The 2020 Conference was held as an online event on the theme of ‘Can Big Data Replace Animal Testing?‘.
Speakers included winners from this and previous years. You can download the conference brochure and watch videos of the sessions below.
Session 1: Can big data replace animal testing?
- 00:15 – Dr Tim Allen, MIE Atlas Team, Cambridge University, UK: In Silico Models to Predict Human Molecular Initiating Events
- 15:25 – Dr Domenico Gadelata, Laboratory of Environmental Chemistry and Toxicology, Istituto di Ricerche Farmacologiche Mario Negri, Italy: Presentation: Modeling of Molecular Initiating Events for Large-Scale Assessment of Chemical Toxicity
- 30:05 – Prof Hao Zhu, The Rutgers Center for Computational & Integrative Biology, USA: Non-animal models for animal toxicity evaluations: applying data-driven profiling and read-across
Fireside Chat with TPI Helpathon
Helpathon run 2-day workshops for scientists who are open to exploring ways of replacing animal use in their research.
Session 2: Big data, animal testing and Covid-19
- 02:07 – Rebecca Ram, Safer Medicines Trust, UK: Participation in the COVID-19 Adverse Outcome Pathway (CIAO) Project
- 18:47 – Prof Thomas Hartung, Centre for Alternatives to Animal Testing, John Hopkins University, USA: The pandemic and alternatives – a panacea for each other?
- 35:50 – Dr Jyotika Varshney, VeriSIM life, USA: BIOiSIM: Next-generation AI/ML-based Hybrid Modeling to significantly reduce reliance on animal models for Preclinical/Clinical Drug Development
- 52:55 – Questions & Answers
Fireside Chat with SOKO Tierschutz
Lush Prize Judge Nick Jukes interviews Friedrich Mülln from SOKO Tierschutz about the organisation’s undercover investigations in animal research laboratories.
Session 3: The Role of Big Data in Next Generation Risk Assessment (NGRA)
- 00:50 – Dr Chloé Raffalli, Toxicologist, Lush Cosmetics, UK: Advancing Animal-free Safety assessment Collaboration overview
- 14:45 – Edoardo Carnesecchi, Institute for Risk Assessment Sciences, Utrecht University: Towards the development of innovative Quantitative Structure-Activity Relationship models for human and ecological risk assessment of emerging contaminants and their mixtures
- 32:56 – Dr Vinicius Alves, National Institute of Environmental Health Sciences: STopTox: An in-silico platform as an alternative to animal testing for acute Systemic and TOPical TOXicity
- 54:00 – Questions & Answers
Fireside Chat with Environment & Animal Society of Taiwan
Hilary Jones, Ethics Director of Lush, speaks to Wu Hung of Environment & Animal Society of Taiwan (EAST)
Session 4: Paving the Way From Big Data to Regulatory Acceptance
- 00:48 – Clemens Wittwehr, European Commission Joint Research Centre, Italy: The triangle of chemical safety – and how it will boost regulatory acceptance of mechanistic data
- 15:35 – Kristie Sullivan, Physicians Committee for Responsible Medicine, USA: It Takes a Village – The Role of NGOs in Regulatory Acceptance of NAMs
- 32:30 – Dr Nicole Kleinstreuer, NTP Interagency Center for the Evaluation of Alternative Toxicological Methods, USA: From the 3Rs to the 4Cs: Communication, Collaboration, Commitment, and Confidence
- 53:05 – Q&A
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.
This animation looks at the role of big data (computational toxicology) in replacing animal testing. The film was made specially for the Lush Prize 2020 Awards Ceremony.
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.
2018 Lush Prize Conference on the theme ‘Is there an end in sight for animal testing? Can Organ-on-a-Chip replace animal use in safety testing with advanced human focused approaches?’
2016 Lush Prize conference on the theme ‘Regulating Chemical Safety – the future for animal use’.
2015 Lush Prize Conference on the theme ‘Adverse Outcome Pathways – What, How and Where Next?’.
2014 Lush Prize conference on the theme ‘Is One R the new Three Rs?’.
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