Accelerating pharma R&D and innovation through technology partnerships
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.
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.
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.
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.
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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Originally published at https://blog.biostrand.be.