Pub Date : 2020-05-20DOI: 10.1080/23808993.2020.1768844
P. Gurbel, A. Rout, U. Tantry
Oral antiplatelet therapy with aspirin and a P2Y12 receptor blocker constitutes a major strategy to prevent thrombotic events in patients with arterial diseases. Among available P2Y12 receptor blockers, ticagrelor is a potent direct-acting drug, whereas clopidogrel and prasugrel (thienopyridines) are prodrugs that require cytochrome (CYP) P450 based in vivo conversion to an active metabolite to irreversibly inhibit the P2Y12 receptor. Wide antiplatelet response variability was observed during clopidogrel treatment, with nearly one in three subjects exhibiting minimal or no inhibition of adenosine diphosphate-induced platelet aggregation. The latter phenomenon is termed clopidogrel non-responsiveness or resistance [1,2]. Despite these limitations and guideline recommendations for the preference of ticagrelor or prasugrel, clopidogrel remains the most used P2Y12 inhibitor in the current practice even in patients with acute coronary syndromes (ACS) [3–5]. Pharmacogenetics associated with P2Y12 receptor blocker therapy, especially clopidogrel, their clinical implications and potential utility of personalized antiplatelet therapy based on genetic testing are discussed here. Pharmacokinetic and pharmacodynamic studies have revealed that variable active metabolite generation is associated with clopidogrel response variability. The latter, in part, is affected by carriage of single nucleotide polymorphism (SNPs) of genes encoding CYPs that are responsible for clopidogrel metabolism. Among > 30 CYP2C19 alleles, CYP2C19 * 1, with normal activity, is the most prevalent allele. Carriage of a loss-of function allele (LoF) is associated with reduced clopidogrel active metabolite generation. In subjects with CYP2C19*2, the most common LoF, a guanine>adenine mutation in exon 5 of CYP2C19 (rs42442850) creates an aberrant splice site resulting in an altered reading frame at amino acid 215 and a premature stop codon 20 amino acids downstream. The final result is a nonfunctional truncated protein, lack of translation resulting from nonsense-mediated messenger RNA decay, or both. Other LoFs are *3-*8. LoF carriage is estimated at ~25%, ~33% and ~55% in Caucasians, African Americans and Asians, respectively. Carriage of a gain-of-function allele (GoF) (CYP2C19*17) is associated with increased clopidogrel active metabolite generation. Carriage of GoF is ~34%, 30% and 4% in Caucasians, African Americans and Asians, respectively [2]. In patients treated with clopidogrel, LoF allele carriage is associated with a reduced antiplatelet response, an increased prevalence of high platelet reactivity to ADP (HPR), and an increased risk for post-stenting ischemic event occurrence, including stent thrombosis. The relation between GoF allele carriage and clinical outcomes is less robust. The association between SNPs of paroxonase-1(PON-1) and ABCB1, clopidogrel metabolism, and clinical outcomes remains controversial [2]. CYP2C19 isoenzyme is not the only factor determining the anti
{"title":"Pharmacogenetic considerations in antiplatelet therapy","authors":"P. Gurbel, A. Rout, U. Tantry","doi":"10.1080/23808993.2020.1768844","DOIUrl":"https://doi.org/10.1080/23808993.2020.1768844","url":null,"abstract":"Oral antiplatelet therapy with aspirin and a P2Y12 receptor blocker constitutes a major strategy to prevent thrombotic events in patients with arterial diseases. Among available P2Y12 receptor blockers, ticagrelor is a potent direct-acting drug, whereas clopidogrel and prasugrel (thienopyridines) are prodrugs that require cytochrome (CYP) P450 based in vivo conversion to an active metabolite to irreversibly inhibit the P2Y12 receptor. Wide antiplatelet response variability was observed during clopidogrel treatment, with nearly one in three subjects exhibiting minimal or no inhibition of adenosine diphosphate-induced platelet aggregation. The latter phenomenon is termed clopidogrel non-responsiveness or resistance [1,2]. Despite these limitations and guideline recommendations for the preference of ticagrelor or prasugrel, clopidogrel remains the most used P2Y12 inhibitor in the current practice even in patients with acute coronary syndromes (ACS) [3–5]. Pharmacogenetics associated with P2Y12 receptor blocker therapy, especially clopidogrel, their clinical implications and potential utility of personalized antiplatelet therapy based on genetic testing are discussed here. Pharmacokinetic and pharmacodynamic studies have revealed that variable active metabolite generation is associated with clopidogrel response variability. The latter, in part, is affected by carriage of single nucleotide polymorphism (SNPs) of genes encoding CYPs that are responsible for clopidogrel metabolism. Among > 30 CYP2C19 alleles, CYP2C19 * 1, with normal activity, is the most prevalent allele. Carriage of a loss-of function allele (LoF) is associated with reduced clopidogrel active metabolite generation. In subjects with CYP2C19*2, the most common LoF, a guanine>adenine mutation in exon 5 of CYP2C19 (rs42442850) creates an aberrant splice site resulting in an altered reading frame at amino acid 215 and a premature stop codon 20 amino acids downstream. The final result is a nonfunctional truncated protein, lack of translation resulting from nonsense-mediated messenger RNA decay, or both. Other LoFs are *3-*8. LoF carriage is estimated at ~25%, ~33% and ~55% in Caucasians, African Americans and Asians, respectively. Carriage of a gain-of-function allele (GoF) (CYP2C19*17) is associated with increased clopidogrel active metabolite generation. Carriage of GoF is ~34%, 30% and 4% in Caucasians, African Americans and Asians, respectively [2]. In patients treated with clopidogrel, LoF allele carriage is associated with a reduced antiplatelet response, an increased prevalence of high platelet reactivity to ADP (HPR), and an increased risk for post-stenting ischemic event occurrence, including stent thrombosis. The relation between GoF allele carriage and clinical outcomes is less robust. The association between SNPs of paroxonase-1(PON-1) and ABCB1, clopidogrel metabolism, and clinical outcomes remains controversial [2]. CYP2C19 isoenzyme is not the only factor determining the anti","PeriodicalId":12124,"journal":{"name":"Expert Review of Precision Medicine and Drug Development","volume":"5 1","pages":"235 - 238"},"PeriodicalIF":1.2,"publicationDate":"2020-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/23808993.2020.1768844","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43858184","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-05-03DOI: 10.1080/23808993.2020.1756770
S. Balendra, Piero Zollet, Gloria Cisa Asinari Di Gresy E Casasca, M. Cordeiro
ABSTRACT Introduction Personalized medicine is the future goal across all specialties. Accurate prediction of optimal treatment beneficial and adverse effects could transform patient management. This is of particular importance in chronic conditions, where a ‘trial and error’ approach over months and years can contribute to significant morbidity. Glaucoma is a chronic irreversible progressive optic neuropathy, a leading cause of blindness worldwide. An ideal personalized approach in glaucoma clinic would be to answer the inevitable question in a patient’s first visit: ‘Which treatment option will work best for me so that I won’t go blind?’ Areas covered This review will give an overview of the knowledge we have acquired to achieve this goal, particularly discussing using patient’s individual risk factors, their genetic profile, and different treatment modalities, including therapy compliance, to personalize care. Expert opinion Pharmacogenomics and genetic profiling are the most tangible ways in which glaucoma management can be personalized. Future challenges will include developing realistic animal models to reflect the underlying genetic patterns in glaucoma to investigate their interaction with different treatments.
{"title":"Personalized approaches for the management of glaucoma","authors":"S. Balendra, Piero Zollet, Gloria Cisa Asinari Di Gresy E Casasca, M. Cordeiro","doi":"10.1080/23808993.2020.1756770","DOIUrl":"https://doi.org/10.1080/23808993.2020.1756770","url":null,"abstract":"ABSTRACT Introduction Personalized medicine is the future goal across all specialties. Accurate prediction of optimal treatment beneficial and adverse effects could transform patient management. This is of particular importance in chronic conditions, where a ‘trial and error’ approach over months and years can contribute to significant morbidity. Glaucoma is a chronic irreversible progressive optic neuropathy, a leading cause of blindness worldwide. An ideal personalized approach in glaucoma clinic would be to answer the inevitable question in a patient’s first visit: ‘Which treatment option will work best for me so that I won’t go blind?’ Areas covered This review will give an overview of the knowledge we have acquired to achieve this goal, particularly discussing using patient’s individual risk factors, their genetic profile, and different treatment modalities, including therapy compliance, to personalize care. Expert opinion Pharmacogenomics and genetic profiling are the most tangible ways in which glaucoma management can be personalized. Future challenges will include developing realistic animal models to reflect the underlying genetic patterns in glaucoma to investigate their interaction with different treatments.","PeriodicalId":12124,"journal":{"name":"Expert Review of Precision Medicine and Drug Development","volume":"5 1","pages":"145 - 164"},"PeriodicalIF":1.2,"publicationDate":"2020-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/23808993.2020.1756770","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41518431","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-05-03DOI: 10.1080/23808993.2020.1758062
C. Carretero-Puche, Santiago García-Martín, R. García-Carbonero, G. Gómez-López, F. Al-Shahrour
The last few years have witnessed the emergence of personalized precision medicine (PPM), a novel approach to improve clinical decisions, integrating individual multi-omic profiles, and heterogeneo...
{"title":"How can bioinformatics contribute to the routine application of personalized precision medicine?","authors":"C. Carretero-Puche, Santiago García-Martín, R. García-Carbonero, G. Gómez-López, F. Al-Shahrour","doi":"10.1080/23808993.2020.1758062","DOIUrl":"https://doi.org/10.1080/23808993.2020.1758062","url":null,"abstract":"The last few years have witnessed the emergence of personalized precision medicine (PPM), a novel approach to improve clinical decisions, integrating individual multi-omic profiles, and heterogeneo...","PeriodicalId":12124,"journal":{"name":"Expert Review of Precision Medicine and Drug Development","volume":"5 1","pages":"115 - 117"},"PeriodicalIF":1.2,"publicationDate":"2020-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/23808993.2020.1758062","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44286489","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-04-28DOI: 10.1080/23808993.2020.1754127
A. Correa, S. Haider, W. Aronow
ABSTRACT Introduction The treatment of arrhythmias is complex and there are many options including drug therapy, ablation techniques, and implantable devices. Selection of the right treatment strategy is complicated by the fact that patients with apparently the same clinical picture may respond differently to a given therapy, indicating some underlying molecular and cellular differences. We now know this is mediated in a large part by the genetics, and patients with similar phenotypes may have differing genotypes. Understanding this genotype–phenotype relationship is key in modern medicine. Areas covered We have conducted an exhaustive review of the literature surrounding the genetic basis of arrhythmias and have presented a comprehensive summary of the available information. We have demonstrated how understanding the underlying genetic and molecular derangements in arrhythmias has resulted in effective targeted treatments. We have further highlighted novel therapies in arrhythmia management based on emerging genomic research. Expert opinion The future of cardiac electrophysiology, and indeed all cardiovascular medicine, lies in the development of targeted therapies that can effectively treat the individual patient, based on their specific genetic attributes and variations. Future genetic research which drives the development of innovative therapies holds the promise of delivering such personalized therapies in cardiac electrophysiology.
{"title":"Precision medicine in cardiac electrophysiology: where we are and where we need to go","authors":"A. Correa, S. Haider, W. Aronow","doi":"10.1080/23808993.2020.1754127","DOIUrl":"https://doi.org/10.1080/23808993.2020.1754127","url":null,"abstract":"ABSTRACT Introduction The treatment of arrhythmias is complex and there are many options including drug therapy, ablation techniques, and implantable devices. Selection of the right treatment strategy is complicated by the fact that patients with apparently the same clinical picture may respond differently to a given therapy, indicating some underlying molecular and cellular differences. We now know this is mediated in a large part by the genetics, and patients with similar phenotypes may have differing genotypes. Understanding this genotype–phenotype relationship is key in modern medicine. Areas covered We have conducted an exhaustive review of the literature surrounding the genetic basis of arrhythmias and have presented a comprehensive summary of the available information. We have demonstrated how understanding the underlying genetic and molecular derangements in arrhythmias has resulted in effective targeted treatments. We have further highlighted novel therapies in arrhythmia management based on emerging genomic research. Expert opinion The future of cardiac electrophysiology, and indeed all cardiovascular medicine, lies in the development of targeted therapies that can effectively treat the individual patient, based on their specific genetic attributes and variations. Future genetic research which drives the development of innovative therapies holds the promise of delivering such personalized therapies in cardiac electrophysiology.","PeriodicalId":12124,"journal":{"name":"Expert Review of Precision Medicine and Drug Development","volume":"5 1","pages":"165 - 180"},"PeriodicalIF":1.2,"publicationDate":"2020-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/23808993.2020.1754127","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44856064","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-04-22DOI: 10.1080/23808993.2020.1760089
E. Cartwright, A. Athauda, I. Chau
ABSTRACT Introduction Gastric cancer (GC) is a leading cause of cancer-related death and a significant global health burden. Chemotherapy remains the mainstay of treatment in advanced disease and only trastuzumab in combination with upfront fluoropyrimidine/platinum chemotherapy for human epidermal growth factor receptor 2 (HER2) positive disease and ramucirumab together with paclitaxel in the second line have demonstrated a significant survival benefit over chemotherapy alone. Despite the focus on a molecularly targeted approach, treatment gains have been modest and GC remains an area of great unmet need. Areas covered In this review, we provide an overview of the continuum of care in GC, the molecular characterization of GC, targeted therapies currently under investigation and the role of immunotherapy. Expert commentary Gastric cancer is a heterogeneous disease. A targeted approach based upon molecular phenotype holds promise for improving outcomes.
{"title":"Emerging precision therapies for gastric cancer","authors":"E. Cartwright, A. Athauda, I. Chau","doi":"10.1080/23808993.2020.1760089","DOIUrl":"https://doi.org/10.1080/23808993.2020.1760089","url":null,"abstract":"ABSTRACT Introduction Gastric cancer (GC) is a leading cause of cancer-related death and a significant global health burden. Chemotherapy remains the mainstay of treatment in advanced disease and only trastuzumab in combination with upfront fluoropyrimidine/platinum chemotherapy for human epidermal growth factor receptor 2 (HER2) positive disease and ramucirumab together with paclitaxel in the second line have demonstrated a significant survival benefit over chemotherapy alone. Despite the focus on a molecularly targeted approach, treatment gains have been modest and GC remains an area of great unmet need. Areas covered In this review, we provide an overview of the continuum of care in GC, the molecular characterization of GC, targeted therapies currently under investigation and the role of immunotherapy. Expert commentary Gastric cancer is a heterogeneous disease. A targeted approach based upon molecular phenotype holds promise for improving outcomes.","PeriodicalId":12124,"journal":{"name":"Expert Review of Precision Medicine and Drug Development","volume":"5 1","pages":"299 - 311"},"PeriodicalIF":1.2,"publicationDate":"2020-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/23808993.2020.1760089","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42725853","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-04-09DOI: 10.1080/23808993.2020.1746640
M. Camilli, G. Iannaccone, M. D. Del Buono, F. Crea, N. Aspromonte
ABSTRACT Introduction Both genetic background and life-style factors influence coronary artery disease (CAD) individual risk and clinical manifestations. Areas covered Over the last decades, cardiovascular disease (CVD) emerged as the most important cause of morbidity and mortality worldwide, leading the scientific community to nourish increased interest in cardiovascular risk factors control. Several cohort studies have shown that a family history of ischemic heart disease is common among patients suffering from CAD, suggesting that genetic factors play a role of utmost relevance. The techniques used to study the genetic basis of these diseases have evolved from linkage studies to candidate gene studies and genome-wide association studies (GWAS). Linkage studies have been able to identify genetic variants associated with monogenic diseases, whereas genome-wide association studies have been more successful in determining genetic variants associated with complex diseases. However, family context transmits not only genetic information, but also attitudes and lifestyle habits which proved to significantly influence the overall cardiovascular risk. Expert opinion In the era of patient-tailored management, the aim of this review is to summarize the genetic background of patients with CAD, focusing on the most updated gene-targeted therapies, providing potential future perspectives of pharmacogenetics utility in daily clinical practice.
{"title":"Genetic background of coronary artery disease: clinical implications and perspectives","authors":"M. Camilli, G. Iannaccone, M. D. Del Buono, F. Crea, N. Aspromonte","doi":"10.1080/23808993.2020.1746640","DOIUrl":"https://doi.org/10.1080/23808993.2020.1746640","url":null,"abstract":"ABSTRACT Introduction Both genetic background and life-style factors influence coronary artery disease (CAD) individual risk and clinical manifestations. Areas covered Over the last decades, cardiovascular disease (CVD) emerged as the most important cause of morbidity and mortality worldwide, leading the scientific community to nourish increased interest in cardiovascular risk factors control. Several cohort studies have shown that a family history of ischemic heart disease is common among patients suffering from CAD, suggesting that genetic factors play a role of utmost relevance. The techniques used to study the genetic basis of these diseases have evolved from linkage studies to candidate gene studies and genome-wide association studies (GWAS). Linkage studies have been able to identify genetic variants associated with monogenic diseases, whereas genome-wide association studies have been more successful in determining genetic variants associated with complex diseases. However, family context transmits not only genetic information, but also attitudes and lifestyle habits which proved to significantly influence the overall cardiovascular risk. Expert opinion In the era of patient-tailored management, the aim of this review is to summarize the genetic background of patients with CAD, focusing on the most updated gene-targeted therapies, providing potential future perspectives of pharmacogenetics utility in daily clinical practice.","PeriodicalId":12124,"journal":{"name":"Expert Review of Precision Medicine and Drug Development","volume":"5 1","pages":"135 - 144"},"PeriodicalIF":1.2,"publicationDate":"2020-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/23808993.2020.1746640","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46532013","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-03-17DOI: 10.1080/23808993.2020.1738217
R. Cacabelos
ABSTRACT Introduction Neuropsychiatric disorders (NPDs) (neurodevelopmental, mental, neurodegenerative, neurotoxic, complex disorders) are the third major problem of health in developed countries. About 10-20% of direct costs are attributed to pharmacological treatment; however, drug effectiveness is lower than 30% in most NPDs. Pharmacogenomics accounts for 60-90% variability in pharmacokinetics and pharmacodynamics. Areas covered Main areas covered include (i) organization of the pharmacogenetic machinery (pathogenic, mechanistic, metabolic, transporter, pleiotropic genes); (ii) pharmacogenomics of antidepressants, antipsychotics, anxiolytics, antiepileptics, anti-Alzheimer, anti-Parkinson, and anti-stroke drugs; and (iii) adverse drug reactions and pharmaco-resistance. Expert commentary The pharmacogenomics of NPDs is still primitive, but sufficient to help physicians to optimize pharmacological treatment by reducing ADRs (extrapyramidal symptoms, tardive dyskinesia, neurotoxicity, cerebrovascular damage) and unnecessary costs. Over 50% of psychotropic drugs are incorrectly prescribed. CYP enzymes participate in the metabolism of over 90% of drugs for the treatment of NPDs. Only 20% of the population is potentially extensive metabolizer for 80% of current psychotropic agents. Consequently, the introduction of pharmacogenomic procedures in the clinical setting is an urgent need for improving drug efficacy and safety.
{"title":"Pharmacogenomics of drugs used to treat brain disorders","authors":"R. Cacabelos","doi":"10.1080/23808993.2020.1738217","DOIUrl":"https://doi.org/10.1080/23808993.2020.1738217","url":null,"abstract":"ABSTRACT Introduction Neuropsychiatric disorders (NPDs) (neurodevelopmental, mental, neurodegenerative, neurotoxic, complex disorders) are the third major problem of health in developed countries. About 10-20% of direct costs are attributed to pharmacological treatment; however, drug effectiveness is lower than 30% in most NPDs. Pharmacogenomics accounts for 60-90% variability in pharmacokinetics and pharmacodynamics. Areas covered Main areas covered include (i) organization of the pharmacogenetic machinery (pathogenic, mechanistic, metabolic, transporter, pleiotropic genes); (ii) pharmacogenomics of antidepressants, antipsychotics, anxiolytics, antiepileptics, anti-Alzheimer, anti-Parkinson, and anti-stroke drugs; and (iii) adverse drug reactions and pharmaco-resistance. Expert commentary The pharmacogenomics of NPDs is still primitive, but sufficient to help physicians to optimize pharmacological treatment by reducing ADRs (extrapyramidal symptoms, tardive dyskinesia, neurotoxicity, cerebrovascular damage) and unnecessary costs. Over 50% of psychotropic drugs are incorrectly prescribed. CYP enzymes participate in the metabolism of over 90% of drugs for the treatment of NPDs. Only 20% of the population is potentially extensive metabolizer for 80% of current psychotropic agents. Consequently, the introduction of pharmacogenomic procedures in the clinical setting is an urgent need for improving drug efficacy and safety.","PeriodicalId":12124,"journal":{"name":"Expert Review of Precision Medicine and Drug Development","volume":"5 1","pages":"181 - 234"},"PeriodicalIF":1.2,"publicationDate":"2020-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/23808993.2020.1738217","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42585222","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-03-10DOI: 10.1080/23808993.2020.1736942
E. Young, D. Gould, S. Hart
ABSTRACT Introduction Rheumatoid arthritis (RA) is an autoimmune disease of the joint, affecting 0.24% of the global population. Many patients only respond partially or not at all to current therapies while the systemic complications of immunosuppression associated with these treatments are unacceptable. Genetic therapies for RA have the potential to improve treatments by targeting delivery to the disease site, enhancing efficacy, and avoiding adverse effects. Areas covered The route of administration, delivery vector, nucleic acid type, and target gene must be carefully selected to develop an effective RA gene therapy. Drawing from examples of RA gene therapies investigated in animal models and clinical trials, this review discusses how these strategies may be used to translate RA gene therapy into the clinic. Expert opinion Existing RA treatments lack specificity to the joint. Genetic delivery systems can include targeting properties, such as disease-responsive promoters or cell-targeting moieties, to overcome this. Non-viral vectors, in particular, can be engineered easily to possess these properties and, unlike viral vectors, display low immunogenicity. Contrary to current drugs, gene therapy can be delivered intra-articularly, providing sustained levels of the therapeutic. Targeted vectors may also achieve this, but with a single systemic injection, simultaneously delivering the therapeutic to all affected joints.
{"title":"Toward gene therapy in rheumatoid arthritis","authors":"E. Young, D. Gould, S. Hart","doi":"10.1080/23808993.2020.1736942","DOIUrl":"https://doi.org/10.1080/23808993.2020.1736942","url":null,"abstract":"ABSTRACT Introduction Rheumatoid arthritis (RA) is an autoimmune disease of the joint, affecting 0.24% of the global population. Many patients only respond partially or not at all to current therapies while the systemic complications of immunosuppression associated with these treatments are unacceptable. Genetic therapies for RA have the potential to improve treatments by targeting delivery to the disease site, enhancing efficacy, and avoiding adverse effects. Areas covered The route of administration, delivery vector, nucleic acid type, and target gene must be carefully selected to develop an effective RA gene therapy. Drawing from examples of RA gene therapies investigated in animal models and clinical trials, this review discusses how these strategies may be used to translate RA gene therapy into the clinic. Expert opinion Existing RA treatments lack specificity to the joint. Genetic delivery systems can include targeting properties, such as disease-responsive promoters or cell-targeting moieties, to overcome this. Non-viral vectors, in particular, can be engineered easily to possess these properties and, unlike viral vectors, display low immunogenicity. Contrary to current drugs, gene therapy can be delivered intra-articularly, providing sustained levels of the therapeutic. Targeted vectors may also achieve this, but with a single systemic injection, simultaneously delivering the therapeutic to all affected joints.","PeriodicalId":12124,"journal":{"name":"Expert Review of Precision Medicine and Drug Development","volume":"5 1","pages":"123 - 133"},"PeriodicalIF":1.2,"publicationDate":"2020-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/23808993.2020.1736942","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43798400","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-03-04DOI: 10.1080/23808993.2020.1735935
A. Kashou, Itzhak Z. Attia, Xiaoxi Yao, P. Friedman, P. Noseworthy
Atrial fibrillation (AF) is known to affect at least 30 million people worldwide [1,2], although this may be an underestimation. AF can be asymptomatic and fleeting and often goes undetected. In fact, it has been estimated that approximately one million Americans live with unrecognized AF [3]. The proportion of patients with paroxysmal AF versus persistent AF varies with age (with paroxysmal AF more common in patients <50 years), and it is estimated that about 25% of patients with AF have a paroxysmal pattern [4]. Identifying patients with undiagnosed AF is important as they have a fivefold increased risk of stroke [1,2] and the first manifestation of AF may be a disabling stroke. Furthermore, AF-related strokes carry a particularly poor prognosis [3,5]. When AF is recognized, interventions including oral anticoagulation or left atrial appendage closure can lower stroke risk and mortality [5,6]. Due to its frequently paroxysmal nature, AF is often under detected. Currently, prolonged electrocardiographic monitoring is implemented to detect patients with suspected AF – a process that is expensive, resource intensive, and at times poorly tolerated. In nearly 5,000 patients referred for continuous 24-hour monitoring, the prevalence of paroxysmal AF was 2.5% [7]. It has been estimated that even among a highrisk cohort of patients with ischemic strokes, 20% remain cryptogenic despite thorough diagnostic evaluation [5]. Apart from the low yield, long-term cardiac monitoring is resource intensive, expensive, and impractical for broad-scale application. A frequent clinical dilemma is whether or not to anticoagulate patients without documented AF based on incomplete information; studies of empiric anticoagulation following embolic stroke of uncertain source have found no benefit and harm (i.e. bleeding) [8,9]. Therefore, it is essential to detect paroxysmal AF to guide therapy to prevent stroke. Recently, we developed an artificial intelligence-enabled electrocardiogram (AI-ECG) algorithm using over 500,000 normal sinus rhythm standard 10-second 12-lead ECGs from over 180,000 patients using machine learning to identify those with a high likelihood of undocumented AF [10]. This work demonstrated that the application of a convolutional neural network (CNN) to a single ECG recorded during sinus rhythm could effectively identify paroxysmal AF, with an area under the receiver operator curve (AUC) of 0.87 (95% confidence interval [CI], 0.86–0.88), sensitivity of 79.0% (95% CI, 77.5–80.4%), specificity of 79.5% (95% CI, 79.0–79.9%), F1 score of 39.2% (95% CI, 38.1–40.3%), and overall accuracy of 79.4% (95% CI, 79.0–79.9%). The diagnostic yield improved when applied to patients with multiple ECGs (AUC 0.90). With the impressive performance of the AI-ECG algorithm, the question becomes: what is the AI seeing that the human eye is missing? Due to the nature of CNNs, identification of the signal features selected by the AI is currently not possible. We presume that
{"title":"Artificial intelligence-enabled electrocardiogram: can we identify patients with unrecognized atrial fibrillation?","authors":"A. Kashou, Itzhak Z. Attia, Xiaoxi Yao, P. Friedman, P. Noseworthy","doi":"10.1080/23808993.2020.1735935","DOIUrl":"https://doi.org/10.1080/23808993.2020.1735935","url":null,"abstract":"Atrial fibrillation (AF) is known to affect at least 30 million people worldwide [1,2], although this may be an underestimation. AF can be asymptomatic and fleeting and often goes undetected. In fact, it has been estimated that approximately one million Americans live with unrecognized AF [3]. The proportion of patients with paroxysmal AF versus persistent AF varies with age (with paroxysmal AF more common in patients <50 years), and it is estimated that about 25% of patients with AF have a paroxysmal pattern [4]. Identifying patients with undiagnosed AF is important as they have a fivefold increased risk of stroke [1,2] and the first manifestation of AF may be a disabling stroke. Furthermore, AF-related strokes carry a particularly poor prognosis [3,5]. When AF is recognized, interventions including oral anticoagulation or left atrial appendage closure can lower stroke risk and mortality [5,6]. Due to its frequently paroxysmal nature, AF is often under detected. Currently, prolonged electrocardiographic monitoring is implemented to detect patients with suspected AF – a process that is expensive, resource intensive, and at times poorly tolerated. In nearly 5,000 patients referred for continuous 24-hour monitoring, the prevalence of paroxysmal AF was 2.5% [7]. It has been estimated that even among a highrisk cohort of patients with ischemic strokes, 20% remain cryptogenic despite thorough diagnostic evaluation [5]. Apart from the low yield, long-term cardiac monitoring is resource intensive, expensive, and impractical for broad-scale application. A frequent clinical dilemma is whether or not to anticoagulate patients without documented AF based on incomplete information; studies of empiric anticoagulation following embolic stroke of uncertain source have found no benefit and harm (i.e. bleeding) [8,9]. Therefore, it is essential to detect paroxysmal AF to guide therapy to prevent stroke. Recently, we developed an artificial intelligence-enabled electrocardiogram (AI-ECG) algorithm using over 500,000 normal sinus rhythm standard 10-second 12-lead ECGs from over 180,000 patients using machine learning to identify those with a high likelihood of undocumented AF [10]. This work demonstrated that the application of a convolutional neural network (CNN) to a single ECG recorded during sinus rhythm could effectively identify paroxysmal AF, with an area under the receiver operator curve (AUC) of 0.87 (95% confidence interval [CI], 0.86–0.88), sensitivity of 79.0% (95% CI, 77.5–80.4%), specificity of 79.5% (95% CI, 79.0–79.9%), F1 score of 39.2% (95% CI, 38.1–40.3%), and overall accuracy of 79.4% (95% CI, 79.0–79.9%). The diagnostic yield improved when applied to patients with multiple ECGs (AUC 0.90). With the impressive performance of the AI-ECG algorithm, the question becomes: what is the AI seeing that the human eye is missing? Due to the nature of CNNs, identification of the signal features selected by the AI is currently not possible. We presume that ","PeriodicalId":12124,"journal":{"name":"Expert Review of Precision Medicine and Drug Development","volume":"5 1","pages":"119 - 121"},"PeriodicalIF":1.2,"publicationDate":"2020-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/23808993.2020.1735935","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45795186","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-03-03DOI: 10.1080/23808993.2020.1735936
S. Sayad, Seyyed Amir Yasin Ahmadi, M. Moradi, Reza Nekouian, K. Anbari, F. Shahsavar
ABSTRACT Background: According to the role of human leukocyte antigen (HLA)-G in tumor progression and tumor escape from immune system as well as diagnostic role of biomarkers in breast cancer, this meta-analysis is designed to reach a pooled diagnostic accuracy for this biomarker. Methods: The present work is a meta-analysis on diagnostic accuracy studies using preferred reporting items for systematic reviews and meta-analyses guideline. All documents studying the serum level of HLA-G both in breast cancer patients and in healthy controls using receiver operating characteristics (ROC) curve with reporting area under ROC curve (AUC) were eligible for inclusion. Results: Five articles including 754 participants were eligible for quantitative synthesis. The range of AUC of the selected studies was 0.735–0.953. The pooled AUC was 0.922 (95% confidence interval [CI] 0.903–0.941) based on fixed effect model (P < 0.001) and 0.896 (95% CI 0.834–0.959) based on random effect model (P < 0.001). Conclusion: This meta-analysis updated the level of evidence for using serum HLA-G in diagnosis of breast cancer. However, this piece of evidence cannot be used as a diagnostic tool. This biomarker can be used for investigation of recurrence and response to treatment in future. Further studies are suggested to complete this evidence gap.
{"title":"A meta-analysis on diagnostic accuracy of serum HLA-G level in breast cancer","authors":"S. Sayad, Seyyed Amir Yasin Ahmadi, M. Moradi, Reza Nekouian, K. Anbari, F. Shahsavar","doi":"10.1080/23808993.2020.1735936","DOIUrl":"https://doi.org/10.1080/23808993.2020.1735936","url":null,"abstract":"ABSTRACT Background: According to the role of human leukocyte antigen (HLA)-G in tumor progression and tumor escape from immune system as well as diagnostic role of biomarkers in breast cancer, this meta-analysis is designed to reach a pooled diagnostic accuracy for this biomarker. Methods: The present work is a meta-analysis on diagnostic accuracy studies using preferred reporting items for systematic reviews and meta-analyses guideline. All documents studying the serum level of HLA-G both in breast cancer patients and in healthy controls using receiver operating characteristics (ROC) curve with reporting area under ROC curve (AUC) were eligible for inclusion. Results: Five articles including 754 participants were eligible for quantitative synthesis. The range of AUC of the selected studies was 0.735–0.953. The pooled AUC was 0.922 (95% confidence interval [CI] 0.903–0.941) based on fixed effect model (P < 0.001) and 0.896 (95% CI 0.834–0.959) based on random effect model (P < 0.001). Conclusion: This meta-analysis updated the level of evidence for using serum HLA-G in diagnosis of breast cancer. However, this piece of evidence cannot be used as a diagnostic tool. This biomarker can be used for investigation of recurrence and response to treatment in future. Further studies are suggested to complete this evidence gap.","PeriodicalId":12124,"journal":{"name":"Expert Review of Precision Medicine and Drug Development","volume":"5 1","pages":"109 - 114"},"PeriodicalIF":1.2,"publicationDate":"2020-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/23808993.2020.1735936","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42553731","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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