Towards early detection of adverse drug reactions: combining pre-clinical drug structures and post-market safety reports.

BACKGROUND: Adverse drug reaction (ADR) is a major burden for patients and healthcare industry. Early and accurate detection of potential ADRs can help to improve drug safety and reduce financial costs. Post-market spontaneous reports of ADRs remain a cornerstone of pharmacovigilance and a series of drug safety signal detection methods play an important role in providing drug safety insights. However, existing methods require sufficient case reports to generate signals, limiting their usages for newly approved drugs with few (or even no) reports. METHODS: In this study, we propose a label propagation framework to enhance drug safety signals by combining drug chemical structures with FDA Adverse Event Reporting System (FAERS). First, we compute original drug safety signals via common signal detection algorithms. Then, we construct a drug similarity network based on chemical structures. Finally, we generate enhanced drug safety signals by propagating original signals on the drug similarity network. Our proposed framework enriches post-market safety reports with pre-clinical drug similarity network, effectively alleviating issues of insufficient cases for newly approved drugs. RESULTS: We apply the label propagation framework to four popular signal detection algorithms (PRR, ROR, MGPS, BCPNN) and find that our proposed framework generates more accurate drug safety signals than the corresponding baselines. In addition, our framework identifies potential ADRs for newly approved drugs, thus paving the way for early detection of ADRs. CONCLUSIONS: The proposed label propagation framework combines pre-clinical drug structures with post-market safety reports, generates enhanced drug safety signals, and can potentially help to accurately detect ADRs ahead of time. AVAILABILITY: The source code for this paper is available at: https://github.com/ruoqi-liu/LP-SDA.

Bad medicine: prescription drugs, preemption, and the potential for a no-fault fix.

For decades, federal regulation of pharmaceutical drugs and medical devices has worked hand in hand with state tort claims to protect the health and safety of the American public. Now, a new trend toward preemption endangers this scheme. In recent years, the Supreme Court has given increasing deference to agency assertions about their preemptive authority and has found preemption in an increasing number of cases. In the process, the Supreme Court has preempted claims for medical device injuries and left claims for pharmaceutical harms in a precarious position. The elimination of common law claims for drug and device harms will leave holes in the FDA's regulatory scheme, endangering the health and safety of Americans. It will also prevent ordinary Americans from seeking compensation for their injuries--even those injuries caused by manufacturer malfeasance. This Article proposes that Congress create a no-fault compensation scheme for drugs and medical devices to close these gaps. Such a scheme could be both practical and politically possible, satisfying manufacturers, tort reformers, patients, and plaintiffs' lawyers alike.

[Research on foreign countries laws and regulations on surveillance and reporting of postmarketing drugs adverse reactions].

Following more and more new drugs are authorized into market, new, serious or unexpected adverse drug reactions appear frequently, which is a serious threat to people health and life. Through making laws and guidelines, governments of various countries aim to strengthen and standardize the surveillance and reporting of postmarketing drugs. The drugs management department of our country are doing related jobs positively, but there are some problems, such as drug risk-menagement is not emphasized well, and the management department lacks clarity on operating related regulations. This article tries to explore foreign countries' laws and regulations on the surveillance and reporting of postmarketing drugs, aiming to provide reference for our courtry.

Decision support methods for the detection of adverse events in post-marketing data.

Spontaneous reporting is a crucial component of post-marketing drug safety surveillance despite its significant limitations. The size and complexity of some spontaneous reporting system databases represent a challenge for drug safety professionals who traditionally have relied heavily on the scientific and clinical acumen of the prepared mind. Computer algorithms that calculate statistical measures of reporting frequency for huge numbers of drug-event combinations are increasingly used to support pharamcovigilance analysts screening large spontaneous reporting system databases. After an overview of pharmacovigilance and spontaneous reporting systems, we discuss the theory and application of contemporary computer algorithms in regular use, those under development, and the practical considerations involved in the implementation of computer algorithms within a comprehensive and holistic drug safety signal detection program.

A computerized system for signal detection in spontaneous reporting system of Shanghai China.

PURPOSE: We developed a computerized system for signal detection in spontaneous reporting system (SRS) of Shanghai. Data acquisition, data mining could be carried out automatically and the process of data preprocessing and cleaning could be facilitated. This system was expected to detect signals from SRS after drug licensing with minimum patient exposure. METHODS: This system was developed by Microsoft visual basic (VB) 6.0. Data preprocessing, data cleaning, and data mining were based upon visual basic for application (VBA) in Microsoft Excel 2003. Database of drug generic name and adverse drug reaction (ADR) standard dictionary were set up initially for data cleaning and coding. Algorithms including reporting odds ratio (ROR), proportional reporting ratio (PRR), measure used by the Medicines and Healthcare Products Regulatory Agency (MHRA), Bayesian confidence propagation neural network (BCPNN) were employed in this system. Crude ADR reports submitted to Shanghai ADR SRS from December 2003 to April 2007 were used as a material in this study to test the feasibility and flexibility of this system. RESULTS: Thirty two thousand seven hundred and fourty six crude ADR reports were acquired from the SRS automatically. Two thousand one hundred and fourty seven drug generic name and 621 ADR name were kept in the database after data preprocessing and cleaning. A total of 1430, 1419, 868 and 997 possible drug-ADR signals were generated by ROR, PRR, BCPNN and MHRA, respectively. CONCLUSIONS: The results indicate that this computerized system is a flexible one that can help to detect possible drug-ADR signals intelligently in SRS of Shanghai now. It is a promising system for post-marketing surveillance on both chemical medicine and Chinese traditional medicine.

Signal detection in the pharmaceutical industry: integrating clinical and computational approaches.

Drug safety profiles are dynamic and established over time using multiple, complimentary datasets and tools. The principal concern of pharmacovigilance is the detection of adverse drug reactions that are novel by virtue of their clinical nature, severity and/or frequency as soon as possible with minimum patient exposure. A key step in the process is the detection of 'signals' that direct safety reviewers to associations that might be worthy of further investigation. Although the 'prepared mind' remains the cornerstone of signal detection safety reviewers seeking potential signals by scrutinising very large, sparse databases may find themselves 'drowning in data but thirsty for knowledge'. Understandably, health authorities, pharmaceutical companies and academic centres are developing, testing and/or deploying computer-assisted database screening tools (also known as data-mining algorithms [DMAs]) to assist human reviewers. The most commonly used DMAs involve disproportionality analysis that project high-dimensional data onto two-dimensional (2 x 2) contingency tables in the context of an independence model. The objective of this paper is to extend the discussion of the evaluation, potential utility and limitations of the commonly used DMAs by providing a 'holistic' perspective on their use as one component of a comprehensive suite of signal detection strategies incorporating clinical and statistical approaches to signal detection -- a marriage of technology and the 'prepared mind'. Data-mining exercises involving spontaneous reports submitted to the US FDA will be used for illustration. Potential pitfalls and obstacles to the acceptance and implementation of data mining will be considered and suggestions for future research will be offered.

Reporting traditional Chinese medicine morbidity--A University of Technology, Sydney, project with an emphasis on developing standards for testing and reporting data.

OBJECTIVES: Morbidity in Traditional Chinese Medicine (TCM) research is an emerging field. Few studies have been published, and there is a lack of international standards for data collection and reporting. Based on the experience of developing a computerized system for patient data collection at the University of Technology, Sydney, (UTS) Acupuncture Clinic (Sydney, Australia), and reporting results from that database, a start can be made toward developing guidelines for reporting similar results from TCM clinical audits. METHODS: This study reports on data relating to 5735 patients who had undergone 29,697 treatments. Patient information is collected by a computerized database recording International Classification of Primary Care (ICPC) reason for encounter (RFE) and symptom for encounter (SFE) data and TCM tongue, pulse, diagnostic, and treatment data. Data coding is automated, and systems for reliability testing and error reporting were developed. RESULTS: UTS data has a 2.7% error rate and is within international standards of 5% error. Sixty-one-point three percent (61.3%) of patients were female and of these, 59.45 were 25-44 years of age. Musculoskeletal disorders are the most common presentation (41.4%) of all RFE, followed by general disorders (13.1%) and digestive disorders (8.1%). CONCLUSIONS: International standards must be set for TCM morbidity data collection methods and reporting. It is hoped that the methods described and reported in this paper are an initial step in the setting of such standards and that they will be adopted by other researchers. In particular, methods for testing and reporting data reliability must be adopted if TCM morbidity studies are to maintain any credibility.

The nature of the scientific evidence leading to drug withdrawals for pharmacovigilance reasons in France.

INTRODUCTION: Because of design, objectives and number of included subjects, clinical studies are insufficient to assess the safety of new drugs. Sometimes, serious adverse drug reactions (ADRs) led to withdrawal of the drug from the market after their approval. The objective of our study was to determine the scientific evidences leading to drug withdrawal for pharmacovigilance reasons in France. METHODS: Data coming from French Health Products Safety Agency, literature and Toulouse Pharmacovigilance Center allowed to identify all drugs withdrawn from the French market for pharmacovigilance reasons from 1998 to 2004. We classified data according to their study design (Randomized Clinical Trial [RCT], case serie or case report, case-control study, cohort study, observational study, animal study), the organ/system affected and the type of ADR. RESULTS: A total of 21 drugs were withdrawn for safety reasons between 1998 and 2004 in France. The most frequent ADRs were hepatic (n = 7), cardiovascular (n = 4) or neurological (n = 3) ones. Eleven withdrawals were due to type-B ('unexpected') reactions (52%). For 19 out of 21 drugs, scientific evidence leading to drug withdrawal came from spontaneous case reports (or case series). Among these, case reports were the sole evidence in 12 cases. Withdrawals were based on evidence from case reports in combination with case-control or cohort study in four cases, in combination with observational study in two cases or in combination with animal study in two other cases. In only one case, a RCT supported the decision. CONCLUSIONS: This study underlines the importance of spontaneous case reports in detecting signals and supporting withdrawal of drug for pharmacovigilance reasons in France. Health authorities suffer from lack of comparative data resource. In this perspective, a pharmaco-epidemiological population-based database could represent a helpful tool to both generate and test safety hypotheses.

Servicio Hotline SOS de Anestesia Locorregional: notificación de incidencias y resolución de dudas / Service of Hotline SOS of Regional Anesthesia: reporting of complications and seeking advice

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[Pilot study for the surveillance of adverse reactions to herbal preparations and dietary supplements]. / Progetto pilota sulla sorveglianza delle reazioni avverse a prodotti a base di piante officinali e integratori alimentari.

Use of herbal medicine is increasing in Italy. Herbal preparations are generally used as self-medication, often in the wrong belief that "natural" can be equated with "harmless". Whereas use of these products has been associated with serious adverse events, due to the quality of the raw materials used, to interactions with synthetic drugs or to their use in particular conditions, such as pregnancy or lactation. To monitor herbal preparation use in the population is necessary in order to point out possible adverse reactions. This was the objective of this pilot study, together with the urgency to inform health personnel and patients on the correct use of these products. After this experience we strongly believe that a system to assess the safety of herbal preparations is needed.

Pharmacovigilance in the 21st century: new systematic tools for an old problem.

The large number of adverse-event reports generated by marketed drugs and devices argues for the application of validated computerized algorithms to supplement traditional methods of detecting adverse-event signals. Difficulties in accurately estimating patient exposure and background rates for a given event in a specific population hinder risk estimation in spontaneous adverse-event databases. The United States Food and Drug Administration (FDA) is evaluating a Bayesian data mining system called Multi-item Gamma Poisson Shrinker (MGPS) to enhance the FDA's ability to monitor the safety of drugs, biologics, and vaccines after they have been approved for use. The MGPS computes adjusted higher-than-expected reporting relationships between drugs and adverse events across 35 years of data relative to internal background rates. The MGPS can also adjust for random noise by using a model derived from the data, and corrects for temporal trends and confounding related to age, sex, and other variables by stratifying over 900 categories. Signals can then be compared with or used in conjunction with other sources (e.g. clinical trials, general practice databases) to further study the adverse-event risk. The example of pancreatitis risk with atypical antipsychotics, valproic acid, and valproate is used to discuss the strengths and limitations of MGPS versus traditional methods. Validated data mining techniques offer great promise to enhance pharmacovigilance practices.

Recognizing, reporting, and reducing adverse drug reactions.

Adverse drug reactions (ADRs) are underreported and consequently are an underestimated cause of morbidity and mortality. Recent epidemiologic evidence estimates that ADRs represent the fourth to the sixth leading cause of death. Public awareness is increasing as reports of ADRs and medication errors become more prevalent in the lay press. Reducing the number of ADRs can be accomplished by systems changes made at an individual and national level. At the individual level, increased diligence should be used with regard to reporting and documenting ADRs. At the national level, ADRs can be reduced by expanding the role of the arm of the Food and Drug Administration (FDA) responsible for postmarketing surveillance.

Farmacovigilancia: detección de reacciones adversas a medicamentos tipo B, factores de riesgo asociados / Pharmacovigilance: detection of adverse reactions to medications type B, associated risks factors

Las reacciones adversas a medicamentos (R.A.M.) se clasifican en de tipo A, relacionadas con la toxicidad del medicamento, y de tipo B, debido a un incremento de la susceptibilidad del paciente. El objetivo del presente trabajo consiste en detectar y evaluar las R.A.M. tipo B, y determinar sus factores de riesgo asociados. Se efectuó un estudio retrospectivo, utilizando estrategias (AU)

Farmacovigilancia: detección de reacciones adversas a medicamentos tipo B, factores de riesgo asociados / Pharmacovigilance: detection of adverse reactions to medications type B, associated risks factors

Las reacciones adversas a medicamentos (R.A.M.) se clasifican en de tipo A, relacionadas con la toxicidad del medicamento, y de tipo B, debido a un incremento de la susceptibilidad del paciente. El objetivo del presente trabajo consiste en detectar y evaluar las R.A.M. tipo B, y determinar sus factores de riesgo asociados. Se efectuó un estudio retrospectivo, utilizando estrategias

Drug utilization studies within the VIDEOFAR project.

The epidemiological use of drug prescription data is getting increasing attention in Italy. All Italian citizens are included in the National Health Service (NHS) which reimburses almost every drug prescription. The potential information content of such systems have to be evaluated within a technological context which will allow, for medium term, drug prescription data collection for all of the individuals served by the NHS. The VIDEOFAR project did explore the possible use of drug prescription data for descriptive epidemiological studies, in the evaluation of General Practitioners activity and as a tool for Post-Marketing Drug Surveillance. Some examples are presented in this paper using data derived from Latium Region.