Think Tank, dGen, releases a new report entitled, “AI, Privacy, and Genomics: The Next Era of Drug Design”. It tackles the issue of privacy and access to genetic data for companies using AI to speed up and improve drug design.
Where the average drug today takes 10-12 years and cost $2 billion, Covid-19 forced this timeline to 12-18 months. Whether or not the cure will be delivered in the next six months remains unclear.
The bottom line is that more companies need access to more genetic data. We spoke with industry leaders from Aidence, Gero, Iktos, Alphanosos, e-Estonia, Qunatlib, Turbine, and more.
With a blockchain-based access network, our top predictions for 2030 are:
● Better collaboration networks will emerge.
● Genetic privacy laws will be overhauled.
● AI will become a fundamental part of drug discovery.
● Pharmaceutical giants won’t be toppled, but they won’t get out unscathed as biotech startups take the field.
Genetic information is central to many AI-enabled drug discovery startups. To expand this innovation, several issues with genetic data must first be resolved:
● secure storage
● availability to multiple research parties.
Ultimately, many privacy-preserving technologies leave the issue of ownership and auditing this system undisturbed. We propose a blockchain-based, decentralised pan-European biobank network to make information available to researchers, but log all access requests. This would also empower individuals to grant or deny these requests and track the use of their information.
Maxim Kholin, Gero Co-Founder
‘We believe that AI can accelerate the drug discovery process by proper understanding of human diseases from large biomedical data. The data-driven approach should help establish the genetic determinants and molecular markers of the disease’.
Pascal Mayer, Alphanosos Founder
‘While currently working really well on bacteria, we are confident [AI-enabled plant-based drug discovery] shall be successful in fighting viruses as well’.
‘Using edible plant extracts, like we do, in the development process of drugs, the risks of side effects are quite low[…] possibilities 10^10 or 10^30 [are available]. This is such a huge number that we use AI to quickly drive us through these possibilities’.
Tamás Török, Turbine Head of Business Development
‘Turbine is able to identify novel molecular targets to overcome the disease, and precisely select patients for whom the therapies will work best. Turbine’s Simulated Cell platform therefore generates novel biological knowledge through simulations rather than mining available biological data’.
Florian Marcus, e-Estonia Speaker
‘The patient will then see in the logbook, that this particular doctor looked at this particular patient dataset at this time for this and that reason. This can be challenged in court […] when the system was introduced, some doctors lost their licence over it’.
‘This logic of a rights-based access system is fundamental to the operation of e-Estonia, as is the notion of truth-by-design so I can always see who checked my data and hold them to account’.
Image source: dGen. Org