Cambridge Healthtech Institute 트레이닝 세미나에서는 학술적인 이론이나 배경을 폭넓게 다루는 것은 물론, 실제 사례 연구, 현장에서 마주친 문제, 적용된 솔루션을 제공합니다. 각 트레이닝 세미나에서는 정식 강의와 인터랙티브 디스커션 및 액티비티를 조합하여 학습 경험을 최대한 높일 수 있습니다. 경험이 풍부한 강사가 현재 연구에 적용 가능한 컨텐츠에 초점을 맞추고, 이 분야에 처음 입문한 분들에게도 중요한 가이던스를 제공합니다.
트레이닝 세미나는 대면으로만 제공
일관성과 집중적인 학습 환경을 확보하기 위해
컨퍼런스 세션과 트레이닝 세미나 간 이동은 허용되지 않습니다.
Monday, 16 November 2026 08:30 - 17:00
TS1A: Introduction to Multispecific Antibodies: History, Engineering, and Applications
Topics to be covered:
- A brief history of bispecific antibodies: 60 years of progress with critical advances and key pioneers
- Bispecific applications and powerful mechanisms-of-action
- Engineering bispecific antibodies: 100 formats and counting
- Bispecific-specific considerations in preclinical development and regulatory landscape
- Developability, manufacturing, and analytical considerations
- Clinical experience, translation, and regulatory approval
- Current trends and future opportunities in regulating immune checkpoints, cell-based therapies, and personalised approaches
INSTRUCTOR BIOGRAPHY:
G. Jonah Rainey, PhD, Associate Vice President, Eli Lilly and Company
TS2A: Everything You Ever Wanted to Know about Immunogenicity
This 1-day training seminar provides a practical, comprehensive overview of immunogenicity-the causes, how to assess an immunogenicity risk, and what to do if you observe immunogenicity during preclinical, clinical, and post-market approval. The seminar begins by detailing the science behind immunogenicity and the latest international guidance, followed by assay and bioanalytical assessment strategies for traditional and emerging biologics. Other topics include non-clinical models, the role of AI/ML, and reporting immunogenicity.
INSTRUCTOR BIOGRAPHIES:
Chloé Ackaert, PhD, Senior Scientist, Immunogenicity, IQVIA Laboratories
Timothy Hickling, PhD, Consultant, Quasor Ltd.
Sofie Pattyn, Founder & CTO, IQVIA Laboratories
TS3A: Introduction to Machine Learning for Biologics Design
- Basics of machine learning and where it fits into drug discovery
- Modern homology modelling and structure prediction
- Predicting antibody affinity and specificity modulation
- Generative design in biologics: library design and language models
- Machine learning applications of T cell and B cell immunogenicity
- Methods and application of ML for chemical, folding, and solution stabilities
INSTRUCTOR BIOGRAPHY:
Christopher R. Corbeil, PhD, Research Officer, Human Health Therapeutics, National Research Council Canada
TS4A: Protein Production 201: Applying End-to-End CEPA Workflow
Topics to be Covered:
Review of host expression systems and their application
- Cell free, bacterial, yeast, plant, insect, and mammalian host systems
- Which expression system should I use to express my protein?
- Can we generate a host expression decision tree to address complex modalities?
Implementing and optimising the CEPA workflow
- Aligning data and biology to optimise expression
- Addressing bottlenecks in harvesting/purification
- Analytical methodologies and their applications
- Establishing/Setting QC standards
Case Studies
- Difficult-to-express proteins
- Structural biology support
- Automation/Screening
- Scale-down/Scale-up
INSTRUCTOR BIOGRAPHIES:
Richard Altman, MS, Field Application Scientist, Thomson Instrument Company
Christopher Cooper, DPhil, Senior Lecturer in Biotechnology, University of Surrey
Dominic Esposito, PhD, Senior Director, Protein Sciences, Septerna
Tuesday, 17 November 2026 08:30 - 18:35
TS7B: AI-Driven Design of Biologics: A Hands-on Guide to Using State-of-the-Art ML Protein Models
Participants are expected to have some prior exposure to computational modeling tools (e.g. Python, R, COOT, Rosetta, AutoDock Vina, etc.) but limited experience applying them to their projects. They should be comfortable using Jupyter notebooks and prepared to explore topics such as evaluating metrics, determining appropriate sampling sizes, and selecting key adjustable parameters. While this seminar does not cover ligand docking or protein-protein docking, it is well-suited for those interested in antibody modeling and, potentially, enzyme design language models.
Hands-on instructional content will be presented as Google Colab notebooks written in python. A basic understanding of general coding principles, such as typing, loops, functions, and classes, will be sufficient. It will not be required to write your own code from scratch, but a sufficient familiarity with python to understand and edit the provided notebooks will be essential to a meaningful experience.
Topics to be covered:
- Building practical experience with AI-based modelling of proteins
- A breakdown of input formats, command lines, and analysis of output
- Hands-on exercises using real-world scenarios in antibody structure prediction, developability pre-screening, immunogen solubilization, and de novo binder design
- Discussion of, and guidance on, questions like: how many models, in silico selection metrics and ranking, and how many to test in the lab
- Pipelining of protein design software and the critical use of an “oracle”
INSTRUCTOR BIOGRAPHY:
David P. Nannemann, PhD, Vice President, Rosetta Commons Foundation
* 주최측 사정에 따라 사전 예고없이 프로그램이 변경될 수 있습니다.










