Lush Prize 2020 Conference and Awards Ceremony
We are delighted to announce the Lush Prize 2020 Conference and Awards Ceremony.
Delayed from May due to the Covid-19 pandemic, the events will now be held online over two days, on Wednesday 11 and Thursday 12 November 2020.
The great news is that more people can virtually attend from anywhere in the world. The Conference is being held over two days to ensure more people from across differing time zones can participate.
The Conference runs 1pm – 3.40pm (UK time) on both days. The theme is ‘Can Big Data Replace Animal Testing?‘ (see below for more details), and will include discussions on ‘Paving the way from big data to regulatory acceptance’, and ‘The role of big data in next generation risk assessment’. Speakers will include winners from this year and previous years.
The Awards Ceremony will be at 4pm – 5pm (UK time) on Wednesday 11th, where we will be celebrating all of the winning and commended projects from this year and awarding a total £250,000 prize money to scientists and campaigners from around the world to support their initiatives to end animal testing.
Free Registration for the Conference and Awards
For free attendance to the Conference, sign up here on Eventbrite and you will receive more details closer to the event.
We will announce registration details for the Awards ceremony soon.
How can big data replace animal testing?
The 2020 Lush Prize Conference will include presentations on big data related approaches, as well as discussion of their critical importance to Covid-19 research.
Big data is the term given to the process of extracting and analysing large quantities of data. This could be from large ‘platforms’ which may be open access or specific data libraries. Cutting edge advances in artificial intelligence (AI) and machine learning aid big data analysis in new ways.
Big data is used in many ways, not only for science, but business, finance or technology (e.g. cloud computing) and the ‘mining’ of big data can find predictive trends or patterns to a far higher quality and speed, where other methods fail.
Animal tests are still considered by many to be the ‘gold standard’ for the safety testing of chemicals. However, machine learning of toxicity test databases allows vast quantities of data to be read and compared using computational modelling to make predictions of chemical safety (a process known as ‘read across’).
This not only improves quality and reliability, but is shown to outperform toxicity tests in animals, which are known to have a limited ability to predict human safety to test substances. Comparing data for many known substances has also demonstrated the high reliability of the computer models used.
Big data analysis has the potential to replace animal use not only in chemicals testing, but disease research too. For example, the mining of biological data, genetic sequences and data from human populations has far greater capacity to investigate and understand disease, providing not only more human relevant – but individual patient relevant – information than animal models, which remain severely limited in their ability to replicate human disease.