Concordance and predictive value of two adverse drug event data sets

Background: Accurate prediction of adverse drug events (ADEs) is an important means of controlling and reducing drug-related morbidity and mortality. Since no single “gold standard” ADE data set exists, a range of different drug safety data sets are currently used for developing ADE prediction models. There is a critical need to assess the degree of concordance between these various ADE data sets and to validate ADE prediction models against multiple reference standards. Methods: We systematically evaluated the concordance of two widely used ADE data sets – Lexi-comp from 2010 and SIDER from 2012. The strength of the association between ADE (drug) counts in Lexi-comp and SIDER was assessed using Spearman rank correlation, while the differences between the two data sets were characterized in terms of drug categories, ADE categories and ADE frequencies. We also performed a comparative validation of the Predictive Pharmacosafety Networks (PPN) model using both ADE data sets. The predictive power of PPN using each of the two validation sets was assessed using the area under Receiver Operating Characteristic curve (AUROC). Results: The correlations between the counts of ADEs and drugs in the two data sets were 0.84 (95% CI: 0.82-0.86) and 0.92 (95% CI: 0.91-0.93), respectively. Relative to an earlier snapshot of Lexi-comp from 2005, Lexi-comp 2010 and SIDER 2012 introduced a mean of 1,973 and 4,810 new drug-ADE associations per year, respectively. The difference between these two data sets was most pronounced for Nervous System and Anti-infective drugs, Gastrointestinal and Nervous System ADEs, and postmarketing ADEs. A minor difference of 1.1% was found in the AUROC of PPN when SIDER 2012 was used for validation instead of Lexi-comp 2010. Conclusions: In conclusion, the ADE and drug counts in Lexi-comp and SIDER data sets were highly correlated and the choice of validation set did not greatly affect the overall prediction performance of PPN. Our results also suggest that it is important to be aware of the differences that exist among ADE data sets, especially in modeling applications focused on specific drug and ADE categories

Evaluating predictive pharmacogenetic signatures of adverse events in colorectal cancer patients treated with fluoropyrimidines

The potential clinical utility of genetic markers associated with response to fluoropyrimidine treatment in colorectal cancer patients remains controversial despite extensive study. Our aim was to test the clinical validity of both novel and previously identified markers of adverse events in a broad clinical setting. We have conducted an observational pharmacogenetic study of early adverse events in a cohort study of 254 colorectal cancer patients treated with 5-fluorouracil or capecitabine. Sixteen variants of nine key folate (pharmacodynamic) and drug metabolising (pharmacokinetic) enzymes have been analysed as individual markers and/or signatures of markers. We found a significant association between TYMP S471L (rs11479) and early dose modifications and/or severe adverse events (adjusted OR = 2.02 [1.03; 4.00], p = 0.042, adjusted OR = 2.70 [1.23; 5.92], p = 0.01 respectively). There was also a significant association between these phenotypes and a signature of DPYD mutations (Adjusted OR = 3.96 [1.17; 13.33], p = 0.03, adjusted OR = 6.76 [1.99; 22.96], p = 0.002 respectively). We did not identify any significant associations between the individual candidate pharmacodynamic markers and toxicity. If a predictive test for early adverse events analysed the TYMP and DPYD variants as a signature, the sensitivity would be 45.5 %, with a positive predictive value of just 33.9 % and thus poor clinical validity. Most studies to date have been under-powered to consider multiple pharmacokinetic and pharmacodynamic variants simultaneously but this and similar individualised data sets could be pooled in meta-analyses to resolve uncertainties about the potential clinical utility of these markers

Methodical advances in reproducibility research : A proof of concept qualitative comparative analysis of reproducing animal data in humans

Background: While the term reproducibility crisis mainly reflects reproducibility of experiments between laboratories, reproducibility between species also remains problematic. We previously summarised the published reproducibility between animal and human studies; i.e. the translational success rates, which varied from 0% to 100%. Based on analyses of individual factors, we could not predict reproducibility. Several potential analyses can assess effect of combinations of predictors on an outcome. Regression analysis (RGA) is common, but not ideal to analyse multiple interactions and specific configurations (≈ combinations) of variables, which could be highly relevant to reproducibility. Qualitative comparative analysis (QCA) is based on set theory and Boolean algebra, and was successfully used in other fields. We reanalysed the data from our preceding review with QCA. Results: This QCA resulted in the following preliminary formula for successful translation: ∼Old*∼Intervention*∼Large*MultSpec*Quantitative Which means that within the analysed dataset, the combination of relative recency (∼ means not; >1999), analyses at event or study level (not at intervention level), n 85%). Other combinations of factors showed less consistent or negative results. An RGA on the same data did not identify any of the included variables as significant contributors. Conclusions: While these data were not collected with the QCA in mind, they illustrate that the approach is viable and relevant for this research field. The QCA seems a highly promising approach to furthering our knowledge on between-species reproducibility

Translating Clinical Findings into Knowledge in Drug Safety Evaluation - Drug Induced Liver Injury Prediction System (DILIps)

Drug-induced liver injury (DILI) is a significant concern in drug development due to the poor concordance between preclinical and clinical findings of liver toxicity. We hypothesized that the DILI types (hepatotoxic side effects) seen in the clinic can be translated into the development of predictive in silico models for use in the drug discovery phase. We identified 13 hepatotoxic side effects with high accuracy for classifying marketed drugs for their DILI potential. We then developed in silico predictive models for each of these 13 side effects, which were further combined to construct a DILI prediction system (DILIps). The DILIps yielded 60–70% prediction accuracy for three independent validation sets. To enhance the confidence for identification of drugs that cause severe DILI in humans, the “Rule of Three” was developed in DILIps by using a consensus strategy based on 13 models. This gave high positive predictive value (91%) when applied to an external dataset containing 206 drugs from three independent literature datasets. Using the DILIps, we screened all the drugs in DrugBank and investigated their DILI potential in terms of protein targets and therapeutic categories through network modeling. We demonstrated that two therapeutic categories, anti-infectives for systemic use and musculoskeletal system drugs, were enriched for DILI, which is consistent with current knowledge. We also identified protein targets and pathways that are related to drugs that cause DILI by using pathway analysis and co-occurrence text mining. While marketed drugs were the focus of this study, the DILIps has a potential as an evaluation tool to screen and prioritize new drug candidates or chemicals, such as environmental chemicals, to avoid those that might cause liver toxicity. We expect that the methodology can be also applied to other drug safety endpoints, such as renal or cardiovascular toxicity

Concurrent Adherence To Multiple Chronic Disease Medications: Examining The Behavior And Issues Concerning Its Measurement

Objectives the objectives were to 1) examine adherence to multiple medications prescribed for a chronic disease (intra-disease multiple medication adherence) and that of multiple chronic diseases (inter-disease multiple medication adherence); 2) determine appropriate measurement paradigm from different intra-disease multiple medication adherence measurement approaches; 3) identify optimal cut-point for a dichotomized composite measure. Methods a retrospective study design was used. The subjects came from the marketscanâ® commercial claims and encounters data 2002-2003 and filled both sulfonylurea (su) and thiazolidinedione (tzd). Adherence was measured by proportion of days covered (pdc) over each period of 30 or 90 days and cumulatively. Random components from multivariate multilevel models were analyzed to examine multiple medication adherence relationships, including associations of evolutions of adherence. Survival analysis was performed on any-cause or diabetes-related emergency services (er) utilization. Concordance statistics were computed to compare different measurement approaches. Results intra-disease multiple medication analysis demonstrated strong and significant (p\u3c0.05) relationships between overall adherence estimates for su and tzd and changes in adherence estimates over time. Patients who were receiving lipid or hypertension medications, or both in addition to su and tzd shostrong and significant (p\u3c0.05) relationships between overall adherence to cross-disease medications or cross-disease adherence slope estimates. However, such results were not observed in diabetic subjects who were prescribed nitrates for angina. Each of six composite measures of intra-disease multiple medication adherence significantly predicted hazard (hazard ratio \u3c1.0) of all-cause or any diabetes-related er utilization. Although each concordance statistic was significant (p\u3c0.05), there were no differences among concordance statistics produced by these measurement approaches. The average and all approach shosome superiority. The optimality of cut-point for categorizing adherence based on a composite measure of intra-disease multiple medication adherence ranged from 75-85%. Conclusion the study population demonstrated good but not optimal levels of adherence to multiple chronic disease medications. Factors that affect adherence to individual medications appear to be related and should be targeted for intervention. Efficacy of a composite measure of intra-disease multiple medications may depend on intervention goals. Further research needs to identify a composite measurement approach that demonstrates superiority in predictive and discriminatory power consistently

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