Ranking adverse drug reactions with crowdsourcing.
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ABSTRACT:
SUBMITTER: Gottlieb A
PROVIDER: S-EPMC4387295 | biostudies-other | 2015 Mar
REPOSITORIES: biostudies-other
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Project description:Considerable progress has been made in identifying genetic risk factors for idiosyncratic adverse drug reactions in the past 30 years. These reactions can affect various tissues and organs, including liver, skin, muscle and heart, in a drug-dependent manner. Using both candidate gene and genome-wide association studies, various genes that make contributions of varying extents to each of these forms of reactions have been identified. Many of the associations identified for reactions affecting the liver and skin involve human leukocyte antigen (HLA) genes and for reactions relating to the drugs abacavir and carbamazepine, HLA genotyping is now in routine use prior to drug prescription. Other HLA associations are not sufficiently specific for translation but are still of interest in relation to underlying mechanisms for the reactions. Progress on non-HLA genes affecting adverse drug reactions has been less, but some important associations, such as those of SLCO1B1 and statin myopathy, KCNE1 and drug-induced QT prolongation and NAT2 and isoniazid-induced liver injury, are considered. Future prospects for identification of additional genetic risk factors for the various adverse drug reactions are discussed.
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Project description:The economic and health burden caused by adverse drug reactions has increased dramatically in the last few years. This is likely to be mediated by increasing polypharmacy, which increases the likelihood for drug-drug interactions. Tools utilized by healthcare practitioners to flag potential adverse drug reactions secondary to drug-drug interactions ignore individual genetic variation, which has the potential to markedly alter the severity of these interactions. To date there have been limited published studies on impact of genetic variation on drug-drug interactions. In this review, we establish a detailed classification for pharmacokinetic drug-drug-gene interactions, and give examples from the literature that support this approach. The increasing availability of real-world drug outcome data linked to genetic bioresources is likely to enable the discovery of previously unrecognized, clinically important drug-drug-gene interactions.
| S-EPMC7253354 | biostudies-literature
Project description:ADRs are immune mediated skin reactions of diverse severity and etiology. The patho-mechanisms are however not well understood. We used a gene expression array for the comparison of the gene expression profile of 2 cutaneous adverse drug reactions (MPR and AGEP) to normal skin.
2019-02-01 | GSE121421 | GEO
Project description:Epidemiologic investigation of cutaneous adverse drug reactions (cADRs) is important in order to evaluate their impact on dermatology and health care in general as well as their burden on affected patients. Few epidemiologic studies have been performed on frequent non-life-threatening cADR, including reactions of both delayed and immediate hypersensitivity, such as maculopapular exanthema (MPE), fixed drug eruption, and urticaria. Concerning rare but life-threatening severe cutaneous adverse reactions, e.g., toxic epidermal necrolysis (TEN), Stevens-Johnson syndrome (SJS), acute generalized exanthematous pustulosis (AGEP), and drug reaction with eosinophilia and systemic symptoms (DRESS), several epidemiologic studies have been performed to date, some of which are still ongoing. Such studies enable the calculation of reliable incidence rates and demographic data, and also allow researchers to perform risk estimation for drugs. The spectrum of drugs causing cADR differs substantially when separating the various clinical conditions. Whereas antibiotics are by far the most frequent inducers of milder cADRs, like MPE, they have a much lower risk of inducing SJS/TEN, for which "high-risk" drugs are anti-infective sulfonamides, allopurinol, certain anti-epileptic drugs, nevirapine, and non-steroidal anti-inflammatory drugs (NSAIDs) of the oxicam-type. In contrast, AGEP is predominantly caused by the antibiotics pristinamycin and aminopenicillins, followed by quinolones, (hydroxy-)chloroquine, and sulfonamides. DRESS can be induced by a number of drugs known to cause SJS/TEN, such as certain antiepileptics and allopurinol, but also other medications (e.g., minocyclin).
| S-EPMC6039997 | biostudies-other
Project description:BackgroundNumerous observational studies suggest that preventable adverse drug reactions are a significant burden in healthcare, but no meta-analysis using a standardised definition for adverse drug reactions exists. The aim of the study was to estimate the percentage of patients with preventable adverse drug reactions and the preventability of adverse drug reactions in adult outpatients and inpatients.MethodsStudies were identified through searching Cochrane, CINAHL, EMBASE, IPA, Medline, PsycINFO and Web of Science in September 2010, and by hand searching the reference lists of identified papers. Original peer-reviewed research articles in English that defined adverse drug reactions according to WHO's or similar definition and assessed preventability were included. Disease or treatment specific studies were excluded. Meta-analysis on the percentage of patients with preventable adverse drug reactions and the preventability of adverse drug reactions was conducted.ResultsData were analysed from 16 original studies on outpatients with 48797 emergency visits or hospital admissions and from 8 studies involving 24128 inpatients. No studies in primary care were identified. Among adult outpatients, 2.0% (95% confidence interval (CI): 1.2-3.2%) had preventable adverse drug reactions and 52% (95% CI: 42-62%) of adverse drug reactions were preventable. Among inpatients, 1.6% (95% CI: 0.1-51%) had preventable adverse drug reactions and 45% (95% CI: 33-58%) of adverse drug reactions were preventable.ConclusionsThis meta-analysis corroborates that preventable adverse drug reactions are a significant burden to healthcare among adult outpatients. Among both outpatients and inpatients, approximately half of adverse drug reactions are preventable, demonstrating that further evidence on prevention strategies is required. The percentage of patients with preventable adverse drug reactions among inpatients and in primary care is largely unknown and should be investigated in future research.
| S-EPMC3305295 | biostudies-literature
Project description:Adverse drug reactions (ADRs) remain a common and major problem in healthcare. Severe cutaneous adverse drug reactions (SCARs), such as Stevens-Johnson syndrome (SJS)/toxic epidermal necrolysis (TEN) with mortality rate ranges from 10% to more than 30%, can be life threatening. A number of recent studies demonstrated that ADRs possess strong genetic predisposition. ADRs induced by several drugs have been shown to have significant associations with specific alleles of human leukocyte antigen (HLA) genes. For example, hypersensitivity to abacavir, a drug used for treating of human immunodeficiency virus (HIV) infection, has been proposed to be associated with allele 57:01 of HLA-B gene (terms HLA-B?57:01). The incidences of abacavir hypersensitivity are much higher in Caucasians compared to other populations due to various allele frequencies in different ethnic populations. The antithyroid drug- (ATDs- ) induced agranulocytosis are strongly associated with two alleles: HLA-B?38:02 and HLA-DRB1?08:03. In addition, HLA-B?15:02 allele was reported to be related to carbamazepine-induced SJS/TEN, and HLA-B?57:01 in abacavir hypersensitivity and flucloxacillin induced drug-induced liver injury (DILI). In this review, we summarized the alleles of HLA genes which have been proposed to have association with ADRs caused by different drugs.
| S-EPMC5733150 | biostudies-literature
Project description:Adverse drug reactions are a common and important complication of drug therapy in children. Over the past decade it has become increasingly apparent that genetically controlled variations in drug disposition and response are important determinants of adverse events for many important adverse events associated with drug therapy in children. While this research has been difficult to conduct over the past decade technical and ethical evolution has greatly facilitated the ability of investigators to conduct pharmacogenomic studies in children. Some of this research has already resulted in changes in public policy and clinical practice, for example in the case of codeine use by mothers and children. It is likely that the use of pharmacogenomics to enhance drug safety will first be realized among selected groups of children with high rates of drug use such as children with cancer, but it also likely that this research will be extended to other groups of children who have high rates of drug utilization and as well as providing insights into the mechanisms and pathophysiology of adverse drug reactions in children.
| S-EPMC3997041 | biostudies-literature
Project description:Background and objectivesLittle is known about the burden of adverse drug reactions in CKD. We estimated the incidence of overall and serious adverse drug reactions and assessed the probability of causation, preventability, and factors associated with adverse drug reactions in patients seen by nephrologists.Design, setting, participants, & measurementsThe Chronic Kidney Disease-Renal Epidemiology and Information Network cohort included 3033 outpatients (65% men) with CKD and eGFR<60 ml/min per 1.73 m2, with follow-up for 2 years. Adverse drug reactions were identified from hospitalization reports, medical records, and participant interviews and finally assessed for causality, preventability, and immediate therapeutic management by experts in pharmacology.ResultsMedian (interquartile range) age was 69 (60-76) years old; 55% had eGFR≥30 ml/min per 1.73 m2, and 45% had eGFR<30 ml/min per 1.73 m2. Participants were prescribed a median (range) of eight (five to ten) drugs. Over 2 years, 536 patients had 751 adverse drug reactions, 150 (in 125 participants) classified as serious, for rates of 14.4 (95% confidence interval, 12.6 to 16.5) and 2.7 (95% confidence interval, 1.7 to 4.3) per 100 person-years, respectively. Among the serious adverse drug reactions, 32% were considered preventable or potentially preventable; 16 caused death, directly or indirectly. Renin-angiotensin system inhibitors (15%), antithrombotic agents (14%), and diuretics (10%) were the drugs to which the most adverse drug reactions were imputed, but antithrombotic agents caused 34% of serious adverse drug reactions. The drug was discontinued in 71% of cases, at least temporarily. Adjusted hazard ratios for serious adverse drug reaction were significantly higher in patients with eGFR<30 versus ≥30 ml/min per 1.73 m2 (1.8; 95% confidence interval, 1.3 to 2.6), in those prescribed more than ten versus less than five medications (2.4; 95% confidence interval, 1.1 to 5.2), or in those with poor versus good adherence (1.6; 95% confidence interval, 1.4 to 2.4).ConclusionsAdverse drug reactions are common and sometimes serious in patients with CKD. Many serious adverse drug reactions may be preventable. Some specific pharmacologic classes, particularly antithrombotic agents, are at risk of serious adverse drug reactions.Clinical trial registry name and registration numberChronic Kidney Disease-Renal Epidemiology and Information Network (CKD-REIN), NCT03381950.
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Project description:Off-target adverse drug reactions (ADRs) are associated with significant morbidity and costs to the healthcare system, and their occurrence is not predictable based on the known pharmacological action of the drug's therapeutic effect. Off-target ADRs may or may not be associated with immunological memory, although they can manifest with a variety of shared clinical features, including maculopapular exanthema, severe cutaneous adverse reactions (SCARs), angioedema, pruritus and bronchospasm. Discovery of specific genes associated with a particular ADR phenotype is a foundational component of clinical translation into screening programmes for their prevention. In this review, genetic associations of off-target drug-induced ADRs that have a clinical phenotype suggestive of an immunologically mediated process and their mechanisms are highlighted. A significant proportion of these reactions lack immunological memory and current data are informative for these ADRs with regard to disease pathophysiology, therapeutic targets and biomarkers which may identify patients at greatest risk. Although many serious delayed immune-mediated (IM)-ADRs show strong human leukocyte antigen associations, only a small subset have successfully been implemented in screening programmes. More recently, other factors, such as drug metabolism, have been shown to contribute to the risk of the IM-ADR. In the future, pharmacogenomic targets and an understanding of how they interact with drugs to cause ADRs will be applied to drug design and preclinical testing, and this will allow selection of optimal therapy to improve patient safety.
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Project description:Recent years have witnessed booming data on drugs and their associated adverse drug reactions (ADRs). It was reported that these ADRs have resulted in a high hospitalisation rate worldwide. Therefore, a tremendous amount of research has been carried out to predict ADRs in the early phases of drug development, with the goal of reducing possible future risks. The pre-clinical and clinical phases of drug research can be time-consuming and cost-ineffective, so academics are looking forward to more extensive data mining and machine learning methods to be applied in this field of study. In this paper, we try to construct a drug-to-drug network based on non-clinical data sources. The network presents underlying relationships between drug pairs according to their common ADRs. Then, multiple node-level and graph-level network features are extracted from this network, e.g., weighted degree centrality, weighted PageRanks, etc. After concatenating the network features to the original drug features, they were fed into seven machine learning models, e.g., logistic regression, random forest, support vector machine, etc., and were compared to the baseline, where there were no network-based features considered. These experiments indicate that all the tested machine-learning methods would benefit from adding these network features. Among all these models, logistic regression (LR) had the highest mean AUROC score (82.1%) across all ADRs tested. Weighted degree centrality and weighted PageRanks were identified to be the most critical network features in the LR classifier. These pieces of evidence strongly indicate that the network approach can be vital in future ADR prediction, and this network-based approach could also be applied to other health informatics datasets.
| S-EPMC9957267 | biostudies-literature