Company: CNTB
Filing Date: 2025-11-12
Form Type: 10-Q
Source: 0001835268-25-000058
Chunk: 77

Company: Connect Biopharma Holdings Ltd
Filing Date: 2025-11-12
Form: 10-Q
Item: Part I, Item 2
Chunk 77
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 introduces new and evolving risks that could harm our business and competitive position.

AI is increasingly playing a role in our industry, being used for target identification, drug discovery, preclinical modeling, clinical operations, and data analysis, among other things. While it is not a significant factor in our current operations, AI may play a future role in our operations based upon our evaluation of its usefulness to us. As with many new and emerging technologies, AI presents numerous risks and challenges that could adversely affect our business.

AI technologies are inherently complex and evolving. They may not function as intended, produce accurate results, or provide actionable insights. The quality of AI outputs depends heavily on the quality and quantity of input data, which in the life sciences context may be limited, biased, incomplete, or subject to regulatory and privacy constraints. If we utilize AI in the future and our AI systems fail to achieve their intended purposes – such as identifying viable therapeutic candidates or targets, predicting biological outcomes, producing reproducible results, optimizing clinical trial designs or operations, and other similar or related purposes – our product development efforts may be delayed or unsuccessful. If we are unable to successfully integrate and manage AI within our business, or if AI fails to deliver the expected benefits, our ability to develop rademikibart or any other new therapies could be materially adversely affected.

The industry in which we compete is characterized by rapid technological advancements, frequent introductions of new products and heavy competition. The development of rademikibart and any other future product candidates is vital to our success. The implementation of AI technologies and processes by us or by any third-party collaborators, including advanced predictive analytics, computational approaches and so-called “generative” AI, has the potential to provide significant benefits in these areas. Use of AI in our efforts may be difficult to deploy successfully due to operational issues inherent in such methods. In particular, AI algorithms utilize machine learning and predictive analytics which may lead to flawed, biased, or inaccurate results, or exposure to competitive and reputational harm. 

Even with the successful implementation of AI, we may fail to correctly identify indications and allocate resources efficiently, which could adversely impact our pipeline and ability to compete effectively. Developing, testing and deploying resource-intensive AI systems may also require additional investment and increase our costs, and there is no guarantee that our investment in such systems would lead to more effective or efficient development of rademikibart or other investigational products, or lead to eventual regulatory approval or commercialization of any new products. 

We also face increased competition from other companies that