J Biomed Inform

Targeted anticancer drugs such as imatinib, trastuzumab and erlotinib dramatically improved treatment outcomes in cancer patients, however, these innovative agents are often associated with unexpected side effects. The pathophysiological mechanisms underlying these side effects are not well understood. The availability of a comprehensive knowledge base of side effects associated with targeted anticancer drugs has the potential to illuminate complex pathways underlying toxicities induced by these innovative drugs. While side effect association knowledge for targeted drugs exists in multiple heterogeneous data sources, published full-text oncological articles represent an important source of pivotal, investigational, and even failed trials in a variety of patient populations. In this study, we present an automatic process to extract targeted anticancer drug-associated side effects (drug-SE pairs) from a large number of high profile full-text oncological articles. We downloaded 13,855 full-text articles from the Journal of Oncology (JCO) published between 1983 and 2013. We developed text classification, relationship extraction, signaling filtering, and signal prioritization algorithms to extract drug-SE pairs from downloaded articles. We extracted a total of 26,264 drug-SE pairs with an average precision of 0.405, a recall of 0.899, and an F1 score of 0.465. We show that side effect knowledge from JCO articles is largely complementary to that from the US Food and Drug Administration (FDA) drug labels. Through integrative correlation analysis, we show that targeted drug-associated side effects positively correlate with their gene targets and disease indications. In conclusion, this unique database that we built from a large number of high-profile oncological articles could facilitate the development of computational models to understand toxic effects associated with targeted anticancer drugs.DP2 HD084068/HD/NICHD NIH HHS/United StatesDP2HD084068/DP/NCCDPHP CDC HHS/United StatesR25 CA094186/CA/NCI NIH HHS/United StatesR25CA094186-06/CA/NCI NIH HHS/United StatesUL1 RR024989/RR/NCRR NIH HHS/United StatesUL1 TR000439/TR/NCATS NIH HHS/United States2016-06-01T00:00:00Z25817969PMC458266

Adverse reactions to oncologic drugs: spontaneous reporting and signal detection

Oncology is one of the areas of medicine with the most active research being conducted on new drugs. New pharmacological entities frequently enter the clinical arena, and therefore, the safety profile of anticancer products deserves continuous monitoring. However, only very severe and (unusual) suspected adverse drug reactions (ADRs) are usually reported, since cancer patients develop ADRs very frequently and some practical selectivity must be used. Notably, a recent study was able to identify 76 serious ADRs reported in updated drug labels of oncologic drugs and 50% of them (n = 38) were potentially fatal. Of these, 49 and 58%, respectively, were not described in initial drug labels. The aims of this article are to provide an overview about spontaneous reporting of ADRs of oncologic drugs and to discuss the available methods to analyze the safety of anticancer drugs using databases of spontaneous ADR reporting

J Biomed Inform

Schizophrenia (SCZ) is a common complex disorder with poorly understood mechanisms and no effective drug treatments. Despite the high prevalence and vast unmet medical need represented by the disease, many drug companies have moved away from the development of drugs for SCZ. Therefore, alternative strategies are needed for the discovery of truly innovative drug treatments for SCZ. Here, we present a disease phenome-driven computational drug repositioning approach for SCZ. We developed a novel drug repositioning system, PhenoPredict, by inferring drug treatments for SCZ from diseases that are phenotypically related to SCZ. The key to PhenoPredict is the availability of a comprehensive drug treatment knowledge base that we recently constructed. PhenoPredict retrieved all 18 FDA-approved SCZ drugs and ranked them highly (recall=1.0, and average ranking of 8.49%). When compared to PREDICT, one of the most comprehensive drug repositioning systems currently available, in novel predictions, PhenoPredict represented clear improvements over PREDICT in Precision-Recall (PR) curves, with a significant 98.8% improvement in the area under curve (AUC) of the PR curves. In addition, we discovered many drug candidates with mechanisms of action fundamentally different from traditional antipsychotics, some of which had published literature evidence indicating their treatment benefits in SCZ patients. In summary, although the fundamental pathophysiological mechanisms of SCZ remain unknown, integrated systems approaches to studying phenotypic connections among diseases may facilitate the discovery of innovative SCZ drugs.20152016-08-01T00:00:00ZDP2 HD084068/HD/NICHD NIH HHS/United StatesDP2HD084068/DP/NCCDPHP CDC HHS/United StatesR25 CA094186/CA/NCI NIH HHS/United StatesR25 CA094186-06/CA/NCI NIH HHS/United StatesUL1 RR024989/RR/NCRR NIH HHS/United StatesUL1 TR000439/TR/NCATS NIH HHS/United States26151312PMC4589865875

The Role of Signal Detection in Pharmacovigilance

The World Health Organisation defines pharmacovigilance as the science and activities relating to the detection, assessment, understanding and prevention of adverse effects or any other possible drug-related problems. Signal detection is a core pharmacovigilance activity. The motivation behind exploring signal detection and evaluation process is the timely detection of safety issues, and ultimately a better protection of public health. This thesis aimed to explore the processes of signal detection and evaluation and to inform regulatory decision making by providing evidence-based solutions to some of the existing questions. We investigated methods of detection and alternative data sources. Furthermore, we were interested to find out which characteristics of drugs make them more prone to have safety issues discovered post-marketing and tried to find predictive characteristics and we tried to gain some insight in the prioritisation process. The current work is relevant for all involved stakeholders, as European Medicines Agency (EMA), national regulatory authorities, pharmaceutical industry and, healthcare professionals and researchers