Aureka Biotechnologies: The 'ChatGPT of Immunotherapy' Accelerates Bispecific Antibody Discovery, Pipeline Enters IND-Enabling Stage

In February this year, the explosive popularity and widespread attention of ChatGPT in China once again directed domestic investors' focus toward the medical AI sector. ChatGPT is expected to bring a new wave of advancements in the medical AI field. According to the "Deep Industry Report on AI Pharmaceuticals," the domestic AI pharmaceuticals market size was 2.7 billion yuan in 2022, and by 2035, this figure will grow to 204 billion yuan.

In April this year, an article published in the Nature sub-journal Communications Biology caught the attention of VCBeat.

The article is titled "A High Throughput Bispecific Antibody Discovery Pipeline." It shows that the research team successfully completed the entire process from design, synthesis, expression, screening to characterization in just a few weeks and screened more than 100 functional bispecific antibody molecules from millions of possibilities. Some of these molecules even outperformed the current positive drugs in clinical use in terms of functionality.

The scientific research transformation carrier behind the team is a start-up AI pharmaceutical company named Aureka Biotechnologies (hereinafter referred to as "Aureka").

"In short, you can understand that we are working on the ChatGPT of the immunotherapy field. The research published in the journal represents the successful validation of our technology platform. Currently, the collaboration pipeline based on this research has entered the IND-enabling stage," Dr. Wei'an Zhao, founder and CEO of Aureka, told VCBeat.

Aureka, founded in March this year and primarily co-founded by Dr. Wei'an Zhao, is a platform-based biotechnology company that combines AI technology with high-throughput digital bio-technology, focusing on protein design and immunotherapy drug discovery.At the beginning of this year, even in the capital winter, Aureka still gained the favor of K2VC and New Alliance Capital with its excellent team and proven technology platform, completing a seed round of financing worth tens of millions of US dollars.

Dr. Zhao Wei'an is not only the founder and CEO of Aureka but also a tenured professor at the University of California, Irvine.Previously, Dr. Wei-An Zhao has co-authored over 100 articles in several prestigious journals, including Science Translational Medicine, Nature Biomedical Engineering, Nature Communications, and PNAS, among others. He has also received numerous awards, such as the MIT Technology Review's TR35 Award, the NIH New Innovator Award, and the University of California, Irvine's Innovator of the Year Award.

"Since Dr. Wei'an Zhao has long been dedicated to fundamental research and scientific translation in the pharmaceutical field, leading his team to publish over a hundred research papers, families of patients often contact me after reading the papers," recalled Dr. Zhao Wei'an. "But every time I have to explain that our technology is still too premature to be applied clinically. These families always reach out to us with great hope but leave disappointed. As a scientist, I feel it’s my duty to help them. However, the traditional drug development process takes far too long from discovery to clinical application—many patients simply can’t afford to wait."

The main reasons for these regrets are the long cycle, high investment, high risk, and low throughput of traditional pharmaceutical R&D. The traditional pharmaceutical process mainly includes target discovery and validation, drug design and discovery, compound synthesis and screening, preclinical research, clinical trials, approval, and market launch. In the process of new drug development, it is generally necessary to screen 5,000 to 10,000 compounds, about 5 drugs will enter the clinical trial stage, and ultimately about 1 drug will be approved for marketing, with a success rate of only 0.01%.

This is the original intention of Dr. Zhao Wei'an to found Aureka, where he wanted to break the traditional trial-and-error model of pharmaceutical research and development and solve the pain points in the research and development of immunology and macromolecular drugs, including bispecific antibodies.

"Aureka's technology platform can generate millions of data sets per experiment, improving R&D efficiency by several orders of magnitude compared to traditional methods. Its technology platform covers multiple aspects such as gene sequences, functions, high-content cell images, and drug-likeness of candidate drugs. Through in-depth analysis and integration by the AI platform, Aureka has revealed new principles of drug discovery and drug design, further advancing the drug R&D process, shortening the time to market for drugs, and providing patients with higher-quality treatment options," Dr. Wei’an Zhao told VCBeat.

Under the leadership of Dr. Zhao Wei'an and with the support of capital, Aureka has now built a multidisciplinary cross-disciplinary team driven by research and development. By leveraging the integration and technological innovation of multiple disciplines such as biology, pharmacy, AI, synthetic biology, microfluidics, biophysics, computational physics, chemistry, and life sciences, Aureka has quickly established its own scientific and technological barriers to support drug discovery.

Led by the team, Aureka has made bispecific antibodies its entry point, focusing on pharmaceutical R&D in the field of large molecules.

Aureka Chooses Large Molecules Over Small Molecules for Three Reasons.

First,The field of AI-driven small molecule drug discovery commonly faces the challenge of slow iterative feedback in chemical synthesis. However, in the realm of AI-driven large molecule drug discovery, researchers can utilize synthetic biology techniques to rapidly generate multimodal datasets on the scale of millions and screen for suitable compounds.

Furthermore,Aureka has chosen bispecific antibodies as the entry point to enter the large-molecule pharmaceuticals field, with a corresponding strategic logic. Traditional bispecific antibody research and development is still at a low-throughput stage that relies on empirical trial-and-error methods using microtiter plates. "Take the development of Hemlibra, a classic bispecific antibody drug, as an example. The development of this bispecific antibody requires the synthesis and characterization of approximately tens of thousands of molecules, which takes about two years. However, Aureka can shorten the synthesis and characterization time for a project of the same scale to within a few weeks."

"Because bispecific antibodies not only need to be evaluated for binding but also for functional expression. Traditional methods cannot quickly address the issues of mismatch and functional screening, but microfluidics-based single-cell technology can screen millions of samples in a day, with the final screened molecules showing good affinity and functionality," emphasized Dr. Zhao Wei'an.

In addition,In addressing pain points such as data silos, AI-driven large molecule drug discovery also holds advantages. In the field of AI small molecule drug discovery, although a significant amount of open-source data exists, most of it is of low quality, with high-quality data primarily held by pharmaceutical companies or research institutes and kept confidential. Conversely, in the AI large molecule drug discovery space, there is an abundance of publicly available data, "but very few people, if any, specialize in cleaning this data. In fact, even if we obtain this data, it holds little significance because the correlation between data from different companies is relatively low."

In the field of large-molecule pharmaceuticals, AI companies are better suited to process data in a customized manner with an excellent AI team when they have the capability to generate large-scale data internally. Feedback and iteration can then be carried out based on this model. Through this approach, AI-driven large-molecule pharmaceutical companies can achieve an experimental closed loop within their organization, essentially without the need for external data support.

"Therefore, there is no data silo in the large molecule pharmaceuticals field. Data for large molecules is relatively accessible; the key lies in how to make customized fine-tuning based on specific projects," explained Dr. Zhao Wei'an.

Currently, Aureka has primarily established a high-throughput single B-cell functional screening technology platform, a yeast protein rapid evolution technology platform based on synthetic biology, and a structure-inspired AI modeling and design technology platform. Notably, Aureka is the world's first company to apply high-throughput single-cell microfluidics technology to bispecific antibody discovery. Moreover, based on the synthetic biology platform, Aureka can generate millions of sequences and multimodal data monthly for rapid feedback and iteration. Combined with the AI modeling and design technology platform, this forms a closed-loop system for its technological platforms.

In fact, globally, there are also many AI pharmaceutical companies that own either a standalone microfluidics technology platform or a standalone synthetic biology technology platform. However, the uniqueness of Aureka's technology platform lies in the simultaneous establishment of three platforms, which, combined with AI technology, form a dry-wet closed loop for large molecule drug development, significantly improving the precision and efficiency of drug research and development.

Based on the three major technology platforms, Aureka has taken bispecific antibodies as the entry point and achieved commercial implementation first. Not limited to bispecific antibodies, Aureka's technology platform will also be used to accelerate the R&D process of TCR-T, CAR-T, ADC, and other difficult drug targets. "In the future, Aureka will realize an integrated solution from discovery to optimization in the entire field of immunotherapy drugs, ultimately achieving the goal of 'one-click generation.'"

Moreover, in the context of the recent cooling down of China's Biotech sector, Aureka, with its global background, is able to quickly achieve commercial implementation. Currently, Aureka has conducted pilot projects with several multinational pharmaceutical giants and achieved results that exceeded expectations, demonstrating the strong potential of its technology platform.

Speaking of the future, Dr. Zhao Wei'an said, "Next, Aureka will actively collaborate with upstream and downstream partners in the industry chain to explore and optimize the pain points in drug research and development, and bring effective and accessible treatment options to patients as soon as possible. We hope that all time-consuming and labor-intensive aspects of the drug development process will be replaced by the processes we have developed. After founding the company, I will do my best to lead the team to focus on and efficiently achieve the transformation of results."

Looking forward to Aureka's rapid development and becoming the ChatGPT of the immunotherapy field as soon as possible.

References:

1. East Wu Securities, "ChatGPT Catalyzes the Further Development of Medical AI, Optimistic About Application Areas Such as AI Pharmaceuticals and Pathological Diagnosis"

2. Guojin Securities, "Technology Upgrade Empowers Industry Applications, AI + Healthcare Development Expected to Accelerate"