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.
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.
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.
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.
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.
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.
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.