Q&A with Dr. Dan Foxler, Principal Scientist @ LabGenius

We caught up with Dr. Dan Foxler to learn more about how he has been getting on in his new role as Principal Scientist and hear about his vision for the future of machine learning-driven drug discovery.

Back in January, we welcomed Dr. Dan Foxler to our team as a Principal Scientist. Dan joins us from Adaptate Biotherapeutics, where he was a Senior Scientist. With over 5 years of immunotherapy discovery experience and over 10 years of biologics screening experience, Dan will lead LabGenius’ Functional Assay Team to design, plan and execute research experiments to deliver our ambitious internal and partnered programmes.

Let’s start with an easy one, can you tell us a bit more about your career to date and what first attracted you to the role at LabGenius?

After completing my PhD, I spent six years as a postdoc at Barts Cancer Institute, London. It was here that my interest and experience in biologics screening began, with a particular focus on the molecular mechanisms of a lung tumor suppressor gene whilst also forming early access industrial collaborations.

I then moved to a cell therapy company, GammaDelta Therapeutics. Here, I developed and executed various immuno-assays that enabled progression of a compound to preclinical/IND phase.

Next, I took on a similar role at an antibody therapeutics company called Adaptate Biotherapeutics, where I developed assays to progress the lead asset from discovery through to pre-clinical development.

I was first attracted to the role at LabGenius as it presented the perfect opportunity for me to combine my assay development and immunology therapeutic skill sets. In doing so, I have the privilege of leading a fantastic team, who, together with machine learning (ML) guidance and expertise, have the potential to accelerate the discovery and optimization of advanced therapeutics.

How have you found the first couple of months in the role, and what has been your main focus?

The first couple of months have been an exciting learning curve. My time has been spent getting up to speed with both current and future projects, whilst also understanding the true potential of ML to improve the traditional wet lab discovery process.

My main focus has been on identifying and building the capacity, capabilities and people that the Assay Team needs to deliver robust, ML-grade data. This functional data is used to train computational models to make predictions about potential future drugs.

Can you tell us about the part your role plays in achieving our mission?

To maximize the development and impact of ML, it’s crucial that we feed only the highest quality wet lab data into our data driven models. In order to do this, all assays need to be fully developed and validated by the expertise of an interdisciplinary team.

By leading the team that produces the data, I can ensure that as a company we utilize machine intelligence to its full potential, such that we can develop a pipeline to expedite the discovery of next-generation therapeutic antibodies.

Where do you see LabGenius four years from now and what excites you about the company’s future?

Four years from now I hope to see one (or more!) new immunotherapeutics in clinical trials that have come from LabGenius’ internal pipeline, with a stream of other drugs behind, ranging from discovery to pre-clinical development stages.

What excites me about the company’s future is that by building a high throughput pipeline that encompasses ML, expression and functional validation of new drugs, it will enable us to reduce the timeline from discovery to clinic; a process that is conventionally slow and expensive.

I’m also looking forward to seeing the company scale, and take on new partnerships — it’s through these partnerships that we will have the opportunity to demonstrate our platform capabilities and showcase EVA™’s full potential.

Where do you think machine intelligence* will have the biggest impact on the discovery and development of new drugs?

In my opinion, the most significant impact of ML will be on the discovery and development of novel drugs by accelerating the initial screening phase to identify lead compounds faster. By predicting and accurately pinpointing the top performing drug sequences to test, we can reduce, and ultimately avoid, the uncertain and repetitive nature of multiple drug screening rounds.

Concurrent to this, machine intelligence should also reduce the failure rate of lead molecules at candidate and lead selection stages. If we can predict the relationship between drug sequence and function , then we can reduce the number of lead drug candidates that fail at the later stages due to safety issues.

Do you have plans to grow your team?

With the development of our internal pipeline and the introduction of new exciting projects, we are continually expanding our in-house capabilities and team capacity to deliver on multiple projects simultaneously.

I have twice joined biotech companies at the early stage of drug discovery and it’s hugely rewarding to look back to see how a drug has progressed from discovery to clinic. With new drug discovery projects coming online at LabGenius, this is a very exciting time for Scientists of all levels to join the team!

Thank you for your insights Dan, and welcome to the team!

To finish, we asked our CSO, Dr. Gino Van Heeke, to comment on the importance and impact of the Principal Scientist role at LabGenius.

“The role of a Principal Scientist is integral to the smooth delivery of our ambitious internal and partnered programmes. In this case, Dan provides his team with continued expert counsel, advocating a drug discovery mindset in project design and execution to drive and maintain high standards for data generation.

We’re always on the lookout for talented scientists to join our discovery team who are passionate about making a positive impact on the world by accelerating the discovery of advanced medicines.”

We’re hiring Principal Scientists amongst many other roles in our Discovery Team. If you would like to learn more about the open roles at LabGenius, visit our careers page.

*At LabGenius, we run the full design-build-test-learn cycle for antibody discovery. This requires a host of technology in various forms, so we use the term ‘Machine Intelligence’ to encompass the fields of software development, automation and data science.