Molecular-level Disease Understanding
PanOmics
PanOmics, Evotec’s multi-omics supported drug discovery platform, is unique in industrializing Omics data generation and AI/ML supported Omics data analysis. Built on the foundation of proprietary molecular patient data, the platform fundamentally improves the understanding of disease processes, disease modeling in vitro and in vivo, identifications of novel high value targets as well as biomarker discovery and patient selection.
This seamless use of Omics data to inform the drug discovery process substantially increases the probabilities of success in translating preclinical discovery efforts successfully into the clinic and, from here, into highly differentiated drug products into the market.
PanOmics Across All Steps of Our Drug & Biomarker Discovery & Development Process
- During Target ID and validation, the identification of the best targets and most relevant pathways as early as possible during your processes, will ensure you avoid spending costly resources on the wrong path.
- PanOmics is also applied during screening, profiling, hit-to-lead and lead optimization campaigns. In many cases, compounds are hitting more than one particular target and thereby generating so-called off-target effects. PanOmics can identify such off-target effects and very efficiently guide your drug discovery efforts towards the identification and development of the right molecules.
- For the prediction of potential toxicity of molecules, PanOmics creates a huge advantage. For example, omics-driven prediction of drug-induced liver injury (DILI) is much more sensitive than any other standard tox prediction method. In addition to this increase in sensitivity, further important information, like which pathways are leading to the tox effect, will be obtained by this new way of tox prediction.
- Patient stratification, based on clinical and multi-omics datasets, enable the discovery of biomarkers early in drug discovery, guiding the application of the drug to the right patient at the right time and improving the probability of success in clinical trials for better patient outcomes.
- The underlying comprehensive clinical and multi-omics datasets of significant patient cohorts enable a deep understanding of disease and can ultimately be developed to monitor clinical trials all the way to companion diagnostics.
- PanOmics in combination with machine learning approaches is a much better predictor of primary diagnosis than standard measurements like histology and clinical blood chemistry and even combinations thereof.