The Experts below are selected from a list of 231 Experts worldwide ranked by ideXlab platform
Weida Tong - One of the best experts on this subject based on the ideXlab platform.
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Mining FDA Drug labels using an unsupervised learning technique - topic modeling
BMC Bioinformatics, 2020Co-Authors: Halil Bisgin, Hong Fang, Xiaowei Xu, Weida TongAbstract:Abstract Background The Food and Drug Administration (FDA) approved Drug labels contain a broad array of information, ranging from adverse Drug reactions (ADRs) to Drug efficacy, risk-benefit consideration, and more. However, the Labeling language used to describe these information is free text often containing ambiguous semantic descriptions, which poses a great challenge in retrieving useful information from the Labeling text in a consistent and accurate fashion for comparative analysis across Drugs. Consequently, this task has largely relied on the manual reading of the full text by experts, which is time consuming and labor intensive. Method In this study, a novel text mining method with unsupervised learning in nature, called topic modeling, was applied to the Drug Labeling with a goal of discovering “topics” that group Drugs with similar safety concerns and/or therapeutic uses together. A total of 794 FDA-approved Drug labels were used in this study. First, the three Labeling sections (i.e., Boxed Warning, Warnings and Precautions, Adverse Reactions) of each Drug label were processed by the Medical Dictionary for Regulatory Activities (MedDRA) to convert the free text of each label to the standard ADR terms. Next, the topic modeling approach with latent Dirichlet allocation (LDA) was applied to generate 100 topics, each associated with a set of Drugs grouped together based on the probability analysis. Lastly, the efficacy of the topic modeling was evaluated based on known information about the therapeutic uses and safety data of Drugs. Results The results demonstrate that Drugs grouped by topics are associated with the same safety concerns and/or therapeutic uses with statistical significance (P
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fda Drug Labeling rich resources to facilitate precision medicine Drug safety and regulatory science
Drug Discovery Today, 2016Co-Authors: Hong Fang, Guangxu Zhou, Joshua Xu, Stephen C Harris, Guoping Zhang, Lilliam A Rosario, Paul C Howard, Weida TongAbstract:Here, we provide a concise overview of US Food and Drug Administration (FDA) Drug Labeling, which details Drug products, Drug–Drug interactions, adverse Drug reactions (ADRs), and more. Labeling data have been collected over several decades by the FDA and are an important resource for regulatory research and decision making. However, navigating through this data is challenging. To aid such navigation, the FDALabel database was developed, which contains a set of approximately 80 000 Labeling data. The full-text searching capability of FDALabel and querying based on any combination of specific sections, document types, market categories, market date, and other Labeling information makes it a powerful and attractive tool for a variety of applications. Here, we illustrate the utility of FDALabel using case scenarios in pharmacogenomics biomarkers and ADR studies.
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quantitative structure activity relationship models for predicting Drug induced liver injury based on fda approved Drug Labeling annotation and using a large collection of Drugs
Toxicological Sciences, 2013Co-Authors: Minjun Chen, Hong Fang, Huixiao Hong, Reagan Kelly, Guanxuan Zhou, Jurgen Borlak, Weida TongAbstract:ing the early stages of the development process would greatly reduce the Drug attrition rate in the pharmaceutical industry but would require the implementation of new research and development strategies. In this regard, several in silico models have been proposed as alternative means in prioritizing Drug candidates. Because the accuracy and utility of a predic tive model rests largely on how to annotate the potential of a Drug to cause DILI in a reliable and consistent way, the Food and Drug Administration–approved Drug Labeling was given prominence. Out of 387 Drugs annotated, 197 Drugs were used to develop a quantitative structure-activity relationship (QSAR) model and the model was subsequently challenged by the left of Drugs serving as an external validation set with an overall pre diction accuracy of 68.9%. The performance of the model was further assessed by the use of 2 additional independent valida tion sets, and the 3 validation data sets have a total of 483 unique Drugs. We observed that the QSAR model’s performance varied for Drugs with different therapeutic uses; however, it achieved a better estimated accuracy (73.6%) as well as negative predictive value (77.0%) when focusing only on these therapeutic catego ries with high prediction confidence. Thus, the model’s appli
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Mining FDA Drug labels using an unsupervised learning technique--topic modeling.
BMC bioinformatics, 2011Co-Authors: Halil Bisgin, Hong Fang, Xiaowei Xu, Weida TongAbstract:The Food and Drug Administration (FDA) approved Drug labels contain a broad array of information, ranging from adverse Drug reactions (ADRs) to Drug efficacy, risk-benefit consideration, and more. However, the Labeling language used to describe these information is free text often containing ambiguous semantic descriptions, which poses a great challenge in retrieving useful information from the Labeling text in a consistent and accurate fashion for comparative analysis across Drugs. Consequently, this task has largely relied on the manual reading of the full text by experts, which is time consuming and labor intensive. In this study, a novel text mining method with unsupervised learning in nature, called topic modeling, was applied to the Drug Labeling with a goal of discovering "topics" that group Drugs with similar safety concerns and/or therapeutic uses together. A total of 794 FDA-approved Drug labels were used in this study. First, the three Labeling sections (i.e., Boxed Warning, Warnings and Precautions, Adverse Reactions) of each Drug label were processed by the Medical Dictionary for Regulatory Activities (MedDRA) to convert the free text of each label to the standard ADR terms. Next, the topic modeling approach with latent Dirichlet allocation (LDA) was applied to generate 100 topics, each associated with a set of Drugs grouped together based on the probability analysis. Lastly, the efficacy of the topic modeling was evaluated based on known information about the therapeutic uses and safety data of Drugs. The results demonstrate that Drugs grouped by topics are associated with the same safety concerns and/or therapeutic uses with statistical significance (P<0.05). The identified topics have distinct context that can be directly linked to specific adverse events (e.g., liver injury or kidney injury) or therapeutic application (e.g., antiinfectives for systemic use). We were also able to identify potential adverse events that might arise from specific medications via topics. The successful application of topic modeling on the FDA Drug Labeling demonstrates its potential utility as a hypothesis generation means to infer hidden relationships of concepts such as, in this study, Drug safety and therapeutic use in the study of biomedical documents.
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fda approved Drug Labeling for the study of Drug induced liver injury
Drug Discovery Today, 2011Co-Authors: Minjun Chen, Vikrant Vijay, Hong Fang, Weida TongAbstract:Drug-induced liver injury (DILI) is a leading cause of Drugs failing during clinical trials and being withdrawn from the market. Comparative analysis of Drugs based on their DILI potential is an effective approach to discover key DILI mechanisms and risk factors. However, assessing the DILI potential of a Drug is a challenge with no existing consensus methods. We proposed a systematic classification scheme using FDA-approved Drug Labeling to assess the DILI potential of Drugs, which yielded a benchmark dataset with 287 Drugs representing a wide range of therapeutic categories and daily dosage amounts. The method is transparent and reproducible with a potential to serve as a common practice to study the DILI of marketed Drugs for supporting Drug discovery and biomarker development.
Ethan Basch - One of the best experts on this subject based on the ideXlab platform.
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patient reported outcomes in cancer Drug development and us regulatory review perspectives from industry the food and Drug administration and the patient
JAMA Oncology, 2015Co-Authors: Ethan Basch, Cindy Geoghegan, Stephen Joel Coons, Ari Gnanasakthy, Ashley Slagle, Elektra J Papadopoulos, Paul G KluetzAbstract:Data reported directly by patients about how they feel and function are rarely included in oncology Drug Labeling in the United States, in contrast to Europe and to nononcology Labeling in the United States, where this practice is more common. Multiple barriers exist, including challenges unique to oncology trials, and industry’s concerns regarding cost, logistical complexities, and the Food and Drug Administration’s (FDA’s) rigorous application of its 2009 guidance on the use of patient-reported outcome (PRO) measures. A panel consisting of representatives of industry, FDA, the PRO Consortium, clinicians, and patients was assembled at a 2014 workshop cosponsored by FDA to identify practical recommendations for overcoming these barriers. Key recommendations included increasing proactive encouragement by FDA to clinical trial sponsors for including PROs in Drug development programs; provision of comprehensive PRO plans by sponsors to FDA early in Drug development; promotion of an oncology-specific PRO research agenda; development of an approach to existing (“legacy”) PRO measures, when appropriate (focused initially on symptoms and functional status); and increased FDA and industry training in PRO methodology. FDA has begun implementing several of these recommendations.
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patient reported outcomes and the evolution of adverse event reporting in oncology
Journal of Clinical Oncology, 2007Co-Authors: Andy Trotti, Dimitrios A Colevas, Ann Setser, Ethan BaschAbstract:Adverse event (AE) reporting in oncology has evolved from informal descriptions to a highly systematized process. The Common Terminology Criteria for Adverse Events (CTCAE) is the predominant system for describing the severity of AEs commonly encountered in oncology clinical trials. CTCAE clinical descriptors have been developed empirically during more than 30 years of use. The method of data collection is clinician based. Limitations of the CTC system include potential for incomplete reporting and limited guidance on data analysis and presentation methods. The Medical Dictionary for Regulatory Activities (MedDRA) is a comprehensive medical terminology system used for regulatory reporting and Drug Labeling. MedDRA does not provide for severity ranking of AEs. CTC-based data presentations are the primary method of AE data reporting used in scientific journals and oncology meetings. Patient-reported outcome instruments (PROs) cover the subjective domain of AEs. Exploratory work suggests PROs can be used wit...
Hong Fang - One of the best experts on this subject based on the ideXlab platform.
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Mining FDA Drug labels using an unsupervised learning technique - topic modeling
BMC Bioinformatics, 2020Co-Authors: Halil Bisgin, Hong Fang, Xiaowei Xu, Weida TongAbstract:Abstract Background The Food and Drug Administration (FDA) approved Drug labels contain a broad array of information, ranging from adverse Drug reactions (ADRs) to Drug efficacy, risk-benefit consideration, and more. However, the Labeling language used to describe these information is free text often containing ambiguous semantic descriptions, which poses a great challenge in retrieving useful information from the Labeling text in a consistent and accurate fashion for comparative analysis across Drugs. Consequently, this task has largely relied on the manual reading of the full text by experts, which is time consuming and labor intensive. Method In this study, a novel text mining method with unsupervised learning in nature, called topic modeling, was applied to the Drug Labeling with a goal of discovering “topics” that group Drugs with similar safety concerns and/or therapeutic uses together. A total of 794 FDA-approved Drug labels were used in this study. First, the three Labeling sections (i.e., Boxed Warning, Warnings and Precautions, Adverse Reactions) of each Drug label were processed by the Medical Dictionary for Regulatory Activities (MedDRA) to convert the free text of each label to the standard ADR terms. Next, the topic modeling approach with latent Dirichlet allocation (LDA) was applied to generate 100 topics, each associated with a set of Drugs grouped together based on the probability analysis. Lastly, the efficacy of the topic modeling was evaluated based on known information about the therapeutic uses and safety data of Drugs. Results The results demonstrate that Drugs grouped by topics are associated with the same safety concerns and/or therapeutic uses with statistical significance (P
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fda Drug Labeling rich resources to facilitate precision medicine Drug safety and regulatory science
Drug Discovery Today, 2016Co-Authors: Hong Fang, Guangxu Zhou, Joshua Xu, Stephen C Harris, Guoping Zhang, Lilliam A Rosario, Paul C Howard, Weida TongAbstract:Here, we provide a concise overview of US Food and Drug Administration (FDA) Drug Labeling, which details Drug products, Drug–Drug interactions, adverse Drug reactions (ADRs), and more. Labeling data have been collected over several decades by the FDA and are an important resource for regulatory research and decision making. However, navigating through this data is challenging. To aid such navigation, the FDALabel database was developed, which contains a set of approximately 80 000 Labeling data. The full-text searching capability of FDALabel and querying based on any combination of specific sections, document types, market categories, market date, and other Labeling information makes it a powerful and attractive tool for a variety of applications. Here, we illustrate the utility of FDALabel using case scenarios in pharmacogenomics biomarkers and ADR studies.
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quantitative structure activity relationship models for predicting Drug induced liver injury based on fda approved Drug Labeling annotation and using a large collection of Drugs
Toxicological Sciences, 2013Co-Authors: Minjun Chen, Hong Fang, Huixiao Hong, Reagan Kelly, Guanxuan Zhou, Jurgen Borlak, Weida TongAbstract:ing the early stages of the development process would greatly reduce the Drug attrition rate in the pharmaceutical industry but would require the implementation of new research and development strategies. In this regard, several in silico models have been proposed as alternative means in prioritizing Drug candidates. Because the accuracy and utility of a predic tive model rests largely on how to annotate the potential of a Drug to cause DILI in a reliable and consistent way, the Food and Drug Administration–approved Drug Labeling was given prominence. Out of 387 Drugs annotated, 197 Drugs were used to develop a quantitative structure-activity relationship (QSAR) model and the model was subsequently challenged by the left of Drugs serving as an external validation set with an overall pre diction accuracy of 68.9%. The performance of the model was further assessed by the use of 2 additional independent valida tion sets, and the 3 validation data sets have a total of 483 unique Drugs. We observed that the QSAR model’s performance varied for Drugs with different therapeutic uses; however, it achieved a better estimated accuracy (73.6%) as well as negative predictive value (77.0%) when focusing only on these therapeutic catego ries with high prediction confidence. Thus, the model’s appli
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Mining FDA Drug labels using an unsupervised learning technique--topic modeling.
BMC bioinformatics, 2011Co-Authors: Halil Bisgin, Hong Fang, Xiaowei Xu, Weida TongAbstract:The Food and Drug Administration (FDA) approved Drug labels contain a broad array of information, ranging from adverse Drug reactions (ADRs) to Drug efficacy, risk-benefit consideration, and more. However, the Labeling language used to describe these information is free text often containing ambiguous semantic descriptions, which poses a great challenge in retrieving useful information from the Labeling text in a consistent and accurate fashion for comparative analysis across Drugs. Consequently, this task has largely relied on the manual reading of the full text by experts, which is time consuming and labor intensive. In this study, a novel text mining method with unsupervised learning in nature, called topic modeling, was applied to the Drug Labeling with a goal of discovering "topics" that group Drugs with similar safety concerns and/or therapeutic uses together. A total of 794 FDA-approved Drug labels were used in this study. First, the three Labeling sections (i.e., Boxed Warning, Warnings and Precautions, Adverse Reactions) of each Drug label were processed by the Medical Dictionary for Regulatory Activities (MedDRA) to convert the free text of each label to the standard ADR terms. Next, the topic modeling approach with latent Dirichlet allocation (LDA) was applied to generate 100 topics, each associated with a set of Drugs grouped together based on the probability analysis. Lastly, the efficacy of the topic modeling was evaluated based on known information about the therapeutic uses and safety data of Drugs. The results demonstrate that Drugs grouped by topics are associated with the same safety concerns and/or therapeutic uses with statistical significance (P<0.05). The identified topics have distinct context that can be directly linked to specific adverse events (e.g., liver injury or kidney injury) or therapeutic application (e.g., antiinfectives for systemic use). We were also able to identify potential adverse events that might arise from specific medications via topics. The successful application of topic modeling on the FDA Drug Labeling demonstrates its potential utility as a hypothesis generation means to infer hidden relationships of concepts such as, in this study, Drug safety and therapeutic use in the study of biomedical documents.
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fda approved Drug Labeling for the study of Drug induced liver injury
Drug Discovery Today, 2011Co-Authors: Minjun Chen, Vikrant Vijay, Hong Fang, Weida TongAbstract:Drug-induced liver injury (DILI) is a leading cause of Drugs failing during clinical trials and being withdrawn from the market. Comparative analysis of Drugs based on their DILI potential is an effective approach to discover key DILI mechanisms and risk factors. However, assessing the DILI potential of a Drug is a challenge with no existing consensus methods. We proposed a systematic classification scheme using FDA-approved Drug Labeling to assess the DILI potential of Drugs, which yielded a benchmark dataset with 287 Drugs representing a wide range of therapeutic categories and daily dosage amounts. The method is transparent and reproducible with a potential to serve as a common practice to study the DILI of marketed Drugs for supporting Drug discovery and biomarker development.
Guangxu Zhou - One of the best experts on this subject based on the ideXlab platform.
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study of pharmacogenomic information in fda approved Drug Labeling to facilitate application of precision medicine
Drug Discovery Today, 2020Co-Authors: Darshan Mehta, Leihong Wu, Taylor Ingle, Shraddha Thakkar, Junshuang Yang, Ryley Uber, Catherine Li, Baitang Ning, Steve Harris, Guangxu ZhouAbstract:Pharmacogenomics (PGx), studying the relationship between Drug response and genetic makeup of an individual, is accelerating advances in precision medicine. The FDA includes PGx information in the Labeling of approved Drugs to better inform on their safety and effectiveness. We herein present a summary of PGx information found in 261 prescription Drug Labeling documents by querying the publicly available FDALabel database. A total of 362 Drug–biomarker pairs (DBPs) were identified. We profiled DBPs using frequency of the biomarkers and their therapeutic classes. Four categories of applications (indication, safety, dosing and information) were discussed according to information in Labeling. This analysis facilitates better understanding, utilization and translation of PGx information in Drug Labeling among researchers, healthcare professionals and the public.
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study of serious adverse Drug reactions using fda approved Drug Labeling and meddra
BMC Bioinformatics, 2019Co-Authors: Leihong Wu, Taylor Ingle, Anna Zhaowong, Shraddha Thakkar, Guangxu Zhou, Junshuang Yang, Joshua Xu, Darshan Mehta, Stephen C Harris, Weigong GeAbstract:Adverse Drug Reactions (ADRs) are of great public health concern. FDA-approved Drug Labeling summarizes ADRs of a Drug product mainly in three sections, i.e., Boxed Warning (BW), Warnings and Precautions (WP), and Adverse Reactions (AR), where the severity of ADRs are intended to decrease in the order of BW > WP > AR. Several reported studies have extracted ADRs from Labeling documents, but most, if not all, did not discriminate the severity of the ADRs by the different Labeling sections. Such a practice could overstate or underestimate the impact of certain ADRs to the public health. In this study, we applied the Medical Dictionary for Regulatory Activities (MedDRA) to Drug Labeling and systematically analyzed and compared the ADRs from the three Labeling sections with a specific emphasis on analyzing serious ADRs presented in BW, which is of most Drug safety concern. This study investigated New Drug Application (NDA) Labeling documents for 1164 single-ingredient Drugs using Oracle Text search to extract MedDRA terms. We found that only a small portion of MedDRA Preferred Terms (PTs), 3819 out of 21,920 or 17.42%, were observed in a whole set of documents. In detail, 466/3819 (12.0%) PTs were in BW, 2023/3819 (53.0%) were in WP, and 2961/3819 (77.5%) were in AR sections. We also found a higher overlap of top 20 occurring BW PTs with WP sections compared to AR sections. Within the MedDRA System Organ Class levels, serious ADRs (sADRs) from BW were prevalent in Nervous System disorders and Vascular disorders. A Hierarchical Cluster Analysis (HCA) revealed that Drugs within the same therapeutic category shared the same ADR patterns in BW (e.g., nervous system Drug class is highly associated with Drug abuse terms such as dependence, substance abuse, and respiratory depression). This study demonstrated that combining MedDRA standard terminologies with data mining techniques facilitated computer-aided ADR analysis of Drug Labeling. We also highlighted the importance of Labeling sections that differ in seriousness and application in Drug safety. Using sADRs primarily related to BW sections, we illustrated a prototype approach for computer-aided ADR monitoring and studies which can be applied to other public health documents.
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fda Drug Labeling rich resources to facilitate precision medicine Drug safety and regulatory science
Drug Discovery Today, 2016Co-Authors: Hong Fang, Guangxu Zhou, Joshua Xu, Stephen C Harris, Guoping Zhang, Lilliam A Rosario, Paul C Howard, Weida TongAbstract:Here, we provide a concise overview of US Food and Drug Administration (FDA) Drug Labeling, which details Drug products, Drug–Drug interactions, adverse Drug reactions (ADRs), and more. Labeling data have been collected over several decades by the FDA and are an important resource for regulatory research and decision making. However, navigating through this data is challenging. To aid such navigation, the FDALabel database was developed, which contains a set of approximately 80 000 Labeling data. The full-text searching capability of FDALabel and querying based on any combination of specific sections, document types, market categories, market date, and other Labeling information makes it a powerful and attractive tool for a variety of applications. Here, we illustrate the utility of FDALabel using case scenarios in pharmacogenomics biomarkers and ADR studies.
Faisal Mehmed - One of the best experts on this subject based on the ideXlab platform.
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patient reported outcomes in oncology Drug Labeling in the united states a framework for navigating early challenges
American health & drug benefits, 2016Co-Authors: Alan L Shields, Meaghan Krohe, A Yaworsky, Iyar Mazar, Catherine Foley, Faisal MehmedAbstract:BACKGROUND: Despite an increased use of patient-reported outcomes (PROs) in oncology clinical trials, integrating the patient perspective into Drug approval decisions and documentation has been challenging. OBJECTIVES: To review important regulatory and measurement terminology, and to provide oncology outcomes researchers and those involved with building oncology programs with tools to plan PRO data collection, particularly in relation to Drug efficacy claims for Drug Labeling in the United States. DISCUSSION: When contemplating a PRO measurement strategy for oncology clinical trials, outcomes researchers are challenged in several ways. First, given multiple stakeholders, researchers must communicate with their scientific, commercial, and regulatory colleagues using often misunderstood terms, such as "label," "claim," "end point," "outcome," and "concept." Second, because stakeholders do not always have access to data from early-stage clinical trials and do not contribute to the target Drug's profile in early development, researchers are often unable to address the most important question in building a measurement strategy: What do we want to say about our Drug? To overcome these challenges, researchers can systematically develop an end point model to facilitate communication among Drug development stakeholders using a common language and to link the building blocks of a PRO measurement strategy, including claims, concepts, questionnaires, and end points. We developed a model that characterizes a disease by its proximal signs and/or symptoms and increasingly distal health outcomes to provide researchers potential measurement concepts that can be instrumental in selecting PRO questionnaires for use in studies. CONCLUSION: PRO data collected in clinical trials should be used in Drug development to evaluate the Drug's efficacy; it is encouraging that US regulators are willing to work with Drug sponsors to overcome the challenges associated with the development, implementation, and interpretation of PROs. The tools discussed in this article can facilitate the planning process for oncology researchers, as well as assist in communicating with US regulators.
