BioFIT 2019 conference programme is tailored to fit the expectations of its audience, whose sharp minds evolve in the fast-paced and dynamic Life Sciences sector. Built around 12 sessions and 1 plenary session, the 2019 programme is rooted in three custom-made tracks, addressing the right funding sources for early-stage innovation, best practices in academia-industry R&D collaborations, and nurturing early-stage assets.
What is pharma looking for nowadays?
A lot of pharma companies declare that they no longer want to be purely developers of drugs, and thus they progressively transition to become healthcare solution providers, trying to seek broader solutions to answer the patient needs, and changing the way they look at assets: What does it mean for the industry in terms of orientation of their scouting efforts, particularly for early-stage assets?
This session aims to understand pharma’s viewpoint, the consequences for the orientation of deals, the way it is going to influence the relationships with academia and biotech start-ups. How does this strategic intention modify the type and the nature of deals that pharma have with start-ups & academic institutions? How is it affecting early-stage licensing deals?
Track 1: BEST PRACTICES IN ACADEMIA-INDUSTRY R&D COLLABORATIONS
What are the outcomes of long-lasting commitment between universities and pharmaceutical companies?
To which needs do these numerous collaboration schemes answer? What are the specificities of these agreements in terms of management models and R&D means mutualisation. Are short-term reports and industry constraints coherent with multi-years academic agreements?
How can big data fuel collaborations between industry and academic institutions?
How can consolidating and mutualising data between industrial and academic players unlock the full potential of these partnerships? How can we make these data available to pharma and how can we make use of big data to fuel the pharma pipeline? Which collaboration models have emerged around big data? How is the central status of big data already entailing academia-industry collaborations?
Managing pre-competitive collaboration in Life Sciences: Testimonials on outcomes & limits of European consortia.
Which research topics are considered good candidates for competitive consortia at the time of precision medicine? Are the current collaborative models (IMI, H2020…) adapted to the research needs? How to balance each party’s interests and define the domain of precompetitive research. How can IP problematics be managed?
What are the recent successful industry-academia collaborations in Artificial Intelligence?
What are the good examples of partnerships in this field? How have they proved to be fertile in innovation for the benefit of both parties?
Would-be CEO Workshop:
– Panorama of European entrepreneurial training programmes
– How do I find the right contacts? What are the ways and networks to be found? How to get access to a seat at the right table? How do we find the risk takers? Hear from savvy entrepreneurs and investors to receive a real know-how.
Track 2: NURTURING AND LICENSING EARLY-STAGE ASSETS
What are the milestones to be reached for an academic asset?
How to valuate an early-stage though promising asset? To what extent do experimented actors help the bio-entrepreneur to aim for a realistic maturation and validation roadmap? How do they help the managing team to understand value inflection points? How do the industry players (pharma, biotech, VC) handle this issue of granting of value to those assets? How could industrial players help academic institutions and TTOs avoid making mistakes at the very early development stages?
How to prepare your package for a due diligence by a pharma player?
What are the key questions an academic project should address to ensure a valuable licensing deal in the view of pharmaceutical industries? How can a biotech company prepare for a due diligence by big pharma? Which areas are usually underestimated when preparing for a due diligence? What is an efficient due diligence plan? How to prepare an attractive asset to pharma and investors? What are the fundamentals of Due diligence? What are the legal aspects to get prepared to?
Which new forms of academia-industry partnerships to better mature assets?
Numerous tools have been created in the last years, how do these early-stage financing vehicles work? On which bases do they ground funds allocation? What are the expectations of the limited partners? Who are the players involved in such investment tools, and what do they await from their involvement in such early-stage funding and investment tools?
What to ensure before the creation of a viable spinout opportunity?
What does viable mean for TTOs, and do we have the same definition everywhere? On which grounds are TTOs advising to create a spin-off rather than out-licensing the IP or a contract-based R&D collaboration? Which are the benefits, limits, constraints and indication of the spin-out model? What are the key factors to create a well-conceived spin-off company, how to exploit and maximise the value creation?
Track 3: FROM PRE-SEED TO SERIES A: ACCESSING EARLY-STAGE INVESTMENT
Working with corporate venture funds?
What are the benefits or constraints to go to a corporate venture tool? Is it better to go with a pharma player that wants to be a limited partner in existing funds rather than corporate ventures? AS both have fundamentally different missions, what are the validating and limitation effects of working with a corporate VC? How to balance these two effects?
Which funding models to accelerate anti-parasitic and anti-infectious innovation?
Which funding to support new vaccines, drugs and treatment strategies in Africa? What is the role of philanthropic funding? How can foundations and governments form alliances to fund better R&D?
Challenging conventional wisdom: Is early-stage capital as satisfyingly available in Europe as we like to believe?
How is it crucial to question the accepted common idea that Europe only lacks bigger funds for bigger roundtables and for more mature companies? Compared to European later-stage investment markets which are positively underfunded, it seems at first glance that the early-stage capital market is healthy and well-fuelled, but is it genuinely the case? Is the European early-stage capital market as rich in players and capital as it is said to be?
How to shape the corporate governance of biotech start-ups at their very first steps to make them successful?
Beyond the sole CEO appointment, how to cope with a pre-established governance from an academic spin-off? How can clashes of culture between researchers and industrials be beneficial and craft a balanced managing team? How are VCs expressing their demands and expectations and how are they filled up? To what extent is leaning on international strategic and scientific committees as soon as they are born a recipe of success?
What’s hot, what’s not? What’s on the investors’ wishlist for 2020?
How do the deals and lessons-learned from 2019 investment let us peak at what can be expected for 2020? What are the tips for success for next year? Which therapeutic area will gain or reinforce interest from the VCs? How can you make sure that your business will be on VC’s radar?
Animal Health Highlights
Which promises to be delivered by high potential vaccines?
How can international and multidisciplinary approaches like the SAPHIR European project help develop innovative vaccination strategies? How are these kinds of projects integrating vaccination in a global health management system? What are the outcomes and expected developments in terms of vaccines production of these multi-million international project? How are these vaccines strengthening the profitability of food systems?
How is AI based on wearables and sensors a major driver for the future of Animal Health and veterinary sciences?
What is the understanding of AI technologies and machine learning within the animal health industry? With the huge amount of data provided by wearables and sensors, how can data be utterly exploited? How is the future lying in real-time predictive analysis? How is AI bringing changes in business models through automated veterinary diagnostic tools?