As the global environment for new drug development changes rapidly, a "prediction-centered" development approach that uses artificial intelligence (AI) and data is emerging as a key trend. The paradigm is shifting away from judging by observing animal test results to predicting drug efficacy and safety based on data.
The Korea Association for Laboratory Animal Science said at the Bio Evolution 2026 forum held at the Westin Chosun Parnas in Gangnam-gu, Seoul, on the 15th that such changes are underway, and argued that it is necessary to build an "AI and new alternative methods (NHP, New Alternative Methods)-based new drug development platform" with participation from the government, industry, and academia.
According to the association, the global regulatory environment has also been changing quickly. Representative examples include the modernization policy promoted by the U.S. Food and Drug Administration (FDA), the introduction of the ISTAND (Innovative Science and Technology Approaches for New Drugs) program, and the enforcement of Korea's Crisis Response Medical Products Act. As these changes open the possibility of approving new drugs based on nonclinical test data alone, new drug development is being reorganized around "prediction," not "observation."
AI-based drug development can be used across the entire process, from discovering candidate substances to designing clinical trials. As NAMs technologies such as Organoid (artificial organs) and organ-on-a-chip are combined, an environment is being created to evaluate drug efficacy and toxicity faster and more precisely at the early stage.
However, experts noted that NAMs alone have limits. That is because it is difficult to fully reproduce complex systemic responses and the immune system like in the human body. To compensate for these limits, they said the importance of non-human primate models (NHP), which are physiologically similar to humans, is actually growing.
Reflecting this trend, an "AI NHP platform" was newly presented at the forum. It is a system that predicts drug responses and disease progression at the whole-body level by integrating and analyzing behavioral, physiological, and molecular data of laboratory animals with AI. It can be seen as an example of applying the "Digital Twin (a model that virtually replicates reality)" concept to new drug development.
Jang Jae-jin, the new chair of the Korea Association for Laboratory Animal Science, said, "A platform integrating AI, NAMs, and NHP will become the core infrastructure of future drug development," adding, "It will greatly contribute not only to shortening development time and reducing expense, but also to realizing patient-centered Precision Medicine."
Jang said, "Through this platform, we can lower the high failure rate of new drug development and raise the clinical success rate," and emphasized, "Even as AI and alternative testing methods expand, the importance of primate research will grow."
He also said, "The association will serve as a policy bridge so that the AI- and NAMs-based innovation strategies discussed this time can take root in actual industrial settings."
Experts who attended the forum agreed that as global drug development shifts from "observation-centered" to "prediction-centered," legislative support from the National Assembly and government-wide input of budgets and personnel are needed in response.