According to the pharmaceutical and biotech industry on the 11th, Insilico Medicine, an AI-based drug discovery company, began a Phase 3 clinical trial in China on the 7th of last month for “Lentosetip,” a candidate drug for the treatment of idiopathic pulmonary fibrosis (IPF). The trial will recruit 320 patients from 47 institutions in China, administer the drug for 52 weeks, and evaluate the annual rate of decline in forced vital capacity (FVC), a measure of lung function.
The reason Lentosetip is drawing attention is that it goes beyond simply using AI to predict the likelihood of success for existing drug candidates in clinical trials; instead, it directly applies AI to the processes of identifying new drug targets and designing novel compounds. InSilicoMedicine used its proprietary AI platform, “PandaOmics,” to identify TNIK as a new target for fibrotic diseases. Subsequently, using the generative chemistry platform “Chemistry 42,” the company designed novel small-molecule compounds that inhibit TNIK and, through actual synthesis and validation, optimized the drug’s efficacy and pharmacokinetic properties.
Since 2021, Insilico Medicine has identified 31 preclinical candidate compounds (PCCs) through its AI platform, 13 of which have received IND approval. For its in-house projects, it took an average of 12 to 18 months from the start of research to the confirmation of a PCC, with approximately 60 to 200 compounds actually synthesized and tested per program. Compared to the typical 2.5 to 4 years required for early-stage drug discovery, this represents a significant reduction in both trial and error and overall time.
While there is growing interest in South Korea in joint research between AI platforms and related companies, cases where actual candidate compounds have advanced to clinical trials remain limited.
The most advanced example in Korea is Pharos iBio Co., Ltd.’s acute myeloid leukemia (AML) treatment candidate “Rasmotinib” (PHI-101). Rasmotinib, discovered through the company’s proprietary AI-based drug discovery platform “Chemiverse,” has completed a Phase 1 clinical trial in South Korea and Australia targeting patients with relapsed or refractory acute myeloid leukemia (AML). Last year, “PHI-501,” a candidate drug for the treatment of intractable solid tumors, also received IND approval for a Phase 1 clinical trial in South Korea.
Oncocross Co.,Ltd.’s sarcopenia candidate “OC514” has also completed a Phase 1 clinical trial in Australia. OC514 is a combination drug that combines two active ingredients; Oncocross Co.,Ltd. utilized its transcriptome-based AI platform, “RaptorAI,” to identify potential new indications such as sarcopenia and cancer-related cachexia. However, it is important to note that OC514 is not a novel compound designed from scratch but rather a drug repositioning effort in which the combination of existing ingredients and their indications were derived using AI.
In addition, companies such as SyntekaBio,Inc.(226330)and #Standardim are pursuing AI-based candidate discovery and the development of their own pipelines; however, their success in repeatedly advancing multiple candidates to the IND and clinical stages has been limited.
Industry insiders cite a lack of capital and specialized personnel as the main reasons for this gap. The fact that investment incentives for pharmaceutical and biotech companies are still insufficient is also cited as a limitation. A pharmaceutical and biotech industry official stated, “Collaboration on AI-driven new drug development is increasing in Korea, and interest in its importance has grown significantly,” but added, “From a corporate perspective, there is still a lack of a pressing need to invest large amounts of manpower and funds to establish the entire process in-house.”
Some observers point out that to enhance competitiveness in AI-driven new drug development, a national-level integrated data management system for new drug development is needed, along with a standardized format that allows for a comprehensive overview of data catalogs.
There are also calls for the government to introduce support measures, including initiatives to attract international talent. The United States, for instance, has been supporting the development of bio and medical data suitable for AI training, establishing standards, and cultivating interdisciplinary talent through the National Institutes of Health’s (NIH) “Bridge2AI” program, while providing initial research funding to AI-driven new drug development startups via the Small Business Innovation Research (SBIR) and Small Business Technology Transfer (STTR) programs.
A bioindustry official stated, “If it is difficult to establish the necessary facilities independently, a long-term joint development model is also possible, in which AI companies, pharmaceutical firms, and contract research organizations (CROs) share various rights and responsibilities—from candidate compound discovery through synthesis, validation, preclinical testing, IND submission, and clinical trials.”
