Pharmacogenomics – A Prospective Journey towards Precision Medicine

Abstract

In personalized medicine, genomic data is utilized to focus on individual reactions to drugs. At the point when a gene variation is related to a specific medication reaction in a patient, there is the potential for settling on clinical choices in light of hereditary factors by changing the dose or picking an alternate drug, for instance. Researchers survey gene variations influencing a person’s medication reaction the same way they evaluate gene variations related to certain illnesses: by recognizing hereditary loci related to known drug reactions and afterward testing people whose reaction is obscure. Current methodologies incorporate multi-gene investigation or entire genome single nucleotide polymorphism (SNP) profiles, and these methodologies are simply coming into clinical use for drug revelation and improvement.

1. Introduction

Pharmacogenetics is the science of how genetic factors affect the variation in drug safety and efficacy between individuals [1]. Regarding the human genome project, pharmacogenomics is another applied study of the entire genome, including genomics and proteomics, for recognizing every human gene, inter-individual and intra-individual variations in articulation, and testing its capability progressively [2]. Customized medication is an incredible chance to take a “one size fits all” approach dealing with diagnostics, medication treatment, and counteraction and transform it into an individualized methodology. Of course, we are all alike, but we are also unique, and this allows us to make individual predictions about disease risk, which can help someone choose a prevention plan that is right for them. Genomics is playing a big role in the development of personalized medicine. Genomics gives us a new window into the differences between us in a very specific molecular way. It likewise permits the chance, in certain cases, of picking the perfect medication at the ideal dosage for the perfect individual rather than the “one size fits all” way to deal with drug treatment. In the end, it will be difficult to see how this will not affect any kind of medicine as we learn more about each person and as many of us find that our entire genomes are being sequenced and available as a ready reckoner to enable that kind of personalized approach. There is much work to be done, however, and it might be the greatest upheaval in medication in seemingly forever. Pharmacogenomics aims to design new drugs and select the best treatment for each individual patient. As a general rule, most pharmacogenetic studies target single genes and their relationships with individual contrasts in drug interactions; at the same time, pharmacogenomics is a science that also deals with genomic interactions between genes in the general variety of drug metabolism and reactions.

2. Pharmacogenomics’ - origin and development

Pharmacogenetics existed more than 2000 years before Pythagoras’ observations, but enzyme polymorphisms like N-acetyltransferase and G6PD were not discovered until the 1950s [3]. In 1959, Friederich Vogel came up with the term “pharmacogenetics” to describe a new field of study that uses genetics and pharmacological knowledge and techniques to investigate the impact of inherited factors on drug response variability [4]. The subject of pharmacogenetics became too esoteric, and as a result, it declined. Proof for a genetic basis for clinical disorders related to the administration of medications arose in the mid-'50s when antimalarial drugs, for instance, primaquine, were proven to stimulate hemolytic anemia in patients who had a deficiency of glucose-6-phosphate dehydrogenase. Numerous observations of pharmacogenetic-based variations in pharmacokinetics were made in the 1970s following the discovery of the CYP2D6 polymorphism and its impact on drug toxicity and response [5]. This examination brought about a few investigations in view of the utilization of molecular innovations connected to traditional pharmacological phenotypization and hereditary studies in populations that allowed the identification of a few polymorphisms in genes engaged in drug metabolism. The term “pharmacogenomics” was first used in the medical literature towards the end of the 1990s. The European Agency for the Evaluation of Medicinal Products (EMEA) characterizes “pharmacogenetics” as “the investigation of inter-individual variations in DNA sequences connected with drug reactions” and “pharmacogenomics” as “the investigation of the expression of individual genes responsible for disease susceptibility as well as drug toxicity at the cell, tissue, individual, or populace level.” The European Agency for the Evaluation of Medicinal Products (EMEA), 2002. The most common meaning of the term, which considers pharmacogenomics to be the evolution of pharmacogenetics on a genomic scale, is in line with this definition. Pharmacogenetics, as a matter of fact, uses genetic innovations to investigate a set number of genes to describe the molecular mechanism of an individual’s reaction to drugs, while pharmacogenomics includes the investigation of the entire genome as it connects with drug reactions using high-throughput advancements.

At the drug metabolizing enzyme, transporter, or receptor level, polymorphisms associated with variable drug response have been identified for an increasing number of genes, mostly through a candidate gene approach. The use of genome-wide analysis is leading to the discovery of previously unknown new genes linked to disease and drug response. Albeit a few old and most new medications going onto the market have a “pharmacogenomic track” the clinical significance of pharmacogenomics has been by and large inadequate. For toxicity (such as azathioprine) and efficacy (such as warfarin) purposes, narrow therapeutic index drugs have been the primary focus of clinical translation of pharmacogenetics to date. Pharmacogenetics and genomics will progress through lower-cost, fast entire genome sequencing techniques joined with complex calculations permitting individualized measurement suggestions, but not really their reception. However, the influence of environmental and genetic factors on gene expression changes complicates this. As a result, the translation of pharmacogenetics into “personalized medicine” will be contingent on a variety of elements, such as clinical relevance, interactions between genes and the environment, costs, and education [3].

3. Single nucleotide polymorphisms: its impact on drug metabolism

Genetic variation in drug metabolism is a major factor in the variation in drug toxicity between individuals. Drug-metabolizing enzymes involving single nucleotide polymorphisms create distinct population subgroups with distinct capabilities for drug-transforming reactions. For the majority of enzymes that break down drugs, genetic polymorphisms have been described. Mutations in these enzyme genes cause polymorphisms by reducing, increasing, or eliminating enzyme expression or activity through a variety of molecular mechanisms. In addition, the population contains recessive alleles with a relatively high frequency [6]. Genes assist with building proteins and their enzymes. Many things happen to enzymes, like breaking down (metabolizing) drugs. People who do not respond to medications as well as expected might have genetic differences that make it harder for enzymes to break down a medication or make them stop working. A person’s response to a medication may be affected by these genetic variations. A typical dose of a drug can cause side effects or have little to no effect on treating the condition at hand if it is broken down too quickly or too slowly. The drug itself also affects how a person responds to it. For instance, whereas increased breakdown renders drug Y ineffective, increased breakdown causes side effects with drug X. The nucleotide sequence of the majority of genes is thought to have evolved casually over time. Mutations situated in a systematizing locale might prompt the replacement of an amino acid in a particular place in a protein and subsequently influence the function of proteins. Mutations in a regulatory region may alter the expression levels of mRNA and proteins and thus transcriptional and translational mechanisms [7].

A polymorphism is a variation in the DNA sequence that occurs in a population with an allelic frequency of at least 1%, while a mutation is a variation that occurs less frequently. Mutations and polymorphisms arrange for enzymes portrayed by various metabolic movements or receptors with affinity for the drug. They alter the pharmacological response of an individual or, in the case of variations that are particularly prevalent in particular ethnic groups, even of a population [7]. Single-nucleotide polymorphisms (SNPs) are the simplest genetic variants. Genetic mutations may include a few nucleotides or long DNA characteristics. In this instance, they are defined as amplifications, translocations, substitutions, insertions, deletions, and large mutations [7]. SNPs can be roughly divided into four main categories, which are as follows: (i) in the gene’s protein coding sequence; (ii) in the gene’s regulatory regions (like the promoter, 5′-untranslated region, 3′-untranslated region, and intronic sequences); (iii) at the gene’s exon-intron boundaries; and (iv) in the gene’s intergenic regions, which are interfering genomic segments that separate genes [8].

Alterations in the structure and function of the encoded proteins, as well as changes in the level of gene expression, are all possible outcomes of these sequence variants. However, there may be no discernible effects on protein function. The inheritance of these alleles by patients receiving standard doses of medication can result in an adverse drug reaction or failure to respond in the latter two scenarios. Such SNPs are potential candidates for drug response-modifying alleles. SNPs in genes’ regulatory regions have the potential to influence gene expression regulation [9]. Short sequences (typically 6–20 bases) known as transcriptional regulatory domains are found mostly in the promoter or intronic region of genes and serves as transcription factor (TF) binding sites. SNPs that change the binding site might possibly increase or reduce the binding efficiency of transcription factors, which results in spatial modifications in gene expression or potentially changes in the degree of gene expression. Alternately, SNPs in the promoter region may result in a gain-of-function by introducing novel TF binding properties. For instance, the minor “A” allele of a SNP found in the promoter of the tumor necrosis factor gene makes a new binding site for the OCT-1 TF, prompting increased transcriptional action [10]. In contrast, OCT-1 does not bind to the same promoter that carries the predominant “G” allele [10]. At long last, one more gene in the regulatory region that can be impacted by SNPs is the 5′- or 3′-untranslated area [9].

Post-transcriptional regulation of the mRNA involves either translational repression or changes in mRNA stability in these regions, which are on either end of the transcribed mRNA molecule. The binding of regulatory factors—short non-coding RNA molecules with a length of 19–21 nucleotides—to sequence motifs in the untranslated region of the mRNA acts as a conduit for post-transcriptional control [11, 12]. Alterations in regulatory protein [13] or microRNA binding characteristics [14] have been linked to changes in mRNA stability caused by SNPs targeting these motifs in the 3′-untranslated region. In pharmacogenetics, prototypes are used to describe monogenic traits. They are made up of polymorphisms in a single gene that code for a protein in a drug’s effects or metabolism, resulting in varying individual responses (Table 1). To be viable, drugs should interact with explicit targets restricted to the plasma, cell layer, or cytoplasm. These effectors can be modified qualitatively in the amino acid sequence or quantitatively (in the levels of gene expression) to cause biological variability as well as genetically determined diseases. The administration of a drug that is safe and effective in the general population may, in either case, have severe side effects in people with the disease gene and manifest a subclinical change in a relatively uncommon but clinically significant syndrome like the long QT syndrome.

Clinical Condition	Genes Associated	Clinical Usage	Reference
Atrial fibrillation	CYP2C9, VKORC1	Dose of Warfarin	Redekop and Mladsi [15]
Breast cancer	HER2	Use of Trastuzumab recommended	Redekop and Mladsi [15]
Epilepsy	HLA-B1502	Use of carbamazepine	Redekop and Mladsi [15]
Chronic myeloid leukemia (CML)	BCR and ABL	Imatinib is recommended	Druker et al. [16]
Cystic fibrosis	G551D, G551D	Ivacaftor is recommended	Ramsey et al. [17]

Table 1.

Examples of genetic variants that influence drug metabolism in clinical conditions.

4. Drug development utilizing pharmacogenomics

The first step in the process of drug discovery is to identify a potential target that the drug could target. A protein involved in signal transduction, a receptor, a transporter, an enzyme in an important pathway, or any protein produced by a disease can all serve as the target. The number of drug targets after sequencing the human genome was estimated to be around 8000, of which 4990 could actually be acted upon—2329 by antibodies and 794 by drug proteins [18]. 399 molecular targets from 130 protein families have been found through ligand binding studies [19, 20]. These targets are known to vary due to genetic polymorphisms. The effects of drugs that are based on targets with wide polymorphisms can vary. As previously mentioned, polymorphisms in the 2 adrenoceptor gene, for instance, have resulted in responder and non-responder phenotypes [21]. This can prompt conflicting outcomes in the preclinical and clinical examinations that would follow in the event that such a compound is sought after as a drug. Such targets can be eliminated as drug compounds, and other appropriate targets can be chosen. So, targets can be characterized early on using pharmacogenetic and proteomic studies, and suitable drug compounds can be chosen for future investment. Variation in a disease’s drug response is typically the result of multiple genes rather than a single gene mutation. The aftereffects of pharmacogenetic studies do not have any significant bearing when utilized clinically, as possibly single gene mutations are considered when, as a matter of fact, multiple genes are involved. In such cases, more than a pharmacogenetic study, it would be proper to do pharmacogenomic investigations looking at single nucleotide polymorphism (SNP) expression and heat maps among patients and controls (Figure 1). This can distinguish the hereditary elements related to the disease condition and hence give more current focuses to describe and assess, with the end goal of drug development [22].

Figure 1.

Consequences of polymorphisms on drug metabolism.

At the point when targets are tried and tested, in view of the pharmacogenetic profile of patients and their classification, it gives the feeling that those with poor use limits are restricted in their use. When viewed from a broader perspective, it should be noted that this method only reveals what was missed in the pregenomic era, when clinical trials and clinical practice were poorly explained. By using pharmacogenetic devices and understanding the reasons for unfavorable impacts, the targets that induce morbidity in poor metabolizers can really be forestalled when pharmacogenetics is recommended in clinical practice with suitably directed dosages. Additionally, it must be recognized that the population with poor metabolizing capacity as a result of genetic polymorphisms is only a small, extremely rare subset. The pharmaceutical company avoids developing such a drug if an enzyme polymorphism is found in a larger population. Another worry would be the expense that the patient would incur for pharmacogenetic testing prior to beginning treatment. The expense of genotyping for single nucleotide polymorphisms may not be reasonable in many developing and immature nations. However, as technology advances, this price may decrease in the near future. The cost of genotyping 1000 DNA samples would be 0.3 USD per genotype, as previously mentioned. Yet, when the expense is determined for a solitary patient example, it adds up to in excess of 130 USD, which includes the cost of the probe as well [23]. Consequently, it appears that genotyping is financially effective provided that it is utilized for a larger scope, which would be the case in the event that it is significant for therapeutic purposes.

5. Clinical interpretations

Genetic biomarkers have been the subject of numerous studies on their clinical utility in drug therapy [24, 25] and their potential to partially replace therapeutic drug level monitoring [26, 27]. Additionally, combining genetic biomarkers and drug levels could further guide optimal dosing, such as for warfarin. It is intuitive to assume that drug response and variants in genes that encode drug-metabolizing enzymes, membrane transporters, and receptors are causally linked. Multiple components of the signaling pathway for drug receptors have the potential to introduce variation in drug response, reducing the impact of drug receptor variants alone. The essential objectives of pharmacogenomic biomarkers are the choice of a reasonable treatment methodology or an endless drug measurement routine. Poor metabolizers, or “null alleles,” should avoid drugs that are mostly metabolized by a single enzyme; however, partially increased or decreased activity can be used to adjust dosages. Even when taking into account the most recent discoveries in genomics, biomarker predictions of graded enzyme activity frequently exhibit large variations, reducing their clinical utility, such as for CYP2D6 variants. Again, a lot of personal factors need to be taken into account when choosing a treatment.

The implementation of pharmacogenetic and genomic biomarkers into clinical practice is difficult and constrained by numerous factors. Most importantly, there is a lot of variation among patients, so any biomarker can only predict a small portion of disease risk or treatment aftereffects (Figure 2). Hence, the overall effect of inter-patient variance, along with cost, are the fundamental models that characterize cost–benefit proportions and decide clinical plausibility. Drugs that are linked to genetic biomarkers are listed in the FDA Pharmacogenomic Biomarkers in Drug Labels, which includes links to complete public drug label information, such as the type of genetic variant and allele frequencies between ethnic groups [28]. As an element of a biomarker’s clinical effect, drug-biomarker matches are either given on specialists advice only or further featured with a boxed advance notice when adverse reactions can be extremely severe. For instance, the main active metabolite endoxifen is activated by CYP2D6 when tamoxifen is used to treat breast cancer. Consequently, poor CYP2D6 metabolizers have a lower chance of responding and may require additional treatments. However, the involvement of a number of metabolizing enzymes, dietary factors, and patients’ compliance all contribute to variations in response. Further examination is expected to improve the clinical utility of CYP2D6-directed treatment [29].

Figure 2.

Drug response variability between individuals.

6. Pharmacogenomics’ applications - a network for complex diseases

For personalized treatment plans, precision medicine now incorporates lifestyle, age, behaviors, and polypharmacy regimens in addition to genomic data. A significant part of customized medication is recognizing genes that can impact the metabolism of a drug, which are referred to as “pharmacogenes”. Proven and factual variations of specific genes, like those coding for the cytochrome P450 catalyst superfamily, have been shown to influence drug vulnerability and, as a rule, increase side effects. Some people have deletions or substitutions that cause some enzymes to have low, high, or no activity at all, resulting in variable drug metabolism. This complicates the genetic influence. Poor reimbursement from third-party funders, a lack of clinician familiarity with personalized treatment, an inadequate workflow agenda, and a lack of organization in reporting are all obstacles to personalized medicine, despite its growing use in therapeutic areas like psychiatry, cardiology, and pain management. Healthcare administrators require electronic medical recording systems, proper orientation, and a constant genotype and phenotype reporting system, while funders and providers have legitimately requested sufficient published data to support the clinical utility of pharmacogenomics. About half of Americans say they take at least one prescription medication, according to data from the National Centre for Health Statistics [30]. In addition, 28% of people receive prescriptions for three or more medications, and approximately 13% report taking five or more medications in the past 45 days [31]. Altogether, roughly 4.8 billion remedies were filled at drug stores in the US in 2019 [32]. Mostly, numerous patients will encounter adverse consequences of prescription use, either as unfavorable adverse reactions to medication or drug inefficacy, requiring supervision in treatment management [33]. It has been more difficult to demonstrate the clinical significance of prospective, randomized clinical trials despite the abundance of scientific evidence for individual gene-drug associations. Personalized guided treatment and conventional treatment systems have typically been compared in these individual trials [34, 35, 36].

These trials are dependent on certain major factors: For instance, a random report with randomized subjects cannot be ethically generated, and these trials cannot be truly triple-blinded. Additionally, patients randomized to conventional treatment receive an effective trial that is proven to be safe and approved by the U.S. FDA. In cases like depression, where superiority over a placebo must be demonstrated, these trials differ significantly from typical industry-sponsored drug trials. At last, the clinician-agents in personalized medicine preliminaries are not typically committed to following the treatment routine characterized by pharmacogenomics. As a result, it should not come as a surprise that such randomized conventional trials lack power or are noted for only marginal outcomes of improvement. There are two reasons why pharmacogenomics will probably be used more frequently in the near future. First, the data will force funders, medical associations, and regulatory agencies to take this approach. Second, and as significant, new generations of doctors and partnered medical care suppliers will turn out to be more educated about the individual, monetary, wellbeing, and cultural ramifications of personalized medicine. Besides, as doctors take up pharmocogenomics in their practices, drug specialists will thusly have to assume a focal role in drug usage and clinical translation [37]. More current and more affordable genetic innovations will further develop the money-saving advantages of personalized medicine. At last, more funders will perceive the financial advantages of pharmacocogenomics and will repay for its utilization.

7. Pharmacogenomics - execution, regulations and ethics

Endless pharmacogenomics biomarker tests keep arising, yet endorsement by administrative organizations and acknowledgment by medical coverage organizations and foundations require legitimate proof of clinical significance. The FDA has established requirements that must be met before a product can be approved. Often, clinical trials are used to compare efficacy in the targeted population to a comparable biomarker-negative control. Cost–benefit analyses are necessary to demonstrate that the biomarker-drug combination is superior or that the drug in and of itself would not meet FDA approval criteria for use in the general patient population, as was the case with trastuzumab in the treatment of breast cancer [38]. Pharmacoeconomics evaluates the money-saving advantage proportions for therapeutic purposes, including the utilization of pharmacogenomics compared with medical care, not surprisingly. The utilization of next-generation sequencing to direct remedial choices raises unexpected issues, for example, coincidental discoveries of pathogenic variations [39]. In the end, it’s important to think about other approaches that might be less expensive than pharmacogenomics when making a clinical decision. When high-dose simvastatin is required to lower cholesterol levels, causing potential toxicity, prescribing alternative statins eliminates the requirement for SLCO1B1 genetic data, for instance, in the case of simvastatin and genetic variants of SLCO1B1. Genuine information is as yet meager and will require a normalized, certifiable proof plan. A few studies have addressed evidence review standards, payer participation in study design, and provider and payer education regarding NGS [39]. Utilization of hereditary information summons delicate issues in regards to privacy, abuse by outsiders, and inquiries with pertinence to the patient’s family, assuming that malicious changes are found. In the United States, the Genetic Information Nondiscrimination Act (GINA) was passed in 2008, ensuring that genetic information would not be used in decisions regarding health insurance or employment. This alleviated concerns regarding the misuse of genetic information and discrimination. Genetic information will now have a foothold in health care thanks to this important civil rights bill. For patients, the option to be aware of or protected from discoveries of pernicious variations should be explained by marked informed consent in clinical investigations.

The real moral standard in medication is to cause no damage and to stick to the vital core values of equality, justice, and beneficence. In the United States, there are significant disparities in access to health care among various populations. The COVID pandemic is only partially to blame for the decline in life expectancy that has been occurring for a number of years, despite advances in medical science. Black people and indigenous Americans have been disproportionately affected. Minority populations’ social exclusion from health care remains a serious issue. Bracic et al. [40], contend that efforts to eliminate such disparities in the context of social behaviors performed by members of the dominant group and members of the minoritized group frequently serve to perpetuate “exclusion cycles.” Extending the utilization of enormous information and man-made intelligence-based frameworks in medication to tackle these issues conveys a risk of building up such cycles when established on one-sided data [40]. However, through the use of widely accessible personal electronic health care records, personalized medicine inevitably moves towards convergence and integration of all medical, genomic, personal, cultural, and socioeconomic factors [41]. It is unclear to what extent this method can replace the intuitive judgments of seasoned healthcare professionals, but it is essential to observe beneficence, justice, and equality. Life expectancy has decreased in the United States over the past few years as a result of the two leading causes of death, heart disease and cancer, despite advancements in medicine. Genomics medication guarantees further developed results, yet it must be reasonably coordinated into normal clinical practice to turn out to be completely compelling.

8. Conclusion

Through enhanced use of existing biomarkers and the detection of early genomic and epigenomic events in disease development, particularly carcinogenesis, knowledge of personalized medicine enables earlier disease detection. Preventative medicine is the primary focus of this approach, which encourages proactive rather than reactive actions. This approach delays or forestalls the need to apply more extreme medicines, which are generally less endured and have expanded personal satisfaction and monetary contemplations. Globally, rising healthcare costs, particularly for end-of-life care, have increased pressure on government-funded healthcare systems. Precision medicine might make existing treatments work better and get rid of the problems that come with other methods. Precision medicine is a growing field of medical services where a doctor can choose a therapy in view of a patient’s hereditary or genetic profile that may not just limit harmful incidental effects and assure more success, but can also be less practical and an ‘experimentation’ way to deal with sickness therapy. The “trial-and-error” non-precision medicine approach, which is less effective and can result in drug toxicity, severe side effects, reactive treatment, and misdiagnosis, continues to drive up healthcare costs. A more unified treatment strategy tailored to each individual and their genome will emerge as a result of advances in personalized medicine. Customized medication might furnish better conclusions with prior intercession, more productive medication advancement, and more designated treatments.

To conclude, progress in personalized health care necessitates the convergence of a number of different fields and technologies in order to uncover connections between various components that are intertwined and influence one another. Seeing such intricacies might arise out of man-made brainpower and artificial intelligence later on. Basic genomics research must strive to integrate key aspects of personalized medicine in order to improve clinical translation, as pharmacogenomics serves as an essential link.

Conflict of interest

The author declares no conflict of interest.