Distilling new insights from a recent podcast with an industry investor from Nordic Capital, ArisGlobal CEO Aman Wasan makes a robust financial case for advanced automation in processing pharma’s multiplying regulatory and safety workloads.
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For life science R&D organisations to have a sustainable financial future, those companies must be in a position to maintain or grow profit while minimising the impact of significant and converging market pressures.
Economically and commercially, those pressures range from high interest rates and soaring costs, to increased global supply chain challenges and intensifying competition. Strategically, they also include market-defining changes to the make-up of drug product lines as blockbuster drugs give way to novel therapies, and as medical devices and personalised data play a greater role. In the US, the biopharma industry is now also subject to the national Inflation Reduction Act. This will have a further, direct impact on organisations’ profit potential and the funds available for R&D.
It is no coincidence that many Boards of Directors are mandating up to 80% cost savings over the next three years 1. Compromising on drug development, or the choice of materials, is not an option however. Rather, the focus now must turn to the performance of R&D services and the resources needed to manage soaring workloads.
Nordic Capital estimates that large pharma companies will need to take out somewhere between 10-15% of their total cost base just to maintain current activity. To support more sustainable business models, meanwhile, they will need to take much more severe action across the value chain, to stay ahead of the rising and converging pressures. Hence the talk of removing up to 80% of current operating costs. It is in this context that advanced automation offers a breakthrough; that AI becomes not just a nice-to-have, but a critical enabler.
A signal of long-term prosperity: investors’ growing appetite for advanced tech use
The imperative around AI is now a prominent theme as part of life science investor strategies for the next 1-2 years. Firms like Nordic Capital are now actively tracking pharma companies that have access to advanced technology and superior data to help alleviate their operational and cost pressures.
This could take the form of advanced technology solutions that enable them to capture data in a more effective and efficient way, or AI-powered platforms and tools that mean they can process data in more effective ways.
Another avenue of interest to investors is companies that are harnessing data or technology to help identify scope for new product/therapeutic innovation, diluting an organisation’s exposure to current market pressures by taking them beyond the realm of the Inflation Reduction Act.
Where investors are looking to buy and own pharma companies, certainly, the preference is for truly innovative companies that are not impacted by the usual market forces; or those that are serving their markets in a hyper-efficient way.
For heads of R&D now, the strategic priority is to maximise the flow of funds to the discovery and development pipeline, and to advance that pipeline cost-efficiently. This in turn requires optimisation of the enabling functions: in other words, everything that supports R&D. And the smartest way to optimise anything is to leverage the latest technology.
Scoping the opportunity: identifying breaking points
To gain a tangible advantage from new technology, companies need to be prepared to step out in front. But what does good look like?
Companies’ relative priorities for AI-powered automation will vary, but the key to maximising the economic advantage will be to remove ‘waste’ from current workflows, and to do this in a way that is directly measurable (something that AI makes very easy).
That could be in applying everyday Safety protocols, or in aspects of the Regulatory workload, for instance. In both of these contexts there is considerable potential to simultaneously streamline and tighten sub-processes, and also empower busy teams to focus their expertise where it can make the most important difference.
Data recorded along the way will clearly show the rate by which workflow is being reduced or accelerated, as time-consuming early stages of a process are reliably and efficiently automated.
Crucially, any individual AI/automation applications should be underpinned by a unifying platform, supporting easy integration and interchangeability of data and insights over time, and a return on investment that keeps building and multiplying.
Crowd-sourcing: looking laterally for emerging best practice
AI-enabled automation of R&D processes has become a prominent topic at major industry events, so attending and networking at those sessions can play an important part in shaping each company’s own vision and next plans. Joining special interest groups and consortia is another way to stay close to developments, hone strategies and roadmaps, and get a first-hand picture of what is working versus what may need a bit more time and maturity.
Having set a sufficiently high overarching ambition, business stakeholders should help identify what future processes will need to look like, to be operating with what should ideally be just 20% of today’s costs. This is then the reality that a modern technology platform needs to be built for.
The role of the prioritised applications and target use cases is to deliver rapid results and showcase what’s possible to the wider organisation. In this way (module by module, use case by use case), companies can move actively and incrementally toward their goal, rather than waiting 3-5 years to determine whether they are where they want to be.
Looking up: preparing for life sciences’ next destination
As already alluded to, R&D transformation isn’t only about improving productivity and cost-efficiency. All life sciences organisations must also heed the call to innovate, to stay relevant and competitive and to buck the downward pressure on the cost of traditional medicine. This, too, is a fundamental expectation of investors.
The trend in 21st century life sciences, to target more specific diseases almost on an individual basis, is enabling new product innovation at a micro scale. Smarter use of data meanwhile will enable new economies of scale, through the aggregation of multiple highly targeted diseases to maintain scale from innovation.
That scale is clearly important, to maintain profitability as the cost of drug discovery and development rises in line with the ambition and complexity of novel and advanced therapies. That the vast majority of all diseases do not yet have a cure is life sciences’ great challenge, both scientifically and commercially. To ensure sufficient payback, companies need to become more cost-efficient in the drug discovery and development cycle. This in turn demands ever smarter use of technology and data. Only once key processes have been optimised, and costs have been contained, can companies truly focus on advancing the pipeline in an economically-sustainable way.