Reducing R&D Cycle Time in Pharma Without Increasing Regulatory Risk - with Vaithi Bharath of Bayer

Bharath maps the complete clinical data lifecycle from EDC systems (Medidata, Veeva) through CTMS, safety databases, statistical computing environments, to final submission deliverables. He explains how data passes through 4-5 major handoff points requiring manual file transfers, format conversions (CRF to SDTM/ADAM), and reconciliation across disparate teams and vendors, causing database lock cycles to stretch from days to weeks.

• •Clinical data flows through EDC → CTMS → Safety DBs → Statistical Computing → Medical Writers, with each requiring manual transformation
• •CRF data must be converted to SDTM and ADAM datasets with variable mapping to CDISC domains
• •Database lock can stretch from days to weeks when upstream changes occur, forcing statistical teams to redo mappings
• •Multi-team workflows often span internal teams, pharma vendors, and CROs with incompatible formats
• •Even after database lock, deviations between data and study analysis plans trigger rework cycles

Bharath distinguishes validation from testing: validation asserts what must and must not happen, not just that something works. He shares a real example where a vendor integration issue forced 6-8 months of manual ZIP file handoffs because the CSV release process for a modern integration was too expensive and time-consuming to fast-track.

• •Validation requires proving both that required functions work AND that prohibited actions cannot occur
• •CSV release processes involve extensive documentation, testing, and multi-team coordination
• •Real example: Teams tolerated 6-8 months of manual ZIP file transfers via email rather than wait for integration validation
• •Manual workarounds require tracking file lineage, timestamps, record counts, and corruption checks for compliance
• •Small system changes can trigger weeks of validation work, forcing teams to accept inefficient processes

Bharath explains FDA's 21 CFR Part 11 requirements for electronic records and signatures, detailing what auditors actually look for: tamper-proof logs, complete access justifications, training evidence, and reproducible results. He emphasizes that FDA expects to reproduce exact results using the same code and data, requiring immutable audit trails at every step.

• •Auditors require tamper-proof, immutable logs with audit trails on the logs themselves
• •Every user access requires training completion, business justification, and manager approval documented in annual periodic reviews
• •E-signatures replace physical signatures but require credential-based frameworks with full traceability
• •FDA reproduces results using submitted code and data to verify integrity—exact reproducibility is mandatory
• •Global trials face varying requirements: China has more stringent rules than FDA/EMA standards

Bharath describes how AI tools from vendors like SAS are introducing guided workflows for SDTM/ADAM transformations and protocol deviation checks. The key is maintaining human clinical review of AI suggestions while creating immutable audit trails. He emphasizes these are pilots and proofs-of-concept, not fully mature implementations, with humans still validating every AI-generated suggestion.

• •AI-guided workflows can complete SDTM transformations in minutes/hours vs. multiple rounds of manual SAS programming
• •Human reviewers validate every AI suggestion against study analysis plans—AI is copilot, not autopilot
• •Vendors like SAS offering AI-assisted quality control checks with traceability to underlying macros and workflows
• •Most implementations are pilots/POCs—not yet fully mature or industry-wide deployed
• •AI suggestions documented identically to human-generated transformations: derivation logic, code, sign-offs

Bharath explains how standardized AI-guided workflows enable reusable validation macros, increasing reusability from 50% to 80% across studies. This translates to faster database locks, fewer study analysis plan amendments (which can cost hundreds of thousands to millions), and more consistent quality regardless of team experience level.

• •Standardized workflows reduce dependency on individual reviewer expertise—less experienced teams produce consistent quality
• •Reusable macros increase from ~50% to ~80% reusability across studies, significantly reducing validation time
• •Study analysis plan amendments can cost hundreds of thousands to millions of dollars depending on study complexity
• •Consistent macros mean validating only new additions rather than re-validating entire workflows
• •Audit trails and logs become more consistent across studies, improving overall compliance posture

Bharath advocates for building API-based wrappers around existing validated systems rather than expensive rip-and-replace projects. He explains how to leverage EDC APIs (like Veeva) to add AI-guided workflow layers, plug QC checks into existing ETL pipelines, and enhance validation frameworks without revalidating the entire stack—saving months to years of implementation time.

• •Build API wrappers around existing systems (EDC, CTMS) to add AI workflow layers without replacing core infrastructure
• •Leverage vendor APIs (Veeva, Medidata) for data access while adding lineage tracking and QC checks on top
• •Plug AI-assisted validation checks into existing data promotion workflows (dev → val → prod) without framework replacement
• •Validation in pharma takes months to years vs. monthly releases in SaaS—complementing beats replacing
• •Teams continue using existing tools while AI workflows handle routine checks, reviews, and data quality validation

Bharath shares Bayer's consolidation approach: moving from 7-8 disparate system integrations to a three-tier stack (data management/prep, lakehouse for transformation, analytics environment). This shifts data wrangling burden upstream, allowing statistical teams to focus on analysis rather than data preparation, while adopting market-standard platforms with plug-and-play API capabilities.

• •Consolidating from 7-8 system integrations down to centralized lakehouse architecture (e.g., Databricks)
• •Three-tier stack: data management/prep → lakehouse transformation → statistical computing/analytics
• •Shifting data wrangling upstream frees statistical teams to focus on analysis and insight generation
• •Adopting market-standard platforms (Veeva, Medidata, Databricks, SAS) with native API support
• •Loosely coupled integrations via APIs replace hardwired endpoints for easier data flow

Bharath and the host synthesize the discussion into a practical roadmap: identify 8-10 routine weekly questions causing the most rework, map data owners and approvers, pilot 2 questions as guided workflows in a governed sandbox, track days saved and rework avoided, then scale what passes audits. The key is complementing validated systems with explainable workflows that maintain human approval and audit-ready traceability.

• •Start by standardizing the 8-10 routine questions that cause the most rework in your organization
• •Map data owners, approvers, and workflow dependencies before building any AI-guided processes
• •Pilot 2 routine questions as guided workflows inside a governed sandbox environment
• •Track concrete metrics: days saved, rework cycles avoided, audit compliance improvements
• •Scale workflows that successfully clear audits while maintaining human-in-the-loop approval and full traceability