China’s Lepu Biopharma and DP Technology Achieve Breakthrough in ADC Cancer Drug Development

21 November 2024 | Thursday | News

Lepu Biopharma Co., Ltd. and Beijing DP Tech Co., Ltd. mark a key milestone in their partnership, integrating advanced AI-driven ADC linker-payload design with ADC development. The collaboration enhances ADC drug optimization, advancing candidates toward clinical trials and setting new standards for innovation in China’s cancer therapy landscape.
Picture Courtesy | Public Domain

Picture Courtesy | Public Domain

This partnership has effectively integrated DP's ADC Linker-Payload design platform with Lepu's ADC technology development platform, capitalizing on their respective strengths. The overall optimization of the ADC drug has been achieved in a relatively short time.

Lepu Biopharma Co., Ltd. (Lepu Biopharma) and Beijing DP Tech Co., Ltd. (DP Technology) have recently announced a significant milestone in their Antibody-Drug Conjugate (ADC) drug collaboration. This partnership has effectively integrated DP's ADC Linker-Payload design platform with Lepu's ADC technology development platform, capitalizing on their respective strengths. The overall optimization of the ADC drug has been achieved in a relatively short time. This project has reached an important milestone, further validating the approach of accelerating ADC innovative drug development through computational design. Moving forward, both parties will continue to deepen their R&D collaboration in this field, jointly committed to promoting the continuous optimization and advancement of innovative drug development processes.

The ADC Linker Payload design platform, meticulously crafted by DP Technology, utilizes "RiDYMO®", an AI-for-Science driven hit discovery platform. Furthermore, "AI + first principles" based computational methods are employed to predict linker cleavage sites and ensure the correct payload release, achieving outstanding cell-killing and bystander effects. Additionally, the designed antibody-drug conjugates (ADCs) maintain excellent hydrophilicity and plasma stability. Leveraging Lepu's mature and complete ADC development system, candidates developed by the new ADC platform have demonstrated significant efficacy in CDX models targeting multiple targets. This project has been successfully validated in animal models and is currently advancing the candidate ADC to the clinical stage.

Lei Fang, Ph.D., Vice President of Lepu Biopharma and CEO of CtM Bio Co., Ltd., expressed high appreciation for this cooperation: "Introducing advanced computational methods to solve scientific problems and jointly pioneering breakthrough explorations is our goal in the ADC project collaboration. As a leader in 'AI for Science', DP Technology has played a crucial role in this project with its newly developed ADC design platform. By integrating Lepu Biopharma's advanced ADC development platform with AI-driven design, we proposed novel perspectives on ADC development while significantly expediting the process. By complementing each other's strengths, we jointly provide new inspiration and ideas for innovative ADC drug design."

Weijie Sun, CEO of DP Technology, expressed great anticipation for future collaboration: "Lepu Biopharma, as an innovative biopharmaceutical company focused on cancer treatment, particularly in the areas of targeted therapy and immunotherapy, has extensive experience and a long-standing track record in developing innovative ADC drugs. We firmly believe that our close cooperation will significantly accelerate the design and development of ADC drugs, and we are optimistic about the potential to develop innovative, highly effective, and differentiated new ADC therapies. I am eagerly anticipating the ongoing and deepened collaboration between both companies in the field of pharmaceutical innovation!"

Concerning the various demands from industry partners in the ADC field, DP Technology's ADC platform has successfully empowered various scenarios and projects. For instance, it utilizes AI combined with first-principles calculations to predict linker cleavage sites and improve attachment stability. By integrating pre-trained models with fine-tuning strategies and expert insights, the platform can predict and modify physicochemical properties such as payload efflux and bystander effects. Additionally, there is ongoing exploration and collaboration in the overall evaluation of properties like aggregation effects in ADCs.

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