A new tripartite, harmonised guideline is proposed in the Quality topic area on the “Comparability of Biotechnological / Biological Products Subject to Changes in their Drug Substance and /or Drug Product Manufacturing Process”. For establishing these comparisons, it is necessary to address both product and process aspects which, while in some instances region-specific, are uniform in their principles and are necessary to support changes in manufacturing processes yielding products defined within the scope of Q6B. When the Quality aspects have been addressed, experts from the Safety and Efficacy groups will be invited to consider issues in the preclinical and in the clinical areas, as appropriate.

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International conference on harmonisation of technical requirements for registration of pharmaceuticals for human use

Objective: The database of adverse drug reactions (ADRs) held by the Uppsala Monitoring Centre on behalf of the 47 countries of the World Health Organization (WHO) Collaborating Programme for International Drug Monitoring contains nearly two million reports. It is the largest database of this sort in the world, and about 35 000 new reports are added quarterly. The task of trying to find new drug–ADR signals has been carried out by an expert panel, but with such a large volume of material the task is daunting. We have developed a flexible, automated procedure to find new signals with known probability difference from the background data. Method: Data mining, using various computational approaches, has been applied in a variety of disciplines. A Bayesian confidence propagation neural network (BCPNN) has been developed which can manage large data sets, is robust in handling incomplete data, and may be used with complex variables. Using information theory, such a tool is ideal for finding drug–ADR combinations with other variables, which are highly associated compared to the generality of the stored data, or a section of the stored data. The method is transparent for easy checking and flexible for different kinds of search. Results: Using the BCPNN, some time scan examples are given which show the power of the technique to find signals early (captopril–coughing) and to avoid false positives where a common drug and ADRs occur in the database (digoxin–acne; digoxin–rash). A routine application of the BCPNN to a quarterly update is also tested, showing that 1004 suspected drug–ADR combinations reached the 97.5% confidence level of difference from the generality. Of these, 307 were potentially serious ADRs, and of these 53 related to new drugs. Twelve of the latter were not recorded in the CD editions of The physician's Desk Reference or

 Martindale's Extra Pharmacopoea and did not appear in Reactions Weekly online. Conclusion: The results indicate that the BCPNN can be used in the detection of significant signals from the data set of the WHO Programme on International Drug Monitoring. The BCPNN will be an extremely useful adjunct to the expert assessment of very large numbers of spontaneously reported ADRs.

A Bayesian neural network method for adverse drug reaction signal generation

SUMMARY Purpose A continuous systematic review of all combinations of drugs and suspected adverse reactions (ADRs) reported to a spontaneous reporting system, is necessary to optimize signal detection. To focus attention of human reviewers, quantitative procedures can be used to sift data in different ways. In various centres, different measures are used to quantify the extent to which an ADR is reported disproportionally to a certain drug compared to the generality of the database. The objective of this study is to examine the level of concordance of the various estimates to the measure used by the WHO Collaborating Centre for International ADR monitoring, the information component (IC), when applied to the dataset of the Netherlands Pharmacovigilance Foundation Lareb. Methods The Reporting Odds Ratio � 1.96 standard errors (SE), proportional reporting ratio � 1.96 SE, Yule’s Q � 1.96 SE, the Poisson probability and Chi-square test of all 17 330 combinations were compared with the IC minus 2 standard deviations. Additionally, the concordance of the various tests, in respect to the number of reports per combination, was examined. Results In general, sensitivity was high in respect to the reference measure when a combination of point- and precision estimate was used. The concordance increased dramatically when the number of reports per combination increased. Conclusion This study shows that the different measures used are broadly comparable when four or more cases per combination have been collected. Copyright # 2002 John Wiley & Sons, Ltd.

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A comparison of measures of disproportionality for signal detection in spontaneous reporting systems for adverse drug reactions.

A common data mining task is the search for associations in large databases Here we consider the search for “interestingly large” counts in a large frequency table, having millions of cells, most of which have an observed frequency of 0 or 1 We first construct a baseline or null hypothesis expected frequency for each cell, and then suggest and compare screening criteria for ranking the cell deviations of observed from expected count A criterion based on the results of fitting an empirical Bayes model to the cell counts is recommended An example compares these criteria for searching the FDA Spontaneous Reporting System database maintained by the Division of Pharmacovigilance and Epidemiology In the example, each cell count is the number of reports combining one of 1,398 drugs with one of 952 adverse events (total of cell counts = 49 million), and the problem is to screen the drug-event combinations for possible further investigation

Bayesian Data Mining in Large Frequency Tables, with an Application to the FDA Spontaneous Reporting System

Adverse drug events remain a leading cause of morbidity and mortality around the world. Many adverse events are not detected during clinical trials before a drug receives approval for use in the clinic. Fortunately, as part of postmarketing surveillance, regulatory agencies and other institutions maintain large collections of adverse event reports, and these databases present an opportunity to study drug effects from patient population data. However, confounding factors such as concomitant medications, patient demographics, patient medical histories, and reasons for prescribing a drug often are uncharacterized in spontaneous reporting systems, and these omissions can limit the use of quantitative signal detection methods used in the analysis of such data. Here, we present an adaptive data-driven approach for correcting these factors in cases for which the covariates are unknown or unmeasured and combine this approach with existing methods to improve analyses of drug effects using three test data sets. We also present a comprehensive database of drug effects (Offsides) and a database of drug-drug interaction side effects (Twosides). To demonstrate the biological use of these new resources, we used them to identify drug targets, predict drug indications, and discover drug class interactions. We then corroborated 47 ( P

Data-Driven Prediction of Drug Effects and Interactions

Post-marketing drug safety data sets are often massive, and entail problems with heterogeneity and selection bias. Nevertheless, quantitative methods have proven a very useful aid to help clinical experts in screening for previously unknown associations in these data sets. The WHO international drug safety database is the world's largest data set of its kind with over three million reports on suspected adverse drug reaction incidents. Since 1998, an exploratory data analysis method has been in routine use to screen for quantitative associations in this data set. This method was originally based on large sample approximations and limited to pairwise associations, but in this article we propose more accurate credibility interval estimates and extend the method to allow for the analysis of more complex quantitative associations. The accuracy of the proposed credibility intervals is evaluated through comparison to precise Monte Carlo simulations. In addition, we propose a Mantel-Haenszel-type adjustment to control for suspected confounders.

Extending the methods used to screen the WHO drug safety database towards analysis of complex associations and improved accuracy for rare events.

The WHO database contains over 2.5 million case reports, analysis of this data set is performed with the intention of signal detection. This paper presents an overview of the quantitative method used to highlight dependencies in this data set. The method Bayesian confidence propagation neural network (BCPNN) is used to highlight dependencies in the data set. The method uses Bayesian statistics implemented in a neural network architecture to analyse all reported drug adverse reaction combinations. This method is now in routine use for drug adverse reaction signal detection. Also this approach has been extended to highlight drug group effects and look for higher order dependencies in the WHO data. Quantitatively unexpectedly strong relationships in the data are highlighted relative to general reporting of suspected adverse effects; these associations are then clinically assessed.

Roland Orre

Papers

A Bayesian neural network method for adverse drug reaction signal generation

A comparison of measures of disproportionality for signal detection in spontaneous reporting systems for adverse drug reactions.

Extending the methods used to screen the WHO drug safety database towards analysis of complex associations and improved accuracy for rare events.

A data mining approach for signal detection and analysis.

Bayesian neural networks with confidence estimations applied to data mining