Company: CNTB
Filing Date: 2025-11-12
Form Type: 10-Q
Source: 0001835268-25-000058
Chunk: 90

Company: Connect Biopharma Holdings Ltd
Filing Date: 2025-11-12
Form: 10-Q
Item: Part I, Item 3
Chunk 90
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Item 1. Legal Proceedings.

From time to time, we may become involved in legal proceedings arising in the ordinary course of our business. We are not currently party to any material legal proceedings, and we are not aware of any pending or threatened legal proceeding against us that we believe could have an adverse effect on our business, operating results or financial condition.

Item 1A. Risk Factors.

We operate in a rapidly changing environment that involves a number of risks that could materially affect our business, financial condition or future results, some of which are beyond our control. In addition to the other information set forth in this report, the risks and uncertainties that we believe are most important for you to consider are discussed in Part I, “Item 1A. Risk Factors” in our 2024 Annual Report and Part II, “Item 1A. Risk Factors” in our Q1 2025 Form 10-Q. Other than the risk factor set forth below, we are not aware of any material changes to the risk factors described in our 2024 Annual Report and Q1 2025 Form 10-Q.

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TABLE OF CONTENTS

The increasing use of artificial intelligence (“AI”) and machine learning in drug discovery and development 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