Ronald H. B. Meyboom

Causal or Casual? The Role of Causality Assessment in Pharmacovigilance

SummaryAs with any other study method, ‘spontaneous reporting’ in pharmacovigilance is a process of data acquisition, assessment, presentation and interpretation. The provision of information (i.e. of interpreted data) concerning previously unknown, or otherwise important adverse drug reactions is a major goal. The assessment of case reports in spontaneous reporting takes place in 2 steps: first the assessment of each case individually, and secondly the interpretation of the aggregated data. The latter step is only completed for a minority of case reports, such as when actions or measures are deemed necessary.Uncertainty in case reports regarding the involvement of the suspected drugs is an inherent drawback of spontaneous reporting. Standardised case-causality assessment has become a routine at pharmacovigilance centres around the world. It aims at a decrease in ambiguity of the data and plays a role in data exchange and the prevention of erroneous conclusions. A variety of systems for standardised causality assessment have been developed, ranging from short questionnaires to comprehensive algorithms. Since none of the available assessment systems has been validated (i.e. shown to consistently and reproducibly produce a fair approximation of the truth), causality assessment has only limited scientific value. Causality assessment neither eliminates nor quantifies uncertainty but, at best, categorises it in a semiquantitative way.Routine causality assessment is usually part of the first step in case assessment, and is based on a general system that is intended for all reactions and all drugs. During the subsequent phase of aggregated assessment, causality assessment is likely to be repeated and the use of a specific aetiological-diagnostic system may be more appropriate. It may be recommended to restrict case-causality assessment to selected case reports that are likely to play an active role in pharmacovigilance and to use specific systems, adapted to the reaction or problem involved.It is an inherent limitation of spontaneous reporting that, with the exception of rare proof-positive case reports, conclusive evidence cannot usually be produced. Standardised causality assessment has not really changed this situation. As a rule, confirmation of the connection between a drug and an adverse reaction requires further analytical or experimental study.

Principles of Signal Detection in Pharmacovigilance

SummaryAdverse drug effects are manifold and heterogenous. Many situations may hamper the signalling (i.e. the detection of early warning signs) of adverse effects and new signals often differ from previous experiences.Signals have qualitative and quantitative aspects. Different categories of adverse effects need different methods for detection. Current pharmacovigilance is predominantly based on spontaneous reporting and is mainly helpful in detecting type B effects (those effects that are often allergic or idiosyncratic reactions, characteristically occurring in only a minority of patients and usually unrelated to dosage and that are serious, unexpected and unpredictable) and unusual type A effects (those effects that are related to the pharmacological effects of the drug and are dosage-related). Examples of other sources of signals are prescription event monitoring, large automated data resources on morbidity and drug use (including record linkage), case-control surveillance and follow-up studies. Type C effects (those effects related to an increased frequency of ‘spontaneous’ disease) are difficult to study, however, and continue to pose a pharmacoepidemiological challenge.Seven basic considerations can be identified that determine the evidence contained in a signal: quantitative strength of the association, consistency of the data, exposure response relationship, biological plausibility, experimental findings, possible analogies and the nature and quality of the data. A proposal is made for a standard signal management procedure at pharmacovigilance centres, including the following steps: signal delineation, literature search, preliminary inventory of data, collection of additional information, consultation with the World Health Organization Centre for International Drug Monitoring and the relevant drug companies, aggregated data assessment and a report in writing. A better understanding of the conditions and mechanisms involved in the detection of adverse drug effects may further improve strategies for pharmacovigilance.

Antipsychotic drugs and heart muscle disorder in international pharmacovigilance: data mining study

Abstract Objectives: To examine the relation between antipsychotic drugs and myocarditis and cardiomyopathy. Design: Data mining using bayesian statistics implemented in a neural network architecture. Setting: International database on adverse drug reactions run by the World Health Organization programme for international drug monitoring. Main outcome measures: Reports mentioning antipsychotic drugs, cardiomyopathy, or myocarditis. Results: A strong signal existed for an association between clozapine and cardiomyopathy and myocarditis. An association was also seen with other antipsychotics as a group. The association was based on sufficient cases with adequate documentation and apparent lack of confounding to constitute a signal. Associations between myocarditis or cardiomyopathy and lithium, chlorpromazine, fluphenazine, haloperidol, and risperidone need further investigation. Conclusions: Some antipsychotic drugs seem to be linked to cardiomyopathy and myocarditis. The study shows the potential of bayesian neural networks in analysing data on drug safety.

A Retrospective Evaluation of a Data Mining Approach to Aid Finding New Adverse Drug Reaction Signals in the WHO International Database

AbstractBackground: The detection of new drug safety signals is of growing importance with ever more new drugs becoming available and exposure to medicines increasing. The task of evaluating information relating to safety lies with national agencies and, for international data, with the World Health Organization Programme for International Drug Monitoring. Rationale: An established approach for identifying new drug safety signals from the international database of more than 2 million case reports depends upon clinical experts from around the world. With a very large amount of information to evaluate, such an approach is open to human error. To aid the clinical review, we have developed a new signalling process using Bayesian logic, applied to data mining, within a confidence propagation neural network (Bayesian Confidence Propagation Neural Network; BCPNN). Ultimately, this will also allow the evaluation of complex variables. Methods: The first part of this study tested the predictive value of the BCPNN in new signal detection as compared with reference literature sources (Martindale’s Extra Pharmacopoeia in 1993 and July 2000, and the Physicians Desk Reference in July 2000). In the second part of the study, results with the BCPNN method were compared with those of the former signalling procedure. Results: In the study period (the first quarter of 1993) 107 drug—adverse reaction combinations were highlighted as new positive associations by the BCPNN, and referred to new drugs. 15 drug—adverse reaction combinations on new drugs became negative BCPNN associations in the study period. The BCPNN method detected signals with a positive predictive value of 44% and the negative predictive value was 85%. 17 as yet unconfirmed positive associations could not be dismissed with certainty as false positive signals.Of the 10 drug—adverse reaction signals produced by the former signal detection system from data sent out for review during the study period, 6 were also identified by the BCPNN. These 6 associations have all had a more than 10-fold increase of reports and 4 of them have been included in the reference sources. The remaining 4 signals that were not identified by the BCPNN had a small, or no, increase in the number of reports, and are not listed in the reference sources. Conclusion: Our evaluation showed that the BCPNN approach had a high and promising predictive value in identifying early signals of new adverse drug reactions.

Use of measures of disproportionality in pharmacovigilance: three Dutch examples.

Spontaneous reporting systems for suspected adverse drug reactions (ADRs) remain a cornerstone of pharmacovigilance. In The Netherlands ‘the Netherlands Pharmacovigilance Foundation Lareb’ maintains such a system. A primary aim in pharmacovigilance is the timely detection of either new ADRs or a change of the frequency of ADRs that are already known to be associated with the drugs involved, i.e. signal detection. Adequate signal detection solely based on the human intellect (case by case analysis or qualitative signal detection) is becoming time consuming given the increasingly large number of data, as well as less effective, especially in more complex associations such as drug-drug interactions, syndromes and when various covariates are involved. In quantitative signal detection measures that express the extent in which combinations of drug(s) and clinical event(s) are disproportionately present in the database of reported suspected ADRs are used to reveal associations of interest. Although the rationale and the methodology of the various quantitative approaches differ, they all share the characteristic that they express to what extent the number of observed cases differs from the number of expected cases.In this paper three Dutch examples are described in which a measure of disproportionality is used in quantitative signal detection in pharmacovigilance: (i) the association between antidepressant drugs and the occurrence of non-puerpural lactation as an example of an association between a single drug and a single event; (ii) the onset or worsening of congestive heart failure associated with the combined use of nonsteroidal anti-inflammatory drugs and diuretics as an example of an association between two drugs and a single event (drug-drug interaction); and (iii) the (co)-occurrence of fever, urticaria and arthralgia and the use of terbinafine as an example of an association between a single drug and multiple events (syndrome).We conclude that the use of quantitative measures in addition to qualitative analysis is a step forward in signal detection in pharmacovigilance. More research is necessary into the performance of these approaches, especially its predictive value, its robustness as well as into further extensions of the methodology.

Pharmacovigilance practice and risk control of Traditional Chinese Medicine drugs in China: current status and future perspective

ETHNOPHARMACOLOGICAL RELEVANCE Traditional Chinese Medicine (TCM), including Traditional Chinese Medicine drugs (TCM drugs), has been playing a very important role in health protection and disease control for thousands of years in China. Relying on natural products, mainly of herbal origin, used either as raw materials for decoction, as prepared herbal medicines or as formulated traditional medicines, TCM is still widely accepted by Chinese people, especially for chronic diseases treatment. This extensive use warrants safety measures and so TCM drug safety monitoring and risk management are becoming increasingly important tasks for the Chinese State Food and Drug Administration (SFDA). METHODS The Adverse Drug Reaction (ADR) monitoring system in China was established both for western and TCM drugs in 1989 as a voluntary reporting system with a National Center collecting and compiling reports. Serious or multi-case reports on individual TCM drug or formulated products are detailed in the Chinese ADR Information Bulletin to inform the public and Drug Administrative authorities for risk management. RESULTS About 10-15% of the ADR reports received by the National Center are related to TCM drugs and mainly pertaining to the formulated products. In certain cases, the suspension of a particular TCM preparation is decided by SFDA China. CONCLUSION The model of safety monitoring and risk management of TCM drugs is still under exploration. Indeed, the characteristics and risk factors associated with these drugs require both proper understanding and control of the risk by strengthening standardization of clinical applications, basic science research, quality control in manufacturing, exploration of the actives monitoring methodology and enhancement of international communication and cooperation.

Signal selection and follow-up in pharmacovigilance.

The detection of unknown and unexpected connections between drug exposure and adverse events is one of the major challenges of pharmacovigilance. For the identification of possible connections in large databases, automated statistical systems have been introduced with promising results. From the large numbers of associations so produced, the human mind has to identify signals that are likely to be important, in need of further assessment and follow-up and that may require regulatory action. Such decisions are based on a variety of clinical, epidemiological, pharmacological and regulatory criteria. Likewise, there are a number of criteria that underlie the subsequent evaluation of such signals. A good understanding of the logic underlying these processes fosters rational pharmacovigilance and efficient drug regulation. In the future a combination of quantitative and qualitative criteria may be incorporated in automated signal detection.

Drug-Induced Immune Thrombocytopenia

Thrombocytopenia can have several causes, including the use of certain drugs. The mechanism behind drug-induced thrombocytopenia is either a decrease in platelet production (bone marrow toxicity) or an increased destruction (immune-mediated thrombocytopenia). In addition, pseudothrombocytopenia, an in vitro effect, has to be distinguished from true drug-induced thrombocytopenia. This article reviews literature on drug-induced immune thrombocytopenia, with the exception of thrombo-haemorrhagic disorders such as thrombotic thrombocytopenic purpura and heparin-induced thrombocytopenia and thrombosis.A literature search in PubMed combined with a check of the reference lists of all the retrieved articles resulted in 108 articles relevant to the subject. The drug classes that are most often associated with drug-induced immune thrombocytopenia are cinchona alkaloid derivatives (quinine, quinidine), sulfonamides, NSAIDs, anticonvulsants, disease modifying antirheumatic drugs and diuretics. Several other drugs are occasionally described in case reports of thrombocytopenia; an updated review of these case reports can be found on the internet. A small number of epidemiological studies, differing largely in the methodology used, describe incidences in the magnitude of 10 cases per 1 000 000 inhabitants per year. No clear risk factors could be identified from these studies. The underlying mechanism of drug-induced immune thrombocytopenia is not completely clarified, but at least three different types of antibodies appear to play a role (hapten-dependent antibodies, drug-induced, platelet-reactive auto-antibodies and drug-dependent antibodies). Targets for drug-dependent antibodies are glycoproteins on the cell membrane of the platelets, such as glycoprotein (GP) Ib/IX and GPIIb/IIIa.Diagnosis of drug-induced immune thrombocytopenia may consist of identifying clinical symptoms (bruising, petechiae, bleeding), a careful evaluation of the causal relationship of the suspected causative drug, general laboratory investigation, such as total blood count and peripheral blood smear (to rule out pseudothrombocytopenia), and platelet serology tests. The sensitivity of these tests is dependent on factors such as the concentration of the drug in the test and the potential sensitisation of the patient by metabolites instead of the parent drug.Drug-induced immune thrombocytopenia can be treated by withholding the causative drug and, in severe cases associated with bleeding, by platelet transfusion.Although drug-induced thrombocytopenia is a relatively rare adverse drug reaction, its consequences may be severe. Therefore it is important to extend our knowledge on this subject. Future research should focus on the identification of potential risk factors, as well as the exact mechanism underlying drug-induced thrombocytopenia.

An ABC of drug-related problems.

The problems relating to the use of medicines are manifold. They may differ in pharmacological, pathological, epidemiological and legal respects, and may have different consequences, for example, as regards scientific study, regulation or rational use. Pharmacovigilance is concerned with all such problems: adverse effects and interactions as well as problems relating to ineffectiveness, inappropriate use, counterfeiting, dependence or poisoning.Practically all medicine-related problems can be classified in one basic system, taking into account their characteristics and distinctions. This system distinguishes between appropriate and inappropriate drug use, dose-related and dose-unrelated problems, and types A (‘drug actions’), B (‘patient reactions’) and C (‘statistical’) adverse effects. This classification may serve as an educational tool and may be useful in when choosing a study method and for the design of effective strategies in pharmacovigilance.

Pharmacovigilance in Perspective

Pharmacovigilance is more than spontaneous reporting alone, and the evaluation of marketed medicines is more than just pharmacovigilance. The positioning of a drug usually takes place during the years following introduction, when worldwide experience has accumulated. Originally a modest appendix of drug regulation, pharmacovigilance has become a major activity. The provision of the information needed for the evaluation of the benefits and risks of drugs is in the first place a scientific challenge. In addition, there are important ethical, logistical, legal, financial and commercial constraints. Good pharmacovigilance practice needs to be developed to ensure that data are collected and used in the right way and for the right purpose.Pharmacovigilance, and more generally the study of the benefits and risks of drugs, plays a major role in pharmacotherapeutic decision-making, be it individual, regional, national or international. In addition, pharmacovigilance is becoming a scientific discipline in its own right.A variety of changes are taking place in the complex system of drug development, regulation and distribution. Pharmacovigilance should be proactive in monitoring their possible consequences.