Prothymosin alpha functions as a cellular oncoprotein by inducing transformation of rodent fibroblasts in vitro.

Prothymosin alpha (ProTalpha), a cellular molecule known to be associated with cell proliferation, is transcriptionally up-regulated on expression of c-myc and interacts with histones in vitro and associates with histone H1 in cells. Previous studies have also shown that ProTalpha is involved in chromatin remodeling. Recent studies have shown that ProTalpha interacts with the acetyl transferase p300 and an essential Epstein-Barr virus protein, EBNA3C, involved in regulation of viral and cellular transcription. These studies suggest a potential involvement in regulation of histone acetylation through the association with these cellular and viral factors. In the current studies, we show that heterologous expression of ProTalpha in the Rat-1 rodent fibroblast cell line results in increased proliferation, loss of contact inhibition, anchorage-independent growth, and decreased serum dependence. These phenotypic changes seen in transfected Rat-1 cells are similar to those observed with a known oncoprotein, Ras, expressed under the control of a heterologous promoter and are characteristic oncogenic growth properties. These results demonstrate that the ProTalpha gene may function as an oncogene when stably expressed in Rat-1 cells and may be an important downstream cellular target for inducers of cellular transformation, which may include Epstein-Barr virus and c-myc.

Data-mining analyses of pharmacovigilance signals in relation to relevant comparison drugs.

OBJECTIVE: The aim of this paper is to demonstrate the usefulness of the Bayesian Confidence Propagation Neural Network (BCPNN) in the detection of drug-specific and drug-group effects in the database of adverse drug reactions of the World Health Organization Programme for International Drug Monitoring. METHODS: Examples of drug-adverse reaction combinations highlighted by the BCPNN as quantitative associations were selected. The anatomical therapeutic chemical (ATC) group to which the drug belonged was then identified, and the information component (IC) was calculated for this ATC group and the adverse drug reaction (ADR). The IC of the ATC group with the ADR was then compared with the IC of the drug-ADR by plotting the change in IC and its 95% confidence limit over time for both. RESULTS: The chosen examples show that the BCPNN data-mining approach can identify drug-specific as well as group effects. In the known examples that served as test cases, beta-blocking agents other than practolol are not associated with sclerosing peritonitis, but all angiotensin-converting enzyme inhibitors are associated with coughing, as are antihistamines with heart-rhythm disorders and antipsychotics with myocarditis. The recently identified association between antipsychotics and myocarditis remains even after consideration of concomitant medication. CONCLUSION: The BCPNN can be used to improve the ability of a signal detection system to highlight group and drug-specific effects.

A Bayesian neural network method for adverse drug reaction signal generation.

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.