Pistoia Alliance USA Conference 2016

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Pistoia Alliance

Keynote: Sohini Chowdhury

Science

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Innovation applications of microphysiological systems (MPS) have been growing over the past decade, especially with respect to the use of complex human tissues for assessing safety of drug candidates – but broad industry adoption of MPS methods has not yet become a reality. This webinar addresses some recent advances in MPS development and begins to explore the barriers to increased incorporation of MPS to improve drug safety assessment and to provide safer, more effective drugs into the clinical pipeline.

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Federated Learning (FL) is a learning paradigm that enables collaborative learning without centralizing datasets. In this webinar, NVIDIA present the concept of FL and discuss how it can help overcome some of the barriers seen in the development of AI-based solutions for pharma, genomics and healthcare. Following the presentation, the panel debate on other elements that could drive the adoption of digital approaches more widely and help answer currently intractable science and business questions.

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It seems that AI is also becoming a buzzword, like design thinking. Everyone is talking about AI or wants to have AI, and sees all the ideas and benefits – that’s fine, but how do you get started? But what’s different now? Three innovations have finally put AI on the fast track: Big Data, with the internet and sensors everywhere; massive computing power, especially through the Cloud; and the development of breakthrough algorithms, so computers can be trained to accomplish more sophisticated tasks on their own with deep learning. If you use new technology, you need to explore and know what’s possible. With design thinking, it aids to outline the steps and define the ways in which you’re going to create the solution. Starting with mapping the customer journey, defining who will be using that service enhanced with intelligent technology, or who will benefit and gain value from it. We discuss how these two worlds are coming together, and how you get started to transform your venture with Artificial Intelligence using Design Thinking. Speaker: Claudio Mirti, Principal Solution Specialist – Data & AI, Microsoft

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Themes and objectives: To position FAIR as a key enabler to automate and accelerate R&D process workflows FAIR Implementation within the context of a use case Grounded in precise outcomes (e.g. faster and bigger science / more reuse of data to enhance value / increased ability to share data for collaboration and partnership) To make data actionable through FAIR interoperability Speakers: Mathew Woodwark,Head of Data Infrastructure and Tools, Data Science & AI, AstraZeneca Erik Schultes, International Science Coordinator, GO-FAIR Georges Heiter, Founder & CEO, Databiology

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This presentation described how data-driven chemoinformatics methods may automate much of what has historically been done by a medicinal chemist. It explored what is reasonable to expect “AI” approaches might achieve, and what is best left with a human expert. The implications of automation for the human-machine interface were explored and illustrated with examples from Bradshaw, GSK’s experimental automated design environment.

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Dr. Dennis Wang discusses possible ways to enable ML methods to be more powerful for discovery and to reduce ambiguity within translational medicine, allowing data-informed decision-making to deliver the next generation of diagnostics and therapeutics to patients quicker, at lowered costs, and at scale. The talk by Dr. Dennis Wang was followed by a panel discussion with Mr. Albert Wang, M. Eng., Head, IT Business Partner, Translational Research & Technologies, Bristol-Myers Squibb.

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With the explosion of interest in both enhanced knowledge management and open science, the past few years have seen considerable discussion about making scientific data “FAIR” — findable, accessible, interoperable, and reusable. The problem is that most scientific datasets are not FAIR. When left to their own devices, scientists do an absolutely terrible job creating the metadata that describe the experimental datasets that make their way in online repositories. The lack of standardization makes it extremely difficult for other investigators to locate relevant datasets, to re-analyse them, and to integrate those datasets with other data. The Center for Expanded Data Annotation and Retrieval (CEDAR) has the goal of enhancing the authoring of experimental metadata to make online datasets more useful to the scientific community. The CEDAR work bench for metadata management will be presented in this webinar. CEDAR illustrates the importance of semantic technology to driving open science. It also demonstrates a means for simplifying access to scientific data sets and enhancing the reuse of the data to drive new discoveries.

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The FAIR (Findable, Accessible, Interoperable and Reusable) principles aim to maximize the discovery and reuse of digital resources. Using recently developed software and metrics to assess FAIRness and supported through an ELIXIR Implementation Study, Michel worked with a subset of ELIXIR Core Data Resources to apply these technologies. In this webinar, he will discuss their approach, findings, and lessons learned towards the understanding and promotion of the FAIR principles.

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Blockchain technology can revolutionise the way information is exchanged between parties by bringing an unprecedented level of security and trust to these transactions. The technology is finding its way into multiple use cases but we are yet to see full adoption and real-world business implementation in the Healthcare industry. In this webinar we will explore the main challenges and considerations for the implementation of Blockchain technology in Healthcare use cases. This is the third webinar in our Blockchain Education series.

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In the late Fall and Winter of 2018, the Pistoia Alliance in cooperation with Elsevier and charitable organizations Cures within Reach and Mission: Cure ran a datathon aiming to find drugs suitable for treatment of childhood chronic pancreatitis, a rare disease that causes extreme suffering. The datathon resulted in identification of four candidate compounds in a short time frame of just under three months. In this webinar our speakers discuss the technologies that made this leap possible

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Creating novel drugs is an extraordinarily hard and complex problem. One of the many challenges in drug design is the sheer size of the search space for novel chemical compounds. Scientists need to find molecules that are active toward a biological target or pathway and at the same time have acceptable ADMET properties. There is now considerable research going on using various AI and ML approaches to tackle these challenges. Our distinguished speakers, Drs. Alex Tropsha and Ola Engkvist, will discuss their recent work in Drug Design involving Deep Reinforcement Learning and Neural Networks, and will answer questions from the audience on the current state of the research in the field. Speakers: Prof Alex Tropsha, Professor at University of North Carolina at Chapel Hill, USA Dr. Ola Engkvist, Associate Director at AstraZeneca R&D, Gothenburg, Sweden

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