Pharmacogenomics: How Your Genes Influence Drug Response

 

Introduction

Have you ever taken a medication that worked wonders for someone else, but had no effect on you — or worse, caused side effects? You're not alone. For decades, medicine has followed a one-size-fits-all approach, assuming that people respond to drugs in roughly the same way. But science is now revealing a more complex truth: our genes play a powerful role in how we respond to medications.

Welcome to the world of pharmacogenomics — the study of how your unique genetic makeup influences your body’s response to drugs. This cutting-edge field is revolutionizing healthcare by shifting the focus from standardized prescriptions to personalized treatments. It seeks to answer crucial questions like:

• Why does a painkiller work for one person but cause allergic reactions in another?

• Why do some people need higher or lower doses of the same drug?

• How can we prevent adverse drug reactions before they happen?

By combining genetics, molecular biology, and bioinformatics, pharmacogenomics helps us understand how small variations in DNA — like single nucleotide polymorphisms (SNPs) — affect the way drugs are metabolized, absorbed, and utilized by the body. With the help of genome sequencing and data-driven tools, doctors are beginning to tailor prescriptions based on an individual’s genetic profile — a concept known as precision medicine.

As the global demand for safe, effective, and personalized treatments grows, pharmacogenomics is stepping up as a key player. In this blog, we’ll dive deep into the science behind drug-gene interactions, explore real-world applications, and discuss the role of bioinformatics in shaping the future of medicine.

What is Pharmacogenomics?

Pharmacogenomics is a branch of science that studies how an individual’s genetic makeup influences their response to medications. It merges the fields of pharmacology (the science of drugs) and genomics (the study of genes and their functions) to create a personalized approach to medicine. Rather than relying on a "one-size-fits-all" model, pharmacogenomics seeks to optimize drug therapy based on a person’s unique genetic profile.

Definition and Scope

• Pharmacogenomics involves the use of genomic technologies to predict how individuals will respond to specific drugs. It aims to:

  • Identify genetic variants that influence drug metabolism and efficacy.

  • Minimize adverse drug reactions (ADRs).

  • Customize treatment plans for maximum effectiveness.

• The ultimate goal is precision medicine—providing the right drug, at the right dose, to the right person.

• Pharmacogenomics is now being used in clinical settings, especially for drugs with narrow therapeutic windows (where small changes in dose can lead to toxicity or ineffectiveness), such as anticoagulants, antidepressants, and cancer therapies.

Pharmacogenetics vs. Pharmacogenomics

Though often used interchangeably, pharmacogenetics and pharmacogenomics have subtle differences:

Term	Description
Pharmacogenetics	Focuses on the effects of a single gene on drug response. It studies how variations in a specific gene can alter how a person reacts to a particular drug.
Pharmacogenomics	Encompasses a broader view, studying the entire genome to understand complex interactions between multiple genes and drugs. It often uses large datasets and bioinformatics tools.

Think of pharmacogenetics as a single-gene approach, while pharmacogenomics takes a systems-level, genome-wide approach to drug response.

Examples of Genetic Variations Affecting Drug Response

1. CYP450 Enzymes (Cytochrome P450 family)

   • These liver enzymes are responsible for metabolizing ~75% of all drugs.

   • Variations in the genes encoding these enzymes (like CYP2D6, CYP2C9, and CYP2C19) can dramatically affect how drugs are processed.

   Examples:

   • CYP2D6:

     • Converts codeine into morphine for pain relief.

     • Poor metabolizers get little pain relief; ultra-rapid metabolizers may overdose on morphine.

   • CYP2C9:

     • Affects metabolism of warfarin, a blood thinner.

     • Genetic testing helps avoid dangerous bleeding or clotting complications.

2. TPMT (Thiopurine Methyltransferase)

   • Influences the breakdown of thiopurine drugs (used in leukemia and autoimmune disorders).

   • Low TPMT activity = high risk of toxicity unless the dose is adjusted.

3. SLCO1B1

   • Affects transport of statins (cholesterol-lowering drugs).

   • Variants can increase risk of muscle toxicity in some individuals.

4. HLA Genes (e.g., HLA-B*15:02)

   • Certain variants are associated with severe allergic reactions to drugs like carbamazepine and abacavir.

   • Screening helps avoid life-threatening conditions like Stevens-Johnson Syndrome.

In Summary:

Pharmacogenomics allows healthcare professionals to:

• Predict drug response before starting therapy.

• Select the safest and most effective medication.

• Avoid adverse drug reactions.

• Reduce time and cost in treatment cycles.

By understanding these gene–drug interactions, we move closer to truly personalized healthcare.

How Genes Influence Drug Response

Understanding how your body reacts to medications is at the heart of pharmacogenomics. Your genes play a critical role in determining how a drug is absorbed, distributed, metabolized, and excreted—collectively known as ADME. These genetic influences can determine whether a drug works for you, causes side effects, or is even dangerous.

ADME: The Pharmacogenomic Breakdown

1. Absorption
   Genes can influence how drugs enter your bloodstream.
   For example, variations in genes encoding transporter proteins like ABCB1 can affect how well drugs are absorbed in the intestines.

2. Distribution
   After absorption, drugs are transported to their target tissues. Genetic differences in plasma proteins (like albumin) or transporters can affect where the drug goes and how much of it reaches the right site.

3. Metabolism
   This is often the most critical step affected by genetics.
   Genes like CYP2D6, CYP2C9, and CYP2C19 encode cytochrome P450 enzymes, which are involved in the metabolism of over 80% of drugs. Variations in these genes can cause people to be:

   • Poor metabolizers – leading to drug accumulation and toxicity.

   • Rapid/ultra-rapid metabolizers – breaking down the drug too quickly, reducing effectiveness.

4. Excretion
   Genes such as SLCO1B1 and ABCC2 control how drugs are eliminated via the liver and kidneys. Variations can cause slower or faster clearance, affecting drug levels in the body.

Role of SNPs (Single Nucleotide Polymorphisms)

SNPs are single base changes in DNA that can influence gene function or expression. In pharmacogenomics, SNPs are crucial because:

• They can alter enzyme activity, making drug metabolism faster or slower.

• They can impact drug targets, such as receptors or enzymes, altering drug effectiveness.

• They can increase side effect risk by changing how your body reacts to certain drugs.

Example:
The CYP2C19 gene SNP rs4244285 causes a splicing defect, leading to reduced metabolism of clopidogrel, a blood thinner. This increases the risk of cardiovascular events in poor metabolizers.

Summary

Genetic differences—especially SNPs—can significantly impact how your body handles medication at every stage. This explains why the same drug can help one person but harm another. Pharmacogenomics seeks to use this information to personalize medicine for safer and more effective treatments.

Benefits of Pharmacogenomics

Pharmacogenomics holds the promise of revolutionizing healthcare by tailoring treatment plans based on an individual’s genetic profile. This approach brings a range of benefits that improve patient care, reduce risks, and increase the efficiency of medical treatments.

1. Personalized and More Effective Treatments

Pharmacogenomics helps clinicians choose the right drug at the right dose for each individual. Instead of relying solely on generalized treatment guidelines, doctors can consider a patient's genetic makeup when prescribing medications.

Example:
Patients with certain HER2 gene expressions respond better to targeted therapies like trastuzumab (Herceptin) in breast cancer. Testing for HER2 ensures the therapy is only given to those who will benefit.

Why it matters: Personalized medicine reduces the chances of treatment failure, which is especially important in cancer, psychiatry, and cardiovascular diseases.

2. Reduced Trial-and-Error in Prescriptions

Traditionally, finding the right medication can involve multiple attempts—trying different drugs, adjusting doses, and dealing with side effects. Pharmacogenomics aims to minimize this trial-and-error process.

Example:
Antidepressants like SSRIs often require several tries before finding an effective one. A pharmacogenomic test can indicate whether a patient has genetic variants that affect how they metabolize certain SSRIs.

Outcome: Faster relief for the patient and lower emotional and financial burden.

3. Lower Risk of Adverse Drug Reactions (ADRs)

Adverse drug reactions are a leading cause of hospitalizations and even deaths globally. Genetic testing can identify patients at high risk for certain drug reactions and help avoid them.

Examples:

• People with the HLA-B*15:02 allele are at increased risk of severe skin reactions when given carbamazepine, a seizure medication.

• Those with TPMT deficiency are more prone to toxicity from azathioprine, an immunosuppressant.

Result: Safer drug prescribing and reduced healthcare costs from managing side effects.

4. Improved Patient Compliance and Health Outcomes

When patients experience fewer side effects and faster improvement, they’re more likely to stick to their treatment plans. Knowing that a drug is chosen based on their DNA can also increase patient trust in the treatment.

Impact: Long-term benefits such as better disease control, fewer hospital visits, and improved quality of life.

5. Economic Benefits and Reduced Healthcare Costs

Pharmacogenomic-guided therapies can lead to:

• Fewer ineffective treatments

• Shorter hospital stays

• Reduced need for emergency care

For healthcare systems, this translates into lower overall costs and more efficient use of resources.

Case Study:
A study showed that preemptive pharmacogenomic testing for heart patients saved over $600 per patient annually by reducing complications and hospital readmissions.

6. Informed Drug Development and Clinical Trials

Pharmaceutical companies use pharmacogenomics to design targeted therapies and select trial participants with specific genetic profiles. This helps:

• Increase success rates of drug trials

• Develop safer, more effective drugs

Summary

Benefit	Impact
Personalized treatment	Right drug, right dose
Reduced trial-and-error	Faster symptom relief
Fewer side effects	Safer medication use
Better compliance	Improved health outcomes
Lower costs	Economic efficiency
Smarter drug development	Better targeted therapies

Challenges in Pharmacogenomics

Despite its immense potential, pharmacogenomics faces several barriers that hinder its widespread adoption in clinical practice. These challenges span technical, financial, ethical, and educational domains.

1. High Cost of Genetic Testing

One of the biggest limitations is the expense of genetic screening and analysis. Although prices have dropped significantly over the past decade, comprehensive pharmacogenomic panels can still be unaffordable for many patients and underfunded healthcare systems.

• Current Costs: Depending on the scope, testing can range from ₹5,000 to ₹30,000 or more ($60–$350) per person.

• Insurance Gaps: In many countries, including India, these tests are not routinely covered by insurance, making them inaccessible to most.

Impact: This restricts pharmacogenomic testing to wealthier individuals or research settings, limiting equitable healthcare delivery.

2. Limited Genetic Diversity in Data (Ethnic Bias)

Many pharmacogenomic studies are heavily skewed toward populations of European ancestry, leading to a lack of data for other ethnic groups.

• Example: A genetic variant common in African populations might be underrepresented in databases, leading to inaccurate drug recommendations.

• Consequence: Certain populations may receive less effective or even harmful treatments due to non-inclusive genetic models.

Solution Needed: Global efforts are required to diversify genomic databases by including samples from Asia, Africa, and indigenous populations.

3. Ethical, Legal, and Social Concerns

The use of genetic data raises serious ethical questions, especially around:

a. Privacy & Confidentiality

• Genetic data is deeply personal and can reveal information about an individual's health risks, family lineage, and more.

• Breaches of this data can lead to discrimination by insurers or employers.

b. Informed Consent

• Patients may not fully understand what they are consenting to, especially if test results may have implications for family members.

c. Data Ownership and Usage

• Who owns the genetic data: the patient, the lab, or the healthcare provider?

• There is growing concern about pharmaceutical companies or third parties using genetic data for profit.

Recommendation: Stronger regulations, transparent data policies, and patient education are essential to uphold trust and ethical standards.

4. Limited Awareness Among Healthcare Professionals and the Public

Despite its clinical potential, pharmacogenomics is not yet a standard part of medical training.

• Physicians and Pharmacists: Many lack the knowledge to interpret genetic test results or integrate them into treatment decisions.

• Patients: Public understanding of genetic testing remains low, with many hesitant due to misconceptions or fear of stigma.

Solution:

• Include pharmacogenomics in medical and pharmacy curricula.

• Increase public health awareness campaigns to improve understanding and trust.

5. Integration into Clinical Workflow

Even when testing is available, integrating pharmacogenomic data into real-time clinical decisions is a challenge.

• Electronic Health Records (EHRs) often lack compatibility with genetic data.

• There are few standardized guidelines for how to act on test results.

Need for:

• Clinical decision support systems that alert doctors to gene-drug interactions.

• Easily accessible pharmacogenomic databases integrated with prescribing software.

6. Scientific and Technical Limitations

• Incomplete Knowledge: Many gene-drug interactions are still being discovered. For some drugs, the pharmacogenomic basis is poorly understood or controversial.

• Variant Interpretation: Not all genetic variants have clear clinical implications, making result interpretation complex.

• Multifactorial Influences: Drug response is not based on genetics alone. Factors like age, diet, microbiome, and co-medications also play a role.

Conclusion: Pharmacogenomics is promising, but it should be used as one piece of the diagnostic puzzle, not in isolation.

Pharmacogenomics in Clinical Practice

Pharmacogenomics is no longer a futuristic concept — it’s already reshaping how healthcare is delivered. Hospitals, clinics, and even pharmacies are starting to use genetic information to guide prescribing decisions. Here’s how:

1. Integration in Hospitals & Clinics

Many hospitals and research centers around the world are integrating pharmacogenomic testing into their routine clinical workflows, especially in:

• Oncology (e.g., selecting chemotherapy agents)

• Cardiology (e.g., blood thinners like warfarin)

• Psychiatry (e.g., antidepressants and antipsychotics)

• Pain management (e.g., opioids like codeine)

In these settings, doctors may request a pharmacogenomic test before prescribing certain medications. Results are then used to:

• Adjust drug doses

• Avoid medications that may cause adverse reactions

• Choose alternatives more likely to be effective based on a patient’s gene profile

Example: Mayo Clinic and St. Jude Children's Research Hospital have implemented pharmacogenomic testing into standard care for many patients.

2. FDA-Approved Pharmacogenomic Drugs

The U.S. Food and Drug Administration (FDA) maintains a list of drugs with pharmacogenomic labeling — meaning the label includes information about how genetics can affect response to the drug. A few notable examples:

Drug	Gene(s) Involved	Clinical Use
Warfarin	VKORC1, CYP2C9	Blood thinner (anticoagulant)
Abacavir	HLA-B*57:01	HIV treatment
Clopidogrel	CYP2C19	Prevents blood clots
Codeine	CYP2D6	Pain management
Carbamazepine	HLA-B*15:02	Epilepsy and bipolar disorder
Trastuzumab	HER2	Breast cancer

These drugs require either genetic testing before prescription or close monitoring based on genotype.

3. Role of Pharmacogenomic Guidelines

To assist clinicians, several expert groups provide actionable guidelines:

• CPIC (Clinical Pharmacogenetics Implementation Consortium): Offers peer-reviewed, evidence-based dosing recommendations based on a patient’s genotype. For example, CPIC has guidelines on how to adjust warfarin or clopidogrel doses.

• PharmGKB (Pharmacogenomics Knowledgebase): A curated resource that links genetic variation to drug response and houses pharmacogenomic data, clinical annotations, and guidelines.

• Dutch Pharmacogenetics Working Group (DPWG): Provides recommendations in Europe.

These guidelines help physicians make confident decisions based on a patient’s genotype, especially in scenarios where multiple drugs are impacted by the same enzyme pathway (e.g., CYP2D6, CYP2C9, CYP3A4).

4. Pharmacogenomic Testing in Practice

Some healthcare providers now offer preemptive genetic testing — where a patient’s pharmacogenomic profile is stored in their electronic health record (EHR). This allows drug-gene interaction alerts to trigger during prescribing.

Examples of pharmacogenomic testing services:

• GeneSight® – common in psychiatry

• OneOme® RightMed – co-developed by Mayo Clinic

• Color Genomics – direct-to-consumer genetic testing

5. Real-World Impact

• Faster symptom relief in depression due to better antidepressant selection

• Avoidance of adverse drug reactions in elderly or polypharmacy patients

• Reduced healthcare costs over time through fewer hospitalizations and failed treatments

Summary

Pharmacogenomics in clinical practice is enhancing the safety, precision, and effectiveness of treatments. As technology advances and testing becomes more affordable, it’s expected that personalized prescribing based on DNA will become a standard part of healthcare — especially in complex or chronic conditions.

Bioinformatics in Pharmacogenomics

Pharmacogenomics wouldn't be possible without bioinformatics. Behind every personalized drug recommendation lies a deep web of computational tools, curated databases, and predictive algorithms. Let’s explore how bioinformatics empowers this precision medicine revolution.

Key Bioinformatics Databases in Pharmacogenomics

1. PharmGKB (Pharmacogenomics Knowledgebase):

   • A central resource for curated information on gene-drug associations.

   • Includes clinical annotations, drug labels, and dosing guidelines.

   • Tracks genetic variants, their impact on drug metabolism, and recommendations for clinicians.

   • Example: TPMT gene variants affecting thiopurine drug metabolism.

2. ClinVar:

   • A public archive of clinical significance of genetic variants.

   • Helps researchers and clinicians understand if a genetic change is benign, pathogenic, or of uncertain significance.

   • Useful for predicting how a genetic mutation may influence drug response or toxicity.

3. dbSNP (Database of Single Nucleotide Polymorphisms):

   • Maintained by NCBI, it houses millions of known SNPs.

   • Pharmacogenomic studies often start here to identify common polymorphisms affecting drug-related genes.

4. CPIC (Clinical Pharmacogenetics Implementation Consortium):

   • While not a database, CPIC provides actionable clinical guidelines based on genotype data.

   • Bioinformatics tools help integrate these guidelines into Electronic Health Records (EHRs).

5. 1000 Genomes / gnomAD:

   • Used to study population-wide genetic variation.

   • Helps researchers understand how frequent a variant is across different populations, which is crucial in pharmacogenomics.

Predictive Algorithms and Modeling

Bioinformatics uses computational algorithms to predict how genetic variants may influence:

• Drug metabolism rates

• Binding affinities of drugs to proteins

• Transporter activity (e.g., P-glycoprotein)

• Toxicity profiles of drugs

These predictions are powered by:

• Molecular docking simulations

• Machine learning models

• Quantitative structure-activity relationship (QSAR) analysis

• Protein-ligand interaction modeling

Example: A machine learning model trained on SNPs in the CYP2D6 gene can predict a person’s metabolizer status (poor, intermediate, extensive, or ultra-rapid) for drugs like codeine or fluoxetine.

Artificial Intelligence & Machine Learning in Pharmacogenomics

The rise of AI and deep learning has accelerated pharmacogenomics in areas such as:

• Variant effect prediction: Tools like DeepVariant and PolyPhen predict the impact of new variants.

• Multi-omics integration: Combining genomics, transcriptomics, and metabolomics data for a holistic drug response model.

• Personalized drug recommendations: AI engines can process vast genomic datasets and suggest drugs with minimal side effects based on a person’s genome.

AI is especially useful in uncovering gene-gene and gene-drug interactions that are too complex for traditional methods to detect.

Role in Clinical Decision Support Systems (CDSS)

• Bioinformatics platforms help clinicians make real-time decisions.

• Tools are integrated into hospital EHR systems to flag potential gene-drug conflicts.

• Example: A patient with a CYP2C19 variant may receive an alert suggesting an alternative to clopidogrel due to reduced activation.

Future Directions

• Cloud-based pharmacogenomic platforms will allow for scalable, accessible analysis.

• Federated learning approaches will enable collaborative AI training on sensitive genomic data without data sharing, protecting privacy.

• Integration with wearables and lifestyle data will enable ultra-personalized drug response predictions.

Summary

Bioinformatics is the engine driving the pharmacogenomics revolution. From variant discovery to drug-response modeling and clinical deployment, computational biology ensures that your genetic data becomes actionable medical insight. As datasets grow and AI advances, expect bioinformatics to unlock a future where medications are not just effective — they’re genetically tailored for you.

Popular Companies and Startups in Pharmacogenomics

1. GeneSight (Myriad Genetics)
Approach: Clinician-guided pharmacogenomic testing
Services:

• Offers a comprehensive test analyzing how your genes may affect your response to more than 60 medications commonly prescribed for depression, anxiety, ADHD, and other mental health conditions.

• The test results help healthcare providers personalize treatment by identifying medications likely to be more effective or cause fewer side effects based on genetic compatibility.

Highlights:

• Focus on psychiatry and mental health.

• Backed by peer-reviewed studies showing improved outcomes and reduced trial-and-error.

2. OneOme
Approach: Provider-integrated pharmacogenomic solutions
Services:

• Offers the RightMed® test, developed with Mayo Clinic, covering 27 genes and hundreds of medications.

• Supports healthcare systems, hospitals, and payers in integrating pharmacogenomics into routine care.

Highlights:

• Clinical decision support tools built into EHRs.

• Covers multiple therapeutic areas: cardiology, pain management, psychiatry, etc.

3. 23andMe (Health + Ancestry Service)
Approach: Direct-to-consumer genetic testing with health insights
Services:

• While not focused solely on pharmacogenomics, 23andMe provides health reports that include information on how your genetics may impact your ability to metabolize certain drugs (e.g., clopidogrel, proton pump inhibitors).

• Offers FDA-approved reports for select drug responses.

Highlights:

• Consumer-friendly.

• Great entry point for people new to genetic testing.

4. Color Genomics
Approach: Population-scale genetic testing for health systems and employers
Services:

• Offers affordable genetic screening for hereditary conditions and pharmacogenomics.

• Pharmacogenomic panel focuses on key genes like CYP2C19, CYP2D6, and SLCO1B1 to guide medication decisions.

Highlights:

• Scalable solutions for large populations.

• Includes telehealth support and access to genetic counselors.

5. Genomind
Approach: Mental health-focused pharmacogenomics
Services:

• Offers Genomind Professional PGx Express, which evaluates 24 genes linked to mental health medication response.

• Includes digital tools, such as GeneSight-like reports for clinicians and patients.

Highlights:

• Integrates pharmacogenomics with behavioral health tools.

• Helps with treatment of conditions like depression, bipolar disorder, and schizophrenia.

6. Admera Health 
Approach: Precision medicine for oncology and general health
Services:

• Offers PGxOne™ Plus, which analyzes 50 genes to predict responses to over 200 medications.

• Used in multiple specialties, including oncology, cardiology, and pain management.

Highlights:

• CLIA-certified and CAP-accredited laboratory.

• Covers drug-drug and gene-drug interactions.

These companies are at the forefront of translating complex genomic information into actionable clinical insights. Whether through direct-to-consumer kits or clinical-grade services, they’re making personalized medicine more accessible and effective.

Future of Pharmacogenomics

The field of pharmacogenomics is rapidly evolving and holds tremendous promise for transforming global healthcare. Here are key developments shaping its future:

1. Integration with Electronic Health Records (EHR)

• Seamless clinical decision-making: Pharmacogenomic data is increasingly being embedded into EHR systems, allowing doctors to receive automatic alerts or suggestions based on a patient’s genetic profile when prescribing medications.

• Example: If a patient has a CYP2C19 variant, the EHR may prompt a warning before a drug like clopidogrel is prescribed.

• Benefits: Enhances personalized care, minimizes adverse drug reactions, and streamlines workflows for healthcare providers.

2.AI-Driven Drug Discovery

• Smarter and faster R&D: AI models can analyze vast genomic datasets to identify gene-drug interactions, accelerating the development of tailored therapies.

• Machine learning in action: Predicts drug efficacy and toxicity profiles based on genetic information, reducing trial-and-error in clinical trials.

• Future impact: AI combined with pharmacogenomics could significantly shorten the time it takes to develop and test new drugs.

3.Global Genomic Initiatives

• Large-scale projects: Programs like the UK Biobank, All of Us (USA), and GenomeAsia 100K are collecting and analyzing genetic data from diverse populations.

• Why it matters: Expanding pharmacogenomic research beyond European ancestry groups ensures equitable access to personalized medicine.

• Goal: Build a more inclusive genomic knowledge base that reflects global genetic diversity.

4.Accessibility of At-Home Pharmacogenomic Tests

• Convenience and privacy: Consumers can now access pharmacogenomic insights using saliva-based kits at home.

• Examples: 23andMe, GeneSight (via physicians), and OneOme offer accessible testing options with online reporting.

• Potential: Democratizes genetic testing and empowers individuals to take control of their health decisions.

10. Conclusion

Pharmacogenomics stands at the forefront of personalized medicine, offering a powerful tool to tailor treatments based on a person’s unique genetic code. From minimizing side effects to enhancing drug efficacy, this field is transforming the way medications are prescribed.

With growing research, digital integration, and increased awareness, gene-based prescriptions are no longer a distant dream but an emerging reality. As we move forward, it's crucial to:

• Encourage open conversations about privacy, data ownership, and consent.

• Ensure equity in access to genetic testing and personalized care.

• Foster responsible innovation that balances technological progress with ethical vigilance.

The future of healthcare is personal, predictive, and precise—and pharmacogenomics is leading the way.

💬 Let’s Discuss!
Would you trust a DNA test to help choose the right medication for you? 🧬💊 Should pharmacogenomic testing become a part of regular health check-ups? 

Share your thoughts in the comments !!1!