Summary
Tailoring treatment to a patient's genetic profile, for example in pharmacogenomics.
In depth
Uses genomic, proteomic and lifestyle data. Examples: HER2 testing for trastuzumab, BRAF testing for vemurafenib, CYP2C19 for clopidogrel. Challenges: cost, data privacy and equity.
Combines genetics with clinical data to predict risk, select therapy and dose. Polygenic risk scores add predictions for common diseases. Limited ancestry diversity in data causes inequities.
Example: companion diagnostics identify patients likely to benefit from a targeted drug.
Personalised or precision medicine uses an individual's genetic, molecular and lifestyle information to choose prevention and treatment. Examples include testing tumours for mutations (such as HER2 or EGFR) to select targeted drugs, screening for BRCA variants, and dosing drugs according to genotype.
Challenges are cost, access, interpretation of rare variants, privacy and ensuring genomic data represent diverse populations. Polygenic risk scores summarise many small effects but have limited accuracy in some groups.
Example: trastuzumab (Herceptin) works only for breast cancers with HER2 overexpression.
Key idea: the same disease name can hide different molecular diseases that need different treatments.
Frequently asked questions
What is Personalised Medicine?
Tailoring treatment to a patient's genetic profile, for example in pharmacogenomics.
Which area of biology covers Personalised Medicine, and how advanced is it?
Personalised Medicine is one of the 56 topics in Biotechnology & Genomics. It is rated Advanced, so it suits learners who want specialist depth. Related topics in this area include Machine Learning in Biology, Pharmacogenomics, Liquid Biopsy.
Can you give an example of Personalised Medicine?
Tumour sequencing matches patients to targeted therapies.
What should I learn before Personalised Medicine?
Work through the Beginner topics in Biotechnology & Genomics first, such as Biotechnology, Genetic Engineering, Selective Breeding, then return to Personalised Medicine. Advanced topics assume the Intermediate vocabulary.
What topics are related to Personalised Medicine?
The closest topics to Personalised Medicine are Machine Learning in Biology, Pharmacogenomics, Liquid Biopsy, Gene Therapy. Comparing them side by side is a good way to see how the ideas connect.
How can I check that I understand Personalised Medicine?
Read the summary and the in-depth section, then use the quick check on the interactive lesson for Personalised Medicine. To practise the whole of Biotechnology & Genomics, use the Biotechnology & Genomics quiz.
How is Personalised Medicine different from Machine Learning in Biology?
Personalised Medicine: Tailoring treatment to a patient's genetic profile, for example in pharmacogenomics. Machine Learning in Biology: Algorithms learn patterns in sequence, image and clinical data to predict function and disease. Read both side by side and note what each one explains.
How should I study Personalised Medicine step by step?
First, read the summary and write the definition in your own words. Second, read the in-depth section and note any example. Third, compare it with Machine Learning in Biology and explain the difference aloud. Finally, take the Biotechnology & Genomics quiz and come back to this page for any question you miss.