Signal Fusion and Semantic Similarity Evaluation for Social Media Based Adverse Drug Event Detection

Recent advancements in pharmacovigilance tasks have shown the usage of social media as a resource to obtain real-time signals for drug surveillance. Researchers demonstrated a good potential for the detection of Adverse Drug Events (ADEs) using social media much earlier than the traditional reporting systems maintained by official regulatory authorities like the United States Food and Drug Administration (FDA). Existing automated drug surveillance systems have used various types of social media channels and search query logs for monitoring ADE signals.;In this thesis, we address two key performance issues related to automated drug surveillance systems. The first is to improve the ADE signal detection by analyzing signals from multiple social media channels, and the second is usage of semantic similarity to evaluate ADE narratives detected by drug surveillance systems. Most current approaches for detecting ADEs from social media rely on a single channel: forums or microblogs or query logs. In this study we propose a new methodology to fuse signals from different social media channels. We use graphical causal models to discover potentially hidden connections between data channels, and then use such associations to generate signals for ADEs. Further, prior work have not emphasized much on the language of healthcare consumers, which is often casual and informal in expressing health issues on social media. There is a high potential to miss the semantic similarity between ADE terms extracted from social media and terms from formal official narratives when the two sets of terms do not share exact text. Thus, we exhibit the usage of semantic similarity to enhance accuracy of detected ADEs, and evaluated similarity measurement algorithms developed over biomedical vocabularies in ADE surveillance domain. We experimented on a dataset of drugs which had FDA black box warnings with a retrospective analysis spanning years 2008 to 2015. The results show a better detection rate and an improved performance in terms of precision, recall and timeliness using our proposed methods

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

Under-reporting of Adverse Drug Reactions to the Food & Drug Administration

This study examined the potential significant differences in the distribution of adverse drug reactions (ADRs) by reporter (consumer versus physician) and patient outcome at case and event level. This study also contains exploratory questions to evaluate reporting of ADRs by consumers versus physician by system organ class (SOC) and reporter demographics within the United States Food & Drug Administration Adverse Event Reporting System (FAERS). The theoretical foundation applied in this quantitative study was the social amplification of risk framework. Data from the second quarter of 2016 were obtained from FAERS, and a total of 87,807 ADR reports corresponding to 143,399 ADRs were analyzed by utilizing the chi-square test, the odds ratio, and logistic regression. Cross-sectional design was employed to compare reporting of ADRs at the case and event level (case-based and event-based analyses, respectively) between reporters (consumer versus physician), specifically, for patient outcome, as well as SOC and reporter demographics. For both the case-based and event-based analyses, findings revealed that consumers reported more serious ADRs in comparison to physicians. Furthermore, findings confirmed a difference in ADR reporting between consumers and physicians depending on SOC groups. Additionally, consumers reported more nonserious ADRs in comparison to physicians. The results from this study may have implications for positive social change by augmenting pharmacovigilance systems at a national and international level to identify risks and risk factors spontaneously reported after drugs have been on the market

A machine learning approach to classification of drug reviews in Russian

© 2017 IEEE. The automatic extraction of drug side effects from social media has gained popularity in pharmacovigilance. Information extraction methods tailored to medical subjects are essential for the task of drug repurposing and finding drug reactions. In this article, we focus on extracting information about side effects and symptoms in users' reviews about medications in Russian. We manually develop a real-world dataset by crawling user reviews from a health-related website and annotate a set of reviews on a sentence level. The paper addresses the classification problem with more than two classes, comparing a simple bag-of-words baseline and a feature-rich machine learning approach

Communicating post-market safety risks of medicines with regulatory safety advisories: an international comparison of policy and perceptions

Background Information about the safety of medicines often emerges after approval. Medicines’ regulators use post-market safety advisories to communicate potential new harms. Advisories can influence medicines use, helping users to weigh benefits and harms. This thesis compared regulatory policy and outcomes for post-market safety communication in Australia, Canada, the United Kingdom (as part of the European Union) and the United States (US). Methods The four regulators were compared using: • A regulatory policy analysis. • An in-depth case study of safety communications for SGLT2 inhibitors (2012-2018). • A content analysis of safety advisories issued for new drugs approved in Australia 2010-2016. • Qualitative interviews exploring prescriber awareness and use of medicines safety information (Boston and Australia). Results Differences in regulatory policy among the European Medicines Agency, the US Food and Drug Administration, Health Canada, and the Therapeutic Goods Administration (TGA) included: their legislated authority for safety advisories, transparency, and interactions with pharmaceutical industry. SGLT2 inhibitor safety advice differed among regulators in number, timing, and strength. TGA advisories were issued for 20.5% of 73 safety concerns communicated by other regulators, for new drugs approved in Australia (2010-2016). Differences were not explained by the seriousness of safety concerns. Prescribers’ awareness of regulatory safety advisories was relatively low, particularly in Australia. While respecting regulators’ institutional authority, regulatory warnings may lack clinical authority. Conclusions There are considerable differences amongst the EMA, FDA, Health Canada and the TGA in policy and use of post-market safety advisories. Recommendations for improving safety and policy are discussed

Ilo Marie Grundberg, Janice Gray v. The Upjohn Company : Brief of Appellant

Appendix to Brief of Petitioner The Upjohn Company on certified question

Ilo Marie Grundberg, Janice Gray v. The Upjohn Company : Brief of Appellant

Appendix to Brief of Petitioner The Upjohn Company on certified question

Pharmacoepidemiologic Exploration of Increased Eating Drives Associated with Antidiabetic Medications

학위논문(박사) -- 서울대학교대학원 : 의과대학 임상의과학과, 2021.8. 최형진.임상 개발 과정에서 대부분의 안전성 탐지, 분석 노력은 동물 독성 연구에서 얻은 중요한 결과 또는 1 차 및 2 차 약력학적 효과에 기반한 가설을 사람에서 유심히 관찰하는 것에 집중되어 있다. 약물이 시장에 출시된 후 약물 역학적 연구는 주로 사망률, 심각한 이환율 또는 객관적으로 정량화 할 수 있는 결과들 (예: 검사 수치, 영상 바이오마커)의 분석에 결과에 집중되어 있다. 현존하는 약물 안전의 제도는 임상 현장에서 혹은 실생활에서 환자의 주관적인 약물 경험을 조사하는 데 큰 관심을 보이지 않았다. 이 논문은 기존 약물 감시 데이터베이스 중 미국 FDA의 약물 부작용 보고 시스템 (FAERS)의 빅데이터가 환자의 주관적 약물 경험을 탐색할 수 있는지를 보고자 했다. 환자의 주관적 경험 중 배고픔, 식욕, 음식에 대한 갈망 등 식이 행동의 동기와 연관된 지각들이 약물 부작용으로 경험되고 보고되는지를 탐색했다. 탐색에는 미국 FDA의 시판 후 약물 부작용 데이터베이스에 보고된 당뇨약과 연관된 식사 동기를 증가시키는 부작용이 사용되었다. 미국의 약물 처방 자료를 참고하여 미국에서 많이 쓰이는 6개의 약물군에 속한 15개의 당뇨약과 식욕 증가와 관련된 부작용 용어가 조합된 보고를 추출했다. 부작용 용어로 배고픔 (hunger), 음식에 대한 갈망 (food craving), 식욕 증가 (increased appetite)가 사용되었다. 이 부작용 들은 개별적 신호 탐색에도 쓰였고 세 부작용 용어의 보고 빈도의 합으로도 탐색 되었다. 미 FDA 데이터베이스에서 1968년부터 2020년 12월 30일까지 전체 데이터에서 약물-부작용 조합을 추출하였다. 부작용 신호 탐색에 흔히 쓰는 기법 중 reporting odds ratio (ROR)을 사용하였다. 이는 다른 모든 약과 비교해서 특정 약물의 특정 부작용 보고 비율의 비교의 균형을 보는 불균형 계산 (disproportionality) 방법 중 하나로 이 값의 95% 신뢰 구간의 하부경계값이 1을 넘으면 부작용 신호로 해석했다. 모든 계열의 당뇨약이 식사 동기 증가 부작용과 2.00 [1.74, 2.31]에서 12.38 [11.81, 12.98] 범위의 ROR [95 % CI] 값의 유의한 연관을 보였다. 개별 당뇨약의 식사 동기 증가 부작용의 ROR [95 % CI]은 다음과 같았다: 메트포르민은 2.00 [1.74, 2.31], 리나글립틴은 2.29 [1.46, 3.59], 삭 사글립틴 1.85 [0.96, 3.55], 시타글립틴 3.20 [2.64, 3.89], 둘라글루타이드 4.69 [4.06, 5.42], 엑세나타이드 16.22 [15.31, 17.18], 리라글루타이드 12.55 [11.42, 13.78], 세마글루타이드 9.63 [7.50, 12.37], 카나글리플로진 2.98 [2.39, 3.73], 다파글리플로진 6.93 [5.17, 9.29], 엠파글리플로진 2.49 [1.84, 3.37], 글리메피리드 3.07 [2.12, 4.45], 글리피지드 5.03 [3.90, 6.48], 글리부라이드 3.31 [2.39, 4.57], 피오글리타존 3.06 [2.42, 3.87]. FAERS에는 상당한 수의 주관적인 환자 경험 ADR이 포함되었다. 20,000 개가 넘는 부작용 용어 중 세 개의 식사 동기 증가 용어가 전체 부작용 보고 사례의 0.1 %를 차지했다. 약물 주관적 경험은 의료인보다 소비자가 더 자주 보고하는 것으로 보인다. 식사 동기 증가 부작용의 보고자 중 69.33 %는 소비자, 23.94 %는 의료인이었다. 모든 계열의 당뇨약에서 의료인 (9.89-35.48 %)보다 소비자 (33.82-89.70 %)가 더 많은 보고를 하였고, 남성 (25.64-34.36 %)보다 여성 (57.26-72.45 %)에서 더 많은 식사 동기 증가가 보고되었다. FAERS는 환자의 주관적 경험에 대한 초기 신호 탐색 및 가설 생성을 위한 유용한 도구로 보여진다. 심각한 부작용이 더 선택적으로 보고되는 것으로 보여지나 환자의 주관적 불편함도 충분한 사례가 보고되어 있다. 환자의 주관적인 약물 경험은 의료인보다는 환자가 보고하는 경우가 많았는데 이것이 환자와 의사가 생각하는 치료의 목표와 그 과정에서 중요하게 생각하는 점의 불일치에서 기인하는지를 이해하는 것은 치료의 관계 및 약물 순응도에 중요할 것으로 보여진다. 약물감시체계가 이런 주관적인 환자 경험을 탐색할 수 있는 유용한 도구가 되기 위해서는 약물과 부작용 관리 시스템에 대한 환자의 지식과 이해를 필요로 한다. 미국 FDA 데이터베이스는 의미 있는 정보원이 될 수 있을 것으로 보인다.During clinical development, much of the safety detection and analysis effort is centered on assessing the human equivalent of significant findings from animal toxicology studies. Moreover, toxicities hypothesized from primary and secondary pharmacologic effects profiling are also used for safety analysis. After a drug launches in the market, postmarketing safety surveillance systems focus mainly on hard outcomes of mortality, serious morbidity, or objectively quantifiable outcomes (e.g., laboratory data, imaging biomarkers). Institutions using these pillars of drug safety have not had much interest in examining “soft signs” or subjective patient drug experiences. This study explores whether an existing pharmacovigilance database, namely the U.S. Food and Drug Administration’s Adverse Events Reporting System (FAERS), can be used to examine soft signals of subjective patient experiences, especially those related to motivational aspects of eating. Antidiabetic drugs were used to examine whether subjective patient drug experiences of increased eating drives could be detected using the FAERS. Referencing U.S. prescription data, 15 non-insulin, single agent antidiabetic drugs (ADDs) most frequently prescribed in the United States from 6 ADD classes were used. Event terms used to extract adverse drug reactions (ADRs) of increased eating drives were hunger, food craving, and increased appetite. An aggregate search was also performed combining the 3 event terms. Drug-event pairs were extracted for periods of FAERS existence from 1968 to December 31, 2020. The reporting odds ratio (ROR) was used for a disproportionality calculation in which a ROR with a lower margin of the 95% CI >1 was defined as a positive ADR signal. All ADD classes yielded positive safety signals of increased eating drives: ROR [95% CI] calculations ranging from 2.00 [1.74, 2.31] to 12.38 [11.81, 12.98]. For the individual ADDs, the RORs [95% CI] for increased eating drives were: 2.00 [1.74, 2.31] for metformin, 2.29 [1.46, 3.59] for linagliptin, 1.85 [0.96, 3.55] for saxagliptin, 3.20 [2.64, 3.89] for sitagliptin, 4.69 [4.06, 5.42] for dulaglutide, 16.22 [15.31, 17.18] for exenatide, 12.55 [11.42, 13.78] for liraglutide, 9.63 [7.50, 12.37] for semaglutide, 2.98 [2.39, 3.73] for canagliflozin, 6.93 [5.17, 9.29] for dapagliflozin, 2.49 [1.84, 3.37] for empagliflozin, 3.07 [2.12, 4.45] for glimepiride, 5.03 [3.90, 6.48] for glipizide, 3.31 [2.39, 4.57] for glyburide, and 3.06 [2.42, 3.87] for pioglitazone. The FAERS contained substantial numbers of subjective patient experience ADRs. Out of over 20,000 event terms, the three event terms for increased eating drives totaled 0.1% of all case reports in the FAERS. Soft signals seem to be more frequently reported by consumers than by healthcare providers. 69.33% of the reports of increased eating drives for all drugs were from consumers and 23.94% from healthcare providers. For all ADD classes, more reports of increased eating drives were received from consumers (33.82-89.70%) than healthcare providers (9.89-35.48%) and from women (57.26-72.45%) than men (25.64-34.36%). Patients may offer information about previously unknown ADRs that physicians cannot observe or quantify. Educated consumers can be valuable partner in the post-marketing surveillance of drug safety. Patient distressful drug experiences can affect treatment adherence and therapeutic. FAERS and other patient reporting systems might be useful tools in detecting adverse patient drug experiences.1. INTRODUCTION 1 1.1. Pharmacovigilance systems 1 1.1.1. Rationale for legislation: protection of public health 1 1.1.2. Definitions 2 1.1.3. Sources of report 2 1.1.4. Valid report 3 1.1.5. Coding of AEs: MedDRA 4 1.1.6. Causality 9 1.1.7. Non-clinical safety 11 1.1.8. Clinical trial safety data 12 1.1.9. Continuous characterization of safety in the post-market authorization 15 1.2. Spontaneous reporting systems 17 1.2.1. Limitations of spontaneous reports 18 1.2.2. Strengths of SRS 21 1.2.3. Databases 22 1.3. Signal detection 24 1.3.1. Disproportionality Analyses 26 1.4. Relatively benign soft ADRs 30 1.4.1. ABCDE Classification 30 1.4.2. Where do subjective patient experience ADRs fit in the ABCDE scheme? 31 1.4.3. Patient drug experience and adherence 31 1.4.4. Examples of soft signals detected from SRS 34 1.5. Increased eating drive as an ADR 35 1.5.1. Hunger, appetite, craving—wanting, liking, needing 36 1.6. Diabetes, antidiabetic drugs and the drive to eat 40 1.6.1. Glucostatic theory 41 1.6.2. Food addiction and diabetes 42 1.6.3. Biologically driven to eat more 45 1.7. Research question: antidiabetic drug—increased eating drives explored with FAERS data 45 2. METHODS 47 2.1. Source of spontaneous report database 47 2.1.1. FAERS 47 2.1.2. Characteristics of individual case safety reports in FAERS 47 2.2. Reaction terms examined 55 2.2.1. ADD-increased eating drives 55 2.3. Drug names 59 2.3.1. ADD-increased eating drives 59 2.4. Disproportionality analyses 62 2.5. Ethical statements 62 3. RESULTS 63 3.1. All cases for all drugs in the FAERS 63 3.2. ICSR characteristics of ADD classes 64 3.3. Disproportionality analyses 67 4. DISCUSSION 80 4.1. Summary of key findings 80 4.1.1. Appearance of a reporting bias of serious and lethal cases in FAERS 80 4.1.2. Numerous spontaneous ADR reports of increased eating drives were in FAERS 81 4.1.3. Three-fold more reports of increased eating drives are received from consumers (especially women) than healthcare professionals 81 4.1.4. Increased eating drives was a positive ADR signal for all ADD classes; strongest signal was observed with GLP1RAs 82 4.1.5. Increased drive to eat, behavioral output, and associated physical exam were not trending together in the FAERS 83 4.2. Interpretations 83 4.2.1. Bias toward reporting ADRs with serious outcomes 83 4.2.2. Patients, especially women, were more likely to report increased eating drives than physicians 84 4.2.3. What was unexpected—associations of GLP1RA and increased eating drives 86 4.2.4. Discordance in signal directions of eating drives, eating behavior, and weight increase 98 4.3. Limitations 104 4.3.1. Cannot assess comparative risk based on strength of the signal 104 4.3.2. The magnitude of the problem cannot be estimated 105 4.3.3. Association is not causation 107 4.4. Implications 108 4.4.1. FAERS can be used to detect signals of subjective patient experience ADRs 108 4.4.2. Consumer reports may be more sensitive to detect soft signals of subjective patient experiences 108 4.4.3. Informed consumers may be key to successful PV activities for signals that matter to patients 110 4.4.4. Increased eating drives associated with ADDs requires further evaluation 113 4.5. Recommendations for signal evaluation of increased eating drives associated with ADDs 114 4.5.1. For characterization 114 4.5.2. Susceptibility factors 114 5. CONCLUSION 115 REFERENCES 116 국문 초록 137박