Pfizer’s Personalized Medicine Initiative Case Study

Pfizer aims to leverage genomics and clinical trial data to analyze drug efficacy and patient response variability. The goal is to identify key genetic markers, demographic factors, and clinical characteristics that influence how patients respond to treatments. By detecting patterns in side effects and progression-free survival (PFS) rates, this analysis will contribute to personalized medicine, enabling tailored treatment plans for improved patient outcomes.

Objectives

1. Analyze Drug Efficacy Across Patient Demographics

   • Investigate how age, sex, ethnicity, and comorbidities affect treatment outcomes.

2. Identify Genomic Biomarkers for Drug Response

   • Detect genetic mutations, SNPs, and gene expression patterns that correlate with positive or negative treatment responses.

3. Detect Patterns in Side Effects

   • Analyze which genetic and clinical factors contribute to higher risk of adverse effects.

4. Predict Progression-Free Survival (PFS)

   • Use statistical and machine learning models to forecast patient survival rates based on their genomic and clinical profiles.

5. Enhance Personalized Medicine and Clinical Trial Optimization

   • Develop models that stratify patients into optimal treatment groups and improve clinical trial recruitment.

   Expected Outcomes

   ✔ Personalized Treatment Plans
   ✔ Reduced Side Effects
   ✔ Optimized Clinical Trials