Celeste Cohen


      Picture credit: National Human Genome Research Institute      
Note: All terms shown in bold are defined in the glossary at the end of the article.

Defining Pharmacogenomics

From chemotherapy to HIV drugs, the past century has seen rapid advances in medical innovation, which have given rise to a wide range of novel treatments and therapies. These innovations have saved millions of lives around the world. However, drug development is not without its challenges. One considerable challenge in modern healthcare is that of Adverse Drug Reactions (ADRs). In the United States alone, the Food and Drug Administration (FDA) estimates that serious side effects resulting from medical treatments affect around two million Americans per year, resulting in up to 100,000 deaths annually [1].

ADRs can result from drug misuse, but can also occur at random in some individuals regardless of the dosage of drug administered [1]. How do we reduce this “random” risk? An emerging field of pharmacology that may provide a viable solution is Pharmacogenomics.  Pharmacogenomics is the study of how the genome of individuals affects an individual´s reaction to different drugs and dosages, and how this knowledge can be used to personalize treatments. 

The term “pharmacogenomics” was first coined by Friederich Vogel in 1959, and is often used interchangeably with the term “pharmacogenetics” [2], despite its nuanced definition. According to the European Agency for the Evaluation of Medicinal Products (EMEA) in 2002, pharmacogenetics analyzes variations in short sequences within genes, whereas pharmacogenomics additionally looks at variations in gene expression [2]. 

Pharmacogenomics partly attributes the “random” risk of ADRs to genetic polymorphism. Genetic polymorphism refers to variation within a DNA sequence that is present in more than 1% of a population. When genetic variations are present in less than 1% of a population, they are considered to be rare mutations [2].

The Biology of Pharmacogenomics

A patient’s reaction to a drug depends on two main factors which are namely, pharmacokinetics and pharmacodynamics [1]. Both of these factors are determined by genomics, and are therefore an important focus in pharmacogenomics. 


Figure 1. Genetic variation linked to adverse drug reactions (ADRs) can have an impact on pharmacodynamics or pharmacokinetics [3].

Pharmacokinetics describes how a drug is absorbed, distributed, metabolized, and excreted throughout the body (Fig. 1). It affects how the body reacts to different doses and to different types of drugs. Understanding a patient’s pharmacokinetics through analysis of their genetic information can help doctors to determine the optimal drug and dosage that is appropriate for a patient [1]. 

Genetic polymorphisms that affect pharmacokinetics can have substantial effects on drug reactions in two main scenarios.

  1. The drug is a prodrug, which is pharmacologically inactive until activated by a patient’s drug metabolism. If a patient metabolizes the drug in a different way (e.g. with different enzymes) or has mutations in metabolic enzymes required for activation, it cannot be activated [4].
  2. The drug is metabolized by one metabolic pathway and has a narrow therapeutic range, meaning that the dosage range in which the drug is non-toxic and therapeutic is very narrow. The loss of function of a single enzyme in the metabolic pathway can result in the drug causing toxic side-effects as it accumulates in the body instead of being metabolized and excreted [4]. 

For instance, cytochrome P450 enzymes (CYP) are key in drug metabolism, and over 85% of people have significant mutations in genes coding for important CYPs (CYP2D6, CYP2C9, CYP2C19, CYP3A4 and CYP3A5). Many CYP mutations affect drug response [5, 6], demonstrating the importance of metabolic genetics in drug treatments.

Pharmacodynamics describes how a drug exerts its therapeutic effect via interactions with cells of the body. This includes a wide range of biological systems such as the immune system, ion channels and specific receptors – which allow communication between a cell and its environment – and enzymes (Fig. 1). Pharmacodynamics influences the efficiency of a patient’s response to a specific drug [1]. For instance, genetic variation affects receptors involved in the therapeutic effect of a drug, such as G-protein-coupled receptors (GPCRs), which are the target of over 30% of US FDA-approved drugs [7]. If the binding site of a target receptor is shaped slightly differently in some patients, a different variation of a drug may be more efficient than another to treat the condition in question (Fig. 2).


Figure 2. Diagram of the differences in drug binding between a common and mutated receptor. Drug A cannot bind as well as drug B to the mutated receptor, so a patient carrying a polymorphism resulting in mutated receptors will respond better to drug B. The receptors are embedded in the cell membrane.

The influence of genetics on drug reactions has been studied since the early 1950s, with research into hereditary reactions to drugs such as isoniazid and succinylcholine [2]. In 1956, concrete evidence for the genetic basis of drug reactions was obtained for the first time when antimalarial drugs such as primaquine were shown to induce hemolytic anemia in patients with glucose-6-phosphate dehydrogenase (G6PD) deficiency [2, 8]. In G6PD deficient patients, red blood cells break down prematurely through a process called hemolysis. Primaquine magnifies the effect of G6PD deficiency, causing hemolysis in patients and subsequent anemia [9].  Today, over 260 US FDA-approved drugs provide information about pharmacogenomic biomarkers on their drug labels [10], with 90% of the population presenting at least one gene variant impacting drug response [11]. 

Pharmacogenomics may prove to be essential in modern healthcare, helping to reduce incidences of serious ADR [1], improve the outcomes of treatments such as chemotherapy for breast cancer [1], and avoid the costly process of trial and error of different drugs during treatments by immediately identifying the genetically optimal drug and dosage [12]. It represents an important step in the field of precision medicine (or personalized medicine), a branch of medicine based on collecting genomic and epigenetic information from patients to determine the best treatment for them and their condition [12]. In the near future, there is hope to democratize precision medicine, tailoring treatments to different patients [12].

DNA Technology in Pharmacogenomics

Novel DNA technologies developed in recent decades have been instrumental to the expansion of pharmacogenomics, making the collection and processing of genomic data from individuals faster and far less costly. DNA sequencing, in particular, has provided the foundation for the development of new DNA analysis methods. DNA sequencing was pioneered in 1977 by Fred Sanger, who decoded DNA sequences by integrating chain-terminating fluorescent nucleotides (i.e. chemically modified DNA building blocks) to existing DNA, in a method called Sanger sequencing [13]. Widely used for decades after its discovery, this method gave rise to the development of Next-generation sequencing (NGS), which uses complex machines to sequence entire genomes within a day. In the implementation of pharmacogenomics, NGS is one of the essential tools that make research and genetic testing possible.

Rapidly-improving sequencing technologies have allowed for large databases of genomic data to be assembled, including the Human Genome Project (2003) in which the entirety of the human genome was sequenced [14]. The Human Genome Project led to the development of numerous DNA analysis projects and tools including the HapMap Project [15, 16], which maps the common polymorphisms in the human genome [12]. The HapMap Project is essential in pharmacogenomics, particularly as used in genome-wide association studies (GWAS), which use statistical tools on genetic variation data to identify the genetic basis of diseases, specific phenotypes (i.e. traits), drug reactions and more [17].

Many pharmacogenomic studies currently use these new techniques and databases, analyzing genetic variations and drug responses in patients to identify gene variants and biomarkers (e.g. the product of a gene) that correlate with drug responses [12]. Such pharmacogenomic data is stored and accessed by relevant institutions such as the Clinical Pharmacogenetics Implementation Consortium (CPIC) [12]. 

In practice, once pharmacogenomic data on a specific drug is discovered, the CPIC provides clinical advice to the general public on usage and dosage of that drug according to patients’ genetic information. A patient that might benefit from this drug could be tested for relevant gene variations, or their blood or bodily fluids tested for specific biomarkers associated with these genetic variations. The subsequent test results would help to determine the ideal dosage of the drug and/or whether another drug should be prescribed.

Nowadays, the number of private companies focusing on pharmacogenomics is rising. Beyond research, many pharmacogenomics-oriented companies provide DNA testing and/or Clinical Decision Support (CDS), often involving both patients and clinicians in the process (Table 1, see end of article). DNA tests screen patients for different gene variants depending on their medication needs, and CDS goes a step further, providing clinical guidance on treatment based on the DNA test results. Several companies, including YouScript, GenXys and InsightRX, have developed specific CDS platforms and softwares. InsightRX Platform, for instance, contains mathematical algorithms to determine exact types and dosages of drugs best suited for patients.

While many of these services are directed towards patient care, some companies like  OneOme provide solutions for the wider implementation of pharmacogenomics in public and private healthcare institutions, for instance through training platforms such as OneOme Institute

Applications of Pharmacogenomics

In recent years, pharmacogenomics has generated increasing interest within the scientific community. It has already been implemented in a number of studies and clinical trials to improve treatments for many conditions, including drug and chemotherapy treatments for cancers. In the treatment of breast cancers that do not affect lymph nodes (node-negative) and bind estrogen for growth (estrogen receptor-positive), chemotherapy is only estimated to improve 4% of cancer outcomes when used in addition to the drug tamoxifen, versus when only tamoxifen is used [18]. However, identifying the genetic mutations and modifications of gene expression in a patient’s cancer cells can help determine which treatments will have the best outcome. In fact, a diagnostic kit was developed to test cancer tissue samples for different levels of expression and variation in 21 genes, predicting the likelihood of recurrence of cancer [18]. In patients determined to be at a higher risk of recurrence according to the results of this test, it was found that chemotherapy decreased the risk of recurrence of cancer by almost 30% [19].

Pharmacogenomics has also been shown to improve treatments in patients with major depressive disorder (MDD). In a 2019 study, patients with MDD were given the GeneSight® Psychotropic test by Assurex Health Inc., testing for 59 gene variants across 8 genes, and treated with specific drugs according to their genomic data [20]. The results demonstrated that patients’ responses to treatment and the remission rate were significantly higher in those treated according to pharmacogenomic data [20]. 

Overall, pharmacogenomics has proven useful in improving treatments for patients with a wide range of medical conditions, including breast cancer, MDD, diabetic retinopathy [21], and high blood lipid levels which put patients at risk of coronary heart disease [22].

Improving Pharmacogenomics

Pharmacogenomics is an emerging field at the intersection of many other areas of science, such as bioinformatics, the study of MicroRNAs and pharmacometabolomics.

Big data and bioinformatic approaches are essential for the storage and analysis of genetic data [5]. Computational tools are needed for data collection and storage, sequence read alignments, sequencing quality control, detecting sequence variations and their frequency in a population, and more protocols that are crucial for the different aspects of pharmacogenomics [5]. Bioinformatics can also be useful in the interpretation of pharmacogenomic results, as seen in the Clinical Decision Support InsightRX Platform. However, a substantial lack of familiarity remains among clinicians of these computational methods, reducing understanding and implementation of pharmacogenomics when treating patients [10].

The study of MicroRNAs can also be used to improve the precision of pharmacogenomic data and treatments. MicroRNAs bind to genes and silence their expression, and can therefore affect the production of proteins involved in drug metabolism and drug targets. This, in turn, can affect drug response [23]. Identifying these MicroRNAs in patients and pharmacogenomics studies would help better understand and predict drug responses, beyond simply sequencing patients’ genes. 

This approach to pharmacogenomics, which favors the use of other indicators in cells as well as DNA to predict drug responses, is also supported by pharmacometabolomics. Pharmacometabolomics analyses patients’ metabolic profiles, which are affected by both genes and the environment. This involves taking into account a range of environmental factors when analyzing a patient’s genomic data, predicting drug response and efficiency more precisely than when relying solely on genetic information [24]. 

Challenges in Pharmacogenomics

As a relatively new field, some of the main challenges in the implementation of pharmacogenomics are related to lack of knowledge and resources. To this day, the Clinical Pharmacogenomics Implementation Consortium (CPIC) has identified less than 50 clinics worldwide (with 30 of these being in the US) that focus on implementing pharmacogenomics [10]. There is a lack of training, incentive, funding, and guidelines for the clinical implementation of pharmacogenomics. Moreover, the subjects and level of depth taught across different Pharmacology programs significantly vary, including regarding pharmacogenomics [10]. That said, as an emerging field in clinical medicine, there is hope that current and future initiatives taken in academic institutions and scientific organizations will resolve training-related issues. As pharmacogenomic approaches become more prevalent in medicine, increased awareness and understanding – amongst the public and professionals alike – are likely to attract greater funding and investment for clinical implementation.

The other main obstacles faced in pharmacogenomics relate to genetic testing, with issues such as inaccuracies in genomic data collection and ethical concerns over storage of genetic information. In many cases, it is also difficult to associate specific genetic variations with drug reactions, and many trials observe a large spectrum of reactions even in patients who all present the same alleles (i.e. variants of genes) in relevant genes. 

Beyond the issue of gene expression, different types of polymorphisms can complicate studies, for instance if the alleles in question are loss-of-function alleles, meaning that the gene variant does not result in a functional protein. In this case, heterozygotes (with one loss-of-function allele and one functional allele) only have a reduction of function where homozygotes show complete loss of function as they possess two loss-of-function alleles and no functional alleles [4] (Fig. 3). Alternatively, if an allele results in a partially functional protein, it will simply be less efficient. 


Figure 3. Diagram of allele combinations for a gene and boxplot of the corresponding amounts of functional protein. The ovals represent chromosomes, each pair belonging to one individual, and the green and red boxes represent the different alleles of the gene at that locus (i.e. location on a chromosome). From left to right, the pairs of chromosomes are homozygous, heterozygous and homozygous for this gene. The boxplot schematically shows the expected amount of functional protein found in each of the three individuals represented.

These scenarios result in a spectrum of protein functionality and thereby a spectrum of drug reactions. This makes it difficult to categorize patients by only identifying whether they carry a specific allele. To solve this problem, studies are being conducted to develop solutions such as using genetic risk scores on a quantitative spectrum, which would provide patients with ADR risk scores according to the different alleles that they might have [4].



Considering the success that pharmacogenomic approaches to treatments have had in a range of clinical studies and treatments, this emerging field could revolutionize medicine, with less trial and error of different therapies, increased efficiency of treatments and improved results. There are, however, many challenges yet to be overcome in the democratization of pharmacogenomics, including improving access, technology and awareness. Meeting these challenges requires a convergence of different areas of science, from pharmacology and the study of MicroRNAs to bioinformatics. Also essential in pharmacogenomics, genetic sequencing technologies are rapidly evolving, helping to broaden the implementation of pharmacogenomics. With DNA tests becoming increasingly accessible to the greater public, we can envision a future in which patients systematically take DNA tests that provide pharmacogenomic and other health-related information as a standard.

Company Test Clinical Decision Support tool  Specialty (Types of Conditions & Treatments)
Admera Health PGxOne Plus Report & RxVisionTM Clinical Decision Support System for Physicians Various (Cardiology, Psychiatry, Oncology, Anesthesiology, etc.)
Genesight GeneSight® Psychotropic Report & Consultation for clinicians with Medical Affairs Team Mental health
Coriell Life Sciences (CLS) PGx testing Report, Phone consultation with a CLS Program pharmacist & GeneDose LIVETM Decision Support for Medication Planning for clinicians  Various
Gene by Gene, Ltd. myDNA Psychotropic  Report for clinicians Mental health
Genemarkers, LLC PGx testing Report for clinicians Various
GENETWORx, LLC. GENETWORx pharmacogenetics Report for clinicians Various
Genomind, Inc. Genomind® Professional PGx Express Report & Expert consultation for clinicians, Patient Gateway for patient level report access Mental health
GenXys TreatGxplus PGx testing Report & TreatGx medication decision support software for patients and clinicians Various
GetMyDNA and Gravity Diagnostics GetMyDNA Report for patients Various (Pain, Mental Health, Cardiology, etc.)
Innovative Gx Laboratories Innovative PGx Report for clinicians Various (Infectious Diseases, Chronic Respiratory Diseases, Cardiovascular Diseases, Allergic reactions to medications, Mental Health, COVID-19 Care)
InsightRX N/A InsightRX Platform (Clinical decision support tool) for clinicians N/A
Invitae CorporationGenelex and YouScript Genelex PGx testing Report & YouScript Precision Prescribing Software for clinicians Various
Luxor Scientific PGx testing Report Various
myDNA Life Australia Pty Ltd. myDNATM Report for clinicians Various (Mental health, Pain, Gastrointestinal, Cardiovascular)
OneOme, LLC and Mayo Clinic (MFMER) RightMed® Solution Report, VantageTM decision support tool & Consultations with PGx experts for clinicians Various
Parallel Profile Parallel ProfileTM Report & Consultation with a Parallel Physician Various (ADHD, Neurology, Osteoporosis, etc.)
Phenomics Health Inc PredictScriptTM Report Various (Neuropsychiatric disorders, Metabolic, Pain, Anticoagulants, etc.)
RPRD Diagnostics LLC Whole Pharmacogenomics Scan (WPS®) Report for patients Various (Pain, Anaesthesiology, Cardiovascular, Oncology, Neurology, Behavioral health)
RxGenomix ExactMedsTM Medication Care Plan report & RxGenomix Hub platform for clinicians Various
Sema4 OpCo, Inc. Pharmacogenetic Genotyping Panels Report for clinicians Various (Cardiovascular, Epilepsy, Oncology, etc.)

Table 1. Some of the main commercially available pharmacogenetics-based DNA tests and Clinical Decision Support (CDS) tools. Listed here are the names of companies in alphabetical order, their corresponding testing and CDS services and information on which groups of drugs (for specific conditions) the testing focuses on. “PGx” is used as an abbreviation for “pharmacogenetics” and/or “pharmacogenomics” by several companies. Commercialized pharmacogenetics and -genomics products and services are not limited to those in this table.



Adverse drug reaction (ADR) – Dangerous and/or unexpected reaction of the body to a drug

Allele – Variant of a gene

Binding site – Region of a protein that binds to a ligand (a molecule it can chemically bind to)

Bioinformatics – Field of biology that uses computational methods to solve biological problems

Biomarker – Consequence of a biological phenomenon, used to measure that phenomenon (e.g. presence of a specific molecule)

Chain-terminating fluorescent nucleotides – Bases of DNA (of the letters A, G, C or T which build the genetic code) chemically altered to fluoresce and stop the elongation of DNA strands during replication, also called dideoxynucleotides, used in Sanger Sequencing

Clinical decision support – Support for clinicians in making decisions regarding treatments of patients

Drug dosage – Amount of a drug administered to a patient

Drug metabolism – Way in which a drug is altered in the body before it is excreted

Enzyme – Protein that catalyzes a chemical reaction

Epigenetics – Study of heritable chemical changes to DNA that do not affect its sequence

G-protein coupled receptors – Receptors in the body that bind signaling molecules to mediate communication between cells and the environment

Gene – Sequence of DNA that codes for a protein, which performs a function in the body; most genes are present in two copies in individuals with one inherited from each parent

Gene expression – Process by which the genetic information of a gene is converted into a functional protein, can be positively or negatively regulated; Can be studied by analyzing RNA, regulatory DNA sequences that flank genes or proteins that are present in a cell

Genome – Entirety of the genetic information of an individual, including coding regions (i.e. genes) and non-coding regions (i.e. regions that do not code for proteins)

Genome-wide association study (GWAS) – Study of entire genomes to determine genetic variations linked to specific phenotypes (e.g. diseases) through statistical analyses

Heterozygous/Heterozygote – When both copies of a gene present different alleles

Homozygous/Homozygote  – When both copies of a gene present the same alleles

Ion channels – Proteins that form pore in cell membranes for the transport of ions in and out of cells and cell compartments (i.e. organelles), e.g. for the transmission of electrical signals between cells

Locus – Location on a chromosome

Loss-of-function allele – Allele that results in a nonfunctional protein or no protein at all

Metabolic pathway – Series of chemical reactions catalyzed by enzymes for various functions in the body

MicroRNAs – Fragments of RNA which can bind to DNA and silence the expression of genes

Pharmacometabolomics – Study of the metabolic profiles of patients to understand the effect of drugs on different individuals

Phenotype – Physical consequence of genomic information; trait

Polymorphism – Genetic variation present in at least 1% of a population

Prodrug – Drug that becomes active once metabolized by the body

Protein – Chain(s) of molecules called amino acids, coded for by genes, which can take specific three-dimensional shapes and perform specific functions

Receptors – Protein that specifically binds to a molecule or protein to trigger different cell functions (e.g. reactions, transport)

Regulatory sequences – Sequences of DNA that often (but not always) flank genes and are involved in the regulation of expression of that gene (i.e. through binding of proteins)

RNA – Chain or sequence of building blocks (nucleic acids) most often synthesized from DNA sequence in order to perform functions or provide a template for protein synthesis

Sequence read alignment – Alignment of a sample sequence of genetic code with a reference sequence (e.g. sample gene against a previously sequenced reference gene)

Therapeutic range – Range of dosage within which a drug has a therapeutic effect and is non-toxic


Useful links

The birth of cancer chemotherapy: accident and research 

The History of HIV Treatment: Antiretroviral Therapy and More

History of Medicine – Medicine in the 20th century

The History of DNA: past, present and future

Milestones in Genomic Sequencing

The race to sequence the human genome – Tien Nguyen (TED-Ed)

What is Sanger sequencing?

What is Next-Generation Sequencing (NGS)?

International HapMap Project

An Introduction to Pharmacokinetics: Four Steps in a Drug’s Journey Through the Body

Overview of Pharmacodynamics

The Chemistry of Depression

Diabetic Retinopathy

What to know about coronary artery disease

How Accurate is Genetic Testing?

Privacy in Genomics

Sequencing technologies: past, present and future



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