Present and future pharmacotherapy for benign prostatic hyperplasia

Around 50% of men 51-60 years of age have pathological benign prostatic hyperplasia (BPH). Pharmacotherapy for BPH includes the 5alpha-reductase inhibitor finasteride, and alpha(1)-adrenoceptor antagonists. Finasteride reduces prostate volume and symptom scores, while increasing peak urinary flow rates. The main problem with finasteride treatment is that it increases the incidence of ejaculation disorders. All of the alpha(1)-adrenoceptor antagonists have been shown to reduce symptom scores and increase peak urinary flow rates in BPH. The nonselective alpha(1)-adrenoceptor antagonists (prazosin, terazosin and doxazosin) were developed as antihypertensives, and hypotensive-related side effects are the main problem with these agents in BPH. These side effects can be diminished by reducing peak concentrations of the drugs, as with once-daily alfuzosin, or by using the uroselective antagonist tamsulosin. Phytopharmaceuticals are commonly used in the treatment of BPH, such as saw palmetto berry which has been shown to improve the symptoms and peak urinary flow rate. Androgen receptor antagonists are not used in BPH because of their adverse effects. Newer drugs under development for the treatment of BPH include alpha(1)-adrenoceptor antagonists that show more selectivity for alpha(1A)-adrenoceptors than tamsulosin, combined 5alpha-reductase/alpha(1)-adrenoceptor inhibitors and combined type 1/type 2 5alpha-reductase inhibitors. New targets for the drug treatment of BPH include indothelin, growth factors, estrogens and the phosphodiesterase isoenzymes.

Caveolae — from ultrastructure to molecular mechanisms

Almost 50 years after the first sighting of small pits that covered the surface of mammalian cells, investigators are now getting to grips with the detailed workings of these enigmatic structures that we now know as caveolae.

An EM-based Semi-Parametric Mixture Model Approach to the Regression Analysis of Competing-Risks Data

We consider a mixture model approach to the regression analysis of competing-risks data. Attention is focused on inference concerning the effects of factors on both the probability of occurrence and the hazard rate conditional on each of the failure types. These two quantities are specified in the mixture model using the logistic model and the proportional hazards model, respectively. We propose a semi-parametric mixture method to estimate the logistic and regression coefficients jointly, whereby the component-baseline hazard functions are completely unspecified. Estimation is based on maximum likelihood on the basis of the full likelihood, implemented via an expectation-conditional maximization (ECM) algorithm. Simulation studies are performed to compare the performance of the proposed semi-parametric method with a fully parametric mixture approach. The results show that when the component-baseline hazard is monotonic increasing, the semi-parametric and fully parametric mixture approaches are comparable for mildly and moderately censored samples. When the component-baseline hazard is not monotonic increasing, the semi-parametric method consistently provides less biased estimates than a fully parametric approach and is comparable in efficiency in the estimation of the parameters for all levels of censoring. The methods are illustrated using a real data set of prostate cancer patients treated with different dosages of the drug diethylstilbestrol. Copyright (C) 2003 John Wiley Sons, Ltd.