Cambridge Healthtech Institute’s Inaugural

Artificial Intelligence for Drug Discovery & Development
(Drug Discovery와 개발을 위한 인공지능)

약제 설계부터 임상시험까지의 응용 분야

2020년 3월 19일

 

Drug Discovery와 개발을 위한 인공지능(AI)을 테마로 한 이 심포지엄에서는 조기 단계의 Drug Discovery, 약물대사와 약물동태(DMPK)/독성의 연구, 임상시험 관리 등 의약품 개발 파이프라인상에서의 작업에 다양한 형태로 관련되어 있는 연구자 및 임상의, 기업 임원이 한자리에 모여 AI, 기계학습(ML), 데이터 마이닝 등 최근 이용이 확대되고 있는 분석 기술에 대해 의견을 교환합니다. 또한 심포지엄에서는 Drug Discovery 및 개발 분야에서의 AI 이용 현황이 소개되며, 사례 연구 및 조사 결과와 함께 첨단 개념의 응용에도 초점을 맞출 예정입니다.


Final Agenda

3월 18일(수)

Recommended Short Course

4:40 pm Dinner Short Course Registration*

5:00 - 8:00 SC4: Gene Editing for Targeted Therapies

Instructors:

Clifford Steer, MD, Professor of Medicine and Genetics, Cell Biology, and Development; Director, Molecular Gastroenterology Program, University of Minnesota Medical School

Khalid Shah, MS, PhD, Director, Center for Stem Cell Therapies and Imaging, Harvard Medical School; Vice Chair of Research, Brigham and Women’s Hospital

Additional Instructors to be Announced

*Separate registration required.

3월 19일(목)

7:30 am Registration and Morning Coffee

의약품 개발에서 보다 나은 예측을 위한 AI 활용

8:15 Welcome Remarks from Conference Director

Tanuja Koppal, PhD, Senior Conference Director, Cambridge Healthtech Institute

8:25 Chairperson’s Opening Remarks

Shruthi Bharadwaj, PhD, Senior Scientist, Novartis Oncology Precision Medicine

8:30 Bringing Precision Drugs to the Clinic Faster Using Artificial Intelligence and Data Science

Olivier Elemento, PhD, Director, The Caryl and Israel Englander Institute for Precision Medicine; Associate Director, Institute for Computational Biomedicine, Weill Cornell Medicine

We have developed novel genomic assays and analytical tools for precision medicine that are being used routinely for personalized medicine for a variety of Weill Cornell patients. We also have developed AI predictive models for improving how drugs are developed, from prediction of mechanisms-of-action to prediction of drug safety, prediction of indication for drug repositioning and predicting effective drug combinations.

9:00 Explainable AI for Data-Driven Medicine: From Data to Models and Treatments

Igor Jurisica, PhD, DrSc, Senior Scientist, Krembil Research Institute; Professor, Medical Biophysics, University of Toronto

To fathom complex diseases, we need to systematically integrate diverse data and link them using relevant annotations and relationships. Graph theory, data mining, machine learning and visualization enables data-driven modeling and precision medicine. Here, we highlight integrative computational biology and AI that help building explainable models, identifying prognostic and predictive signatures, re-positioning existing drugs for novel use, unraveling mechanism of action for therapeutics, and prioritizing them based on predicted toxicity.

9:30 Leveraging Image-Derived Phenotypic Measurements for Drug-Target Interaction Predictions

Arvind Rao, PhD, Associate Professor, Department of Computational Medicine and Bioinformatics, University of Michigan

We propose a novel in silico drug discovery approach to identify kinase targets that impinge on nuclear receptor signaling with data generated using high-content analysis (HCA). Using imaging-derived descriptors, we provide prediction results of drug-kinase-target interactions based on single-task learning, multi-task learning, and collaborative filtering methods. Our promising results suggest that imaging-based information can be used as an additional source of information to existing virtual screening methods, thereby making the drug discovery process more time and cost efficient.

10:00 Networking Coffee Break

10:30 AI-Based Method for Predicting and Validating Therapeutic Peptides

Paul Rohricht, MS, MBA, Chief Business Officer – Pharma, Nuritas Corporation

We are a drug discovery company that uses AI to accelerate the identification of bioactive peptides across multiple therapeutic areas. Whereas current pharma drug discovery takes years and has seen several late-stage failures in the clinic, Nuritas’ platform takes months to deliver molecules that have a high success rate (>60% predicted). We are currently addressing several indications across multiple therapeutic areas, including inflammation, diabetes, muscle health, anti-aging, hypertension, and anti-microbials. Efforts to expand into other therapeutic areas will continue, both internally and through external collaborations.

11:00 AI and ML Approaches for Clinical Trials

Shruthi Bharadwaj, PhD, Senior Scientist, Novartis Oncology Precision Medicine

With the increase in availability of clinical trial data, AI and machine learning approaches are becoming imperative in mining and finding clinically significant insights. In this talk, I will provide an overview of the various approaches currently used to tackle the big-data problem in pharma.

11:30 Sponsored Presentation (Opportunity Available)

12:00 pm Luncheon Presentation (Sponsorship Opportunity Available) or Enjoy Lunch on Your Own

12:30 Session Break

신요법 개발을 위한 AI

1:15 Chairperson’s Remarks

Barun Bhhatarai, PhD, Investigator, Novartis Institute for Biomedical Research

1:20 PANEL DISCUSSION: How Is AI/ML Addressing Real-World Healthcare Problems?

Moderator: Michael Liebman, PhD, Managing Director, IPQ Analytics, LLC

Panelists:

Mary Jo Lamberti, PhD, Research Assistant Professor; Associate Director of Sponsored Research, Tufts Center for the Study of Drug Development

Debbie Lin, PhD, Executive Director, Venture Fund Digital Health, Boehringer-Ingelheim

Michael Montgomery, MD, Former Global Head of Medical Affairs, Incyte Pharmaceuticals

The methodologies of AI (e.g., machine learning, deep learning) are increasingly focused on healthcare to provide analysis with the goal of identifying critical relationships that can enhance clinical decision-making (e.g., diagnosis and treatment) and drug development. The availability of big data, however, may enable the application of these methods, but we must evaluate if the results actually address the clinical questions that exist in real-world medicine and in real-world patients.

2:20 Networking Refreshment Break

2:40 ML and AI on ADME/Tox-Accelerating Drug Discovery

Barun Bhhatarai, PhD, Investigator, Novartis Institute for Biomedical Research

This talk will focus on the application of ML and AI approaches to accelerate drug discovery in ADME/Tox with some case studies and the spirited path traditional pharma has to navigate aiming towards the end goal.

3:10 Talk Title to be Announced

Peter Hagedorn, Senior Principal Scientist and Team Leader of Bioinformatics and RNA Biology, Roche Innovation Center Copenhagen

3:40 Accelerating Research in Rare Disease through Patient-Partnered Collaborations

Ryan Leung, Vice President, Strategy & Corporate Development, Research to the People

Patient-centricity is becoming increasingly important in all areas of healthcare, particularly in rare diseases. With so few patients, it is critical that we make the most out of every patients’ story and experience, engaging them at every point of research, development, care, and treatment. Leveraging advances in -omics, bioinformatics, deep learning, and cloud computing, we partner with patients directly to help them access and understand their health data while creating new opportunities for rare disease research. With 5 successful collaborations to date, we’ll share our thoughts on the impact and potential of patient-partnered research.

4:10 Close of Symposium

* 주최측 사정에 따라 사전 예고없이 프로그램이 변경될 수 있습니다.

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