- 最適化と発展性 -
最適化と発展性をテーマにしたこのカンファレンスプログラムでは、薬剤候補の選定と最適化のための戦略を開発するために利用可能な革新的な手法やモデルに光が当てられます。現在この分野ではデジタル化に向けた動きが進んでおり、多くの研究者が、機械学習、深層学習、コンピューター分析などの手法を用いて発展性と製造可能性を評価するようになっています。

Final Agenda

11月18日(月)

Recommended Short Course*

SC3: Mutation and Selection Strategies Beyond Affinity Optimisation - LEARN MORE

SC4: Surfactants in Biotherapeutics: Can't Live with Them, Can't Live without Them - LEARN MORE

*Separate registration required.

12:00 Conference Registration

複合分子の発展性に基づくスクリーニング

13:30 Organiser’s Welcome

Mimi Langley, MBA, Senior Conference Director, Cambridge Healthtech Institute

13:35 Chairperson’s Opening Remarks

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


13:45 KEYNOTE PRESENTATION: Developability Assessment to Enable Candidate Selection of Therapeutic Proteins

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

 

 

 

 

 

14:15 Developability of Hexabody®-Based IgG Antibodies: The Impact of Formulation on Colloidal and Conformational Stability

vanKampen_MurielMuriel van Kampen, PhD, Senior Scientist, Genmab

The HexaBody format is a novel platform for the potentiation of therapeutic antibodies by enhancement of antigen-dependent hexamer formation at the cell surface, which may drive subsequent target receptor activation or complement activation. The biophysical characteristics and stability of HexaBody-based model compounds in different formulations will be discussed, probed by a variety of analytical techniques.

14:45 An Integrated Approach for Optimization and Developability Assessment of Peptides Intended for Multiple-Dose Pen Devices

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

Physicochemical properties of peptides need to be compatible with the manufacturing process and formulation requirements to ensure developability toward the commercial drug product. This aspect is often disregarded and only evaluated late in discovery, imposing a high risk for delays in development, increased costs, and finally for the project in general. In the presentation, a general roadmap is proposed to optimize physicochemical properties towards developability of peptide drugs by combining experimental and in silico profiling to provide stable peptide formulations at the end of discovery.

Horiba_Scientific 15:15 Presentation to be Announced

 

15:45 Networking Refreshment Break


PLENARY KEYNOTE SESSION

16:15 Moderator’s Opening Remarks

Kerry Chester, PhD, Professor, Molecular Medicine, University College London Cancer InstituteKerry Chester, PhD, Professor, Molecular Medicine, University College London Cancer Institute

 

 

 

 

 

16:20 Bispecific, Soluble TCR as the Next Therapeutic Platform

Bahija Jallal, PhD, CEO and Director of the Board, ImmunocoreBahija Jallal, PhD, CEO and Director of the Board, Immunocore

Of the two adaptive immunity recognition motifs, only antibodies have been brought to patients. However, antibody therapeutics only recognize 10% of human proteome (surface-expressed). The other motif, T cell receptor (TCR), has potential to unlock 90% of the human proteome, but requires converting a low-affinity, specificity membrane receptor into a soluble therapeutic. IMCgp100, a soluble, TCR bispecific-targeting melanoma, is the most advanced soluble TCR therapeutic in development.

17:20 Attacking Cancer Cell Surfaceomes with Recombinant Antibodies

James A. Wells, PhD, Professor, Departments of Pharmaceutical Chemistry and Cellular & Molecular Pharmacology, University of California,
	San FranciscoJames A. Wells, PhD, Professor, Departments of Pharmaceutical Chemistry and Cellular & Molecular Pharmacology, University of California, San Francisco

The cell surface proteome (surfaceome) is the primary hub for cells to communicate with the outside world. Oncogenes are known to cause huge changes in cells and we find this translates to significant remodeling of the surfaceome. We generate recombinant antibodies to detect and then attack these cells by toxifying the antibodies or recruiting immune cells to kill. I’ll discuss the technologies for surface protein analysis, an industrialized platform for rapid antibody generation using phage display, and using these tool reagents for target validation.

18:20 Welcome Reception in the Exhibit Hall with Poster Viewing

19:30 End of Day

11月19日(火)

07:45 Registration and Morning Coffee

発展性評価のための手法とモデル

08:30 Chairperson’s Remarks

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

08:35 Physicochemical Predictors of Antibody Solution Behavior

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

The development of successful high-concentration biologic drugs requires that the therapeutic protein have properties amenable to achieving the target product profile. Selection of molecules that are resistant to unfavorable solution behaviors, such as high viscosity and poor colloidal stability is enabled by developability assessment. A framework for developability is presented, which is centered on assessing the fit to the required dosage form and to the established manufacturing platform. The measurement of molecular and dilute solution properties predictive of high concentration behaviors will be discussed within the context of the underlying solution phenomena and illustrated with examples.

09:05 Developability Assessment to Select Candidates for Clinical Development

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

We have developed a highly versatile next generation biologics platform with a number of candidates in clinical development. During lead identification and optimization of candidates, we typically rank molecules based on their potential for successful future development. Such developability assessments provide important information about potential liabilities, e.g., chemical degradation of amino acids or unfavorable CMC properties. We have recently expanded our developability concept to systematically combine in-silico analysis, including pharmacokinetics analysis with biophysical and functional testing. In summary, this concept provides a more holistic picture of a candidate’s fitness for future development.

09:35 Problem-Solving Breakout Discussions*

Topic: Developability of Biologic Drug:  Current Trends, Challenges and Opportunities

Moderator: Johnathan Kingsbury, PhD, Head, Developability and Preformulation, Biologics Development, Sanofi

 

10:30 Coffee Break in the Exhibit Hall with Poster Viewing

11:15 Biophysical Screening of Unwanted Protein Interactions

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

Stickiness is a critical parameter to measure during developability assessment of antibodies, as it can lead to non-specific interactions, reversible self-association, and aggregation. I will give examples on how we at Novo Nordisk screen for such unwanted protein interactions and how we collaborate with leading academic groups to develop new sophisticated biophysical screening assays.

11:45 Re-Examination of the Hydrophobic Effect at Antibody-Antigen Interfaces

Warwicker_JimJim Warwicker, PhD, Reader, School of Chemistry, University of Manchester

Prediction of developability requires a molecular level understanding of the behaviour of therapeutic proteins. We find that interactions at antibody CDRs challenge current empirical models for the hydrophobic effect. Improvements can be made with introduction of shape-dependence, and this coupling of modern protein science with traditional protein engineering concepts will lead to better predictive models for the biologics community.

UnchainedLabs 12:15 Presentation to be Announced


creoptix 12:45 Luncheon Presentation I to be Announced

 

13:15 Luncheon Presentation II (Sponsorship Opportunity Available)

 

13:45 Dessert Break in the Exhibit Hall with Poster Viewing

抗体最適化のためのディープラーニングとコンピューターによるスクリーニング

14:15 Chairperson’s Remarks

Jim Warwicker, PhD, Reader, School of Chemistry, University of Manchester

14:20 Toward in silico Lead Discovery

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

  • How will artificial intelligence and machine learning change and impact the way big pharma performs antibody lead discovery and optimization processes in the future?
  • What is already there and what is needed on the journey to in silico drug discovery?

 

14:50 Combining Deep Sequencing and High Throughput B Cell Technologies to Maximize Functional Activity Guided Antibodies Discovery and Optimization

Cheung_Gabriel_WCGabriel WC Cheung, PhD, Senior Director, BioMedicine Design, Pfizer, Inc.

Successful biotherapeutic discovery follows some basic principles. At Pfizer, we strategically integrate technologies to enable fast and focused interrogation of B cell repertoire with functionally relevance.

 

15:20 Using Structural Information to Aid in silico Therapeutic Design from Next Generation Sequencing Repertoires of Antibodies

Deane_CharlotteCharlotte Deane, PhD, Professor of Structural Bioinformatics & Head of Department, Department of Statistics, University of Oxford

We have built the freely available Observed Antibody Space database of over a billion antibody sequences. Using this data, I will show how predicted structural information can enrich data from next-generation sequencing experiments. In particular, TAP, our novel therapeutic antibody profiler that provides five computational developability guidelines.

RapidNovor 15:50 Talk Title to be Announced

Anthony Stajduhar, Business Development Manager, Rapid Novor, Inc.



Fusion-Antibodies_horizontal 16:05 Presentation to be Announced

 

16:20 Refreshment Break in the Exhibit Hall with Poster Viewing

 

17:00 Deep Learning Enables Therapeutic Antibody Optimization in Mammalian Cells

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

Deep learning, as part of a family of tools related to machine learning, is an emerging field of information and computer science that uses large data to identify complex relationships. Here, I will describe how we are moving beyond experimental screening by applying deep learning to augment multi-parameter optimization of therapeutic antibodies in mammalian cells.

生物物理学的特性を最適化する新たな工学的手法

17:30 Begin with Quality in Mind: Identifying CQAs from Early Stage of Product Lifecycle

Shah_ArchanaArchana Shah, Investigator, Analytical and Product Characterisation, Biopharm Process Research, GlaxoSmithKline UK

This presentation will cover the approach used to identify CQAs from early stage by using QbD principles. It will also cover developability screens used to assess the developability risks and risk ranking tool to assess the criticality of quality attributes.

18:00 Importance of Vernier Zone Residues in Antibody Engineering Approaches

Kalyoncu_SibelSibel Kalyoncu, PhD, Research Group Leader, Antibody Engineering Lab, Izmir Biomedicine and Genome Center, Turkey

Vernier zone residues locate in framework regions of antibodies affecting conformations of CDR loops and they are underrepresented in the literature. In this talk, an antibody engineering approach based on vernier zone has been applied to improve biophysical characteristics of an anti-VEGF antibody fragment. According to our preliminary results, solubility and, surprisingly, affinity increased with rationally designed mutation(s) on vernier zone residues. My talk will show one of important ways to improve certain biophysical and affinity characteristics of antibodies.

18:30 End of Optimisation & Developability

* 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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