Using Nonexperts for Annotating Pharmacokinetic Drug-Drug Interaction Mentions in Product Labeling: A Feasibility Study

Reconciliation of patient/doctor vocabulary in a structured resource

Today, social media is increasingly used by patients to openly discuss their health. Mining automatically such data is a challenging task because of the non-structured nature of the text and the use of many abbreviations and the slang terms. Our goal is to use Patient Authored Text to build a French Consumer Health Vocabulary on breast cancer field, by collecting various kinds of non-experts' expressions that are related to their diseases and then compare them to biomedical terms used by health care professionals. We combine several methods of the literature based on linguistic and statistical approaches to extract candidate terms used by non-experts and to link them to expert terms. We use messages extracted from the forum on ' cancerdusein.org ' and a vocabulary dedicated to breast cancer elaborated by the Institut National Du Cancer. We have built an efficient vocabulary composed of 192 validated relationships and formalized in Simple Knowledge Organization System ontology.

An Innovative, Collaborative, and Strategic Approach to Proactively Evaluate and Update Drug Interactions Based on Prescribing Information of Newly Approved Medicinal Products

BACKGROUND

Drug-drug interaction (DDIs) are evaluated using pharmacokinetic (PK) simulation models, clinical studies, and scientific publications throughout drug development. DDIs with Norvir (ritonavir) and combination products (eg, Kaletra [lopinavir/ritonavir]) containing ritonavir as a PK enhancer are relevant, because these drugs could affect exposures of CYP3A4 substrates. Application of algorithms proactively identified recently approved drugs, which potentially cause adverse outcomes when given with drugs containing ritonavir.

METHODS

An evidence-based medicine technology platform was used to identify newly approved products. PK-related information from the products' prescribing information was reviewed to identify DDIs with ritonavir. Algorithms were used to further evaluate PK, clinical, and postmarketing information pertinent to the interaction to determine if prescribing information required revision.

RESULTS

From January 1, 2014, through December 31, 2015, 39 newly approved drugs were identified as having potential interactions with Norvir and/or Kaletra. Ten drugs were excluded, 19 drugs went through initial screening, and 10 drugs underwent in-depth algorithm-based analyses for DDIs. No changes to prescribing information for Norvir or Kaletra were recommended from evaluation of the DDIs. Regulatory concurrence with AbbVie decisions was 93.1% (27/29, 93.1%); in 6.9% (2/29, 6.9%) of the evaluated interactions, at least 1 local regulatory authority disagreed with recommendations, requiring label changes to incorporate the DDI information.

CONCLUSIONS

This proactive algorithmic approach identifies and complements existing methods used to detect DDIs with newly approved products. Additionally, this approach facilitates timely communication of risks to patients and healthcare providers via label revisions, publications, or other regulatory communications.

Information needs for making clinical recommendations about potential drug-drug interactions: a synthesis of literature review and interviews

BACKGROUND

Drug information compendia and drug-drug interaction information databases are critical resources for clinicians and pharmacists working to avoid adverse events due to exposure to potential drug-drug interactions (PDDIs). Our goal is to develop information models, annotated data, and search tools that will facilitate the interpretation of PDDI information. To better understand the information needs and work practices of specialists who search and synthesize PDDI evidence for drug information resources, we conducted an inquiry that combined a thematic analysis of published literature with unstructured interviews.

METHODS

Starting from an initial set of relevant articles, we developed search terms and conducted a literature search. Two reviewers conducted a thematic analysis of included articles. Unstructured interviews with drug information experts were conducted and similarly coded. Information needs, work processes, and indicators of potential strengths and weaknesses of information systems were identified.

RESULTS

Review of 92 papers and 10 interviews identified 56 categories of information needs related to the interpretation of PDDI information including drug and interaction information; study design; evidence including clinical details, quality and content of reports, and consequences; and potential recommendations. We also identified strengths/weaknesses of PDDI information systems.

CONCLUSIONS

We identified the kinds of information that might be most effective for summarizing PDDIs. The drug information experts we interviewed had differing goals, suggesting a need for detailed information models and flexible presentations. Several information needs not discussed in previous work were identified, including temporal overlaps in drug administration, biological plausibility of interactions, and assessment of the quality and content of reports. Richly structured depictions of PDDI information may help drug information experts more effectively interpret data and develop recommendations. Effective information models and system designs will be needed to maximize the utility of this information.