GOAL: an inverse toxicity-related algorithm for daily clinical practice decision making in advanced kidney cancer

Area of Science:

• Oncology
• Pharmacology
• Clinical Decision-Making

Background:

• Metastatic renal cell carcinoma (mRCC) treatment landscape has rapidly evolved, presenting a complex therapeutic environment.
• The proliferation of seven active drugs has created physician uncertainty regarding optimal treatment strategies.
• A critical barrier to effective mRCC management is the absence of predictive biomarkers for therapies.

Purpose of the Study:

• To address the challenge of selecting optimal treatments for mRCC.
• To propose an inverse decision-making algorithm focused on polarizing toxicity.
• To aid clinicians in identifying patient profiles unsuitable for specific mRCC therapies based on comorbidities.

Main Methods:

• Review of pivotal mRCC studies and post-marketing surveillance reports.
• Identification of the most frequent (polarizing) toxicity for each available mRCC therapy.
• Development of an algorithm to match patient profiles, particularly comorbidities, with suitable therapies by avoiding contraindicating toxicities.

Main Results:

• Defined the polarizing toxicity for each mRCC therapeutic agent.
• Created an algorithm to identify patient profiles with comorbidities unsuitable for specific treatments.
• The proposed GOAL algorithm aims to reduce the risk of limiting toxicities in mRCC treatment selection.

Conclusions:

• The GOAL inverse decision-making algorithm offers a novel approach to mRCC treatment selection.
• By focusing on polarizing toxicities, the algorithm helps mitigate risks associated with patient comorbidities.
• This strategy supports personalized mRCC therapy by identifying contraindications and optimizing drug selection.