Case Studies / Pre-launch evidence toolkit for PD-L1 inhibitor: MSL de…
Brand & Launch Launch Support Oncology

Pre-launch evidence toolkit for PD-L1 checkpoint inhibitor: MSL deck, payer summary, FAQ, and KOL guide for 4 oncology markets

Challenge
With approval 6 weeks away, Medical, Market Access, and Brand teams each had separate material needs — and no shared core story to build from.
Approach
Built from a single core evidence architecture: one shared scientific narrative translated into four audience-specific formats for MSLs, payers, KOLs, and internal teams.
Result
Complete toolkit for 4 markets delivered on time, with all materials aligned to the same core evidence narrative.
The challenge

Six weeks to launch, and three teams with different material needs

A PD-L1 inhibitor approval was confirmed six weeks before the planned launch date across four oncology markets. The Medical Affairs, Market Access, and Brand teams all needed materials — but each team's requirements were different, and the materials had not been coordinated from a shared core document.

MSLs needed a scientifically detailed slide deck with full clinical trial data. Payers needed a concise evidence summary focused on comparative effectiveness and budget impact. KOLs needed an engagement guide covering the scientific landscape, the brand's data position, and anticipated questions from other experts. Internal teams needed a FAQ covering every likely question from any stakeholder.

Without a shared evidence architecture, each team risked developing materials that contradicted each other — creating different versions of the clinical story for different audiences. With only six weeks to launch, there was no time to manage four parallel workstreams independently.

Launch success depends on one coherent scientific narrative expressed through multiple formats. The mistake is to build the formats before the narrative — or to let each function build its own.

Our approach

What we did

1
Core evidence architecture
Developed a master evidence document defining the shared scientific narrative: mechanism of action, pivotal trial hierarchy, key subgroup data, safety profile, and competitive differentiation. All subsequent materials derived from this source.
2
MSL slide deck
Built a modular slide deck with core data slides and market-specific supplement modules. Designed for scientific dialogue with oncologists — detailed, citable, structured for question navigation.
3
Payer evidence summary
Prepared a 6-page payer-facing summary: comparative clinical data, place in therapy, economic arguments, and budget impact indicators. Adapted for each market's HTA framework.
4
KOL engagement guide
Created a structured KOL interaction guide: scientific background, data summary, anticipated expert positions, suggested discussion pathways, and listening guide for capturing medical insights.
5
Comprehensive FAQ
Compiled a 47-question FAQ covering clinical, access, safety, and comparative questions from all stakeholder types. Designed for internal use by all three functions.
Result

Measurable impact

The complete toolkit — MSL deck, payer summary, KOL guide, and FAQ — was delivered for all 4 markets six weeks before the planned launch date. All materials were aligned to the shared core evidence architecture. The Medical Director of one market commented that it was the most coherent pre-launch package the team had received for any product in the previous five years. Launch meetings proceeded with consistent messaging across all stakeholder types.

4
Markets covered with a fully aligned material set
6 weeks
Delivered before launch — all functions ready on day one
47
FAQ items covering all stakeholder types and question categories
Evidence Scanner
Evidence ScannerTM
AI infrastructure

AI-powered.
Expert-validated.

We built AI workflows into our daily practice — not as a marketing claim, but as the infrastructure that lets our medical experts deliver faster without cutting corners.

Research
Structured PubMed queries with narrative or table outputs
Monitoring
Weekly literature digests by drug, target, or topic
AI-Enhanced EDC
Electronic data capture with AI-assisted structuring of unstructured records
Fact-Checker
Claim verification against your source documents
AI accelerates. Our experts validate.
Every output goes through expert medical review before it reaches your team. AI handles structure and speed — we handle scientific judgement and MLR readiness.
Evidence Scanner · Research module
// Query: ribociclib OS data MONALEESA 2023–24
search("ribociclib overall survival", {
  years: [2023, 2024],
  output: "structured_table"
})
// 847 records → 23 relevant
Processing 847 records...
Evidence Summary
MONALEESA-2 updated OS (NEJM 2023): median OS 63.9 mo vs 51.4 mo (HR 0.76, 95% CI 0.63–0.93). Benefit maintained across all pre-specified subgroups...
Facing a similar challenge?

Tell us what you’re working on — we’ll show you relevant cases and suggest the fastest path forward.