As AI-led drug discovery transforms the pharmaceutical and biotech sectors Jay Shah, a Managing Director and Head of Healthcare and Life sciences at Interpath Advisory, considers the impact of the technology on investment in the eyes of dealmakers.
It has been four years since Sumitomo and Exscientia made history by developing DSP-1181, the first ever molecule to be created using artificial intelligence (AI). Since that landmark event, interest in AI-enabled biotech has surged, not least from the pharmaceutical industry as it looks to speed up the drug discovery process and maintain cost efficiency.
Several compounding factors have come together to drive this trend. Firstly, pharmaceutical companies are searching for the next blockbuster drugs to mitigate revenue losses after losing exclusivity on key products. Biotech can offer access to innovative new treatments and transformative technologies in key therapeutic areas. Cutting edge tools, such as CRISPR, CAR-T therapy and mRNA-based technology, enable more efficient R&D compared to traditional processes.
By turning to biotech, pharma can access tools and techniques to navigate complex disease areas, such as cancer, autoimmune disease, and genetic disorders, potential sources for new blockbusters. Furthermore, advancements in biotech space in stem cell therapy and tissue engineering go beyond the scope of many pharma companies.
But above all, we’re seeing a strong emerging focus on precision medicine where biotech is using technology to create targeted treatments to improve patient outcomes. No doubt, this creates an exciting new frontier for the pharmaceutical industry.
Capital to fuel the AI and biotech boom
It shouldn’t be a surprise that M&A activity between biotech firms and pharma companies is increasing. They hold complementary goals and are symbiotic in nature. Biotechs need the infrastructure and financial support that a larger pharmaceutical company can offer, and in return, biotechs can provide the innovative pipeline that is needed to maintain a competitive edge in the pharmaceutical industry.
The proof is in the dealmaking. The market has seen a number of notable pharma-biotech transactions in recent years that show this M&A trend in action, not least Merck and Acceleron Pharma (2021), Bristol Myers Squibb and Celgene (2019), Roche and Spark therapeutics (2019), Novartis and AveXis (2019), and Gilead and Kite Pharma (2017).
But there is much more to M&A than just corporate takeovers. Any conversation about raising finance should consider venture capital and private equity funds, which have a critical role to play too. Venture capital funds are important to support early-stage investments and facilitate ongoing innovation. On the other hand, private equity houses act as a key enabler when it comes to scaling the company and providing it with the necessary commercial and strategic guidance to achieve its growth potential.
PE interest in biotech.
Private equity is one of the most prominent sources of investment and essential for many businesses in the biotech and wider healthcare and life sciences sector as they look to scale up. Despite significant interest and investment flowing into the sector, private equity can still view biotechs with caution. This is partly due the associated risk profile, such as the company is pre-revenue, drug or technology outcomes may uncertain and there maybe significant regulatory hurdles to overcome. Additionally, when considering that biotech requires sizable, long-term levels of capital, biotech can sometimes be pushed out of private equity’s investment criteria.
This is not to say that private equity will not invest, Biotech is no doubt an exciting and dynamic sector with significant potential for scientific breakthroughs. For private equity the drug discovery process becomes more interesting as it approaches commercialisation, this is often the point at which biotechs need growth capital to scale their business. Successful investment requires a combination of thorough diligence and specialist industry knowledge.
Overcoming AI limitations.
For AI to become more commonplace in the drug discovery process, there are still broader limitations that need to be addressed, examples include, but are not limited to:
The Black Box Problem
The Black Box Problem sees complex AI algorithms creating a lack of transparency as to how decisions are made, which can drive a level of distrust in AI-based outcomes. This is a particular issue when it comes to healthcare where both clinicians and patients need to trust the drug discovery process and the end product. Overtime, being able to provide insight and clarity on how an AI algorithm works will help reduce perceived risk and help get investors comfortable.
Quality of Data
AI-based approaches require large amounts of data for training purposes. In certain cases, the volume of data available may be limited or of low quality; therefore, compromising the accuracy and/or reliability of results. Given high susceptibility to data bias and unrepresentative results, AI-based approaches may provoke ethical and legal arguments. To increase confidence in results, it is important to demonstrate that a diverse range of data is used for training to curtail the risk of biases.
Data Privacy
AI systems process large volumes of personal information and so companies must carefully manage sensitive information in a way that maintains confidentiality but also allows one to harness AI’s technological capabilities. With precision medicine becoming a reality, the issue of data privacy is amplified and forces biotech and pharmaceutical companies to have mitigation strategies in place for potential data breaches. Investors will be looking for assurance that data privacy is taken seriously, and that any risks are robustly mitigated.
Given that AI in drug discovery is still in early stages, there’s no doubt that the challenges and opportunities presented by AI will evolve. While addressing those limitations is critical, there are also other areas that can be supported to reassure investors. Not least, as with any fundraising effort, it is essential to have a clearly articulated business plan with supporting financials, a practical commercialisation and go to market strategy, and a robust pipeline.
Whilst it will take time to fully convince some investor groups, momentum and interest is there to deploy capital. As AI becomes ever more mainstream, there’s no doubt that we’ll see more venture capital and private equity jumping into the market to back some of the most ambitious and promising businesses out there in the sector. Perhaps creating the next DSP-1181 and the next landmark moment in biotech and pharma.
This piece was originally published in HealthInvestorUK
