NPV Framework Enhances Financial Decision-Making for Biosimilar Investments
As biosimilars gain ground in the global health care landscape, the need for precise financial evaluation tools has grown more urgent. A recent study has introduced a novel Net Present Value (NPV) modeling framework designed to assist pharmaceutical companies and policymakers in navigating the high-risk, high-cost terrain of biosimilar development.
Unlike generic small-molecule drugs, biosimilars require significant investment, ranging from $100 million to $250 million, and extended development periods of 6 to 8 years. The study’s authors propose a risk-adjusted NPV framework tailored specifically to the biosimilar sector, addressing this investment complexity through a blend of technical, regulatory, and market-oriented parameters.
Validated through case studies of 3 monoclonal antibody biosimilar programs, the model incorporates standard NPV metrics alongside biosimilar-specific elements such as market penetration, development timelines, and manufacturing efficiency. Findings suggest that projects must achieve at least $250 to $300 million in peak sales to generate a positive NPV, with clinical development accounting for approximately 57% of total project costs.
The model’s comprehensive risk assessment mechanism is the key to its success, according to the authors. Sensitivity analyses explore how fluctuations in core assumptions—ranging from regulatory delays to changes in production efficiency—impact the investment outlook. The framework is adaptable, allowing stakeholders to apply it to a range of biosimilar projects across diverse market conditions and molecule types.
However, the model has its limitations. Its accuracy is tightly bound to the reliability of market forecasts and assumptions, which can be volatile in the evolving biosimilar ecosystem. Regional differences in market access and regulation further complicate projections, highlighting the importance of continuously updating inputs and assumptions.
Looking ahead, the study suggests several enhancements. For example, incorporating machine learning could bolster predictive accuracy, while real-time market data integration would enable more dynamic financial modeling. The framework may also evolve into a multiproject assessment tool, supporting portfolio optimization, capacity planning, and strategic partnership valuation. Furthermore, future iterations aim to expand the model’s relevance in emerging markets by incorporating regional regulatory nuances and health care infrastructure variables.
This study marks a critical step toward standardized, evidence-based financial decision-making in biosimilar development—offering stakeholders a practical, adaptable, and forward-looking investment evaluation tool.
Reference
Ranbhor R, Kulkarni P. Evaluating biosimilar development projects: an analytical framework utilizing net present value. Biologics. 2025;19:125-135. doi:10.2147/BTT.S514767. eCollection 2025
