Precision Medicine and Adverse Drug Reactions Related to Cardiovascular Drugs.
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ABSTRACT:
SUBMITTER: Noyes JD
PROVIDER: S-EPMC8396016 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:ObjectiveTo review the evidence for ethnic differences in susceptibility to adverse drug reactions (ADRs) to cardiovascular drugs.DesignSystematic review and meta-analysis.Data sourcesWe searched Medline and Embase to March 2005. Reference lists of identified articles were hand searched for further relevant articles.Review methodsStudies were eligible for inclusion if they included at least two ethnic groups and one or more ADRs. We excluded case reports and case series.Results564 studies contained some description of ethnicity and an ADR, and 132 of them related to cardiovascular therapies. Twenty four studies provided data for ADRs for at least two ethnic groups and were therefore eligible for inclusion. In pooled analyses the relative risk of angio-oedema from angiotensin converting enzyme (ACE) inhibitors in black compared with non-black patients was 3.0 (95% confidence interval 2.5 to 3.7); the relative risk of cough from ACE inhibitors was 2.7 (1.6 to 4.5) in East Asian compared with white patients; and the relative risk of intracranial haemorrhage with thrombolytic therapy was 1.5 (1.2 to 1.9) in black compared with non-black patients.ConclusionPatients from different ethnic groups have different risks for important ADRs to cardiovascular drugs. Ethnic group may therefore be one determinant of harms of a given treatment in the individual patient, either because it acts as a surrogate measure of genetic make up or because cultural factors alter the risk. Data are sparse, and regulators should consider asking for better data before licensing.
| S-EPMC1463974 | biostudies-literature
Project description:Epilepsy is one of the most common neurologic disorders in dogs and life-long treatment with antiepileptic drugs (AED) is frequently required. Adverse events of AED targeting the skin are only rarely reported in veterinary medicine and the true incidence and spectrum of cutaneous reactions in epileptic dogs remains unknown. In this study, we hypothesized that cutaneous reactions commonly occur in epileptic dogs and are related to AED treatment. A retrospective case review of 185 dogs treated for epilepsy identified 20.0% with simultaneous appearance of dermatologic signs. In a subsequent prospective case investigation (n = 137), we identified newly appearing or distinct worsening of skin lesions following initiation of AED therapy in 10.9% of dogs treated for epilepsy (95% CI 6.8-17.7%). Cutaneous lesions were classified as probably drug-induced in 40.0% of these cases. Patch testing and intradermal testing were further investigated as potential diagnostic methods to confirm AED hypersensitivity. They were of high specificity but sensitivity and positive predictive value appeared inappropriate to recommend their routine use in clinical practice.
| S-EPMC4830846 | biostudies-literature
Project description:BACKGROUND:Predicting adverse drug reactions (ADRs) has become very important owing to the huge global health burden and failure of drugs. This indicates a need for prior prediction of probable ADRs in preclinical stages which can improve drug failures and reduce the time and cost of development thus providing efficient and safer therapeutic options for patients. Though several approaches have been put forward for in silico ADR prediction, there is still room for improvement. METHODS:In the present work, we have used machine learning based approach for cardiovascular (CV) ADRs prediction by integrating different features of drugs, biological (drug transporters, targets and enzymes), chemical (substructure fingerprints) and phenotypic (therapeutic indications and other identified ADRs), and their two and three level combinations. To recognize quality and important features, we used minimum redundancy maximum relevance approach while synthetic minority over-sampling technique balancing method was used to introduce a balance in the training sets. RESULTS:This is a rigorous and comprehensive study which involved the generation of a total of 504 computational models for 36 CV ADRs using two state-of-the-art machine-learning algorithms: random forest and sequential minimization optimization. All the models had an accuracy of around 90% and the biological and chemical features models were more informative as compared to the models generated using chemical features. CONCLUSIONS:The results obtained demonstrated that the predictive models generated in the present study were highly accurate, and the phenotypic information of the drugs played the most important role in drug ADRs prediction. Furthermore, the results also showed that using the proposed method, different drugs properties can be combined to build computational predictive models which can effectively predict potential ADRs during early stages of drug development.
| S-EPMC6530172 | biostudies-literature
Project description:In a move indicative of the enthusiastic support of precision medicine, the U.S. President Barack Obama announced the Precision Medicine Initiative in January 2015. The global precision medicine ecosystem is, thus, receiving generous support from the United States ($215 million), and numerous other governments have followed suit. In the context of precision medicine, drug treatment and prediction of its outcomes have been important for nearly six decades in the field of pharmacogenomics. The field offers an elegant solution for minimizing the effects and occurrence of adverse drug reactions (ADRs). The Clinical Pharmacogenetics Implementation Consortium (CPIC) plays an important role in this context, and it aims at specifically guiding the translation of clinically relevant and evidence-based pharmacogenomics research. In this forward-looking analysis, we make particular reference to several of the CPIC guidelines and their role in guiding the treatment of highly relevant diseases, namely cardiovascular disease, major depressive disorder, cancer, and human immunodeficiency virus, with a view to predicting and managing ADRs. In addition, we provide a list of the top 10 crosscutting opportunities and challenges facing the fields of precision medicine and pharmacogenomics, which have broad applicability independent of the drug class involved. Many of these opportunities and challenges pertain to infrastructure, study design, policy, and science culture in the early 21st century. Ultimately, rational pharmacogenomics study design and the acquisition of comprehensive phenotypic data that proportionately match the genomics data should be an imperative as we move forward toward global precision medicine.
| S-EPMC5072285 | biostudies-literature
Project description:BackgroundIn recent years, Traditional Chinese Medicine (TCM) and alternative medicine have been widely used along with western drugs as a complementary form of treatment. In this study, we first use the scientific literature to identify western drugs with obvious side effects. Then, we find TCM alternatives for these western drugs to ameliorate their side effects.ResultsWe used depression as a case study. To evaluate our method, we showed the relation between herb-ingredients-target-disease for representative alternative herbs of western drugs. Further, a protein-protein interaction network of western drugs and alternative herbs was produced, and we performed enrichment analysis of the targets of the active ingredients of the herbs and examined the enrichment of Gene Ontology terms for Biological Process, Cellular Component, and Molecular Function and KEGG Pathway levels, to show how these targets affect different levels of gene expression.ConclusionOur proposed method is able to select herbs that are highly relevant to the target indication (depression) and are able to treat the side effects caused by the target drug. The compounds from our selected alternative herbal medicines can therefore be complementary to the western drugs and ameliorate their side effects, which may help in the development of new drugs.
| S-EPMC7586667 | biostudies-literature
Project description:Project Description
Esophagitis is a frequent, but at the molecular level poorly characterized condition with diverse underlying etiologies and treatments. Correct diagnosis can be challenging due to partially overlapping histological features. By proteomic profiling of 55 biopsy specimens representing controls, Reflux- (GERD), Eosinophilic-(EoE), Crohns-(CD), and Herpes simplex (HSV)-esophagitis, as well as Candida albicans infection by LC-MS/MS, we identified distinct signatures and functional networks. Our integrated AI-assisted morphoproteomic approach allows deeper insights in disease-specific molecular alterations and represents a promising tool in esophagitis-related precision medicine.
The FFPE samples were further processed including macrodissection, protein extraction, protein precipitation, protein digestion, and peptide clean up according to a previously published and modified protocol (Buczak et al., 2020). For further details, please refer to the Materials and Methods section of the manuscript.
In depth proteomic characterization of the samples a label free high-resolution LC-MS/MS approach using an Orbitrap Tribrid Fusion mass spectrometer was chosen (operated in DIA mode). Tryptic peptides were loaded onto a µPAC Trapping Column with a pillar diameter of 5 µm, inter-pillar distance of 2.5 µm, pillar length/bed depth of 18 µm, external porosity of 9%, bed channel width of 2 mm and length of 10 mm; pillars are superficially porous with a porous shell thickness of 300 nm and pore sizes in the order of 100 to 200 Å at a flow rate of 10 µl per min in 0.1% trifluoroacetic acid in HPLC-grade water. Peptides were eluted and separated on the PharmaFluidics µPAC nano-LC column: 50 cm µPAC C18 with a pillar diameter of 5 µm, inter-pillar distance of 2.5 µm, pillar length/bed depth of 18 µm, external porosity of 59%, bed channel width of 315 µm and bed length of 50 cm; pillars are superficially porous with a porous shell thickness of 300 nm and pore sizes in the order of 100 to 200 Å by a linear gradient from 2% to 30 % of buffer B (80% acetonitrile and 0.08% formic acid in HPLC-grade water) in buffer A (2% acetonitrile and 0.1% formic acid in HPLC-grade water) at a flow rate of 300 nl per min. The remaining peptides were eluted by a short gradient of 10 minutes from 30% to 95% buffer B; followed by 25 minutes at 2% of buffer B, the total gradient run was 120 min.
Spectra were acquired in DIA mode using 50 variable-width windows over the mass range 350-1500 m/z. The Orbitrap was used for MS1 and MS2 detection, with an AGC target for MS1 set to 20x104 and a maximum injection time to 100 ms. MS2 scan range was set between 200 and 2000 m/z, with a minimum of 6 points across the peak. Orbitrap resolution for MS2 was set to 30K, isolation window set to 1.6, AGC target to 50x104 and maximum injection time to 54 ms. MS1 and MS2 data were acquired in centroid mode.
In order to check for retention time (RT) stability, iRT standards (Biognosys) were spiked in each sample according to the manufacturer recommendations, the 11 iRT peptide sequences were manually added to the database and used during DIA-NN search to generate the precursor ion library used for MS data analysis. To reduce the possibility of carry over and cross contamination between the samples, two BSA washes were used between samples, and a trap column wash followed by 2 BSA washes was used every 10 samples sequence.
The above-mentioned workflow is schematically displayed in Fig. 1B.
LC-MS/MS was performed at the Proteome Center Tuebingen (PCT). Here, 250 ng of peptides were loaded onto an Easy-nLC 1200 system coupled to a quadrupole Orbitrap Exploris 480 mass spectrometer (all Thermo Fisher Scientific, Waltham, MA, USA) as previously described (Krauss et al, 2023).
MS raw data files were analyzed using DIA-NN 1.8.1 (Demichev et al, 2020) in library-free mode against the human database (UniProt release March 2024, 20412 proteins). First, a precursor ion library was generated using FASTA digest for library-free search in combination with deep learning-based spectra prediction. An experimental library generated from the DIA-NN search was used for cross-run normalization and mass accuracy correction. Only high-accuracy spectra with a minimum precursor FDR of 0.01, and only tryptic peptides (2 missed Tryptic cleavages) were used for protein quantification. The match between runs option was activated and no shared spectra were used for protein identification. Similarly, Normal, HSV, and Candida samples were searched against reviewed entries of HSV1 (taxonomy id 10298, 125 entries), HSV2 (taxonomy id 10310, 95 entries), and C. albicans (taxonomy id 5476, 1412 entries) downloaded on 04.03.2024, in addition to the human database.
2024-06-10 | MSV000094966 | MassIVE
Project description:BackgroundTo assess the pharmacokinetic correlates of reported adverse drug reactions (ADRs) under antidepressant treatment with escitalopram (ESC) using a large therapeutic drug monitoring database.MethodsA large naturalistic sample of inpatients and outpatients prescribed ESC was analyzed. ADRs were classified using the Udvalg for Kliniske Undersogelser side effect rating scale. We compared ESC-treated patients with (n = 35) and without ADRs (n = 273) using ESC plasma concentrations as the primary outcome. We also compared ADR rates in the 2 groups based on 2 cut-off ESC levels reflecting the recommended upper thresholds of the therapeutic reference range of 80 ng/mL, suggested by the consensus therapeutic drug monitoring guidelines, and 40 ng/mL, based on recent meta-analysis data. The effects of age, sex, smoking, daily ESC dose, plasma concentrations, and concentrations corrected for daily dose were included in a binary logistic regression model to predict ADRs.ResultsNo differences in clinical, demographic, or pharmacokinetic parameters were observed between patients with and without ADRs (P > 0.05). Patients with ESC-related ADRs were more frequently diagnosed with psychotic disorders than those without (25% vs. 7.1%, P = 0.004). None of the variables was associated with ADR risk. Overall, ADR rates were not significantly different in patients above versus below thresholds of ESC concentrations (ESC concentrations >40 [n = 59] vs. ≤40 ng/mL [n = 249] and >80 [n = 8] vs. ≤80 ng/mL [n = 300]; P = 0.56 and P = 1.0, respectively).ConclusionsNo distinct pharmacokinetic patterns underlying ESC-associated ADRs were observed. Further studies with more specific assessments of ADRs in larger cohorts are required to better identify potential underlying patterns.
| S-EPMC10930353 | biostudies-literature
Project description:Adverse drug reactions (ADRs) are an important and frequent cause of morbidity and mortality. ADR can be related to a variety of drugs, including anticonvulsants, anaesthetics, antibiotics, antiretroviral, anticancer, and antiarrhythmics, and can involve every organ or apparatus. The causes of ADRs are still poorly understood due to their clinical heterogeneity and complexity. In this scenario, genetic predisposition toward ADRs is an emerging issue, not only in anticancer chemotherapy, but also in many other fields of medicine, including hemolytic anemia due to glucose-6-phosphate dehydrogenase (G6PD) deficiency, aplastic anemia, porphyria, malignant hyperthermia, epidermal tissue necrosis (Lyell's Syndrome and Stevens-Johnson Syndrome), epilepsy, thyroid diseases, diabetes, Long QT and Brugada Syndromes. The role of genetic mutations in the ADRs pathogenesis has been shown either for dose-dependent or for dose-independent reactions. In this review, we present an update of the genetic background of ADRs, with phenotypic manifestations involving blood, muscles, heart, thyroid, liver, and skin disorders. This review aims to illustrate the growing usefulness of genetics both to prevent ADRs and to optimize the safe therapeutic use of many common drugs. In this prospective, ADRs could become an untoward "stress test," leading to new diagnosis of genetic-determined diseases. Thus, the wider use of pharmacogenetic testing in the work-up of ADRs will lead to new clinical diagnosis of previously unsuspected diseases and to improved safety and efficacy of therapies. Improving the genotype-phenotype correlation through new lab techniques and implementation of artificial intelligence in the future may lead to personalized medicine, able to predict ADR and consequently to choose the appropriate compound and dosage for each patient.
| S-EPMC8120428 | biostudies-literature
Project description:ObjectivesTo prospectively determine the nature and rate of adverse drug reactions (ADRs) in children on antiepileptic drugs (AEDs) and to prospectively evaluate the effect of AEDs on behaviour.SettingA single centre prospective observational study.ParticipantsChildren (<18 years old) receiving one or more AEDs for epilepsy, at each clinically determined follow-up visit.Primary and secondary outcomesPrimary outcome was adverse reactions of AEDs. Behavioural and cognitive functions were secondary outcomes.Results180 children were recruited. Sodium valproate and carbamazepine were the most frequently used AEDs. A total of 114 ADRs were recorded in 56 of these children (31%). 135 children (75%) were on monotherapy. 27 of the 45 children (60%) on polytherapy had ADRs; while 29 (21%) of those on monotherapy had ADRs. The risk of ADRs was significantly lower in patients receiving monotherapy than polytherapy (RR: 0.61, 95% CI 0.47 to 0.79, p<0.0001). Behavioural problems and somnolence were the most common ADRs. 23 children had to discontinue their AED due to an ADR.ConclusionsBehavioural problems and somnolence were the most common ADRs. Polytherapy significantly increases the likelihood of ADRs in children.Trail registration numberEudraCT (2007-000565-37).
| S-EPMC4458612 | biostudies-literature
Project description:We performed a retrospective cohort study in the Dutch patient population to identify active substances with a relatively high number of adverse drug reactions (ADRs) potentially related to drug switching. For this, we analyzed drug switches and reported ADRs related to switching between June 1, 2009, and December 31, 2016, for a selection of 20 active substances. We also compared pharmacovigilance analyses based on the absolute, switch-corrected, and user-corrected numbers of ADRs. In total, 1,348 reported ADRs and over 23.8 million drug switches were obtained from the National Health Care Institute in The Netherlands and from Lareb, which is The Netherlands Pharmacovigilance Centre. There was no correlation between the number of ADRs and the number of switches, but, on average, we found 5.7 reported ADRs per 100,000 switches. The number was relatively high for rivastigmine, levothyroxine, methylphenidate, and salbutamol, with 74.9, 50.9, 47.6, and 26.1 ADRs per 100,000 switches, respectively. When comparing analyses using the absolute number and the switch-corrected number of ADRs, we demonstrate that different active substances would be identified as having a relatively high number of ADRs, and different time periods of increased numbers of ADRs would be observed. We also demonstrate similar results when using the user-corrected number of ADRs instead of the switch-corrected number of ADRs, allowing for a more feasible approach in pharmacovigilance practice. This study demonstrates that pharmacovigilance analyses of switch-related ADRs leads to different results when the number of reported ADRs is corrected for the actual number of drug switches.
| S-EPMC7214643 | biostudies-literature