Personalized (Precision) Medicine

🔬 What is Personalized Medicine?

Personalized medicine (also called precision medicine) refers to a medical model that customizes healthcare decisions, treatments, and preventive measures based on the individual genetic, environmental, and lifestyle factors of each patient.

🌐 Core Principles of Personalized Medicine:

Use of Genetic and Omics Data:
- Individual biological characteristics are analyzed using data from genomics, transcriptomics, proteomics, and metabolomics.
- Example: People carrying BRCA1/2 gene mutations are at higher risk for breast cancer — this info can lead to early preventive action.
Pharmacogenomics – Tailored Drug Usage:
- Genetic differences cause varied responses to the same drug among patients.
- Example: In 6-Mercaptopurine (6-MP) therapy (used in childhood ALL), patients with low TPMT enzyme activity face higher toxicity risks. Dose adjustment is essential.
Targeted Therapies:
- Especially in oncology, tumors are genetically profiled, and drugs are selected based on specific mutations.
- Example: HER2-positive breast cancer is treated effectively with trastuzumab (Herceptin).

⚙️ Technologies Used:

Next-Generation Sequencing (NGS)
Artificial Intelligence and Machine Learning
Bioinformatics Platforms
Integrated Electronic Health Records (EHRs)
Digital Twin Models (just like your project!)

✅ Potential Benefits:

Advantage	Explanation
🎯 Right drug, right dose	Minimizes side effects, boosts success
⏱ Early disease detection	Enables preventive actions via genetic risk profiling
🧠 Data-driven clinical support	Helps physicians make more informed decisions
💰 Cost-efficiency	Avoids unnecessary treatments

🚧 Challenges and Ethical Issues:

Data privacy and genetic confidentiality
Equity of access to personalized healthcare (especially in developing regions)
High cost of omics and genetic testing
Algorithmic fairness in AI-based decision systems

💡 Application Suggestion (For Your Work):

You could create a digital twin platform for childhood ALL patients, simulating personalized treatment paths based on genetic, physiological, and lifestyle data. This could: