Pistoia Alliance conference April 2016: Robotics: Steve Rees

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The document discusses the impact of robotics on drug discovery. It notes that automation allows researchers to do existing work at lower cost and higher quality, and also enables new processes that were previously not possible. Specifically, automation has helped scale up sample management and compound screening processes. Advances in robotics, miniaturization, and other technologies have been driven by factors like the omics revolution and increased target knowledge from structural biology. However, challenges for the future include managing large data sets and training scientists to work with robotic systems.

Science

An early example of drug discovery automation
2

Why use automation?
3
• Allows you to do something
you do today at reduced
cost, higher quality
• Allows you to do something
that you could not do before

Automation in Drug Discovery
Sample Management Compound Screening
4
Allow the application of manufacturing standards for research studies

Drug Discovery Automation
5
Drivers for change
Only possible through advances in automation
-omics revolution
leads to explosion of
targets within
stratified patient
populations and
identification of
clinical biomarkers
Structural Biology,
pathway mapping
and HPC
revolutionize target
knowledge
Cheminformatics
capability allows
virtual screening of
all chemical space
Miniaturisation and
automation of
medicinal chemistry

‘Make-Test’ Chemistry labsLabs
2015 2020
• all activities in a single lab
IMED Biotech Unit I Discovery Sciences6
• many manual steps & a long
journey from ‘make to test’
Mölndal 

A toddler can make an iPhone do things; my
teenage daughters can really use an iPhone, I use
it as a phone
• Ability to store, structure, annotate and mine
huge data sets to derive knowledge
• Machine learning
• Scientist training and skill sets
• Human robot interaction
Future Challenges
7 IMED Biotech Unit I Option to include TA or IMED function

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8

https://youtu.be/C95pl11zdAs Many countries around the world are starting to organise national health data research networks, investing hundreds of millions of dollars, euros, yuan, pounds, etc. in such initiatives. Some of these are organized by the government, such as the NIH All of Us Program in the U.S. and Genomics England, others are started by businesses such as PatientsLikeMe or by patient organizations such as DuchenneConnect. And then there are research-driven infrastructures such as ELIXIR and BBMRI and multi-stakeholder initiatives such as PCORI and HealthRI. During this webinar, we are going to discuss the current technical practice of establishing health data research infrastructures, our experience in building them or advising on that, and the key elements of success that one should not overlook in order to build a healthy, long-lasting health data research network.

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Pistoia Alliance conference April 2016: Robotics: Steve Rees