Evinova and Merck KGaA Forge Multi-Year AI Partnership to Transform Clinical Development
Key Insights
Evinova (search) and Merck KGaA, Darmstadt, Germany, announced a multi-year strategic collaboration to accelerate global clinical development using Evinova's AI-native platform.
Merck KGaA will adopt the end-to-end Evinova (search) platform covering study design, document authoring, and downstream automation to reduce time, costs, and operational bottlenecks.
Merck KGaA gains priority access to Evinova (search)'s AI capabilities and a seat on the Strategic Roadmap Steering Committee to shape future product innovations.
Evinova (search), a global leader in AI-native clinical development technology, today announced a new multi-year strategic collaboration with Merck KGaA, Darmstadt, Germany, aimed at accelerating global clinical development through the deployment of artificial intelligence across the entire trial lifecycle. Under the agreement, Merck KGaA will adopt the end-to-end capabilities of the Evinova AI-Native platform, spanning study design, document authoring, and downstream automation, with the shared goal of significantly reducing time, costs, and operational bottlenecks in clinical development.
"Merck KGaA, Darmstadt, Germany's forward thinking and bold approach to transforming clinical development end-to-end raises the bar for the industry, making them an exceptional partner for Evinova (search)'s efforts," said Cristina Duran, President of Evinova. "With a seat on Evinova's Strategic Roadmap Steering Committee, Merck KGaA, Darmstadt, Germany will help, and together we will push the next frontier of clinical development to accelerate the delivery of high-quality medicines to patients."
Platform Capabilities and Integration
Under the terms of the agreement, Merck KGaA will gain priority access to Evinova (search)'s continuously advancing suite of AI-native capabilities. The end-to-end platform supports study design, endpoints and benchmarks, schedule of assessments with implications, integrated timeline feasibility, and costing modelling. It enables multi-modal study optimisation, patient and site burden reduction, and complexity reduction through multi-scenario simulation, all fully integrated with document authoring via a multi-agentic approach and downstream automation.
A key technical feature includes automatic conversion with models into USDM 4.0, enabling seamless conversion of study designs and documents to the standardized format, digitized either during the design phase or through automatic digitization by AI agents. The document authoring component employs a multi-agentic approach designed to reduce the risk of protocol amendments, improve quality relative to historical baselines, incorporate the latest regulatory guidelines, and enhance the patient experience while accelerating clinical documentation workflows.
Cross-Industry Data Collaboration
The partnership also features a holistic intelligence layer that integrates operational benchmarks across participating pharmaceutical companies through an opt-in model to inform smarter design decisions. Merck KGaA has agreed to participate in Evinova (search)'s cross-industry operational data consortium, contributing to and benefiting from shared operational insights across the industry.
Evinova (search), a separate health tech company within the AstraZeneca group, has demonstrated measurable impact with its platform. According to the company, solutions on its end-to-end platform have been proven to accelerate timelines, reduce costs, improve data quality, enhance patient experiences, and achieve better outcomes. Published results in Nature Medicine have shown up to 60% improvement in patient experience, 6-month acceleration in trial delivery, and 32% reduction in costs.
The collaboration positions both companies to jointly shape the next era of AI-driven R&D transformation, with Merck KGaA's participation on the Strategic Roadmap Steering Committee ensuring that the platform's evolution aligns with the practical needs of large-scale pharmaceutical development.