The path from basic scientific research to industrial application remains narrow. Despite ongoing efforts to transfer public technologies developed through national research and development investments to the industrial sector, the commercialization results from technology transfers are still lacking. According to the '2025 Public Research Institution Technology Transfer and Commercialization Status Report,' only 9.4% of public technology transfer contracts have led to sales.
Commercializing technology involves bridging a significant gap between research and industry. iBeam Technology, where I work, originated from a core technology developed in a KAIST laboratory for biomicroscopy. This innovative technology allows for direct observation of phenomena occurring within living organisms, and we aimed to realize its value outside the lab. After founding the company and entering the global market, I learned that having good technology is not enough; it must also solve industry problems.
Currently, the biggest challenge facing the bio industry is the low success rate of new drug development. Various analyses indicate that only 7.9% of candidate substances that enter Phase 1 clinical trials reach approval, with an average of 10.5 years required for approval and total costs estimated at over 2 trillion won. The most significant hurdle is Phase 2 clinical trials, where only 28.9% of candidates progress to the next stage. The approval success rate for anticancer drugs is even lower, at 5.3%. The success rate for candidates entering Phase 1 continues to decline, worsening the situation. A major reason for this is that preclinical results do not replicate in humans. Traditional methods, which involve observing fixed tissue samples, can only provide a snapshot of how drugs move and interact with cells in the body.
Advanced biomicroscopy technology directly addresses this limitation. By observing cells and microenvironments within living animal tissues in real-time over extended periods, we can precisely track whether drugs reach their targets and how they respond, all within a single organism over time. This approach significantly reduces the number of experimental animals needed, increases data reliability, and allows for the early elimination of less promising candidates. While AI has accelerated the search for candidate substances, the verification stage remains a bottleneck that hinders the reduction of development timelines. It has become increasingly important to quickly identify viable substances among the many candidates discovered. Ultimately, the key to accelerating new drug development lies in creating a structure that allows for rapid failures and precise successes. This trend has already been confirmed in global research settings, and our company's equipment is being utilized by leading research institutions and pharmaceutical companies in the U.S. and Europe.
However, this is not just a success for one company; it highlights the challenges facing the domestic research industry. An analysis of the purchasing status of national research facilities and equipment shows that the foreign-made share of microplate readers is 100%, sample slicers 95.8%, optical microscopes 93.3%, and gas chromatography 91.0%. Even basic equipment used daily in laboratories is largely dependent on imports. Although national research and development budgets are invested from basic research to candidate substance discovery, the research equipment that produces the data needed to verify these results relies on foreign sources. In the AI era, having unique data is a competitive advantage, and precision analysis technologies that produce differentiated data will have a ripple effect across industries that integrate AI. Competing with the same equipment producing the same data makes it difficult to stay ahead. A well-structured investment is needed to create a virtuous cycle where foundational technologies from research institutions are transferred to companies, validated in the market, and lead to reinvestment.
In the future, precision biovision technology will become a core data foundation for new drug development, precision medicine, and digital healthcare. High-resolution images obtained from within living bodies, combined with AI, will enable earlier detection of disease progression, identification of new drug targets, and more accurate matching of treatments to patients. This transformation will not be driven by the efforts of a single company but by an ecosystem created through collaboration among research, industry, and policy. As part of this ecosystem, I aim to contribute to human health by developing technologies that reveal the previously unseen movements of life.
* This article has been translated by AI.
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