Leveraging AI and ML in Drug R&D: Current and Future Impacts on the Life Sciences Industry

Written by: Andy Cronin

How AI and ML Are Revolutionizing Drug R&D

The drug research and development domain is renowned for its intricate and protracted processes, typically entailing expenditures exceeding billions of dollars. On average, ushering a drug from its initial discovery to the market consumes over a decade and a staggering $2.5 billion. Despite the pharmaceutical industry’s relentless efforts, a staggering 90% of clinical drug development endeavors culminate in failure. Consequently, an annual outlay of nearly $100 billion is channeled into pharmaceutical research and development, with companies eagerly seeking ways to optimize their return on investment (ROI). Within this context, AI and ML offer a ray of hope, potentially bolstering ROI while concurrently trimming the timelines for drug approval.

AI introduces innovative mechanisms to expedite drug discovery, design, and optimization, enhancing the overall efficiency of the entire process from inception to fruition. By swiftly determining the viability of a drug target early in the process, AI translates into significant long-term cost savings. The aspiration to accelerate time-to-market by three to five years while extending the period before patent expiration has become a strategic imperative for numerous companies.

Contrary to its reputation for sluggish adaptation, the pharmaceutical sector now exhibits substantial interest in embracing these transformative technologies. The adoption and implementation of AI are becoming increasingly integrated into biotech and pharma strategies.

In our recent podcast focused on AI and ML, Dr. Iris Grossman, Chief Therapeutics Officer at Eleven Therapeutics, shed light on her extensive use of AI across the entire spectrum of discovery and development processes as she has worked in four pharma companies and biotech companies. Dr. Grossman remarked, “From the early stages of sifting through potential candidates to understanding disease biology, interpreting oodles of data through assays, stratifying patients for clinical trials, and developing what we now refer to as ‘digital twins,’ AI has a role to play in all these applications.”

The Current Landscape of AI and ML in Drug R&D

A Cowen Group study reveals that AI currently accounts for approximately 16% of drug discovery efforts, poised to grow by a remarkable 106% over the next three to five years. Furthermore, AI adoption in preclinical and clinical phases steadily increases, with an anticipated growth rate exceeding 90%. The most immediate potential for AI lies in new candidate discovery, followed by identifying new drug biomarkers and targets.

Realizing the full potential of AI and ML demands more than just advanced algorithms. Investors seek access to comprehensive, dynamic databases enriched with diverse data types, including genomics, proteomics, and chemistry-based information. Additionally, having wet-lab capabilities is crucial. AI tool providers and biotech companies meeting these criteria stand well-positioned for commercial success and future expansion.

A Surge in Investment in Drug Discovery

The pharmaceutical industry’s interest in AI is unmistakable, with AI-focused collaborations between major pharma companies and biotechs valued at over $32 billion in recent years. One prominent example is Insitro, an ML-powered drug discovery firm that secured $143 million in series B funding in 2020, followed by an additional $400 million in series C funding and a $2 billion collaboration deal with Bristol Myers Squibb (BMS) the following year. BMS has also collaborated with other AI-powered companies, such as Exscientia, Evotec, and SyntheX, contributing to a total investment of approximately $8.75 billion in AI-focused deals. Other major players in the pharmaceutical industry, including Roche/Genentech and Sanofi, have likewise invested heavily in AI-led drug discovery initiatives, further exemplifying the growing trend.

Emerging players continue to enter the field. In 2021, AstraZeneca, Merck, Pfizer, and Teva joined forces to create AION Labs, a new venture dedicated to supporting firms employing AI in drug development. By March 2023, AION had already launched two AI-powered start-ups: OmecAI, focusing on enhancing the clinical trial readiness of drug candidates, and DenovAI, dedicated to identifying potential antibodies for novel treatments.

Illustrative Case Studies Across the R&D Spectrum

Companies that are incorporating AI and ML capabilities across the various stages of R&D are experiencing a positive feedback loop that validates the efficacy of AI in biotech and pharma. Several notable case studies underscore this phenomenon:

1. DeepMind: Renowned for its breakthroughs in drug discovery, DeepMind developed the AlphaFold AI platform to address the challenge of 3D protein modeling, successfully mapping all 200 million known proteins in less than four years. This groundbreaking work significantly accelerated research efforts in biology and has already been applied to develop molecular “syringes” for delivering cancer-killing drugs and gene therapies.
2. Mendel: This AI company employs its deep-learning engine, Mendel.ai, to streamline clinical trial matching. By harnessing natural language processing of clinical records and automated clinical reasoning, Mendel.ai has demonstrated its ability to boost patient enrollment by 20% to 50%, facilitating access to potentially life-saving treatments.
3. BioXcel Therapeutics: Utilizing AI, BioXcel expedites identifying and developing medicines in neuroscience and immuno-oncology. The company’s first proof-of-concept product, BXCL501 (IGALMI™), transitioned from discovery to approval in less than four years since human trials commenced. Positive data for another drug candidate, BXCL701, further validates the effectiveness of BioXcel’s AI-powered drug discovery and development approach.

The Road Ahead: The Quest for AI and ML Talent

The biotech and pharma industries are determined to incorporate AI and ML into their drug R&D endeavors, foreseeing substantial long-term benefits. Companies are embracing these technologies and planning to expand their adoption. Securing individuals with the requisite skills and expertise will maximize ROI.

However, the industry faces a twofold challenge. Firstly, AI talent scarcity is driven by soaring demand from AI tool providers, biotech, pharma, and major tech companies. The competition for top AI talent is fierce. Dr. Grossman believes that biotech and pharma have an appeal for AI specialists to consider as it is extremely multidisciplinary with opportunities to learn, converse, and collaborate quite a lot every day which doesn’t exist in other industries. She finds that she can leverage those elements and bring great talent to their side.

Secondly, AI and ML roles are relatively new to the industry, necessitating a learning curve for companies seeking to fill them. We have encountered this challenge with some of our biotech clients seeking AI and ML leaders to build novel platform technologies. Hiring managers often lack AI/ML expertise, necessitating presenting a diverse range of candidates with relevant experience, and numerous candidate discussions to pinpoint the best fit.

Another panelist from our podcast, Operating Partner at ARCHIMED and Former Chief Data Officer at Novartis, Dr. Shahram Ebadollahi, highlights that the lack of AI expertise at the top is something to be acknowledged as boards of top pharma companies often lack individuals with expertise in AI, and many senior executives responsible for technology do not possess AI backgrounds. “So, in your company, a business that is very much all about data and extracting information and knowledge from that data to do their business, you better use AI; therefore, you need to have those AI experts or senior executives. It’s an interesting question for companies to address.”

The Future of AI/ML in Drug R&D

The demand for AI and ML roles is poised for growth, intensifying competition, and underscoring the necessity for life science companies to redouble their efforts in talent acquisition. AI and ML have ushered the drug development industry to a critical inflection point, where recruiting the right talent, from top to bottom, will be instrumental in successfully harnessing and integrating AI/ML tools.

Dr. Ebadollahi advocates for the thoughtful integration of AI and ML across all facets of a pharma or biotech company’s pipeline. He emphasizes embedding AI as “cogs within workflow processes”, a strategy he believes will drive efficiency, growth, and new opportunities. However, he also cautions against overhyping AI’s capabilities, emphasizing the need to temper expectations until we see a drug that has been fully developed by AI-driven processes.

As AI and ML continue to demonstrate value in delivering proof of concept in drug discovery and development with increased efficiency and quality, tangible benefits will accrue. Ultimately, the hope is that these advancements will translate into more targeted, safer, and cost-effective therapies delivered at a faster pace to many patients in need.

To discuss this topic further, we invite you to contact Andy Cronin.