Drug Induced Cardiotoxicity: Mechanism, Prevention and Management

Drug-induced cardiotoxicity is a major adverse effect that has been encountered for some clinically important drugs especially antineoplastic agents. This toxicity has previously led to the post-marketing withdrawal of numerous pharmacologically active drugs and limits the efficacy of other clinically useful ones. Currently, assessing the cardiotoxicity potential is a crucial parameter in drug development, and many models have been established to facilitate its prediction to avoid such toxicity. In this chapter, we will briefly discuss the mechanism of drug-induced cardiotoxicity, risk factors, how to prevent, early detection and/or management from a pharmacological and toxicological point of view

Mining multi-item drug adverse effect associations in spontaneous reporting systems

<p>Abstract</p> <p>Background</p> <p>Multi-item adverse drug event (ADE) associations are associations relating multiple drugs to possibly multiple adverse events. The current standard in pharmacovigilance is bivariate association analysis, where each single drug-adverse effect combination is studied separately. The importance and difficulty in the detection of multi-item ADE associations was noted in several prominent pharmacovigilance studies. In this paper we examine the application of a well established data mining method known as association rule mining, which we tailored to the above problem, and demonstrate its value. The method was applied to the FDAs spontaneous adverse event reporting system (AERS) with minimal restrictions and expectations on its output, an experiment that has not been previously done on the scale and generality proposed in this work.</p> <p>Results</p> <p>Based on a set of 162,744 reports of suspected ADEs reported to AERS and published in the year 2008, our method identified 1167 multi-item ADE associations. A taxonomy that characterizes the associations was developed based on a representative sample. A significant number (67% of the total) of potential multi-item ADE associations identified were characterized and clinically validated by a domain expert as previously recognized ADE associations. Several potentially novel ADEs were also identified. A smaller proportion (4%) of associations were characterized and validated as known drug-drug interactions.</p> <p>Conclusions</p> <p>Our findings demonstrate that multi-item ADEs are present and can be extracted from the FDA’s adverse effect reporting system using our methodology, suggesting that our method is a valid approach for the initial identification of multi-item ADEs. The study also revealed several limitations and challenges that can be attributed to both the method and quality of data.</p

Rough-set-based ADR signaling from spontaneous reporting data with missing values

AbstractSpontaneous reporting systems of adverse drug events have been widely established in many countries to collect as could as possible all adverse drug events to facilitate the detection of suspected ADR signals via some statistical or data mining methods. Unfortunately, due to privacy concern or other reasons, the reporters sometimes may omit consciously some attributes, causing many missing values existing in the reporting database. Most of research work on ADR detection or methods applied in practice simply adopted listwise deletion to eliminate all data with missing values. Very little work has noticed the possibility and examined the effect of including the missing data in the process of ADR detection.This paper represents our endeavor towards the exploration of this question. We aim at inspecting the feasibility of applying rough set theory to the ADR detection problem. Based on the concept of utilizing characteristic set based approximation to measure the strength of ADR signals, we propose twelve different rough set based measuring methods and show only six of them are feasible for the purpose. Experimental results conducted on the FARES database show that our rough-set-based approach exhibits similar capability in timeline warning of suspicious ADR signals as traditional method with missing deletion, and sometimes can yield noteworthy measures earlier than the traditional method

The differential effects of alcohol consumption and dependence on adverse alcohol-related consequences: Implications for the workforce.

Previous literature has supported the hypothesis that high rates of alcohol consumption are associated with adverse social consequences and that dependence on alcohol has an effect on that relationship. The purpose of this paper is to further specify the alcohol consumption-adverse consequences linkage by developing and estimating a latent variable model that incorporates the mediating effects of loss of control over alcohol consumption. This model is applied to measures for three alcohol-related constructs—consumption, loss of control and adverse consequences—incorporated in the 1991 National Household Survey on Drug Abuse, for members of the primary workforce in the US. The research suggests that workplace decision makers attempting to minimize the adverse workplace consequences of alcohol abuse should implement procedures that assess and respond to alcohol dependency rather than relying exclusively on detection of and intervention with alcohol consumption per se

Therapeutic Drug Monitoring and Methods of Quantitation for Carbamazepine

Carbamazepine is an early anticonvulsant still used today in the treatment of several forms of epilepsy. An active metabolite in the human body contributes to its pharmacological effect. Carbamazepine metabolism has high inter-individual variability, such that it is relatively difficult to establish a direct link between dose and concentration, or between concentration and pharmacological effect. Carbamazepine is thus a good candidate for therapeutic drug monitoring (TDM). Good UV specific absorbance and high plasmatic concentrations allow for the use of UV detection, which is often more accessible than other methods of detection. This paper presents several methods used for the detection of carbamazepine in plasma, methods that are capable of detecting drug and metabolites at adequate levels/ acceptance criteria. These methods have possible application not only in pharmacokinetic, bioequivalence, and permeability studies, but also in the therapeutic drug monitoring of carbamazepine

Geriatric pharmacotherapy : optimisation through integrated approach in the hospital setting

Since older patients are more vulnerable to adverse drug-related events, there is a need to ensure appropriate prescribing in these patients in order to prevent misuse, overuse and underuse of drugs. Different tools and strategies have been developed to reduce inappropriate prescribing; the available measures can be divided into medication assessment tools, and specific interventions to reduce inappropriate prescribing. Implicit criteria of inappropriate prescribing focus on appropriate dosing, search for drug-drug interactions, and increase adherence. Explicit criteria are consensus-based standards focusing on drugs and diseases and include lists of drugs to avoid in general or lists combining drugs with clinical data. These criteria take into consideration differences between patients, and stand for a medication review, by using a systematic approach. Different types of interventions exist in order to reduce inappropriate prescribing in older patients, such as: educational interventions, computerized decision support systems, pharmacist-based interventions, and geriatric assessment. The effects of these interventions have been studied, sometimes in a multifaceted approach combining different techniques, and all types seem to have positive effects on appropriateness of prescribing. Interdisciplinary teamwork within the integrative pharmaceutical care is important for improving of outcomes and safety of drug therapy. The pharmaceutical care process consists offour steps, which are cyclic for an individual patient. These steps are pharmaceutical anamnesis, medication review, design and follow-up of a pharmaceutical care plan. A standardized approach is necessary for the adequate detection and evaluation of drug-related problems. Furthermore, it is clear that drug therapy should be reviewed in-depth, by having full access to medical records, laboratory values and nursing notes. Although clinical pharmacists perform the pharmaceutical care process to manage the patient’s drug therapy in every day clinical practice, the physician takes the ultimate responsibility for the care of the patient in close collaboration with nurses

Drug-drug interactions: A machine learning approach

Automatic detection of drug-drug interaction (DDI) is a difficult problem in pharmaco-surveillance. Recent practice for in vitro and in vivo pharmacokinetic drug-drug interaction studies have been based on carefully selected drug characteristics such as their pharmacological effects, and on drug-target networks, in order to identify and comprehend anomalies in a drug\u27s biochemical function upon co-administration.;In this work, we present a novel DDI prediction framework that combines several drug-attribute similarity measures to construct a feature space from which we train three machine learning algorithms: Support Vector Machine (SVM), J48 Decision Tree and K-Nearest Neighbor (KNN) using a partially supervised classification algorithm called Positive Unlabeled Learning (PU-Learning) tailored specifically to suit our framework.;In summary, we extracted 1,300 U.S. Food and Drug Administration-approved pharmaceutical drugs and paired them to create 1,688,700 feature vectors. Out of 397 drug-pairs known to interact prior to our experiments, our system was able to correctly identify 80% of them and from the remaining 1,688,303 pairs for which no interaction had been determined, we were able to predict 181 potential DDIs with confidence levels greater than 97%. The latter is a set of DDIs unrecognized by our source of ground truth at the time of study.;Evaluation of the effectiveness of our system involved querying the U.S. Food and Drug Administration\u27s Adverse Effect Reporting System (AERS) database for cases involving drug-pairs used in this study. The results returned from the query listed incidents reported for a number of patients, some of whom had experienced severe adverse reactions leading to outcomes such as prolonged hospitalization, diminished medicinal effect of one or more drugs, and in some cases, death