How Can AWS Bedrock Agents Accelerate Clinical Trial Design?
AWS Bedrock agents can accelerate clinical trial design by automating protocol drafting, analyzing historical trial data, and assisting researchers in evaluating trial parameters more efficiently.
Overview
A research-driven biotech is advancing its pipeline through data-intensive drug discovery and clinical development. Among the most resource-heavy steps in this journey is clinical trial design—a process requiring teams to comb through thousands of historical studies, extract eligibility criteria and endpoints, and draft complex protocols that meet regulatory standards.
While critical to bringing new therapies to patients, protocol design is time-consuming, repetitive, and a frequent bottleneck. The Company sought to test whether Generative AI (GenAI) agents built on AWS Bedrock could streamline trial design, accelerate protocol drafting, and improve consistency across its development programs. Partnering with PTP, the Company launched a proof of concept (POC) centered on two Bedrock-powered clinical development agents, laying the foundation for an extensible GenAI framework to support future R&D needs.
The Challenge
Designing and validating clinical trial protocols introduced two major challenges for The Company:
1. Historical Trial Review
Researchers manually searched ClinicalTrials.gov and related datasets to identify prior studies by condition, intervention, and outcome measures. This repetitive task often took hours or days, with results varying by individual researcher skill and experience.
2. Protocol Drafting
Even with access to templates, drafting trial protocols remained slow and labor-intensive. Researchers had to synthesize best practices from multiple studies, structure content into regulator-ready formats, and iterate through multiple internal reviews.
These inefficiencies slowed R&D progress, delayed hypothesis testing, and consumed valuable researcher time. The Company’s goal was clear: use GenAI to automate repetitive tasks, generate consistent protocol drafts, and free its scientists to focus on innovation—all while staying within compliance boundaries by using public, non-sensitive data.
The Use Case: Clinical Development Protocol Design & Trial Planning
The Company evaluated several possible agentic AI applications but chose to focus the POC on clinical development protocol design, recognizing it as one of the highest-impact areas for immediate improvement.
Two AWS Bedrock Agents were deployed:
- Clinical Study Search Agent – Retrieves structured data from ClinicalTrials.gov, enabling researchers to explore prior study designs by condition, intervention, or sponsor. It highlights eligibility criteria, endpoints, and outcome measures from past trials.
- Clinical Trial Protocol Generator Agent – Builds draft study protocols using best practices and the Common Data Model (CDM), assisting in drafting inclusion/exclusion criteria, endpoints, and statistical plans.
Together, these agents demonstrated how Bedrock could reduce trial design from weeks of manual work to hours, giving The Company a repeatable foundation for scaling future AI-driven research workflows.
The Solution
PTP deployed a modular, AWS-native architecture leveraging Bedrock Agents and supporting services to meet the Company’s requirements.
Key Solution Components
AWS Bedrock Agents for Orchestration
Orchestrated two agents—Study Search and Protocol Generator—designed to work together in surfacing insights and generating structured drafts.
Amazon S3 + Amazon Textract
Public datasets and trial documentation were securely stored in Amazon S3. Amazon Textract converted files into machine-readable formats, ensuring compatibility with Bedrock for indexing and retrieval.
Amazon OpenSearch & Amazon Kendra
Clinical trial datasets were indexed and enhanced with Amazon Kendra for intelligent, natural language search. This allowed researchers to quickly filter and retrieve trial data with higher accuracy than manual searches.
AWS Lambda & Amazon API Gateway
Provided orchestration and secure endpoints, connecting data sources and Bedrock agents into seamless, researcher-facing workflows using AWS Lambda and Amazon API Gateway.
Reference Code Integration
Leveraged AWS’s open-source Bedrock Agents for Healthcare & Life Sciences catalog as a foundation, adapting orchestration chains and prompt templates to the Company’s unique use case.
Demo Interfaces
Delivered a lightweight chat-style interface and Jupyter notebook integration, giving researchers natural, interactive access to the agents and trial drafting workflows.
Why AWS
The company selected AWS as the backbone for this project because of three critical advantages:
Security and Compliance
With sensitive research data at the core of operations, AWS provided a secure, compliance-ready environment. S3, SageMaker, and Bedrock operated within the company’s isolated VPC, ensuring data never left the secure boundary.
Breadth of Model Choice
AWS Bedrock offered access to multiple foundation models through a unified API, allowing experimentation with ProtGPT2, ProtBERT, and other specialized models without costly redevelopment.
Scalability
AWS’s elastic infrastructure meant the company could scale computationally intensive protein folding workloads up or down as research demands shifted. This flexibility allowed acceleration without overinvesting in static infrastructure.
Why PTP
The company chose PTP as its partner because of its deep expertise in both AWS consulting and life sciences R&D.
Life Sciences Competency
As an AWS Life Sciences Competency partner, PTP brought domain-specific knowledge of biotech workflows, regulatory constraints, and scientific data handling.
Proven AWS Delivery
With years of AWS consulting experience, PTP designed and delivered a pipeline that adhered to AWS best practices while meeting the company’s unique research needs.
Innovation and Enablement
Beyond building the system, PTP enabled the company’s team with training, documentation, and extensibility—ensuring they could independently grow the framework to support future research initiatives.
The Results
The POC delivered measurable improvements to The Company’s clinical trial design workflows:
Time Efficiency
Trial dataset search times reduced by ~60%, with relevant study details surfaced in seconds.
Accelerated Drafting
Protocol drafts were generated in minutes, saving 2–3 person weeks per protocol.
Improved Consistency
Standardized retrieval and drafting reduced duplication and variability across teams.
Extensibility
Modular design enabled The Company’s team to extend the framework to additional agent use cases beyond the POC.
Conclusion
The Company’s deployment of AWS Bedrock Agents illustrates how Generative AI can revolutionize clinical trial design, one of the most demanding stages in the drug development lifecycle. By automating historical trial search and protocol drafting, the Company accelerated R&D timelines, reduced costs, and freed researchers to focus on higher-value work.
This successful POC establishes a foundation for expanding Bedrock agent use into adjacent areas such as literature reviews, biomarker discovery, and competitive intelligence—further strengthening the Company’s mission to advance life-saving therapies.
Accelerate Your Clinical Development with AI + AWS
See how Generative AI and AWS Bedrock Agents can streamline trial design, reduce costs, and speed innovation. Partner with PTP to bring efficiency and scalability to your R&D programs.
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