Signal generation and clarification: use of case-control data

Role of Pharmacovigilance in India: An overview

Pharmacovigilance (PV) plays a key role in the healthcare system through assessment, monitoring and discovery of interactions amongst drugs and their effects in human. Pharmaceutical and biotechnological medicines are designed to cure, prevent or treat diseases; however, there are also risks particularly adverse drug reactions (ADRs) can cause serious harm to patients. Thus, for safety medication ADRs monitoring required for each medicine throughout its life cycle, during development of drug such as pre-marketing including early stages of drug design, clinical trials, and post-marketing surveillance. PV is concerns with the detection, assessment, understanding and prevention of ADRs. Pharmacogenetics and pharmacogenomics are an indispensable part of the clinical research. Variation in the human genome is a cause of variable response to drugs and susceptibility to diseases are determined, which is important for early drug discovery to PV. Moreover, PV has traditionally involved in mining spontaneous reports submitted to national surveillance systems. The research focus is shifting toward the use of data generated from platforms outside the conventional framework such as electronic medical records, biomedical literature, and patient-reported data in health forums. The emerging trend in PV is to link premarketing data with human safety information observed in the post-marketing phase. The PV system team obtains valuable additional information, building up the scientific data contained in the original report and making it more informative. This necessitates an utmost requirement for effective regulations of the drug approval process and conscious pre and post approval vigilance of the undesired effects, especially in India. Adverse events reported by PV system potentially benefit to the community due to their proximity to both population and public health practitioners, in terms of language and knowledge, enables easy contact with reporters by electronically. Hence, PV helps to the patients get well and to manage optimally or ideally, avoid illness is a collective responsibility of industry, drug regulators, clinicians and other healthcare professionals to enhance their contribution to public health. This review summarized objectives and methodologies used in PV with critical overview of existing PV in India, challenges to overcome and future prospects with respect to Indian context.

Fluoxetine and infantile hypertrophic pylorus stenosis: a signal from a birth defects-drug exposure surveillance study

PURPOSE

We report an association found in a surveillance study which systematically evaluated combinations of specific birth defects and drugs used in the first trimester of pregnancy.

METHOD

The database of a population-based birth defects registry (birth years 1997-2007) was systematically screened for combinations of drugs and malformations that were disproportionately present compared to the rest of the database. Combinations with at least three exposed cases and a p < 0.01 (Fisher Exact test) were studied to analyse details of the malformation, timing of exposure, and additional case-control analyses.

RESULTS

Among the significant associations found, an association between maternal use of fluoxetine and infantile hypertrophic pyloric stenosis (IHPS) was of particular interest. In total 3/178 (1.7%) of the children with a HPS were exposed to fluoxetine in the first trimester compared to 8/4077 (0.2%) fluoxetine exposures among the children with other malformations (p = 0.009, OR = 8.7, 95%CI = 2.3-33.2). The three exposed cases were all isolated and fluoxetine was used in gestational weeks 4-8, 2-8 and -10-19, respectively. In additional case-control analyses, using controls with a genetic disorder and after adjustment for maternal age and smoking in the first trimester of pregnancy, the adjusted odds ratio was 9.8 (95% confidence interval: 1.5-62.0).

CONCLUSIONS

Because we cannot rule out the possibility that the association between IHPS and fluoxetine is caused by chance, we encourage other investigators to study the association between IHPS and fluoxetine in their data.

The Art and Science of Risk Management

The research-based pharmaceutical industry in the US strongly supports the concepts of risk management and sees formal risk management as playing a major role in the development of safe medicines for the public, as well as providing a mechanism to ensure that decisions concerning individual drug benefit and risk are made based on scientific evidence. Safe medicines refer to those drugs whose benefits have been found to outweigh their risks when they are used according to the approved labelling. Risk management is the comprehensive and proactive application of scientifically based methodologies to identify, assess, communicate and minimise risk throughout the life cycle of a drug so as to establish and maintain a favourable benefit-risk balance in patients. Although there are certainly a number of global risk management initiatives in place or being undertaken, harmonisation has yet to be achieved. Industry is faced with a variety of different risk management approaches and tools. There is a need to move the focus of risk management from the post-approval arena to earlier in the development process and tools need to be developed to support risk management throughout the lifecycle of a drug. The focus in the US on risk minimalisation strategies will also be an area for methodological development. A key factor in the success of overall risk management is the dialogue between industry and regulators throughout the development, review and marketing of the product. It is through such dialogue that appropriate, efficient and effective risk management strategies will be developed and implemented and the best decisions regarding the safe use of pharmaceutical products will be made.

Application of data mining techniques in pharmacovigilance

AIMS

To discuss the potential use of data mining and knowledge discovery in databases for detection of adverse drug events (ADE) in pharmacovigilance.

METHODS

A literature search was conducted to identify articles, which contained details of data mining, signal generation or knowledge discovery in relation to adverse drug reactions or pharmacovigilance in medical databases.

RESULTS

ADEs are common and result in significant mortality, and despite existing systems drugs have been withdrawn due to ADEs many years after licensing. Knowledge discovery in databases (KDD) is a technique which may be used to detect potential ADEs more efficiently. KDD involves the selection of data variables and databases, data preprocessing, data mining and data interpretation and utilization. Data mining encompasses a number of statistical techniques including cluster analysis, link analysis, deviation detection and disproportionality assessment which can be utilized to determine the presence of and to assess the strength of ADE signals. Currently the only data mining methods to be used in pharmacovigilance are those of disproportionality, such as the Proportional Reporting Ratio and Information Component, which have been used to analyse the UK Yellow Card Scheme spontaneous reporting database and the WHO Uppsala Monitoring Centre database. The association of pericarditis with practolol but not with other beta-blockers, the association of captopril and other angiotensin-converting enzymes with cough, and the association of terfenadine with heart rate and rhythm disorders could be identified by mining the WHO database.

CONCLUSION

In view of the importance of ADEs and the development of massive data storage systems and powerful computer systems, the use of data mining techniques in knowledge discovery in medical databases is likely to be of increasing importance in the process of pharmacovigilance as they are likely to be able to detect signals earlier than using current methods.

Quantitative methods in pharmacovigilance: focus on signal detection

Pharmacovigilance serves to detect previously unrecognised adverse events associated with the use of medicines. The simplest method for detecting signals of such events is crude inspection of lists of spontaneously reported drug-event combinations. Quantitative and automated numerator-based methods such as Bayesian data mining can supplement or supplant these methods. The theoretical basis and limitations of these methods should be understood by drug safety professionals, and automated methods should not be automatically accepted. Published evaluations of these techniques are mainly limited to large regulatory databases, and performance characteristics may differ in smaller safety databases of drug developers. Head-to-head comparisons of the major techniques have not been published. Regardless of previous statistical training, pharmacovigilance practitioners should understand how these methods work. The mathematical basis of these techniques should not obscure the numerous confounders and biases inherent in the data. This article seeks to make automated signal detection methods transparent to drug safety professionals of various backgrounds. This is accomplished by first providing a brief overview of the evolution of signal detection followed by a series of sections devoted to the methods with the greatest utilisation and evidentiary support: proportional reporting rations, the Bayesian Confidence Propagation Neural Network and empirical Bayes screening. Sophisticated yet intuitive explanations are provided for each method, supported by figures in which the underlying statistical concepts are explored. Finally the strengths, limitations, pitfalls and outstanding unresolved issues are discussed. Pharmacovigilance specialists should not be intimidated by the mathematics. Understanding the theoretical basis of these methods should enhance the effective assessment and possible implementation of these techniques by drug safety professionals.

Proactive safety surveillance

Growth in health information systems presents opportunities to enhance postmarketing safety surveillance of medical products. Spontaneous suspected side effect reports provide the foundation, but we need to 'proactively' improve their quality and our strategies to seek signals. In our more familiar 'reactive' mode, we examine hypotheses from inquiries or publicity. Such responsive evaluations remain essential but may miss latent information on unsuspected risks. Efficient techniques to disclose hidden clusters and associations may emerge through adaptation of approaches from industrial quality control and other disciplines. Data-driven techniques like exploratory analysis, control charts, and time series modeling may help in sifting through accumulated data and in screening consecutive submissions to discern hints of new product hazards or of more specific understanding about previously identified potential side effects. We also need to cultivate non-spontaneous data for hypothesis generation as well as testing, the systematic epidemiologic evaluation of questions and concerns. This hypothesis testing function will assume greater importance if proactive safety surveillance methods yield larger numbers of putatively positive findings. Whether from spontaneous reports or other sources, signals that could have arisen by chance alone usually represent only clues to potential hazards until or unless they can be verified through independent studies.