{"id":62283,"date":"2024-12-30T11:56:59","date_gmt":"2024-12-30T11:56:59","guid":{"rendered":"https:\/\/biomedpharmajournal.org\/?p=62283"},"modified":"2025-01-06T17:51:17","modified_gmt":"2025-01-06T17:51:17","slug":"role-of-personalized-medicine-in-clinical-practice-an-overview-of-current-and-future-perspectives","status":"publish","type":"post","link":"https:\/\/biomedpharmajournal.org\/staging\/vol17no4\/role-of-personalized-medicine-in-clinical-practice-an-overview-of-current-and-future-perspectives\/","title":{"rendered":"Role of Personalized Medicine in Clinical Practice: An Overview of Current and Future Perspectives"},"content":{"rendered":"\n

Introduction<\/strong><\/p>\n\n\n\n

The prospective medical and financial achievement of\nresearch in drug discovery depends on increased prophecy of drug effects and\nsafety. The approval of new, innovative medicines will be facilitated by\nfocusing therapies on the patients who anticipate benefiting and lowering the\nthreat of adverse effects1<\/sup>. Pharmacogenetics, a rapidly escalating field in\nmolecular biology and clinical medicine, will play a crucial role in this\ntransformation. Since 1959, the “pharmacogenetics” word has been in\nuse. Nowadays, with the trend of appending the suffix “omics”, numerous\nresearch domains have adopted the term “pharmacogenomics”, which has\nbeen used in many of them. While the former phrase is usually used concerning\ngenes affecting drug metabolism, the latter is a more generic term that covers\nany genes in the DNA that could affect how effectively a medicine works2<\/sup>. Pharmacogenomics\nis a branch of science that focuses on identifying the genetic characteristics\nof an individual that influence how they respond to medications. It’s\ninteresting to note that science has progressed to consider hereditary change arrangements\nin specified people, such as particular ethnic groups, that are responsible for\naccounting for variation in pharmacotherapeutic effects3<\/sup>.<\/p>\n\n\n\n

The effectiveness of an investigational medicine for patient\ngenotypes and phenotypes can be studied by patient population stratification\naccording to their pharmacogenomic profile. Variations in a gene may affect the\npharmacokinetics and pharmacodynamics of a drug, which in turn have an impact\non clinical results. Therefore, the idea of personalized medicine represents a\nsignificant conceptual shift from the traditional lore of pharmacotherapy,\nwhich claims the administration of drugs universally in extensive patient\npopulations rather than smaller subgroups where medications may demonstrate\nimproved effectiveness and ideal safety4<\/sup>.<\/p>\n\n\n\n

Personalized medicine or precision drug is a medical\napproach in which patient information, based on environmental, lifestyle and\ngenetic factors refers to the healthcare sector to make therapeutic decisions.\nIt is a strategy for treating all patients with the same ailment with tailor-made\nmedication and dosage through molecular diagnostics5<\/sup>. Personalized medicine increases\npatient confidence is cost-effective and will make a difference in the\ntreatment approach6<\/sup>.\nPersonalized medicine emphasizes the identification of the biomarkers that help\nin identifying the clinical signs and indications7, 8<\/sup>.<\/p>\n\n\n\n

Food and Drug Administration (FDA), has acknowledged the\nneed for more clinical data regarding the utilization of biomarkers, has\npublished a list of more than 100 medications since 2007, with pharmacogenomic\nprofile on their labelling and issued a black box warning in multiple of these\ndrugs9<\/sup>. To\ndevelop new genetic biomarkers and conduct pharmacogenetics research, they are\nprepared to devote greater resources to these endeavours. More than 20% of the Novel\nMolecular Entities (NMEs) authorized by the FDA in the United States are\nclassified as personalized drugs according to the Personalized Medicine\nCoalition’s (PMC) report in 201610<\/sup>. These kinds of pieces of evidence (as given below) focus\non prevention and early intervention for any disease. <\/p>\n\n\n\n

Herceptin is an effective drug for 20-30% of patients\nhaving breast cancer. The raised expression of the gene HER2 and its mutations\ncause patient resistance towards herceptin. So, genetic characterization of the\npatient having breast cancer can have effective use of Herceptin11<\/sup>. The World Health\nOrganization (WHO) has recommended primaquine to cure the liver infection\ncaused by malaria (Plasmodium vivax<\/em> and Plasmodium ovale<\/em>).\nPrimaquine causes hemolytic anaemia, so to eliminate this side effect and bring\nbetter therapeutic outcomes, the WHO has issued guidelines to reduce the\nadverse effect of this drug among patients with Glucose-6-phosphate\ndehydrogenase deficiency. The discovery of the relationship between antimalarial\ndrugs and G-6-PD deficiency developed a new outlook for a more individualized\nperspective on the disease12<\/sup>. Cystic Fibrosis is a recessive disease that is caused\nbecause of Cystic Fibrosis Transmembrane Conductance Regulator (CFTR) gene\nmutation. In this case, the approach of personalized medicine was performed\nthat was based on the patient\u2019s symptoms and genetic traits. Here the\nsupplementation of digestive enzymes was done along with dose adjustment. Many\nfactors were taken into consideration like the patient\u2019s physiological\ncharacteristics, response to the enzymes, eating habits, etc.13, 14<\/sup>.To date,\nmany projects are running as hormone receptor and ribonucleic acid-based\nmolecular diagnosis of breast cancer15<\/sup>. <\/p>\n\n\n\n

In the year 2005, a 15-year research project was funded\nby the National Institute of Health (NIH) to research into understanding the\ngenetic basis of coronary heart disease, stroke, and breast cancer in\ncorrelation to postmenopausal hormone therapy16<\/sup>. Another running project is the Personal\nGenome Project by George M. Church of Harvard University in 2005 to make\npersonal genomes available to the general public. The gathered information is\naimed at individualizing ancestors\u2019 history, disease risk factors, and\nbiological characteristics17<\/sup>. Since the completion of this project, genetic-based\nvariation has been found in the risk factors of Type-2 diabetes, heart\ndisorders, Parkinson\u2019s disease, obesity, prostate cancer, and Crohn\u2019s disease18<\/sup>.<\/p>\n\n\n\n

Translational Science is another area of advancement for\nthe individualization of treatment. It stands as the science of transferring\npreclinical technologies to clinical applications. The methods applied for\ntranslational science are like personalized medicine in which the biomarkers\nare used to envisage potencies and toxicities, the development of animal models\nto imitate the disease pattern of humans, bioinformatics, and preclinical and\nclinical analysis to decrease the non-success rate of drug development19<\/sup>. These studies\nbring hope to analyze whether the genetic studies of an individual contribute\nto making healthy lifestyle choices like proper diet habits and exercising16<\/sup>.This\narticle describes the benefits, challenges, and strategies for the execution of\nan individualized approach to adopting changes in medical practices. Therefore,\nthe objective of this paper is to present a summary of the medications whose\npharmacogenomic applications could show their value in predicting\npharmacological efficacy, toxicity, and dosage. Several impediments to using\npharmacogenomic testing in clinical practice are discussed at the end of this\npaper.<\/p>\n\n\n\n

Epidemiological Variation due to Genetics<\/strong><\/p>\n\n\n\n

Previously, genetic studies influenced the prevalence of\ndisease in communities and have been referred to as genetic epidemiology. This\nfield mainly focuses on the study of familial aggregation of ailment and\nstatistical techniques for family-based gene discovery investigations. Some of\nthe well-researched heterogeneity in response to pharmacological treatment may\nbe explained in part by genetic variances20<\/sup>. Numerous factors other than genetics\nsuch as ethnicity, race, age, and pregnancy may also be responsible for\nvariations in drug response. Surprisingly, age, gender, and even endemic regional\ninequalities may manifest as phenotypic effects of distinct epigenetic control.\nHowever, genetic pleiotropy and polymorphisms in the targets of pharmacological\ntreatment (such as metabolizing enzymes or protein receptors) and hereditary\nvariations in the metabolism and disposition of pharmaceuticals can have an\neven bigger impact on the effectiveness and toxicity of medications21<\/sup>.<\/p>\n\n\n\n

Ethnicity<\/strong><\/p>\n\n\n\n

Ethnic or racial groups are the most common categories\nfor people with similar physical characteristics and shared genetic ancestry\nwhich may influence therapeutic outcomes22<\/sup>. Differences occur due to variations in\ngenes of the genetic germline which are involved in pharmacokinetic and\npharmacodynamic is thought to account for 20\u201330% of drug response variability.\nThe most frequent form of gene disparity in the human genome is called a Single\nNucleotide Polymorphism (SNP), and it can function as a genetic markers of\npopulation organization and genetic diversity. When the specific SNPs were\nidentified, our comprehension of pharmacogenomics and pharmacogenetics proliferated23<\/sup>. Research released\nin 2011 by Li, Zhang, Zhou, Stoneking, and Tang on the diversity of genes that\nmetabolize drugs in the worldwide population has offered insightful information\non the significance of SNP-activated distinction in drug metabolism. This\ninvestigation analyzed variation in 283 drug-metabolizing enzymes and\ntransporter genes among 62 diverse racial categories worldwide and established\nemergence sequences of SNPs in particular populations dispersed globally. These\ndisparities in SNPs play a significant role in the varied medication responses\nwithin any population. This research not only supports and explains the genetic\npolymorphism in drug-metabolizing enzymes, but it also allegedly offers an\nevolutionary explanation for such variations between ethnic groups24<\/sup>. Another\nresearch by Sahana has revealed notable variations between Indians and the global\npopulation in the gene regularities of clinically relevant pharmacogenetic\npolymorphisms and they have found the presence of 18 SNP and 34 haplotype\nvariants with HLA alleles which are allied with 85 clinical illustrations among\nIndians. In India, three variants of the VKORC1 gene (rs9934438, rs9923231, and\nrs7294) are responsible for the pharmacodynamic variation of warfarin. This\ngenotype information for the VKORC1 gene provides strong support for using\noptimal doses of warfarin in individual patients before beginning therapy in India.\nIn comparison to the worldwide population, it was discovered that the Indian\npopulation had greater allele frequencies for four CYP2D6 haplotype variations.\nIndians have a noticeably greater prevalence of the abridged function allele\nCYP2D6*41, which is linked to a variety of frequently prescribed\nantipsychotics, opioids, and antidepressant drugs25<\/sup>.<\/p>\n\n\n\n

Pregnancy<\/strong><\/p>\n\n\n\n

Due to the prolonged exclusion of pregnant women from\nclinical drug studies, there is little information is available about drug\nlevels in pregnant women for various treatments. Now, it appears that this\ntrend is shifting26<\/sup>.\nThere have been 264 recorded clinical trials of medications utilized during\npregnancy in the past two years, out of which 10.6% describe pharmacokinetic\ninformation in the expected mother. This is significant because, according to\nrecent findings, drug concentrations for several treatments, such as\nantibiotics, antihypertensives, and antiretrovirals, are significantly lower in\npregnant women than in non-pregnant controls27<\/sup>. This occurs from a wide range of\nphysiological changes which have been extensively documented in Table 1. The Obstetric-Fetal\nPharmacology Research Units Network, supported by US-NIH, aims to fill this\nknowledge gap on pharmacokinetic and pharmacodynamic data during pregnancy28<\/sup>. Clinicians can\nacquire methods for treatment from the contemplation of both mother and fetal\ngenetics and develop curative models. To populate and evaluate the models they\nwill depend on reliable data then only it will be possible to use\npharmacogenetics for personalized prenatal medication26<\/sup>.<\/p>\n\n\n\n

Table 1: The pharmacogenetic liability of usually prescribed drugs during pregnancy<\/strong><\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
\n

Commonly used drugs during pregnancy<\/strong><\/p>\n<\/td>\n

\n

Metabolizing Enzyme<\/strong><\/p>\n<\/td>\n

\n

Phenotype Metabolize<\/strong><\/p>\n<\/td>\n

\n

Pharmacogenomic liability linked to drug<\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

Metoprolol<\/p>\n

(Antihypertensive)<\/p>\n

 <\/p>\n<\/td>\n

\n

CYP2D6<\/p>\n<\/td>\n

\n

Ultra-rapid metabolizer<\/p>\n<\/td>\n

\n

Increased Drug Clearance and decreased efficacy<\/p>\n<\/td>\n<\/tr>\n

\n

Poor-Metabolizer<\/p>\n<\/td>\n

\n

High Plasma concentration is associated with a high frequency of side effects like bradycardia, hypoglycemia in the neonate<\/p>\n<\/td>\n<\/tr>\n

\n

Citalopram<\/p>\n

Paroxetine<\/p>\n

and Escitalopram<\/p>\n

(Anti-depressant)<\/p>\n<\/td>\n

\n

CYP2C19<\/p>\n

CYP2D6<\/p>\n<\/td>\n

\n

Ultra-rapid metabolizer<\/p>\n<\/td>\n

\n

Decreased Plasma concentration and potentially elevate the treatment failure<\/p>\n<\/td>\n<\/tr>\n

\n

Poor-Metabolizer<\/p>\n<\/td>\n

\n

Increased plasma concentration and probably reduced the starting dose by 50%.<\/p>\n<\/td>\n<\/tr>\n

\n

Nitrofurantoin<\/p>\n

(Anti-microbial)<\/p>\n<\/td>\n

\n

G6PD<\/p>\n<\/td>\n

\n

Ultra-rapid metabolizer<\/p>\n<\/td>\n

\n

Risk of hemolytic anaemia<\/p>\n<\/td>\n<\/tr>\n

\n

Ondansetron and Metoclopramide<\/p>\n

(Anti-emetics)<\/p>\n<\/td>\n

\n

CYP2D6<\/p>\n<\/td>\n

\n

Ultra-rapid metabolizer<\/p>\n<\/td>\n

\n

Elevated metabolism of enzymes associated with reduced medication response<\/p>\n<\/td>\n<\/tr>\n

\n

Poor-Metabolizer<\/p>\n<\/td>\n

\n

Dystonia and other adverse reactions may be increased<\/p>\n<\/td>\n<\/tr>\n

\n

Codeine, Tramadol and Hydromorphone<\/p>\n

(Opioid Analgesic)<\/p>\n<\/td>\n

\n

CYP2D6<\/p>\n<\/td>\n

\n

Ultra-rapid metabolizer<\/p>\n<\/td>\n

\n

Rapid rise in morphine levels in the blood, there is a risk of toxicity, including respiratory depression in both the mother and fetus.<\/p>\n<\/td>\n<\/tr>\n

\n

Poor-Metabolizer<\/p>\n<\/td>\n

\n

Decreased therapeutic effect<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n\n

Age<\/strong><\/p>\n\n\n\n

Age-related undesirable effects or therapeutic adversity\nare becoming more common in older people. Significant polypharmacy may play a\nrole in this, which may facilitate the probability of interaction between\ndrug-gene and drug-drug. The precision drug, which is rooted in unique genetic\nvariants, makes it possible to identify patients at risk for unwanted drug\nreactions and execute individualized treatment plans. It customizes\npreventative and disease-management measures, including pharmacotherapy, by\nfusing genomic and genetic information with environmental and clinical aspects.\nIndividualized treatment is made possible by the discovery of genetic variables\nthat affect how well drugs are absorbed, distributed, metabolized, excreted,\nand function at the drug target level. It is essential to provide methods for\nthe forecast of various phenoconversions that are frequent in older patients\nalong with co-morbidity29<\/sup>.\nHowever, a single drug’s gene interaction is evaluated from the various\npharmacogenetic recommendations. A study by Hagstrom identified various single\nnucleotide polymorphisms in multiple genes that are associated with age-related\nmacular degeneration (AMD). This study offers the groundwork for the hypothesis\nthat SNPs linked to the onset of AMD may influence treatment response. Mainly\nfour SNP rs10490924 (ARMS2 A69S), rs1061170 (CFH Y402H), rs2230199 (C3 R80G) and\nrs11200638 (HTRA1 promoter), have been continuously demonstrated and found the\nlargest correlations with the onset and development of AMD, and they may also hypothesize\nto affect therapeutic reaction30<\/sup>.<\/p>\n\n\n\n

Genetic Pleiotropy<\/strong><\/p>\n\n\n\n

When a single gene is responsible for a variety of unique\nand unrelated phenotypic features is called genetic pleiotropy. This phenomenon\nis significant to pharmacogenetics because it could undermine the\npharmacogenetic relationship31<\/sup>. Recent research has demonstrated that genetically\nsupported target drugs, identified by Genome-Wide Association Studies (GWAS)\nseem to get clinical consent rather than whose targets are not genetically\nreinforced. That outcome is most pronounced when the gene responsible for the genetic\nlink has been identified (e.g., Mendelian genes), indicating a fundamental\nissue with the clinical application of GWAS discoveries32<\/sup>. According to\nFinan, 2017, only 4,479 human genes encoded proteins are modulated drugs\nresponsible for therapeutic response out of which only 1,427 are already\napproved or are being tested in clinical trials as drug targets33<\/sup>. Aromatase\ninhibitors used in breast cancer imply that genetic diversity in allelic\nassociation with the pleiotropic CYP19A1 GWAS variation may be associated with\nimproved results in progressive disease, but additional research is needed to\nconfirm this conclusion34<\/sup>.\nPharmacokinetics-related genes are frequently pleiotropic because they can\ninfluence traits, SLCO1B1 and CYP2C19, which are linked to drug transport and\nmetabolism, respectively, are two examples and both are associated with the\nresponse of pharmacokinetic profile of anti-platelet agent ticagrelor35<\/sup>.<\/p>\n\n\n\n

Genetic Polymorphism<\/strong><\/p>\n\n\n\n

When the genomic DNA sequences of two individuals are\ncompared, significant sequence variations can be found at various locations\nthroughout the entire genome. An allele is a gene that is present at any\nlocation on a chromosome in two distinct forms, or alternate sequences and this\nmultiple form of a single gene exists in more than 1% population responsible\nfor phenotypic variation is called polymorphism36<\/sup>. Individual vulnerability to both\ndose-dependent and dose-independent adverse drug reactions can be impacted by\npolymorphisms. Single nucleotide polymorphisms (SNPs), copy-number variants\n(CNVs), gene insertions and deletions (del), variable number tandem repeats\n(VNTRs), and premature stop codons are some examples of the various forms of\npolymorphisms. Both kinetic (e.g. genetic variability of cytochrome P450 enzymes)\nand dynamic factors (e.g. Drug targets’ polymorphism like enzyme and receptors)\nare prone to determinants37<\/sup>. The relationship between the polymorphism and clinical\nrelevance could be determined when the drug and disease should be studied for a\nspecific person. The polymorphism may have an impact on drug dose,\neffectiveness, toxicity, and pharmacokinetic and pharmacodynamic features; it\nmay also affect a disease’s prognosis, and susceptibility, or indeed function\nas a screening test for specific illnesses38<\/sup>. Some examples of clinically relevant\npolymorphic genes39<\/sup>\nwhich are associated with drug response are mentioned in Table 2.<\/p>\n\n\n\n

Table 2: Examples of Genetic Polymorphisms Linked to drug response39<\/sup>.<\/strong><\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
\n

Polymorphic gene <\/strong><\/p>\n<\/td>\n

\n

Drug<\/strong><\/p>\n<\/td>\n

\n

Drug Effect<\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

Drug – Metabolizing Enzymes<\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

CYP2C9<\/p>\n<\/td>\n

\n

Phenytoin<\/p>\n<\/td>\n

\n

Toxicity<\/p>\n<\/td>\n<\/tr>\n

\n

Warfarin<\/p>\n<\/td>\n

\n

Bleeding Risk<\/p>\n<\/td>\n<\/tr>\n

\n

Tolbutamide and Glipizide<\/p>\n<\/td>\n

\n

Hypoglycemia<\/p>\n<\/td>\n<\/tr>\n

\n

Losartan<\/p>\n<\/td>\n

\n

Altered drug response<\/p>\n<\/td>\n<\/tr>\n

\n

NSAIDs<\/p>\n<\/td>\n

\n

Altered drug response<\/p>\n<\/td>\n<\/tr>\n

\n

CYP2D6<\/p>\n<\/td>\n

\n

Tamoxifen<\/p>\n

Rucaparib<\/p>\n

Fluoxetine<\/p>\n

Codeine<\/p>\n

Beta- blockers<\/p>\n<\/td>\n

\n

Toxicity<\/p>\n<\/td>\n<\/tr>\n

\n

CYP2C19<\/p>\n<\/td>\n

\n

Omeprazole<\/p>\n

Amoxicillin<\/p>\n

Diazepam<\/p>\n

Proguanil<\/p>\n<\/td>\n

\n

Altered drug response<\/p>\n<\/td>\n<\/tr>\n

\n

G-6-PD<\/p>\n<\/td>\n

\n

Rasburicase<\/p>\n

Dabrafenib<\/p>\n<\/td>\n

\n

Toxicity<\/p>\n

\n<\/p><\/td>\n<\/tr>\n

\n

UGT1A1<\/p>\n<\/td>\n

\n

Belinostat<\/p>\n

Nilotinib<\/p>\n

Pazopanib<\/p>\n

Irinotecan<\/p>\n<\/td>\n

\n

Toxicity<\/p>\n<\/td>\n<\/tr>\n

\n

N-acetyltransferases<\/p>\n<\/td>\n

\n

Isoniazid, Sulfonamides<\/p>\n

Hydralazine Procainamide<\/p>\n<\/td>\n

\n

Toxicity and Hypersensitivity<\/p>\n<\/td>\n<\/tr>\n

\n

TPMT<\/p>\n<\/td>\n

\n

Cisplatin<\/p>\n

Mercaptopurine<\/p>\n

Thioguanine<\/p>\n<\/td>\n

\n

Toxicity<\/p>\n<\/td>\n<\/tr>\n

\n

DYPD<\/p>\n<\/td>\n

\n

Capecitabine<\/p>\n

Fluorouracil<\/p>\n<\/td>\n

\n

Toxicity<\/p>\n<\/td>\n<\/tr>\n

\n

HLA (Human leukocyte antigen)<\/p>\n<\/td>\n

\n

Abacavir<\/p>\n<\/td>\n

\n

Allergic reactions<\/p>\n<\/td>\n<\/tr>\n

\n

Drug \u2013Targets<\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

Angiotensin Converting Enzyme<\/p>\n<\/td>\n

\n

Lisinopril, Enalapril, Captopril<\/p>\n<\/td>\n

\n

Kidney protective effects, cardiac index, blood pressure,<\/p>\n

immunoglobulin A nephrosis<\/p>\n<\/td>\n<\/tr>\n

\n

hERG<\/p>\n<\/td>\n

\n

Cisapride<\/p>\n<\/td>\n

\n

Torsade de pointes induced by drugs<\/p>\n<\/td>\n<\/tr>\n

\n

Potassium channels<\/p>\n<\/td>\n

\n

Quinidine<\/p>\n<\/td>\n

\n

 Prolong QT syndrome induced by drug<\/p>\n<\/td>\n<\/tr>\n

\n

KvLQT1<\/p>\n<\/td>\n

\n

Disopyramide, Terfenadine, mefloquine<\/p>\n<\/td>\n

\n

Drug-induced Prolong QT disorder<\/p>\n<\/td>\n<\/tr>\n

\n

hKCNE2<\/p>\n<\/td>\n

\n

Clarithromycin<\/p>\n<\/td>\n

\n

Arrhythmia induced by drugs<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n\n

Impact of Genetic Variations<\/strong><\/p>\n\n\n\n

Genes influence the expression of proteins involved in\nthe drug ADME, which has an impact on pharmacodynamics. Variation is typically\nquantitative, meaning that the medicine has a greater or lesser effect or acts\nfor a longer or shorter period. Due to genetic\/immunological variations, the\neffect may have a qualitatively different impact on susceptible individuals.<\/p>\n\n\n\n

When genotypic data became accessible, a novel\nnomenclature was created to describe an individual metabolic rate. Especially\ndiplotypes, which are made up of one maternal and one paternal allele\u2014have been\nused depicted by a star (*). Each star allele has a unique sequence variation\nwithin the gene locus, for example, SNPs may be given a functional activity\nscore when the functional characterization is known, \u20180\u2019 for non-functional, \u20180.5\u2019\nfor diminished function, and \u20181\u2019 for fully functional. The sum of allelic\nactivity score, which runs from 0 to 3, is most frequently used to define the\nfollowing phenotypes: <\/p>\n\n\n\n

Poor metabolizers are given a score of 0; <\/p>\n\n\n\n

Moderate metabolizers score 0.5; <\/p>\n\n\n\n

Extensive metabolizers score 1-2, <\/p>\n\n\n\n

Ultra-rapid metabolizers score greater than 2.5<\/p>\n\n\n\n

Role of Polymorphism\nof Phase I Enzymes in Response Variation <\/strong>Phase I Enzymes<\/strong><\/p>\n\n\n\n

CYP450 is a superfamily of cytochrome enzymes present\nmainly in liver cells on the membrane of the rough endoplasmic reticulum and is\nresponsible for the biotransformation of 75% of prescription medicines. Among\nthe various reactions catalyzed by cytochromes are oxidative reactions,\ndealkylation, aromatic hydroxylation, deamination, and hydrolytic reactions.\nThe maximum drug metabolism is reported in CYP2C, CYP2D, and CYP3A subfamilies.\nAs determined by both clinical pharmacologic investigations and examination of\nexpression in human liver samples, there are significant variations in each\nCYP’s levels of expression between individuals. As a result, variations in\ndrug-metabolizing enzymes can change how various medications interact with the\nbody.  <\/p>\n\n\n\n

CYP2D6<\/strong><\/p>\n\n\n\n

Up to 25 percent of all pharmaceuticals used in\nclinical settings, mostly basic medicines including beta blockers,\nantidepressants, antipsychotics, and opioid analgesics, are metabolized by\ncytochrome CYP2D6 and it aids in activating various prodrugs. When comparing\nmetabolic capability within and between populations, CYP2D6 exhibits the most\nphenotypic variability. It is possible to predict therapeutic and unfavourable\nreactions after administering CYP2D6 substrates using the terms poor,\nintermediate, extensive, and ultra-rapid metabolizers. The gene that codes for\nCYP2D6 has more than 100 known alleles. Over 95% of attributes can be explained\nby just 9 alleles. The CYP2D6 alleles *1 and *2 is fully functional, while\n*10*17*41 and *10*4*5*6 have reduced function. The response of many drugs\nalters due to changes in the metabolic activity of enzyme variants.<\/p>\n\n\n\n

The opioid analgesic prodrug codeine is accepted\nfor the therapy of pain. Codeine’s analgesic activity depends on its conversion\nto morphine. CYP2D6 is the enzyme that converts codeine to morphine through\nO-demethylation. Codeine is sufficiently converted to morphine (5\u201310% of the\nsupplied dose) in patients with normal CYP2D6 activity to deliver the necessary\nanalgesic effect. While ultra-rapid metabolizers are at a higher hazard for\nadverse effects such as drowsiness and respiratory depression due to raised\nsystemic concentrations of morphine, poor metabolizers, and intermediate\nmetabolizers are more likely to have insufficient pain alleviation.\nConstipation and other GIT side effects are less common in poor metabolizers,\nalthough sedation and vertigo are common in both. CYP2D6 activity does not\naffect the antitussive actions of codeine. The Clinical Pharmacogenetics Implementation\nConsortium (CPIC) advises the application of substitute agents40<\/sup>.<\/p>\n\n\n\n

Ondansetron is an active drug\nmetabolized by CYP2D6 and is given for the treatment of nausea and vomiting,\nespecially post-surgical as well as chemotherapy-induced vomiting. CYP2D6 is\nprone to deletions, gene duplications, or multiplications. Certain cases\nreported failure of the therapy and on analyzing the cases, it was seen that\nthe variant was found to be an ultra-rapid metabolizer and have multiple gene\ncopies. <\/p>\n\n\n\n

Likewise, the responses of many drugs\ncan vary based on the variation in their alleles. Some examples are depicted in\nTable 3.<\/p>\n\n\n\n

Table 3: Responses shown by allele variants of CYP2D6 for specific drugs.<\/strong><\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n
\n

Allele Variant<\/strong><\/p>\n<\/td>\n

\n

Drugs Affected<\/strong><\/p>\n<\/td>\n

\n

Drug Class<\/strong><\/p>\n<\/td>\n

\n

Effect of allele expression<\/strong><\/p>\n<\/td>\n

\n

Reference<\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

CYP2D6 *4\/*4<\/p>\n<\/td>\n

\n

Amitriptyline, Clomipramine, Doxepin, Imipramine, Maprotiline, Nortriptyline, Opipramol<\/p>\n<\/td>\n

\n

Tricyclic Antidepressant<\/p>\n<\/td>\n

\n

Risk of toxicity decreased metabolism<\/p>\n<\/td>\n

\n

41<\/p>\n<\/td>\n<\/tr>\n

\n

CYP2D6 *1\/*1xN, 1\/*2xN, *2\/*2xN<\/p>\n<\/td>\n

\n

Ondansetron<\/p>\n<\/td>\n

\n

Anti-emetic (5-HT3<\/sub> antagonist)<\/p>\n<\/td>\n

\n

Increased metabolism (therapeutic failure)<\/p>\n<\/td>\n

\n

42<\/p>\n<\/td>\n<\/tr>\n

\n

CYP2D6 *9, 10,17,29,36,41<\/p>\n<\/td>\n

\n

Metoprolol<\/p>\n<\/td>\n

\n

Beta-blocker (cardioprotective)<\/p>\n<\/td>\n

\n

Decreased clearance<\/p>\n<\/td>\n

\n

43<\/p>\n<\/td>\n<\/tr>\n

\n

*3-*8, *11-*16, *19-*21, *38, *40, *42<\/em><\/p>\n<\/td>\n

\n

Metoprolol<\/p>\n<\/td>\n

\n

Beta-blocker (cardioprotective)<\/p>\n<\/td>\n

\n

No clearance<\/p>\n<\/td>\n

\n

43<\/p>\n<\/td>\n<\/tr>\n

\n

CYP2D6 *2, *10, *87, *88, *89, *90, *91, *93, *94, *95, *97, *98<\/p>\n<\/td>\n

\n

Venlafaxine<\/p>\n<\/td>\n

\n

Selective Serotonin and Norepinephrine Reuptake Inhibitors (SNRI)<\/p>\n<\/td>\n

\n

Decreased clearance<\/p>\n<\/td>\n

\n

44<\/p>\n<\/td>\n<\/tr>\n

\n

10, 4, 5,6<\/p>\n<\/td>\n

\n

Venlafaxine<\/p>\n<\/td>\n

\n

SNRI<\/p>\n<\/td>\n

\n

Increased concentration in drug level<\/p>\n<\/td>\n

\n

44<\/p>\n<\/td>\n<\/tr>\n

\n

CYP2D6 *1\/*3, *1\/*4, *1\/*5, *1\/*6, *4\/*41, *6\/*10, *10\/*10, *41\/*41<\/p>\n<\/td>\n

\n

Tramadol<\/p>\n<\/td>\n

\n

Opioid analgesic<\/p>\n<\/td>\n

\n

Increased risk of sedation<\/p>\n<\/td>\n

\n

45<\/p>\n<\/td>\n<\/tr>\n

\n

CYP2D6 *10, *87, *90, *93, *95, *98<\/p>\n<\/td>\n

\n

Gefitinib<\/p>\n<\/td>\n

\n

Anti-cancer<\/p>\n<\/td>\n

\n

Decreased clearance (Gefitinib-induced hepatotoxicity)<\/p>\n<\/td>\n

\n

46<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

 <\/p>\n\n\n

CYP2C9<\/strong><\/p>\n\n\n\n

The most prevalent member of the CYP2C subfamily in the human liver and the enzyme that contributes most significantly to the metabolism of drugs is CYP2C9. The CYP2C9 gene is prone to polymorphisms that result in lower enzyme activity, and this, along with the fact that numerous essential pharmacological substrates have limited therapeutic indexes, raises some crucial questions about the safety and effectiveness of medications. CYP2C9 metabolizes substrates from many drug classes such as nonsteroidal anti-inflammatory drugs (NSAIDs), anti-diabetics, anticoagulants like Warfarin, and anticonvulsants like Phenytoin. Approximately 50 alleles have been defined for CYP2C9 in comparison with the wild-type enzyme (CYP2C9*1), the catalytic activity of two frequent allelic variations of the enzyme is significantly decreased (by less than 10% for CYP2C9*3 and about 20% for CYP2C9*2). Therefore, homozygous carriers of these variant alleles have profoundly impaired metabolism of drugs like phenytoin, glibenclamide, and warfarin. <\/p>\n\n\n\n

Warfarin has a narrow safety margin, and its side effects include the risk of bleeding that becomes severe, especially in CYP2C9*3 individuals. The S-enantiomer of warfarin, which is mostly metabolized by CYP2C9 is responsible for most of the anticoagulant action. So, lower dosages of warfarin are required for the tiny subset of individuals carrying the homozygous CYP2C9*3 genotype to achieve the goal of anticoagulation (1 to 1.5 mg once a day as compared to 4 to 6 mg a day for patients with normal genotype). Such persons with increased warfarin sensitivity may have a lower ability to metabolize phenytoin and other drugs too47<\/sup>. Below Table-4 summarizes the effects of allele expression with the susceptible drugs. <\/p>\n\n\n\n

Table 4: Responses shown by allele variants of CYP2C9 for specific drugs.<\/strong><\/p>\n\n\n\n\n\n\n\n\n\n\n
\n

Allele Variant<\/strong><\/p>\n<\/td>\n

\n

Drugs Affected<\/strong><\/p>\n<\/td>\n

\n

Drug Class<\/strong><\/p>\n<\/td>\n

\n

Effect of allele expression<\/strong><\/p>\n<\/td>\n

\n

References<\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

CYP2C9 *3\/*3<\/p>\n<\/td>\n

\n

Warfarin<\/p>\n<\/td>\n

\n

Anti-coagulant<\/p>\n<\/td>\n

\n

Increased risk of haemorrhage<\/p>\n<\/td>\n

\n

48<\/p>\n<\/td>\n<\/tr>\n

\n

CYP2C9*1\/*3<\/p>\n<\/td>\n

\n

Piroxicam, Aceclofenac, Celecoxib, Diclofenac, Ibuprofen. Indomethacin, Naproxen<\/p>\n<\/td>\n

\n

NSAID<\/p>\n<\/td>\n

\n

Increased risk of git haemorrhage<\/p>\n<\/td>\n

\n

49<\/p>\n<\/td>\n<\/tr>\n

\n

CYP2C9 *2 & *3<\/p>\n<\/td>\n

\n

Glibenclamide, Gliclazide, Glimepiride, Glipizide Gliquidone<\/p>\n<\/td>\n

\n

Anti-diabetic (Sulphonylurea)<\/p>\n<\/td>\n

\n

Decreased metabolism (Increased risk of hypoglycemia<\/p>\n<\/td>\n

\n

50<\/p>\n<\/td>\n<\/tr>\n

\n

CYP2C9 *1\/*3<\/p>\n<\/td>\n

\n

Nateglinide<\/p>\n<\/td>\n

\n

Antidiabetic (meglitinide)<\/p>\n<\/td>\n

\n

Increased risk of hypoglycemia<\/p>\n<\/td>\n

\n

51<\/p>\n<\/td>\n<\/tr>\n

\n

CYP2C9 *2<\/p>\n<\/td>\n

\n

Fluvastatin<\/p>\n<\/td>\n

\n

Anti-hyperlipidemic<\/p>\n<\/td>\n

\n

Increased concentrations<\/p>\n<\/td>\n

\n

52<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

 <\/p>\n\n\n

CYP2C19<\/strong><\/p>\n\n\n\n

Antidepressants, proton pump inhibitors, and\nantiplatelet medications are among the pharmaceuticals that are known to be\npreferentially metabolized by cytochrome P450 CYP2C19. Four alleles can explain\nmost of the phenotypic diversity in the highly polymorphic CYP2C19 gene, which\nhas been identified as having over 30 alleles. CYP2C19*1 is the normal fully functional allele, while alleles *1 and *17\nhave increased function, and *2 and *3 to *8 are non-functional. The major\ndefective allele responsible for the poor metabolizing activity is CYP2C19*2\nfollowed by CYP2C19*3. Asians (30%) are about twice as likely as Africans and\nEuropeans (13%), to have the most prevalent non-functional allele, CYP2C19 *2.\nLess than 3% of Asians experience *17, although certain Europeans and Africans\nexperience it more commonly (16\u201321%). Individuals who are homozygous for\nCYP2C19*1 show a higher metabolism of omeprazole than those homozygous for\nCYP2C19*17. <\/p>\n\n\n\n

In another instance, a thienopyridine\nantiplatelet prodrug like clopidogrel is indicated to prevent atherothrombotic\nevents. ADP-induced platelet aggregation is selectively and permanently\ninhibited by active metabolites. About 85% of a dose of clopidogrel supplied is\nquickly hydrolyzed by hepatic esterase into inactive carboxylic acid\nderivatives, one of the two primary processes by which clopidogrel is\nmetabolized in the body. The remaining 15%, however, undergoes two subsequent\nCYP-mediated oxidation reactions (mostly CYP2C19) that result in active thiol\nmetabolites with antiplatelet activity. Variability in response to clopidogrel\nis linked to genetic variations in the CYP2C19 gene that diminish the active\nmetabolite’s synthesis and, as a result, lower the drug’s antiplatelet activity.\nWhen taking clopidogrel, people with the CYP2C19*2 allele having loss of\nmetabolic function, are more likely to have significant cardiovascular\nevents, especially if they have acute coronary syndrome treated with\npercutaneous coronary intervention (PCI). In EMs and UMs, standard starting\ndoses are advised, while in PMs and IMs, CPIC advises using an alternative\nantiplatelet medication such as prasugrel or ticagrelor53, 54<\/sup>. Likewise, the responses of\nthe drugs can vary based on the variation in their alleles as depicted in Table\n5.<\/p>\n\n\n\n

Table 5: Responses shown by allele variants of CYP2C19 for specific drugs.<\/strong><\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n
\n

Allele Variant<\/strong><\/p>\n<\/td>\n

\n

Drugs Affected<\/strong><\/p>\n<\/td>\n

\n

Drug Class<\/strong><\/p>\n<\/td>\n

\n

Effect of allele expression<\/strong><\/p>\n<\/td>\n

\n

References<\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

CYP2C19 *2<\/p>\n<\/td>\n

\n

Clopidogrel<\/p>\n<\/td>\n

\n

Anti-coagulant<\/p>\n<\/td>\n

\n

Increased platelet reactivity<\/p>\n<\/td>\n

\n

55<\/p>\n<\/td>\n<\/tr>\n

\n

CYP2C19 *1\/*17, *17\/*17<\/p>\n<\/td>\n

\n

Warfarin<\/p>\n<\/td>\n

\n

Anti-coagulant<\/p>\n<\/td>\n

\n

Increased clearance<\/p>\n<\/td>\n

\n

56<\/p>\n<\/td>\n<\/tr>\n

\n

CYP2C19 *2\/*2<\/p>\n<\/td>\n

\n

Labetalol<\/p>\n<\/td>\n

\n

Anti-hypertensive (alpha and beta blocker)<\/p>\n<\/td>\n

\n

Decreased metabolism<\/p>\n<\/td>\n

\n

57<\/p>\n<\/td>\n<\/tr>\n

\n

CYP2C19 *1\/*1<\/p>\n<\/td>\n

\n

Pantoprazole, Lansoprazole, Omeprazole<\/p>\n<\/td>\n

\n

Proton pump inhibitors<\/p>\n<\/td>\n

\n

Decreased response<\/p>\n<\/td>\n

\n

58<\/p>\n<\/td>\n<\/tr>\n

\n

CYP2C19 *1\/*2, *1\/*3<\/p>\n<\/td>\n

\n

Lansoprazole<\/p>\n<\/td>\n

\n

PPI<\/p>\n<\/td>\n

\n

Increased clearance of lansoprazole<\/p>\n<\/td>\n

\n

59<\/p>\n<\/td>\n<\/tr>\n

\n

CYP2C19 *17<\/p>\n<\/td>\n

\n

Aspirin<\/p>\n<\/td>\n

\n

NSAID<\/p>\n<\/td>\n

\n

Decreased platelet reactivity<\/p>\n<\/td>\n

\n

60<\/p>\n<\/td>\n<\/tr>\n

\n

CYP2C19 *2\/*2 +*3\/*3, *2\/*3<\/p>\n<\/td>\n

\n

Escitalopram<\/p>\n<\/td>\n

\n

Anti-depressant (Selective Serotonin Reuptake Inhibitor)<\/p>\n<\/td>\n

\n

Decreased metabolism<\/p>\n<\/td>\n

\n

61<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

 <\/p>\n\n\n

CYP1A2<\/strong><\/p>\n\n\n\n

CYP1A2 makes up roughly 13% of all cytochrome\nprotein, making it a major metabolizing enzyme in the liver. For CYP1A2, more\nthan 100 substrates have been documented, including numerous clinically\nsignificant medications like tacrine, theophylline, clozapine, and endogenous\nsubstrates like steroidal hormones.<\/p>\n\n\n\n

Since up to 15% of a patient population can be\ndefined as having poor metabolism due to CYP1A2 genetic variation. Moreover,\nthere is a significant racial variation in CYP1A2 activity. As reported, people\nin Sweden had 1.54 times more CYP1A2 activity than people in Korea, whereas a\nreduced CYP1A2 activity has been reported in Asian and African populations62<\/sup>.<\/p>\n\n\n\n

Fluvoxamine is a substrate and strong inhibitor\nof CYP1A2, which results in significant interactions with medications like\ntheophylline, imipramine, amitriptyline, clomipramine, and clozapine that are\npartially metabolized by this enzyme. <\/p>\n\n\n\n

The CYP1A2 gene has been reported to have 177\nSNPs, more than 15 variant alleles (*1B to *16), and several subvariants.\nCYP1A2*1C, *1D, *1F, and *1K have been linked to changed enzyme activity among\nthe polymorphic CYP1A2 alleles that display polymorphism in the promoter\nregion. Although there have been reports of enhanced activity for CYP1A2*1F,\nthis trait is only known to manifest when smoking or consuming large amounts of\ncaffeine.<\/p>\n\n\n\n

CYP2B6<\/strong><\/p>\n\n\n\n

CYPB2B6 is considered a minor drug-metabolizing\nenzyme among all cytochrome enzymes present in the human liver.  Artemisinin, bupropion, cyclophosphamide,\nefavirenz, ketamine, and methadone are some of the medications that CYP2B6\nmetabolizes primarily. The most frequent functionally defective variant,\nCYP2B6*6 is found in several groups with rates ranging from 15 to over 60%. Due\nto incorrect splicing, the allele causes decreased expression in the liver.\nAnother significant mutation, known as CYP2B6*18 is primarily found in Africans\n(4\u201312%) and does not express functional protein. Although CYP2B6 polymorphism\nis increasingly being discovered for other drug substrates, it is clinically\nsignificant for HIV-infected patients using the reverse transcriptase inhibitor\nefavirenz.<\/p>\n\n\n\n

CYP3A4\/5<\/strong><\/p>\n\n\n\n

CYP3A4, the most profusely expressed enzyme in the liver catalyzes approximately \u00bd of the clinically used medications and oxidizes foreign particles. Inhibition of CYP3A4 will lead to the accumulation of drugs that, on prolonged exposure, can lead to toxicity, and induction will result in reduced efficacy of substrate. Because of CYP3A\u2019s large concentrations in both the epithelial cells of the small intestine and liver, it contributes to the pre-systemic metabolic effect after oral drug delivery. When two or more CYP3A substrates are administered, drug-drug interactions involving enzyme inhibition or induction are prevalent. In many cases, the severity of such medication interactions is severe enough to make therapeutic use of the drugs involved impossible.<\/p>\n\n\n\n

Cyclosporine, primarily metabolized by CYP3A4 and to a lesser extent partially metabolized by CYP3A5 has been used to avoid complications or rejections after organ transplantation. Polymorphism in CYP3A4 has been shown to decrease the activity of the enzyme, so a low dose of cyclosporine is enough to reach its target levels. Likewise, the effect on substrates by a polymorphism in CYP3A4 and CYP3A5 is summarized in Table 6.<\/p>\n\n\n\n

Table 6: Responses shown by allele variants of CYP3A4 for specific drugs.<\/strong><\/p>\n\n\n\n\n\n\n\n\n\n\n
\n

Allele Variant<\/strong><\/p>\n<\/td>\n

\n

Drugs Affected<\/strong><\/p>\n<\/td>\n

\n

Drug Class<\/strong><\/p>\n<\/td>\n

\n

Effect of allele expression<\/strong><\/p>\n<\/td>\n

\n

References<\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

CYP3A4 *1\/*1, *1\/*18<\/p>\n<\/td>\n

\n

Cyclosporine<\/p>\n<\/td>\n

\n

Immunosuppressant<\/p>\n<\/td>\n

\n

increased trough concentrations<\/p>\n<\/td>\n

\n

63<\/p>\n<\/td>\n<\/tr>\n

\n

CYP3A4 *22\/*22<\/p>\n<\/td>\n

\n

Lopinavir<\/p>\n<\/td>\n

\n

Antiretroviral (protease inhibitor)<\/p>\n<\/td>\n

\n

decreased clearance<\/p>\n<\/td>\n

\n

64<\/p>\n<\/td>\n<\/tr>\n

\n

CYP3A4 *18\/*18<\/p>\n<\/td>\n

\n

Fentanyl<\/p>\n<\/td>\n

\n

Synthetic Opioid analgesic<\/p>\n<\/td>\n

\n

decreased dose<\/p>\n<\/td>\n

\n

65<\/p>\n<\/td>\n<\/tr>\n

\n

CYP3A4 *36\/*36<\/p>\n<\/td>\n

\n

Sufentanil<\/p>\n<\/td>\n

\n

Synthetic Opioid analgesic<\/p>\n<\/td>\n

\n

decreased concentrations<\/p>\n<\/td>\n

\n

66<\/p>\n<\/td>\n<\/tr>\n

\n

CYP3A4*22<\/p>\n<\/td>\n

\n

Tacrolimus<\/p>\n<\/td>\n

\n

Immunosuppressant<\/p>\n<\/td>\n

\n

Decreased metabolism (overexposure to Tacrolimus)<\/p>\n<\/td>\n

\n

67<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

 <\/p>\n\n\n

Dihydropyrimidine Dehydrogenase (DPD)<\/strong><\/p>\n\n\n\n

The DPD enzyme, coded by the DPYD gene, is 1st<\/sup>\nrate-limiting step in the breakdown of pyrimidines and a crucial mechanism for\nthe elimination of fluoropyrimidine chemotherapy medicines. There are three\nnon-functional alleles, with DPYD*2A, *13, and *rs67376798 *2A being the most\ncommon. Three fluoropyrimidine medications can be used in clinical settings to\ntreat solid tumours such as breast and colorectal cancer. These are Tegafur,\nCapecitabine, and 5-fluorouracil (Tegafur is only approved in Europe). After\noral administration, Tegafur and Capecitabine are transformed into\n5-Fluorouracil in the human body. Only one-to-three percent of the dosage of a\nprodrug is changed into cytotoxic metabolites like 5-FUMP and 5-FdUMP,\ntargeting cancer cells that also stop DNA synthesis. DPD converts 80% of the\ndrug’s given dosage into pyrimidines, which are then excreted in the urine.\nPeople with entire or partial DPD deficiency are more prone to experience\nsubstantial dose-dependent toxicities like bone marrow\nsuppression, mucositis, neurodegeneration, hand, and foot syndrome, and\ndiarrhoea68<\/sup>.<\/p>\n\n\n\n

Polymorphism in Phase II Enzymes<\/strong><\/p>\n\n\n\n

To remove the foreign drug from the human body,\nphase II enzyme biotransformation reactions frequently conjugate endogenous\nchemicals, such as acetic acid, glucuronic acid, and sulfuric acid, with\ndifferent substrates. Transferases make up many phase II\ndrug-metabolizing enzymes. These are UDP-glucuronosyltransferases,\nN-acetyltransferases, glutathione S-transferases, sulfotransferases, and\nmethyltransferases. About 30% of all metabolites are produced during phase\nII metabolism69<\/sup>.\nPolymorphic Phase II\nenzymes may reduce medication elimination and raise the likelihood of\ntoxicities.<\/p>\n\n\n\n

Uridine 5 Diphosphoglucuronyl Transferase<\/strong><\/p>\n\n\n\n

The glucuronic acid is conjugated onto tiny\nlipophilic molecules, such as bilirubin and a wide range of medicinal\nmedication substrates, by the uridine 5-diphosphoglucuronyl transferase UGT1A1\nenzyme, which is represented by the UGT1A1 gene. There are more than 30 identified\nalleles at the UGT1A1 gene locus, some of which result in diminished or\neliminated function. Gilbert Syndrome is clinically identified in 10% of\nEuropeans who are homozygous carriers of the *28 alleles, or *28\/*28 genotype.\nDue to a 30% decrease in UGT1A1 activity, such affected people may have 60 to\n70% higher levels of circulating unconjugated bilirubin. Owing to decreased\nbiliary clearance, people with the UGT1A1*28\/*28 genotype are more likely to experience\nadverse drug reactions (ADRs) with UGT1A1 drug substrates70, 71<\/sup>.<\/p>\n\n\n\n

Combined with 5-Fluorouracil and Leucovorin,\nirinotecan, a topoisomerase 1 inhibitor prodrug, is recommended as first-line\nchemotherapy for the treatment of metastatic colon or rectum cancer. The\nhepatic carboxylesterase enzyme hydrolyses it into the active metabolite SN-38,\nwhich obstructs topoisomerase 1 and ultimately causes the end of DNA\nreplication and cell death. Most therapeutic effects and dose-limiting bone\nmarrow and GIT toxicities are caused by the active SN-38 metabolite.\nPolymorphism in UGT1A1 renders the metabolite inactive. Due to impaired SN-38\nclearance, carriers of the UGT1A1*28 variation are subsequently at increased\ndanger of fatal life-threatening toxicities, like low neutrophil count and\ndiarrhoea72, 73<\/sup>.\nSimilarly, comparing individuals with the UGT1A1*28\/*28 genotype to those with\nthe UGT1A1*1\/*1 genotype, the UGT1A1*28\/*28 patients had higher exposure to\ndrug raloxifene and its glucuronides, thus a significantly higher hip bone\nmineral density74<\/sup>.\nThis subset of the population is usually advised lower doses of the drugs as\ncompared to the normal population.<\/p>\n\n\n\n

N-Acetyl Transferase and Glutathione\nS-Transferase<\/strong><\/p>\n\n\n\n

N-acetyltransferases (NATs) and\nGlutathione S-transferases (GST) make up for around 25% of phase II metabolic\nactivity. N-acetyltransferases are enzymes that catalyze the acetylation of\narylamines that are exposed through dietary, occupational, and environmental\nexposures. Humans have hepatic N-acetyltransferase genetic variants that result\nin rapid, intermediate, and slow acetylator phenotypes. It has been proposed\nthat an individual’s acetylator phenotype may play a role in their propensity\nto develop specific malignancies linked to arylamine exposures. The two human\narylamine N-acetyltransferases, NAT1 and NAT2, are encoded by two genetic\nvariants that are closely connected on chromosome 8. More than 25 polymorphic\nvariants exist for both NAT genes, however, NAT2 null alleles are more common.\nThe sluggish acetylator phenotype is linked to NAT2*5A, NAT2*6A, and NAT2*7A in\nhumans75, 76<\/sup>.\nDue to their slower metabolism, NAT2 slow acetylators are prone to a higher\nrisk of hepatotoxicity, liver damage, and hepatitis brought on by anti-TB\nmedication treatment. NAT1 polymorphisms typically have less interindividual\nvariability and relatively minimal impacts on acetylation function than NAT2\npolymorphisms as depicted in Table 7. <\/p>\n\n\n\n

Table 7: Effect of NAT1 and NAT2 allele expression on the substrates<\/strong><\/p>\n\n\n\n\n\n\n\n\n\n
\n

Enzyme<\/strong><\/p>\n<\/td>\n

\n

Allele<\/strong><\/p>\n<\/td>\n

\n

Drug substrate<\/strong><\/p>\n<\/td>\n

\n

Drug Class<\/strong><\/p>\n<\/td>\n

\n

Effect of allele expression<\/strong><\/p>\n<\/td>\n

\n

References<\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

NAT1<\/p>\n<\/td>\n

\n

NAT*4<\/p>\n<\/td>\n

\n

Sulfamethoxazole<\/p>\n<\/td>\n

\n

Antibiotic<\/p>\n<\/td>\n

\n

more likely to develop hypersensitivity to SMX<\/p>\n<\/td>\n

\n

77<\/p>\n<\/td>\n<\/tr>\n

\n

Nat1*3, *14,<\/p>\n<\/td>\n

\n

Para-aminosalicylic acid<\/p>\n<\/td>\n

\n

Anti-tubercular<\/p>\n<\/td>\n

\n

Expressed slow phenotype. Therefore, higher drug exposure.<\/p>\n<\/td>\n

\n

77<\/p>\n<\/td>\n<\/tr>\n

\n

NAT2<\/strong><\/p>\n<\/td>\n

\n

NAT2*6A, *6B, *7A, *7B, *14A<\/p>\n<\/td>\n

\n

Isoniazid<\/p>\n<\/td>\n

\n

Anti-tubercular<\/p>\n<\/td>\n

\n

Increased risk of developing toxic liver disease<\/p>\n<\/td>\n

\n

78<\/p>\n<\/td>\n<\/tr>\n

\n

NAT2*5B<\/p>\n<\/td>\n

\n

Rifampicin<\/p>\n<\/td>\n

\n

Anti-tubercular<\/p>\n<\/td>\n

\n

Increased risk of hepatoxicity<\/p>\n<\/td>\n

\n

79<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n\n

Due to the polymorphism of many\nGST genes, there is a great deal of interest in figuring out whether specific\nallelic variants affect the risk (or outcome) of several diseases. In humans,\nthere are eight classes of cytosolic GSTs: (GSTA), (GSTM), (GSTP), (GSTT),\n(GSTZ), (GSTS), (GSTO), and (GSTK). Each class contains one or more homodimeric\nor heterodimeric isoforms of the protein80<\/sup>. The detoxification of electrophilic\nsubstances, such as carcinogens, medicinal agents, environmental pollutants,\nand by-products of oxidative stress, is carried out by the GSTM (a mu class of\nenzymes through conjugation with glutathione). Genetic variants can alter a\nperson’s vulnerability to carcinogens and poisons as well as the toxicity and\nefficacy of medications. A modest increase in the number of malignancies has\nbeen associated with mutations of this class mu gene, most likely because of\nexposure to environmental pollutants. Mitogen-activated protein kinase (MAPK)\nsignal transduction pathway is modulated by GSTM1, which also regulates\napoptosis. While overexpression of the GSTM1 isozyme has been linked to\nchemotherapeutic resistance, GSTM1 deficiency has been linked to impaired\nmetabolic clearance of carcinogenic chemicals from the body, which may raise\nthe risk of cancer81,\n82<\/sup>.<\/p>\n\n\n\n

In ovarian cancer cells, GSTP1\nis crucial for cisplatin and carboplatin metabolism83, 84<\/sup>. Patients with ovarian cancer\nmay respond differently to platinum-based chemotherapy due to variations in\nGSTP1 expression as depicted in Table 885<\/sup>.<\/p>\n\n\n\n

Table 8: Effect of GST Mu-1 allele expression on the substrates<\/strong><\/p>\n\n\n\n\n\n\n\n\n\n\n\n
\n

Enzyme<\/strong><\/p>\n<\/td>\n

\n

Allele<\/strong><\/p>\n<\/td>\n

\n

Drug<\/strong><\/p>\n<\/td>\n

\n

Drug Class<\/strong><\/p>\n<\/td>\n

\n

Effect of allele expression<\/strong><\/p>\n<\/td>\n

\n

References<\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

GST Mu 1<\/strong><\/p>\n<\/td>\n

\n

GSTM1-Non-Null<\/p>\n<\/td>\n

\n

Vincristine<\/p>\n<\/td>\n

\n

Anticancer (Vinca alkaloid)<\/p>\n<\/td>\n

\n

Development of drug resistance<\/p>\n<\/td>\n

\n

86, 82<\/p>\n<\/td>\n<\/tr>\n

\n

Busulphan<\/p>\n<\/td>\n

\n

Alkylating agent<\/p>\n<\/td>\n

\n

Development of drug resistance and Low clearance<\/p>\n<\/td>\n

\n

86, 82, 87<\/p>\n<\/td>\n<\/tr>\n

\n

Nevirapine<\/p>\n<\/td>\n

\n

Antiviral (reverse transcriptase inhibitor)<\/p>\n<\/td>\n

\n

Steven Johnson Syndrome and Toxic Epidermal Necrolysis<\/p>\n<\/td>\n

\n

88<\/p>\n<\/td>\n<\/tr>\n

\n

GSTM1 Null<\/p>\n<\/td>\n

\n

 <\/p>\n

Cisplatin<\/p>\n<\/td>\n

\n

Anticancer (Platinum-based drugs<\/p>\n<\/td>\n

\n

Fewer side effects (thrombocytopenia, anaemia, and neuropathy) but Increased risk of ototoxicity.<\/p>\n<\/td>\n

\n

89, 90<\/p>\n<\/td>\n<\/tr>\n

\n

Cyclophosphamide<\/p>\n<\/td>\n

\n

Alkylating agent<\/p>\n<\/td>\n

\n

Fewer side effects (thrombocytopenia, anaemia and neuropathy)<\/p>\n<\/td>\n

\n

90<\/p>\n<\/td>\n<\/tr>\n

\n

Oxaliplatin<\/p>\n<\/td>\n

\n

Platinum-based drugs<\/p>\n<\/td>\n

\n

Risk of developing toxic injury in patients with metastatic colorectal cancer<\/p>\n<\/td>\n

\n

91<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

 <\/p>\n\n\n

Table 9: Effect of GSTT1 allele expression on the substrates<\/strong><\/p>\n\n\n\n\n\n\n\n\n\n\n\n
\n

Enzyme<\/strong><\/p>\n<\/td>\n

\n

Allele<\/strong><\/p>\n<\/td>\n

\n

Drug\/Multidrug therapy<\/strong><\/p>\n<\/td>\n

\n

Effect<\/strong><\/p>\n<\/td>\n

\n

References<\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

GSTT1<\/p>\n<\/td>\n

\n

GSTT1 non-null<\/p>\n<\/td>\n

\n

Cisplatin<\/p>\n<\/td>\n

\n

Higher likelihood of presenting vomiting and ototoxicity<\/p>\n<\/td>\n

\n

92, 89<\/p>\n<\/td>\n<\/tr>\n

\n

Busulphan<\/p>\n<\/td>\n

\n

Low clearance, so higher toxicity<\/p>\n<\/td>\n

\n

87<\/p>\n<\/td>\n<\/tr>\n

\n

GST1 null<\/p>\n<\/td>\n

\n

Cisplatin\/Doxorubicin\/Methotrexate therapy<\/p>\n<\/td>\n

\n

Decreased likelihood of progression-free survival in osteocarcinoma<\/p>\n<\/td>\n

\n

93, 94<\/p>\n<\/td>\n<\/tr>\n

\n

Oxaliplatin<\/p>\n<\/td>\n

\n

Risk of developing toxic injury in patients with metastatic colorectal cancer<\/p>\n<\/td>\n

\n

91<\/p>\n<\/td>\n<\/tr>\n

\n

Carboplatin<\/p>\n<\/td>\n

\n

Risk of developing ototoxicity<\/p>\n<\/td>\n

\n

95<\/p>\n<\/td>\n<\/tr>\n

\n

Etoposide<\/p>\n<\/td>\n

\n

Increased risk of lymphocytopenia<\/p>\n<\/td>\n

\n

96<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

 <\/p>\n\n\n

Table 10: Effect of GSTP1 allele expression on the substrates<\/strong><\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n
\n

Enzyme<\/strong><\/p>\n<\/td>\n

\n

Allele<\/strong><\/p>\n<\/td>\n

\n

Drug<\/strong><\/p>\n<\/td>\n

\n

Drug Class<\/strong><\/p>\n<\/td>\n

\n

Effect<\/strong><\/p>\n<\/td>\n

\n

References<\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

GSTP1<\/p>\n<\/td>\n

\n

Ile105Val<\/p>\n<\/td>\n

\n

Cisplatin<\/p>\n<\/td>\n

\n

Platinum-based drugs<\/p>\n<\/td>\n

\n

Higher likelihood of presenting grade 2 or 3 vomiting and lower GFR<\/p>\n<\/td>\n

\n

92<\/p>\n<\/td>\n<\/tr>\n

\n

Epirubicin<\/p>\n<\/td>\n

\n

Anthracycline<\/p>\n<\/td>\n

\n

Good response to chemotherapy and light toxicity in breast cancer treatment.<\/p>\n<\/td>\n

\n

97<\/p>\n<\/td>\n<\/tr>\n

\n

Cyclophosphamide<\/p>\n<\/td>\n

\n

Alkylating agent<\/p>\n<\/td>\n

\n

Stronger progression-free survival<\/p>\n<\/td>\n

\n

97<\/p>\n<\/td>\n<\/tr>\n

\n

(105)Ile\/Ile<\/p>\n<\/td>\n

\n

Epirubicin<\/p>\n<\/td>\n

\n

Anthracycline<\/p>\n<\/td>\n

\n

Good response to chemotherapy and light toxicity in breast cancer treatment.<\/p>\n<\/td>\n

\n

97<\/p>\n<\/td>\n<\/tr>\n

\n

Cisplatin<\/p>\n<\/td>\n

\n

Alkylating agent<\/p>\n<\/td>\n

\n

 Less chance of disease progression<\/p>\n<\/td>\n

\n

92<\/p>\n<\/td>\n<\/tr>\n

\n

(105)Val\/Val<\/p>\n<\/td>\n

\n

Cyclophosphamide<\/p>\n<\/td>\n

\n

Alkylating agent<\/p>\n<\/td>\n

\n

Weaker response to chemotherapy and heavy toxicity in breast cancer treatment<\/p>\n<\/td>\n

\n

97<\/p>\n<\/td>\n<\/tr>\n

\n

Epirubicin<\/p>\n<\/td>\n

\n

Anthracycline<\/p>\n<\/td>\n

\n

Weaker response to chemotherapy and heavy toxicity in breast cancer treatment<\/p>\n<\/td>\n

\n

97<\/p>\n<\/td>\n<\/tr>\n

\n

Cisplatin<\/p>\n<\/td>\n

\n

Platinum-based drugs<\/p>\n<\/td>\n

\n

more chance of presenting disease progression<\/p>\n<\/td>\n

\n

92<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

 <\/p>\n\n\n

Thiopurine 5-Methyl\nTransferase (TPMT)<\/strong><\/p>\n\n\n\n

TPMT is responsible\nfor the covalent binding of a methyl group to heterocyclic sulfhydryl moieties\nthereby inactivating thiopurine drugs. Though a large percentage of people (86\nto 97 percent) inherit two functioning TPMT alleles and have significant\nTPMT activity, 10 percent of people in Europe and Africa inherit two\nfaulty alleles and have little to no TPMT activity. Genetic polymorphism in the\nTPMT gene may produce clinical TPMT activity phenotypes, (i.e. high,\nintermediate, and low) that are connected to different rates of thiopurine drug\ninactivation as well as risk for toxicities. With just three-point mutations,\nTPMT *2, *3A, *3B, and *3C, are defined by four non-functional alleles. <\/p>\n\n\n\n

Azathioprine (AZT),\n6-Mercaptopurine, and 6-thioguanine (6-TG) are the three thiopurine medications\nutilized clinically. 6-thioguanine is an active metabolite of AZT and 6-MP. To\ncreate 6-thioguanine nucleotides, 6-MP and 6-TG are activated by the salvage\npathway enzyme hypoxanthine-guanine phosphoribosyl transferase (HGPRTase),\nwhich is responsible for both bone marrow toxicity and the majority of\ntherapeutic efficacy. As an alternative, 6-MP and 6-TG might be rendered\ninactive by enzymes Thiopurine methyl transferase as well as Xanthine oxidase,\nwhich would reduce the amount of accessible substrate for HGPRTase to activate62<\/sup>.<\/p>\n\n\n\n

Sulphotransferase<\/strong><\/p>\n\n\n\n

The sulfate\nconjugation of many pharmacologically significant endo- and xenobiotics is\ncatalyzed by the superfamily of sulfotransferase (SULT) enzymes99<\/sup>.\nNeurotransmitters, anti-estrogen steroid hormones, paracetamol and\np-nitrophenol are sulfated by SULT1A1. Alleles of SULT1A1 have shown an altered\nresponse to Tamoxifen as depicted in Table 11.<\/p>\n\n\n\n

Table 11: Altered response of SULT for tamoxifen, an anti-oestrogen<\/strong><\/p>\n\n\n\n\n\n\n\n
\n

Allele<\/strong><\/p>\n<\/td>\n

\n

Drug<\/strong><\/p>\n<\/td>\n

\n

Effect<\/strong><\/p>\n<\/td>\n

\n

References<\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

SULT1A1*2<\/p>\n<\/td>\n

\n

Tamoxifen<\/p>\n<\/td>\n

\n

Reduced metabolism of 4-OH-N-desmethyl-tamoxifen activity, over three times the risk of death in breast cancer patients.<\/p>\n<\/td>\n

\n

98<\/p>\n<\/td>\n<\/tr>\n

\n

SULT1A1*1<\/p>\n<\/td>\n

\n

Tamoxifen<\/p>\n<\/td>\n

\n

With higher activity, more likely to have other tumours in addition to breast cancer<\/p>\n<\/td>\n

\n

99<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n\n

Other Enzymes<\/strong><\/p>\n\n\n\n

Glucose-6-Phosphate\nDehydrogenase (G-6-PD)<\/strong><\/p>\n\n\n\n

The enzyme is coded\nby the G-6-PD gene present on the X chromosome and is known to be highly\npolymorphic. It can detoxify the unstable oxygen species and thus able to\nproduce NADPH and reduced glutathione that play a vital role in the prevention\nof oxidative damage for RBCs. Following exposure to external oxidative stresses\nlike infection, consumption of fava beans, and therapeutic drugs like\nprimaquine, the enzyme activity in RBCs rises significantly to fulfil the\nrequisite NADPH demand that can prevent the haemoglobin from getting oxidized.\nHowever, people with G-6-PD deficiency (i.e. less than 60 percent enzyme activity)\nare at a greater risk for aberrant RBC destruction, or hemolysis in the presence\nof oxidative stress180 genetic variants have been identified that have resulted\nin G-6-PD deficiency. More than 90 percent of variation is in the single base\nsubstitutions that alter amino acids, leading to the formation of abnormal\nproteins with decreased activity. G-6-PD deficiency affects over 400 million\npeople worldwide100<\/sup>.\nHeterozygous males and homozygous deficient females express reduced activity\nphenotypes.<\/p>\n\n\n\n

In individuals with G-6-PD\ndeficiency, Rasburicase therapy has shown a higher risk for severe hemolytic anaemia\nand methemoglobinemia. Rasburicase is a recommended drug for reducing uric acid\nlevels. The drug converts uric acid into Allantoin, which is a more soluble\nmolecule to be excreted easily from the human body. During this transformation,\na by-product hydrogen peroxide is produced which is a highly reactive oxidant.\nIt needs to be scavenged by glutathione to avoid the formation of free\nradicals. In people with G-6-PD deficiency, glutathione stores are diminished\nso they are prone to develop higher toxicity if receive drugs like Rasburicase.<\/p>\n\n\n\n

Therefore, it’s recommended\nthat patients at greater risk (especially individuals of African\/Mediterranean ancestry)\nmust be screened before initiating therapy and that this drug need not be used\nin patients with G-6-PD deficiency101, 102<\/sup>.<\/p>\n\n\n\n

Genetic Variation in\nTransporters<\/strong><\/strong><\/p>\n\n\n\n

Cell membrane\ntransporters are present in different tissues of the intestine, kidney, and\nliver. They mediate the selective influx & efflux of endogenous substances\nas well as foreign substances. Transporters as well as metabolic enzymes serve\nto determine the blood and tissue concentration of drugs and their metabolites.\nGenetic variations in transporter genes can change a drug’s disposition and\nfunction, which raises the possibility of toxicity. OATP1B1 is an organic anion\ntransporter encoded by the SLCO1B1 gene and is present on the sinusoidal\nmembrane of hepatic cells and mediates the uptake of acidic drugs like Statins,\nMethotrexate, and endogenous compounds like bilirubin from the blood.\nApproximately 40 SNP\u2019s are known that lead to decreased function of\ntransporter. <\/p>\n\n\n\n

A common polymorphism\nin rs4149056 decreases the transport of OATP1B1 substrates in vitro and alters\npharmacokinetics as well as pharmacodynamics. The variant displays a change in\namino acid that results in decreased expression. It’s common in most European\nand Asian populations103<\/sup>.<\/p>\n\n\n\n

HMG-CoA reductase\ninhibitors (Statins) are routinely prescribed drugs that effectively lower\nserum lipid levels to prevent cardiovascular events. The variant rs4149056 in\nSLCO1B1 increases the systemic exposure of Simvastatin and associated myopathy\nin a genome-wide association analysis. Therefore, CPIC advises a lower dose of\nSimvastatin or another statin in such cases104<\/sup>.<\/p>\n\n\n\n

Genetic Variations in\nImmune System Function<\/strong><\/p>\n\n\n\n

Genetic variation in\nthe human leukocyte antigen system has been implicated as the cause of\npopulation-based hypersensitivity reactions. HLA-B, HLA-DQ, and HLA-DR\npolymorphism among other HLA forms have been linked to several drug-induced\nhypersensitivity reactions to allopurinol, carbamazepine, abacavir, and flucloxacillin105, 106, 107<\/sup>.<\/p>\n\n\n\n

Table 12: Genetic Variations in Immune System Function<\/strong><\/p>\n\n\n\n\n\n\n\n\n\n\n\n
\n

S.No<\/strong><\/p>\n<\/td>\n

\n

HLA Gene variant<\/strong><\/p>\n<\/td>\n

\n

Suspected Drug<\/strong><\/p>\n<\/td>\n

\n

Drug Category<\/strong><\/p>\n<\/td>\n

\n

Adverse effect<\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

1.<\/p>\n<\/td>\n

\n

HLA-B*57:01<\/p>\n<\/td>\n

\n

Abacavir<\/p>\n<\/td>\n

\n

Anti-viral<\/p>\n<\/td>\n

\n

Steven Johnson Syndrome<\/p>\n<\/td>\n<\/tr>\n

\n

2.<\/p>\n<\/td>\n

\n

HLA-B*57:01<\/p>\n<\/td>\n

\n

Flucloxacillin<\/p>\n<\/td>\n

\n

Beta-lactam antibiotic<\/p>\n<\/td>\n

\n

Hepatocytes injury<\/p>\n<\/td>\n<\/tr>\n

\n

3.<\/p>\n<\/td>\n

\n

HLA-B*58:01, 53:01, HLA-A*34:02<\/p>\n<\/td>\n

\n

Allopurinol<\/p>\n<\/td>\n

\n

Antigout<\/p>\n<\/td>\n

\n

Hepatotoxicity<\/p>\n<\/td>\n<\/tr>\n

\n

3.<\/p>\n<\/td>\n

\n

HLA-DRB1*07*01<\/p>\n<\/td>\n

\n

Ximelagatron<\/p>\n<\/td>\n

\n

Thrombin inhibitor (now withdrawn)<\/p>\n<\/td>\n

\n

Increased ALT<\/p>\n<\/td>\n<\/tr>\n

\n

4.<\/p>\n<\/td>\n

\n

HLA-B*1502<\/p>\n<\/td>\n

\n

Carbamazepine<\/p>\n<\/td>\n

\n

Anticonvulsant<\/p>\n<\/td>\n

\n

Cutaneous toxicity<\/p>\n<\/td>\n<\/tr>\n

\n

5.<\/p>\n<\/td>\n

\n

HLA-DRB1*15:01, DRB5*01:01, DQB1*01:02,<\/p>\n

HLA-B*15:02<\/p>\n<\/td>\n

\n

Amoxycillin & Clavulanate combination<\/p>\n<\/td>\n

\n

Antibiotic Combination<\/p>\n<\/td>\n

\n

Liver toxicity<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

 <\/p>\n\n\n

Abacavir is a prodrug\nthat gets converted to carbovir triphosphate, a reactive molecule that may\ncontribute to Abacavir immunogenicity. Cytotoxic CD8 T cells that have been\nactivated are most likely the mechanism. An abacavir-related peptide may bind\nto the HLA-B*57:01 protein, according to reports. Genetic testing of\nHLA-B*57:01 indicators linked to Abacavir hypersensitivity has quickly entered\nclinical practice due to the significance of Abacavir in therapies.\nHypersensitivity reactions can lead to drug-induced liver injury108<\/sup>.<\/p>\n\n\n\n

Among 51 reported\ncases of liver damage linked to the antibiotic flucloxacillin, a specific genetic\nvariation (HLA-B*57:01) emerged as a risk factor. Researchers further observed\nthat flucloxacillin triggered the activation of certain immune cells (T-cells)\nwhich exhibited specific markers (CCR4 and CCR9) and responded by releasing\ninflammatory molecules (IFN-\u03b3, cytokines, perforin, and granzyme B).\nInterestingly, a time-dependent binding of flucloxacillin to a protein in the\nblood (albumin) was directly linked to the extent of T-cell activation,\nsuggesting a potential mechanism for this adverse reaction109, 110, 111<\/sup>.<\/p>\n\n\n\n

Patients with\nAmoxycillin-Clavulanic acid drug-induced liver injury were shown to have\ndrug-specific T cells, which suggests that the adaptive immune system is\nimplicated in the development of the disease. The antigens produced by Amoxycillin\nand clavulanate combination and the antigenic determinants that activate T\ncells were studied using mass spectrometric techniques. Similarly, Isoniazid,\nRifampicin, and Ethambutol are among the medications used to treat tuberculosis\nthat can also cause liver damage, which may be associated with HLA polymorphism109<\/sup>.<\/p>\n\n\n\n

Future Perspectives and Challenges to Personalized Medicines<\/strong><\/p>\n\n\n\n

Personalized medicine (PM) is the latest, futuristic, and novel area of research in the field of the healthcare industry. It is an idea in which health care professionals use diagnostic tests to identify specific biological markers mostly genomics, proteomics, and epigenomic profile of an individual to be mindful in providing any sort of treatment to the patient112, 113<\/sup>. All such information helps healthcare professionals to target a specific treatment according to the diagnostic results. Resistance to certain treatment strategies for individual patients has led to the urge for more development in this personalized medicine area. Also, the patient goes on one plan of medication and afterwards switches on to another, such practices lead to poorer results, in terms of undesirable effects, drug interactions, or any evolvement or advancement of diseases114, 115<\/sup>. Most people are not even aware of Personalized medicine. According to one survey, only 11% of patients became aware of personalized medicine through their doctors116<\/sup>. PM has made it possible to diagnose and treat a rapidly growing number of diseases, especially cancer, more precisely than ever before. This practice has empowered doctors to customize the therapy, maximize the effectiveness of drug treatments and minimize their side effects117<\/sup>. The main motive of personalized medicine is to provide the \u201cright drug with the right dose at the right time to the right patient\u201d115<\/sup>.<\/p>\n\n\n\n

As of now, personalized medicine has facilitated\ncommunication about incorporating genetic diagnostic results into treatment\nplans, but recent investigations confirm that there is a lack of awareness of\nimplementing this practice. Development in technology brings challenges along\nwith it. We have seen that personalized medicine is a mere basic step toward a\nmore defined approach to patient treatment. It not only prevents the\nadversities of drugs but also strengthens the preventive and therapeutic\nefforts of the patient. To combine a personalized medicine approach with\nhealthcare practices, more technologies and diagnostic tools need to be\nintroduced. It is always difficult to get and handle large data of patients.\nAlso, reaching the immediate goal of treating a patient and the end objective\nof discovering the etiology of the drug with the given data is challenging as\nwell as demanding as it needs real-time analysis and interpretation118<\/sup>. The challenge\nof personalized medicine concerning bioinformatics is large-scale robust\ngenomic data. We can do genomic resequencing through orthogonal resequencing.\nIt is still expensive and time-consuming119<\/sup>. The interpretation of the functional\neffect and effect of genomic variation is also difficult. Calculations and\npredictions do not provide the pathophysiology of the diseases, so for genetic\npredictions, experiments are required to be performed which is time-consuming.\nThe analytical methods of single nucleotide polymorphism are limited to the\nprediction of the impact of mis-sense in it. Also, it is required to analyze\nthe functional region in the genome. So, we can see the major challenge is to\ndevelop a method that combines multiple data sources with the inclusion of\nstatistics in it120<\/sup>.\nMany healthcare professionals are not willing to incorporate personal genetic\ntesting into their treatment strategy121<\/sup>. Also, there is a huge demand for\ngovernment agencies to check out the safety of medicines on people in a\ncost-effective manner. For bridging the slit between the typical medicine\nsystem and personalized medicine, already one move has been taken in the USA by\npassing Genomics and Personalized Medicine Act 2006122<\/sup>.<\/p>\n\n\n\n

Conclusion<\/strong><\/p>\n\n\n\n

We have seen personalized medicine start from a profile like genomics, proteomics, epigenomics, metagenomics, etc. to data integration, analysis, and interpretation including Bioinformatics, Biostatistics, and Biomathematics. Every technology inclusion comes with pros and cons and PM is also not different. The main motive of this article is to stimulate the medical fraternity to initiate research in the field of handling and analyzing data and its integration. So basically, personalized medicine is the science of transferring preclinical technologies to clinical applications. The study of personalized medicine brings hope to analyzing whether the genetic habits of an individual contribute to making healthy lifestyle choices. The large data, identification of variants in genomes, and prediction of pathophysiology are still the major challenges. Moreover, it is of the utmost requirement that the concept of personalized medicine should be within the reach of every person, so it should be cost-effective and more approachable in terms of inclusion in normal clinical practices.<\/p>\n\n\n\n

Acknowledgement<\/strong><\/p>\n\n\n\n

The authors are heartily thankful to the management and\nhigher administrative authorities of KIET Group of Institutions, Ghaziabad,\nIndia for their continuous support.  <\/p>\n\n\n\n

Funding<\/strong> <\/strong>Sources<\/strong><\/p>\n\n\n\n

The author(s) received no financial support for the\nresearch, authorship, and\/or publication of this article.<\/p>\n\n\n\n

Conflict of Interest<\/strong><\/p>\n\n\n\n

The author(s) do not have any conflict of interest.<\/p>\n\n\n\n

Data Availability<\/strong> Statement<\/strong> <\/p>\n\n\n\n

This\nstatement does not apply to this article.<\/p>\n\n\n\n

Ethical Statement<\/strong><\/p>\n\n\n\n

This study did not involve human participants, so informed\nconsent was not required.<\/p>\n\n\n\n

Informed Consent Statement<\/strong><\/p>\n\n\n\n

This study did not involve human participants, and therefore, informed consent was not required.<\/p>\n\n\n\n

Clinical Trial Registration<\/strong><\/p>\n\n\n\n

This research does not involve\nany clinical trials<\/p>\n\n\n\n

Author Contributions<\/strong><\/p>\n\n\n\n

Roma Ghai (RG), Ashu Mittal (AM), Shamsheer Alam (SA)\nsuggested the plot and framework of this manuscript, whereas Yogita Kaushik (YK),\nPasha Ishtiyaq (PI), Deepali Pandey (DP), SK, RG, Shardendu Kumar Mishra (SKM)\nwritten different parts of this manuscript. The proofreading and final editing\npart was done by RG, SKM. <\/p>\n\n\n\n

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    Abbreviations<\/strong><\/p>\n\n\n\n

    MHC:\nMajor Histocompatibility Complex<\/p>\n\n\n\n

    FDA:\nFood and Drug Administration<\/p>\n\n\n\n

    PMC:\nPersonalized Medicine Coalition’s<\/p>\n\n\n\n

    NME:\nNovel Molecular Entities<\/p>\n\n\n\n

    WHO:\nWorld Health Organization<\/p>\n\n\n\n

    CFTR:\nCystic Fibrosis Transmembrane Conductance Regulator<\/p>\n\n\n\n

    NIH:\nNational Institute of Health<\/p>\n\n\n\n

    SNP:\nSingle Nucleotide Polymorphism<\/p>\n\n\n\n

    HLA:\nHuman Leukocyte Antigen<\/p>\n\n\n\n

    VKORC1:\nVitamin K Epoxide Reductase Complex Subunit 1<\/p>\n\n\n\n

    CYP:\nCytochrome P450<\/p>\n\n\n\n

    AMD:\nAge-Related Macular Degeneration<\/p>\n\n\n\n

    GWAS:\nGenome-Wide Association Studies<\/p>\n\n\n\n

    SNP:\nSingle nucleotide polymorphisms<\/p>\n\n\n\n

    CNV:\ncopy-number variants<\/p>\n\n\n\n

    VNTR:\nvariable number tandem repeats<\/p>\n\n\n\n

    G-6-D: Glucose-6-phosphate dehydrogenase<\/p>\n\n\n\n

    UGT:\nUDP-glucuronosyltransferases<\/p>\n\n\n\n

    TPMT: Thiopurine S-methyltransferase<\/p>\n\n\n\n

    DYPD:\nDihydropyrimidine dehydrogenase<\/p>\n\n\n\n

    HLA:\nHuman leukocyte antigen<\/p>\n\n\n\n

    hERG:\nhuman ether-\u00e0-go-go-related gene<\/p>\n\n\n\n

    ADME: Absorption, Distribution, Metabolism, and\nExcretion<\/p>\n\n\n\n

    GIT: Gastrointestinal Tract<\/p>\n\n\n\n

    CPIC: Clinical Pharmacogenetics Implementation\nConsortium<\/p>\n\n\n\n

    NSAID: Nonsteroidal Anti-Inflammatory Drugs<\/p>\n\n\n\n

    PCI: Percutaneous Coronary Intervention<\/p>\n\n\n\n

    PPI:\nProton Pump Inhibitors<\/p>\n\n\n\n

    HIV: Human Immunodeficiency Virus<\/p>\n\n\n\n

    DPD: Dihydropyrimidine Dehydrogenase<\/p>\n\n\n\n

    ADR: Adverse Drug Reactions<\/p>\n\n\n\n

    NAT: N-acetyltransferases<\/p>\n\n\n\n

    GST: Glutathione S-transferases<\/p>\n\n\n\n

    TB: Tuberculosis<\/p>\n\n\n\n

    MAPK: Mitogen-activated protein kinase<\/p>\n\n\n\n

    GSTM: Glutathione S-Transferase mu<\/p>\n\n\n\n

    GSTP1: Glutathione S-Transferase pi-1<\/p>\n\n\n\n

    GSTT1: Glutathione\nS-Transferase theta-1<\/p>\n\n\n\n

    AZT: Azathioprine<\/p>\n\n\n\n

    6-TG: 6-thioguanine<\/p>\n\n\n\n

    6-MP: 6-Mercaptopurine<\/p>\n\n\n\n

    HGPRTase: Hypoxanthine-Guanine Phosphoribosyl Transferase<\/p>\n\n\n\n

    SULT: Sulfotransferase<\/p>\n\n\n\n

    NADPH: Nicotinamide Adenine Dinucleotide Phosphate<\/p>\n\n\n\n

    OATP: Organic Anion-Transporting Polypeptides<\/p>\n\n\n\n

    HMG-CoA: 3-hydroxy-3-methyl-glutaryl-CoA<\/p>\n\n\n\n

    CCR4: C-chemokine receptor type 4<\/p>\n\n\n\n

    IFN: Interferon<\/p>\n\n\n\n

    PM: Personalized\nmedicine<\/p>\n","protected":false},"excerpt":{"rendered":"

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