Biosimilar Target Product Profiles (Immunology)

To determine the trade-offs across key biosimilar attributes that surveyed rheumatologists are willing to make when considering which biosimilar to prescribe, we included an adaptive choice-based conjoint (ACBC) module in the survey. Following appropriate analysis, ACBC data allow for the simulation of physician preference share and likelihood to prescribe user-defined target product profiles (TPPs). Conjoint analysis is based on the idea that any product or service is seen by consumers as a combination of attributes or features, each of which contributes to their overall interest in the product or service.

We report on key ACBC analysis findings and one market simulation performed on three hypothetical TPPs (see the “Conjoint Analysis-Based Simulation of a Market Scenario”). The Excel-based Target Product Profile Simulator allows for the creation of up to seven user-defined TPPs with varying performance across the attributes included in the survey. To access the Excel-based Target Product Profile Simulator, see the “Downloads” section of the report.

Through research conducted by Clarivate, we identified seven clinical and nonclinical attributes relevant to the assessment of biosimilar development opportunities in the rheumatology space to be included in our analysis. We included attributes known to be key differentiators of biosimilars in the rheumatology space, including price:

• Availability of clinical data in the indication I am prescribing for.
• Inclusion in treatment guidelines.
• Type of manufacturer.
• Reimbursement restrictions.
• Postmarketing data (time on market).
• Drug delivery.
• List price.

In choosing these attributes, we considered the impact of these attributes on physicians’ prescribing of biosimilars as well as regulatory guidance on and trends in biosimilar development. Surveyed physicians’ concerns about biosimilars’ efficacy, safety, and immunogenicity are a major factor influencing biosimilar use. Lack of long-term safety and efficacy data, insufficient clinical data compared with branded biologics, and the quality of clinical data are concerns regarding biosimilar use. Therefore, we included availability of clinical data in the indication I am prescribing for as an attribute to explore how clinical data impacts the use of a biosimilar, allowing for performance levels that include no clinical data (i.e., biosimilar approved via extrapolation) and Phase III data in the indication of interest. Furthermore, the availability and length of postmarketing data impact physicians’ comfort with and prescribing of a biosimilar. The level of trust physicians and patients have in a biosimilar will impact their willingness to use the biosimilar; our data suggest that the type of manufacturer influences clinicians’ trust and, ultimately, biosimilar prescribing behavior. External factors (e.g., market access) also influence biosimilar prescribing choice; here, we included attributes that capture inclusion in local or national treatment guidelines and general payer policy. Cost is a key driver of biosimilar use, and price relative to the brand is included in our conjoint analysis to gauge the impact of price relative to a reference brand on physicians’ likelihood of prescribing a biosimilar.