Dissecting sources of variability in patient response to targeted therapy: anti-HER2 therapies as a case study

Area of Science:

• Oncology
• Pharmacology
• Computational Biology

Background:

• Targeted therapies show variable patient responses despite targeting specific genetic aberrations.
• Understanding sources of response heterogeneity is crucial for advancing targeted therapy drug development.
• No current methods effectively quantify the contribution of different factors to treatment response variability.

Purpose of the Study:

• To develop and validate a computational platform for dissecting sources of variability in patient response to targeted therapies.
• To compare the relative contributions of pharmacokinetics, tumor characteristics, and drug sensitivity to response heterogeneity.
• To apply the platform to HER2-amplified breast cancer treated with neratinib and lapatinib.

Main Methods:

• Developed a simulation platform integrating pharmacokinetics, tumor burden/growth kinetics, clonal composition, and drug sensitivity.
• Utilized population pharmacokinetic models for systemic exposure variability.
• Incorporated clinical data for tumor growth kinetics and HER2 immunohistochemistry for sensitive/resistant cell fractions.
• Predicted response using growth rate-corrected drug potency and simulated virtual patient outcomes.

Main Results:

• The platform accurately predicted clinical outcomes, including response rate and progression-free survival (PFS).
• Resistant clone growth rate significantly impacted PFS more than systemic drug exposure for both neratinib and lapatinib.
• Drug sensitivity strongly influenced neratinib response, while HER2 immunohistochemistry variability affected lapatinib response.

Conclusions:

• The developed platform effectively dissects sources of variability in targeted therapy response.
• This approach can aid decision-making processes in targeted therapy drug development.
• Identifying key variability drivers can optimize treatment strategies and drug design.