Pharmacointeraction Network Models Predict Unknown Drug-Drug Interactions

Drug-drug interactions (DDIs) can lead to serious and potentially lethal adverse events. In recent years, several drugs have been withdrawn from the market due to interaction-related adverse events (AEs). Current methods for detecting DDIs rely on the accumulation of sufficient clinical evidence in the post-market stage – a lengthy process that often takes years, during which time numerous patients may suffer from the adverse effects of the DDI. Detection methods are further hindered by the extremely large combinatoric space of possible drug-drug-AE combinations. There is therefore a practical need for predictive tools that can identify potential DDIs years in advance, enabling drug safety professionals to better prioritize their limited investigative resources and take appropriate regulatory action. To meet this need, we describe Predictive Pharmacointeraction Networks (PPINs) – a novel approach that predicts unknown DDIs by exploiting the network structure of all known DDIs, together with other intrinsic and taxonomic properties of drugs and AEs. We constructed an 856-drug DDI network from a 2009 snapshot of a widely-used drug safety database, and used it to develop PPIN models for predicting future DDIs. We compared the DDIs predicted based solely on these 2009 data, with newly reported DDIs that appeared in a 2012 snapshot of the same database. Using a standard multivariate approach to combine predictors, the PPIN model achieved an AUROC (area under the receiver operating characteristic curve) of 0.81 with a sensitivity of 48% given a specificity of 90%. An analysis of DDIs by severity level revealed that the model was most effective for predicting “contraindicated” DDIs (AUROC = 0.92) and less effective for “minor” DDIs (AUROC = 0.63). These results indicate that network based methods can be useful for predicting unknown drug-drug interactions

Development of a Scalable Pharmacogenomic Clinical Decision Support Service

Advances in sequencing technology are making genomic data more accessible within the healthcare environment. Published pharmacogenetic guidelines attempt to provide a clinical context for specific genomic variants; however, the actual implementation to convert genomic data into a clinical report integrated within an electronic medical record system is a major challenge for any hospital. We created a two-part solution that integrates with the medical record system and converts genetic variant results into an interpreted clinical report based on published guidelines. We successfully developed a scalable infrastructure to support TPMT genetic testing and are currently testing approximately two individuals per week in our production version. We plan to release an online variant to clinical interpretation reporting system in order to facilitate translation of pharmacogenetic information into clinical practice

Workspace Disorder Does Not Influence Creativity and Executive Functions

Recent research by Vohs et al. (2013) garnered media attention after reporting that disordered environments increase creativity. The present research was designed to conceptually replicate and extend this finding by exploring the effect of workspace disorder on creativity. Participants were randomly assigned to work at a neatly organized (Order condition) or a messy desk (Disorder condition), where they completed several paper-and-pencil and computerized tasks, including two validated creativity measures (Abbreviated Torrance Test for Adults; ATTA; Goff and Torrance, 2002; Alternative Uses Task; adapted from Guilford, 1967). We also included several executive control measures from the NIH EXAMINER (Kramer, 2011), to explore the role of reduced top-down control in explaining a possible creativity-disorder connection. Independent-samples t-tests failed to replicate any significant difference in creativity between the Order and Disorder conditions. Furthermore, the conditions did not differentially affect executive control. Despite implementing an experimental setup similar to the one in Vohs et al. (2013), including a larger sample size, and adopting multiple measures of the constructs of interest, we did not find any effect of workspace clutter on cognitive performance. At this stage, the relationship between disorder and cognition seems elusive and does not warrant the claims it generated in the popular press

Action to protect the independence and integrity of global health research

Storeng KT, Abimbola S, Balabanova D, et al. Action to protect the independence and integrity of global health research. BMJ GLOBAL HEALTH. 2019;4(3): e001746

Patients providing the answers: narrowing the gap in data quality for emergency care

Objective The authors examined the validity of documentation produced during paediatric emergency care to determine if a patient-driven health information technology called ParentLink produced higher-quality data than documentation completed by nurses and physicians. Design The authors analysed the quality of information across elements of allergies to medications and the history of present illness (HPI) collected during a quasi-experimental intervention study where control periods with usual care alternated with intervention periods when ParentLink was operational. Documentation by emergency department (ED) providers was abstracted and compared with information generated through ParentLink. The criterion standard for the history of allergies to medications was a structured telephone interview with parents after the ED visit. A valid report for a medication allergy was one that was both accurate and complete. Completeness of the HPI for acute head trauma was evaluated across seven elements relevant to an evidence-based risk assessment. Results Of 1410 enrolled parents, 1111/1410 (79%) completed the criterion standard interview. Parents' valid reports of allergies to medications were higher than those of nurses (parents 94%, nurses 88%, p<0.0001). Parents' valid reports of allergies to medications were greater than those of physicians (parent 94%, physicians 83%, p<0.0001). ParentLink produced more complete information on HPI for head trauma than the medical record for five of seven elements. Conclusion ParentLink provided electronic information that met or exceeded the quality of data documented by ED nurses and physicians.Agency for Healthcare Research and Quality (grant R01 HS014947

Accuracy of DDI type prediction.

<p>Analysis is based on DDI types occurring in the set of true positives predicted by the GLM model when its specificity was fixed at 0.95. (A) The true positive pairs are shown ordered according to the number of unique interaction IDs found in the pair’s neighborhood (blue bars); the rank of each pair’s true ID according to the 2012 snapshot (purple crosses) is also shown; (B) Cumulative distribution of the rank of true ID.</p

A visualization of the DDI network.

<p>(A) The DDIs present in the 2009 training dataset are shown in blue. (B) The DDIs newly reported in the 2012 validation dataset are shown in red. In both parts, the size of each node is proportional to the node degree in the 2009 network. Some of the most highly-connected drugs are labeled for illustrative purposes.</p

Predictive performance plots.

<p>(A) ROC curves for similarity-based covariates; (B) ROC curves for non-similarity covariates; (C) ROC curves for multivariate models; (D) ROC curves for three severity-based DDI classes: minor, major and contraindicated.</p