Bridging the gap: Transforming multiomic endpoints into actionable human disease discoveries

In the current era of drug discovery, the primary challenge is no longer the generation of data, but its translation. While technological advances have made multiomics a daily reality, a fundamental hurdle remains: how to distil the biological findings from model organisms into a "wiring diagram" that is truly reflective of human disease.

The infrastructure of translational insight

As datasets grow in complexity — spanning epigenomics, multidimensional transcriptomics, and proteomics — traditional analytical methods often fail to account for the biological "noise" inherent in cross-species research. Robust computational structures are now absolute requirements to translate these complex data volumes into actionable insights.

Cloud-based computing and advanced algorithms provide the infrastructure to manage this complexity. By interrogating multiomic datasets, biopharma companies can identify biological signals that persist from epigenetic regulation to protein translation. Mapping these cross-species patterns allows researchers to bridge the gap between preclinical models and human clinical outcomes.

The next frontier: Cross-species model qualification

Dr. Matt Westfall views the bridging of multiomics data from in vivo and in vitro models to human disease biology as the definitive frontier for drug pharmacology.

“The challenge is not related to the generation of large data sets within a model, but evaluating the translational fidelity of that model,” Westfall explains. “We need to determine how precisely the molecular signatures in a model reflect human biochemistry and clinical phenotypes. Selecting a model with high human relevance saves significant costs during drug discovery and may also result in an accelerated transition to clinical trials.”

A prime example is the characterization of preclinical models of Inflammatory Bowel Disease (IBD). By generating the global proteomic signatures from commonly employed rodent IBD models, pharmaceutical researchers are able to match those signatures to clinically relevant biopsies, effectively establishing an early endpoint of translation well before the clinic. Moreover, the ability to query the biological system through multidimensional ‘omics enables the identification of portable biomarkers for utilization in clinical trials.

Strategic synergy: The Inotiv-VUGENE translational engine

To realize this vision, Inotiv has entered a strategic collaboration with VUGENE, a leader in scalable multiomics data analysis. This partnership integrates VUGENE’s AI-empowered bioinformatics platform into Inotiv’s Discovery & Translational Sciences Division.

VUGENE’s platform is purpose-built to address the "translational gap" through three core pillars:

• Comprehensive disease and model characterization: Conducting in-depth multiomic profiling to thoroughly understand the biological landscape of both the disease state and its preclinical models.
• Identification of cross-species and multiomic patterns: Pinpointing the conserved molecular signatures that persist across species and multiple layers of biological regulatory proteins.
• Mechanistic alignment to human disease biology: Translating complex model discoveries into biologically plausible human frameworks, ensuring that therapeutic targets identified in the lab are mapped directly to relevant human clinical phenotypes.

“To maximize the value of discovery programs, we must interpret complex datasets through a human-centric lens,” says Scott Daniels, PhD, Senior Vice President of Drug Discovery & Translational Sciences at Inotiv. “VUGENE’s platform gives our team a powerful tool to advance AI-assisted discovery, helping clients make faster, data-driven decisions that are grounded in human biology from day one.”

Moving beyond standard endpoints

The goal of this integrated approach is to improve the prediction of drug efficacy and safety in early-stage discovery. Whether it is through spatial transcriptomics to localize gene expression or proteomic profiling to verify pathway-level changes, the focus remains the same: providing an understanding of a drug’s action beyond standard histological endpoints or clinical pathology.

Together, Inotiv and VUGENE are moving beyond fragmented data analysis. By combining high-throughput experimental data with cloud-based machine-learned bioinformatics, the acceleration of new target biology discoveries is within reach, as are breakthroughs in improving human health.