Could artificial intelligence be the new key to competitive advantage in the pharmaceutical industry? Given reports of advances and promise in AI-driven drug discovery, one might think so. The pharmaceutical industry has been fascinated by clinical trial milestones. AI-discovered medicines And valuations of companies in this space are skyrocketing.
But when every company buys the same breakthrough tools, those tools lose their ability to differentiate. AI's potential to help patients should not be underestimated. At the same time, pharma companies have always honed their competitive edge through creativity and ingenuity; AI won't change that. Life sciences companies must remain focused on the fundamentals of drug development: investing in R&D, effectively managing the resulting data, and making the most of their intellectual property.
Bridging the digital transformation gap
AI is only as good as the data that is fed into it. This means that if a company doesn't have the right data or the underlying data is not robust enough, the AI model won't produce useful results. For life sciences companies, this includes data from the entire R&D lifecycle, including flow data, assay data, protein data, data from various instruments, scientist insights, experiments, and other data capturing successes and failures. Only with enough information can researchers match the right candidates and the right targets to the right diseases, with or without the help of AI.
Moreover, AI will not benefit companies that are still struggling with the fundamentals of data management. As CEO of life science software company Dotmatics, I know all too well that you can't code around bad business practices. AI needs clean, standardized, and organized data to produce usable outputs. Meanwhile, companies need a strong data governance culture to ensure consistency in data generated and collected by different teams. To that end, my company developed a Scientific Intelligence Data Platform that flexibly aggregates all relevant data into intelligent data structures, enabling clean and reliable data analysis and paving the way for meta-analysis and AI-based algorithms.
The pharmaceutical industry is said to be lagging behind other industries in terms of digitalization. McKinsey Report Life sciences companies are reportedly “2-3x behind industry leaders in digital maturity,” meaning the pharmaceutical industry is significantly behind other industries in adopting and integrating digital technologies, often due to legacy systems and infrastructure, and regulatory and compliance challenges.
That said, the industry has made great strides during the pandemic, as healthcare companies have increased their digital capabilities. More than any other industry Excludes consumer goods from 2019 onwards.
Under pressure to deliver new therapies and diversify research, life sciences companies have broken down silos that prevent data sharing and digital collaboration. They have set digitalization goals from the C-suite to the lab, but they still struggle to collect, integrate and analyze data throughout the R&D lifecycle. PwC Predictions In the near future, “the ability to extract value from and manage data will determine a large part of biopharmaceutical company shareholder value.”
Even in a competitive environment, life sciences companies need to push each other, and the vendors that support them, toward using technologies that will ultimately support multimodal research into an AI future. This is the rising tide that floats all boats. AI can help create a competitive advantage and reduce the average 10 years and $2.6 billion in costs required to bring a therapy to market.
Leveraging intellectual property
Data is also the lifeblood of pharmaceutical companies: their intellectual property (IP). A company's IP encapsulates its unique knowledge.
Conversely, AI is or will be a commodity. Vendors are building compelling AI products based on public knowledge. Their models make it easier to derive relevant insights from the chemical structures and properties of molecules, advancing the research starting point for every company that buys them. But when computational models built on public knowledge are used to design new drugs, debates arise about who owns, or should own, the resulting IP.
AI only becomes a unique advantage when deployed on a unique data foundation optimized for use in R&D. When companies own the data used to build models, IP and ownership are less contestable. This means life sciences companies need to invest in R&D and the data science that underpins it, so that scientists and researchers have the freedom to experiment, learn, and iterate.
The biopharmaceutical industry is experiencing a major shift that offers hope for patients and healthcare providers. AI will make drug discovery faster and more cost-effective by augmenting scientists' creativity, ingenuity, and diligence. Now it is up to life sciences companies to lay the foundation that will enable AI-enabled drug discovery to realize its potential.
