トレーニングセミナー | 24th Annual Discovery on Target 2026 公式サイト

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Cambridge Healthtech Instituteのトレーニングセミナーでは、学術的な理論や背景を幅広くカバーするとともに、実際のケーススタディ、遭遇した問題、適用されたソリューションを提供します。各トレーニングセミナーでは、正式な講義とインタラクティブなディスカッションやアクティビティを組み合わせ、学習経験を最大限に高めることができます。経験豊富な講師が、現在の研究に適用可能なコンテンツに焦点を当て、この分野に不慣れな方にも重要なガイダンスを提供します。

トレーニングセミナーは、対面のみで提供されます。

Monday, September 28, 2026 9:00 am - 6:00 pm

TS3A: AI-Driven Design of Biologics: State-of-the-Art ML Models & Real-World Applications

Artificial intelligence has driven remarkable breakthroughs in structure prediction, sequence design, and protein engineering. This course equips researchers - from those seeking a first foothold in the field to practitioners looking for deeper insight into emerging models and their application - with a rigorous, grounded understanding of foundational tools including AlphaFold, Boltz, ImmuneBuilder, ESM, AntiBERTy, ProteinMPNN, and RFDiffusion, as well as emerging pipelines such as BindCraft, RFAntibody and BoltzGen. Three building sessions move from foundational principles to real-world application, examining where the models come from, model selection and thoughtful application, and how performance benchmarks hold up - or don't - in active discovery pipelines.

Session 1: Foundations

Origins and evolution of AI protein models: from early sequence models to modern structure-based diffusion approaches
Understanding training data: sources, biases, and implications for model generalizability with best practices for model selection

Session 2: Using AI Models

AI model selection and thoughtful application, with discussion of inputs and outputs
The role of the oracle in generative AI protein design

Session 3: Real-World Applications

Benchmarked performance vs. real-world biologics discovery: navigating the gap
In silico selection metrics, sampling strategies, and translating computational results to the lab

INSTRUCTOR BIOGRAPHY:

David P. Nannemann, PhD, Vice President, Rosetta Commons Foundation

David is an expert in protein engineering and computational design, with extensive experience applying AI-driven modeling tools in an industry setting. He serves as Vice President of the Rosetta Commons Foundation and Industry Chair on the Rosetta Commons board, helping bridge academic advancements with industry applications. As Managing Member of Rosetta Design Group, he collaborates with companies of all sizes to tackle complex challenges in biologics design. David's deep expertise in leveraging cutting-edge tools like Rosetta, AlphaFold, and diffusion-based models for protein design make him an invaluable guide for participants looking to apply AI-driven biologics design in real-world settings.

TS4A: The End Game: From Lead Optimization to Drug Candidate Selection

Drugs interact with complex physiology to give different activities under different conditions. The discovery candidate selection process must demonstrate these different activities in anticipation of therapeutic outcomes; failure at this stage is the most costly and the translation of drug activity to human pathophysiology is a notorious graveyard for projects. Pharmacology is the unique discipline to prevent this. This course will take registrants through the unique aspects of Pharmacologic analysis of drug-target interaction to convert descriptive data (what we see) to predictive data (what will be seen in other systems). Data from a range of candidate targets (GPCRs, ion channels, enzymes) and ligands (small molecules, biologics, allosterics) will be used to demonstrate the techniques Pharmacology has to offer to quantify drug potency, efficacy and modulatory activity in a variety of settings. In addition to discussion of techniques, a series of case histories will be used to illustrate the concepts.

Detailed Agenda
Session 1 (9:00-11:00 am)

Introduction to candidate selection
Some unique features of pharmacology as a discipline
The drug discovery landscape and discovery infrastructure
Pharmacologic assays: The Eyes to See
Pharmacologic tools: The Dose-Response Curve

Session 2 (1:30-3:30 pm)

Determining mechanism of action
Ligand affinity
Ligand efficacy
Allosteric protein function

Session 3 (4:00-6:00 pm)

Pharmacokinetics for discovery
Early safety studies
In Vivo residence time and kinetics
The Endgame: case studies

INSTRUCTOR BIOGRAPHY:

Terrence P. Kenakin, PhD, Professor, Pharmacology, University of North Carolina at Chapel Hill

Beginning his career as a synthetic chemist, Terry Kenakin received a PhD in Pharmacology at the University of Alberta in Canada. After a postdoctoral fellowship at University College London, UK, he joined Burroughs-Wellcome as an associate scientist for 7 years. From there, he continued working in drug discovery for 25 years first at Glaxo, Inc., then Glaxo Wellcome, and finally as a Director at GlaxoSmithKline Research and Development Laboratories at Research Triangle Park, North Carolina, USA. Dr. Kenakin is now a professor in the Department of Pharmacology, University of North Carolina School of Medicine, Chapel Hill. Currently he is engaged in studies aimed at the optimal design of drug activity assays systems, the discovery and testing of allosteric molecules for therapeutic application, and the quantitative modeling of drug effects. In addition, he is Director of the Pharmacology graduate courses at the UNC School of Medicine. He is a member of numerous editorial boards, as well as Editor-in-Chief of the Journal of Receptors and Signal Transduction. He has authored numerous articles and has written 10 books on pharmacology.

TS5A: Drug Exposure at the Target: The Role of ADME and Pharmacokinetics

This training seminar describes how pharmacokinetics (PK) affects drug exposure at the intended target. The seminar opens with a foundation of clinical PK including the determination of key PK parameters from Cp-time data. Course materials also cover common preclinical ADME assays that allow estimation of a compound’s human PK properties. The materials bridge the idea of a compound’s PK and its observed pharmacodynamic effects (PD) through coverage of PK/PD modeling. Various drug modalities (e.g., small molecules, antibodies, and peptides) illustrate the concepts of the course.

Session 1

Drug discovery-typical order of operations
ADME and key pharmacokinetic parameters
Modeling Cp-time curves from an IV dose
Modeling Cp-time curves from an oral dose

Session 2

Oral drug space and membrane permeability
Metabolic stability and intrinsic clearance
Plasma, PPB, and the free drug hypothesis
Compartment models

Session 3

Pre-formulation and formulation
Preclinical species and PBPK
Non-small molecule drug modalities
PK/PD modeling

INSTRUCTOR BIOGRAPHY:

Erland Stevens, PhD, James G. Martin Professor, Department of Chemistry, Davidson College

Erland Stevens is formally trained as a synthetic organic chemist, with a PhD from the Department of Chemistry at the University of Michigan at Ann Arbor. He specialized in nitrogen heterocycle synthetic methodology. After completing his postdoctoral research at The Scripps Research Institute in La Jolla, CA, he joined the chemistry faculty at Davidson College in Davidson, NC. In addition to teaching organic chemistry, he created an undergraduate medicinal chemistry course and later published a textbook, Medicinal Chemistry: The Modern Drug Discovery Process, with Pearson Education. He then created an online medicinal chemistry course, which has been continuously revised and publicly available for approximately 10 years. He subsequently worked with Novartis to create additional online materials that are used with employees for continuing education purposes. He maintains two YouTube channels - Chem Help ASAP and Inside Drug Discovery.

* 不測の事態により、事前の予告なしにプログラムが変更される場合があります。

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