Layoffs in the biotech and pharma industries are gaining speed and reaching the C-level. Every week, we see announcements from new companies about laying off their staff or cutting R&D projects.
It’s understandable that organizations are striving to cut their operating costs as much as possible in the present climate. But when doing so, they lose on the opportunity the crisis brings.
McKinsey found that organizations that maintained their innovation focus through the 2009 financial crisis emerged stronger and outperformed the market average by more than 30% over the subsequent three to five years.
What can leaders do today to keep that focus and persevere through the economic downturn? I believe the answer lies in AI and its potential for redefining business processes and models.
Since the beginning of the year, we have had layoff announcements from 80+ companies. There’s even a layoffs tracker helping to make sense of the situation.
Here’s a taste of what the industry has been going through only this September:
If we learned anything from the 2009 financial crisis is that crises are brief. If you let talented people go now, hiring new staff once the industry picks up the pace again will be more expensive, time-consuming, and difficult. Instead of reaping the benefits of the new opportunities, organizations may suffer setbacks because of the lack of available talent
Many layoff stories are directly connected to cuts in R&D budgets. Many companies are closing their R&D projects for reasons ranging from savings to disappointing research results.
For example, Clarus Therapeutics reduced staff by 40% and eliminated certain R&D projects as it faced debt troubles. Atara Biotherapeutics ran a mid-phase study for a multiple sclerosis cell therapy that brought inconclusive results and is now slashing its R&D focus, laying off 20% of staff.
Eisai is closing its US oncology R&D wing H3 Biomedicine and cutting 88 jobs. Agios Pharmaceuticals is changing its focus to later-stage assets, cutting its R&D personnel by half. The Japanese pharma company Daiichi Sankyo shut down its Plexxikon R&D project in South San Francisco after buying the biotech for $805 million a decade ago.
While some of these cuts have sound motivation, others could have been avoided if companies assessed their processes and implemented tools that make R&D initiatives much more cost-effective, especially in the drug discovery area.
Machine learning systems and computational tools are becoming industry standards for a reason. AI technologies allow scientists to draw insights rapidly from vast data sets that previously would have taken large teams years to analyze.
We all know the success story of AI in vaccine development: the Covid-19 vaccine was brought to societies at lightning speed thanks to AI.
In a press release announcing the $325 million funding series, Prof. Ugur Sahin, CEO and co-founder of BioNTech, noted the company’s ongoing focus on “accelerating convergence of biology with bioinformatics, robotics, and artificial intelligence as an opportunity to develop more precise, efficacious and cost-effective individualized immunotherapies.” BioNTech’s recent partnership with InstaDeep indicates the need for even greater expertise in AI.
By accelerating processes and allowing quick analysis of massive data sets, AI technologies create tangible value for companies, whether it’s cost savings or faster drug development.
The McKinsey Global Institute estimates that AI solutions applied in the pharma industry could generate almost $100 billion annually across the US healthcare system.
The best use cases for AI technologies are drug discovery, drug manufacturing, diagnostic assistance, and optimizing medical treatment processes.
Over the years, drug discovery has become increasingly competitive and expensive, driving pharmaceutical companies to look into AI as a new method to reduce research and development costs. AI and machine learning algorithms are able to identify molecules that may have failed in clinical trials and predict how these compounds could be applied to target other diseases.
NaturalAntibody solutions have supported many teams in finding suitable candidates faster thanks to an extensive antibody database and AI-driven antibody analytics.
Learn how AI can help from our latest publication: from Briefings in Bioinformatics: Machine-designed biotherapeutics: opportunities, feasibility, and advantages of Deep Learning in antibody discovery
If you work with antibodies and want to see how AI can help you streamline the drug discovery process, get in touch with me and book a demo to see how our platform works.