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AI Takes Center Stage in Drug Development Race: Big Pharma Joins the Fray

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This year’s keyword is “AI”
Big Tech and Big Pharma participate in development competitions one after another
It is expected to grow to 5.3 trillion won in 2027
South Korean companies also compete for dominance

Developing new drugs using artificial intelligence (AI) is emerging as a hot topic in the pharmaceutical and biotech industries. As global big techs and big pharma collaborate to develop new AI drugs, South Korean companies are also taking steps to dominate the related market.

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According to industry sources on the 13th, the Korea Trade-Investment Promotion Agency (KOTRA) listed AI as a keyword in the bio-industry to watch this year, along with obesity treatments (GLP-1) and antibody-drug conjugates (ADC), after participating in the JP Morgan Healthcare Conference (JPMHC), the world’s largest pharmaceutical and bio investment event held in San Francisco last month.

The AI platform for new drug development unveiled by US IT company Nvidia was also mentioned as one of the most noticeable presentations at this year’s JPMHC. Nvidia announced that global pharmaceutical giant Amgen is adopting its new AI drug development platform ‘Bionimo’ and building a supercomputer ‘Freja’ in Iceland. At the time, Kimberly Powell, vice president of Nvidia’s healthcare division, explained, “Using Nvidia’s AI system can process data seven times faster and save seven times the cost,” adding, “AI will further expand the $250 billion new drug development market.”

Insilico Labs, a new AI drug development company established by Alphabet, Google’s parent company, recently collaborated with global pharmaceutical companies Eli Lilly and Novartis to develop new drug candidates. The contract amounts to a maximum of $1.7 billion and $1.2 billion, respectively. Last September, Microsoft (MS) agreed to collaborate with clinical AI company Paige to construct an AI cancer diagnosis model. It embarked on the development of disease diagnosis technology using AI.

New drug development is a typical high-risk, high-return business. It requires a lot of time and money, but the success rate is extremely low. Typically, in the pharmaceutical and bio industries, about 90% of candidate drugs that reach the clinical stage fail to pass, and it is estimated that an average of $2.5 billion is spent over 10-15 years to enter the U.S. market. However, it is expected that using AI from the initial stage of new drug development can drastically reduce the time and cost invested. This is why global corporations are rushing into the AI new drug development business. The market outlook is also bright. According to the Food and Drug Safety R&D Issue Report, the global market size for new AI drugs, which was only $609.8 million in 2022, is expected to grow to $4.035 billion by 2027, with an annual growth rate of 45.7%.

Daewoong Pharmaceutical has signed a business agreement with Merck Life Science to construct an AI-based new drug development platform and full-cycle technology support for new drug development. Park Joon Seok (left), head of Daewoong Pharmaceutical’s New Drug Center, and Jung Ji Young, representative of Merck Life Science’s Science and Lab Solutions, are taking a commemorative photo after signing the business agreement. [Photo=Daewoong Pharmaceutical]

South Korean companies are also paying attention to AI’s new drug development. Daewoong Pharmaceutical signed a memorandum of understanding (MOU) with Germany’s Merck Life Science last month to ” construct an AI-based new drug development platform and full-cycle technology support for new drug development.” The agreement will allow the two companies to improve the efficiency and productivity of new drug development. In particular, Merck will provide technical support needed throughout the whole cycle of new drug development through AI for the first time in the industry through this business agreement. Daewoong Pharmaceutical expects to enhance the efficiency of the new drug development process by utilizing Merck’s new drug development software, Sincia, and the platform AMS, which supports the synthesis of low-molecular libraries.

JW Pharmaceutical is building an R&D platform based on its own technology for innovative new drug development and is pushing for open innovation with promising biotechs with AI technology. In 2021, JW Pharmaceutical plans to develop innovative new drugs through drug 3D simulation technology in collaboration with Syntekabio in 2021 and Oncocross in 2022. Last year, it entered a business agreement with Merck and began researching and developing raw material drugs using AI.

Hanmi Pharmaceutical signed a business agreement with biotech Eisen Science for AI-based anticancer drug research and development. Eisen Science is developing 14 new drug pipelines through its transcriptome data-based AI new drug development platform, which can derive potential targets and mechanisms of drugs. GC Cell is collaborating with AI company Lunit. They are researching the solid cancer treatment candidate AB-201 using Lunit Scope IO, an AI biomarker under development by Lunit.

Paros AiBio, a specialized company for AI-based innovative new drug development, conducts phase 1 clinical trials of the acute myeloid leukemia drug “PHI-101” using its self-developed AI new drug development platform Chemiverse. Chemiverse assists the new drug development process from the action point discovery stage to the candidate substance derivation stage based on about 230 million pieces of big data and complex algorithms. The company explained that it had reduced the cost and time for developing PHI-101 by a maximum of 80.2% and 63.6%, respectively, using Chemiverse.

An industry insider explained, “AI’s new drug development is attracting attention because of its economic efficiency. The industry expects that the time and cost of new drug development will be dramatically shortened through the use of AI.” and added, “In particular, the effect is expected to be maximized when applied to the development of treatments for rare and intractable diseases, which are difficult to recruit from test subject recruitment stage due to the small number of patients.”

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