Malaria parasites are haploid for most of their life cycle, with zygote formation and meiosis occurring during the mosquito phase of development. The parasites can be analyzed genetically by transmitting mixtures of cloned parasites through mosquitoes to permit cross-fertilization of gametes to occur. A cross was made between two clones of Plasmodium falciparum differing in enzymes, drug sensitivity, antigens, and chromosome patterns. Parasites showing recombination between the parent clone markers were detected at a high frequency. Novel forms of certain chromosomes, detected by pulsed-field gradient gel electrophoresis, were produced readily, showing that extensive rearrangements occur in the parasite genome after cross-fertilization. Since patients are frequently infected with mixtures of genetically distinct parasites, mosquito transmission is likely to provide the principal mechanisms for generating parasites with novel genotypes.

Since 1998, the US Food and Drug Administration (FDA) has been exploring new automated and rapid Bayesian data mining techniques. These techniques have been used to systematically screen the FDA's huge MedWatch database of voluntary reports of adverse drug events for possible events of concern. The data mining method currently being used is the Multi-Item Gamma Poisson Shrinker (MGPS) program that replaced the Gamma Poisson Shrinker (GPS) program we originally used with the legacy database. The MGPS algorithm, the technical aspects of which are summarised in this paper, computes signal scores for pairs, and for higher-order (e.g. triplet, quadruplet) combinations of drugs and events that are significantly more frequent than their pair-wise associations would predict. MGPS generates consistent, redundant, and replicable signals while minimising random patterns. Signals are generated without using external exposure data, adverse event background information, or medical information on adverse drug reactions. The MGPS interface streamlines multiple input-output processes that previously had been manually integrated. The system, however, cannot distinguish between already-known associations and new associations, so the reviewers must filter these events. In addition to detecting possible serious single-drug adverse event problems, MGPS is currently being evaluated to detect possible synergistic interactions between drugs (drug interactions) and adverse events (syndromes), and to detect differences among subgroups defined by gender and by age, such as paediatrics and geriatrics. In the current data, only 3.4% of all 1.2 million drug-event pairs ever reported (with frequencies > or = 1) generate signals [lower 95% confidence interval limit of the adjusted ratios of the observed counts over expected (O/E) counts (denoted EB05) of > or = 2]. The total frequency count that contributed to signals comprised 23% (2.4 million) of the total number, 10.4 million of drug-event pairs reported, greatly facilitating a more focused follow-up and evaluation. The algorithm provides an objective, systematic view of the data alerting reviewers to critically important, new safety signals. The study of signals detected by current methods, signals stored in the Center for Drug Evaluation and Research's Monitoring Adverse Reports Tracking System, and the signals regarding cerivastatin, a cholesterol-lowering drug voluntarily withdrawn from the market in August 2001, exemplify the potential of data mining to improve early signal detection. The operating characteristics of data mining in detecting early safety signals, exemplified by studying a drug recently well characterised by large clinical trials confirms our experience that the signals generated by data mining have high enough specificity to deserve further investigation. The application of these tools may ultimately improve usage recommendations.

This publication describes uniform definitions for cardiovascular and stroke outcomes developed by the Standardized Data Collection for Cardiovascular Trials Initiative and the U.S. Food and Drug Administration (FDA). The FDA established the Standardized Data Collection for Cardiovascular Trials Initiative in 2009 to simplify the design and conduct of clinical trials intended to support marketing applications. The writing committee recognizes that these definitions may be used in other types of clinical trials and clinical care processes where appropriate. Use of these definitions at the FDA has enhanced the ability to aggregate data within and across medical product development programs, conduct meta-analyses to evaluate cardiovascular safety, integrate data from multiple trials, and compare effectiveness of drugs and devices. Further study is needed to determine whether prospective data collection using these common definitions improves the design, conduct, and interpretability of the results of clinical trials.

In the last 5 years, regulatory agencies and drug monitoring centres have been developing computerised data-mining methods to better identify reporting relationships in spontaneous reporting databases that could signal possible adverse drug reactions. At present, there are no guidelines or standards for the use of these methods in routine pharmaco-vigilance. In 2003, a group of statisticians, pharmaco-epidemiologists and pharmaco-vigilance professionals from the pharmaceutical industry and the US FDA formed the Pharmaceutical Research and Manufacturers of America-FDA Collaborative Working Group on Safety Evaluation Tools to review best practices for the use of these methods.In this paper, we provide an overview of: (i) the statistical and operational attributes of several currently used methods and their strengths and limitations; (ii) information about the characteristics of various postmarketing safety databases with which these tools can be deployed; (iii) analytical considerations for using safety data-mining methods and interpreting the results; and (iv) points to consider in integration of safety data mining with traditional pharmaco-vigilance methods. Perspectives from both the FDA and the industry are provided. Data mining is a potentially useful adjunct to traditional pharmaco-vigilance methods. The results of data mining should be viewed as hypothesis generating and should be evaluated in the context of other relevant data. The availability of a publicly accessible global safety database, which is updated on a frequent basis, would further enhance detection and communication about safety issues.

Immunization of mice withPasnodiumyoeli sporozoite surface protein 2 (PySSP2) and cfrcumorozoite protein protects completely against P. yoelii. The amino acid sequence of PySSP2 suggested that the thrombospondinrelated anonymous protein (TRAP) [Robson, K.

Sera from patients with American cutaneous leishmaniasis and Chagas disease and from monkeys infected with either Trypanosoma cruzi or Trypanosoma rhodesiense show, in RIAs, strong binding to mouse laminin. A distinct although weaker binding activity is also detected in normal human sera. The antibodies recognize a common carbohydrate epitope present on mouse laminin, which was assigned to a terminal galactosyl(alpha 1-3)-galactose group. Distinct crossreactions were observed with some other basement membrane proteins, rabbit glycosphingolipids, defucosylated human B blood group substance and components produced by some human tumor cells. Only little activity was, however, found on laminin obtained from human placenta. The data indicate that the antibodies arising in infectious diseases are stimulated by similar carbohydrate epitopes present on the surface of parasites. Tissue-specific occurrence of such epitopes may exist and explain the involvement of distinct tissues in autoimmune disorders.

Topics include routine and developmental data mining activities, short descriptions of mined FDA data, advantages and challenges of data mining at the FDA, and future directions of data mining at the FDA.

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.