The Optimisation & Developability conference at PEGS Europe looks at innovative approaches, methods and models that scientists use to develop strategies for candidate selection and optimization. Today, the field of optimization and developability takes a step toward digitalization, where scientists are starting to apply machine learning, deep learning and in silico approaches to assess developability and manufacturability.

Preliminary Agenda

DEVELOPABILITY SCREENING FOR COMPLEX MOLECULES

KEYNOTE PRESENTATION: Developability Assessment to Enable Candidate Selection of Therapeutic Proteins

Steffen Hartmann, PhD, Head, Characterization, Formulation and Bioinformatics, Novartis Pharma AG

Developability of Hexabody®-Based IgG Antibodies: The Impact of Formulation on Colloidal and Conformational Stability

Muriel van Kampen, PhD, Senior Scientist, Genmab

An Integrated Approach for Optimisation and Developability Assessment of Peptides Intended for Multiple-Dose Pen Devices

Andreas Evers, PhD, Senior Scientist, Synthetic Molecular Design, Integrated Drug Discovery, Sanofi

METHODS AND MODELS FOR CANDIDATE SELECTION

Physiochemical Predictors of Antibody Solution Behavior

Jonathan Kingsbury, PhD, Head, Developability and Preformulation, Biologics Development, Sanofi

Developability Assessment to Select Candidates for Clinical Development

Anup Arumughan, PhD, Principal Scientist, Antibody Analytics, Roche

Biophysical Screening of Unwanted Protein Interactions

Nikolai Lorenzen, PhD, Specialist, Biophysics and Formulation, Novo Nordisk A/S

DEEP LEARNING and in silico APPROACHES FOR ANTIBODY OPTIMISATION

Toward in silico Lead Discovery

Lars Linden, PhD, Director & Head, Protein Biochemistry, Bayer Healthcare AG 

A Comprehensive Screening Platform to Identify the Next-Generation Targeted Cancer Immunotherapy Targets

Stefanie Urlinger, PhD, VP Antibody Development, iOmx Therapeutics AG

Using Structural Information to Aid in silico Therapeutic Design from Next-Generation Sequencing Repertoires of Antibodies

Charlotte Deane, PhD, Professor, Structural Bioinformatics; Head, Department of Statistics, University of Oxford

Deep Learning Enables Therapeutic Antibody Optimization in Mammalian Cells

Derek Mason, MSc, PhD Candidate, Department of Biosystems Science & Engineering (D-BSSE), ETH Zurich



* The program is subject to change without notice, due to unforeseen reason.


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