Date: Tuesday 21st October 2025
Time: 14:00-15:30 BST | 15:00-16:30 CEST
Location: Online via Zoom
Speakers: Warren Roche (Sanofi), Bernard Francq (GSK) and Thomas Zahel (Körber-Pharma)
Who is this event intended for?: Statisticians and interested parties working in the CMC and Manufacturing area of Pharmaceutical Industry.
What is the benefit of attending?: Presentation and active discussion of technical statistical topics in this field.
This webinar is free to both Members of PSI and Non-Members.
To register for this event, please click here
Webinar dedicated to the topic of stability modelling; opportunity to hear from statisticians working in the CMC field, with open question and answers. Brought to you by the CMC Statistical Network Europe (CSNE) SIG.
|
Speaker |
Biography |
Abstract |
|
|
Warren Roche is from Waterford, Ireland. Warren has worked as a mathematics and statistics lecturer previously at the South East Technological University in Ireland, and also has extensive statistical experience in clinical trial research and pharmaceutical manufacturing. He originally earned his BSc in Mathematics from Trinity College Dublin in 2012. |
A universal tool for stability predictions of biotherapeutics, vaccines and in vitro diagnostic products It is of particular interest for biopharmaceutical companies developing and distributing fragile biomolecules to warrant the stability and activity of their products during long-term storage and shipment. In accordance with quality by design principles, advanced kinetic modeling (AKM) has been successfully used to predict long-term product shelf-life and relies on data from short-term accelerated stability studies that are used to generate Arrhenius-based kinetic models that can, in turn, be exploited for stability forecasts. The AKM methodology was evaluated through a cross-company perspective on stability modeling for key stability indicating attributes of different types of biotherapeutics, vaccines and biomolecules combined in in vitro diagnostic kits. It is demonstrated that stability predictions up to 3 years for products maintained under recommended storage conditions (2–8 °C) or for products that have experienced temperature excursions outside the cold-chain show excellent agreement with experimental real-time data, thus confirming AKM as a universal and reliable tool for stability predictions for a wide range of product types. |
 Bernard Francq, GSK
|
Bernard drives statistical innovation at GSK for CMC projects worldwide and fosters collaborations with industry and health authorities. He holds MSc degrees in Molecular Biology Engineering, and Industrial Statistics, along with a PhD in Statistics. He has earned several international prizes for his research and excellence in technical communication. He is the principal author of the award-winning paper on tolerance intervals in bridging studies (Stat in Med, 2020), and (co)-authored two of Wiley’s most downloaded papers (Stat in Med, 2019; Analytical Science Advances, 2022). He also created the R packages BivRegBLS (for method comparison studies) and AccelStab. Bernard participates in IQ Pharma working groups, serves on scientific committees (e.g., the non-clinical statistics conference in Europe), and sits on the board of directors of ENBIS (European Network for Business and Industrial Statistics). His research focuses on design of experiments, (non)-linear mixed models, tolerance intervals, and (accelerated) stability analysis. He is passionate about sharing his expertise, mentoring (PhD) students, and delivering innovative statistical training. |
Accelerated stability studies: a hybrid frequentist-Bayesian approach Accelerated stability studies model the degradation of biological drug products using arbitrary order kinetics, which are superior to the traditional Arrhenius plot. We compare the delta method, resampling techniques, and a hybrid frequentist-Bayesian approach, with the latter offering the best coverage probabilities and ease of implementation. Case studies will be demonstrated using the R package AccelStab. |
 Thomas Zahel, Körber-Pharma |
Thomas Zahel is Head of CMC Innovation Consulting at Körber. He holds a master’s degree in Biochemical Engineering from the Technical University of Graz and a PhD in applied statistics from Vienna Technical University. He has expert knowledge in theory and implementation of a multitude of statistical disciplines and long experience in developing statistical workflows for process characterization. |
Advanced Hybrid Kinetic Modelling Using Physics-Informed AI for Stability Data Stability data in drug development is inherently non-linear and often influenced by complex, poorly understood factors beyond temperature. We present a novel hybrid modelling approach using neural ODEs that integrates physics-informed AI to capture hidden kinetic effects—such as pH or material variability—offering greater flexibility and predictive power across the product lifecycle. |
Date: Tuesday 21st October 2025
Time: 14:00-15:30 BST | 15:00-16:30 CEST
Location: Online via Zoom
Speakers: Warren Roche (Sanofi), Bernard Francq (GSK) and Thomas Zahel (Körber-Pharma)
Who is this event intended for?: Statisticians and interested parties working in the CMC and Manufacturing area of Pharmaceutical Industry.
What is the benefit of attending?: Presentation and active discussion of technical statistical topics in this field.
This webinar is free to both Members of PSI and Non-Members.
To register for this event, please click here
Webinar dedicated to the topic of stability modelling; opportunity to hear from statisticians working in the CMC field, with open question and answers. Brought to you by the CMC Statistical Network Europe (CSNE) SIG.
|
Speaker |
Biography |
Abstract |
|
|
Warren Roche is from Waterford, Ireland. Warren has worked as a mathematics and statistics lecturer previously at the South East Technological University in Ireland, and also has extensive statistical experience in clinical trial research and pharmaceutical manufacturing. He originally earned his BSc in Mathematics from Trinity College Dublin in 2012. |
A universal tool for stability predictions of biotherapeutics, vaccines and in vitro diagnostic products It is of particular interest for biopharmaceutical companies developing and distributing fragile biomolecules to warrant the stability and activity of their products during long-term storage and shipment. In accordance with quality by design principles, advanced kinetic modeling (AKM) has been successfully used to predict long-term product shelf-life and relies on data from short-term accelerated stability studies that are used to generate Arrhenius-based kinetic models that can, in turn, be exploited for stability forecasts. The AKM methodology was evaluated through a cross-company perspective on stability modeling for key stability indicating attributes of different types of biotherapeutics, vaccines and biomolecules combined in in vitro diagnostic kits. It is demonstrated that stability predictions up to 3 years for products maintained under recommended storage conditions (2–8 °C) or for products that have experienced temperature excursions outside the cold-chain show excellent agreement with experimental real-time data, thus confirming AKM as a universal and reliable tool for stability predictions for a wide range of product types. |
| Bernard Francq, GSK
|
Bernard drives statistical innovation at GSK for CMC projects worldwide and fosters collaborations with industry and health authorities. He holds MSc degrees in Molecular Biology Engineering, and Industrial Statistics, along with a PhD in Statistics. He has earned several international prizes for his research and excellence in technical communication. He is the principal author of the award-winning paper on tolerance intervals in bridging studies (Stat in Med, 2020), and (co)-authored two of Wiley’s most downloaded papers (Stat in Med, 2019; Analytical Science Advances, 2022). He also created the R packages BivRegBLS (for method comparison studies) and AccelStab. Bernard participates in IQ Pharma working groups, serves on scientific committees (e.g., the non-clinical statistics conference in Europe), and sits on the board of directors of ENBIS (European Network for Business and Industrial Statistics). His research focuses on design of experiments, (non)-linear mixed models, tolerance intervals, and (accelerated) stability analysis. He is passionate about sharing his expertise, mentoring (PhD) students, and delivering innovative statistical training. |
Accelerated stability studies: a hybrid frequentist-Bayesian approach Accelerated stability studies model the degradation of biological drug products using arbitrary order kinetics, which are superior to the traditional Arrhenius plot. We compare the delta method, resampling techniques, and a hybrid frequentist-Bayesian approach, with the latter offering the best coverage probabilities and ease of implementation. Case studies will be demonstrated using the R package AccelStab. |
| Thomas Zahel, Körber-Pharma |
Thomas Zahel is Head of CMC Innovation Consulting at Körber. He holds a master’s degree in Biochemical Engineering from the Technical University of Graz and a PhD in applied statistics from Vienna Technical University. He has expert knowledge in theory and implementation of a multitude of statistical disciplines and long experience in developing statistical workflows for process characterization. |
Advanced Hybrid Kinetic Modelling Using Physics-Informed AI for Stability Data Stability data in drug development is inherently non-linear and often influenced by complex, poorly understood factors beyond temperature. We present a novel hybrid modelling approach using neural ODEs that integrates physics-informed AI to capture hidden kinetic effects—such as pH or material variability—offering greater flexibility and predictive power across the product lifecycle. |
Date: Tuesday 21st October 2025
Time: 14:00-15:30 BST | 15:00-16:30 CEST
Location: Online via Zoom
Speakers: Warren Roche (Sanofi), Bernard Francq (GSK) and Thomas Zahel (Körber-Pharma)
Who is this event intended for?: Statisticians and interested parties working in the CMC and Manufacturing area of Pharmaceutical Industry.
What is the benefit of attending?: Presentation and active discussion of technical statistical topics in this field.
This webinar is free to both Members of PSI and Non-Members.
To register for this event, please click here
Webinar dedicated to the topic of stability modelling; opportunity to hear from statisticians working in the CMC field, with open question and answers. Brought to you by the CMC Statistical Network Europe (CSNE) SIG.
|
Speaker |
Biography |
Abstract |
|
|
Warren Roche is from Waterford, Ireland. Warren has worked as a mathematics and statistics lecturer previously at the South East Technological University in Ireland, and also has extensive statistical experience in clinical trial research and pharmaceutical manufacturing. He originally earned his BSc in Mathematics from Trinity College Dublin in 2012. |
A universal tool for stability predictions of biotherapeutics, vaccines and in vitro diagnostic products It is of particular interest for biopharmaceutical companies developing and distributing fragile biomolecules to warrant the stability and activity of their products during long-term storage and shipment. In accordance with quality by design principles, advanced kinetic modeling (AKM) has been successfully used to predict long-term product shelf-life and relies on data from short-term accelerated stability studies that are used to generate Arrhenius-based kinetic models that can, in turn, be exploited for stability forecasts. The AKM methodology was evaluated through a cross-company perspective on stability modeling for key stability indicating attributes of different types of biotherapeutics, vaccines and biomolecules combined in in vitro diagnostic kits. It is demonstrated that stability predictions up to 3 years for products maintained under recommended storage conditions (2–8 °C) or for products that have experienced temperature excursions outside the cold-chain show excellent agreement with experimental real-time data, thus confirming AKM as a universal and reliable tool for stability predictions for a wide range of product types. |
| Bernard Francq, GSK
|
Bernard drives statistical innovation at GSK for CMC projects worldwide and fosters collaborations with industry and health authorities. He holds MSc degrees in Molecular Biology Engineering, and Industrial Statistics, along with a PhD in Statistics. He has earned several international prizes for his research and excellence in technical communication. He is the principal author of the award-winning paper on tolerance intervals in bridging studies (Stat in Med, 2020), and (co)-authored two of Wiley’s most downloaded papers (Stat in Med, 2019; Analytical Science Advances, 2022). He also created the R packages BivRegBLS (for method comparison studies) and AccelStab. Bernard participates in IQ Pharma working groups, serves on scientific committees (e.g., the non-clinical statistics conference in Europe), and sits on the board of directors of ENBIS (European Network for Business and Industrial Statistics). His research focuses on design of experiments, (non)-linear mixed models, tolerance intervals, and (accelerated) stability analysis. He is passionate about sharing his expertise, mentoring (PhD) students, and delivering innovative statistical training. |
Accelerated stability studies: a hybrid frequentist-Bayesian approach Accelerated stability studies model the degradation of biological drug products using arbitrary order kinetics, which are superior to the traditional Arrhenius plot. We compare the delta method, resampling techniques, and a hybrid frequentist-Bayesian approach, with the latter offering the best coverage probabilities and ease of implementation. Case studies will be demonstrated using the R package AccelStab. |
| Thomas Zahel, Körber-Pharma |
Thomas Zahel is Head of CMC Innovation Consulting at Körber. He holds a master’s degree in Biochemical Engineering from the Technical University of Graz and a PhD in applied statistics from Vienna Technical University. He has expert knowledge in theory and implementation of a multitude of statistical disciplines and long experience in developing statistical workflows for process characterization. |
Advanced Hybrid Kinetic Modelling Using Physics-Informed AI for Stability Data Stability data in drug development is inherently non-linear and often influenced by complex, poorly understood factors beyond temperature. We present a novel hybrid modelling approach using neural ODEs that integrates physics-informed AI to capture hidden kinetic effects—such as pH or material variability—offering greater flexibility and predictive power across the product lifecycle. |
Date: Tuesday 21st October 2025
Time: 14:00-15:30 BST | 15:00-16:30 CEST
Location: Online via Zoom
Speakers: Warren Roche (Sanofi), Bernard Francq (GSK) and Thomas Zahel (Körber-Pharma)
Who is this event intended for?: Statisticians and interested parties working in the CMC and Manufacturing area of Pharmaceutical Industry.
What is the benefit of attending?: Presentation and active discussion of technical statistical topics in this field.
This webinar is free to both Members of PSI and Non-Members.
To register for this event, please click here
Webinar dedicated to the topic of stability modelling; opportunity to hear from statisticians working in the CMC field, with open question and answers. Brought to you by the CMC Statistical Network Europe (CSNE) SIG.
|
Speaker |
Biography |
Abstract |
|
|
Warren Roche is from Waterford, Ireland. Warren has worked as a mathematics and statistics lecturer previously at the South East Technological University in Ireland, and also has extensive statistical experience in clinical trial research and pharmaceutical manufacturing. He originally earned his BSc in Mathematics from Trinity College Dublin in 2012. |
A universal tool for stability predictions of biotherapeutics, vaccines and in vitro diagnostic products It is of particular interest for biopharmaceutical companies developing and distributing fragile biomolecules to warrant the stability and activity of their products during long-term storage and shipment. In accordance with quality by design principles, advanced kinetic modeling (AKM) has been successfully used to predict long-term product shelf-life and relies on data from short-term accelerated stability studies that are used to generate Arrhenius-based kinetic models that can, in turn, be exploited for stability forecasts. The AKM methodology was evaluated through a cross-company perspective on stability modeling for key stability indicating attributes of different types of biotherapeutics, vaccines and biomolecules combined in in vitro diagnostic kits. It is demonstrated that stability predictions up to 3 years for products maintained under recommended storage conditions (2–8 °C) or for products that have experienced temperature excursions outside the cold-chain show excellent agreement with experimental real-time data, thus confirming AKM as a universal and reliable tool for stability predictions for a wide range of product types. |
| Bernard Francq, GSK
|
Bernard drives statistical innovation at GSK for CMC projects worldwide and fosters collaborations with industry and health authorities. He holds MSc degrees in Molecular Biology Engineering, and Industrial Statistics, along with a PhD in Statistics. He has earned several international prizes for his research and excellence in technical communication. He is the principal author of the award-winning paper on tolerance intervals in bridging studies (Stat in Med, 2020), and (co)-authored two of Wiley’s most downloaded papers (Stat in Med, 2019; Analytical Science Advances, 2022). He also created the R packages BivRegBLS (for method comparison studies) and AccelStab. Bernard participates in IQ Pharma working groups, serves on scientific committees (e.g., the non-clinical statistics conference in Europe), and sits on the board of directors of ENBIS (European Network for Business and Industrial Statistics). His research focuses on design of experiments, (non)-linear mixed models, tolerance intervals, and (accelerated) stability analysis. He is passionate about sharing his expertise, mentoring (PhD) students, and delivering innovative statistical training. |
Accelerated stability studies: a hybrid frequentist-Bayesian approach Accelerated stability studies model the degradation of biological drug products using arbitrary order kinetics, which are superior to the traditional Arrhenius plot. We compare the delta method, resampling techniques, and a hybrid frequentist-Bayesian approach, with the latter offering the best coverage probabilities and ease of implementation. Case studies will be demonstrated using the R package AccelStab. |
| Thomas Zahel, Körber-Pharma |
Thomas Zahel is Head of CMC Innovation Consulting at Körber. He holds a master’s degree in Biochemical Engineering from the Technical University of Graz and a PhD in applied statistics from Vienna Technical University. He has expert knowledge in theory and implementation of a multitude of statistical disciplines and long experience in developing statistical workflows for process characterization. |
Advanced Hybrid Kinetic Modelling Using Physics-Informed AI for Stability Data Stability data in drug development is inherently non-linear and often influenced by complex, poorly understood factors beyond temperature. We present a novel hybrid modelling approach using neural ODEs that integrates physics-informed AI to capture hidden kinetic effects—such as pH or material variability—offering greater flexibility and predictive power across the product lifecycle. |
Date: Tuesday 21st October 2025
Time: 14:00-15:30 BST | 15:00-16:30 CEST
Location: Online via Zoom
Speakers: Warren Roche (Sanofi), Bernard Francq (GSK) and Thomas Zahel (Körber-Pharma)
Who is this event intended for?: Statisticians and interested parties working in the CMC and Manufacturing area of Pharmaceutical Industry.
What is the benefit of attending?: Presentation and active discussion of technical statistical topics in this field.
This webinar is free to both Members of PSI and Non-Members.
To register for this event, please click here
Webinar dedicated to the topic of stability modelling; opportunity to hear from statisticians working in the CMC field, with open question and answers. Brought to you by the CMC Statistical Network Europe (CSNE) SIG.
|
Speaker |
Biography |
Abstract |
|
|
Warren Roche is from Waterford, Ireland. Warren has worked as a mathematics and statistics lecturer previously at the South East Technological University in Ireland, and also has extensive statistical experience in clinical trial research and pharmaceutical manufacturing. He originally earned his BSc in Mathematics from Trinity College Dublin in 2012. |
A universal tool for stability predictions of biotherapeutics, vaccines and in vitro diagnostic products It is of particular interest for biopharmaceutical companies developing and distributing fragile biomolecules to warrant the stability and activity of their products during long-term storage and shipment. In accordance with quality by design principles, advanced kinetic modeling (AKM) has been successfully used to predict long-term product shelf-life and relies on data from short-term accelerated stability studies that are used to generate Arrhenius-based kinetic models that can, in turn, be exploited for stability forecasts. The AKM methodology was evaluated through a cross-company perspective on stability modeling for key stability indicating attributes of different types of biotherapeutics, vaccines and biomolecules combined in in vitro diagnostic kits. It is demonstrated that stability predictions up to 3 years for products maintained under recommended storage conditions (2–8 °C) or for products that have experienced temperature excursions outside the cold-chain show excellent agreement with experimental real-time data, thus confirming AKM as a universal and reliable tool for stability predictions for a wide range of product types. |
| Bernard Francq, GSK
|
Bernard drives statistical innovation at GSK for CMC projects worldwide and fosters collaborations with industry and health authorities. He holds MSc degrees in Molecular Biology Engineering, and Industrial Statistics, along with a PhD in Statistics. He has earned several international prizes for his research and excellence in technical communication. He is the principal author of the award-winning paper on tolerance intervals in bridging studies (Stat in Med, 2020), and (co)-authored two of Wiley’s most downloaded papers (Stat in Med, 2019; Analytical Science Advances, 2022). He also created the R packages BivRegBLS (for method comparison studies) and AccelStab. Bernard participates in IQ Pharma working groups, serves on scientific committees (e.g., the non-clinical statistics conference in Europe), and sits on the board of directors of ENBIS (European Network for Business and Industrial Statistics). His research focuses on design of experiments, (non)-linear mixed models, tolerance intervals, and (accelerated) stability analysis. He is passionate about sharing his expertise, mentoring (PhD) students, and delivering innovative statistical training. |
Accelerated stability studies: a hybrid frequentist-Bayesian approach Accelerated stability studies model the degradation of biological drug products using arbitrary order kinetics, which are superior to the traditional Arrhenius plot. We compare the delta method, resampling techniques, and a hybrid frequentist-Bayesian approach, with the latter offering the best coverage probabilities and ease of implementation. Case studies will be demonstrated using the R package AccelStab. |
| Thomas Zahel, Körber-Pharma |
Thomas Zahel is Head of CMC Innovation Consulting at Körber. He holds a master’s degree in Biochemical Engineering from the Technical University of Graz and a PhD in applied statistics from Vienna Technical University. He has expert knowledge in theory and implementation of a multitude of statistical disciplines and long experience in developing statistical workflows for process characterization. |
Advanced Hybrid Kinetic Modelling Using Physics-Informed AI for Stability Data Stability data in drug development is inherently non-linear and often influenced by complex, poorly understood factors beyond temperature. We present a novel hybrid modelling approach using neural ODEs that integrates physics-informed AI to capture hidden kinetic effects—such as pH or material variability—offering greater flexibility and predictive power across the product lifecycle. |
Date: Tuesday 21st October 2025
Time: 14:00-15:30 BST | 15:00-16:30 CEST
Location: Online via Zoom
Speakers: Warren Roche (Sanofi), Bernard Francq (GSK) and Thomas Zahel (Körber-Pharma)
Who is this event intended for?: Statisticians and interested parties working in the CMC and Manufacturing area of Pharmaceutical Industry.
What is the benefit of attending?: Presentation and active discussion of technical statistical topics in this field.
This webinar is free to both Members of PSI and Non-Members.
To register for this event, please click here
Webinar dedicated to the topic of stability modelling; opportunity to hear from statisticians working in the CMC field, with open question and answers. Brought to you by the CMC Statistical Network Europe (CSNE) SIG.
|
Speaker |
Biography |
Abstract |
|
|
Warren Roche is from Waterford, Ireland. Warren has worked as a mathematics and statistics lecturer previously at the South East Technological University in Ireland, and also has extensive statistical experience in clinical trial research and pharmaceutical manufacturing. He originally earned his BSc in Mathematics from Trinity College Dublin in 2012. |
A universal tool for stability predictions of biotherapeutics, vaccines and in vitro diagnostic products It is of particular interest for biopharmaceutical companies developing and distributing fragile biomolecules to warrant the stability and activity of their products during long-term storage and shipment. In accordance with quality by design principles, advanced kinetic modeling (AKM) has been successfully used to predict long-term product shelf-life and relies on data from short-term accelerated stability studies that are used to generate Arrhenius-based kinetic models that can, in turn, be exploited for stability forecasts. The AKM methodology was evaluated through a cross-company perspective on stability modeling for key stability indicating attributes of different types of biotherapeutics, vaccines and biomolecules combined in in vitro diagnostic kits. It is demonstrated that stability predictions up to 3 years for products maintained under recommended storage conditions (2–8 °C) or for products that have experienced temperature excursions outside the cold-chain show excellent agreement with experimental real-time data, thus confirming AKM as a universal and reliable tool for stability predictions for a wide range of product types. |
| Bernard Francq, GSK
|
Bernard drives statistical innovation at GSK for CMC projects worldwide and fosters collaborations with industry and health authorities. He holds MSc degrees in Molecular Biology Engineering, and Industrial Statistics, along with a PhD in Statistics. He has earned several international prizes for his research and excellence in technical communication. He is the principal author of the award-winning paper on tolerance intervals in bridging studies (Stat in Med, 2020), and (co)-authored two of Wiley’s most downloaded papers (Stat in Med, 2019; Analytical Science Advances, 2022). He also created the R packages BivRegBLS (for method comparison studies) and AccelStab. Bernard participates in IQ Pharma working groups, serves on scientific committees (e.g., the non-clinical statistics conference in Europe), and sits on the board of directors of ENBIS (European Network for Business and Industrial Statistics). His research focuses on design of experiments, (non)-linear mixed models, tolerance intervals, and (accelerated) stability analysis. He is passionate about sharing his expertise, mentoring (PhD) students, and delivering innovative statistical training. |
Accelerated stability studies: a hybrid frequentist-Bayesian approach Accelerated stability studies model the degradation of biological drug products using arbitrary order kinetics, which are superior to the traditional Arrhenius plot. We compare the delta method, resampling techniques, and a hybrid frequentist-Bayesian approach, with the latter offering the best coverage probabilities and ease of implementation. Case studies will be demonstrated using the R package AccelStab. |
| Thomas Zahel, Körber-Pharma |
Thomas Zahel is Head of CMC Innovation Consulting at Körber. He holds a master’s degree in Biochemical Engineering from the Technical University of Graz and a PhD in applied statistics from Vienna Technical University. He has expert knowledge in theory and implementation of a multitude of statistical disciplines and long experience in developing statistical workflows for process characterization. |
Advanced Hybrid Kinetic Modelling Using Physics-Informed AI for Stability Data Stability data in drug development is inherently non-linear and often influenced by complex, poorly understood factors beyond temperature. We present a novel hybrid modelling approach using neural ODEs that integrates physics-informed AI to capture hidden kinetic effects—such as pH or material variability—offering greater flexibility and predictive power across the product lifecycle. |
Warren Roche, Sanofi