I understand you have been developing new technology platforms. How will these be integrated into the wider life sciences industry?

DesignPharma is a drug discovery platform company which focuses its development pipeline on G-protein coupled receptors (GPCR). These GPCRs influence most physiological activities (sight, smell, taste, etc.) and are the prime targets for drug discovery programs. Nearly 30% of all therapeutics sales which have been introduced to the market, and about 34% of all FDA approved medicines, are GPCR based. Although these receptors are involved across many disease areas, science as a whole knows relatively very little information about them: so our mission at DesignPharma is to unravel their secrets. 

The technology platforms we have innovated allow us to explore both the physical drug target and the drug compound spaces together in an unprecedentedly short time frame and depth. This milestone record is the result of next-generation-sequencing and droplet-microfluidics - which will allow us to run millions of chemical or cellular reactions in parallel. Artificial intelligence complements our work as we generate enormous amounts of high-quality data: we are able to run 100,000 compounds and more on a subset of the most interesting 300 GPCR drug targets concurrently. This process not only provides higher quality data, but also improves the depth of the information we can generate. 

Is AI technology disrupting the life sciences industry? What is your attitude towards AI?

I have worked in both life sciences research and industry for over 30 years, and I was surprised when even now, I widely observe the reluctance towards using AI to generate data for drug discovery and development processes. However, I believe this is a natural response to any novel technology, and that’s one reason why I believe Design Pharma is poised for unique success. The public wants to see instant results, but science takes a lot of work which, ultimately, is paying off, - we’ve seen this throughout the Covid pandemic as one example. 

During those years, the decision-making process had to adapt to the wealth of new information about COVID - which the scientific community learned within an intense timeframe - however these new approaches and attitudes saved lives. In our case, AI technologies are helping with the initial process of drug discovery, by showing us which are the best ways to select the most suitable candidate molecules or antibodies, and doing this in a highly standardized way to generate in depth data. As a result, our current footprint of the GPCR multiplex platform for data generation is 300 times higher than any other platform. 

Traditionally, drug compound screening is limited by the size of the chemical libraries that are screened, as well as by the throughput. For very complex libraries, typically, pharma companies attempt to utilize an in vitro-expressed drug target, so as to look for what binds to it, but this process is variable. The stringency of the conditions, and the binding itself does not necessarily mean there exists physiological activity. 

Our approach at Design Pharma is different: we are always looking at the physiological activity because we are working with cell lines as a readout system for the biological drug target.

With the help of our microfluidics platform, we will be able to even screen very complex libraries for drug function as we generate 10,000 droplets in a second, and these then encapsulate drug target cells. Later, these are merged with DNA encoded libraries or biologics libraries which contain millions of different compounds. In conclusion, I am certain that the new technologies we work on here will be the first to truly deliver on the propositions enabled by AI in the life sciences field.

Could you talk a little about your vertical pipeline, your relationship with big Pharma and expansion?

Last year, we emerged from stealth mode. We are now validating our platform in collaboration with other pharma companies. In late 2022, we concluded a successful collaboration with a partner company in Boston, and now we are running four  other partnerships that will soon be hitting milestones - we look forward to convincing results within the next 4 to 12 months. Besides all our hard work in this regard, we are also actively reaching out to other pharma partners whom have expressed marked interest. Right now, we are raising funds in order to support our further business expansion and internal development, so it is a very exciting time for us here. 

In the coming two to three years what makes you the most excited? 

New computing power and technologies will yield novel insights into better understanding physiological mechanisms. I deeply believe in the capabilities of our machine learning models and this is what we are striving to achieve. Coalescing these with disease models, we will be met with an unparalleled range of new types of data that will help Design Pharma push the frontier of scientific discovery forward. 

By merging different life science tools, together we will arrive at much more effective drug candidates and evaluations, and this will lead to increased success rates in later clinical phases. Moreover, the more we understand about drug candidates, the better we can match them to specific patient populations with particular phenotypes. Open innovation, as is the domain of Design Pharma, will become the new status quo of doing business: it will not be possible to absorb and integrate all these state-of-the-art technology platforms into the R&D departments of big pharma alone.