Antiverse: Taking drug development from 18 months to 18 hours.
We first met Rowina whilst running a workshop at Imperial’s incubator on exploring the potential of personalised medicine. With a background in cell free synthesis systems for high yield protein expression she seemed perfect for DSV. One problem, she was passionate about changing the model of drug development but it just seemed too big all her ideas were around small businesses such as meditation training. We decided to invite her to join as a part time Exec Fellow and see if we could gradually work her around to focusing on something a little more game changing, turns out that didn’t require much encouragement at all!
Next came Murat, he applied directly after seeing the write up in New Scientists, he had worked as a lab automation programme for 11 years and had initially intended to join as an Exec Fellow but when his employer wouldn’t allow that he quit to join DSV as the only full time member of the team.
The final member of the team is Ben, a mechanical engineer by training who had previously taken easily adjustable eye glasses from concept to manufacture and since taken a career break to pursue machine learning whilst raising his son.
The team didn’t hit it off right away, instead exploring other ideas with other members of the cohort including distributed cloud labs using dormant equipment, an integrated synbio development environment, cell activity simulation, parasitic worms as a treatment, predicting the response of the microbiome to particular drugs and reducing the impact of cytokine storms during CAR-T cell therapy.
However, after around a month it was clear that they were all equally passionate about chipping away at the insane time and cost it takes to develop a new treatment (6–10 years and $2.6bn). Not because it’s particularly compelling to save Pharma companies money, but because if you can make the processes quicker and cheaper it vastly increases the statistical chance of developing effective treatments.
The team undertook a sprint to examine the drug development processes and identified that antibody development sits at the base of around 50% of treatments and is increasing in importance year on year. Moreover it is still conducted largely by exposing animals to the antigen and an arduous processes of filtering and scaling up quantities of the antibody over around 18 months and at a cost of £300–500k per candidate.
The idea for the underlying technology to replace this process started to come together within the next couple of weeks. A very rough proof of concept showed that it could provide results that equaled those of traditional methods in just a few hours vs. the current 18 months. With this data the team quickly found traction with major Contract Research Organisations who handle around 60% of the antibody development for large pharma companies.
Then disaster struck, it was clear that under the initial model they would be giving all the value away making it unsustainable, moreover it was beginning to look incredibly difficult to raise money for what is predominately software in the field of pharma (more on that madness in another post).
As ever in startups, what initially appeared to be a disaster turned out to be the window to a much larger opportunity and the company has now pivoting to become an early stage drug development company. The team are currently building out the core technology, working up partnerships and raising investment.