Challenges and opportunities for mining adverse drug reactions: perspectives from pharma, regulatory agencies, healthcare providers and consumers

Monitoring drug safety is a central concern throughout the drug life cycle. Information about toxicity and adverse events is generated at every stage of this life cycle, and stakeholders have a strong interest in applying text mining and artificial intelligence (AI) methods to manage the ever-increasing volume of this information. Recognizing the importance of these applications and the role of challenge evaluations to drive progress in text mining, the organizers of BioCreative VII (Critical Assessment of Information Extraction in Biology) convened a panel of experts to explore ‘Challenges in Mining Drug Adverse Reactions’. This article is an outgrowth of the panel; each panelist has highlighted specific text mining application(s), based on their research and their experiences in organizing text mining challenge evaluations. While these highlighted applications only sample the complexity of this problem space, they reveal both opportunities and challenges for text mining to aid in the complex process of drug discovery, testing, marketing and post-market surveillance. Stakeholders are eager to embrace natural language processing and AI tools to help in this process, provided that these tools can be demonstrated to add value to stakeholder workflows. This creates an opportunity for the BioCreative community to work in partnership with regulatory agencies, pharma and the text mining community to identify next steps for future challenge evaluations.M.K.: This work was supported in part through the collaboration between the Spanish Plan for the Advancement of Language Technology (Plan TL) and the Barcelona Supercomputing Center; we also acknowledge the 2020 Proyectos de I+D+i - RTI Tipo A (PID2020-119266RA-I00) for support. Ö.U.: This study was supported in part by the National Library of Medicine under Award Number R15LM013209 and R13LM013127.Peer ReviewedPostprint (published version

\u3ci\u3ePLIVA v. Mensing\u3c/i\u3e and Its Implications

The U.S. Supreme Court ruling in PLIVA Inc. v. Mensing will immunize generic drug manufacturers facing failure-to-warn claims from state-law liability, and may also have implications for preemption jurisprudence more generally, says attorney Brian Wolfman and co-author Dena Feldman in this BNA Insight. The authors analyze the ruling, and offer their views on the questions that PLIVA raises about the ongoing vitality of the presumption against preemption, the standard for determining ‘‘impossibility’’ preemption, and the propriety of deference to an agency’s views on preemption

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

Analyzing Adverse Events from Publicly Available Web Sources

Data mining for drug-reaction associations is a major topic in the pharmaceutical industry. Historically the focus has been on using privately owned and maintained datasets consisting of information that has been transformed via the FDA Adverse Event Reporting System (FAERS) and privatized reporting systems that house the data from clinical trials. Our focus will be on building a pipeline that demonstrates an open source solution for building a drug’s safety profile from data collection through signal detection. In contrast this pipeline primarily uses the openFDA and social media data available through Reddit with all analysis being done in the R statistical programming language. The aim was to collect the information available in these public sources and apply popular data mining methodologies used to identify and predict the occurrence of adverse events. The results show the ability of the openFDA and social media sites to create real-time drug safety occurrence profiles by applying the same statistical methods applied in clinical trials. Social media will be shown to provide the best results when applied to prescribed daily use medications compared to common over-the-counter drugs or last line of defense medications. The information and results reported in this paper are not intended or implied to be a substitute for professional medical advice, diagnosis, or treatment. Do not delay seeking medical treatment or advice because of something you have read in this paper

IMS Health v. Sorrell – Implications for Federal Regulation of Pharmaceutical Marketing?

ABSTRACT In an era of increased scrutiny of laws regulating corporate speech, state and federal regulators must balance regulation of the prescription drug market with the budgetary and public health needs. One area of contention is the use of prescriber data for pharmaceutical marketing. Claiming a need to protect physician privacy and the state budget, Vermont limited access to this data. Pharmaceutical companies and data processors filed suit, claiming a violation of their First Amendment rights. The Supreme Court recently heard the case. The outcome could have broad implications not only for states’ ability to protect privacy but also for FDA’s restrictions on pharmaceutical marketing. If the Supreme Court chooses to invalidate Vermont’s law, FDA’s regulation of off-label promotion could be ripe for a judicial challenge

Translating Clinical Findings into Knowledge in Drug Safety Evaluation - Drug Induced Liver Injury Prediction System (DILIps)

Drug-induced liver injury (DILI) is a significant concern in drug development due to the poor concordance between preclinical and clinical findings of liver toxicity. We hypothesized that the DILI types (hepatotoxic side effects) seen in the clinic can be translated into the development of predictive in silico models for use in the drug discovery phase. We identified 13 hepatotoxic side effects with high accuracy for classifying marketed drugs for their DILI potential. We then developed in silico predictive models for each of these 13 side effects, which were further combined to construct a DILI prediction system (DILIps). The DILIps yielded 60–70% prediction accuracy for three independent validation sets. To enhance the confidence for identification of drugs that cause severe DILI in humans, the “Rule of Three” was developed in DILIps by using a consensus strategy based on 13 models. This gave high positive predictive value (91%) when applied to an external dataset containing 206 drugs from three independent literature datasets. Using the DILIps, we screened all the drugs in DrugBank and investigated their DILI potential in terms of protein targets and therapeutic categories through network modeling. We demonstrated that two therapeutic categories, anti-infectives for systemic use and musculoskeletal system drugs, were enriched for DILI, which is consistent with current knowledge. We also identified protein targets and pathways that are related to drugs that cause DILI by using pathway analysis and co-occurrence text mining. While marketed drugs were the focus of this study, the DILIps has a potential as an evaluation tool to screen and prioritize new drug candidates or chemicals, such as environmental chemicals, to avoid those that might cause liver toxicity. We expect that the methodology can be also applied to other drug safety endpoints, such as renal or cardiovascular toxicity

Systematic review on the prevalence, frequency and comparative value of adverse events data in social media

Aim: The aim of this review was to summarize the prevalence, frequency and comparative value of information on the adverse events of healthcare interventions from user comments and videos in social media. Methods: A systematic review of assessments of the prevalence or type of information on adverse events in social media was undertaken. Sixteen databases and two internet search engines were searched in addition to handsearching, reference checking and contacting experts. The results were sifted independently by two researchers. Data extraction and quality assessment were carried out by one researcher and checked by a second. The quality assessment tool was devised in-house and a narrative synthesis of the results followed. Results: From 3064 records, 51 studies met the inclusion criteria. The studies assessed over 174 social media sites with discussion forums (71%) being the most popular. The overall prevalence of adverse events reports in social media varied from 0.2% to 8% of posts. Twenty-nine studies compared the results from searching social media with using other data sources to identify adverse events. There was general agreement that a higher frequency of adverse events was found in social media and that this was particularly true for ‘symptom’ related and ‘mild’ adverse events. Those adverse events that were under-represented in social media were laboratory-based and serious adverse events. Conclusions: Reports of adverse events are identifiable within social media. However, there is considerable heterogeneity in the frequency and type of events reported, and the reliability or validity of the data has not been thoroughly evaluated