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第14回年次会議「最適化と開発可能性」

分子の特性を調整して、その開発可能性、半減期、物理化学的特性を改善することは、臨床での成功の可能性を大幅に向上させ、分子の創薬可能性を判断するための重要な最初のステップです。第14回年次会議「最適化と開発可能性」会議では、分子モデリング、深層学習、in silicoでのアプローチのほか、最新のAIや機械学習のツールボックスを使用して、薬物特性を最適化し、安定性・凝集・免疫原性リスクを評価・予測するための戦略を紹介します。

11月14日(火)

Registration Open and Morning Coffee07:30

OPTIMISING DRUG PROPERTIES
薬物特性の最適化

08:25

Chairperson's Remarks

Andreas Evers, PhD, Principal Scientist, Computational Chemistry & Biology, Global Research & Development Discovery Technology, Merck Healthcare KGaA

08:30 KEYNOTE PRESENTATION:

Optimising a Small Protein Platform to Develop Inhalable Biologics to Treat Respiratory Diseases

Hitto Kaufmann, PhD, CSO & Senior Vice President, Pieris Pharmaceuticals GmbH

Respiratory diseases-including asthma, COPD, and pulmonary fibrosis-represent serious disorders with high unmet medical need. While the recent approvals of injectable biologics have improved therapeutic options for some, innovation is needed to move beyond inhaled small molecules and systemic therapies. The local administration of biologics represents the opportunity to not only create therapies with a high level of convenience but also achieve biological effects not possible with other modalities and routes of administration. We have advanced a pipeline of inhaled Anticalin proteins, including clinical-stage programs for asthma and idiopathic pulmonary fibrosis. Our optimised platform includes a developability workflow tailored to the needs of inhalable biologics, machine-learning based predictive models to drive rapid development, and a robust manufacturing platform for efficient production of inhaled proteins compatible with a broad range of devices. To illustrate the power and potential of the Pieris inhaled biologic platform, we will present a case study focused on Pieris’ inhaled Jagged-1 antagonist, PRS-400, designed to address mucus hypersecretion in a broad range of respiratory diseases.

09:00

Non-mAb Biotherapeutics: A Paradigm Change for the Developability Assessment Concept?

Paul Wassmann, PhD, Senior Principal Scientist, NIBR Biologics Center, Novartis, Switzerland

Unmet needs in pharmaceutical areas require development of complex, heavily engineered biotherapeutics, which are often based on non-mAb formats. Limited applicability of the mAb-centric developability assessment concept for these new formats will be highlighted. Importance of early identification of critical molecular parameters, which influence efficacy and safety, will be shown in examples such as assessment of parameters governing (short-term) stability during lead optimization phases and assessment of stability in relevant biofluids.
09:30

Embedding Dynamics in Intrinsic Physicochemical Profiles of Market-Stage Antibody-Based Biotherapeutics

Giuseppe L. Licari, PhD, Lead Scientist, Computational Structural Biology, Global Drug Product Development - BDC, Merck Serono SA

This presentation explores the integration of dynamics into the intrinsic physicochemical properties of antibody-based therapeutics with the aim to better understand and predict protein behavior in different environments and formulations.

10:00 Comparing Potential Bispecific Formats of Trastuzumab and a Humanized OKT3

Catherine Bladen, PhD, COO, Absolute Biotech

Not every antibody can be combined to produce well-behaved multi-specifics.  The valency and geometry of each design can determine the production, target engagement and ultimately the requisite biological functions.  In this case study, we selected two established antibody therapeutics, trastuzumab and a humanized OKT3 to produce 20 different bispecific formats to compare the feasibility of each format.

10:15 Case Studies About Innovative Recombinant Protein Vaccines of the VRI/Linkinvax Dendritic Cell-Targeting Platform

Thierry Menguy, PhD, Head of CMC projects, LinkinVax

LinKinVax’s ambition is to disrupt vaccine development using a unique, clinically safe, dendritic cell targeting vaccine platform inherited from VRI/INSERM allowing the development of recombinant protein vaccines against multiple pathogens and cancer.

cGMP manufacturing at LinKinVax relies some of downstream process and formulation adaptations specific to physicochemical properties of vaccines. We will present how we achieved large scale productions of candidates of the pipeline with GTP Bioways.

Grand Opening Coffee Break in the Exhibit Hall with Poster Viewing10:30

IMMUNOGENICITY RISK ASSESSMENT
免疫原性のリスク評価

11:15

Approaches to Immunogenicity Risk Assessment of mRNA-LNP Products

Sophie Tourdot, PhD, Immunogenicity Sciences Lead, BioMedicine Design, Pfizer Inc.

The LNP-mRNA platform is generating considerable interest in the field of immunotherapy. To date, there are no specific regulatory guidelines for the identification and mitigation of unwanted immunogenicity risk factors for LNP-mRNA products. Here, we present a strategy utilizing a suite of in vitro immunogenicity/reactogenicity assays that could be applied early in drug discovery to guide the design and optimization of LNP-mRNA therapeutics to reduce immunogenicity risk factors.

11:45

Graph-pMHC: Graph Neural Network Approach to MHC Class II Peptide Presentation and Antibody Immunogenicity

Will Thrift, PhD, Senior Artificial Intelligence Scientist, Genentech

Antigen presentation of MHC Class II plays an essential role in mediating the anti-drug response to large-molecule drugs. Such a response reduces drug efficacy and potentially causes safety concerns. Here, we develop graph-pMHC, a graph neural network approach to predict pMHCII presentation. We demonstrate that graph-pMHC dramatically outperforms other methods, such as NetMHCIIpan-4.0 (+22.84% average precision). We further create an antibody drug immunogenicity dataset from clinical trial data, and develop a method for measuring anti-antibody immunogenicity risk using pMHCII presentation models. Our approach outperforms Biophi’s Sapeins score by 7.14% ROCAUC for predicting antibody drug immunogenicity.

12:15 Accelerating Antibody Discovery for Difficult Targets through mRNA Immunization and Beacon Single Cell Technology

Francois Romagne, PhD, Scientific Director, MI-mAbs

.Despite demonstrated efficiency in antibody generation, classical immunization strategies and subsequent hybridoma generation often face strong limitations when it comes to poorly immunogenic membrane proteins with short extracellular domains. Indeed, even if a few antibodies can be obtained with repeated campaigns, only limited diversity and molecular characteristics are achieved, resulting in difficulties in selecting good candidates for pharmaceutical developments. Innovative approaches combining RNA immunization and single cell screening provide unique opportunities to dramatically speed up antibody discovery against such challenging targets. In the presentation, obtention of large collections of antibodies with both molecular and function diversity against a difficult GPCR and ion channel will be described using these strategies.

Session Break12:45

12:55 LUNCHEON PRESENTATION:Advanced Cell Line Development Platforms

David Calabrese, PhD, Senior Director Cell Line Services, CLD Department - Selexis, KBI /Selexis

Our first-in-class cell line development platforms are specially designed for the fast isolation of high-producing CHO cell lines. Strong from our expertise in mAbs and bsAbs expression, we have optimized any single stage of our workflow by using a combination of proprietary elements, high throughput characterization and cutting-edge technologies. This results in the generation of high-performance research cell banks (RCBs) in less than 11 weeks and with titers reaching up to 10g/L.

Luncheon Presentation (Sponsorship Opportunity Available) or Enjoy Lunch on Your Own13:25

Session Break13:55

IN SILICO AND MACHINE LEARNING APPROACHES TO DEVELOPABILITY AND BIOLOGICS DRUG DESIGN
開発可能性とバイオ医薬品設計向けin silico・機械学習のアプローチ

14:05

Chairperson's Remarks

Hitto Kaufmann, PhD, CSO & Senior Vice President, Pieris Pharmaceuticals GmbH

14:10 KEYNOTE PRESENTATION:

Updated Therapeutic Antibody Profiling: The Developability Risk of Antibodies with Lambda Light Chains

Charlotte M. Deane, PhD, Professor, Structural Bioinformatics, Statistics, University of Oxford; Chief Scientist, Biologics AI, Exscientia

There is a huge kappa (?) dominance amongst clinical-stage therapeutics. However, the identification of increasing numbers of epitopes preferentially engaged by lambda (? )-antibodies shows there is a functional cost to neglecting them as potential lead candidates during discovery campaigns. Here, we update our Therapeutic Antibody Profiler tool to use the latest data and machine learning-based structure prediction methods, and apply this new protocol to evaluate developability risk profiles for ?-antibodies and ?-antibodies. We provide context to the differing developability of ?- and ?-antibodies, enabling a rational approach to then incorporate more diversity into the initial pool of immunotherapeutic candidates.

14:40

Towards Biologics by Design: Computational & AI-Based Optimization of Multi-Specific Protein Therapeutics

Norbert Furtmann, PhD, Head, Computational & High-Throughput Protein Engineering, Large Molecule Research, Sanofi

Sanofi's automated high-throughput engineering platform enables the rapid generation of large panels of multi-specific antibody variants, resulting in the accumulation of big data sets. By mining these data sets, we were able to extract engineering patterns and develop AI-based virtual screening workflows to guide the exploration of vast design spaces in biologics drug discovery.

15:10

Next-Generation Biologics Engineering Platform: From Conventional Screening to Early Multiparameter Deep Characterization and Machine Learning-Based Properties Prediction

Ernst Weber, PhD, Head, Molecular Design & Engineering, Bayer AG

The presentation will focus on a new end-to-end high-throughput biologics engineering platform. It describes the generation and multiparameter characterization of large panels of biological molecules enabling short design and learning cycles. Here, we report on how we apply this new high-throughput engineering platform for parallel multiparametric optimization of protein therapeutics and how these high-quality datasets can be applied for machine learning applications.

Sponsored Presentation (Opportunity Available)15:40

Refreshment Break in the Exhibit Hall with Poster Viewing16:10

17:00

Predicting Antibody Developability Using Machine Learning

Peter M. Tessier, PhD, Albert M. Mattocks Professor, Pharmaceutical Sciences & Chemical Engineering, University of Michigan

We report a high-throughput protein engineering method for rapidly identifying antibody candidates with both low self-association and high affinity. We conjugate IgGs that strongly self-associate to quantum dots and use these conjugates to enrich yeast-displayed antibody libraries for variants with low levels of immunoconjugate binding. Deep sequencing and machine learning analysis enables identification of extremely rare variants with co-optimized levels of low self-association and high affinity.

17:30

Developability Strategy for Large Molecule Therapeutics: Integrating in silico and Wet Lab Approaches

Maniraj Bhagawati, PhD, Lab Head, Functional Characterization, Large Molecule Research, Roche pRED

Developability assessment of drugs during the discovery phase is critical to ensure the manufacturability, safety, and efficacy of selected candidates and thus improve the likelihood of clinical success. In this presentation, I will describe the developability framework at Large Molecule Research, pRED, Roche, with a special focus on assessment of molecule suitability for high concentration formulations and automation approaches for high-throughput developability analysis.

18:00

Optimisation of Antibody Developability Properties Using Deep-Learning Predictive Models

James R. Apgar, PhD, Associate Research Fellow, BioMedicine Design, Pfizer Inc.

For an antibody to be a successful therapeutic candidate many competing factors must be optimised simultaneously including desired binding affinities, good biophysical characteristics, and low immunogenicity. Here we will discuss the development of interpretable, biophysically-meaningful, deep-learning predictive models to optimised viscosity and other developability properties to accelerate the discovery and development process. These methods, along with high-throughput screening allow for rapid identification of lead molecules with good biophysical characteristics.

Welcome Reception in the Exhibit Hall with Poster Viewing18:30

Close of Optimisation and Developability Conference19:30


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