Fibroblast growth factor receptors (FGFRs) are critical drivers of oncogenesis in various solid tumors, including urothelial carcinoma, hepatocellular carcinoma, ovarian cancer and lung adenocarcinoma. However, developing highly selective FGFR2/3 inhibitors and overcoming drug resistance remain two of the most significant challenges in the field.
Recently, Insilico Medicine ("Insilico") announces that the team, with the support of its generative chemistry engine, has developed a novel and highly selective FGFR2/3 dual inhibitor, which maintains efficacy against resistance mutations, shows potency in gastric cancer animal model, and demonstrates a more favorable safety profile compared to existing FGFR inhibitors. The findings have been published in the Journal of Medicinal Chemistry (JMC, IF=7.2)
In this research, scientists conducted structural modifications and optimizations to search for new structures with appropriate balance among potency, selectivity, drug-like properties, and safety. Notably, Insilico's self-developed Chemistry42 played a crucial role in this research by generating the pyrrolopyrazine carboxamide core as a starting point for further molecular design. Moreover, Chemistry42 facilitated molecular modeling for binding affinity and selectivity prediction, helping the research team identify compound 10 with unique structure.
Compound 10 demonstrated broader potency against FGFR2/3 mutants acquired after the approved FGFR drug treatments. It also exhibited robust selectivity, sparing FGFR1/4, and showed minimal off-target effects against a broad panel of kinases. In preclinical models, compound 10 displayed favorable pharmacokinetics, high oral bioavailability, and induced tumor stasis or regression in gastric cancer mouse models, showing robust antitumor efficacy and pharmacodynamic suppression, highlighting its potential as an effective cancer treatment.
The publication marks another milestone of real-world AI drug discovery application cases, and Insilico Medicine looks forward to further investigation on the precandidate compound, for comprehensive safety profile, combination potentials, efficacy across broader settings, and indication exploration.
In 2016, Insilico first described the concept of using generative AI for the design of novel molecules in a peer-reviewed journal, which laid the foundation for the commercially available Pharma.AI platform. Since then, Insilico keeps integrating technical breakthroughs into Pharma.AI platform, which is currently a generative AI-powered solution spanning across biology, chemistry, medicine development and science research. Powered by Pharma.AI, Insilico has nominated 22 preclinical candidates in its comprehensive portfolio of over 30 assets since 2021 and has received IND clearance for 10 molecules.
In early 2024, Insilico published a Nature Biotechnology paper presenting the entire R&D journey from AI algorithms to Phase II clinical trials of ISM001-055, the company's lead drug pipeline with AI-discovered target and AI-designed structure. Following that, Insilico has recently announced positive preliminary results from a Phase IIa trial (NCT05938920), where ISM001-055 showed favorable safety and tolerability across all dose levels, as well as dose-dependent response in forced vital capacity (FVC), after only 12 weeks of dosage .
References
[1] Discovery of Pyrrolopyrazine Carboxamide Derivatives as Potent and Selective FGFR2/3 Inhibitors that Overcome Mutant Resistance
Yazhou Wang, Yihong Zhang, Jinxin Liu, Jichen Zhao, Chao Wang, Fanye Meng, Xin Cai, Man Zhang, Alex Aliper, Tao Liang, Feng Yan, Feng Ren, Jiong Lan, Qiang Lu, Fusheng Zhou, Alex Zhavoronkov, and Xiao Ding
Journal of Medicinal Chemistry Article ASAP
DOI: 10.1021/acs.jmedchem.4c03205
About Insilico Medicine
Insilico Medicine, a global clinical stage biotechnology company powered by generative AI, is connecting biology, chemistry and clinical trials analysis using next-generation AI systems. The company has developed AI platforms that utilize deep generative models, reinforcement learning, transformers and other modern machine learning techniques for novel target discovery and the generation of novel molecular structures with desired properties. Insilico Medicine is developing breakthrough solutions to discover and develop innovative drugs for cancer, fibrosis, immunity, central nervous system diseases, infectious diseases, autoimmune diseases, and aging-related diseases.
www.insilico.com
