Cambridge Healthtech Instituteでの初開催
Digital Integration in Biotherapeutic Analytics
- 5:00 pm Main Conference Registration1:00 pm
Recommended Pre-Conference Short Course2:00 pm
SC3: In silico and Machine Learning Tools for Antibody Design and Developability Predictions
*Separate registration required. See short courses page for details.
Registration and Morning Coffee7:00 am
DATA HANDLING AND CONSOLIDATION
Case Studies in Data Automation at Pfizer
In this talk, two case studies will demonstrate how Pfizer data scientists use the power of automation to analyze and understand the vast quantities of data that are generated on a daily basis. Discussion topics will include why data automation is so useful, data automation strategy, and the impact of two data automation projects that were completed at Pfizer.
Executing a Digital Strategy for BioTherapeutics Development - Data Lakes Generating Data Flow: A Journey of Standards, Systems, and Culture
This presentation will highlight Janssen BioTherapeutics development digital maturity journey. Beginning with defining a holistic program partnering with IT to build a scalable, supportable infrastructure and deliver useful tools/apps/data access to project teams. Approaches to enabling the data to flow with context will be shared with examples of key successes and opportunities ranging from structured data capture to applied data science and e-reporting.
Biotherapeutic R&D relies on a range of biological assays that inform screening, optimization, and characterization. To scale and accelerate discovery and development, manual data processing must be eliminated, by digitalizing and automating data workflows. We will present case studies in which Genedata supported Genmab, Amgen, and others in streamlining and pioneering novel approaches for the discovery, developability assessment, and production of bispecifics, multispecifics, and other biotherapeutic modalities.
Networking Coffee Break10:00 am
The Lab of the Future - Instrument and Data Flow via AutoLab
As a central interface in Roche pRED’s vision of the lab of the future, AutoLab enables seamless and automated data flow between instruments and various data sources. AutoLab increases research efficiency by providing easy-to-use digital workflows, guiding the scientists in their daily work and automating several time-consuming tasks. The digital representation of lab and data workflows supports FAIRification of data collected along the entire value chain.
Best Practices for Successful Digital Transformations
Digital Transformation of a more than 130-year-old pharmaceutical company is no small feat. Integration of well-established data infrastructure with modern tools is highly complex. An even greater challenge is changing the mindset and culture of data-generating scientists. In this talk, we will share our journey thus far from the perspective of Biotherapeutics Discovery at Boehringer Ingelheim.
Luncheon Presentation (Sponsorship Opportunity Available) or Enjoy Lunch on Your Own11:30 am
Interactive Discussions are informal, moderated discussions, allowing participants to exchange ideas and experiences and develop future collaborations around a focused topic. Each discussion will be led by a facilitator who keeps the discussion on track and the group engaged. To get the most out of this format, please come prepared to share examples from your work, be a part of a collective, problem-solving session, and participate in active idea sharing. Please visit the Interactive Discussions page on the conference website for a complete listing of topics and descriptions.
TABLE 5: Acceleration of Analytical Development by Digital Transformation - IN-PERSON ONLY
- At what stages and areas of analytical development do you see big opportunities for digital applications?
- Success stories, major challenges and your solutions
- Types of modeling applied for analytical development and the value they bring
- Different needs for different modalities
TABLE 6: Launching Digitalization Initiatives in Pharma - IN-PERSON ONLY
- Current state assessments: what are we doing and how - and what aren’t we doing
- Data capture and standards: pain points and opportunities (ELN, systems, instruments & context)
- Setting digital goals: what has worked and at what levels of detail
- User requirements: best approaches for science and engineering
- Data flow end user experiences: good or bad and lessons learned
Session Break1:30 pm
IMPLEMENTATION AND ORGANIZATION
Implementation Challenges: Staffing, IT/Data Landscape, and Change Management
The success or failure of projects centered around digital transformation can be determined overall by an organization’s vision, alignment, resources, and capabilities. Focusing specifically on digital integration in busy research laboratories, we’ll explore how these themes manifest and interact to shape the outcomes and impact of projects.
Implementation of Data Science and Digital Applications in Analytical Development
Data science and digital applications are being more widely used nowadays to accelerate analytical development. In this talk, a few case studies will be shared to show how CMC statisticians, data scientists, analytical scientists, and IT partners work together to implement solutions that help gain deeper insights, quantify risks, enable quality-by-design and data-driven decisions, and improve efficiency.
Networking Refreshment Break3:20 pm
Transition to Plenary Keynote Session3:50 pm
PLENARY KEYNOTE SESSION
Advances in CAR T Therapy
Carl H. June, MD, Richard W. Vague Professor in Immunotherapy; Professor of Medicine; Director, Center for Cellular Immunotherapies; Director, Parker Institute for Cancer Immunotherapy, University of Pennsylvania Perelman School of Medicine
Advances in the understanding of basic immunology have ushered in two major approaches for cancer therapy over the past 10 years. The first is checkpoint therapy to augment the function of the natural immune system. The second uses the emerging discipline of synthetic biology and the tools of molecular biology and genome engineering to create new forms of engineered cells with enhanced functionalities. The emergence of synthetic biology approaches for cellular engineering provides a broadly expanded set of tools for programming immune cells for enhanced function. Barriers to therapy of solid tumors will be discussed.
The Next Frontier in Machine Learning and Biologics: "Lab in a Loop" Large Molecule Drug Discovery, From Optimization to de novo Discovery
A key opportunity in applying machine learning to augment biologic drug discovery and development is through constant iteration - a process we call "lab in a loop." By developing integrated methods for optimizing affinity and multiple developability parameters, as well as a close integration of antibody engineering, machine learning, and structural biology, we have the potential to more rapidly identify and test novel candidate molecules. Sophisticated machine learning frameworks allow us to integrate later stages of optimization into the earliest stages of discovery, while high-throughput experimental systems allow rapid improvement of all methods and molecules. This process starts with the integration of people and scientific culture and ends with tightly integrated computational and experimental systems.
Welcome Reception in the Exhibit Hall with Poster Viewing5:40 pm
PEGS BOSTON COMMON: YOUNG SCIENTIST MEET UP
Young Scientist Meet Up - IN-PERSON ONLY
The young scientist meet up is an opportunity for scientists entering the field to develop connections across institutions, and for established leaders to come build relationships with the next generation of scientists. The meet-up will pave the way for mentorships, professional opportunities, and scientific discovery.
- Get to know fellow peers and colleagues
- Make connections and network with other institutions
- Inspire others and be inspired!
Close of Day7:00 pm
Registration and Morning Coffee8:00 am
MODELING APPLICATIONS AND IMPACTS
Integration with Discovery Stage Computational Models and Machine Learning
Model-based prediction of biologics developability will increase speed to clinic. Previous program data can be used to train models but requires data quality control and compensation for biased sampling of sequence space. In my talk, I will describe how we incorporated historical data using data quality control protocols and used sequence similarity clustering to improve prediction of critical quality attributes of monoclonal antibodies.
Integration of Process Analytical Technology and Model Predictive Control for Bioprocessing
As Process Analytical Technology (PAT) matures and becomes more robust, it offers more incentive for companies to integrate PAT into real-time processing. Additionally, when PAT is implemented, it opens up additional opportunity for companies to explore higher level of control. In this presentation, we will share a case study of how PAT can be integrated with Model Predictive Control to improve bioprocess efficiency.
Adapting Antibody Developability Assays to Machine Learning
In silico assessment of antibody developability has the potential to speed up antibody development, especially lead optimization. However, advances in computational tools such as machine learning are often limited by the lack of suitable training data sets of high quality. Here, I present improvements of common antibody developability assays, e.g., AC-SINS, with the aim of enabling optimal data for modelling. Important parameters like dynamic range, calibration, and data processing are discussed.
Despite the promises of Biopharma 4.0, most organizations struggle with tools and platforms that create data silos, integrity risks from manual manipulation, and difficulty aligning data for analysis and modelling. Simply digitizing and consolidating data is not enough, unlocking insight requires a different strategy. We discuss how a holistic contextualized data backbone, linked to an advanced analytics engine, can expedite process understanding for faster decisions and efficient knowledge transfer.
Coffee Break in the Exhibit Hall with Poster Viewing10:30 am
PROBLEMS AND SOLUTIONS
Introduction to Ontology-Based Standards for Biopharmaceutical Manufacturing
This talk will address the benefits of utilizing smart data models (ontologies) in transitioning to Biopharma 4.0. First, a comparison between ontologies and traditional data standards will be given. Next, success stories of utilizing ontologies will be outlined, along with the most prominent ontologies in the field. Finally, the application of ontologies to two critical use cases will be demonstrated: cross-domain data integration and improvements in data-driven and hybrid modeling.
Empowering Analytical Scientists in the Digital Age
With Pharma 4.0, the digital revolution, analytical scientists have come to expect access to their scientific information in much the same way as they do with their personal devices. More than just accessing data, scientist want to use their information to gain insights, build applications, and make their work empowered by the very information they generate. To meet this demand pharma companies will need to overcome challenges in data access, making their information FAIR, and developing the skills necessary to accelerate their science in the digital age.
Structured Content and Data Management to Enable Digital Pharmaceutical Development and Automated Dossier Authoring
Throughout years of cross-functional collaboration, research and development of each pharmaceutical candidate accumulates tremendous amounts of data. However, due to the segregation and heterogeneity of these data, knowledge extraction has been tedious and limited while dossier authoring becomes an excruciating exercise. Enterprise data lake and ontology enabled data curation and semantic transformation enables structured content and data management that democratizes scientific data, enabling deep data analytics and automated dossier authoring.
Luncheon Presentation (Sponsorship Opportunity Available) or Enjoy Lunch on Your Own12:40 pm
Close of Digital Integration in Biotherapeutic Analytics Conference1:40 pm
Recommended Dinner Short Course6:30 pm
SC5: Introduction to Gene Therapy Product Manufacturing and Analytics
*Separate registration required. See short courses page for details.