Enabling predictive, preventive, precision and personalized medicine

“Every individual is different from another and hence should be considered as a different entity.  As many variations are there in the universe, all are seen in human beings”.

Charaka Samhita

“It’s far more important to know what person has the disease than what disease the person has”

– Hippocrates

Clinical practice is being evolved during decades to improve the standards of patient care, diagnosis, and treatment. Evolution of medical practice from empirical treatment to evidence-based medicine happened with the development of newer techniques and tools for accurate diagnosis and practice.  However, treatment options for many ailments were developed based on trials conducted in non-homogeneous numbers distinctly many times less than the population receiving medication after approval. During past years, we also witnessed awareness   about   pharmacovigilance and many medications were withdrawn after being associated with rare fatal conditions, which might occur in few tens and hundreds of patients among thousand and millions receiving the medication.  This procedure of drug development (clinical) has always been a trial and error method to evaluate the benefits and risks; and due to the involvement of lots of investments, there is always peer pressure for the marketing of medication.  Reasons for most of these rare events, and basis for the occurrence of these events in only a few patients, but not in all, have always been a problem to be investigated and were attributed to idiosyncrasy. The concept of medication i.e. “One fits for all” is in transformation towards the concept of “One fits One or Few” only.

There is a concept described in ancient literature and earlier dictums about differences in the response to a drug by different ethnicities, and races across the globe.  In ancient Greece, Pythagoras described how fava beans could be poisonous to certain individuals, causing hemolytic anemia. The basis for this favism has been attributed to the mutations in glucose-6-phosphate dehydrogenase encoding. At the beginning of 19th century, Archibald Garrod hypothesized and demonstrated that alkaptonuria is not a disease but the result of an alternative course of metabolism, caused by chemical individuality and recurring in interbred families, therefore   suggesting a genetic   basis for the condition. Similar   descriptions   were   given   in   Ayurveda   which originated 5000 years ago in India, wherein all individuals were classified into different ‘Prakriti’ types based on the theory of trishaw and each type had varying degree of predisposition to different diseases. The classification is independent of caste, race, and ethnic background and is in practice to administer different medications or amount of medication to individuals with similar ailment but belonging to different osha (Vata, Pitta and Kapha). The reasons for this type of practice   and   different   phenotypes is now attributed to different genetic makeup of an individual and chemical modifications of the genes in an individual in his life time.

History of Pharmacogenetics as a tool for individualized or personalized medicine*

In modern era (Modern medicine), a new path was paved with the development of molecular methods and successful sequencing of whole genome in 2000. Reasons for most of these idiosyncrasy events are being investigated in relation to genetic makeup of an individual. It has also been hypothesized to be associated with the differences in the blood circulation, metabolism of the drugs, affinity to the receptors and drug interactions.  This genetic knowledge has also helped us understand the biology, molecular basis of a disease, thus helping us to design effective medication targeting a specific path   of a disease, minimizing the adverse outcomes.

According to modern   science, all humans   have similar genetic makeup by 99.9%.  The whole genome (23 pairs received from both father and mother) is composed of 3 billion base pairs of DNAs, and we all are similar in our genetic content among us, which is sufficient evidence to let us know that we have evolved from the same species. If we are similar by 99.9%, then what makes us respond differently to medication?  The 0.1% mismatch between individuals that consists of around 1.4 million variations in the whole genome is sufficient to cause some changes in the protein produced from the genome in either the composition or the amount of protein, or the process of production of protein.  Since many drug targets are proteins and the enzymes metabolizing drugs are proteins, these changes result in altered response to same medicament in different individuals. In addition to these changes, there are several insertions, copy number variations, deletions of genes, which last with the individual for his entire life from his birth. Some changes might occur in specific tissues (either germline, or somatic) during the life time of an individual and either will diminish (somatic) with the individual or pass on to next generations (germline). Moreover, chemical modifications of bases in genomic DNA such as methylation and acetylation will happen now and then during the lifetime of an individual, which also direct the expression of genes and thus respond to drugs or disease phenotypes. All these aspects define an individual or a group of individuals responding differently to a similar treatment. From this individualized genetic makeup, concepts of “Pharmacogenetics”, “Pharmacogenomics” and “Pharmacoepigenetics” were evolved. The principle concept is different individuals will have differences in the efficacy and toxicity of a single medication owing to their genetic makeup.

Pharmacogenomics aims to identify patients at risk for toxicity or reduced response to therapy prior to medication selection. Thus enabling prediction of right dose, prevention of toxicity and administration of precise medication to achieve personalized treatment.

Food and Drug Administration defines “Pharmacogenomics” as the science of determining how genetic variability influences physiological responses to drugs, from absorption and metabolism to pharmacologic action and therapeutic effect. With increasing knowledge of the molecular basis for a drug’s action has come the recognition of the importance of an individual’s genetic makeup in influencing how he or she may respond to a drug.  The terms ‘Pharmacogenetics’ and Pharmacogenomics’ are used   interchangeably. However, ‘Pharmacogenetics’ refers to the use of genetic knowledge of a candidate gene for improving treatment with a drug.  On the other hand, ‘Pharmacogenomics’ considers the genetic information about the whole genome of an individual.

Drug metabolism is often responsible for variable response by individuals to same drug and same dose.  This variability in drug metabolism might occur in both phase 1 (oxidation, reduction, hydrolysis) and phase 2 (conjugating) drug metabolizing   enzymes, predominantly    characterized   in phase 1 enzymes i.e. cytochrome P 450 enzymes. Based on the activity of the enzyme, and   genetic   makeup   of an individual defining enzyme activity and expression, individuals can be categorized as extensive, intermediate and slow metabolizers.   Thus, screening   individuals for their genetic makeup before initiation of treatment with a particular drug will help improve efficacy and avoid toxicity of a particular drug or selection of appropriate drug. Most of the pharmacogenetic markers studies in relation to drug metabolism   are single nucleotide   polymorphisms (SNP), which are single nucleotide   base   changes   in the   DNA sequence of a gene, seen frequently in a particular population.  Since we all receive one chromosome from father, and another from mother, most of the SNPs are bi- allelic (i.e. Base representation on a single chromosome for a particular location). Genotyping for a bi-allelic SNP could be homozygous normal genotype (containing   normal alleles on both chromosomes at that location), or heterozygous (containing one normal and one variant alleles), or homozygous variant genotypes (containing variant alleles on both chromosomes).

Classification of phenotypes and the prediction of clinical outcome in different phenotype categories. The clinical outcome varies Between phenotype categories depending upon the drug whether it is active or inactive and the metabolite whether it is Therapeutically active or harmful. UM: Ultra Rapid Metabolizers, NM: Normal Metabolizers, IM: Intermediate Metabolizers,

PM: Poor Metabolizers, ADRs: Adverse Drug Reactions.

Depending on the impact of a particular SNP, whether   it changing an amino acid, or creating a stop   codon, or increasing transcription of mRNA or improving stability and vice versa, individuals could be grouped into any of the above-mentioned categories. From the evidence obtained from multiple genotype-phenotype association studies, dosing guidelines based on genotype and other demographics could be developed to implement in routine clinical practice. Since some of the enzymes are also involved in metabolism of endogenous substrates, genotyping of enzyme coding gene might give us an idea of endogenous metabolism, especially in case of catecholamine’s (in brain), estrogens and the response to respective medication. Preliminary research and recent findings have shown promising utility of pharmacogenetic markers defining pharmacokinetics of a drug and thus its dynamic action (especially   related   to   the   metabolism, transport of the drugs) for optimized treatment. In oncology and infectiology, pharmacogenetic markers of specific proteins involved in defining pharmacodynamic activity of the medications have been described and are now being widely used.  Studies across the globe have validated several of these markers for optimized treatment with medicament and the details are available in public domain (FDA website: ).

Several studies undergone and undergoing to elucidate the utility of genetic markers not only predict response to treatment, but also predict the occurrence of disease, and shift the medical treatment to an era of predictive medicine. However, one must not forget that genetic makeup alone is not responsible for the disease occurrence or outcomes of treatment. Environmental factors   also play a large role; hence one must carefully analyze the information in their clinic, because for some of the conditions environmental factor has a bigger role than genetic component. For e.g. cigarette smoking is a major cause for lung cancer, and even though the person has a protective genetic makeup, if he is a smoker there is more chance of having lung cancer and vice versa. Similarly, one must understand the utility of genetic information in relation to drug interactions. For e.g. an individual might be having a defective allele on a   single chromosome resulting in intermediate enzyme activity, but if the same individual is receiving another drug which increases the expression of an enzyme (usually glucocorticoids acting on nuclear receptors or many enzyme inducers like rifampicin), then he will exhibit extensive metabolizer   phenotype.   Likewise, one    must    carefully consider   other   factors   like demographics (age, gender). Though utility of cytochromes P 450 enzyme genotypes for predicting dose of some drugs is established, it is not known whether in neonates, infants and children these markers have similar predictability function. Maturation of these enzymes does not occur in neonates, infants, and no data is available in children.  However, definitely, exploration for these markers has improved   the practice of medicine   in an effective way, though it is not that promising at this time as it was thought to be before. However, recent understanding about elements regulating gene expression, chemical modifications during life time of individual, somatic mutations and their interactions with environmental factors, will land us in the era of predictive medicine from the era of evidence-based medicine.

 To conclude, pharmacogenetics has definitely improved the practice medicine, and helped us optimize treatment significantly with commonly used medicaments in oncology, cardiology and infectiology. Its utility is being spread widely across all branches of medicine.  Limiting factors for its implementation are the cost of genotyping and ethical issues related to sharing of this genetic information.  Regarding cost of the genotyping, the cost has been minimized from the past few decades, and will be in the same direction, since many competitors have entered this area to provide cost effective methods of getting genetic information.  Ethical issues can be dealt easily by increasing awareness among clinicians, patients and genetic information companies and by enforcing strict regulations.   Finally, the clinician must not forget   individual demographics, environment and   must wisely use this information for optimized treatment in each individual.


Uppugunduri S Chakradhara Rao


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