Tanja Dowe is the CEO of Debiopharm Innovation Fund, the company enterprise capital arm of Debiopharm Group. The fund invests in digital health firms that have an effect on cancer, infectious diseases, and drug development.
Debiopharm Innovation Fund is thought for investing in firms which are leveraging AI and massive data. How essential is AI to accelerating drug discovery?
A typical drug discovery process takes 4.5-6 years and requires iterative experiments within the lab with uncertain outcomes. Lower than 12% of drug candidates resulting from the standard drug discovery process make it throughout the clinical trials and to the market.
As healthcare costs proceed to extend, our society cannot afford costlier drugs. Up to now 10 years, the ROI from drug development has declined by 80% – making drug discovery (and development) as we comprehend it unsustainable in the long run resulting from its high costs.
AI can cut the time spent on drug discovery to as little as a number of months to a 12 months. AI techniques have been improving fast – computing power has accelerated exponentially, and increasingly more high-quality data sets can be found to coach the AI models. Combined, this permits more precise understanding of the chemical and the biological spaces and the acceleration of drug discovery.
What do you search for in firms which are leveraging machine learning and AI?
We search for a mix of technical and scientific skills. The team must have biological or medical expertise, in addition to state-of-the-art data science capabilities. AI is an excellent tool, however the team also has to know the issue they try to unravel.
On the information science side, we search for the backgrounds of the engineers and developers: do they arrive with the latest knowledge and a track record of applying AI to complex problems?
As competition continues to grow within the space, we also search for a track record of existing customers before we invest to indicate that the corporate is in a position to interact with customers and solve relevant problems.
Generative AI is all of the trend, in your view what are the very best use cases for Generative AI within the healthcare sector?
There are numerous areas where generative AI may be utilized in healthcare – ranging from easy opportunities equivalent to content development for patient education and patient support programs, providing clinical decision support systems with up-to-date information from scientific literature, and stretching all of the solution to drug discovery.
In drug discovery, generative AI learns the relationships between chemical structures and their activity on a given biological goal to suggest de novo molecule designs which have the specified properties.
A few of Debiopharm’s previous investments concentrate on genomics, what are your views on how machine learning may be incorporated in genomics?
The human genome consists of 20,000-25,000 genes, but only just a little over 800 disease-related molecular targets are used today by the drugs available in the marketplace. We all know only a fraction of how genomics affects diseases. However the complexity and the increasing amount of genomic data combined with other omics and clinical data need higher evaluation methods. Machine learning has the potential to point to latest connections between genomics and health conditions and enables us to develop higher and more targeted treatments for diseases.
The Debiopharm Innovation Fund focuses on Series A investments, these are sometimes successful at proof of concept, but proof of scalability should still be a problem. How do you discover firms that may scale?
There are two axes to scalability. The primary is whether or not the market is able to scale, and the second is whether or not the start-up’s technologies and business model is scalable.
Available on the market side, we see that greater than 20 drug development programs from AI discovery firms have already reached clinical trials today. We also see a critical mass of pharma collaborations with AI firms. So, we consider the market is prepared.
On the start-up side, we wish to see that the tech platform is generally together, getting used no less than internally for customer projects, even when UX/UI haven’t been fully optimized. We also need to see access to top quality data. There must be a transparent development roadmap for the platform to indicate what must be built to make sure scalability and value in the client’s hands.
A lot of the firms we see have began with a service model and have a plan to evolve towards a recurring revenue model. Our investment thesis is to take a position in firms with software business models relatively than biotech asset models, and we steer away from AI firms that solely consider in developing drug assets for licensing. So, we’d like to see a reputable roadmap towards a recurring revenue model and a pricing strategy that is smart.
You’ve spoken concerning the importance of the education that is required on each side of massive pharma and start-ups for them to know one another, how does Debiopharm assist with this?
Once we put money into a start-up company, we organize a ‘Meet-the-Startup Day’ at Debiopharm. We invite the start-ups to offer a company-wide presentation, and we open our doors for the start-up to access Debiopharm’s expertise. Whether it’s for translational medicine, drug development or market access teams, we connect the start-up with experts that they should test their hypothesis on customer needs or to know which technical features are crucial for connecting with pharma’s internal tech stacks. Often, we also facilitate collaborative discussions between the start-up and Debiopharm. On this process, the start-up can refine their understanding of their customer groups. We also educate our internal teams to work with start-ups – to access the latest innovation, you can not expect turn-key solutions, but you must adopt a mindset of co-creation.
What do you personally search for in entrepreneurs that you simply are considering investing in?
I get asked this quite a bit. I search for that entrepreneurial ingredient that is difficult to clarify – passion, energy, enthusiasm, strong conviction that you may overcome difficulties, curiosity and suppleness of mind. The entrepreneur also must be an optimist. You get beat down so persistently that it just isn’t possible to construct a thriving company without being an optimist. And you have got to know that you simply are an optimist, so that you simply mitigate over-optimism by bringing the form of people around you that keep you grounded.
There’s one concrete feature that I search for in entrepreneurs that I can share though. It’s responsiveness. We live in a fast-paced world, and, as an entrepreneur, you have got to maintain up. Responsiveness builds relationships and trust, whether it’s with a customer or an investor. Irrespective of how great a technology you have got, communication between people is what is going to make or break you.
What advice do you have got for startups and founders which are considering approaching Debiopharm or other VC funds to lift capital?
Investors are at all times searching for latest, interesting start-ups, so don’t hesitate to achieve out to us at events, through networks or digitally. Nevertheless, keep in mind that we undergo 400-500 investment opportunities per 12 months so be crystal clear about what you do, how your customers work with you and the way much money you might be searching for. We’re very efficient in screening and filtering opportunities and wish to simply discover if your organization could possibly be a fit with our investment thesis.
What’s your vision for the long run of digital health?
It is straightforward: individualized, accessible, preventive.
Individualized implies that your health data (whether it’s your health history, genetic profile or continuous monitoring data from wearables) is digitally available and is efficiently used for treatment selection and treatment management.
Accessible implies that you have got access to all of your data, in addition to digital access to your healthcare providers, and that the standard of diagnosis or treatment decisions remain constant no matter where you might be situated on the earth – due to AI-assisted diagnosis methods and clinical decision support systems.
Preventive implies that, based in your health data, digital diagnostics discover potential health issues early and personalized digital therapeutics show you how to modify your behavior with a view to maintain a healthier lifestyle and stop – or even reverse – a health risk.