It’s no secret that drug development costs are ballooning, while the number of products that actually make it market is decreasing. This phenomenon has become a kind of truism in the life sciences – so much so that it has spawned its own “law”. The so-called “Eroom’s Law” states that, adjusted for inflation, the cost of drug production doubles every 9 years.
That may or may not be technically true. But what’s undeniable is that managing the costs of drug development is now a critical priority for the pharmaceutical industry. Advanced AI holds the promise of shortening the timeframe and reducing the overall costs of drug production through in silico modeling. But a long process of research and ideation has to take place before an asset reaches that stage.
And traditionally, that process has been resource-intensive, both in terms of cost and personnel. In this article, we’re taking a look at how this stage can be optimized and accelerated with AI-enabled search capacities that augment human research teams. By saving time and cost here, companies can move from white space to asset more quickly and more confidently than ever before.
But what’s really new? Drug development has never been cheap
The high costs of drug development are nothing new. Neither are the factors that go into it: extensive research and development, clinical trials, regulatory approval, and marketing. Drug development has also always required investment in infrastructure and personnel. Back in 2021, the average cost of developing a single asset was estimated at just over $2 billion.
But while the fundamental inputs that go into that cost haven’t changed, their relationship to market conditions have.
Cost up, ROI down: the conundrum for Pharma
According to a Deloitte study, 2023 is putting drug developers between the rock of higher development costs and the hard place of lower sales forecasts. In 2022, costs rose to pre-pandemic levels, with sales dropping almost as low as 2019. Looking beyond 2023, pharmaceutical companies in the United States will also need to be agile enough to adjust their clinical and commercial strategies as the Inflation Reduction Act changes the commercial landscape for prescription drugs.
The perfect storm of scarce talent and tight budgets
Another factor complicating the question of cost is the prevailing state of the market. The economic outlook for 2023 is bleak, and companies across the board are being forced to trim the fat, and find ways to do much more, sometimes with much less. At the same time, employees still hold the upper hand in a historically tight labor market. Skill shortages are being felt everywhere from high-tech to marketing.
For business leaders, the problem is made even more insoluble by global employee mobility and remote work: highly skilled professionals are able to be selective about who they work for, and they’re confident enough to strike out on their own in search of better conditions.
Managing costs with artificial intelligence
These challenges underscore the importance of leveraging AI solutions to manage costs, optimize processes, and make up for personnel shortages. Here’s how AI-enabled research impacts every stage in the development process.
Hit identification: automatically sorting the hits from the misses
The average cumulative cost for this initial stage is around $102 million. AI can help to control this cost by analyzing large datasets of biological and chemical data before it reaches human researchers. They’re then able to focus their efforts on only the most promising targets. This can radically enhance high-throughput screening, by accurately picking the best compounds for analysis.
Progressing hits to leads and optimizing them
Tools like Similari make it possible for researchers to instantly survey the landscape and technical literature to accurately assess the viability of leads. By automating away the bulk of their otherwise manual search time, these tools accelerate the hit-to-lead and lead-optimization phases. And by leveraging proactive analytics, researchers can respond to emergent threats or new findings, to adjust their strategy accordingly.
Streamlining preclinical development & clinical trials
Always-on market surveillance allows researchers to “fail early” (and cheaply) by knowing the results of prior research. With the volume of clinical data growing exponentially year on year, AI offers a way for human teams to cut through the noise and identify salient insights that can make or break their innovation initiatives.
Streamlining approvals: the last hurdle
By the time an asset reaches the approval stage, the cumulative cost will have reached around $3 billion. AI has a crucial role to play in managing these ballooning costs, before, and during the approvals phase.
Before an asset gets here, AI like Similari can help reduce the costs of regulatory approval by predicting the likelihood of approval and identifying potential safety issues early on. This helps companies make better decisions about which drugs to invest in and reduce the risk of costly regulatory failures.
Companies can also leverage market intelligence to identify potential partners with the requisite expertise and experience to achieve compliance. By entering licensing or partnership agreements with these (usually larger) entities, startups in particular stand to gain a great deal when it comes to expedited approval and go-to-market.
Breaking free of Eroom’s Law starts with data
The way out of the bind is to improve the quality and the quantity of data available to decision makers. Similari puts thousands of clinical trials, along with millions of patents, articles and press releases at their fingertips. R&D professionals and innovation leaders are leveraging Similari’s next-generation AI capabilities to accelerate drug development, and improve outcomes with accurate and up-to-date insights, moment to moment.