Associating adverse drug effects with protein targets by integrating adverse event, in vitro bioactivity, and pharmacokinetic data

Adverse drug effects are unintended and undesirable effects of medicines, causing attrition of molecules in drug development and harm to patients. To anticipate potential adverse effects early, drug candidates are commonly screened for pharmacological activity against a panel of protein targets. However, there is a lack of large-scale, quantitative information on the links between routinely screened proteins and the reporting of adverse events (AEs). This work describes a systematic analysis of associations between AEs observed in humans and bioactivities of drugs while taking into account drug plasma concentrations. In the first chapter, post-marketing drug-AE associations are derived from the United States Food and Drug Administration Adverse Event Reporting System using disproportionality methods, while applying Propensity Score Matching to reduce confounding factors. The resulting drug-AE associations are compared to those from the Side Effect Resource, which are primarily derived from clinical trials. The analysis reveals that the datasets generally share less than 10% of reported AEs for the same drug and have different distributions of AEs across System Organ Classes (SOCs). Using the drugs from the two AE datasets described in the first chapter, the second chapter integrates corresponding bioactivities, i.e. measured potencies and affinities from the ChEMBL database and ligand-based target predictions obtained with the tool PIDGIN, with drug plasma concentrations compiled from literature, such as Cmax. Compared to a constant bioactivity cut-off of 1 uM, using the ratio of the unbound drug plasma concentration over the drug potency, i.e. Cmax/XC50, results in different binary activity calls for protein targets. Whether deriving activity calls in this way results in the selection of targets with greater relevance to human AEs is investigated in the third chapter, which computes relationships between targets and AEs using different measures of statistical association. Using the Cmax/XC50 ratio results in higher Likelihood Ratios and Positive Predictive Values (PPVs) for target-AE associations that were previously reported in the context of secondary pharmacology screening, at the cost of a lower recall, possibly due to the smaller size of the dataset with available plasma concentrations. Furthermore, a large-scale quantitative assessment of bioactivities as indicators of AEs reveals a trade-off between the PPV and how many AE-associated drugs can potentially be detected from in vitro screening, although using combinations of targets can improve the detection rate in ~40% of cases at limited cost to the PPV. The work highlights AEs most strongly related to bioactivities and their SOC distribution. Overall, this thesis contributes to knowledge of the relationships between in vitro bioactivities and empirical evidence of AEs in humans. The results can inform the selection of proteins for secondary pharmacology screening and the development of computational models to predict AEs.Lhasa Limite

Examining current practice for the analysis and reporting of harm outcomes in phase II and III pharmacology trials: exploring methods to facilitate improved detection of adverse drug reactions

Introduction Randomised controlled trials (RCTs) provide data to help establish the harm-profile of drugs but evidence suggests that this data is underutilised and analysis practices are suboptimal. Aims To develop and assess methods for the analysis and presentation of harm outcomes in phase II/III drug trials that can facilitate the detection of adverse drug reactions (ADRs) and enable communication of informative harm-profiles.Methods A systematic review looked at current practice for collection, analysis and reporting of harm outcomes and a scoping review to identify statistical methods proposed for their analysis was undertaken. A survey of clinical trial statisticians measured awareness of methods for the analysis of harm outcomes, barriers to their use and opinions on solutions to improve practice. Alternative strategies for analysis and presentation of harm outcomes were explored. Results The review of current practice confirmed that data on harm outcomes is not being fully utilised, providing evidence of inappropriate and inconsistent practices. The scoping review revealed a broad range of methods for the analysis of both prespecified and emerging harms. The survey confirmed sub-optimal practices and while there was a moderate level of awareness of alternative approaches, use was limited. Guidance and training on more appropriate methods was unanimously supported. Recommendation were devised via consensus to encourage trialists to use visualisations for analysing and reporting harm outcomes. Of the evaluated methods for the analysis of emerging harms none were appropriate in trials ≤5000 participants with some utility in specific scenarios, recommendations for use are provided. Conclusion Clinical trial statisticians agree that there is a need to improve how we analyse and report harm outcomes in RCTs. Efforts to date have focused on prespecified harm outcomes, with little thought given to emerging harms. Several solutions for immediate adoption are proposed but there remains the need for an easy to implement, objective, signal detection approach. Guidelines for best analysis practice that are endorsed by key stakeholders would also enable a more coherent and consistent path for change.Open Acces

Stochastic Ordering under Conditional Modelling of Extreme Values: Drug-Induced Liver Injury

Drug-induced liver injury (DILI) is a major public health issue and of serious concern for the pharmaceutical industry. Early detection of signs of a drug's potential for DILI is vital for pharmaceutical companies' evaluation of new drugs. A combination of extreme values of liver specific variables indicate potential DILI (Hy's Law). We estimate the probability of severe DILI using the Heffernan and Tawn (2004) conditional dependence model which arises naturally in applications where a multidimensional random variable is extreme in at least one component. We extend the current model by including the assumption of stochastically ordered survival curves for different doses in a Phase 3 study.Comment: 24 pages, 5 figure

Annotation analysis for testing drug safety signals using unstructured clinical notes

BackgroundThe electronic surveillance for adverse drug events is largely based upon the analysis of coded data from reporting systems. Yet, the vast majority of electronic health data lies embedded within the free text of clinical notes and is not gathered into centralized repositories. With the increasing access to large volumes of electronic medical data-in particular the clinical notes-it may be possible to computationally encode and to test drug safety signals in an active manner.ResultsWe describe the application of simple annotation tools on clinical text and the mining of the resulting annotations to compute the risk of getting a myocardial infarction for patients with rheumatoid arthritis that take Vioxx. Our analysis clearly reveals elevated risks for myocardial infarction in rheumatoid arthritis patients taking Vioxx (odds ratio 2.06) before 2005.ConclusionsOur results show that it is possible to apply annotation analysis methods for testing hypotheses about drug safety using electronic medical records

Pattern discovery in adverse event data

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Development of models for predicting Torsade de Pointes cardiac arrhythmias using perceptron neural networks

Blockage of some ion channels and in particular, the hERG cardiac potassium channel delays cardiac repolarization and can induce arrhythmia. In some cases it leads to a potentially life-threatening arrhythmia known as Torsade de Pointes (TdP). Therefore recognizing drugs with TdP risk is essential. Candidate drugs that are determined not to cause cardiac ion channel blockage are more likely to pass successfully through clinical phases II and III trials (and preclinical work) and not be withdrawn even later from the marketplace due to cardiotoxic effects. The objective of the present study is to develop an SAR model that can be used as an early screen for torsadogenic (causing TdP arrhythmias) potential in drug candidates. The method is performed using descriptors comprised of atomic NMR chemical shifts and corresponding interatomic distances which are combined into a 3D abstract space matrix. The method is called 3D-SDAR (3 dimensional spectral data-activity relationship) and can be interrogated to identify molecular features responsible for the activity, which can in turn yield simplified hERG toxicophores. A dataset of 55 hERG potassium channel inhibitors collected from Kramer et al. consisting of 32 drugs with TdP risk and 23 with no TdP risk was used for training the 3D-SDAR model.An ANN model with multilayer perceptron was used to define collinearities among the independent 3D-SDAR features. A composite model from 200 random iterations with 25% of the molecules in each case yielded the following figures of merit: training, 99.2 %; internal test sets, 66.7%; external (blind validation) test set, 68.4%. In the external test set, 70.3% of positive TdP drugs were correctly predicted. Moreover, toxicophores were generated from TdP drugs. A 3D-SDAR was successfully used to build a predictive model for drug-induced torsadogenic and non-torsadogenic drugs.Comment: Accepted for publication in BMC Bioinformatics (Springer) July 201

Machine learning liver-injuring drug interactions with non-steroidal anti-inflammatory drugs (NSAIDs) from a retrospective electronic health record (EHR) cohort

Drug-drug interactions account for up to 30% of adverse drug reactions. Increasing prevalence of electronic health records (EHRs) offers a unique opportunity to build machine learning algorithms to identify drug-drug interactions that drive adverse events. In this study, we investigated hospitalizations\u27 data to study drug interactions with non-steroidal anti-inflammatory drugs (NSAIDS) that result in drug-induced liver injury (DILI). We propose a logistic regression based machine learning algorithm that unearths several known interactions from an EHR dataset of about 400,000 hospitalization. Our proposed modeling framework is successful in detecting 87.5% of the positive controls, which are defined by drugs known to interact with diclofenac causing an increased risk of DILI, and correctly ranks aggregate risk of DILI for eight commonly prescribed NSAIDs. We found that our modeling framework is particularly successful in inferring associations of drug-drug interactions from relatively small EHR datasets. Furthermore, we have identified a novel and potentially hepatotoxic interaction that might occur during concomitant use of meloxicam and esomeprazole, which are commonly prescribed together to allay NSAID-induced gastrointestinal (GI) bleeding. Empirically, we validate our approach against prior methods for signal detection on EHR datasets, in which our proposed approach outperforms all the compared methods across most metrics, such as area under the receiver operating characteristic curve (AUROC) and area under the precision-recall curve (AUPRC)

Report on methods of safety signal generation in paediatrics from pharmacovigilance databases

This deliverable is based on the need to develop and test methods for safety signal detection in children. Signal detection is the mainstay of detecting safety issues, but so far very few groups have specifically looked at children. We developed reference sets for positive and negative drugevent combinations and vaccine-event combinations by a systematic literature review on all combinations. We retrieved the FDA AERS database, the CDC VAERS database and EUDRAVIGILANCE database. In order to analyse the datasets we had a stepwise approach from extraction of data, cleaning (e.g. mapping MedDRA and ATC codes) and transformation into a a common data model that we defined for the spontaneous reporting databases. A statistical analysis plan was created for the testing of methods and we provided some descriptive analyses of the FAERS data. Next steps will be to complete the analyses