The antibacterial activity of some substituted cysteine sulphonamides and peptides containing cysteine sulphonamide

The authors investigated the antibacterial activity of some cysteine sulphonamides substituted in the sulphonamide group as well as that of some peptides containing cysteine sulphonamide. They found that the antibacterial activity of these compounds was in part considerably stronger than that of cysteine sulphonamide.     

Design of potent and selective linear antagonists of vasopressor (V1-receptor) responses to vasopressin

We report the solid-phase synthesis of 21 linear analogues of A and D, two nonselective antagonists of the vasopressor (V1) and antidiuretic (V2) responses to arginine vasopressin (AVP). A is Aaa-D-Tyr(Et)-Phe-Val-Asn-Abu-Pro-Arg-Arg-NH2 (where Aaa = adamantylacetyl at position 1). D is the des-Arg9 analogue of A. Nine new analogues of A (1-9) and 12 new analogues of D (10-21) were obtained. The following substitutions either alone or in combination were incorporated in A and/or in D: phenylacetic acid (Phaa) and tert-butylacetic acid (t-Baa) at position 1; D-Tyr2, D-Tyr(Me)2; Gln4; Arg6, Lys6, Orn6, MeAla7. The nine new analogues of A are (1) [Arg6], (2) [Lys6], (3) [Orn6], (4) [Phaa1,Lys6], (5) [Phaa1,Orn6], (6) [D-Tyr2], (7) [D-Tyr2,Arg6], (8) [Phaa1,D-Tyr2], (9) [Phaa1,D-Tyr2,Arg6]. The 12 new analogues of D are (10) [Arg6], (11) [Lys6], (12) [Orn6], (13) [Phaa1,Lys6], (14) [Phaa1,Gln4,Lys6], (15) [Phaa,D-Tyr(Me)2,Lys6], (16) [Phaa,D-Tyr(Me)2,Gln4,Lys6], (17) [Phaa1,D-Tyr2,Gln4,Lys6], (18) [t-Baa1,Lys6], (19) [t-Baa1,Gln4,Lys6], (20) [Arg6,MeAla7], (21) [t-Baa1,Arg6,MeAla7]. All 21 peptides were examined for agonistic and antagonistic potencies in AVP V2 and V1 assays in rats. With the exception of 6, the eight remaining new analogues of A are equipotent or more potent than A as V1 antagonists. Peptides 2-9 are less potent than A as V2 antagonists. Three, 4, 5, and 9, exhibit significant gains in anti-V1/anti-V2 selectivities (selectivity ratio = 41, 14, and infinite, respectively), compared to A (anti-V1, pA2 = 7.75 +/- 0.07; selectivity ratio = 0.44). Peptide 9 is unique in both series. It is a highly potent V1 antagonist (anti-V1 pA2 = 8.62 +/- 0.11 and is the first linear peptide to exhibit substantial antidiuretic agonism (4.1 +/- 0.2 units/mg). With the exception of 12, the remaining 11 analogues of D are 8-40 times more potent than D as V1 antagonists. Eight of these peptides exhibit significant gains in anti-V1/anti-V2 selectivities compared to D (anti-V1 pA2 = 7.43 +/- 0.06; selectivity ratio = 1.6).(ABSTRACT TRUNCATED AT 400 WORDS)     

Ligand-based design, synthesis and primary in vivo screening of pyrrole derivatives as potential tricyclic anti-inflammatory agents

Twelve new compounds were designed as 5-aryl-1H-pyrrole analogs of celecoxib (CAS 169590-42-5) and were synthesized by Paal-Knorr cyclization in three series according to 1H-substitution: derivatives with salicylic acid, pyrazolone or isonicotinamide residues. The average physico-chemical and steric similarity between the prototype and the new analogs (completed with two previously synthesized related products) was assessed to be 82 % and therefore considered as a reliable prerequisite for spatial compatibility and effective binding to the cyclooxygenase (COX) enzymes. The anti-inflammatory effects were determined in acute inflammation model using the carrageenan-induced rat paw edema assay on male Wistar rats (180-200 g) at doses of 10, 20 and 40 mg/kg, i.p. Six of the new products showed higher percent of inhibition (up to 100 %) compared to the highly selective COX-2 inhibitor celecoxib and the nonselective indometacin (CAS 53-86-1) used as reference compounds. Ethyl 1-(1,5-di-methyl-3-oxo-2-phenyl-2,3-dihydro-1H-4-pyrazolyl)-2-methyl-5-phenyl-1H-3-pyrrolecarboxylate (2b), ethyl 5-(4-chlor-ophenyl)-2-methyl-1-[(4-pyridylcarbonyl) amino]-1H-3-pyrrolecarboxylate (3c) and 5-[3-acetyl-2-methyl-5-(4-methylphenyl)-1H-1-pyrrolyl] -2-hydroxybenzoic acid (4b) were pointed out as the most active representatives of each of the three tested sub-series.     

New Evidences about the Carcinogenic Effects of Ochratoxin A and Possible Prevention by Target Feed Additives

A review of the carcinogenic effects of ochratoxin A (OTA) on various tissues and internal organs in laboratory and farm animals is made. Suggestions are made regarding how to recognize and differentiate the common spontaneous neoplastic changes characteristic for advanced age and the characteristic neoplasia in different tissues and organs in laboratory animals/poultry exposed to OTA. The synergistic effects of OTA together with its natural combination of penicillic acid are also investigated regarding possible carcinogenic effects. The malignancy and the target location of OTA-induced neoplasia is studied. The sex-differences of such neoplasia are investigated in the available literature. The time of appearance of the first neoplasia is investigated in long-term carcinogenic studies with OTA-treated animals. The possibility of target feed additives or herbs to counteract the toxic and carcinogenic effects of OTA is studied in the available literature. Some effective manners of prophylaxis and/or prevention against OTA contamination of feedstuffs/foods or animal production are suggested. The suitability of various laboratory animals to serve as experimental model for humans with regard to OTA-induced tumorigenesis is investigated.     

Inference for dynamic and latent variable models via iterated,perturbed Bayes maps

Iterated filtering algorithms are stochastic optimization procedures for latent variable models that recursively combine parameter perturbations with latent variable reconstruction. Previously, theoretical support for these algorithms has been based on the use of conditional moments of perturbed parameters to approximate derivatives of the log likelihood function. Here, a theoretical approach is introduced based on the convergence of an iterated Bayes map. An algorithm supported by this theory displays substantial numerical improvement on the computational challenge of inferring parameters of a partially observed Markov process.An iterated filtering algorithm was originally proposed for maximum likelihood inference on partially observed Markov process (POMP) models by Ionides et al. (1). Variations on the original algorithm have been proposed to extend it to general latent variable models (2) and to improve numerical performance (3, 4). In this paper, we study an iterated filtering algorithm that generalizes the data cloning method (5, 6) and is therefore also related to other Monte Carlo methods for likelihood-based inference (7–9). Data cloning methodology is based on the observation that iterating a Bayes map converges to a point mass at the maximum likelihood estimate. Combining such iterations with perturbations of model parameters improves the numerical stability of data cloning and provides a foundation for stable algorithms in which the Bayes map is numerically approximated by sequential Monte Carlo computations.We investigate convergence of a sequential Monte Carlo implementation of an iterated filtering algorithm that combines data cloning, in the sense of Lele et al. (5), with the stochastic parameter perturbations used by the iterated filtering algorithm of (1). Lindström et al. (4) proposed a similar algorithm, termed fast iterated filtering, but the theoretical support for that algorithm involved unproved conjectures. We present convergence results for our algorithm, which we call IF2. Empirically, it can dramatically outperform the previous iterated filtering algorithm of ref. 1, which we refer to as IF1. Although IF1 and IF2 both involve recursively filtering through the data, the theoretical justification and practical implementations of these algorithms are fundamentally different. IF1 approximates the Fisher score function, whereas IF2 implements an iterated Bayes map. IF1 has been used in applications for which no other computationally feasible algorithm for statistically efficient, likelihood-based inference was known (10–15). The extra capabilities offered by IF2 open up further possibilities for drawing inferences about nonlinear partially observed stochastic dynamic models from time series data.Iterated filtering algorithms implemented using basic sequential Monte Carlo techniques have the property that they do not need to evaluate the transition density of the latent Markov process. Algorithms with this property have been called plug-and-play (12, 16). Various other plug-and-play methods for POMP models have been recently proposed (17–20), due largely to the convenience of this property in scientific applications.