The University of Tokyo · Engineering
Professor Sara Badr's research lab specializes in the development of advanced modeling and control strategies for biopharmaceutical manufacturing, with a focus on monoclonal antibody (mAb) production. The lab integrates mechanistic, data-driven, and hybrid modeling approaches to capture complex cell metabolism, process dynamics, and impurity formation across cultivation phases. Key research directions include kinetic modeling of metabolic shifts (e.g., lactate production/consumption), long-term equipment condition monitoring in aseptic filling lines, and system-wide process optimization under variability and uncertainty. The lab emphasizes model robustness, data quality, and early integration of plant-wide models to accelerate process development and improve manufacturing efficiency.
Figures are computed from collected data and may differ slightly.
Representative cultivation models are needed for designing efficient monoclonal antibody (mAb) production processes. Simple Monod-type kinetic models could fail to capture changes at different phases and conditions. Current models rarely account for process-related impurities, which hinders optimizing integrated processes. A three-module hybrid approach is thus introduced here. Module 1 is a kinetic metabolism model until cell death based on Monod-type equations. Module 2 is a data-driven module
In a highly complex and interconnected system as that for monoclonal antibody production, integrated design approaches are necessary to avoid unforeseen consequences. This review presents developments in process modeling approaches: mechanistic, data-driven, and hybrid modeling. Challenges and requirements of process control in terms of the underlying models, data availability, and quality are presented. Impacts of system variations and their propagation across the production chain are discussed
A two-stage data-driven methodology for long-term equipment condition assessment in drug product manufacturing is presented with a case study for a commercially operating aseptic filling line. The methodology leverages process monitoring data. Sensor measurements are partitioned using process information and maintenance schedules that are available on different databases. Data is processed to tackle heterogeneity in sources and formats. The data is cleaned to remove the effects of short-term var
Demand for monoclonal antibodies (mAbs) is rapidly increasing. To achieve higher productivity, there have been improvements to cell lines, operating modes, media, and cultivation conditions. Representative mathematical models are needed to narrow down the growing number of process alternatives. Previous studies have proposed mechanistic models to depict cell metabolic shifts (e.g., lactate production to consumption). However, the impacts of variations of some operating conditions have not yet be
Bacteria have evolved multiple protein secretion systems to survive and cope with surrounding environmental stresses. So far, there are seven secretion systems (type I to type VII), which have been identified and demonstrated the structural and molecular mechanisms. Among them, type three secretion system (T3SS), hallmark of acute infection, is considered as the most complicated system and can translocate effector proteins directly into host cell through a needle-like apparatus. Type six secreti
Representative kinetic models to describe monoclonal antibody (mAb) production processes are needed for effective process design. The development of mechanistic models can be impeded by the lack of complete understanding of changes in cell metabolism, e.g., lactate metabolic shifts. State-estimation-based methods were applied to assess the fit of available kinetic models over experimental runs. The results indicated the regions where model parameter updates were required. Different clustering st
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