Acridone alkaloids as potent inhibitors of cathepsin V

Cathepsin V is a lysosomal cysteine peptidase highly expressed in thymus, testis and corneal epithelium. Eleven acridone alkaloids were isolated from Swinglea glutinosa (Bl.) Merr. (Rutaceae), with eight of them being identified as potent and reversible inhibitors of cathepsin V (IC(50) values ranging from 1.2 to 3.9 mu M). Detailed mechanistic characterization of the effects of these compounds on the cathepsin V-catalyzed reaction showed clear competitive inhibition with respect to substrate, with dissociation constants (K(i)) in the low micromolar range (2, K(i) = 1.2 mu M; 6, K(i) = 1.0 mu M; 7, K(i) = 0.2 mu M; and 11, K(i) = 1.7 mu M). Molecular modeling studies provided important insight into the structural basis for binding affinity and enzyme inhibition. Experimental and computational approaches, including biological evaluation, mode of action assessment and modeling studies were successfully employed in the discovery of a small series of acridone alkaloid derivatives as competitive inhibitors of catV. The most potent inhibitor (7) has a K(i) value of 200 nM. (C) 2011 Elsevier Ltd. All rights reserved.

Bayesian Estimation of the Logistic Positive Exponent IRT Model

A Bayesian inference approach using Markov Chain Monte Carlo (MCMC) is developed for the logistic positive exponent (LPE) model proposed by Samejima and for a new skewed Logistic Item Response Theory (IRT) model, named Reflection LPE model. Both models lead to asymmetric item characteristic curves (ICC) and can be appropriate because a symmetric ICC treats both correct and incorrect answers symmetrically, which results in a logical contradiction in ordering examinees on the ability scale. A data set corresponding to a mathematical test applied in Peruvian public schools is analyzed, where comparisons with other parametric IRT models also are conducted. Several model comparison criteria are discussed and implemented. The main conclusion is that the LPE and RLPE IRT models are easy to implement and seem to provide the best fit to the data set considered.