Connecting the impossible.
Moving the bottom line.

We identified HELC, a previously unannotated long non-coding RNA that scaffolds the AP1 transcription factor complex to the fatty acid synthase promoter. The result is a coherent molecular axis where HELC drives lipogenic reprogramming, Skp2 upregulation, p27Kip1 degradation, and uncontrolled cell cycle progression. We then designed, synthesized, and validated HELCi-7, a first-in-class small molecule inhibitor that disrupts the HELC-AP1 interface.

The same mechanistic pattern-recognition framework that found HELC powers our diagnostic testing gap identification and non-responder stratification capabilities. The algorithm detects subtle molecular and clinical signals in harmonized patient data that code-matching approaches cannot see. For pharma and payers, this means finding the patients who should be tested but have not been, and identifying which patients on a $100K+ specialty therapy are not actually responding.

Through single-cell chromatin accessibility profiling, transcriptomics, and proteomics across treatment-naive and drug-resistant NSCLC models, we identified 847 differentially accessible regulatory regions organized into 23 coordinated enhancer clusters that hierarchically regulate metabolic reprogramming, DNA repair, and immune evasion. We developed DT-847, a first-in-class dual DNMT/HDAC inhibitor, and derived the Epigenetic Resistance Index (ERI), a 47-region signature predicting resistance with 94.7% sensitivity.

Mechanistic understanding of how tumors actually evade therapy is what powers our non-adherence prediction and specialty drug outcomes accountability engines. We can flag non-responders earlier, stratify patients for value-based contract enforcement, and give market access teams real evidence that therapies are or are not delivering the promised outcomes. This is not keyword matching. It is pathway-level intelligence.

A federated multi-center framework combining deep learning and radiomics features, validated on 1,060 patients across five institutions on both 1.5T and 3.0T scanners from Siemens, GE, and Philips. Prospective clinical evaluation showed the model influenced surgical planning in 28% of cases and molecular testing prioritization in 42%, saving a mean of 32 minutes per case compared to conventional multidisciplinary review.

The federated validation architecture that grades gliomas across institutional boundaries is the same architecture that powers our clinical trial data reconciliation and cross-site quality monitoring. If it can harmonize oncology imaging across five hospital systems without centralizing data, it can harmonize pharma trial data across fifty sites the same way. Same substrate, different deployment.

A harmonization-aware forecasting framework that estimates the future intersection between a population's clinical state and an external decision object, such as a therapy class or policy change. Critically, the framework separates clinical need from realized utilization. A forecast showing high need but low utilization exposes access barriers. High utilization against low need exposes overprescribing. The system maintains strong calibration and fairness across demographic subgroups and is built to operate under federated learning constraints.

This is the direct scientific foundation for our Global Market Access Intelligence capability. For pharma launch teams working across NICE, G-BA, HAS, and ICER, it produces evidence packages that distinguish genuine unmet need from suppressed or inflated demand. For payers, it exposes where benefit design is creating access gaps. For supply chain and formulary planning, it forecasts at population scale, across borders, without moving data.