TL;DR:
- Takeda Pharmaceutical acquired an experimental psoriasis drug using AI, marking a breakthrough in drug discovery.
- Pharmaceutical companies globally are embracing AI to reduce costs and speed up time to market.
- Investments in AI-driven drug discovery companies have tripled in the past four years, reaching $24.6 billion in 2022.
- Collaboration between pharma companies and AI startups, such as Recursion Pharmaceuticals and Exscientia, has led to potential drugs in human trials.
- Pfizer utilized AI to develop the Comirnaty Covid vaccine and the Covid pill Paxlovid, speeding up the approval process.
- GSK has a dedicated team of 160 experts in AI and ML to support R&D and generate data for machine learning models.
- Chinese drugmakers are also investing in AI to enhance global competitiveness.
- Challenges include the need for validation through laboratory experiments and the presence of biases in training data.
- Venture capitalists are increasingly interested in investing in AI drug discovery companies.
- The future of pharmaceutical research and development relies on the potential of AI and ML for groundbreaking discoveries and life-saving medicines.
Main AI News:
Discovering a groundbreaking pharmaceutical usually demands painstaking laboratory analysis, with research teams meticulously examining data and test outcomes in search of a promising contender. However, Takeda Pharmaceutical Co., a prominent Japanese company, achieved an extraordinary feat by acquiring an experimental psoriasis drug for a staggering $4 billion from a Boston startup. What sets this acquisition apart is that the compound was selected within a mere six months using artificial intelligence (AI).
In the following months, this drug, chosen from a vast pool of potential molecules through the implementation of AI and machine learning algorithms, will advance to the final stages of clinical trials. If these trials prove successful, it could mark one of the first therapeutic breakthroughs made with the assistance of AI. Jefferies analysts anticipate that this pioneering drug could amass annual sales of up to 500 billion yen ($3.7 billion).
Takeda’s strategic move aligns with the broader trend of pharmaceutical companies worldwide embracing AI. They are forging alliances with tech-savvy startups and bolstering their in-house data science teams in a bid to reduce costs and expedite the time required to bring drugs to market. Morgan Stanley estimates that over the next decade, incorporating AI into early-stage drug development could yield an additional 50 innovative therapies, collectively generating more than $50 billion in sales.
According to research firm Deep Pharma Intelligence, investments in AI-driven drug discovery firms have tripled in the past four years, reaching a staggering $24.6 billion in 2022. In a noteworthy example, Sanofi agreed in January last year to provide UK-based Exscientia Plc with an upfront payment of $100 million. The collaboration also includes the potential for milestone payments amounting to $5.2 billion, aimed at conducting research on novel medications and developing up to 15 candidates in the fields of oncology and immunology using AI systems.
Bayer, Roche Holding, and Takeda are among the companies collaborating with Recursion Pharmaceuticals Inc., based in Salt Lake City, to explore the potential of machine learning in drug discovery. Meanwhile, AstraZeneca Plc has formed partnerships with BenevolentAI in the UK and Illumina Inc. in San Diego to pursue similar endeavors.
Alex Devereson, a partner at McKinsey & Co. who advises pharmaceutical companies on digital processes and analytics, emphasizes the significant impact that AI can have when applied effectively in research and development. Devereson anticipates that within five years, these AI-driven approaches will become firmly integrated into the fabric of pharmaceutical R&D processes, yielding substantial results at scale.
While AI undoubtedly aids the drug discovery process, scientists must still undertake considerable traditional legwork following the selection of a molecule. In the case of Takeda’s compound, several more years of human clinical trials and rigorous testing were necessary. Furthermore, AI possesses certain limitations. For instance, it cannot predict intricate biological properties, such as a compound’s effectiveness and potential side effects.
The interest of Big Pharma in investing in artificial intelligence (AI) and machine learning (ML) gained significant momentum after 2018, when DeepMind, a unit of Google’s parent company Alphabet Inc., utilized their AI program called AlphaFold to outperform a biologist in predicting protein structures—a crucial aspect of understanding diseases. Deciphering the complex shapes of proteins, a longstanding challenge in biology aids drug researchers in narrowing down potential molecules for interaction and identifying effective treatments.
The traditional process of bringing a new drug to market has been an expensive endeavor, costing nearly $3 billion and facing a high failure rate of around 90% for experimental medicines. Consequently, technologies that expedite this process have the potential to drive substantial profits. With AlphaFold, determining the three-dimensional structure of a protein now takes seconds, a remarkable improvement compared to the months or years it previously required. Eric Topol, the founder, and director of the Scripps Research Translational Institute in California, as cited on DeepMind’s website, attests to this groundbreaking advancement.
The adoption of AI by pharmaceutical companies received a significant boost during the Covid-19 pandemic, as the industry urgently sought to develop effective weapons against an unknown virus. Pfizer Inc., in partnership with BioNTech SE, turned to AI to develop the Comirnaty Covid vaccine. Additionally, Pfizer expanded its collaboration with XtalPi Inc., an AI-driven drug discovery company based in Shenzhen, China, to expedite the chemical formulation process of the Covid pill Paxlovid.
Both the vaccine and the pill received approval from the US Food and Drug Administration in under two years, a remarkably faster timeline compared to the typical 10-year journey for most drugs to reach the market. The regulatory agencies played a pivotal role in accelerating the process of providing weapons against Covid to the public.
Takeda’s acquisition of an experimental drug from Nimbus Therapeutics LLC, based in Boston, could potentially become one of the few oral treatments available for psoriasis, a widespread skin condition affecting 125 million individuals worldwide. Moreover, the drug, currently known as TAK-279, shows promise for treating other conditions such as Crohn’s disease—an inflammatory bowel disorder. TAK-279 has successfully progressed through the initial two stages of human trials. Jeb Keiper, the CEO of Nimbus, highlights that algorithms were able to identify the present molecule in approximately a quarter of the time it would take using a traditional approach.
Conducting manual tests on chemicals in beakers would necessitate evaluating an overwhelming number of molecules—an impractical task. However, instead of grappling with tens of thousands of compounds, computers propose testing just ten compounds in a laboratory and gathering feedback from the results. The machines then learn from these outcomes to refine their predictions, providing the next set of hundred candidates for testing. Ultimately, this iterative process filters down to a single molecule with the highest potential, as described by Jeb Keiper.
In the pursuit of breakthrough medicines, Takeda has assembled a team of over 500 quantitative scientists and technology experts across its R&D centers in Boston, San Diego, and Shonan, Japan. These dedicated individuals harness the power of artificial intelligence (AI) and machine learning (ML) to analyze vast amounts of data, identify promising molecules for protein targeting, comprehend disease characteristics, and assess variations among different patient populations. Takeda collaborates with the Massachusetts Institute of Technology (MIT) and various AI startups, recognizing the value of leveraging cutting-edge technology to enhance scientific insights and accelerate discovery.
Takeda’s competitors, including Pfizer, are also embracing AI. Pfizer’s partnership with DeepMind’s AlphaFold, for instance, aids in designing and validating highly effective therapeutic targets that were previously unknown. Lidia Fonseca, Pfizer’s Chief Digital and Technology Officer, highlights the significant reduction in computational time—up to 80% to 90%—achieved through the utilization of powerful supercomputing capabilities, AI, and ML models. This accelerated the development of Paxlovid, a crucial treatment in the fight against Covid-19.
Across the globe, numerous potential drugs identified by AI-driven startups have already entered human trials. Recursion Pharmaceuticals Inc. is investigating five such candidates for rare diseases and oncology, while Exscientia has three candidates for conditions like cancer and obsessive-compulsive disorder. Insilico Medicine, based in Hong Kong, has a candidate in mid-stage human trials for treating the most prevalent form of pulmonary fibrosis.
GSK Plc, headquartered in the UK, boasts a team of over 160 experts dedicated to AI and ML, supporting the company’s R&D and manufacturing efforts. GSK generates data to build and feed its own machine-learning models, enabling all scientists within the organization to benefit from the data accumulated over time. Kim Branson, Head of AI at GSK since 2019, emphasizes the value of AI in integrating disparate data sources but cautions that when applied to complex systems, AI may require additional validation through laboratory experiments to ensure safety.
China, too, recognizes the potential of AI to enhance the global competitiveness of its drugmakers. Companies like XTalpi, partly funded by Chinese tech giant Tencent Holdings Ltd., and BioMap, an AI-driven drug discovery firm founded by Baidu Inc. CEO Robin Li, are at the forefront of leveraging AI in their research and development efforts.
Despite the tremendous investments and advancements in AI, challenges remain. Complex systems often necessitate validation through laboratory experiments to ensure safety. Moreover, biases can be present in the data used to train algorithms, potentially leading to biased clinical recommendations, as highlighted by researchers at Stanford University.
Nevertheless, investment in AI drug discovery companies has experienced an unprecedented surge. Venture capitalists seeking evaluations of potential AI-driven ventures have shown a remarkable increase in interest over the past few years. Russ Altman, a bioengineering professor at Stanford with extensive experience conducting due diligence for biotech startups, attests to the rapid growth in demand for assessments of AI drug companies.
The future of pharmaceutical research and development is undoubtedly intertwined with AI and ML. As the industry continues to harness the potential of these technologies, the possibilities for groundbreaking discoveries and the development of life-saving medicines become increasingly promising.
Conlcusion:
The widespread adoption of artificial intelligence (AI) and machine learning (ML) in the pharmaceutical industry signifies a significant shift in drug discovery and development. This paradigm shift presents a lucrative market opportunity, with investments in AI-driven drug discovery companies skyrocketing and collaborations between pharma giants and AI startups flourishing. The integration of AI and ML technologies not only expedites the process of bringing new drugs to market but also has the potential to revolutionize treatment options for various diseases.
As AI continues to unlock cutting-edge insights, reduce manual work, and drive scientific discoveries, the market can expect to witness an influx of innovative therapies and substantial advancements in the pharmaceutical landscape. The future of the pharmaceutical market lies in harnessing the power of AI and ML to propel breakthroughs, improve patient outcomes, and generate substantial returns for stakeholders.
