Novartis: Accelerating Drug Development with AI-Powered Clinical Trial Transformation
Novartis
Novartis partnered with AWS Professional Services and Accenture to modernize their drug development infrastructure and integrate AI across clinical trials, with the goal of reducing trial development cycles by at least six months. The initiative built a GXP-compliant data mesh platform on AWS with Databricks for processing, enabling AI use cases including protocol generation and an intelligent decision system (digital twin). Early results from the patient safety domain showed 72% query speed improvements, 60% storage cost reduction, and 160+ hours of manual work eliminated. The protocol generation use case achieved 83-87% acceleration in producing compliant protocols.
Overview
Novartis partnered with AWS Professional Services and Accenture to modernize their drug development infrastructure and integrate AI across clinical trials, with the goal of reducing trial development cycles by at least six months. The initiative built a GXP-compliant data mesh platform on AWS with Databricks for processing, enabling AI use cases including protocol generation and an intelligent decision system (digital twin). Early results from the patient safety domain showed 72% query speed improvements, 60% storage cost reduction, and 160+ hours of manual work eliminated. The protocol generation use case achieved 83-87% acceleration in producing compliant protocols.
The challenge
Novartis's drug development and clinical trial data was fragmented across heterogeneous sources — file shares, relational databases, life sciences platforms like Veeva Vault, and master data management systems like Reltio — hindering AI adoption across the R&D continuum in an industry where developing new medicines typically takes 15 years. Novartis set the goal of reducing clinical trial development cycles by at least six months.
The solution
Novartis partnered with AWS Professional Services and Accenture to build a next-generation GXP-compliant data platform on AWS with Databricks for processing, following a data mesh architecture with five components: an ingestion framework grouping similar source types, a three-tier data product storage/processing model, data management and governance (catalog, lineage, quality), self-service data consumption via BI tools and AI/ML platforms, and a central observability platform. The platform is designed to support AI use cases including protocol generation and an Intelligent Decision System (digital twin) for simulating clinical trial operational plans; compliance was built in from the design phase using Architectural Decision Records and threat modeling rather than added afterward.
Reported business value
The patient safety domain, an early adopter, built 16 data pipelines processing approximately 17 terabytes of data, yielding a 72% reduction in query speeds, a 60% reduction in storage costs, and 160+ hours of manual work eliminated. The protocol generation use case, which currently runs on demo data or legacy on-premises systems, achieved 83-87% acceleration in producing protocols that meet compliance standards. The six-month clinical trial reduction goal remains to be fully proven, but early results suggest substantive progress toward that target.
Sources
Open any source and check the claim yourself — that is the point of the register.
Other life sciences entries in the register.
Novo Nordisk accelerates clinical documentation and drug development with Claude
Novo Nordisk, a global pharmaceutical company headquartered in Europe, built NovoScribe, an AI-powered documentation platform using Claude Code, Amazon Bedrock and MongoDB Atlas, to automate clinical study report (CSR) generation. Claude helped cut writing times on CSRs by 90%, reducing time spent producing clinical study documentation from 10+ weeks to 10 minutes, and delivered a 95% reduction in resources needed to create device verification protocols. The platform expanded from clinical trial reports to device protocol documentation and patient materials, and an 11-person development team, including non-technical staff, now prototypes features using Claude Code.
Phagos uses generative AI on AWS to match bacteriophages to bacterial infections
Phagos, a Paris-based biotech startup, uses generative AI models built with Amazon SageMaker AI to match bacteriophages to target bacteria for phage therapy, replacing a manual trial-and-error process. The AI models cut wet-lab testing needs by 50% and reduced phage-candidate screening time by 99.5%, from 29 hours to 10 minutes per bacteria. Phagos has treated more than half a million animals in France and can now develop a new treatment in two months versus 10+ years for traditional antibiotic development.
Was this helpful?
Your feedback helps us improve our use case database