A Guided Tour to Building and Integrating LLM Based Tooling with R

From prototypes to production: Enterprise AI solutions in pharma

LLM

AWS

Advanced

Authors

Devin Pastoor (Chief Technology and Product Officer, A2-AI)

Xu Fei (Senior Solutions Engineer, A2-AI)

Aathira Anil Kumar (A2-AI)

Overview

Intermediate AI/LLM Enterprise GxP

A practical, 2-hour workshop demonstrating how to integrate Generative AI (GenAI) into pharmaceutical workflows. This session focuses on bridging the R and Python ecosystems to deliver scalable, GxP-compliant solutions.

What You’ll Learn

🏗️ Architecture patterns for LLM-enabled applications
🔐 Enterprise integration with AWS Bedrock and internal systems
🤖 MCP servers for reproducible analytics
✅ GxP compliance in AI application deployment
🔗 R-Python interoperability for GenAI workflows

Prerequisites

Required Knowledge:

Intermediate R programming
Basic understanding of APIs
Familiarity with clinical trial workflows

Recommended:

Experience with Python (helpful but not required)
Knowledge of cloud services (AWS)
Understanding of GxP requirements

Key Technologies

{ellmer}

{mcpr}

AWS Bedrock

Python

LangChain

MCP (Model Context Protocol)

Workshop Content

1. From Prototype to Production

The Reality Check:

Why most AI prototypes fail in production
Common pitfalls in enterprise AI deployment
Testing and validation approaches for GenAI
Maintaining AI applications over time

2. Architecture Patterns

Building Scalable AI Systems:

# Example: MCP Server for Clinical Data
library(mcpr)

# Define a clinical data tool
clinical_server <- mcp_server() %>%
  add_tool(
    name = "query_adverse_events",
    description = "Query adverse events from clinical database",
    parameters = list(
      study_id = "string",
      severity = "string"
    ),
    handler = function(study_id, severity) {
      # Connect to database and query
      query_clinical_db(study_id, severity)
    }
  )

3. Real-World Applications

A. Interactive Chatbots for Clinical Study Reporting

Conversational interfaces for study data exploration
Natural language queries on CDISC datasets
Automated report generation from templates

B. SOP Management Systems

Document retrieval and summarization
Compliance checking against SOPs
Version control and change tracking

C. MCP Server Implementations

Reproducible analytics workflows
Tool registration and management
Cross-language interoperability (R ↔︎ Python)

4. AWS Bedrock Integration

Enterprise LLM Deployment:

Model selection and configuration
Security and access control
Cost optimization strategies
Monitoring and logging

# Python example: AWS Bedrock with LangChain
from langchain_aws import ChatBedrock

llm = ChatBedrock(
    model_id="anthropic.claude-3-sonnet",
    region_name="us-east-1"
)

5. GxP Compliance Strategies

Making AI Production-Ready:

✅ Validation approaches for LLM applications
📝 Documentation requirements
🔍 Audit trails and logging
🧪 Testing strategies (unit, integration, UAT)
📊 Performance monitoring

6. Navigating IT Constraints

Real-World Enterprise Challenges:

Air-gapped environments
Data privacy requirements
Integration with legacy systems
Regulatory approval processes

Hands-On Exercises

Exercise 1: Build a Clinical Data Chatbot

Create an interactive chatbot that can:

Query SDTM/ADaM datasets
Generate summary statistics
Create basic visualizations
Answer questions about study design

Exercise 2: Implement an MCP Server

Build a reusable MCP server for:

Data validation
Statistical computations
Report generation

Exercise 3: AWS Bedrock Integration

Connect to AWS Bedrock and:

Configure Claude for pharma-specific tasks
Implement rate limiting and error handling
Add logging for audit purposes

Practical Applications in Pharma

Clinical Study Reporting

Automated CSR generation
Table/Listing/Figure creation from natural language
Cross-referencing and consistency checking

Regulatory Submissions

Document preparation assistance
Compliance verification
Response to regulatory queries

Data Analysis

Exploratory data analysis via natural language
Statistical model selection guidance
Results interpretation and explanation

Workshop Instructors

Devin Pastoor - Chief Technology and Product Officer at A2-AI, expert in GxP-compliant AI systems and pharmaceutical DevOps.

Xu Fei - Senior Solutions Engineer at A2-AI, specializes in LLM-enabled applications across R and Python stacks, with focus on enterprise DevOps and cloud APIs.

Aathira Anil Kumar - Engineer at A2-AI, extensive experience with life-science organizations and GenAI integration.

Tools & Frameworks Covered

R Ecosystem

{ellmer} - LLM integration
{mcpr} - Model Context Protocol
{shinychat} - Chatbot interfaces

Python Ecosystem

LangChain - LLM application framework
LangGraph - Multi-agent orchestration
AWS SDK - Cloud integration

Infrastructure

AWS Bedrock - Managed LLM service
Docker - Containerization
GitHub Actions - CI/CD

Learning Outcomes

By the end of this workshop, you will be able to:

✅ Design architecture for production GenAI applications
✅ Integrate LLMs with enterprise pharmaceutical systems
✅ Implement GxP-compliant AI workflows
✅ Build MCP servers for reproducible analytics
✅ Navigate IT constraints in regulated environments
✅ Bridge R and Python ecosystems for AI solutions

Real-World Case Studies

Case Study 1: Clinical Study Report Automation

How A2-AI helped a pharma client reduce CSR preparation time by 60% using LLM-powered automation while maintaining GxP compliance.

Case Study 2: SOP Management System

Implementation of an enterprise-wide SOP chatbot serving 500+ users across multiple departments.

Case Study 3: Data Quality Checks

Automated data validation using LLMs to identify anomalies and suggest corrections in clinical trial data.

Next Steps

After this workshop:

Getting Started with LLM APIs - For foundational knowledge
LLM-Powered Clinical Data Review - Privacy considerations
Explore A2-AI’s GitHub for example implementations

Additional Resources

AWS Bedrock documentation: aws.amazon.com/bedrock
MCP specification: modelcontextprotocol.io
A2-AI blog: Industry insights and case studies

Workshop Materials

This is a hands-on workshop with extensive code examples and exercises. All materials will be provided during the session, including:

Starter code templates
Example datasets (CDISC SDTM/ADaM)
AWS sandbox environment access
Reference documentation

Similar Workshops

Getting Started with LLM APIs - Foundation concepts
Integrating LLM with Clinical Data - Privacy focus

Next Steps

Prerequisites: Start with Getting Started with LLM APIs
Career path: AI Specialist Track

Last updated: November 2025 | R/Pharma 2025 Conference

--- title: "A Guided Tour to Building and Integrating LLM Based Tooling with R" subtitle: "From prototypes to production: Enterprise AI solutions in pharma" author: - "Devin Pastoor (Chief Technology and Product Officer, A2-AI)" - "Xu Fei (Senior Solutions Engineer, A2-AI)" - "Aathira Anil Kumar (A2-AI)" categories: [AI, LLM, AWS, Advanced] --- ## Overview [Intermediate]{.badge .badge-intermediate} [AI/LLM]{.badge .badge-category} [Enterprise]{.badge .badge-category} [GxP]{.badge .badge-category} A practical, 2-hour workshop demonstrating how to integrate Generative AI (GenAI) into pharmaceutical workflows. This session focuses on bridging the R and Python ecosystems to deliver scalable, **GxP-compliant solutions**. ### What You'll Learn - 🏗️ **Architecture patterns** for LLM-enabled applications - 🔐 **Enterprise integration** with AWS Bedrock and internal systems - 🤖 **MCP servers** for reproducible analytics - ✅ **GxP compliance** in AI application deployment - 🔗 **R-Python interoperability** for GenAI workflows ## Prerequisites ::: requirements **Required Knowledge:** - Intermediate R programming - Basic understanding of APIs - Familiarity with clinical trial workflows **Recommended:** - Experience with Python (helpful but not required) - Knowledge of cloud services (AWS) - Understanding of GxP requirements ::: ## Key Technologies ::: tool-tag {ellmer} ::: ::: tool-tag {mcpr} ::: ::: tool-tag AWS Bedrock ::: ::: tool-tag Python ::: ::: tool-tag LangChain ::: ::: tool-tag MCP (Model Context Protocol) ::: ## Workshop Content ### 1. From Prototype to Production **The Reality Check:** - Why most AI prototypes fail in production - Common pitfalls in enterprise AI deployment - Testing and validation approaches for GenAI - Maintaining AI applications over time ### 2. Architecture Patterns **Building Scalable AI Systems:** ``` r # Example: MCP Server for Clinical Data library(mcpr) # Define a clinical data tool clinical_server <- mcp_server() %>% add_tool( name = "query_adverse_events", description = "Query adverse events from clinical database", parameters = list( study_id = "string", severity = "string" ), handler = function(study_id, severity) { # Connect to database and query query_clinical_db(study_id, severity) } ) ``` ### 3. Real-World Applications #### A. Interactive Chatbots for Clinical Study Reporting - Conversational interfaces for study data exploration - Natural language queries on CDISC datasets - Automated report generation from templates #### B. SOP Management Systems - Document retrieval and summarization - Compliance checking against SOPs - Version control and change tracking #### C. MCP Server Implementations - Reproducible analytics workflows - Tool registration and management - Cross-language interoperability (R ↔ Python) ### 4. AWS Bedrock Integration **Enterprise LLM Deployment:** - Model selection and configuration - Security and access control - Cost optimization strategies - Monitoring and logging ``` python # Python example: AWS Bedrock with LangChain from langchain_aws import ChatBedrock llm = ChatBedrock( model_id="anthropic.claude-3-sonnet", region_name="us-east-1" ) ``` ### 5. GxP Compliance Strategies **Making AI Production-Ready:** - ✅ Validation approaches for LLM applications - 📝 Documentation requirements - 🔍 Audit trails and logging - 🧪 Testing strategies (unit, integration, UAT) - 📊 Performance monitoring ### 6. Navigating IT Constraints **Real-World Enterprise Challenges:** - Air-gapped environments - Data privacy requirements - Integration with legacy systems - Regulatory approval processes ## Hands-On Exercises ### Exercise 1: Build a Clinical Data Chatbot Create an interactive chatbot that can: - Query SDTM/ADaM datasets - Generate summary statistics - Create basic visualizations - Answer questions about study design ### Exercise 2: Implement an MCP Server Build a reusable MCP server for: - Data validation - Statistical computations - Report generation ### Exercise 3: AWS Bedrock Integration Connect to AWS Bedrock and: - Configure Claude for pharma-specific tasks - Implement rate limiting and error handling - Add logging for audit purposes ## Practical Applications in Pharma ### Clinical Study Reporting - Automated CSR generation - Table/Listing/Figure creation from natural language - Cross-referencing and consistency checking ### Regulatory Submissions - Document preparation assistance - Compliance verification - Response to regulatory queries ### Data Analysis - Exploratory data analysis via natural language - Statistical model selection guidance - Results interpretation and explanation ## Workshop Instructors ::: author-info **Devin Pastoor** - Chief Technology and Product Officer at A2-AI, expert in GxP-compliant AI systems and pharmaceutical DevOps. **Xu Fei** - Senior Solutions Engineer at A2-AI, specializes in LLM-enabled applications across R and Python stacks, with focus on enterprise DevOps and cloud APIs. **Aathira Anil Kumar** - Engineer at A2-AI, extensive experience with life-science organizations and GenAI integration. ::: ## Tools & Frameworks Covered ### R Ecosystem - `{ellmer}` - LLM integration - `{mcpr}` - Model Context Protocol - `{shinychat}` - Chatbot interfaces ### Python Ecosystem - LangChain - LLM application framework - LangGraph - Multi-agent orchestration - AWS SDK - Cloud integration ### Infrastructure - AWS Bedrock - Managed LLM service - Docker - Containerization - GitHub Actions - CI/CD ## Learning Outcomes By the end of this workshop, you will be able to: ✅ Design architecture for production GenAI applications\ ✅ Integrate LLMs with enterprise pharmaceutical systems\ ✅ Implement GxP-compliant AI workflows\ ✅ Build MCP servers for reproducible analytics\ ✅ Navigate IT constraints in regulated environments\ ✅ Bridge R and Python ecosystems for AI solutions ## Real-World Case Studies ### Case Study 1: Clinical Study Report Automation How A2-AI helped a pharma client reduce CSR preparation time by 60% using LLM-powered automation while maintaining GxP compliance. ### Case Study 2: SOP Management System Implementation of an enterprise-wide SOP chatbot serving 500+ users across multiple departments. ### Case Study 3: Data Quality Checks Automated data validation using LLMs to identify anomalies and suggest corrections in clinical trial data. ## Next Steps After this workshop: - [Getting Started with LLM APIs](getting-started-llm-apis.qmd) - For foundational knowledge - [LLM-Powered Clinical Data Review](llm-clinical-data-privacy.qmd) - Privacy considerations - Explore A2-AI's GitHub for example implementations ## Additional Resources - **AWS Bedrock documentation:** [aws.amazon.com/bedrock](https://aws.amazon.com/bedrock) - **MCP specification:** [modelcontextprotocol.io](https://modelcontextprotocol.io) - **A2-AI blog:** Industry insights and case studies ::: callout-tip ## Workshop Materials This is a hands-on workshop with extensive code examples and exercises. All materials will be provided during the session, including: - Starter code templates - Example datasets (CDISC SDTM/ADaM) - AWS sandbox environment access - Reference documentation ::: ------------------------------------------------------------------------ ### Similar Workshops - [Getting Started with LLM APIs](getting-started-llm-apis.qmd) - Foundation concepts - [Integrating LLM with Clinical Data](llm-clinical-data-privacy.qmd) - Privacy focus ### Related Presentations - [{llumen}: Agentic Framework](../presentations/europe-us-sessions.qmd#llumen-agentic-llm-framework-for-biomedical-documents) - Merck's internal solution - [Building the Ultimate R AI Assistant](../presentations/europe-us-sessions.qmd#building-the-ultimate-r-ai-assistant) - Roche's multi-agent system ### Next Steps - **Prerequisites:** Start with [Getting Started with LLM APIs](getting-started-llm-apis.qmd) - **Career path:** [AI Specialist Track](../summary/career-insights.qmd#path-1-aidata-science-specialist) ------------------------------------------------------------------------ *Last updated: November 2025 \| R/Pharma 2025 Conference*