Recommendations for the Use of Social Media in Pharmacovigilance: Lessons from IMI WEB-RADR

Leveraging patient experience data to guide medicines development, regulation, access decisions and clinical care in the EU

Patient experience data (PED), provided by patients/their carers without interpretation by clinicians, directly capture what matters more to patients on their medical condition, treatment and impact of healthcare. PED can be collected through different methodologies and these need to be robust and validated for its intended use. Medicine regulators are increasingly encouraging stakeholders to generate, collect and submit PED to support both scientific advice in development programs and regulatory decisions on the approval and use of these medicines. This article reviews the existing definitions and types of PED and demonstrate the potential for use in different settings of medicines' life cycle, focusing on Patient-Reported Outcomes (PRO) and Patient Preferences (PP). Furthermore, it addresses some challenges and opportunities, alluding to important regulatory guidance that has been published, methodological aspects and digitalization, highlighting the lack of guidance as a key hurdle to achieve more systematic inclusion of PED in regulatory submissions. In addition, the article discusses opportunities at European and global level that could be implemented to leverage PED use. New digital tools that allow patients to collect PED in real time could also contribute to these advances, but it is equally important not to overlook the challenges they entail. The numerous and relevant initiatives being developed by various stakeholders in this field, including regulators, show their confidence in PED's value and create an ideal moment to address challenges and consolidate PED use across medicines' life cycle.

Insights from the Twittersphere: a cross-sectional study of public perceptions, usage patterns, and geographical differences of tweets discussing cocaine

Introduction

Cocaine abuse represents a major public health concern. The social perception of cocaine has been changing over the decades, a phenomenon closely tied to its patterns of use and abuse. Twitter is a valuable tool to understand the status of drug use and abuse globally. However, no specific studies discussing cocaine have been conducted on this platform.

Methods

111,508 English and Spanish tweets containing "cocaine" from 2018 to 2022 were analyzed. 550 were manually studied, and the largest subset underwent automated classification. Then, tweets related to cocaine were analyzed to examine their content, types of Twitter users, usage patterns, health effects, and personal experiences. Geolocation data was also considered to understand regional differences.

Results

A total of 71,844 classifiable tweets were obtained. Among these, 15.95% of users discussed the harm of cocaine consumption to health. Media outlets had the highest number of tweets (35.11%) and the most frequent theme was social/political denunciation (67.88%). Regarding the experience related to consumption, there are more tweets with a negative sentiment. The 9.03% of tweets explicitly mention frequent use of the drug. The continent with the highest number of tweets was America (55.44% of the total).

Discussion

The findings underscore the significance of cocaine as a current social and political issue, with a predominant focus on political and social denunciation in the majority of tweets. Notably, the study reveals a concentration of tweets from the United States and South American countries, reflecting the high prevalence of cocaine-related disorders and overdose cases in these regions. Alarmingly, the study highlights the trivialization of cocaine consumption on Twitter, accompanied by a misleading promotion of its health benefits, emphasizing the urgent need for targeted interventions and antidrug content on social media platforms. Finally, the unexpected advocacy for cocaine by healthcare professionals raises concerns about potential drug abuse within this demographic, warranting further investigation.

Patient listening on social media for patient-focused drug development: a synthesis of considerations from patients, industry and regulators

Patients, life science industry and regulatory authorities are united in their goal to reduce the disease burden of patients by closing remaining unmet needs. Patients have, however, not always been systematically and consistently involved in the drug development process. Recognizing this gap, regulatory bodies worldwide have initiated patient-focused drug development (PFDD) initiatives to foster a more systematic involvement of patients in the drug development process and to ensure that outcomes measured in clinical trials are truly relevant to patients and represent significant improvements to their quality of life. As a source of real-world evidence (RWE), social media has been consistently shown to capture the first-hand, spontaneous and unfiltered disease and treatment experience of patients and is acknowledged as a valid method for generating patient experience data by the Food and Drug Administration (FDA). While social media listening (SML) methods are increasingly applied to many diseases and use cases, a significant piece of uncertainty remains on how evidence derived from social media can be used in the drug development process and how it can impact regulatory decision making, including legal and ethical aspects. In this policy paper, we review the perspectives of three key stakeholder groups on the role of SML in drug development, namely patients, life science companies and regulators. We also carry out a systematic review of current practices and use cases for SML and, in particular, highlight benefits and drawbacks for the use of SML as a way to identify unmet needs of patients. While we find that the stakeholders are strongly aligned regarding the potential of social media for PFDD, we identify key areas in which regulatory guidance is needed to reduce uncertainty regarding the impact of SML as a source of patient experience data that has impact on regulatory decision making.

Improving drug safety with adverse event detection using natural language processing

INTRODUCTION

Pharmacovigilance (PV) involves monitoring and aggregating adverse event information from a variety of data sources, including health records, biomedical literature, spontaneous adverse event reports, product labels, and patient-generated content like social media posts, but the most pertinent details in these sources are typically available in narrative free-text formats. Natural language processing (NLP) techniques can be used to extract clinically relevant information from PV texts to inform decision-making.

AREAS COVERED

We conducted a non-systematic literature review by querying the PubMed database to examine the uses of NLP in drug safety and distilled the findings to present our expert opinion on the topic.

EXPERT OPINION

New NLP techniques and approaches continue to be applied for drug safety use cases; however, systems that are fully deployed and in use in a clinical environment remain vanishingly rare. To see high-performing NLP techniques implemented in the real setting will require long-term engagement with end users and other stakeholders and revised workflows in fully formulated business plans for the targeted use cases. Additionally, we found little to no evidence of extracted information placed into standardized data models, which should be a way to make implementations more portable and adaptable.

Barriers to opioid use disorder treatment: A comparison of self-reported information from social media with barriers found in literature

Introduction

Medications such as buprenorphine and methadone are effective for treating opioid use disorder (OUD), but many patients face barriers related to treatment and access. We analyzed two sources of data-social media and published literature-to categorize and quantify such barriers.

Methods

In this mixed methods study, we analyzed social media (Reddit) posts from three OUD-related forums (subreddits): r/suboxone, r/Methadone, and r/naltrexone. We applied natural language processing to identify posts relevant to treatment barriers, categorized them into insurance- and non-insurance-related, and manually subcategorized them into fine-grained topics. For comparison, we used substance use-, OUD- and barrier-related keywords to identify relevant articles from PubMed published between 2006 and 2022. We searched publications for language expressing fear of barriers, and hesitation or disinterest in medication treatment because of barriers, paying particular attention to the affected population groups described.

Results

On social media, the top three insurance-related barriers included having no insurance (22.5%), insurance not covering OUD treatment (24.7%), and general difficulties of using insurance for OUD treatment (38.2%); while the top two non-insurance-related barriers included stigma (47.6%), and financial difficulties (26.2%). For published literature, stigma was the most prominently reported barrier, occurring in 78.9% of the publications reviewed, followed by financial and/or logistical issues to receiving medication treatment (73.7%), gender-specific barriers (36.8%), and fear (31.5%).

Conclusion

The stigma associated with OUD and/or seeking treatment and insurance/cost are the two most common types of barriers reported in the two sources combined. Harm reduction efforts addressing barriers to recovery may benefit from leveraging multiple data sources.

Drug_SNSMiner: standard pharmacovigilance pipeline for detection of adverse drug reaction using SNS data

As society continues to age, it is becoming increasingly important to monitor drug use in the elderly. Social media data have been used for monitoring adverse drug reactions. The aim of this study was to determine whether social network studies (SNS) are useful sources of drug side effects information. We propose a method for utilizing SNS data to plot the known side effects of geriatric drugs in a dosing map. We developed a lexicon of drug terms associated with side effects and mapped patterns from social media data. We confirmed that well-known side effects may be obtained by utilizing SNS data. Based on these results, we propose a pharmacovigilance pipeline that can be extended to unknown side effects. We propose the standard analysis pipeline Drug_SNSMiner for monitoring side effects using SNS data and evaluated it as a drug prescription platform for the elderly. We confirmed that side effects may be monitored from the consumer's perspective based on SNS data using only drug information. SNS data were deemed good sources of information to determine ADRs and obtain other complementary data. We established that these learning data are invaluable for AI requiring the acquisition of ADR posts on efficacious drugs.

Can Twitter posts serve as early indicators for potential safety signals? A retrospective analysis

BACKGROUND

As Twitter has gained significant popularity, tweets can serve as large pool of readily available data to estimate the adverse events (AEs) of medications.

OBJECTIVE

This study evaluated whether tweets were an early indicator for potential safety warnings. Additionally, the trend of AEs posted on Twitter was compared with AEs from the Yellow Card system in the United Kingdom.

METHODS

English Tweets for 35 drug-event pairs for the period 2017-2019, two years prior to the date of EMA Pharmacovigilance Risk Assessment Committee (PRAC) meeting, were collected. Both signal and non-signal AEs were manually identified and encoded using the MedDRA dictionary. AEs from Yellow Card were also gathered for the same period. Descriptive and inferential statistical analysis was conducted using Fisher's exact test to assess the distribution and proportion of AEs from the two data sources.

RESULTS

Of the total 61,661 English tweets, 1,411 had negative or neutral sentiment and mention of at least one AE. Tweets for 15 out of the 35 drugs (42.9%) contained AEs associated with the signals. On pooling data from Twitter and Yellow Card, 24 out of 35 drug-event pairs (68.6%) were identified prior to the respective PRAC meetings. Both data sources showed similar distribution of AEs based on seriousness, however, the distribution based on labelling was divergent.

CONCLUSION

Twitter cannot be used in isolation for signal detection in current pharmacovigilance (PV) systems. However, it can be used in combination with traditional PV systems for early signal detection, as it can provide a holistic drug safety profile.

The cross-over of statistical thinking and practices: A pandemic catalyst

Written during the SARS‐CoV‐2 pandemic, and in recognition of Andy Grieve, the polymath, this article looks at an eclectic mix of topics where statistical thinking and practices should transcend typical dividing lines—with a particular focus on the areas of drug development, public health and social science. The case is made for embedding an experimental (or quasi‐experimental) framework within clinical practice for vaccines and treatments following their marketing authorisation. A similar case is made for public health interventions—facilitated by pre‐specification of effect size and by the greater use of data standards. A number of recommendations are made whilst noting that progress is being made in some areas.

Automated gathering of real-world data from online patient forums can complement pharmacovigilance for rare cancers

Current methods of pharmacovigilance result in severe under-reporting of adverse drug events (ADEs). Patient forums have the potential to complement current pharmacovigilance practices by providing real-time uncensored and unsolicited information. We are the first to explore the value of patient forums for rare cancers. To this end, we conduct a case study on a patient forum for Gastrointestinal Stromal Tumor patients. We have developed machine learning algorithms to automatically extract and aggregate side effects from messages on open online discussion forums. We show that patient forum data can provide suggestions for which ADEs impact quality of life the most: For many side effects the relative reporting rate differs decidedly from that of the registration trials, including for example cognitive impairment and alopecia as side effects of avapritinib. We also show that our methods can provide real-world data for long-term ADEs, such as osteoporosis and tremors for imatinib, and novel ADEs not found in registration trials, such as dry eyes and muscle cramping for imatinib. We thus posit that automated pharmacovigilance from patient forums can provide real-world data for ADEs and should be employed as input for medical hypotheses for rare cancers.

Engaging Patients via Online Healthcare Fora: Three Pharmacovigilance Use Cases

Increasingly, patient-generated safety insights are shared online, via general social media platforms or dedicated healthcare fora which give patients the opportunity to discuss their disease and treatment options. We evaluated three areas of potential interest for the use of social media in pharmacovigilance. To evaluate how social media may complement existing safety signal detection capabilities, we identified two use cases (drug/adverse event [AE] pairs) and then evaluated the frequency of AE discussions across a range of social media channels. Changes in frequency over time were noted in social media, then compared to frequency changes in Food and Drug Administration Adverse Event Reporting System (FAERS) data over the same time period using a traditional disproportionality method. Although both data sources showed increasing frequencies of AE discussions over time, the increase in frequency was greater in the FAERS data as compared to social media. To demonstrate the robustness of medical/AE insights of linked posts we manually reviewed 2,817 threads containing 21,313 individual posts from 3,601 unique authors. Posts from the same authors were linked together. We used a quality scoring algorithm to determine the groups of linked posts with the highest quality and manually evaluated the top 16 groups of posts. Most linked posts (12/16; 75%) contained all seven relevant medical insights assessed compared to only one (of 1,672) individual post. To test the capability of actively engage patients via social media to obtain follow-up AE information we identified and sent consents for follow-up to 39 individuals (through a third party). We sent target follow-up questions (identified by pharmacovigilance experts as critical for causality assessment) to those who consented. The number of people consenting to follow-up was low (20%), but receipt of follow-up was high (75%). We observed completeness of responses (37 out of 37 questions answered) and short average time required to receive the follow-up (1.8 days). Our findings indicate a limited use of social media data for safety signal detection. However, our research highlights two areas of potential value to pharmacovigilance: obtaining more complete medical/AE insights via longitudinal post linking and actively obtaining rapid follow-up information on AEs.

Smartphone-based mobile applications for adverse drug reactions reporting: global status and country experience

BACKGROUND

Smartphone technology can support paperless reporting of adverse drug reactions (ADRs). The aims of this study were to systematically assess smartphone ADR-reporting applications, understand their qualitative and quantitative impact on ADR reporting, and garner key lessons from owners and developers.

METHODS

This study had three components: (1) An assessment of ADR-reporting apps, (2) an online survey on the impact of app implementation on ADR reporting and the experiences of app developers and owners, and (3) a search of VigiBase, the World Health Organization global database of individual case safety reports (ICSRs), to observe trends in the number of ADR reports targeting countries where the apps were implemented.

RESULTS

Twenty-two apps were included. Eight out of the 22 apps were for countries in the WHO African region. Features observed included E2B data elements (E stands for efficacy) and functions supporting reporting and user engagement. Seventeen app developers and owners answered to the survey and reported overall positive experiences with app features, and post-launch increases in the total number of ICSRs. User type and user environment were cited as factors influencing app use: Respondents said younger people and/or those with an inclination to use technology were more likely to use apps compared to older or more technology-averse people, while respondents in countries with limited internet connectivity reported persistent difficulties in app use.

CONCLUSIONS

Smartphone apps for reporting ADRs offer added value compared to conventional reporting tools. Reporting tools should be selected based on interface features and factors that may influence app usage.

Applications of quantitative social media listening to patient-centric drug development

Social media listening has been increasingly acknowledged as a tool with applications in many stages of the drug development process. These applications were created to meet the need for patient-centric therapies that are fit-for-purpose and meaningful to patients. Such applications, however, require the leverage of new quantitative approaches and analytical methods that draw from developments in artificial intelligence and real-world data (RWD) analysis. Here, we review the state-of-the-art in quantitative social media listening (QSML) methods applied to drug discovery from the perspective of the pharmaceutical industry.

Social Media Analytics for Pharmacovigilance of Antiepileptic Drugs

Epilepsy is a common neurological disorder worldwide and antiepileptic drug (AED) therapy is the cornerstone of its treatment. It has a laudable aim of achieving seizure freedom with minimal, if any, adverse drug reactions (ADRs). Too often, AED treatment is a long-lasting journey, in which ADRs have a crucial role in its administration. Therefore, from a pharmacovigilance perspective, detecting the ADRs of AEDs is a task of utmost importance. Typically, this task is accomplished by analyzing relevant data from spontaneous reporting systems. Despite their wide adoption for pharmacovigilance activities, the passiveness and high underreporting ratio associated with spontaneous reporting systems have encouraged the consideration of other data sources such as electronic health databases and pharmaceutical databases. Social media is the most recent alternative data source with many promising potentials to overcome the shortcomings of traditional data sources. Although in the literature some attempts have investigated the validity and utility of social media for ADR detection of different groups of drugs, none of them was dedicated to the ADRs of AEDs. Hence, this paper presents a novel investigation of the validity and utility of social media as an alternative data source for the detection of AED ADRs. To this end, a dataset of consumer reviews from two online health communities has been collected. The dataset is preprocessed; the unigram, bigram, and trigram are generated; and the ADRs of each AED are extracted with the aid of consumer health vocabulary and ADR lexicon. Three widely used measures, namely, proportional reporting ratio, reporting odds ratio, and information component, are used to measure the association between each ADR and AED. The resulting list of signaled ADRs for each AED is validated against a widely used ADR database, called Side Effect Resource, in terms of the precision of ADR detection. The validation results indicate the validity of online health community data for the detection of AED ADRs. Furthermore, the lists of signaled AED ADRs are analyzed to answer questions related to the common ADRs of AEDs and the similarities between AEDs in terms of their signaled ADRs. The consistency of the drawn answers with the existing pharmaceutical knowledge suggests the utility of the data from online health communities for AED-related knowledge discovery tasks.

Supervised Machine Learning-Based Decision Support for Signal Validation Classification

INTRODUCTION

Signal validation in pharmacovigilance is the process of evaluating data to decide whether evidence is sufficient to justify further assessment of a detected signal. During the signal validation process, safety experts in our organization are required to review signals of disproportionate reporting (SDRs) and classify them into one of six predefined categories.

OBJECTIVE

This experiment explored the extent to which predictive machine learning (ML) models can support the decision making of safety experts by accurately identifying the most appropriate predefined signal validation category.

METHODS

We extracted cumulative data for six medicinal products, consisting of historic SDR validations and Individual Case Safety Reports, from the company's safety database for training and testing of the ML model. We implemented a decision tree-based supervised multiclass classifier model termed Gradient Boosted Trees followed by a SHapley Additive exPlanations (SHAP) analysis to mitigate the "black box" effect of the ensemble model by identifying the key predicting features in the model. Following a retrospective analysis, a prospective experiment was conducted to test the model accuracy and user acceptance in a real-life setting.

RESULTS

The prediction accuracy of our ML model ranged from 83 to 86% over 3 months for the six medicinal products. The applicability of the model was confirmed by the company's safety experts. Additionally, the systematic predictions provided valuable information to the safety experts and assisted them in reviewing the SDRs efficiently and consistently.

CONCLUSIONS

This experiment demonstrated that it is possible to train a multiclass classification model to accurately predict signal validation categories for SDRs. More importantly, the transparency of the predictions provided by the SHAP analysis led to high acceptance by the safety experts.

Intelligent Telehealth in Pharmacovigilance: A Future Perspective

Pharmacovigilance improves patient safety by detecting and preventing adverse drug events. However, challenges exist that limit adverse drug event detection, resulting in many adverse drug events being underreported or inaccurately reported. One challenge includes having access to large data sets from various sources including electronic health records and wearable medical devices. Artificial intelligence, including machine learning methods, such as natural language processing and deep learning, can detect and extract information about adverse drug events, thus automating the pharmacovigilance process and improving the surveillance of known and documented adverse drug events. In addition, with the increased demand for telehealth services, for managing both acute and chronic diseases, artificial intelligence methods can play a role in detecting and preventing adverse drug events. In this review, we discuss two use cases of how artificial intelligence methods may be useful to improve the quality of pharmacovigilance and the role of artificial intelligence in telehealth practices.

Information Patients With Melanoma Spontaneously Report About Health-Related Quality of Life on Web-Based Forums: Case Study

Background

There is a general agreement on the importance of health-related quality of life (HRQoL). This type of information is becoming increasingly important for the value assessment of health technology assessment agencies in evaluating the benefits of new health technologies, including medicines. However, HRQoL data are often limited, and additional sources that provide this type of information may be helpful.

Objective

We aim to identify the HRQoL topics important to patients with melanoma based on web-based discussions on public social media forums.

Methods

We identified 3 public web-based forums from the United States and the United Kingdom, namely the Melanoma Patient Information Page, the Melanoma International Forum, and MacMillan. Their posts were randomly selected and coded using qualitative methods until saturation was reached.

Results

Of the posts assessed, 36.7% (150/409) of posts on Melanoma International Forum, 45.1% (198/439) on MacMillan, and 35.4% (128/362) on Melanoma Patient Information Page focused on HRQoL. The 2 themes most frequently mentioned were mental health and (un)certainty. The themes were constructed based on underlying and more detailed codes. Codes related to fear, worry and anxiety, uncertainty, and unfavorable effects were the most-often discussed ones.

Conclusions

Web-based forums are a valuable source for identifying relevant HRQoL aspects in patients with a given disease. These aspects could be cross-referenced with existing tools and they might improve the content validity of patient-reported outcome measures, including HRQoL questionnaires. In addition, web-based forums may provide health technology assessment agencies with a more holistic understanding of the external aspects affecting patient HRQoL. These aspects might support the value assessment of new health technologies and could therefore help inform topic prioritization as well as the scoping phase before any value assessment.

Safety of medicines and vaccines - building next generation capability

The systematic safety surveillance of real-world use of medicinal products and related activities (pharmacovigilance) started in earnest as a scientific field only in the 1960s. While developments have occurred over the past 50 years, adding to its complexity and sophistication, the extent to which some of these advances have positively impacted the capability for ensuring patient safety is questionable. We review how the conduct of safety surveillance has changed, highlight recent scientific advances, and argue how they need to be harnessed to enhance pharmacovigilance in the future. Specifically, we describe five changes that we believe should and will need to happen globally in the coming years: (i) better, more diverse data used for safety; (ii) the switch from manual activities to automation; (iii) removal of limited value, extraneous transactional activities and replacement with sharpened focus on scientific efforts to improve patient safety; (iv) patient-involved and focussed safety; and (v) personalised safety.

Assessing Patient Perceptions and Experiences of Paracetamol in France: Infodemiology Study Using Social Media Data Mining

BACKGROUND

Individuals frequently turning to social media to discuss medical conditions and medication, sharing their experiences and information and asking questions among themselves. These online discussions can provide valuable insights into individual perceptions of medical treatment, and increasingly, studies are focusing on the potential use of this information to improve health care management.

OBJECTIVE

The objective of this infodemiology study was to identify social media posts mentioning paracetamol-containing products to develop a better understanding of patients' opinions and perceptions of the drug.

METHODS

Posts between January 2003 and March 2019 containing at least one mention of paracetamol were extracted from 18 French forums in May 2019 with the use of the Detec't (Kap Code) web crawler. Posts were then analyzed using the automated Detec't tool, which uses machine learning and text mining methods to inspect social media posts and extract relevant content. Posts were classified into groups: Paracetamol Only, Paracetamol and Opioids, Paracetamol and Others, and the Aggregate group.

RESULTS

Overall, 44,283 posts were analyzed from 20,883 different users. Post volume over the study period showed a peak in activity between 2009 and 2012, as well as a spike in 2017 in the Aggregate group. The number of posts tended to be higher during winter each year. Posts were made predominantly by women (14,897/20,883, 71.34%), with 12.00% (2507/20,883) made by men and 16.67% (3479/20,883) by individuals of unknown gender. The mean age of web users was 39 (SD 19) years. In the Aggregate group, pain was the most common medical concept discussed (22,257/37,863, 58.78%), and paracetamol risk was the most common discussion topic, addressed in 20.36% (8902/43,725) of posts. Doliprane was the most common medication mentioned (14,058/44,283, 31.74%) within the Aggregate group, and tramadol was the most commonly mentioned drug in combination with paracetamol in the Aggregate group (1038/19,587, 5.30%). The most common unapproved indication mentioned within the Paracetamol Only group was fatigue (190/616, with 16.32% positive for an unapproved indication), with reference to dependence made by 1.61% (136/8470) of the web users, accounting for 1.33% (171/12,843) of the posts in the Paracetamol Only group. Dependence mentions in the Paracetamol and Opioids group were provided by 6.94% (248/3576) of web users, accounting for 5.44% (342/6281) of total posts. Reference to overdose was made by 245 web users across 291 posts within the Paracetamol Only group. The most common potential adverse event detected was nausea (306/12843, 2.38%) within the Paracetamol Only group.

CONCLUSIONS

The use of social media mining with the Detec't tool provided valuable information on the perceptions and understanding of the web users, highlighting areas where providing more information for the general public on paracetamol, as well as other medications, may be of benefit.

[Utilizing social media data in post-market safety surveillance]

Post-marketing surveillance is the principal means to ensure drug use safety. The spontaneous report is the essential method of post-marketing surveillance for drug safety. Often, most spontaneous reports come from medical staff and sometimes come from patients who use the drug. The posts published by individuals on social media platforms that contain drugs and related adverse reaction content have gradually been seen as a new data source similar to spontaneous reports from drug users in recent years. Those user-generated posts potentially provide researchers and regulators with new opportunities to conduct post-marketing surveillance for drug safety from patients' perspectives mostly rather than medical professionals and can afford the possibility theoretically to discover drug-related safety issues earlier than traditional methods. Social media data as a new data source for safety signal detection and signal reinforcement have the unique advantages, such as population coverage, type of drugs, type of adverse reactions, data timeliness and quantity. Most of the social media data used in post-marketing surveillance research for drug safety are still text data in English, and even multiple languages are used by different people worldwide on several social media platforms. Unfortunately, there is still a controversy in the academic circles whether social media data can be used as reliable data sources for routine post-marketing surveillance for drug safety. A couple of obstacles of data, methods and ethics must be overcome before leveraging social media data for post-marketing surveillance. The number of Chinese social media users is large, and the social media data in the Chinese language is rapidly snowballing, which can be employed as the potential data source for post-marketing surveillance for drug safety. However, due to the Chinese language's specific characteristics, the text's diversity is different from the English text, and there is not enough accepted corpus in medical scenarios. Besides, the lack of domestic laws and regulations on privacy and security protection of social media data poses more challenges for applying Chinese social media data for post-market surveillance. The significance of social media data to post-marketing surveillance for drug safety is undoubtedly significant. It will be an essential development direction for future research to overcome the challenges of using social media data by developing new technologies and establishing new mechanisms.

Digital health, big data and smart technologies for the care of patients with systemic autoimmune diseases: Where do we stand?

The past decade has seen tremendous development in digital health, including in innovative new technologies such as Electronic Health Records, telemedicine, virtual visits, wearable technology and sophisticated analytical tools such as artificial intelligence (AI) and machine learning for the deep-integration of big data. In the field of rare connective tissue diseases (rCTDs), these opportunities include increased access to scarce and remote expertise, improved patient monitoring, increased participation and therapeutic adherence, better patient outcomes and patient empowerment. In this review, we discuss opportunities and key-barriers to improve application of digital health technologies in the field of autoimmune diseases. We also describe what could be the fully digital pathway of rCTD patients. Smart technologies can be used to provide real-world evidence about the natural history of rCTDs, to determine real-life drug utilization, advanced efficacy and safety data for rare diseases and highlight significant unmet needs. Yet, digitalization remains one of the most challenging issues faced by rCTD patients, their physicians and healthcare systems. Digital health technologies offer enormous potential to improve autoimmune rCTD care but this potential has so far been largely unrealized due to those significant obstacles. The need for robust assessments of the efficacy, affordability and scalability of AI in the context of digital health is crucial to improve the care of patients with rare autoimmune diseases.

Integrating Real-World Evidence in the Regulatory Decision-Making Process: A Systematic Analysis of Experiences in the US, EU, and China Using a Logic Model

Real world evidence (RWE) and real-world data (RWD) are drawing ever-increasing attention in the pharmaceutical industry and drug regulatory authorities (DRAs) all over the world due to their paramount role in supporting drug development and regulatory decision making. However, there is little systematic documentary analysis about how RWE was integrated for the use by the DRAs in evaluating new treatment approaches and monitoring post-market safety. This study aimed to analyze and discuss the integration of RWE into regulatory decision-making process from the perspective of DRAs. Different development strategies to develop and adopt RWE by the DRAs in the US, Europe, and China were reviewed and compared, and the challenges encountered were discussed. It was found that different strategies on development of RWE were applied by FDA, EMA, and NMPA. The extent to which RWE was adopted in China was relatively limited compared to that in the US and EU, which was highly related to the national pharmaceutical environment and development stages. A better understanding of the overall goals, inputs, activities, outputs, and outcomes in developing RWE will help inform actions to harness RWD and leverage RWE for better health care decisions.

A New Era in Pharmacovigilance: Toward Real-World Data and Digital Monitoring

Adverse drug reactions (ADRs) are a major concern for patients, clinicians, and regulatory agencies. The discovery of serious ADRs leading to substantial morbidity and mortality has resulted in mandatory phase IV clinical trials, black box warnings, and withdrawal of drugs from the market. Real‐world data, data collected during routine clinical care, is being adopted by innovators, regulators, payors, and providers to inform decision making throughout the product life cycle. We outline several different approaches to modern pharmacovigilance, including spontaneous reporting databases, electronic health record monitoring and research frameworks, social media surveillance, and the use of digital devices. Some of these platforms are well‐established while others are still emerging or experimental. We highlight both the potential opportunity, as well as the existing challenges within these pharmacovigilance systems that have already begun to impact the drug development process, as well as the landscape of postmarket drug safety monitoring. Further research and investment into different and complementary pharmacovigilance systems is needed to ensure the continued safety of pharmacotherapy.

Public Perspectives of Using Social Media Data to Improve Adverse Drug Reaction Reporting: A Mixed-Methods Study

Introduction

Information on suspected adverse drug reactions (ADRs) voluntarily submitted by patients can be a valuable source of information for improving drug safety; however, public awareness of reporting mechanisms remains low. Whilst methods to automatically detect ADR mentions from social media posts using text mining techniques have been proposed to improve reporting rates, it is unclear how acceptable these would be to social media users.

Objective

The objective of this study was to explore public opinion about using automated methods to detect and report mentions of ADRs on social media to enhance pharmacovigilance efforts.

Methods

Users of the online health discussion forum HealthUnlocked participated in an online survey (N = 1359) about experiences with ADRs, knowledge of pharmacovigilance methods, and opinions about using automated data mining methods to detect and report ADRs. To further explore responses, five qualitative focus groups were conducted with 20 social media users with long-term health conditions.

Results

Participant responses indicated a low awareness of pharmacovigilance methods and ADR reporting. They showed a strong willingness to share health-related social media data about ADRs with researchers and regulators, but were cautious about automated text mining methods of detecting and reporting ADRs.

Conclusions

Social media users value public-facing pharmacovigilance schemes, even if they do not understand the current framework of pharmacovigilance within the UK. Ongoing engagement with users is essential to understand views, share knowledge and respect users’ privacy expectations to optimise future ADR reporting from online health communities.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40264-021-01042-6.

Pharmacovigilance and the digital world in Italy: presentation of the results of a national survey

Background:

The digital world has undergone an essential metamorphosis in recent years, making the easy sharing of information possible, including those related to pharmacovigilance and the safety aspects of pharmaceutical and other healthcare products. These new interactive ways pose both opportunities and challenges to healthcare/pharmaceutical companies. The Pharmacovigilance Working Group “Ernesto Montagna” of the Italian Society of Pharmaceutical Medicine (SIMeF) decided to carry out a survey to gain a better understanding of the role of pharmacovigilance in digital activities.

Methods:

The Pharmacovigilance Working Group “Ernesto Montagna” sent a questionnaire via Computer-Assisted Web Interview (CAWI) technology to the members of the Pharmacovigilance Working Group (N = 257). The questionnaire was composed of 11 questions in four clusters exploring: (i) digital channels and projects implemented by the healthcare/pharmaceutical companies; (ii) governance tools in place for digital channels and projects; (iii) management of adverse events collected from digital channels and projects; (iv) impact of artificial intelligence on pharmacovigilance activities.

Results:

Ninety-three members of the Group “Ernesto Montagna” completed the questionnaire. The results show that, in the panorama of Italian healthcare/pharmaceutical companies, digital activities are ongoing, but there are still areas of uncertainty: on when a pharmacovigilance team should be involved, on the governance tools and on the guidance to be used to ensure effective governance of digital projects.

Conclusion:

In a scenario which is evolving very quickly, a critical factor is the availability of specific and updated regulations. Scientific societies, such as SIMeF and Farmindustria, the Italian national Pharma-Companies Association, could give a valuable contribution to the development of appropriate guidance together with the competent authorities.

Plain Language Summary

Results of an Italian survey on pharmacovigilance and digital world

Background: The digital world allows and makes the sharing of information easy, including information related to the health status of patients and side effects of drugs. Healthcare/pharmaceutical companies are faced with both opportunities and challenges provided by such new ways of interaction among patients and healthcare professionals. The Pharmacovigilance Working Group “Ernesto Montagna” of the Italian Society of Pharmaceutical Medicine (SIMeF) carried out a survey to gain a better understanding of the role of pharmacovigilance in digital activities.

Methods: The Pharmacovigilance Working Group “Ernesto Montagna” distributed a questionnaire to the 257 members of the Pharmacovigilance Working Group. The questionnaire was composed of 11 questions exploring: (i) digital channels and projects implemented by the companies; (ii) governance tools in place for digital channels and projects; (iii) management of adverse events collected from digital channels and projects; (iv) impact of artificial intelligence on pharmacovigilance activities.

Results: Ninety-three members completed the questionnaire. The results show that digital activities are ongoing in the Italian healthcare/pharmaceutical companies. Despite this, there are still areas of uncertainty, in particular: on when pharmacovigilance team should be involved and on the tools and guidance to be used to ensure effective governance of digital projects.

Conclusion: In a scenario that is evolving very quickly, an important factor is represented by the availability of straightforward and updated pharma-regulations and guidelines. Scientific societies like SIMeF and Farmindustria, the Italian national Pharma-Companies Association, could give a valuable contribution to the development of appropriate guidance together with the qualified authorities, in order to coordinate and standardize the approach among pharmaceutical companies.

Developing Crowdsourced Training Data Sets for Pharmacovigilance Intelligent Automation

INTRODUCTION

Machine learning offers an alluring solution to developing automated approaches to the increasing individual case safety report burden being placed upon pharmacovigilance. Leveraging crowdsourcing to annotate unstructured data may provide accurate, efficient, and contemporaneous training data sets in support of machine learning.

OBJECTIVE

The objective of this study was to evaluate whether crowdsourcing can be used to accurately and efficiently develop training data sets in support of pharmacovigilance automation.

MATERIALS AND METHODS

Pharmacovigilance experts created a reference dataset by reviewing 15,490 de-identified social media posts of narratives pertaining to 15 drugs and 22 medically relevant topics. A random sampling of posts from the reference dataset was published on Amazon Turk and its users (Turkers) were asked a series of questions about those same medical concepts. Accuracy, price elasticity, and time efficiency were evaluated.

RESULTS

Accuracy of crowdsourced curation exceeded 90% when compared to the reference dataset and was completed in about 5% of the time. There was an increase in time efficiency with higher pay, but there was no significant difference in accuracy. Additionally, having a social media post reviewed by more than one Turker (using a voting system) did not offer significant improvements in terms of accuracy.

CONCLUSIONS

Crowdsourcing is an accurate and efficient method that can be used to develop training data sets in support of pharmacovigilance automation. More research is needed to better understand the breadth and depth of possible uses as well as strengths, limitations, and generalizability of results.

The utility of real-world evidence for benefit-risk assessment, communication, and evaluation of pharmaceuticals: Case studies

PURPOSE

In recent years, novel types of real-world evidence (RWE) have played a role in various decision-making processes relating to medicinal products, including regulatory approval, patient access, health technology assessment, safety monitoring, clinical use, and post-approval lifecycle management. We therefore reviewed the potential utility of RWE in the cycle of medicinal product benefit-risk (BR) assessment, communication/risk minimization and evaluation ("BRACE").

METHODS

A convenience sample of illustrative studies was drawn from the published literature and examined. Specifically, we examined the purpose for using RWE, the type of RWE used, its novelty and how it might be integrated with other data and activities of the BRACE cycle, and how it contributed to regulatory decision-making.

RESULTS

Eight studies were selected with each illustrating a different activity in the BRACE cycle ranging from BR assessment in the preapproval setting, post-approval assessment of safety or effectiveness, communicating BR information to patients and healthcare professionals, and evaluating the effectiveness of risk minimization initiatives to support a positive BR balance.

CONCLUSIONS

RWE has an important role in informing regulatory decision-making regarding the BR management of medicines. With increasing digitalization, facilitating data collection and stakeholder engagement in health, this role is only expected to expand in the future. To reach the full potential of RWE, both regulators and sponsors will need to be familiar with a range of existing and emerging methods for generating and analyzing such evidence appropriately and achieve convergence regarding how different types of RWE can best be used to inform BR management and decision-making.

Artificial Intelligence, Real-World Automation and the Safety of Medicines

Despite huge technological advances in the capabilities to capture, store, link and analyse data electronically, there has been some but limited impact on routine pharmacovigilance. We discuss emerging research in the use of artificial intelligence, machine learning and automation across the pharmacovigilance lifecycle including pre-licensure. Reasons are provided on why adoption is challenging and we also provide a perspective on changes needed to accelerate adoption, and thereby improve patient safety. Last, we make clear that while technologies could be superimposed on existing pharmacovigilance processes for incremental improvements, these great societal advances in data and technology also provide us with a timely opportunity to reconsider everything we do in pharmacovigilance operations to maximise the benefit of these advances.

A Comparative View of Reported Adverse Effects of Statins in Social Media, Regulatory Data, Drug Information Databases and Systematic Reviews

INTRODUCTION

There are few studies assessing how data on adverse drug events from consumers on social media compare with other sources.

AIM

The aim of this study was to assess the consistency of adverse event data of statin medications from social media as compared with other sources.

METHODS

We collected data on the adverse events of statins from Twitter, the US FDA Adverse Event Reporting System (FAERS), the UK Medicines and Healthcare products Regulatory Agency (MHRA), drug information databases (DIDs) and systematic reviews. We manually annotated 12,649 tweets collected between June 2013 and August 2018. We collected 45,447 reports from FAERS, 10,415 from MHRA, identified 17 systematic reviews with relevant data and extracted data from Facts and Comparisons^® and Clinical Pharmacology^®. We compared the proportion, relative frequencies and rank of each category of adverse event from each source using MedDRA^® primary System Organ Class codes.

RESULTS

Compared with other sources, patients on social media are proportionally far more likely to complain about musculoskeletal symptoms than other adverse events. Most adverse events showed a high level of agreement between Twitter and regulatory data. DIDs tend to demonstrate similar patterns but not as strongly. Systematic reviews tend to examine pre-specified adverse events or those reported by trial investigators.

CONCLUSIONS

Combining the data from multiple sources, albeit challenging, may provide a broader safety profile of any medication. Systematically collected social media reports may be able to contribute information on the most pertinent adverse effects to patients.

Complementing the US Food and Drug Administration Adverse Event Reporting System With Adverse Drug Reaction Reporting From Social Media: Comparative Analysis

Background

Adverse drug reactions (ADRs) can occur any time someone uses a medication. ADRs are systematically tracked and cataloged, with varying degrees of success, in order to better understand their etiology and develop methods of prevention. The US Food and Drug Administration (FDA) has developed the FDA Adverse Event Reporting System (FAERS) for this purpose. FAERS collects information from myriad sources, but the primary reporters have traditionally been medical professionals and pharmacovigilance data from manufacturers. Recent studies suggest that information shared publicly on social media platforms related to medication use could be of benefit in complementing FAERS data in order to have a richer picture of how medications are actually being used and the experiences people are having across large populations.

Objective

The aim of this study is to validate the accuracy and precision of social media methodology and conduct evaluations of Twitter ADR reporting for commonly used pharmaceutical agents.

Methods

ADR data from the 10 most prescribed medications according to pharmacy claims data were collected from both FAERS and Twitter. In order to obtain data from FAERS, the SafeRx database, a curated collection of FAERS data, was used to collect data from March 1, 2016, to March 31, 2017. Twitter data were manually scraped during the same time period to extract similar data using an algorithm designed to minimize noise and false signals in social media data.

Results

A total of 40,539 FAERS ADR reports were obtained via SafeRx and more than 40,000 tweets containing the drug names were obtained from Twitter’s Advanced Search engine. While the FAERS data were specific to ADRs, the Twitter data were more limited. Only hydrocodone/acetaminophen, prednisone, amoxicillin, gabapentin, and metformin had a sufficient volume of ADR content for review and comparison. For metformin, diarrhea was the side effect that resulted in no difference between the two platforms (P=.30). For hydrocodone/acetaminophen, ineffectiveness as an ADR that resulted in no difference (P=.60). For gabapentin, there were no differences in terms of the ADRs ineffectiveness and fatigue (P=.15 and P=.67, respectively). For amoxicillin, hypersensitivity, nausea, and rash shared similar profiles between platforms (P=.35, P=.05, and P=.31, respectively).

Conclusions

FAERS and Twitter shared similarities in types of data reported and a few unique items to each data set as well. The use of Twitter as an ADR pharmacovigilance platform should continue to be studied as a unique and complementary source of information rather than a validation tool of existing ADR databases.

Patient Organizations' Barriers in Pharmacovigilance and Strategies to Stimulate Their Participation

INTRODUCTION

European drug regulations aim for a patient-centered approach, including involving patients in the pharmacovigilance (PV) systems. However many patient organizations have little experience on how they can participate in PV activities.

AIM

The aim of this study was to understand patient organizations' perceptions of PV, the barriers they face when implementing PV activities, and their interaction with other stakeholders and suggest methods for the stimulation of patient organizations as promoters of PV.

METHODS

A sequential qualitative method study was conducted and integrated with the quantitative study performed by Matos, Weits, and van Hunsel to complete a mixed method study.

RESULTS

The qualitative phase expands the understanding of the quantitative results from a previous study by broadening the knowledge on external barriers and internal barriers that patient organizations face when implementing PV activities. The strategies to stimulate patient-organization participation are the creation of more awareness campaigns, more research that creates awareness, education for patient organizations, communication of real PV examples, creation of a targeted PV system, creation of a PV communication network that provides feedback to patients, improvement of understanding of all stakeholders, and a more proactive approach from national competent authorities.

CONCLUSION

Both study phases show congruent results regarding patients' involvement and the activities patient organizations perform to promote drug safety. Patient organizations progressively position themselves as stakeholders in PV, carrying out many activities that stimulate awareness and participation of their members in drug safety, but still face internal and external barriers that can hamper their involvement.

Pharmacovigilance of anti-cancer medicines: opportunities and challenges

INTRODUCTION

The foundations of pharmacovigilance are the monitoring of drug safety in real-world medicine, and identification of new adverse effects, unknown at the time of market approval. Cancer patients are prone to adverse drug reactions due to the complexity of the neoplastic disease and its treatment. Pharmacovigilance of anti-cancer medicines is further complicated because patients have comorbidities, as for elderly patients. It is even more challenging when complete safety and risk data for a drug are lacking, as may occur for new molecules or when it comes to drugs for children.

AREAS COVERED

This article introduces the field of pharmacovigilance of anti-cancer drugs, describing the various layers of complexity that make the recognition of adverse drug events in oncology particularly problematic, including the type of medicines, the phenomenon of underreporting and polypharmacy. Finally, it reviews new digital tools to help pharmacovigilance activities in oncology.

EXPERT OPINION

The authors outline some crucial challenges and opportunities that can be useful for pharmacovigilance to keep up with the times and follow the current technological and scientific progress. In addition to the evaluations made by researchers, it will, of course, be necessary to have an equality important concrete response from the institutions and regulatory bodies.

Use of Social Media for Pharmacovigilance Activities: Key Findings and Recommendations from the Vigi4Med Project

The large-scale use of social media by the population has gained the attention of stakeholders and researchers in various fields. In the domain of pharmacovigilance, this new resource was initially considered as an opportunity to overcome underreporting and monitor the safety of drugs in real time in close connection with patients. Research is still required to overcome technical challenges related to data extraction, annotation, and filtering, and there is not yet a clear consensus concerning the systematic exploration and use of social media in pharmacovigilance. Although the literature has mainly considered signal detection, the potential value of social media to support other pharmacovigilance activities should also be explored. The objective of this paper is to present the main findings and subsequent recommendations from the French research project Vigi4Med, which evaluated the use of social media, mainly web forums, for pharmacovigilance activities. This project included an analysis of the existing literature, which contributed to the recommendations presented herein. The recommendations are categorized into three categories: ethical (related to privacy, confidentiality, and follow-up), qualitative (related to the quality of the information), and quantitative (related to statistical analysis). We argue that the progress in information technology and the societal need to consider patients' experiences should motivate future research on social media surveillance for the reinforcement of classical pharmacovigilance.

Drug Safety Issues Covered by Lay Media: A Cohort Study of Direct Healthcare Provider Communications Sent between 2001 and 2015 in The Netherlands

Background

Some drug safety issues communicated through direct healthcare professional communications (DHPCs) receive substantial media coverage, while others do not.

Objectives

The objective of this study was to assess the extent of coverage of drug safety issues that have been communicated through DHPCs in newspapers and social media. A secondary aim was to explore which determinants may be associated with media coverage.

Methods

Newspaper articles covering drug safety issues communicated through 387 DHPCs published between 2001 and 2015 were retrieved from LexisNexis Academic™. Social media postings were retrieved from Coosto™ for drugs included in 220 DHPCs published between 2010 and 2015. Coverage of DHPCs by newspapers and social media was assessed during the 2-month and 14-day time periods following issuance of the DHPC, respectively. Multivariate logistic regression was used to assess potential DHPC- and drug-related determinants of media coverage.

Results

41 (10.6%) DHPC safety issues were covered in newspaper articles. Newspaper coverage was associated with drugs without a specialist indication [adjusted odds ratio 5.32; 95% confidence interval (2.64–10.73)]. Negative associations were seen for time since market approval [3–5 years 0.30; (0.11–0.82), 6–11 years 0.18; (0.06–0.58)] and year of the DHPC [0.88; (0.81–0.96)]. In the social media, 180 (81.8%) drugs mentioned in 220 DHPCs were covered. Social media coverage was associated with drugs without a specialist indication [6.92; (1.56–30.64)], and for DHPCs communicating clinical safety issues [5.46; (2.03–14.66)].

Conclusions

Newspapers covered a small proportion of DHPC safety issues only. Most drugs mentioned in DHPCs were covered in social media. Coverage in both media were higher for drugs without a specialist indication.

Electronic supplementary material

The online version of this article (10.1007/s40264-020-00922-7) contains supplementary material, which is available to authorized users.

WHODrug: A Global, Validated and Updated Dictionary for Medicinal Information

The WHODrug medicinal information dictionary is a worldwide source of global medicinal information with the aim to facilitate the coding of medications in clinical trials as well as identification of medication-related problems when monitoring patient safety, thereby supporting the development and usage of effective and safe medications. WHODrug contains individual trade names, active ingredients and additional information such as marketing authorisation holder, country of sale, pharmaceutical form and strength. All related medications are linked using a structured WHODrug alphanumeric code, connecting trade names and variation of the ingredient with the active moiety of the ingredient. Medications in WHODrug are classified using the ATC system and clustered into Standardised Drug Groupings, to allow for grouping of medications with one or more properties in common. The built-in data structure and the classification of medications in WHODrug facilitate various ways of aggregating medications for identification and analysis of possible adverse drug reactions. The different information levels in WHODrug are used to explore the relationship between a medication or a class of medications and an adverse event. By using WHODrug in clinical trials and post-marketing safety, accurate and standardised medication information can be achieved globally and allow easy information exchange. To meet the demands of WHODrug users from the pharmaceutical industry, academia and regulatory authorities, it is relevant to keep the dictionary comprehensive, validated and constantly updated on a global scale.

Beyond the Randomized Clinical Trial: Innovative Data Science to Close the Pediatric Evidence Gap

Despite the application of advanced statistical and pharmacometric approaches to pediatric trial data, a large pediatric evidence gap still remains. Here, we discuss how to collect more data from children by using real-world data from electronic health records, mobile applications, wearables, and social media. The large datasets collected with these approaches enable and may demand the use of artificial intelligence and machine learning to allow the data to be analyzed for decision making. Applications of this approach are presented, which include the prediction of future clinical complications, medical image analysis, identification of new pediatric end points and biomarkers, the prediction of treatment nonresponders, and the prediction of placebo-responders for trial enrichment. Finally, we discuss how to bring machine learning from science to pediatric clinical practice. We conclude that advantage should be taken of the current opportunities offered by innovations in data science and machine learning to close the pediatric evidence gap.