Prediction of Drug-Drug Interactions by Using Profile Fingerprint Vectors and Protein Similarities.

OBJECTIVES: Drug-drug interaction (DDI) is a vital problem that threatens people's health. However, the prediction of DDIs through in-vivo experiments is not only extremely costly but also difficult as many serious side effects are hard to detect in in-vivo and in-vitro settings. The aim of this study was to assess the effectiveness of similarity-based in-silico computational DDI prediction approaches and to provide a cost effective and scalable solution to predict potential DDIs. METHODS: In this study, widely known similarity-based computational DDI prediction methods were utilized to discover novel potential DDIs. More specifically, known interactions, drug targets, adverse effects, and protein similarities of drug pairs were used to construct drug fingerprints for the prediction of DDIs. RESULTS: Using the drug interaction profile, our approach achieved an area under the curve (AUC) of 0.975 in the prediction of a potential DDI. The drug adverse effect profile and protein profile similarity-based methods resulted in AUC values of 0.685 and 0.895, respectively, in the prediction of DDIs. CONCLUSIONS: In this study, we developed a computational approach to the prediction of potential drug interactions. The performance of the similarity-based computational methods was comparatively evaluated using a comprehensive real-world DDI dataset. The evaluations showed that the drug interaction profile information is a better predictor of DDIs compared to drug adverse effects and protein similarities among DDI pairs.

A Minimal Information Model for Potential Drug-Drug Interactions.

Despite the significant health impacts of adverse events associated with drug-drug interactions, no standard models exist for managing and sharing evidence describing potential interactions between medications. Minimal information models have been used in other communities to establish community consensus around simple models capable of communicating useful information. This paper reports on a new minimal information model for describing potential drug-drug interactions. A task force of the Semantic Web in Health Care and Life Sciences Community Group of the World-Wide Web consortium engaged informaticians and drug-drug interaction experts in in-depth examination of recent literature and specific potential interactions. A consensus set of information items was identified, along with example descriptions of selected potential drug-drug interactions (PDDIs). User profiles and use cases were developed to demonstrate the applicability of the model. Ten core information items were identified: drugs involved, clinical consequences, seriousness, operational classification statement, recommended action, mechanism of interaction, contextual information/modifying factors, evidence about a suspected drug-drug interaction, frequency of exposure, and frequency of harm to exposed persons. Eight best practice recommendations suggest how PDDI knowledge artifact creators can best use the 10 information items when synthesizing drug interaction evidence into artifacts intended to aid clinicians. This model has been included in a proposed implementation guide developed by the HL7 Clinical Decision Support Workgroup and in PDDIs published in the CDS Connect repository. The complete description of the model can be found at https://w3id.org/hclscg/pddi.

Clinical significance of weight changes at diagnosis in solid tumours.

PURPOSE: Weight changes occur throughout the cancer trajectory. Most research has focused on changes during or after treatment, so clinical significance of change at diagnosis remains unclear. This study aimed to determine prevalence, predictors and prognostic significance of weight changes at diagnosis in outpatients with solid tumours presenting to a tertiary academic medical centre. METHODS: A retrospective study of the electronic medical record was conducted (n = 6477). Those with weight recorded within 6 months of cancer diagnosis (pre-diagnosis, T0) and 2 subsequent weights (diagnosis, T1; final visit, T2) were identified (n = 4258). Percentage weight change was categorised into four bands (0.1-2.4%; 2.5-5%; 5.01-9.9%; ≥ 10%) for gain and loss. A stable category was also included. RESULTS: Mean age is 61 ± 12.5 years. Common tumour sites: breast (17%; n = 725), prostate (16%; n = 664), lung (14%; n = 599). 15% (n = 652) had metastatic disease at T1. 98% (n = 4159) had weight change at T1. Head & neck and upper gastrointestinal cancers were significantly associated with weight loss (p < 0.001). Worst survival occurred with ≥ 10% weight gain or ≥ 10% weight loss. Overweight or obese body mass index with any percentage weight change band was associated with better overall survival. CONCLUSIONS: Most had evidence of clinically significant weight changes at diagnosis. Weight loss at diagnosis was associated with a higher risk of further weight loss. A detailed weight history at cancer diagnosis is essential to identify and intervene for those most at risk of weight change-related early mortality.

Serum C-reactive protein is an important and powerful prognostic biomarker in most adult solid tumors.

INTRODUCTION: Prognostication in cancer is challenging and inaccurate. C-Reactive Protein (CRP), a cheap and sensitive marker of inflammation may help. This study investigated the relationship between CRP and prognosis in a large cohort of solid tumors with mixed cancer diagnoses and stages. METHODS: Electronic medical records of 4931 adults with solid tumors who attended the Taussig Cancer Institute from 2006-2012 were reviewed. Demographic and clinical characteristics were recorded. Maximum CRP (mCRP) was identified for each individual. CRP was analysed as a time-dependent, continuous and categorical variable for association with survival. RESULTS: Two thirds of patients had a high mCRP. This was consistently associated with shorter survival, even after correction for time from diagnosis, and when analysed as a continuous or a categorical variable. When mCRP values above 10 mg/L were subcategorized, a higher mCRP was always worse. Even among those with normal values, statistically and clinically significant shorter survival was noted at mCRP levels >5 mg/L. CONCLUSIONS: In a large representative cohort of consecutive solid tumor patients the risk of death was clinically and statistically significantly greater with a high mCRP. This was independent of other variables and regardless of statistical method from both dates of diagnosis and test. CRP appeared to be underutilized. Our results support the routine use of CRP as a universal cost-effective independent prognostic indicator in most solid tumors.

Drug-drug interaction discovery and demystification using Semantic Web technologies.

OBJECTIVE: To develop a novel pharmacovigilance inferential framework to infer mechanistic explanations for asserted drug-drug interactions (DDIs) and deduce potential DDIs. MATERIALS AND METHODS: A mechanism-based DDI knowledge base was constructed by integrating knowledge from several existing sources at the pharmacokinetic, pharmacodynamic, pharmacogenetic, and multipathway interaction levels. A query-based framework was then created to utilize this integrated knowledge base in conjunction with 9 inference rules to infer mechanistic explanations for asserted DDIs and deduce potential DDIs. RESULTS: The drug-drug interactions discovery and demystification (D3) system achieved an overall 85% recall rate in terms of inferring mechanistic explanations for the DDIs integrated into its knowledge base, while demonstrating a 61% precision rate in terms of the inference or lack of inference of mechanistic explanations for a balanced, randomly selected collection of interacting and noninteracting drug pairs. DISCUSSION: The successful demonstration of the D3 system's ability to confirm interactions involving well-studied drugs enhances confidence in its ability to deduce interactions involving less-studied drugs. In its demonstration, the D3 system infers putative explanations for most of its integrated DDIs. Further enhancements to this work in the future might include ranking interaction mechanisms based on likelihood of applicability, determining the likelihood of deduced DDIs, and making the framework publicly available. CONCLUSION: The D3 system provides an early-warning framework for augmenting knowledge of known DDIs and deducing unknown DDIs. It shows promise in suggesting interaction pathways of research and evaluation interest and aiding clinicians in evaluating and adjusting courses of drug therapy.

Toward a complete dataset of drug-drug interaction information from publicly available sources.

Although potential drug-drug interactions (PDDIs) are a significant source of preventable drug-related harm, there is currently no single complete source of PDDI information. In the current study, all publically available sources of PDDI information that could be identified using a comprehensive and broad search were combined into a single dataset. The combined dataset merged fourteen different sources including 5 clinically-oriented information sources, 4 Natural Language Processing (NLP) Corpora, and 5 Bioinformatics/Pharmacovigilance information sources. As a comprehensive PDDI source, the merged dataset might benefit the pharmacovigilance text mining community by making it possible to compare the representativeness of NLP corpora for PDDI text extraction tasks, and specifying elements that can be useful for future PDDI extraction purposes. An analysis of the overlap between and across the data sources showed that there was little overlap. Even comprehensive PDDI lists such as DrugBank, KEGG, and the NDF-RT had less than 50% overlap with each other. Moreover, all of the comprehensive lists had incomplete coverage of two data sources that focus on PDDIs of interest in most clinical settings. Based on this information, we think that systems that provide access to the comprehensive lists, such as APIs into RxNorm, should be careful to inform users that the lists may be incomplete with respect to PDDIs that drug experts suggest clinicians be aware of. In spite of the low degree of overlap, several dozen cases were identified where PDDI information provided in drug product labeling might be augmented by the merged dataset. Moreover, the combined dataset was also shown to improve the performance of an existing PDDI NLP pipeline and a recently published PDDI pharmacovigilance protocol. Future work will focus on improvement of the methods for mapping between PDDI information sources, identifying methods to improve the use of the merged dataset in PDDI NLP algorithms, integrating high-quality PDDI information from the merged dataset into Wikidata, and making the combined dataset accessible as Semantic Web Linked Data.

Drug-drug interaction data source survey and linking.

As an initial step towards the goal of a common data model for potential drug-drug interactions, we surveyed the data elements provided by the publicly available sources. Our analysis found that there is very little overlap between or across publicly available resources and that the information provided is very heterogeneous.