Interview with Intelligencia: "Take the risk out of clinical drug development"

Vangelis Vergetis

1. Can you please describe what Intelligencia does in a few sentences?

In a nutshell, we use Machine Learning on a vast amount of proprietary data in order to assess and reduce the risk clinical development. To make this a bit more tangible, we help answer two questions: First, how likely is it that my program (say, for example, a PD-1 / IDO combo for melanoma) will receive regulatory approval? And second, what can I do (for example, as I think of different trial design options) to reduce any risk and therefore maximize that probability?


Separately, we also help our clients predict the specific areas of science that are likely to produce the next generation of innovative drugs. If we are to place ourselves back in 2000 or so, the question we are answering is how could one predict that immunotherapy would be the next wave of innovative drugs for cancer, or PCSK9 would lead to innovations in cardiovascular disease?


2. What is the disruptive / innovative part that Intelligencia brings to the Healthcare industry?

In one word: "Moneyball" - for anyone who has watched the movie with Brad Pitt about using data to build a winning baseball team. In a similar way, we combine carefully curated data (biological, clinical, genomic, etc.) together with AI in order to revolutionize how drug developers think about and assess the risk of their pipelines and specific development programs. 



3. What is your strongest value-proposition / use-case to a pharmaceutical company?

An incredibly accurate data-driven methodology to consistently assess risk across (i) all internal development programs, and (ii) any external programs that are considered from a Business Development point of view.



4. What is your biggest challenge at the moment?

Augmenting how pharmaceutical and biotechnology companies make portfolio and BD decisions is never easy. As complex as our Machine Learning models are, the technology is perhaps the easier part. The hardest part is to incorporate AI-driven decision-making tools into the current workflows of R&D and Business Development teams.



5. When you are recruiting for leadership roles in a startup, what capabilities and characteristics are you looking for in a candidate?

We can spend a lot of time on this. But, at the risk of generalizing a bit, perhaps one way to describe it is more emphasis on "attitude" than on "aptitude". Of course, anyone we would recruit for, say, a data science role, needs to pass a fairly high bar on technical expertise. But the key here is to recruit someone who has a can-do attitude and wants to "build stuff". The metabolic rate of a startup is incredibly fast, and it needs people that are constantly looking not just for good ideas, but for practical ways to make these ideas into software products and tools that people can use.

If you want to learn more about Intelligencia or get in touch, please reach out to or

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