CN Bio Expands FDA-Recognized DILI Assay with Cross-Species Models to Improve Drug Safety Predictions
核心洞察
CN Bio (搜索) has introduced two new animal microphysiological system models that extend their FDA-recognized drug induced liver injury (搜索) assay to include rat and dog models alongside human models for comparative safety studies.
The expanded PhysioMimix (搜索) DILI assay enables rapid comparative studies between animal and human liver (搜索)-on-a-chip models to identify interspecies differences early in preclinical development and reduce costly late-stage failures.
The new cross-species platform addresses critical gaps in drug safety prediction by providing 14-day longitudinal testing capabilities that can flag hepatotoxicity (搜索) risks before in vivo studies, potentially reducing animal testing requirements.
CN Bio (搜索) has expanded its FDA-recognized drug induced liver injury (搜索) (DILI) assay with two new animal microphysiological system models, enabling pharmaceutical companies to conduct comparative safety studies across human, rat, and dog liver (搜索)-on-a-chip platforms. The Cambridge-based organ-on-a-chip provider announced the enhancement to address critical gaps in predicting drug safety during preclinical development.
Addressing Preclinical Safety Prediction Challenges
Traditional human in vitro methods have demonstrated limited capacity to accurately determine drug toxicity, while discrepancies between these methods and in vivo animal studies create significant challenges in predicting safety risks for humans during preclinical testing. According to CN Bio (搜索), these limitations often result in unsafe drug candidates being wrongly progressed through development pipelines, while potentially life-saving therapies are misclassified as toxic and abandoned.
The expanded PhysioMimix (搜索) DILI assay now incorporates human-, rat-, and dog-derived liver (搜索)-on-a-chip models, allowing researchers to flag interspecies differences early in the development process. This cross-species approach aims to provide more accurate in vitro to in vivo extrapolation (IVIVE) capabilities and better inform in vivo study design decisions.
Enhanced Testing Capabilities
The new offering, accessible through CN Bio (搜索)'s Contract Research Services, enables comprehensive longitudinal and endpoint testing for DILI-specific biomarkers from both single- and repeat-dosing studies over a 14-day experimental window. This extended testing period provides researchers with deeper insights into underlying mechanisms of hepatotoxicity (搜索) and latent effects of drug candidates that may not be apparent in shorter-term assays.
Dr. Emily Richardson, Lead Scientist for Safety and Toxicology at CN Bio (搜索), emphasized the critical importance of understanding safety risks in drug development. "Fundamental physiological and biological differences between species can lead to inaccuracies in predictions, often causing drug candidates to be wrongfully abandoned as toxic, or worse, mistakenly classified as safe," Richardson stated.
Reducing Development Risks and Animal Testing
The enhanced assay platform is designed to mitigate the risk of costly, late-stage conflicting data by providing early warning of hepatotoxicity (搜索) prior to in vivo studies. By identifying potential safety issues earlier in the development process, the technology aims to reduce unnecessary animal testing while improving the accuracy of safety predictions.
CN Bio (搜索)'s scientific team provides detailed data analysis and data-driven conclusions that extend beyond what is achievable using existing in vitro models. The company positions this service as addressing growing market demand for tools that can better predict human safety risks during preclinical testing phases.
Richardson noted that the expansion builds upon CN Bio (搜索)'s established expertise in organ-on-a-chip technology: "Having established our DILI assay as an industry leading option to garner more valuable insights across the development pipeline, we were in an ideal position to expand its capabilities and address this crucial gap in understanding hepatotoxicity (搜索) using the most commonly used animal models."