Drug development is a long and challenging journey. From target discovery to Investigational New Drug (IND) clearance, bringing a novel therapy into clinical trials requires substantial time, investment, and scientific rigor. Every stage of development is interconnected, and even a minor setback can jeopardize an entire program. As artificial intelligence (AI) continues to transform drug discovery by accelerating target identification and candidate optimization, the downstream nonclinical evaluation process must also evolve to translate AI-generated discoveries into robust scientific evidence that meets global regulatory standards.

SIGX1094R, developed by Higgs Therapeutics Inc., is the world’s first targeted therapy for diffuse gastric cancer developed through the integration of organoid disease models and AI technologies. The program advanced from novel target discovery to IND clearances in both China and the United States in just over three years. It was subsequently nominated for the 2025 Prix Galien USA Award for Best Biotechnology Product, becoming the first drug developed in Shenzhen to receive a Prix Galien nomination and the only product from a Chinese biopharmaceutical company nominated in this category that year.

Behind both the accelerated development timeline and this international recognition was the close collaboration between Higgs Therapeutics Inc. and InnoStar. Translating rapidly generated AI-driven in vitro predictions into scientifically rigorous in vivo toxicology studies required innovative development strategies and seamless coordination. At the same time, meeting multiple regulatory submission objectives and ambitious project timelines demanded meticulous planning and execution. By combining AI-enabled innovation with GLP-compliant nonclinical expertise, Higgs Therapeutics and InnoStar successfully established an efficient and scientifically robust development pathway, supporting the program’s rapid progress toward global regulatory milestones.

01

Early Engagement: Building the First Line of Defense Against Development Risks

Unlike conventional small-molecule programs, AI-enabled drug development often enters the nonclinical stage with a significant amount of non-standardized information. Formulation strategies, mechanisms of action, and metabolic characteristics may all lack established industry references, presenting unique scientific and regulatory challenges. Recognizing these complexities, the InnoStar team became deeply involved at the earliest stages of the project, working alongside Higgs Therapeutics Inc. throughout the drug development process.

Leveraging its integrated nonclinical development platform, InnoStar simultaneously advanced several critical activities. The team optimized early-stage formulations and evaluated sample stability while conducting comprehensive assessments of the candidate’s physicochemical properties, mechanism of action, and metabolic profile. Combined with the client’s early findings generated from organoid disease models and AI platforms, these efforts enabled the proactive identification of potential risks across formulation development, pharmacokinetics, toxicology, and overall safety evaluation.

“From the very beginning, our goal was to provide an integrated evaluation strategy for innovative therapies—supporting every stage from early formulation development and pharmacokinetic assessment to GLP safety studies and clinical translation, rather than simply executing individual studies,” said the InnoStar scientific team.

Faced with an entirely new class of data generated through organoid disease models, InnoStar rapidly assembled a multidisciplinary team of experts in pharmacokinetics, pathology, and toxicology. Working closely with Higgs Therapeutics, the team repeatedly evaluated the available evidence and translated in vitro predictions into scientifically robust, GLP-compliant study designs. This early-engagement approach established a rigorous and traceable scientific framework for all subsequent nonclinical evaluations, ensuring that development decisions were guided by evidence rather than precedent.

02

Resolving the Time Paradox: Aligning the Speed of AI with the Rigor of GLP

One of the defining advantages of AI-enabled drug discovery is speed. From target validation to preclinical candidate (PCC) selection, timelines can be compressed from years to just a few months. In contrast, nonclinical development must adhere to rigorous scientific principles and regulatory requirements, with GLP studies governed by well-established biological and compliance timelines. Balancing the rapid pace of AI-driven innovation with the uncompromising standards of GLP became one of the central challenges of the SIGX1094R program.

Rather than simply accelerating individual studies, InnoStar addressed this challenge through its integrated development platform, coordinating formulation development, pharmacokinetics, GLP safety assessment, and clinical translation within a unified project framework. Based on the client’s regulatory submission milestones, the project team strategically aligned study scheduling, resource allocation, and data generation to maximize efficiency while maintaining scientific rigor. Study initiation, sample management, and data analysis were coordinated under a centralized project management system, while continuous communication with Higgs Therapeutics Inc. ensured that nonclinical activities remained closely synchronized with the overall drug development timeline.

The program’s greatest challenges, however, often emerged from unexpected technical details. Due to the unique characteristics of the SIGX1094R formulation, the project required the development of a new solvent system and preparation method that fell outside InnoStar’s routine experience. Rather than treating this as a project risk, multidisciplinary teams from analytical sciences, formulation development, and toxicology rapidly worked together to optimize the formulation strategy and validate its stability. This collaborative effort enabled the team to overcome a seemingly minor but technically critical bottleneck within a very short timeframe, ensuring the smooth progression of subsequent GLP studies.

At the same time, the program was designed from the outset to support parallel IND submissions in both China and the United States. Study protocols, animal welfare practices, and data management were developed in accordance with the most stringent requirements across NMPA, OECD, and FDA guidelines. FDA-compliant electronic data capture and audit trail systems were implemented early in the project, while InnoStar’s experienced regulatory team proactively identified and addressed potential review concerns during study design. By emphasizing a “right-first-time” approach, the team minimized the need for additional studies and delivered a complete, high-quality regulatory data package—demonstrating that the speed of AI-driven innovation and the rigor of GLP can advance together without compromise.

03

 Delivering Results That Stand Up to Regulatory Review

The ultimate measure of success was reflected in the project’s regulatory outcome. The toxicology data package generated by InnoStar progressed through subsequent regulatory review without requiring additional clarification or supplementary studies related to the nonclinical safety assessment, providing a solid compliance foundation for successful IND clearances in both China and the United States. The subsequent nomination of SIGX1094R for the 2025 Prix Galien USA Award for Best Biotechnology Product further recognized the program’s scientific innovation and the value of its nonclinical development strategy.

Reflecting on the collaboration, the team at Higgs Therapeutics Inc. commented:

“Developing AI-enabled medicines presents unprecedented scientific and technical challenges. We sincerely appreciate InnoStar’s professional support throughout the entire development process. With its rigorous international quality standards and deep scientific expertise, InnoStar helped us overcome multiple technical challenges and successfully complete dual IND submissions in China and the United States, enabling SIGX1094R to reach the global stage. Its commitment to innovation, scientific excellence, and efficient execution has made InnoStar a trusted long-term partner.”

For InnoStar, the collaboration represented more than the successful completion of a single project. It demonstrated the company’s ability to support next-generation AI-enabled drug development through integrated nonclinical expertise while helping innovative therapies developed in China advance toward global regulatory milestones. It also reflected the dedication and scientific commitment of InnoStar’s multidisciplinary research teams in addressing complex development challenges.

From organoid disease models and AI-driven target discovery to GLP-compliant nonclinical evaluation and global IND submissions, the SIGX1094R program illustrates how scientific innovation and regulatory rigor can advance together. As AI continues to reshape the future of drug discovery, InnoStar remains committed to providing integrated, high-quality nonclinical solutions that accelerate the translation of breakthrough science into therapies for patients worldwide.