How ZS Leveraged Intelligencia AI’s Deep Data to Optimize Tumor Selection
Core Challenges
Complex Tumor Evaluation: A global
biopharma company needed to
assess 16 tumor types and 60
segments (e.g., populations by line of
treatment (LoT) and/or subdivisions
like biomarker-targeted) for its novel,
early-stage ADC.
Time Constraints: The
pharmaceutical company’s asset
team had only two months to meet
an internal deadline.
Data Overload: Traditional methods
could not efficiently aggregate the
vast clinical and commercial data for
timely decision-making.
Key Outcomes
Accelerated Decision-Making: The
project was completed in just over
two months, substantially less time
than the typical four to six months
that projects of the same size
normally take.
Targeted Tumor Selection: Using
clinical and competitive intelligence
data from Intelligencia AI paired with
other data sources and assessments,
ZS successfully narrowed 60 tumor
segments to 10 based on
commercial and clinical feasibility.
Data-Driven Confidence:
Intelligencia AI’s granular clinical and
competitive data, paired with its
analytics and AI-driven probability of
technical and regulatory success
(PTRS) assessments, provided clear,
actionable insights for smart
investment decisions.
Identify the Most Promising Tumor Targets for a Novel Drug Treatment
In this engagement, ZS supported a global biopharmaceutical company’s oncology team. The company needed to assess the best tumor types for its antibody-drug conjugate (ADC) with a novel target.
Given limited experience or existing footprint in many of these tumor types, the pharmaceutical company sought an external partner and framework to evaluate tumor segments based on commercial and clinical feasibility.
“Our client needed support in developing a framework to evaluate 16 tumor types, each with multiple segments, and determine the most commercially attractive areas,” explained Charlotte Anne Miller, strategy insights and planning associate consultant at ZS. “This was a massive undertaking, and we needed a comprehensive, up-to-date, curated and user-friendly database to accelerate the process.”
Reduced a Labor-Intensive Process With Access to Comprehensive Clinical and Biological Data
Before utilizing Intelligencia AI’s SaaS platform, Portfolio Optimizer, conducting this analysis was labor-intensive and time-consuming. It required extensive literature reviews, expert interviews and manual data aggregation. Historically, such projects took four to six months due to the complexity and the volume of data across multiple tumor types and their competitive landscapes.
ZS leveraged Intelligencia AI’s vast, expertly curated clinical and biological data to efficiently pull key metrics across the 60 tumor segments. Intelligencia AI’s in-house, scientifically trained data curators are dedicated to the timely, accurate and detailed capturing of the data behind clinical programs and drug entities. This process differs from other data providers that leverage automatically embedded information, which results in inaccuracies and missed data that are unstructured and buried in documents such as abstracts and other announcements.
The ZS process, combined with access to Intelligencia AI’s comprehensive data, enabled:
1. Rapid extraction of key data points, such as the number of Phase III trials in the pipeline and competitive landscape projections or the number of recently approved modalities in the segment.
2. Assessment of clinical and commercial viability using AI-driven PTRS assessments, which helped estimate the probability of success for future programs
3. Data triangulation from multiple sources which provided a holistic view of tumor viability
“Intelligencia AI Portfolio Optimizer proved to be an incredible tool for quickly pulling key metrics for each segment,” said Miller. “For example, when evaluating HER2-positive breast cancer, we could instantly see how many ongoing Phase III trials existed and estimate the probability of success for relevant programs. This helped us determine whether it was worth further investment.”
The Result: An Expedited, Data-Driven Approach
By leveraging Portfolio Optimizer, ZS quickly narrowed the pharmaceutical client’s options from 60 tumor segments to 10, a process traditionally taking up to 3 times longer. The results and insights informed the pharma company’s budget discussions, guiding decisions on where to allocate resources for clinical trials and preclinical data generation. “Analyzing 60 tumor segments in just over two months was unheard of,” Miller noted. “I’ve been on projects where this took 6+ months. Moving quickly was critical to getting it done before our client’s impending leadership meeting.”
Reduce Risk Around Complex, High-Stakes Decisions
Pharmaceutical companies must make complex and high-stakes investment decisions. They’re responsible for determining where to focus their resources, considering clinical feasibility and commercial potential. Intelligencia AI’s predictive analytics platform provides a powerful tool for reducing uncertainty and expediting these critical decisions.
“When making investment decisions, you need to consider whether a drug will likely succeed. Intelligencia AI helped us shed light on this question in a way we could not before,” Miller explained. “The ability to assess the probability of success and competitive intensity quantified by the number of Phase III trials, the number of new modalities, etc., was a game-changer.”
A Unique Differentiator: Granular Tumor Segmentation
One of the standout capabilities of Intelligencia AI’s platform was the ability to analyze tumor subtypes with unmatched specificity. “We could double-click into segments—like second-line patients with a specific mutation—to go deeper into the data, which is not something I’ve ever seen done in other databases,” Miller shared. “This helped us get incredibly precise in narrowing down the options.”
While outside the scope and need for this engagement, Portfolio Optimizer provides additional data dimensions such as target, modality, disease severity, therapy type, primary sponsor, adjunct therapy, and other population characteristics, which can all be used for further modeling needs.
Conclusion
ZS’s collaboration with Intelligencia AI transformed the global biopharma company’s tumor selection process by providing a faster, data-driven approach to decision-making. By leveraging AI-powered predictive analytics, ZS helped the company confidently prioritize the most promising tumor segments, enabling better resource allocation and strategic planning in oncology development.
