The University of Tokyo · 공학
Sara Badr 교수의 연구실은 단일클론 항체(mAb) 생산 공정의 설계 및 최적화를 위한 혼합형 수학적 모델링에 중점을 두고 있습니다. 기계적 원리 기반 모델, 데이터 기반 분석, 그리고 공정 내 잡질 등 복잡한 요소를 통합한 하이브리드 모델링 기법을 개발하여, 세포 대사 변화와 공정 조건 변화에 대한 정밀한 예측을 가능하게 합니다. 특히, 산소 농도, 글루타민 고갈 등 핵심 공정 변수의 영향을 반영한 메커니즘 기반 모델링과 장기적인 장비 상태 평가를 위한 데이터 기반 진단 기법도 함께 연구하고 있습니다.
표시된 성과는 수집된 데이터 기준으로 산출되며, 일부 차이가 있을 수 있습니다.
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
