Short Communication - Clinical Practice (2023) Volume 20, Issue 2
Introducing Neuroendocrinology to bridge health sciences and social sciences for positive social cohesion outcomes
- Corresponding Author:
- Kelli Kemenah Mauric
Whnp-Bc, Ms, Bsn Balance The Brain Professional Corporation, USA - E-mail: thisiswhy@kellimauric.com
Abstract
The history of the term, social cohesion, involves the research fields of social science, political science, and health science. Social science is the study of human society and social relationships. Political science is the analysis of political behavior and the study of politics at the state and national levels of government. Health science is the study of the human body itself and the human body’s response to its environment, also known as human science and includes the study of psychology. Public Health focuses on communities rather than individuals. Community is researched for data on health behaviors, physical environment, social economics, clinical care. Health behaviors includes data on tobacco, diet, exercise, alcohol, drug, and sexual activity. Physical environment data includes air and water quality, housing, and transportation. Social economics includes data on education, employment, income, family and social support, and community safety. Clinical care is data on access to and quality of healthcare. The Healthy People initiative of the US Department of Health and Human Services (https://www.healthypeople.gov/), views the practice of public health to involve partnerships within community, health and social services, industry and business, academia, and the media.
Keywords
pharmacogenomics, rheumatoid arthritis, DMARDs, biomarkers, pain management
Introduction
Rheumatoid Arthritis (RA) is a chronic autoimmune disorder characterized by inflammation of the synovial joints. The disease affects approximately 1% of the population and is associated with significant morbidity and mortality [1]. While the pathogenesis of RA is complex and not completely understood, both genetic and environmental factors are thought to contribute to disease susceptibility [2]. In recent years, there has been growing interest in the role of pharmacogenomics in the treatment of RA. Pharmacogenomics is the study of how genetic variation affects an individual's response to drugs. The identification of pharmacogenomics biomarkers may enable personalized treatment regimens that are more effective and have fewer adverse effects. This review will provide an overview of pharmacogenomics biomarkers and their applications in the treatment of RA.„
■Pharmacogenomics and pharmacogenomics biomarkers
Pharmacogenomics biomarkers can be broadly divided into two categories: predictive and prognostic. Predictive biomarkers are used to identify patients who are likely to respond to a particular treatment, while prognostic biomarkers are used to identify patients who are at higher risk of developing adverse effects [3]. In the context of RA, predictive biomarkers may be used to identify patients who are likely to respond to Disease-Modifying Anti-rheumatic Drugs (DMARDs), while prognostic biomarkers may be used to identify patients who are at higher risk of developing cardiovascular disease, a common comorbidity in RA [4].
There are several types of pharmacogenomics biomarkers that have been identified in RA. One important class of biomarkers is the human leukocyte antigen (HLA) system. The HLA system plays a critical role in the immune response and is involved in the presentation of antigens to T cells. HLA genes have been shown to be associated with susceptibility to RA, and certain HLA alleles have been found to be associated with responsiveness to specific DMARDs [5]. For example, the HLA-DRB104 allele has been associated with responsiveness to methotrexate (MTX), while the HLA-DRB101 allele has been associated with responsiveness to tumor necrosis factor (TNF) inhibitors [6].
Another important class of pharmacogenomics biomarkers is genetic polymorphisms in drug metabolizing enzymes and transporters. These enzymes and transporters play a critical role in drug metabolism and disposition and can affect the efficacy and toxicity of drugs. For example, polymorphisms in the Cytochrome P450 (CYP) enzymes can affect the metabolism of MTX, which is a commonly used DMARD in RA [7]. Polymorphisms in the ATP-Binding Cassette (ABC) transporters can affect the disposition of several DMARDs, including MTX and TNF inhibitors [8].
Pharmacogenomics is a field of study that aims to understand how genetic variations affect an individual's response to drugs. It involves analyzing genetic variations and gene expression patterns to identify pharmacogenomics biomarkers that can predict a patient's response to a particular drug or class of drugs.
Pharmacogenomics biomarkers are specific genetic variations or gene expression patterns that are associated with drug response or toxicity. These biomarkers can be used to predict how a patient will respond to a particular drug, guide personalized treatment decisions, and identify patients who may be at risk for adverse drug reactions.
Examples of pharmacogenomics biomarkers in rheumatoid arthritis include HLA genes, drug metabolizing enzymes and transporters, gender, age, disease duration, and gut microbiome. These biomarkers have been associated with drug response and toxicity, and can be used to guide personalized treatment decisions in rheumatoid arthritis.
Pharmacogenomics biomarkers have the potential to revolutionize the field of medicine by enabling personalized treatment decisions based on a patient's individual genetic makeup. By using pharmacogenomics biomarkers to predict drug response and toxicity, healthcare providers can improve treatment outcomes, minimize adverse drug reactions, and reduce healthcare costs.
■ Pharmacogenomics Biomarkers and DMARDs
Disease-Modifying Antirheumatic Drugs (DMARDs) are a class of drugs that are used to treat rheumatoid arthritis (RA) by suppressing the immune system and reducing inflammation. However, not all patients respond to DMARD therapy, and some patients may experience adverse drug reactions. This is where pharmacogenomics biomarkers can be particularly useful in guiding personalized treatment decisions.
Here are some examples of pharmacogenomics biomarkers associated with DMARD therapy in RA
1. HLA-DRB104 allele and methotrexate (MTX) response: The HLA-DRB104 allele has been associated with a poorer response to MTX therapy in RA patients. Patients who are homozygous for this allele may require higher doses of MTX to achieve a therapeutic response.
2. HLA-DRB101 allele and TNF inhibitor response: The HLA-DRB101 allele has been associated with a better response to TNF inhibitor therapy in RA patients. Patients who are homozygous for this allele may require lower doses of TNF inhibitors to achieve a therapeutic response.
3. CYP enzymes and MTX toxicity: Methotrexate is primarily metabolized by the liver enzyme CYP2C9. Patients who have genetic variations that result in decreased CYP2C9 activity may be at higher risk for MTX toxicity, such as liver damage or bone marrow suppression.
4. ABC transporters and DMARD disposition: ATP-binding cassette (ABC) transporters are a family of proteins that transport drugs across cell membranes. Genetic variations in ABC transporters can affect the disposition of DMARDs, such as increasing the halflife of drugs in the body or altering drug distribution.
By using pharmacogenomics biomarkers to guide personalized treatment decisions, healthcare providers can optimize the use of DMARDs in RA patients, improving treatment outcomes and minimizing adverse drug reactions. For example, a patient who is homozygous for the HLA-DRB1*04 allele and is not responding to MTX therapy may benefit from a higher dose of MTX or a switch to a different DMARD. Similarly, a patient who has genetic variations in ABC transporters that affect drug disposition may require a lower dose of DMARDs to achieve a therapeutic response.
Personalized Treatment Approaches
Personalized treatment approaches based on pharmacogenomics biomarkers have the potential to improve treatment outcomes and minimize adverse drug reactions in a number of ways
1. Improved drug efficacy: By tailoring treatment based on a patient's genetic makeup, healthcare providers can identify which drugs are most likely to be effective for a given patient. This can improve drug efficacy and reduce the time it takes to find an effective treatment.
2. Reduced risk of adverse drug reactions: Pharmacogenomics biomarkers can identify patients who are at higher risk of experiencing adverse drug reactions, allowing healthcare providers to avoid or minimize these risks by selecting alternative treatments or adjusting drug dosages.
3. Reduced healthcare costs: Personalized treatment approaches can reduce healthcare costs by improving treatment outcomes and reducing the need for unnecessary treatments or hospitalizations due to adverse drug reactions.
4. Reduced treatment time: Personalized treatment approaches can reduce the time it takes to find an effective treatment, thereby reducing the duration of the disease and its impact on the patient's quality of life.
5. More efficient use of healthcare resources: Personalized treatment approaches can help healthcare providers to use resources more efficiently by tailoring treatment to individual patients, rather than using a onesize-fits-all approach
In addition to these benefits, personalized treatment approaches can also help to advance our understanding of disease mechanisms and drug response. By analyzing pharmacogenomics biomarkers in large patient populations, researchers can gain insights into the genetic basis of diseases and drug responses, which can help to identify new targets for drug development and improve our understanding of disease pathogenesis.
■ Challenges to implementing personalized treatment approaches in rheumatoid arthritis
Despite the potential benefits of personalized treatment approaches based on pharmacogenomics biomarkers in Rheumatoid Arthritis (RA), there are several challenges that must be addressed in order to implement these approaches successfully:
1. Limited availability of pharmacogenomics testing: One of the biggest challenges to implementing personalized treatment approaches is the limited availability of pharmacogenomics testing. Currently, many healthcare providers do not have access to genetic testing or may not be aware of its potential benefits. Additionally, genetic testing can be expensive, and insurance coverage may not be available in all cases.
2. Lack of standardization in genetic testing: There is currently a lack of standardization in genetic testing, with different laboratories using different methods and criteria for interpreting genetic test results. This can lead to variability in test results and make it difficult for healthcare providers to interpret the results and make treatment decisions.
3. Limited knowledge of pharmacogenomics among healthcare providers: Another challenge to implementing personalized treatment approaches is the limited knowledge of pharmacogenomics among healthcare providers. Many healthcare providers may not be familiar with the concept of pharmacogenomics or may not have the training needed to interpret genetic test results and make treatment decisions based on this information.
4. Ethical and legal considerations: Personalized treatment approaches raise ethical and legal considerations, particularly around issues of informed consent, data privacy, and discrimination. Patients may be hesitant to undergo genetic testing if they are concerned about the potential consequences of having their genetic information on file.
5. Cost-effectiveness: Finally, there is a need to determine the cost-effectiveness of personalized treatment approaches. While personalized treatments have the potential to improve outcomes and reduce costs in the long run, the up-front costs of genetic testing and personalized treatments may be prohibitive in some cases.
To overcome these challenges, there is a need for increased education and awareness of pharmacogenomics among healthcare providers and patients, as well as the development of standardized testing and interpretation protocols. Additionally, healthcare providers and policymakers must work together to address ethical and legal considerations and determine the cost-effectiveness of personalized treatment approaches in RA.
There are several personalized treatment approaches that are being explored for Rheumatoid Arthritis (RA), including genetic testing and monitoring drug levels. Some examples of these approaches are:
1. Genetic testing: Genetic testing can help to identify patients who are more likely to respond to certain drugs or who are at higher risk of adverse drug reactions. For example, genetic testing can identify patients with the HLA-DRB1 gene, which has been associated with a poorer response to methotrexate and a higher risk of methotrexate toxicity. Similarly, genetic testing can identify patients with the TNFAIP3 gene, which has been associated with a better response to certain biologic therapies.
2. Pharmacokinetic monitoring: Pharmacokinetic monitoring involves measuring drug levels in the blood to ensure that patients are receiving the optimal dose of a medication. This can be particularly useful for drugs with a narrow therapeutic index, such as biologics. By monitoring drug levels, healthcare providers can adjust drug dosages as needed to ensure that patients are receiving the right amount of medication to achieve the desired effect.
3. Treat-to-target approach: The treat-to-target approach involves setting specific treatment goals and adjusting therapy as needed to achieve those goals. This approach is based on the idea that early and aggressive treatment can help to prevent joint damage and improve long-term outcomes in RA. The treat-to-target approach may involve frequent monitoring of disease activity, imaging studies to assess joint damage, and adjusting medications as needed to achieve treatment goals.
4. Biomarker-based approaches: Biomarkerbased approaches involve using biomarkers to identify patients who are more likely to respond to certain therapies or who are at higher risk of disease progression. For example, levels of C-reactive protein (CRP) and erythrocyte sedimentation rate (ESR) can be used as biomarkers of disease activity in RA. Biomarkers can also be used to monitor response to therapy and adjust treatment as needed.
Overall, personalized treatment approaches for RA are still in the early stages of development, and more research is needed to determine their efficacy and cost-effectiveness. However, these approaches have the potential to improve outcomes and reduce adverse drug reactions in patients with RA.
■ Overview of prognostic biomarkers in rheumatoid arthritis
Prognostic biomarkers in rheumatoid arthritis (RA) are biomarkers that can help predict disease outcome and guide treatment decisions. They can be used to identify patients who are at higher risk of disease progression or joint damage, as well as patients who are more likely to respond to specific treatments. Here is an overview of some of the most promising prognostic biomarkers in RA:
1. Anti-cyclic citrullinated peptide (antiCCP) antibodies: Anti-CCP antibodies are autoantibodies that are present in the serum of many patients with RA. They are highly specific for RA and are associated with a more severe disease course and an increased risk of joint damage. Patients with high levels of anti-CCP antibodies may benefit from more aggressive treatment.
2. Rheumatoid factor (RF): RF is an autoantibody that is present in the serum of many patients with RA. Like anti-CCP antibodies, RF is associated with a more severe disease course and an increased risk of joint damage. Patients with high levels of RF may benefit from more aggressive treatment.
3. C-Reactive Protein (CRP): CRP is a protein that is produced by the liver in response to inflammation. Elevated levels of CRP are associated with disease activity in RA and can be used to monitor response to treatment.
4. Erythrocyte sedimentation rate (ESR): ESR is a measure of how quickly red blood cells settle to the bottom of a test tube. Elevated ESR is associated with disease activity in RA and can be used to monitor response to treatment.
5. Matrix metalloproteinases (MMPs): MMPs are enzymes that are involved in the breakdown of extracellular matrix proteins. Elevated levels of MMPs are associated with joint damage in RA and may be a marker of disease severity.
6. Synovial biomarkers: Biomarkers can be measured in synovial fluid or tissue to provide information about disease activity and joint damage. Examples of synovial biomarkers include cytokines, chemokines, and growth factors.
7. Genetic markers: Genetic markers, such as the HLA-DRB1 gene, have been associated with a more severe disease course and an increased risk of joint damage in RA. Genetic testing can be used to identify patients who are at higher risk of disease progression.
Overall, prognostic biomarkers have the potential to improve outcomes in patients with RA by guiding treatment decisions and identifying patients who are at higher risk of disease progression or joint damage. However, more research is needed to determine the clinical utility of these biomarkers and to develop personalized treatment approaches based on biomarker profiles.
Here are some examples of pharmacogenomics biomarkers associated with cardiovascular risk in rheumatoid arthritis. Cardiovascular disease (CVD) is a major cause of morbidity and mortality in patients with Rheumatoid Arthritis (RA), and pharmacogenomics biomarkers can play an important role in identifying patients at higher risk of CVD and guiding treatment decisions. Here are some examples of pharmacogenomics biomarkers associated with CVD risk in RA:
1. CYP2C9 and VKORC1 genotypes: These genetic variants are associated with response to warfarin, a commonly used anticoagulant in patients with CVD. Patients with certain CYP2C9 and VKORC1 genotypes may require lower or higher doses of warfarin to achieve therapeutic anticoagulation, and genetic testing can help optimize dosing and reduce the risk of bleeding.
2. SLCO1B1 genotype: This genetic variant is associated with statin-induced myopathy, a common side effect of statin therapy. Patients with certain SLCO1B1 genotypes may be at higher risk of developing myopathy and may require lower statin doses or alternative lipid-lowering therapies.
3. CYP2C19 genotype: This genetic variant is associated with response to clopidogrel, a commonly used antiplatelet agent in patients with CVD. Patients with certain CYP2C19 genotypes may have reduced clopidogrel efficacy and may require alternative antiplatelet therapies.
4. IL-1β genotype: This genetic variant is associated with increased CVD risk in patients with RA. IL-1β is a proinflammatory cytokine that plays a role in atherosclerosis and CVD, and patients with certain IL-1β genotypes may benefit from targeted IL-1β inhibition to reduce CVD risk.
Overall, pharmacogenomics biomarkers have the potential to improve outcomes in patients with RA by optimizing drug dosing and reducing the risk of adverse drug reactions. However, more research is needed to determine the clinical utility of these biomarkers and to develop personalized treatment approaches based on biomarker profiles in patients with RA and CVD.
&# Future directions in emerging areas of research in pharmacogenomics and rheumatoid arthritis
Pharmacogenomics is a rapidly evolving field, and emerging technologies are expanding our understanding of the genetic and molecular mechanisms underlying Rheumatoid Arthritis (RA) and its treatment [9]. Here are some examples of emerging areas of research in pharmacogenomics and RA:
1. Epigenetics: Epigenetic modifications, such as DNA methylation and histone acetylation, can regulate gene expression without changing the underlying DNA sequence. Emerging evidence suggests that epigenetic modifications play a role in RA pathogenesis and treatment response. For example, DNA methylation of the IL6 promoter region has been associated with response to methotrexate, a commonly used disease-modifying antirheumatic drug (DMARD), in patients with RA. In addition, histone acetylation has been shown to regulate the expression of genes involved in inflammation and joint destruction in RA.
2. RNA sequencing: RNA sequencing, also known as transcriptomics, can provide a comprehensive view of gene expression patterns in RA and identify novel biomarkers and therapeutic targets. For example, RNA sequencing has identified differentially expressed genes and pathways in RA synovial tissue and peripheral blood mononuclear cells, including genes involved in immune activation, cell adhesion, and extracellular matrix remodeling. In addition, RNA sequencing has been used to identify predictive biomarkers of treatment response to DMARDs and biologic agents in patients with RA.
3. Proteomics: Proteomics is the study of the complete set of proteins in a biological sample, and emerging proteomic technologies are providing new insights into RA pathogenesis and treatment. For example, mass spectrometry-based proteomics has identified differentially expressed proteins in synovial fluid and serum of patients with RA, including proteins involved in inflammation, angiogenesis, and tissue remodeling. In addition, proteomics has been used to identify biomarkers of treatment response to DMARDs and biologic agents in patients with RA.
Overall, emerging technologies in pharmacogenomics are expanding our understanding of the genetic and molecular mechanisms underlying RA and its treatment and have the potential to improve outcomes in patients with RA by identifying novel biomarkers and therapeutic targets.
Pharmacogenomics has the potential to improve outcomes in patients with rheumatoid arthritis (RA) by optimizing drug therapy and reducing the risk of adverse drug reactions. However, the application of pharmacogenomics in RA is not limited to Disease-Modifying Antirheumatic Drugs (DMARDs) alone [10]. Here are some potential applications of pharmacogenomics in RA beyond DMARDs:
1. Pain management: Pain is a common symptom of RA, and pharmacogenomics biomarkers may help identify patients who are more likely to respond to certain analgesic medications. For example, genetic variants in the mu-opioid receptor gene (OPRM1) have been associated with response to opioid medications, and genetic testing may help optimize opioid therapy in patients with RA and chronic pain.
2. Non-steroidal anti-inflammatory drugs (NSAIDs): NSAIDs are commonly used to treat pain and inflammation in RA, but they can also cause gastrointestinal and cardiovascular side effects. Pharmacogenomics biomarkers may help identify patients who are at higher risk of NSAID-induced adverse effects and guide the selection of alternative therapies. For example, genetic variants in the CYP2C9 gene are associated with NSAID metabolism and the risk of gastrointestinal bleeding, and genetic testing may help optimize NSAID therapy in patients with RA.
3. Corticosteroids: Corticosteroids are often used as adjunct therapy in patients with RA, but they can cause a wide range of side effects, including osteoporosis, diabetes, and cardiovascular disease. Pharmacogenomics biomarkers may help identify patients who are more likely to experience corticosteroidrelated side effects and guide the selection of alternative therapies. For example, genetic variants in the glucocorticoid receptor gene (NR3C1) have been associated with response to corticosteroids and the risk of osteoporosis.
4. Comorbidities: Patients with RA are at increased risk of comorbidities, such as cardiovascular disease, osteoporosis, and depression. Pharmacogenomics biomarkers may help identify patients who are at higher risk of these comorbidities and guide the selection of preventative and therapeutic interventions. For example, genetic variants in the vitamin D receptor gene (VDR) have been associated with the risk of osteoporosis, and genetic testing may help optimize vitamin D supplementation in patients with RA.
Overall, pharmacogenomics has the potential to improve outcomes in patients with RA by optimizing drug therapy and reducing the risk of adverse drug reactions, not only for DMARDs but also for other medications used to manage pain, inflammation, and comorbidities. However, more research is needed to validate these findings and translate them into clinical practice.
Conclusion
Pharmacogenomics is an important area of research that has the potential to improve outcomes in patients with Rheumatoid Arthritis (RA) by optimizing drug therapy and reducing the risk of adverse drug reactions. By identifying pharmacogenomics biomarkers that are associated with drug response and toxicity, clinicians can personalize treatment approaches for individual patients with RA, selecting medications that are most likely to be effective and safe based on their genetic profile.
The application of pharmacogenomics in RA is not limited to Disease-Modifying Antirheumatic Drugs (DMARDs) alone. Pharmacogenomics biomarkers may also be useful in guiding the selection of other medications commonly used to manage pain, inflammation, and comorbidities in patients with RA, such as NSAIDs, corticosteroids, and vitamin D supplements.
Despite the potential benefits of pharmacogenomics in RA, there are still several challenges that must be addressed, such as the high cost of genetic testing and the need for more research to validate pharmacogenomics biomarkers and translate them into clinical
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