We have built a proprietary platform and operating model called PathOS™ to gain crucial insights and act on them at scale. Our approach combines insilico analyses using a genetically validated framework with biological modeling to design clinical trials optimized for speed, patient response, and, ultimately, drug approval.
Pathos has access to some of the largest integrated spatiotemporal datasets in oncology and years of working experience to translate this unique asset into actionable opportunities. We create a continuous feedback loop of target refinement using advanced AI approaches fueled by petabytes of patient data, generating hypotheses for patient subpopulations likely to respond to a given drug.
We leverage our in-house lab to run functional tests using approved and experimental drugs against patient-derived in vitro organoids and fresh patient samples to validate our insilico derived hypotheses. By examining the behavior of cancer cells ex vivo with a diverse library of compounds, we look to determine whether specific patient populations are likely to respond. Validated hypotheses drive our precision clinical development plans.
Pathos has access to some of the largest multimodal datasets in oncology and years of working experience to translate this unique asset into actionable opportunities. We are routinely incorporating our knowledge base along with Pathos proprietary data sets generated through our partner network.
We’re building a Discovery Engine that has the ability to automate the process of target identification and prioritization using multiple orthogonal methods. Our Clinical Development Suite will harmonize data from multiple sources and enable a generalizable machine learning framework that can be leveraged to establish asset-specific analytics and AI models.
Our PathOS platform is built on a modern data infrastructure and has the ability to continuously enhance the predictive power with every data point from discovery to development.
We are integrating all data consistently across all functions. The ability to manage and integrate data generated or acquired — from discovery to clinical and real-world patient data — is a fundamental requirement to allow us to derive maximum benefit. Data are the foundation upon which the analytics are built. Data integration enables comprehensive searches for subsets of data based on the linkages established rather than on the information itself.