Biopharmas are increasingly turning to alliances & partnerships to drive external innovation. Having raised over $80 billion in follow-on financing, venture funding and IPOs between January and November 2021, the focus in 2022 is expected to be on the more sustainable allocation of capital by leveraging the potential of alliances and strategic partnerships to access new talent and innovation.
The recent race to market for COVID-19 vaccines has only accentuated the value of alliances as companies with core vaccine capabilities turned to external partnerships to leverage the value of nascent mRNA technology. And with alliances historically delivering higher ROIs, major biopharmas have been deploying more capital towards alliances and strategic partnerships since 2020.
Within this broader trend, the AI-enabled drug discovery and development space continues to attract a lot of Big Pharma interest spanning investments, acquisitions and partnerships.
Partnering for AI-enabled drug discovery
In a recent two-parter, we noted how AI technologies are driving the next big innovation cycle in drug discovery and development. As a result, Big Pharma, where AI is currently the top investment priority, and a host of other deep-pocketed players, including Big Tech and biotech venture capital firms, are channelling record volumes of funding into the AI drug development market.
Even though the number of funding rounds in AI-driven drug development has been on the decline since its peak in 2018, 2020 witnessed new heights in total annual investment value with the average funding size increasing nearly five-fold over a five-year period. In 2021, investors doubled down on the sector with a 36% increase in total investments over the previous year.
Biopharma majors, like Pfizer, Takeda, and AstraZeneca, have unsurprisingly been leading the way in terms of AI start-up deals. However, these industry players are also focusing on forging partnerships in the AI space that would help them improve R&D activities. Just in the first quarter of this year, leading industry players including Pfizer, Sanofi, GlaxoSmithKline, and Bristol-Myers Squibb, have announced multi-billion-dollar strategic partnerships with AI vendors.
Pharma-startup partnerships represent the fastest-moving model for externalising innovation to accelerate R&D productivity and drive portfolio growth. However, for a sector that has traditionally preferred to keep R&D and innovation, managing these strategic partnerships introduces some new challenges that are not as easily managed as a Build vs Buy decision involving informatics solutions.
Managing strategic AI partnerships
According to research data from Accenture, the success rate of pharma-tech partnerships, assessed across a total of 149 partnerships between companies of all sizes, is around 60%. For early-stage partnerships, there are additional risks that can impact the success rate. However, the company distilled these four most common pitfalls that can impact every pharma-tech partnership.
SOURCE: Accenture
- Failing to prepare internally: Among life sciences executives, the lack of a clearly defined partnership strategy and/or partner management functions are key challenges to the success of tech partnerships.
It is therefore important to start by defining the appropriate partnership structure and governance for the alliance, with mutually agreed partnership objectives, a dedicated team with the right technical knowledge and resources and clearly defined partnership management functions.
- Engaging with the wrong partner: Despite the most stringent due diligence around technological relevance and strategic alignment, tech partnerships can still fail because of organisational and cultural differences.
Sometimes the distinctive and complementary characteristics of each partner that make collaboration attractive can themselves create a “paradox of asymmetry” that makes working together difficult. Most corporations may be well equipped to deal with the two main phases of collaboration between large companies and startups: the Design phase, where the businesses meet and decide to engage, and the Process phase, where the organization interactions and collaborations actually kick-off. New research shows that a preceding Upstream phase, to define and create conditions conducive to the Design and Process phases, can be decisive in the success of startup partnerships.
- Undefined partnership roadmap: Technology partnerships can be structured in myriad ways. For instance, the financial structure could be based on revenue sharing, milestone-based payments etc. It is necessary to clearly define each engagement structure in terms of its operations, organisational, financial, legal and IP implications. Formalise the roles, responsibilities, and accountabilities expected of each party. Establish short to medium-term goals, metrics, key milestones and stage gates that build towards long-term partnership outcomes. Continuously reassess and fine-tune based on milestones and KPIs.
- Poor execution: Effective long-term partnerships are based on executional excellence. Successful partnerships require a dedicated leader accountable for the execution and result. This role is essential for providing daily oversight of operational issues, addressing inter-organizational bottlenecks and enforcing accountability on both sides. There also should be bipartisan partnership meetings involving senior leadership to discuss how to accelerate progress if everything is on track or how to change tactics in the face of challenges or changing market conditions.
Building successful technology partnerships offers a fast, efficient and cost-effective model for pharma and life sciences companies to develop new capabilities, accelerate R&D processes and drive innovation. However, the scale and complexity of these partnerships, and the challenges of managing partnership networks, are only bound to increase over time.
Building end-to-end AI partnerships
In the race to become pharma AI leaders, many companies are looking at end-to-end AI coverage spanning biology (target discovery and disease modeling), chemistry (virtual screening, retrosynthesis and small molecule generation) and clinical development (patient stratification, clinical trial design and prediction of trial outcomes).
This is where AI platforms like BioStrand based on multi-dimensional information models will become key to value realisation at scale. These platforms not only automate data aggregation across different biological layers, multiple domains and diverse nodes in a partnership network but also provide an AI-enabled, unified and versatile analytics framework that researchers can leverage for a wide range of research applications, from single-cell analysis to analysing microbiota to early-stage drug development.
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