'Generic to genetic' transition in cardiovascular and neuropsychiatric drugs: opportunity for personalized medicine.
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ABSTRACT:
SUBMITTER: Duconge J
PROVIDER: S-EPMC3505752 | biostudies-literature | 2012 Jul
REPOSITORIES: biostudies-literature
Publications
'Generic to genetic' transition in cardiovascular and neuropsychiatric drugs: opportunity for personalized medicine.
Duconge Jorge J Ruaño Gualberto G
Pharmacogenomics 20120701 10
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Project description:Dendrimers are highly branched macromolecules tailorable at will to fulfil precise requirements. They have generated a great many expectations and a huge number of publications and patents in relation to medicine, including in relation to personalized medicine, but have resulted in very poor clinical translation up to now. As clinical trials are the first steps in view of developing new compounds for (a personalized) medicine, this review focusses on the clinical trials carried out with dendrimers. Many of these clinical trials have been recently posted (2020-2022); thus, only very few concern phase 3. The safety and efficiency of essentially two main types of dendrimers, based on polylysine and polyamidoamide scaffolds, have been assessed up to now. These dendrimers were tested with the aim of treating mainly bacterial vaginosis, cancers, and COVID-19.
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Project description:Some of the most commonly prescribed medications are those for cardiovascular maladies. The beneficial effects of these medications have been well documented. However, there can be substantial variation in response to these medications among patients, which may be due to genetic variation. For this reason pharmacogenomic studies are emerging across all aspects of cardiovascular medicine. The goal of pharmacogenomics is to tailor treatment to an individual's genetic makeup in order to improve the benefit-to-risk ratio. This review examines the potential pharmacogenomic parameters which may lead to a future of personalized medicine. For example, it has been found that patients with CYP2C9 and VKORC1 gene variations have a different response to warfarin. Other studies looking at β-blockers, ACE inhibitors, ARBs, diuretics and statins have shown some results linking genetic variations to pharmacologic response. However these studies have not impacted clinical use yet, unlike warfarin findings, as the small retrospective studies need to be followed up by larger prospective studies for definitive results.
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Project description:This meta-analysis aimed to compare the efficacy and adverse events, either serious or mild/moderate, of all generic versus brand-name cardiovascular medicines. We searched randomized trials in MEDLINE, Scopus, EMBASE, Cochrane Controlled Clinical Trial Register, and ClinicalTrials.gov (last update December 1, 2014). Attempts were made to contact the investigators of all potentially eligible trials. Two investigators independently extracted and analyzed soft (including systolic blood pressure, LDL cholesterol, and others) and hard efficacy outcomes (including major cardiovascular adverse events and death), minor/moderate and serious adverse events. We included 74 randomized trials; 53 reported ≥1 efficacy outcome (overall sample 3051), 32 measured mild/moderate adverse events (n = 2407), and 51 evaluated serious adverse events (n = 2892). We included trials assessing ACE inhibitors (n = 12), anticoagulants (n = 5), antiplatelet agents (n = 17), beta-blockers (n = 11), calcium channel blockers (n = 7); diuretics (n = 13); statins (n = 6); and others (n = 3). For both soft and hard efficacy outcomes, 100 % of the trials showed non-significant differences between generic and brand-name drugs. The aggregate effect size was 0.01 (95 % CI -0.05; 0.08) for soft outcomes; -0.06 (-0.71; 0.59) for hard outcomes. All but two trials showed non-significant differences in mild/moderate adverse events, and aggregate effect size was 0.07 (-0.06; 0.20). Comparable results were observed for each drug class and in each stratified meta-analysis. Overall, 8 serious possibly drug-related adverse events were reported: 5/2074 subjects on generics; 3/2076 subjects on brand-name drugs (OR 1.69; 95 % CI 0.40-7.20). This meta-analysis strengthens the evidence for clinical equivalence between brand-name and generic cardiovascular drugs. Physicians could be reassured about prescribing generic cardiovascular drugs, and health care organization about endorsing their wider use.
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Project description:BackgroundGeneric medications cost less than brand-name medications and are similarly effective, but brand-name medications are still prescribed. We evaluated patterns in generic cardiovascular medication fills and estimated the potential cost savings with increased substitution of generic for brand-name medications.MethodsThis was a cross-sectional study of cardiovascular therapies using the Medicare Part D database of prescription medications in 2017. We evaluated drug fill patterns for therapies with available brand-name and generic options. We determined the generic substitution ratio and estimated the potential savings with increased generic substitution at the national, state, and clinician level. We compared states with laws related to mandatory pharmacist generic substitution and patient consent for substitution.ResultsOf ≈$22.9 billion spent on cardiovascular drugs in Medicare Part D prescription programs in 2017, ≈$11.0 billion was spent on medications with both brand-name and generic options. Although only 2.4% of medication fills were for the brand-name choice, they made up 21.2% of total spending. Accounting for estimated brand-name rebates, generic substitution for these medications would save $641 million, including $135 million in costs shouldered by patients. Furthermore, the minority of clinicians with the lowest generic utilization was responsible for a large proportion of the potential cost savings.ConclusionsThere are substantial potential cost savings from substituting brand-name medications with generic medications. These savings would be primarily driven by lower use of brand-name therapies by the minority of clinicians who prescribe them at increased rates.
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Project description:Human genetic diversity has long been studied both to understand how genetic variation influences risk of disease and infer aspects of human evolutionary history. In this article, we review historical and contemporary views of human genetic diversity, the rare and common mutations implicated in human disease susceptibility, and the relevance of genetic diversity to personalized medicine. First, we describe the development of thought about diversity through the 20th century and through more modern studies including genome-wide association studies (GWAS) and next-generation sequencing. We introduce several examples, such as sickle cell anemia and Tay-Sachs disease that are caused by rare mutations and are more frequent in certain geographical populations, and common treatment responses that are caused by common variants, such as hepatitis C infection. We conclude with comments about the continued relevance of human genetic diversity in medical genetics and personalized medicine more generally.
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Project description:Adult congenital heart disease (ACHD) is a growing population that requires life-long care due to advances in pediatric care and surgical or catheter procedures. Despite this, drug therapy in ACHD remains largely empiric due to the lack of clinical data, and formalized guidelines on drug therapy are currently lacking. The aging ACHD population has led to an increase in late cardiovascular complications such as heart failure, arrhythmias, and pulmonary hypertension. Pharmacotherapy, with few exceptions, in ACHD is largely supportive, whereas significant structural abnormalities usually require interventional, surgical, or percutaneous treatment. Recent advances in ACHD have prolonged survival for these patients, but further research is needed to determine the most effective treatment options for these patients. A better understanding of the use of cardiac drugs in ACHD patients could lead to improved treatment outcomes and a better quality of life for these patients. This review aims to provide an overview of the current status of cardiac drugs in ACHD cardiovascular medicine, including the rationale, limited current evidence, and knowledge gaps in this growing area.
| S-EPMC10219196 | biostudies-literature
Project description:The past decade has seen tremendous advances in our understanding of the genetic factors influencing response to a variety of drugs, including those targeted at treatment of cardiovascular diseases. In the case of clopidogrel, warfarin, and statins, the literature has become sufficiently strong that guidelines are now available describing the use of genetic information to guide treatment with these therapies, and some health centers are using this information in the care of their patients. There are many challenges in moving from research data to translation to practice; we discuss some of these barriers and the approaches some health systems are taking to overcome them. The body of literature that has led to the clinical implementation of CYP2C19 genotyping for clopidogrel, VKORC1, CYP2C9; and CYP4F2 for warfarin; and SLCO1B1 for statins is comprehensively described. We also provide clarity for other genes that have been extensively studied relative to these drugs, but for which the data are conflicting. Finally, we comment briefly on pharmacogenetics of other cardiovascular drugs and highlight β-blockers as the drug class with strong data that has not yet seen clinical implementation. It is anticipated that genetic information will increasingly be available on patients, and it is important to identify those examples where the evidence is sufficiently robust and predictive to use genetic information to guide clinical decisions. The review herein provides several examples of the accumulation of evidence and eventual clinical translation in cardiovascular pharmacogenetics.
| S-EPMC3698938 | biostudies-literature
Project description:PurposePersonalized medicine (PM) is the concept of managing patients based on their characteristics, including genotypes. In the field of cardiology, advantages of PM could be found in the diagnosis and treatment of several conditions such as arrhythmias and cardiomyopathies; moreover, it may be beneficial to prevent adverse drug reactions (ADR) and select the best medication. Genetic background can help us in selecting effective treatments, appropriate dose requirements, and preventive strategies in individuals with particular genotypes.MethodIn this review, we provide examples of personalized medicine based on human genetics for the most used pharmaceutics in cardiology, including warfarin, clopidogrel, and statins. We also review cardiovascular diseases, including coronary artery disease, arrhythmia, and cardiomyopathies.ConclusionGenetic factors are as important as environmental factors and they should be tested and evaluated more in the future by improving in genetic testing tools.Supplementary informationThe online version contains supplementary material available at 10.1007/s40200-021-00840-0.
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Project description:The use of intelligent techniques in medicine has brought a ray of hope in terms of treating leukaemia patients. Personalized treatment uses patient's genetic profile to select a mode of treatment. This process makes use of molecular technology and machine learning, to determine the most suitable approach to treating a leukaemia patient. Until now, no reviews have been published from a computational perspective concerning the development of personalized medicine intelligent techniques for leukaemia patients using molecular data analysis. This review studies the published empirical research on personalized medicine in leukaemia and synthesizes findings across studies related to intelligence techniques in leukaemia, with specific attention to particular categories of these studies to help identify opportunities for further research into personalized medicine support systems in chronic myeloid leukaemia. A systematic search was carried out to identify studies using intelligence techniques in leukaemia and to categorize these studies based on leukaemia type and also the task, data source, and purpose of the studies. Most studies used molecular data analysis for personalized medicine, but future advancement for leukaemia patients requires molecular models that use advanced machine-learning methods to automate decision-making in treatment management to deliver supportive medical information to the patient in clinical practice.
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