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Clinical development is one of the most data-intensive undertakings in medicine, and for years the hard problem was not collecting the data but coordinating it. Protocol design, feasibility, site selection, and recruitment have traditionally happened in disconnected phases, with teams reconciling outputs across many systems before a single decision could be made. The gap between data availability and actionable insight has stayed stubbornly wide.
NVIDIA recently announced a step forward in their support for life sciences through the launch of the BioNeMo Agent Toolkit. As BioNeMo's open models, microservices, and agent frameworks come to market, they offer a path to connect biological reasoning more directly to the patient-level evidence our agentic platform and digital twin work already bring together. The opportunity is a layer where a scientist can pose a question, initiate an analysis grounded in real-world oncology data, and move quickly to the next decision, with the underlying logic visible at every step and human judgment central where risk and regulatory accountability demand it.
We continue to partner with NVIDIA, leveraging components of the BioNeMo Agent Toolkit. Through ConcertAI's Cara AI engine and our ACT (Accelerated Clinical Trials) solution, we embed orchestrated intelligence directly inside clinical development workflows rather than bolting analytics on after the fact. Specialized agents retrieve the right evidence, invoke the right models, and coordinate tasks across the lifecycle, with every step traceable back to its data sources and methods. NVIDIA provides the accelerated compute foundation that makes this work at enterprise scale, including NVIDIA NIM microservices that standardize how models are packaged and help ensure consistent, reproducible behavior across environments. That reproducibility is not a nice-to-have in a regulated field. It is the difference between a directional answer and a trustworthy one.
Orchestration is the present. Discovery is where this is heading. In our colorectal cancer work presented at AACR in 2025, we showed that digital twin frameworks can support outcome simulation and more precise target population identification, surfacing prognostic relationships that inform trial design while reducing cost and patient burden. Grounded in Translational360, our multi-lab clinically-linked data foundation, those simulations are anchored in real patients rather than abstractions. The natural next step is to evaluate protocol scenarios against virtual patient populations before a trial ever opens.
We have spent years building the data, the orchestration, and the governance to make that kind of reasoning safe in oncology. The tools arriving now make it possible to do it at greater scale, and we are glad to be building it alongside NVIDIA.