PostDoc at the Karlsruhe Institute of Technology (KIT) focusing on Process Analytical Technology (PAT) for the downstream process of biologics. Returning to industry, Matthias worked at Novartis and Roche in clinical and commercial manufacturing before assuming his current position as associate professor at HES-SO Vallais-Wallis.

During his previous career, Matthias Rüdt authored publications on PAT, downstream processing, chemometrics, mechanistic modeling, issued a patent and received multiple prices for conference publications. His current research interests are:

  • Downstream processing of biotechnological products, scale-up and scale-down
  • Biopharmaceutical process development
  • Real-time monitoring of downstream processes
  • Optical spectroscopy for PAT applications (including UV/Vis, fluorescence, NIR, Raman)
  • Machine learning for process-engineering
  • Chemometrics
BSc HES-SO en Ingénierie des sciences du vivant - HES-SO Valais-Wallis - Haute Ecole d'Ingénierie
  • Bioprocess engineering
  • Downstream processing
  • Process engineering

2025

Quantitation of antibiotics in fresh fermentation medium by hydrophilic interaction chromatography mass spectrometry
Article scientifique

Marcon Nadia, Rüdt Matthias, Joachim Klein, Miladinovic Sasa

Analytical and Bioanalytical Chemistry, 2025 , pp.  1-8

2024

Generative data augmentation and automated optimization of convolutional neural networks for process monitoring
Article scientifique ArODES

Robin Schiemer, Matthias Rüdt, Jürgen Hubbuch

Frontiers in Bioengineering and Biotechnology,  2024, 12

Lien vers la publication

Résumé:

Chemometric modeling for spectral data is considered a key technology in biopharmaceutical processing to realize real-time process control and release testing. Machine learning (ML) models have been shown to increase the accuracy of various spectral regression and classification tasks, remove challenging preprocessing steps for spectral data, and promise to improve the transferability of models when compared to commonly applied, linear methods. The training and optimization of ML models require large data sets which are not available in the context of biopharmaceutical processing. Generative methods to extend data sets with realistic in silico samples, so-called data augmentation, may provide the means to alleviate this challenge. In this study, we develop and implement a novel data augmentation method for generating in silico spectral data based on local estimation of pure component profiles for training convolutional neural network (CNN) models using four data sets. We simultaneously tune hyperparameters associated with data augmentation and the neural network architecture using Bayesian optimization. Finally, we compare the optimized CNN models with partial least-squares regression models (PLS) in terms of accuracy, robustness, and interpretability. The proposed data augmentation method is shown to produce highly realistic spectral data by adapting the estimates of the pure component profiles to the sampled concentration regimes. Augmenting CNNs with the in silico spectral data is shown to improve the prediction accuracy for the quantification of monoclonal antibody (mAb) size variants by up to 50% in comparison to single-response PLS models. Bayesian structure optimization suggests that multiple convolutional blocks are beneficial for model accuracy and enable transfer across different data sets. Model-agnostic feature importance methods and synthetic noise perturbation are used to directly compare the optimized CNNs with PLS models. This enables the identification of wavelength regions critical for model performance and suggests increased robustness against Gaussian white noise and wavelength shifts of the CNNs compared to the PLS models.

2023

Monitoring of ultra- and diafiltration processes by Kalman-filtered Raman measurements
Article scientifique ArODES

Laura Rolinger, Jürgen Hubbuch, Matthias Rüdt

Analytical and Bioanalytical Chemistry,  2023, vol. 415, pp. 841-854

Lien vers la publication

Résumé:

Monitoring the protein concentration and buffer composition during the Ultrafiltration/Diafiltration (UF/DF) step enables the further automation of biopharmaceutical production and supports Real-time Release Testing (RTRT). Previously, in-line Ultraviolet (UV) and Infrared (IR) measurements have been used to successfully monitor the protein concentration over a large range. The progress of the diafiltration step has been monitored with density measurements and Infrared Spectroscopy (IR). Raman spectroscopy is capable of measuring both the protein and excipient concentration while being more robust and suitable for production measurements in comparison to Infrared Spectroscopy (IR). Regardless of the spectroscopic sensor used, the low concentration of excipients poses a challenge for the sensors. By combining sensor measurements with a semi-mechanistic model through an Extended Kalman Filter (EKF), the sensitivity to determine the progress of the diafiltration can be improved. In this study, Raman measurements are combined with an EKF for three case studies. The advantages of Kalman-filtered Raman measurements for excipient monitoring are shown in comparison to density measurements. Furthermore, Raman measurements showed a higher measurement speed in comparison to Variable Pathlength (VP) UV measurement at the trade-off of a slightly worse prediction accuracy for the protein concentration. However, the Raman-based protein concentration measurements relied mostly on an increase in the background signal during the process and not on proteinaceous features, which could pose a challenge due to the potential influence of batch variability on the background signal. Overall, the combination of Raman spectroscopy and EKF is a promising tool for monitoring the UF/DF step and enables process automation by using adaptive process control.

2021

Comparison of UV- and Raman-based monitoring of the protein A load phase and evaluation of data fusion by PLS models and CNNs
Article scientifique ArODES

Laura Rolinger, Matthias Rüdt, Jürgen Hubbuch

Biotechnology and Bioengineering,  2021, vol. 118, no. 11, pp. 4255-4268

Lien vers la publication

Résumé:

A promising application of Process Analytical Technology to the downstream process of monoclonal antibodies (mAbs) is the monitoring of the Protein A load phase as its control promises economic benefits. Different spectroscopic techniques have been evaluated in literature with regard to the ability to quantify the mAb concentration in the column effluent. Raman and Ultraviolet (UV) spectroscopy are among the most promising techniques. In this study, both were investigated in an in-line setup and directly compared. The data of each sensor were analyzed independently with Partial-Least-Squares (PLS) models and Convolutional Neural Networks (CNNs) for regression. Furthermore, data fusion strategies were investigated by combining both sensors in hierarchical PLS models or in CNNs. Among the tested options, UV spectroscopy alone allowed for the most precise and accurate prediction of the mAb concentration. A Root Mean Square Error of Prediction (RMSEP) of 0.013 g L−1 was reached with the UV-based PLS model. The Raman-based PLS model reached an RMSEP of 0.232 g L−1. The different data fusion techniques did not improve the prediction accuracy above the prediction accuracy of the UV-based PLS model. Data fusion by PLS models seems meritless when combining a very accurate sensor with a less accurate signal. Furthermore, the application of CNNs for UV and Raman spectra did not yield significant improvements in the prediction quality. For the presented application, linear regression techniques seem to be better suited compared with advanced nonlinear regression techniques, like, CNNs. In summary, the results support the application of UV spectroscopy and PLS modeling for future research and development activities aiming to implement spectroscopic real-time monitoring of the Protein A load phase.

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