Prostate Cancer – Unmet Need – Unmet Need – Metastatic Hormone-Sensitive Prostate Cancer (US/EU)
Prostate cancer is the second-most-prevalent cancer in men, significantly impacting male mortality rates. Hormonal therapy is a cornerstone of drug treatment for all stages of prostate cancer. Androgen receptor inhibitors such as abiraterone and Xtandi (Pfizer / Astellas) are common treatments for metastatic hormone-sensitive prostate cancer (mHSPC). These drugs compete with next-generation hormonal therapies such as Erleada (Johnson & Johnson) and Nubeqa (Bayer). Despite the availability of these androgens in combination with androgen deprivation therapy (ADT), mHSPC often progresses, highlighting the need for more-effective, safer therapies and underscoring the significant commercial opportunity in this space.
QUESTIONS ANSWERED
- Which attributes most influence prescribers’ choice of therapy for mHSPC?
- How do key current therapies, in combination with ADT, perform on key treatment drivers and goals for this patient population?
- What are the prevailing areas of unmet need and hidden opportunities in the treatment of this patient population?
- How much are medical oncologists willing to compromise on the clinical benefits of a hypothetical new treatment for newly diagnosed mHSPC, taking into consideration a better price, and vice versa?
Markets covered: United States, France, Germany, United Kingdom
Primary research: Survey of 60 U.S. and 32 European medical oncologists fielded in December 2024
Key drugs: Abiraterone, Xtandi, Erleada, Nubeqa
PRODUCT DESCRIPTION
- Evaluate clinical and nonclinical product attributes that influence treatment decisions through physicians’ weightings and analysis of stated vs. derived importance.
- Pinpoint areas of high unmet need by assessing current drug performance against key attributes and treatment drivers.
- Analyze market scenarios for different target product profiles using the TPP Simulator.
KEY FEATURE
Target Product Profile (TPP) simulator tool allows for customizable market simulations based on conjoint analysis. Compare up to seven TPPs across multiple disease-specific attributes and price points to gauge which variables influence prescribing behavior.