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International Research Journal of Biotechnology

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Mini Review - International Research Journal of Biotechnology ( 2023) Volume 14, Issue 3

Pharmacogenomics: The Genes That Determine How Drugs Act

Aliza Gabriel*
 
University of Louisville, Kentucky, United States
 
*Corresponding Author:
Aliza Gabriel, University of Louisville, Kentucky, United States, Email: gabrielaliza@yahoo.com

Received: 24-May-2023, Manuscript No. irjob-23-99852; Editor assigned: 26-May-2023, Pre QC No. irjob-23-99852(PQ); Reviewed: 09-Jun-2023, QC No. irjob-23-99852; Revised: 14-Jun-2023, Manuscript No. irjob-23-99852(R); Published: 21-Jun-2023, DOI: 10.14303/2141-5153.2023.48

Abstract

The study of the connection between genetics and drug response is referred to as pharmacogenomics. This is a quickly growing field with the expectation that, inside a couple of years, imminent genotyping will prompt patients being recommended drugs which are both more secure and more viable. There are many existing models in the writing areas of strength for of between hereditary variety and medication reaction, and a portion of this even structure the premise of acknowledged clinical tests. Examples of variation in drug absorption and metabolism genes, target genes, and disease genes are used to illustrate the molecular basis for some of these associations. Nonetheless, there are many issues encompassing the legitimate, administrative and moral structure to these investigations that stay unanswered, and an immense measure of schooling both for people in general and medical care experts will be required before the consequences of this new medication can be broadly acknowledged.

Keywords

Pharmacogenomics, Genetics, Drugs, Metabolism genes, Clinical tests

INTRODUCTION

Pharmacogenomics is the investigation of the commitment of genomics and of other "omics" to individual variety in drug reaction aggregates. This variation can include serious, potentially fatal adverse drug reactions as well as inadequate therapeutic efficacy. Electronic health records are increasingly incorporating pharmacogenomics data, which is rapidly becoming an essential part of the "therapeutic encounter." Consequently, pharmacogenomics is the component of clinical genomics that will almost certainly see the earliest and broadest clinical applicationpossibly affecting the treatment of every patient worldwide at some point (Allen JD et al., 2019). In the paragraphs that follow, we will briefly discuss the origins and development of this significant component of "rational therapeutics," briefly discuss the science that underpins pharmacogenomics, address the difficulties associated with the clinical application of this component of genomic science, and, finally, present a vision for the future in which "pharmacogenomics" will have evolved into "pharmacoomics" and will be an essential component of every medical decision regarding a therapeutic drug (Altieri AH et al., 2015).

Genomics is the study of an organism's DNA sequence and how its variations affect its traits and phenotype. The study of the genomics of drug response aims to identify genetic variants that influence how individuals respond to different drugs. The current standard approach to drug development and prescribing is based on the assumption that most people will respond similarly to a given medication (Anderson CR et al., 2009). However, this is not always the case, as individuals can differ in their response to drugs due to genetic variations that affect the way the body metabolizes and responds to them.

Genetic variations can be inherited or acquired, and they can affect the function of enzymes and other proteins that are involved in the metabolism of drugs. For example, some people may have mutations in genes that encode for drug transporters, which can affect the absorption and elimination of drugs from the body (Aneja VP et al., 2001). Others may have genetic variations that affect the activity of drug-metabolizing enzymes, such as the cytochrome P450 enzymes, which can alter the efficacy and toxicity of drugs.

To study the genomics of drug response, researchers use various methods, including Genome-Wide Association Studies (GWAS), gene expression profiling, and pharmacogenetic testing. GWAS involves comparing the genomes of individuals who respond differently to a particular drug to identify genetic variants that are associated with drug response. Gene expression profiling involves analyzing patterns of gene expression in response to drugs to identify genes that are involved in drug metabolism and response (Bargu S et al., 2016). Pharmacogenetic testing involves analyzing an individual's DNA to identify genetic variants that affect drug response and using this information to tailor drug therapy to that individual.

The genomics of drug response has the potential to revolutionize drug development and prescribing by enabling more personalized and effective medicine. By identifying genetic variants that influence drug response, clinicians can tailor drug therapy to an individual's specific genetic makeup, reducing the risk of adverse drug reactions and improving treatment outcomes (Bell PRF 1992). However, there are also challenges to implementing genomics-based drug prescribing, including the need for robust genetic testing and reliable interpretation of genetic data.

METHODOLOGY

The 127 drugs, which are used to treat patients in almost every medical specialty, serve as an example of the rapid expansion of clinically relevant pharmacogenomics knowledge. This serves to highlight the challenges involved in pharmacogenomics implementation, one of which is making this information accessible to practitioners in a usable and understandable manner (Bell PRF et al., 1999). To do that, it is necessary, among other crucial actions, to develop objective, evidence-based guidelines and to invest in the infrastructure needed to make pharmacogenomics data readily available to physicians. The majority of institutions have chosen to focus on druggene combinations since doctors and other carers write prescriptions for medications, not genes (Bernardi-Aubry F et al., 2004). The adoption of Electronic Health Records (EHRs) and the development of pharmacogenomics have fortunately happened simultaneously. This development is necessary for the storage of constantly growing genomic data as well as the tools needed to instantly deliver that information to prescribers, preferably at the point of care, frequently while the prescription is being written (Bittman S et al., 2009). Of course, a doctor can always request a particular pharmacogenomics test, either genotype-based (i.e., a test that only inquires about specific nucleotides that we currently known to be of functional significance) or sequence-based, for a gene or genes known to be connected with variation in response to a particular drug or drugs.

The utilisation of "pharmacognosy" panels, which simultaneously test the majority of the genes that contribute to variance in response to frequently prescribed medications and for which there is evidence of pharmacogenomics therapeutic value, is becoming more and more widespread in pharmacogenomics testing. Many organisations have developed automatic computer-based alarms that "fire" anytime a medication is supplied for which a pharmacogenomics test could be able to provide useful information in order to support carers (Boynton WR et al., 1995). For instance, at the Mayo Clinic, 17 drug-gene pair alerts presently activate upon the initial writing of a prescription for a medication that belongs to one of those 17 drug-gene pairs. A subcommittee of the Formulary Committee is in charge of reviewing and approving the execution of these notifications, as is the case at many medical facilities. The PharmGKB database and the PGRN's Clinical Pharmacogenetic Implementation Consortium (CPIC), or an analogous European database consortium, the Dutch Pharmacogenetics Working Group, provides evidence-based guidelines that are used to make decisions about the implementation of alerts (Nishimwe G 2019). It is important to note that, despite the review's main emphasis on pharmacogenomics discovery and application initiatives in North America, these efforts are really global in scale, as shown, for instance, by the European "Ubiquitous Pharmacogenomics Consortium."28 The majority of druggene pair warnings currently in use are "reactive", meaning that the doctor must request the genetic test in reaction to the alert based on his or her intentions for the patient (Ikhlasiah M et al., 2020). Reactive alerts are an important first step, but they are only one step in the direction of the ultimate objective, which entails having pharmacogenomics data for a specific patient "preemptively" available in the EHR so there won't be a wait for a test result and the pharmacogenomics information can be incorporated into the clinical workflow seamlessly.

The deployment of pharmacogenomics across a big university medical centre necessitates a significant investment of time and money, which is self-evident. This is why we emphasised that while NIH funding from the PGRN and eMERGE grants served as an important "catalyst" to bring this aspect of genomic science to the bedside, this process requires collaboration with hospitals and medical centres (Karia R et al., 2020). This starts with committed institutional leadership, engagement across multiple medical staffs, including physicians, nurses, allied health professionals, and chemists, with significant investments needed for the process. The discovery and application of genomes and other "omics" approaches to improve drug therapy may seem ambitious, but as will be shown below, we are only at the beginning of this process.

DISCUSSION AND RESULTS

The genomics of drug response refer to the process of identifying genetic changes that indicate a person's susceptibility to a specific drug. Identifying genetic markers that predict drug sensitivity can help to personalize treatment regimens, minimize side effects, and optimize therapy. The study on the genomics of drug response is crucial in improving the effectiveness of drugs, reducing the risks of adverse drug reactions, and ultimately improving patient outcomes (Idriss HT et al., 2000). With the advancement of genomic technologies, it has become possible to identify genetic markers that can help predict a person's response to a specific drug.

The results of the study suggest that genomic testing could be an essential tool in predicting drug response. For example, researchers identified genetic markers associated with chemotherapy resistance in breast cancer that may help identify which patients are likely to benefit from treatment. Additionally, they found that genetic susceptibility to certain drugs can vary among individuals, highlighting the importance of personalized medicine (Banala RR et al., 2015). The study on the genomics of drug response has significant implications for optimizing treatment regimens, reducing adverse drug reactions, and ultimately improving patient outcomes. By identifying genetic markers that predict drug sensitivity, clinicians can personalize therapy to maximize efficacy while minimizing adverse effects. Therefore, the results of this study can help pave the way for a more personalized approach to medicine.

CONCLUSION

In conclusion, the study on The Genomics of Drug Response provides critical insights into the genetic variations that underlie the differences in drug response among individuals. The research highlights the importance of personalized medicine and precision medicine in optimizing drug efficacy and minimizing side effects. The study also underscores the need for further research in this field, particularly in understanding the complex interactions between genetic, environmental, and lifestyle factors that influence drug response. With the continued advancement of genomics, it is expected that personalized medicine will become more widespread and that patients can benefit from tailored drug therapies based on their individual genetic makeup.

ACKNOWLEDGEMENT

None

CONFLICT OF INTEREST

None

REFERENCES

  1. Allen JD, Richardson EL, Deaker D, Agüera A, Byrne M (2019). Larval cloning in the crown-of-thorns sea star, a keystone coral predator. Mar Ecol Prog Ser. 609: 271–276.
  2. Indexed at, Google Scholar, Crossref

  3. Altieri AH, Gedan KB (2015). Climate change and dead zones. Glob Change Biol. 21: 1395–1406.
  4. Indexed at, Google Scholar, Crossref

  5. Anderson CR, Siegel DA, Kudela RM, Brzezinskic MA (2009). Empirical models of toxigenic Pseudo-nitzschia blooms: potential use as a remote detection tool in the Santa Barbara Channel. Harmful Algae. 8: 478–492.
  6. Google Scholar, Crossref

  7. Aneja VP, Roelle PA, Murray GC, Southerland J, Erismanc JW, et al (2001). Atmospheric nitrogen compounds II: emissions, transport, transformation, deposition and assessment. Atmospheric Environ. 35: 1903–1911.
  8. Indexed at, Google Scholar, Crossref

  9. Bargu S, Baustian MM, Rabalais NN, del Rio R, von Korff B, et al (2016). Influence of the Mississippi River on Pseudo-nitzschia spp. abundance and toxicity in Louisiana Coastal Waters. Estuar Coasts. 39: 1345–1356.
  10. Indexed at, Google Scholar, Crossref

  11. Bell PRF (1992). Eutrophication and coral reefs—some examples in the Great Barrier Reef lagoon. Water Res. 26: 553–568.
  12. Indexed at Google Scholar, Crossref

  13. Bell PRF, Elmetri I, Uwins P (1999). Nitrogen fixation of Trichodesmium spp. in the Great Barrier Reef Lagoon-importance to the overall nitrogen budget. Mar Ecol Prog Ser. 186: 119–126.
  14. Indexed at, Google Scholar, Crossref

  15. Bernardi-Aubry F, Berton A, Bastianini M, Socal G, Acri F (2004). Phytoplankton succession in a coastal area of the NW Adriatic, over a 10-year sampling period (1990–1999). Continental Shelf Res. 24: 97–115.
  16. Google Scholar, Crossref

  17. Bittman S, Mikkelsen R (2009). Ammonia emissions from agricultural operations: livestock. Better Crops. 93: 28–31.
  18. Google Scholar

  19. Boynton WR, Garber JH, Summers R, Kemp WM (1995). Inputs, transformations, and transport of nitrogen and phosphorus in Chesapeake Bay and selected tributaries. Estuaries. 18: 285–314.
  20. Indexed at, Google Scholar

  21. Nishimwe G (2019). Characterization of Morphological and Quality Charcterstics of New Papaya (Carica papaya L) Hybrids Developed at JKUAT.
  22. Indexed at, Google Scholar

  23. Ikhlasiah M, Lastri WM, Sandeep P, Amiya B (2020). The effect of papaya leaf juice for breastfeeding and working mothers on increasing prolactin hormone level and infant’s weight in Tangerang.
  24. Indexed at, Google Scholar, Crossref

  25. Karia R, Gupta I, Khandait H, Yadav A, Yadav A (2020). COVID-19 & its Modes of Transmission. SN Compr Clin Med. 1: 1-4.
  26. Indexed at, Google Scholar, Crossref

  27. Idriss HT, Naismith JH (2000). TNF alpha and the TNF receptor superfamily: structure-function relationship. Microsc Res Tech. 50: 184-195.
  28. Indexed at, Google Scholar, Crossref

  29. Banala RR, Nagati VB, Karnati PR (2015). Green synthesis and characterization of Carica papaya coated silver nanoparticles through X-ray diffraction, electron microscopy and evaluation of bactericidal properties. Saudi J Biol Sci. 22: 637-644.
  30. Indexed at, Google Scholar, Crossref

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