Richard D. Boyce

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.

Consensus recommendations for systematic evaluation of drug-drug interaction evidence for clinical decision support.

BackgroundHealthcare organizations, compendia, and drug knowledgebase vendors use varying methods to evaluate and synthesize evidence on drug–drug interactions (DDIs). This situation has a negative effect on electronic prescribing and medication information systems that warn clinicians of potentially harmful medication combinations.ObjectiveThe aim of this study was to provide recommendations for systematic evaluation of evidence for DDIs from the scientific literature, drug product labeling, and regulatory documents.MethodsA conference series was conducted to develop a structured process to improve the quality of DDI alerting systems. Three expert workgroups were assembled to address the goals of the conference. The Evidence Workgroup consisted of 18 individuals with expertise in pharmacology, drug information, biomedical informatics, and clinical decision support. Workgroup members met via webinar 12 times from January 2013 to February 2014. Two in-person meetings were conducted in May and September 2013 to reach consensus on recommendations.ResultsWe developed expert consensus answers to the following three key questions. (i) What is the best approach to evaluate DDI evidence? (ii) What evidence is required for a DDI to be applicable to an entire class of drugs? (iii) How should a structured evaluation process be vetted and validated?ConclusionEvidence-based decision support for DDIs requires consistent application of transparent and systematic methods to evaluate the evidence. Drug compendia and clinical decision support systems in which these recommendations are implemented should be able to provide higher-quality information about DDIs.

Bridging Islands of Information to Establish an Integrated Knowledge Base of Drugs and Health Outcomes of Interest

The entire drug safety enterprise has a need to search, retrieve, evaluate, and synthesize scientific evidence more efficiently. This discovery and synthesis process would be greatly accelerated through access to a common framework that brings all relevant information sources together within a standardized structure. This presents an opportunity to establish an open-source community effort to develop a global knowledge base, one that brings together and standardizes all available information for all drugs and all health outcomes of interest (HOIs) from all electronic sources pertinent to drug safety. To make this vision a reality, we have established a workgroup within the Observational Health Data Sciences and Informatics (OHDSI, http://ohdsi.org) collaborative. The workgroup’s mission is to develop an open-source standardized knowledge base for the effects of medical products and an efficient procedure for maintaining and expanding it. The knowledge base will make it simpler for practitioners to access, retrieve, and synthesize evidence so that they can reach a rigorous and accurate assessment of causal relationships between a given drug and HOI. Development of the knowledge base will proceed with the measureable goal of supporting an efficient and thorough evidence-based assessment of the effects of 1,000 active ingredients across 100 HOIs. This non-trivial task will result in a high-quality and generally applicable drug safety knowledge base. It will also yield a reference standard of drug–HOI pairs that will enable more advanced methodological research that empirically evaluates the performance of drug safety analysis methods.

A review of the effectiveness of antidepressant medications for depressed nursing home residents

BACKGROUND Antidepressant medications are the most common psychopharmacologic therapy used to treat depressed nursing home (NH) residents. Despite a significant increase in the rate of antidepressant prescribing over the past several decades, little is known about the effectiveness of these agents in the NH population. OBJECTIVE To conduct a systematic review of the literature to examine and compare the effectiveness of antidepressant medications for treating major depressive symptoms in elderly NH residents. METHODS The following databases were searched with searches completed prior to January 2011 and no language restriction: MEDLINE, Embase, PsycINFO, CINHAL, CENTRAL, LILACS, ClinicalTrials.gov, International Standard Randomized Controlled Trial Number Register, and the WHO International Clinical Trial Registry Platform. Additional studies were identified from citations in evidence-based guidelines and reviews as well as book chapters on geriatric depression and pharmacotherapy from several clinical references. Studies were included if they described a clinical trial that assessed the effectiveness of any currently-marketed antidepressant for adults aged 65 years or older, who resided in the NH, and were diagnosed by DSM criteria and/or standardized validated screening instruments with Major Depressive Disorder, minor depression, dysthymic disorder, or Depression in Alzheimers disease. RESULTS A total of eleven studies, including four randomized and seven non-randomized open-label trials, met all inclusion and exclusion criteria. It was not feasible to conduct a meta-analysis because the studies were heterogeneous in terms of study design, operational definitions of depression, participant characteristics, pharmacologic interventions, and outcome measures. Of the four randomized trials, two had a control group and did not demonstrate a statistically-significant benefit for antidepressant pharmacotherapy over placebo. While six of the seven non-randomized studies identified a response to an antidepressant, their results must be interpreted with caution as they lacked a comparison group. CONCLUSIONS The limited amount of evidence from randomized and non-randomized open-label trials suggests that depressed NH residents have a modest response to antidepressant medications. Further research using rigorous study designs are needed to examine the effectiveness and safety of antidepressants in depressed NH residents, and to determine the various facility, provider, and patient factors associated with response to treatment.

Computing with evidence Part I: A drug-mechanism evidence taxonomy oriented toward confidence assignment.

We present a new evidence taxonomy that, when combined with a set of inclusion criteria, enable drug experts to specify what their confidence in a drug mechanism assertion would be if it were supported by a specific set of evidence. We discuss our experience applying the taxonomy to representing drug-mechanism evidence for 16 active pharmaceutical ingredients including six members of the HMG-CoA-reductase inhibitor family (statins). All evidence was collected and entered into the Drug-Interaction Knowledge Base (DIKB); a system that can provide customized views of a body of drug-mechanism knowledge to users who do not agree about the inferential value of particular evidence types. We provide specific examples of how the DIKBs evidence model can flag when a particular use of evidence should be re-evaluated because its related conjectures are no longer valid. We also present the algorithm that the DIKB uses to identify patterns of evidence support that are indicative of fallacious reasoning by the evidence-base curators.

Computing with evidence Part II: An evidential approach to predicting metabolic drug-drug interactions.

We describe a novel experiment that we conducted with the Drug Interaction Knowledge-base (DIKB) to determine which combinations of evidence enable a rule-based theory of metabolic drug-drug interactions to make the most optimal set of predictions. The focus of the experiment was a group of 16 drugs including six members of the HMG-CoA-reductase inhibitor family (statins). The experiment helped identify evidence-use strategies that enabled the DIKB to predict significantly more interactions present in a validation set than the most rigorous strategy developed by drug experts with no loss of accuracy. The best-performing strategies included evidence types that would normally be of lesser predictive value but that are often more accessible than more rigorous types. Our experimental methods represent a new approach to leveraging the available scientific evidence within a domain where important evidence is often missing or of questionable value for supporting important assertions.

Modeling Drug Mechanism Knowledge Using Evidence and Truth Maintenance

To protect the safety of patients, it is vital that researchers find methods for representing drug mechanism knowledge that support making clinically relevant drug-drug interaction (DDI) predictions. Our research aims to identify the challenges of representing and reasoning with drug mechanism knowledge and to evaluate potential informatics solutions to these challenges through the process of developing a knowledge-based system capable of predicting clinically relevant DDIs that occur via metabolic mechanisms. In previous work, we designed a simple, rule-based, model of metabolic inhibition and induction and applied it to a database containing assertions about 267 drugs. This pilot system taught us that drug mechanism knowledge is often dynamic, missing, or uncertain. In this paper, we propose methods to address these properties of mechanism knowledge and describe a new prototype system, the Drug Interaction Knowledge-base (DIKB), that implements our proposed methods so that we can explore their strengths and limitations. A novel feature of the DIKB is its use of a truth maintenance system to link changes in the evidence support for assertions about drug properties to the set of interactions and non-interactions the system predicts.

Age-Related Changes in Antidepressant Pharmacokinetics and Potential Drug-Drug Interactions: A Comparison of Evidence-Based Literature and Package Insert Information

BACKGROUND Antidepressants are among the most commonly prescribed psychotropic agents for older patients. Little is known about the best source of pharmacotherapy information to consult about key factors necessary to safely prescribe these medications to older patients. OBJECTIVE The objective of this study was to synthesize and contrast information in the package insert (PI) with information found in the scientific literature about age-related changes of antidepressants in systemic clearance and potential pharmacokinetic drug-drug interactions (DDIs). METHODS A comprehensive search of two databases (MEDLINE and EMBASE from January 1, 1975 to September 30, 2011) with the use of a combination of search terms (antidepressants, pharmacokinetics, and drug interactions) was conducted to identify relevant English language articles. This information was independently reviewed by two researchers and synthesized into tables. These same two researchers examined the most up-to-date PIs for the 26 agents available at the time of the study to abstract quantitative information about age-related decline in systemic clearance and potential DDIs. The agreement between the two information sources was tested with κ statistics. RESULTS The literature reported age-related clearance changes for 13 antidepressants, whereas the PIs only had evidence about 4 antidepressants (κ < 0.4). Similarly, the literature identified 45 medications that could potentially interact with a specific antidepressant, whereas the PIs only provided evidence about 12 potential medication-antidepressant DDIs (κ < 0.4). CONCLUSION The evidence-based literature compared with PIs is the most complete pharmacotherapy information source about both age-related clearance changes and pharmacokinetic DDIs with antidepressants. Future rigorously designed observational studies are needed to examine the combined risk of antidepressants with age-related decline in clearance and potential DDIs on important health outcomes such as falls and fractures in older patients.

Computing with evidence

We present a new evidence taxonomy that, when combined with a set of inclusion criteria, enable drug experts to specify what their confidence in a drug mechanism assertion would be if it were supported by a specific set of evidence. We discuss our experience applying the taxonomy to representing drug-mechanism evidence for 16 active pharmaceutical ingredients including six members of the HMG-CoA-reductase inhibitor family (statins). All evidence was collected and entered into the Drug-Interaction Knowledge Base (DIKB); a system that can provide customized views of a body of drug-mechanism knowledge to users who do not agree about the inferential value of particular evidence types. We provide specific examples of how the DIKBs evidence model can flag when a particular use of evidence should be re-evaluated because its related conjectures are no longer valid. We also present the algorithm that the DIKB uses to identify patterns of evidence support that are indicative of fallacious reasoning by the evidence-base curators.

Dynamic enhancement of drug product labels to support drug safety, efficacy, and effectiveness

Out-of-date or incomplete drug product labeling information may increase the risk of otherwise preventable adverse drug events. In recognition of these concerns, the United States Federal Drug Administration (FDA) requires drug product labels to include specific information. Unfortunately, several studies have found that drug product labeling fails to keep current with the scientific literature. We present a novel approach to addressing this issue. The primary goal of this novel approach is to better meet the information needs of persons who consult the drug product label for information on a drug’s efficacy, effectiveness, and safety. Using FDA product label regulations as a guide, the approach links drug claims present in drug information sources available on the Semantic Web with specific product label sections. Here we report on pilot work that establishes the baseline performance characteristics of a proof-of-concept system implementing the novel approach. Claims from three drug information sources were linked to the Clinical Studies, Drug Interactions, and Clinical Pharmacology sections of the labels for drug products that contain one of 29 psychotropic drugs. The resulting Linked Data set maps 409 efficacy/effectiveness study results, 784 drug-drug interactions, and 112 metabolic pathway assertions derived from three clinically-oriented drug information sources (ClinicalTrials.gov, the National Drug File – Reference Terminology, and the Drug Interaction Knowledge Base) to the sections of 1,102 product labels. Proof-of-concept web pages were created for all 1,102 drug product labels that demonstrate one possible approach to presenting information that dynamically enhances drug product labeling. We found that approximately one in five efficacy/effectiveness claims were relevant to the Clinical Studies section of a psychotropic drug product, with most relevant claims providing new information. We also identified several cases where all of the drug-drug interaction claims linked to the Drug Interactions section for a drug were potentially novel. The baseline performance characteristics of the proof-of-concept will enable further technical and user-centered research on robust methods for scaling the approach to the many thousands of product labels currently on the market.