The Challenge
For a major global healthcare and pharmaceutical company approaching a key portfolio gate, the forecast for a rare hematology asset relied on a straightforward market-size calculation: eligible patients × market share × price.
While simple to build, this approach provided limited visibility into the patient dynamics behind the commercial opportunity.
The treatable population was unclear
Prevalence and incidence were not differentiated, making it difficult to distinguish the existing patient pool from future patient inflow.
Patient journeys were hidden
Diagnosis, treatment initiation, line progression, switching, and discontinuation were aggregated rather than explicitly modeled.
Competitive dynamics were simplified
Market share was assumed rather than derived from patient movement across therapies.
Launch dynamics were difficult to assess
Annual outputs provided limited visibility into monthly uptake, patient flows, and the impact of market events.
Key assumptions lacked transparency
Inputs were drawn from multiple sources, but the link between evidence, assumptions, and forecast outcomes was not consistently traceable.
The portfolio team needed to move beyond market-size estimates and understand how patient journeys, treatment decisions, and competitive dynamics would shape the asset’s commercial opportunity over time.
Our Solution
Chryselys rebuilt the forecast from the patient up, creating a transparent, evidence-based framework that connected every commercial outcome to an underlying patient population.
Defined the patient pool
Built a hybrid prevalence–incidence framework capturing both the existing diagnosed population and annual patient inflow.
Mapped the patient journey
Modeled diagnosis, treatment initiation, line progression, switching, and discontinuation by patient segment.
Modeled the competitive landscape
Built explicit competitor patient flows, allowing market share to emerge from patient dynamics rather than being imposed as an assumption.
Triangulated the evidence
Combined published literature, primary research, and analog launches to validate and reconcile key assumptions.
Calibrated uptake and persistence
Applied analog-based adoption and treatment persistence assumptions from comparable rare-disease launches.
Quantified uncertainty
Used Monte Carlo simulation to test key assumptions and generate forecast ranges rather than relying on a single point estimate.
Enabled launch-level visibility
Developed monthly outputs to support launch planning, portfolio reviews, and key event-based scenarios.
Business Impact
The patient-based framework shifted the portfolio discussion from “How large is the market?” to “How many patients can we realistically diagnose, treat, retain, and capture?”
- Improved decision confidence through a transparent link between patient populations, assumptions, evidence, and commercial outcomes.
- Clarified the addressable opportunity by separating existing patients from future incidence and treatment inflow.
- Made market share an output, reflecting competitive patient movement rather than a top-down assumption.
- Quantified uncertainty and risk, helping stakeholders distinguish robust conclusions from assumption-sensitive outcomes.
- Enabled more informed portfolio-gate decisions through a transparent, patient-centric view of the opportunity.
- Created a reusable forecasting asset supporting portfolio reviews, scenario planning, launch strategy, and investment decisions beyond the initial gate.
