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Neyarapally GA, Wu L, Xu J, Zhou EH, Dang O, Lee J, Mehta D, Vaughn RD, Pinnow E, Fang H. Description and Validation of a Novel AI Tool, LabelComp, for the Identification of Adverse Event Changes in FDA Labeling. Drug Saf 2024:10.1007/s40264-024-01468-8. [PMID: 39085589 DOI: 10.1007/s40264-024-01468-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/15/2024] [Indexed: 08/02/2024]
Abstract
INTRODUCTION The accurate identification and timely updating of adverse reactions in drug labeling are crucial for patient safety and effective drug use. Postmarketing surveillance plays a pivotal role in identifying previously undetected adverse events (AEs) that emerge when a drug is used in broader and more diverse patient populations. However, traditional methods of updating drug labeling with new AE information have been manual, time consuming, and error prone. This paper introduces the LabelComp tool, an innovative artificial intelligence (AI) tool designed to enhance the efficiency and accuracy of postmarketing drug safety surveillance. Utilizing a combination of text analytics and a trained Bidirectional Encoder Representations from Transformers (BERT) model, the LabelComp tool automatically identifies changes in AE terms from updated drug labeling documents. OBJECTIVE Our objective was to create and validate an AI tool with high accuracy that could enable researchers and FDA reviewers to efficiently identify safety-related drug labeling changes. RESULTS Our validation study of 87 drug labeling PDF pairs demonstrates the tool's high accuracy, with F1 scores of overall performance ranging from 0.795 to 0.936 across different evaluation tiers and a recall of at least 0.997 with only one missed AE out of 483 total AEs detected, indicating the tool's efficacy in identifying new AEs. CONCLUSION The LabelComp tool can support drug safety surveillance and inform regulatory decision-making. The publication of this tool also aims to encourage further community-driven enhancements, aligning with broader interests in applying AI to advance regulatory science and public health.
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Affiliation(s)
- George A Neyarapally
- Office of Surveillance and Epidemiology, Center for Drug Evaluation and Research (CDER), FDA, Silver Spring, MD, USA.
| | - Leihong Wu
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research (NCTR), US Food and Drug Administration (FDA), Jefferson, AR, USA
| | - Joshua Xu
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research (NCTR), US Food and Drug Administration (FDA), Jefferson, AR, USA
| | - Esther H Zhou
- Office of Surveillance and Epidemiology, Center for Drug Evaluation and Research (CDER), FDA, Silver Spring, MD, USA
| | - Oanh Dang
- Office of Surveillance and Epidemiology, Center for Drug Evaluation and Research (CDER), FDA, Silver Spring, MD, USA
| | - Joann Lee
- Office of Surveillance and Epidemiology, Center for Drug Evaluation and Research (CDER), FDA, Silver Spring, MD, USA
| | - Dharmang Mehta
- Office of Surveillance and Epidemiology, Center for Drug Evaluation and Research (CDER), FDA, Silver Spring, MD, USA
| | - Rochelle D Vaughn
- Office of Surveillance and Epidemiology, Center for Drug Evaluation and Research (CDER), FDA, Silver Spring, MD, USA
| | - Ellen Pinnow
- Office of Surveillance and Epidemiology, Center for Drug Evaluation and Research (CDER), FDA, Silver Spring, MD, USA
| | - Hong Fang
- Office of Scientific Coordination, National Center for Toxicological Research (NCTR), FDA, Jefferson, AR, USA
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Ying L, Liu Z, Fang H, Kusko R, Wu L, Harris S, Tong W. Text summarization with ChatGPT for drug labeling documents. Drug Discov Today 2024; 29:104018. [PMID: 38723763 DOI: 10.1016/j.drudis.2024.104018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2024] [Revised: 04/22/2024] [Accepted: 05/02/2024] [Indexed: 05/13/2024]
Abstract
Text summarization is crucial in scientific research, drug discovery and development, regulatory review, and more. This task demands domain expertise, language proficiency, semantic prowess, and conceptual skill. The recent advent of large language models (LLMs), such as ChatGPT, offers unprecedented opportunities to automate this process. We compared ChatGPT-generated summaries with those produced by human experts using FDA drug labeling documents. The labeling contains summaries of key labeling sections, making them an ideal human benchmark to evaluate ChatGPT's summarization capabilities. Analyzing >14000 summaries, we observed that ChatGPT-generated summaries closely resembled those generated by human experts. Importantly, ChatGPT exhibited even greater similarity when summarizing drug safety information. These findings highlight ChatGPT's potential to accelerate work in critical areas, including drug safety.
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Affiliation(s)
- Lan Ying
- FDA National Center for Toxicological Research, Jefferson, AR 72079, USA
| | - Zhichao Liu
- FDA National Center for Toxicological Research, Jefferson, AR 72079, USA; Nonclinical Drug Safety, Boehringer Ingelheim Pharmaceuticals, Inc, Ridgefield, CT 06877, USA
| | - Hong Fang
- FDA National Center for Toxicological Research, Jefferson, AR 72079, USA
| | | | - Leihong Wu
- FDA National Center for Toxicological Research, Jefferson, AR 72079, USA
| | - Stephen Harris
- FDA National Center for Toxicological Research, Jefferson, AR 72079, USA
| | - Weida Tong
- FDA National Center for Toxicological Research, Jefferson, AR 72079, USA.
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Mu W, Kleter GA, Bouzembrak Y, Dupouy E, Frewer LJ, Radwan Al Natour FN, Marvin HJP. Making food systems more resilient to food safety risks by including artificial intelligence, big data, and internet of things into food safety early warning and emerging risk identification tools. Compr Rev Food Sci Food Saf 2024; 23:e13296. [PMID: 38284601 DOI: 10.1111/1541-4337.13296] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Revised: 11/25/2023] [Accepted: 12/15/2023] [Indexed: 01/30/2024]
Abstract
To enhance the resilience of food systems to food safety risks, it is vitally important for national authorities and international organizations to be able to identify emerging food safety risks and to provide early warning signals in a timely manner. This review provides an overview of existing and experimental applications of artificial intelligence (AI), big data, and internet of things as part of early warning and emerging risk identification tools and methods in the food safety domain. There is an ongoing rapid development of systems fed by numerous, real-time, and diverse data with the aim of early warning and identification of emerging food safety risks. The suitability of big data and AI to support such systems is illustrated by two cases in which climate change drives the emergence of risks, namely, harmful algal blooms affecting seafood and fungal growth and mycotoxin formation in crops. Automation and machine learning are crucial for the development of future real-time food safety risk early warning systems. Although these developments increase the feasibility and effectiveness of prospective early warning and emerging risk identification tools, their implementation may prove challenging, particularly for low- and middle-income countries due to low connectivity and data availability. It is advocated to overcome these challenges by improving the capability and capacity of national authorities, as well as by enhancing their collaboration with the private sector and international organizations.
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Affiliation(s)
- Wenjuan Mu
- Wageningen Food Safety Research, Wageningen University and Research, Wageningen, The Netherlands
| | - Gijs A Kleter
- Wageningen Food Safety Research, Wageningen University and Research, Wageningen, The Netherlands
| | - Yamine Bouzembrak
- Information Technology, Wageningen University, Wageningen University and Research, Wageningen, The Netherlands
| | - Eleonora Dupouy
- Food and Agriculture Organization of the United Nations, Rome, Italy
| | - Lynn J Frewer
- School of Natural and Environmental Sciences, Newcastle University, Newcastle upon Tyne, UK
| | | | - H J P Marvin
- Hayan Group B.V., Research department, Rhenen, The Netherlands
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Tan SC, Poh WT, Yong ACH, Chua EW, Ooi DJ, Mahmud R, Thiagarajan M, Stanslas J. Challenges and Strategies for Improving Access to Cancer Drugs in Malaysia: Summary of Opinions Expressed at the 2nd MACR International Scientific Conference 2022. Cancer Manag Res 2023; 15:851-862. [PMID: 37636030 PMCID: PMC10457461 DOI: 10.2147/cmar.s420890] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Accepted: 08/10/2023] [Indexed: 08/29/2023] Open
Abstract
Considerable progress has been made in cancer drug development in recent decades. However, for people in low- and middle-income countries, including Malaysia, many of these drugs are not readily available. During the 2nd Malaysian Association for Cancer Research (MACR) International Scientific Conference, a forum discussion was held to address these challenges and explore strategies to improve access to cancer medicines in the country. This paper presents the results of the said forum discussion. A few challenges to cancer drug access were highlighted, including lengthy approval and regulatory practices, cost of medicines, and manufacturing barriers. Besides, a few strategies for mitigating some of these challenges were proposed, such as mechanisms for cost reduction, uptake of biosimilars and generics, local manufacturing, public-private partnerships, strengthening the role of insurance companies, funding and regulation, and advocacy for fair pricing, by drawing examples from cancer medicines access initiatives in Malaysia and initiatives for different disease groups. Overall, this paper provides a comprehensive overview of the challenges and strategies for improving access to cancer medicines in Malaysia and provides valuable insights for policymakers, healthcare providers, the pharmaceutical industry, cancer patients, cancer support groups, and other stakeholders working on this important issue.
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Affiliation(s)
- Shing Cheng Tan
- UKM Medical Molecular Biology Institute, Universiti Kebangsaan Malaysia, Kuala Lumpur, Malaysia
| | - Wen Tsin Poh
- Pharmacotherapeutic Unit, Department of Medicine, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia, Serdang, Selangor, Malaysia
| | | | - Eng Wee Chua
- Drug and Herbal Research Centre, Faculty of Pharmacy, Universiti Kebangsaan Malaysia, Kuala Lumpur, Malaysia
| | - Der Jiun Ooi
- Department of Oral Biology and Biomedical Sciences, Faculty of Dentistry, MAHSA University, Jenjarom, Selangor, Malaysia
| | - Rozi Mahmud
- Department of Radiology, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia, Serdang, Selangor, Malaysia
| | | | - Johnson Stanslas
- Pharmacotherapeutic Unit, Department of Medicine, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia, Serdang, Selangor, Malaysia
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Beeler PE, Stammschulte T, Dressel H. Hospitalisations Related to Adverse Drug Reactions in Switzerland in 2012-2019: Characteristics, In-Hospital Mortality, and Spontaneous Reporting Rate. Drug Saf 2023; 46:753-763. [PMID: 37335465 PMCID: PMC10344833 DOI: 10.1007/s40264-023-01319-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/09/2023] [Indexed: 06/21/2023]
Abstract
INTRODUCTION Adverse drug reactions (ADRs) contribute to morbidity, and serious ADRs may cause hospitalisation and death. This study characterises and quantifies ADR-related hospitalisations and subsequent in-hospital deaths, and estimates the spontaneous reporting rate to regulatory authorities in Switzerland, where healthcare professionals are legally obliged to report ADRs. METHODS This retrospective cohort study from 2012 to 2019 analysed nationwide data from the Federal Statistical Office. ICD-10 coding rules identified ADR-related hospitalisations. To estimate the reporting rate, individual case safety reports (ICSRs) collected in the Swiss spontaneous reporting system during the same period were considered. RESULTS Among 11,240,562 inpatients, 256,550 (2.3%) were admitted for ADRs, 132,320 (51.6%) were female, 120,405 (46.9%) were aged ≥ 65 (median of three comorbidities, interquartile range [IQR] 2-4), and 16,754 (6.5%) were children/teenagers (0 comorbidities, IQR 0-1). Frequent comorbidities were hypertension (89,938 [35.1%]), fluid/electrolyte disorders (54,447 [21.2%]), renal failure (45,866 [17.9%]), cardiac arrhythmias (37,906 [14.8%]), and depression (35,759 [13.9%]). Physicians initiated 113,028 (44.1%) of hospital referrals, and patients/relatives 73,494 (28.6%). Frequently ADR-affected were the digestive system (48,219 [18.8%], e.g. noninfective gastroenteritis and colitis), the genitourinary system (39,727 [15.5%], e.g. acute renal failure), and the mental/behavioural state (39,578 [15.4%], e.g. opioid dependence). In-hospital mortality was 2.2% (5669). Since ICSRs indicated 14,109 hospitalisations and 700 in-hospital deaths, estimated reporting rates were 5% and 12%, respectively. CONCLUSIONS This 8-year observation in Switzerland revealed that 2.3%, or roughly 32,000 admissions per year, were caused by ADRs. The majority of ADR-related admissions were not reported to the regulatory authorities, despite legal obligations.
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Affiliation(s)
- Patrick E. Beeler
- Division of Occupational and Environmental Medicine, Epidemiology, Biostatistics and Prevention Institute, University of Zurich and University Hospital Zurich, Zurich, Switzerland
- Center for Primary and Community Care, University of Lucerne, Lucerne, Switzerland
| | - Thomas Stammschulte
- Pharmacovigilance, Safety of Medicines Division, Swissmedic, Swiss Agency for Therapeutic Products, Berne, Switzerland
| | - Holger Dressel
- Division of Occupational and Environmental Medicine, Epidemiology, Biostatistics and Prevention Institute, University of Zurich and University Hospital Zurich, Zurich, Switzerland
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Singh AV, Bansod G, Mahajan M, Dietrich P, Singh SP, Rav K, Thissen A, Bharde AM, Rothenstein D, Kulkarni S, Bill J. Digital Transformation in Toxicology: Improving Communication and Efficiency in Risk Assessment. ACS OMEGA 2023; 8:21377-21390. [PMID: 37360489 PMCID: PMC10286258 DOI: 10.1021/acsomega.3c00596] [Citation(s) in RCA: 27] [Impact Index Per Article: 27.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/29/2023] [Accepted: 05/09/2023] [Indexed: 06/28/2023]
Abstract
Toxicology is undergoing a digital revolution, with mobile apps, sensors, artificial intelligence (AI), and machine learning enabling better record-keeping, data analysis, and risk assessment. Additionally, computational toxicology and digital risk assessment have led to more accurate predictions of chemical hazards, reducing the burden of laboratory studies. Blockchain technology is emerging as a promising approach to increase transparency, particularly in the management and processing of genomic data related with food safety. Robotics, smart agriculture, and smart food and feedstock offer new opportunities for collecting, analyzing, and evaluating data, while wearable devices can predict toxicity and monitor health-related issues. The review article focuses on the potential of digital technologies to improve risk assessment and public health in the field of toxicology. By examining key topics such as blockchain technology, smoking toxicology, wearable sensors, and food security, this article provides an overview of how digitalization is influencing toxicology. As well as highlighting future directions for research, this article demonstrates how emerging technologies can enhance risk assessment communication and efficiency. The integration of digital technologies has revolutionized toxicology and has great potential for improving risk assessment and promoting public health.
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Affiliation(s)
- Ajay Vikram Singh
- Department
of Chemical and Product Safety, German Federal
Institute for Risk Assessment (BfR), Max-Dohrn-Straße 8-10, 10589 Berlin, Germany
| | - Girija Bansod
- Rajiv
Gandhi Institute of IT and Biotechnology, Bharati Vidyapeeth (deemed to be) University, Pune 411045, India
| | - Mihir Mahajan
- Department
of Informatics, Technical University of
Munich, 85758 Garching, Germany
| | - Paul Dietrich
- SPECS
Surface Nano Analysis GmbH, Voltastrasse 5, 13355 Berlin, Germany
| | - Shivam Pratap Singh
- School
of Computer and Mathematical Sciences, University
of Greenwich, London SE10 9LS, U.K.
| | - Kranti Rav
- Delta
Biopharmaceutical, Andhra Pradesh 524126, India
| | - Andreas Thissen
- SPECS
Surface Nano Analysis GmbH, Voltastrasse 5, 13355 Berlin, Germany
| | - Aadya Mandar Bharde
- Guru
Nanak Khalsa College of Arts Science and Commerce, Mumbai 400 037, India
| | - Dirk Rothenstein
- Institute
for Materials Science, Department of Bioinspired Materials, University of Stuttgart, 70569 Stuttgart, Germany
| | - Shilpa Kulkarni
- Seeta
Nursing Home, Shivaji
Nagar, Nashik, Maharashtra 422002, India
| | - Joachim Bill
- Institute
for Materials Science, Department of Bioinspired Materials, University of Stuttgart, 70569 Stuttgart, Germany
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