A comparison of the inhibitory action of 5-(substituted-benzyl)-2,4-diaminopyrimidines on dihydrofolate reductase from chicken liver with that from bovine liver

Antibiotic adjuvants: multicomponent anti-infective strategies

The unremitting emergence of multidrug-resistant bacterial pathogens highlights a matching need for new therapeutic options. For example, new carbapenemases such as KPC (class A Klebsiella pneumoniae) and NDM-1 (New Delhi metallo-β-lactamase 1) are surfacing, resulting in almost total resistance to β-lactam antibiotics. Furthermore, resistance is quickly disseminated, not only in the healthcare sector, but also within the community at large, because many resistance determinants are carried on mobile genetic elements readily shared among pathogens. The absence of new antibiotics has led to a growing reliance on older, more toxic drugs such as colistin, but resistance to these is already arising. One approach to combat this growing problem is the use of combination drug antibiotic adjuvant therapy, which potentiates the activity of antibiotics. Here, we review the current situation and discuss potential drug combinations that may increase the potency of antibiotics in the future. Adjuvant therapies include antibiotic combinations, synergy between antibiotics and nonantibiotics, inhibition of resistance and molecules that alter the physiology of antibiotic-insensitive cells, such as those in biofilms. We provide a rationale for these multicomponent strategies, highlighting current research and important considerations for their clinical use and pharmacological properties.

Recombinant bovine dihydrofolate reductase produced by mutagenesis and nested PCR of murine dihydrofolate reductase cDNA

Recent reports of the slow-tight binding inhibition of bovine liver dihydrofolate reductase (bDHFR) in the presence of polyphenols isolated from green tea leaves has spurred renewed interest in the biochemical properties of bDHFR. Earlier studies were done with native bDHFR but in order to validate models of polyphenol binding to bDHFR, larger quantities of bDHFR are necessary to support structural studies. Bovine DHFR differs from its closest sequence homologue, murine DHFR, by 19 amino acids. To obtain the bDHFR cDNA, murineDHFR cDNA was transformed by a series of nested PCRs to reproduce the amino acid coding sequence for bovine DHFR. The bovine liver DHFR cDNA has an open reading frame of 561 base pairs encoding a protein of 187 amino acids that has a high level of conservation at the primary sequence level with other DHFR enzymes, and more so for the amino acid residues in the active site of the mammalian DHFR enzymes. Expression of the bovine DHFR cDNA in bacterial cells produced a stable recombinant protein with high enzymatic activity and kinetic properties similar to those previously reported for the native protein.

Synthesis and estrogen receptor affinity of a 4-hydroxytamoxifen-labeled ligand for diagnostic imaging

A 10-step synthesis of a novel 4-hydroxytamoxifen-DTPA ligand (HOTam-DTPA) is reported. Tamoxifen and its primary metabolite 4-hydroxytamoxifen are common estrogen receptor ligands. Consequently, tamoxifen has found utility as the targeting component of various diagnostic agents for selective imaging of estrogen receptor-rich tissue, specifically breast cancer. An L-aspartic acid-derived DTPA analogue was attached to the ethyl side chain of 4-hydroxy-tamoxifen using N,N'-dimethylethylenediamine as a hydrophilic linker. A competitve estrogen receptor binding assay using [3H]-17beta-estradiol was performed to determine the effect of the ethyl side chain modification on estrogen receptor affinity. The results show that while the relative affinity of HOTam-DTPA for the estrogen receptor is approximately 10-fold lower than that of tamoxifen, it still remains a potent ligand at relatively low concentrations.

Quantitative structure-activity relationships by neural networks and inductive logic programming. I. The inhibition of dihydrofolate reductase by pyrimidines

Neural networks and inductive logic programming (ILP) have been compared to linear regression for modelling the QSAR of the inhibition of E. coli dihydrofolate reductase (DHFR) by 2,4-diamino-5-(substituted benzyl)pyrimidines, and, in the subsequent paper [Hirst, J.D., King, R.D. and Sternberg, M.J.E. J. Comput.-Aided Mol. Design, 8 (1994) 421], the inhibition of rodent DHFR by 2,4-diamino-6,6-dimethyl-5-phenyl-dihydrotriazines. Cross-validation trials provide a statistically rigorous assessment of the predictive capabilities of the methods, with training and testing data selected randomly and all the methods developed using identical training data. For the ILP analysis, molecules are represented by attributes other than Hansch parameters. Neural networks and ILP perform better than linear regression using the attribute representation, but the difference is not statistically significant. The major benefit from the ILP analysis is the formulation of understandable rules relating the activity of the inhibitors to their chemical structure.

Papain hydrolysis of X-phenyl-N-methanesulfonyl glycinates: a quantitative structure-activity relationship and molecular graphics analysis

The hydrolysis of 32 X-phenyl-N-methanesulfonyl glycinates by papain was investigated. It was found that the variation in the Michaelis constants could be rationalized by the following correlation equation: log 1/Km = 0.61 pi '3 + 0.46 MR4 + 0.55 sigma + 2.00 with a correlation coefficient of 0.945. In this expression, pi '3 is the hydrophobic constant for the more lipophilic of the two possible meta substituents, MR4 is the molar refractivity of 4-substituents, and sigma is the Hammett constant summed for all substituents. Using this equation, we designed, synthesized, and successfully predicted Km for a new congener intended to maximize binding (1/Km). The interactions involved in enzyme-substrate binding, as characterized by the correlation equation, are interpreted using a computer-constructed color three-dimensional-graphics molecular model of the enzyme active site. The nonenzymatic hydrolysis (both acid and basic) of phenyl hippurates yield rate constants which are well correlated by Hammett equations; however, log k for both acid and alkaline hydrolysis are not linearly related to log 1/Km or log kcat/Km.