Solebury Trout
Market Commentary: Solebury Trout BD takeaways from Boston BD Roundtable and Hamptons BD Panel
Solebury Trout

Download the PDF

 

BD Panel Participants:

  • Anton Xavier, MBA, MSc, Assistant Director of Technology and Business Development, NY State Center for Biotechnology
  • Axel Hoos, MD, PhD, SVP of Oncology and R&D, GSK
  • Ed Saltzman, Executive Chairman & Founder, Defied Health
  • Stephanie Oestreich, PhD, MPA, Executive Vice President, BRIDGE Partnerships, Evotec AG

Current trends in BD – where are you seeing BD opportunities, where are you focused?

  • In general, we are seeing BD deals going earlier and earlier. Many large pharma’s are getting out of R&D and early stage research. They are partnering earlier and earlier. We are seeing financing going into universities directly to partner with them (Deerfield).
  • We are seeing more early stage partnering with KOLs, but with some conflicts. There’s a need for large pharma to define who the rising star principal investigators are and forming relationships with them. Finding those rising star PIs is a challenge, early stage innovation is something more people are jumping into.
  • Be careful speaking to pharma about early stage. There’s a difference between what ‘early stage’ for academia is and what biotech’s would consider early stage. At Pfizer, we were looking at assets that were 1 year to clinic. From an academic perspective, early stage is 1 bar graph with in vitro data. Bridging the gap is what we do at biotech.
  • Trend is clearly leaning towards early stage. Biggest takeaway is that pharma is almost entirely out of internal R&D. Pharma is a buyer.
    • Provides a bottom to this market for valuations and deals because always will have financially strong industry desperate for innovation.
    • This trend is not universal. We do see lot more deals to fill discovery pipelines. Pharma productivity is an important aspect. If we are not productive, the ecosystem shifts. More desperation in system.
    • Mixing – at GSK, we have in house R&D in areas we have strength and in the areas we don’t have strength, we go external. The trend needs to stay that way - some expertise and discovery from within and some outside. Balanced ecosystem

Valuations:

  • There is a lot of research on going. In general, biology coming from academics, biotech companies take over the research, and de-risk it. Early stage research is very risky & too risky for pharma. Pharma ends up buying biotech’s once they are de-risked and have the capital & marketing power. Need 10k molecules to arrive at 1 that is profitable. Pharma has capital; it’s easier to buy de risked molecules in the market and focus on developing and marketing them.
  • There’s a lot of capital out there, seeing the trends: series A, huge rounds of $100M and higher- going public earlier and earlier because valuations are so high and see less M&A. Need to provide translation from early stage science to clinic.

Biotech needs Pharma:

  • Pharma brings integration of different technologies
    • Example: Cell and Gene Therapy - none will be successful in the long term unless multiple technologies come together and enhance the drug. All big cell therapy driving excitement ended up in pharma (Kite and Juno). It can’t be fully developed unless in major pharma in their platform. Integration of different technologies isn’t that easy for biotech. Integration of things that are complicated to do.
    • Pharma is better positioned to do commercialization, they have a global footprint & is easier to go into new markets. There are examples of biotech who made it and became ‘pharma’. Nothing wrong with either model - main stream, majority of complex stuff will rely on pharma.
    • Avexis SMA drug sold to NVS for $8.8b on efficacy data on 14 patients. Single product. Why did it drive that valuation - did they need pharma?
      • They will benefit from pharma, from the global resources pharma has. Pharma has built tremendous amount of scale over 20 years. Global footprint of pharma is needed in big indications. We have seen some consolidation; scientists are pushing towards personalization. Pharma can be helpful, but they’re learning on the job. Learning together in ecosystem. ALNY is a good example of a biotech company that can accomplish on their own, different marketing model.

Challenges of Biopharma partnering with pharma:

  • Culture piece is one of the biggest drivers of productivity - going through culture change, get away from ‘feel good’ to what is a balance between good performances. National culture vs corporate culture. Decision making, prioritization, execution. If you don’t find commitment, milestones payments, etc., down the road are at risk.
  • Effective - each major player in ecosystem, VC, analysts, banking, biotech, and pharma as muscle, all of them has a role to play. You don’t see one goes away over the other that easily, otherwise will be a gap.
  • Partnerships - being a good partner: looking at value for both sides continuously is so important for organization. At GSK, we pride ourselves on being a good partner, we try to advance their technology before we think of taking them over. It helps fund the technology and hit big milestones then decide if we take them. How to best optimize the path for the molecule. Good value in doing that. You need a partner who is committed to the science and program. GSK does a lot of partner/option deals because of this.

Creative financial Models:

  • One of the big differences culturally is the speed of decision making because of the complexity in biotech’s. Different governance and speed is different. Interested to hear pharma perspective: how important, significant expertise is in pharma in development. We are seeing new models come up in development, such as Clarus doing deals with pharma, taking risk for clinical trial off of a balance sheet and sharing milestones and royalties.
    • No concerns about that. Our universe is so big, and there’s so many drugs that need to be developed. Some of these don’t get buried in a shelf in pharma, so important for them to be involved. This provides a lot more value for ecosystem and patients.
    • Pharma accumulates a lot of assets, they have to prioritize internally, and can’t develop everything - so they either focus on some, raise more money or find other creative ways to get these drugs through. It’s a very creative ecosystem. At the moment, we are flushed with cash.
    • Setting clear strategy, making good decisions. Its worse having a good medicine not developed then having it developed in partnership. We will see more of this going forward.
    • Drug development is a random process since its inception, we are getting better at innovation.
    • Big trend to externalize. Companies are getting out of urology and CV - challenge for BD role in pharma right now is the fact dealing with so many disruptive technologies that it’s hard to process that into therapeutic areas and a strategy. So many platforms cut across a lot of therapeutic areas.

Valuations – “IO Bubble”

  • The current deals in oncology - cell / gene therapy are wonderful source of potential efficacy, but valuations to me have been unusual taking into consideration you have autologous models. There future of IO, is looking at next gen trials, moving to allogenic models where you get an off the shelf product, reducing costs, attrition on next gen bispecifics that can move and take over the CART space. This costs a lot of money - consider where future is going. Impact field as whole.
  • Therapeutic areas - narrow it down to where you feel you can best win. Oncology is one, but every company is leading in IO, worries me. This IO bubble we have is important that pharma has a diversified portfolio and realizes that IO drugs are great, but they don’t work in all patients. We do see resistance and adaptive resistance. We cannot just frame oncology in terms of immunology, we have learned that’s not the way to go. The immune component is only one component. We also need to address many others, such as the tumor micro environment, and metabolism factors. Cancer is a unique disease to one’s state, and it will drive towards personalized approach based on many factors, which is not scalable. Interesting to see how this model will work out.

Tech Transfer:

  • Trust is important – from an academic standpoint, building companies, trust gap between those early stage and large pharma - needs to be built up. PFE reached out to early stage companies, developed mentoring relationships to guide them through development process (no money). Different modality of how to go about forming relationships with early stage companies.
  • Relationships between tech transfer offices at universities; there’s a huge difference between what tech thinks is a billion dollar drug and what pharma thinks is a billion dollar drug. There is no need for NPV value at such an early stage as there’s so much risk associated. Pharma is coming in at lower valuations based on risks. Building an understanding between tech transfer offices and pharma on true valuation of a compound will be so important going forward. Don’t see that happening right now - more discussions around that - need both parties to understand it.
  • Trying to move research that’s a possibility to probability. De risk it in a way you can assign a probability. Partnered with universities, one with 15 hospitals in Toronto with all 3 parties, formed a joint steering committee. We select the science, which can be very early, then form a team around that, and write a very detailed research plan. Identify what would be the experiments needed to validate our hypothesis and bring it to the next inflection point. That data package will be incredible to pharma. If it’s successful, start companies, and get equity. Then stay with the company - capital efficiency and flexibility - build virtual company, select CEO - until they hit next valuation inflection point. In academic realm - to develop in risk reduced environment, new drugs, use of big data. See that more and more and has to start at pre-clinical level - scientists and big data to work together - target identification and optimization and later on in clinical trials. See lot more of that in future.
  • Talking about early stage innovation - multiple resources, we have incubators that we manage, house those companies at their earlier stage. They take very early compounds from academia all the way to a potential inflection point - still little too early to take to pharma. Typically risk averse (pharma) and we do provide capital for development. Still earlier stage where go to VC network to form company. Avoid pharma at selecting innovations because pharma and strategy changes a lot over the years, even over the months.
    • Example: ADC’s were a hot topic in pharma world, now things moved to IO, what do we do if we find an ADC asset - some companies exiting ADC, from there, where is future of oncology - moving to protein degradation, synthetic validation screens, everything around there. If develop IO drug late, pharma’s strategy changes, we are left without a partner. We choose the best asset we can move forward regardless of biotech / pharma interest.
    • In science, there’s no such thing that predicts the outcome. Not all ADCs will be good, not all will be bad. Sometimes platform concept plays a role. We are seeing trends now; going away from rigid application of technology to one particular area. Immunology example - signs of immune system, involved in many diseases - has a role. Understanding immunology can help you understand diseases. Only work in cancer with this particular thing - think more broadly and create organization that can cater to that. Massive specialization.
    • Start agnostic then go to specialization, need to have model to make that happen. If you do build around a notion of immunology, not only targeting one aspect of oncology but a lot of aspects of auto immunity and other disease areas. Typically find with pharma - the research units that study different therapeutic areas are silos. One location is oncology and one is for autoimmunity, another for CV. With silo’d approach, sometimes there is a conflict between research to collaborate, working on own things, no time to collaborate and put together consortia or because of distance, don’t collaborate. How do you go about silo research?
      • Historically, we were working in more silos. Connections between different units, as they grow, there’s a bigger disconnect.
      • AT GSK, immunology is an example of what we are doing today, trying to optimize our research and create a concrete model around this. We made immunology theme very visible to cater to any disease with this. Our new CEO will reduce the number of therapeutic areas from 8-4 and to now focus on areas we are strong in - have to keep eye on it.

International Landscape:

  • There’s good science in EU - but the translational effort is lagging. UK is a shining example - Oxford, Cambridge, has done a lot to bridge this with dedicated funds. Oxford has 600m fund to commercialize research. Japan has a lot of interesting science, but it’s a long process. Government is putting lot of public funding in Universities.
  • There are a lot of big players coming out of China.
    • evolution going on
    • westernization of process
  • US strongest geography
  • Australia - no funding, great science

Payers and category crowding

  • Intersection between commercial / science - always changing
  • Moving to market more quickly, with high prices
    • "me too’s"
    • 50 anti PD1s, 18 PARP inhibitors – crowded
  • Pharma may be too comfortable with commercial risk
  • Need to do payer, commercial, etc. work needs to be done early
  • Will it be approved and will it be reimbursed
  • Need to think about evidence generation, i.e. CAR-T superiority - includes regulators, payers and whole ecosystem
    • Think about the end at the beginning
  • Pharma / biotech control price but not reimbursement
    • Value vs price per unit