Oncology Asset Prioritization: How Evaluate Supported Smarter Oncology Portfolio Strategy
A leading biotech company needed to identify high-potential oncology assets for in-licensing aligned with its strategic goals. Evaluate's consulting team screened over 4,700 assets using Evaluate Pharma data and built "a dynamic asset prioritization model" comparing assets on market factors, unmet need, and development feasibility, ultimately selecting 5 assets for detailed secondary research and negotiation. The engagement delivered "4,700 assets screened" and "1 custom asset prioritization model created," enabling "confident, data-backed decisions" for portfolio expansion.
The customer is anonymised in Norstella’s own case study — not withheld by us.
Norstella is a holding company merging several pharma-intelligence brands — Citeline (trial intelligence), Evaluate (forecasting), MMIT (payer/formulary data) and Panalgo (real-world analytics) — into one data asset.
This sits in the Data Providers stage of the AI use-case lifecycle: Named examples from the real-world-data, claims and competitive-intelligence providers pharma AI systems are trained on, benchmarked against, or run inside of.
Published by Norstella on its own site — read the original case study on evaluate.com ↗
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