Successful application of serum shift prediction models to the design of dual targeting inhibitors of bacterial gyrase B and topoisomerase IV with improved in vivo efficacy.

A series of dual targeting inhibitors of bacterial gyrase B and topoisomerase IV were identified and optimized to mid-to-low nanomolar potency against a variety of bacteria. However, in spite of seemingly adequate exposure achieved upon IV administration, the in vivo efficacy of the early lead compounds was limited by high levels of binding to serum proteins. To overcome this limitation, targeted serum shift prediction models were generated for each subclass of interest and were applied to the design of prospective analogs. As a result, numerous compounds with comparable antibacterial potency and reduced protein binding were generated. These efforts culminated in the synthesis of compound 10, a potent inhibitor with low serum shift that demonstrated greatly improved in vivo efficacy in two distinct rat infection models.

Novel dual-targeting benzimidazole urea inhibitors of DNA gyrase and topoisomerase IV possessing potent antibacterial activity: intelligent design and evolution through the judicious use of structure-guided design and structure-activity relationships.

The discovery of new antibacterial agents with novel mechanisms of action is necessary to overcome the problem of bacterial resistance that affects all currently used classes of antibiotics. Bacterial DNA gyrase and topoisomerase IV are well-characterized clinically validated targets of the fluoroquinolone antibiotics which exert their antibacterial activity through inhibition of the catalytic subunits. Inhibition of these targets through interaction with their ATP sites has been less clinically successful. The discovery and characterization of a new class of low molecular weight, synthetic inhibitors of gyrase and topoisomerase IV that bind to the ATP sites are presented. The benzimidazole ureas are dual targeting inhibitors of both enzymes and possess potent antibacterial activity against a wide spectrum of relevant pathogens responsible for hospital- and community-acquired infections. The discovery and optimization of this novel class of antibacterials by the use of structure-guided design, modeling, and structure-activity relationships are described. Data are presented for enzyme inhibition, antibacterial activity, and in vivo efficacy by oral and intravenous administration in two rodent infection models.

N-Substituted amino acid N'-benzylamides: synthesis, anticonvulsant, and metabolic activities.

Amino acid amides (AAA) were prepared and evaluated in seizure models. The AAA displayed moderate-to-excellent activity in the maximal electroshock seizure (MES) test and were devoid of activity in the subcutaneous Metrazol-induced (scMet) seizure test. The AAA anticonvulsant activity was neither strongly influenced by the C(2) substituent nor by the degree of terminal amine substitution. An in vitro metabolism study suggested that the structure-activity relationship pattern was due, in part, to metabolic processes that occurred at the N-terminal amine unit.

Enantioselective total synthesis of borrelidin.

The first total synthesis of the natural product borrelidin is described. The propionate fragment of the molecule was concisely synthesized through catalytic enantioselective reductive aldol reactions, a catalytic Negishi coupling, and a catalytic directed hydrogenation. The propionate segment was then fused to the vinyl iodide fragment through a catalytic Sonogashira coupling. Subsequent catalytic hydrostannylation and catalytic cyanation allowed access to the target structure.

Design and evaluation of affinity labels of functionalized amino acid anticonvulsants.

Studies have shown that functionalized amino acids (FAA) exhibit outstanding activity in the maximal electroshock-induced seizure (MES) test in rodents. Affinity labels patterned in part after the potent antiepileptic (R)-N-benzyl-2-acetamido-3-methoxypropionamide ((R)-2) have been prepared as mechanistic probes to learn the pharmacological basis for FAA function. The chemical reactivity of the affinity labels with nucleophiles was assessed, and the labels were evaluated in in vitro radioligand assays and in the MES tests in rodents. The affinity labels did not bind to receptors known to effect seizure spread. Three affinity labels, (R,S)-N-benzyl-2-acetamido-6-isothiocyanatohexanamide ((R,S)-5), (R)-N-(4-isothiocyanatobenzyl)-2-acetamido-3-methoxypropionamide ((R)-6), and (R)-N-(3-isothiocyanatobenzyl)-2-acetamido-3-methoxypropionamide ((R)-7), possessed excellent in vivo anticonvulsant activity and exhibited maximal activity at later time periods than typically observed for FAA. The anticonvulsant activity of 6 and 7 resided primarily in the (R)-enantiomer and the activity of (R)-6 and (R)-7 in rats (po) exceeded that of phenytoin. The chemical properties, pharmacological profile, and marked stereospecificity associated with 6 and 7 anticonvulsant activity make these compounds useful pharmacological tools for the study of the mode of action of FAA.

Quantitative structure-activity relationship analysis of functionalized amino acid anticonvulsant agents using k nearest neighbor and simulated annealing PLS methods.

We report the development of rigorously validated quantitative structure-activity relationship (QSAR) models for 48 chemically diverse functionalized amino acids with anticonvulsant activity. Two variable selection approaches, simulated annealing partial least squares (SA-PLS) and k nearest neighbor (kNN), were employed. Both methods utilize multiple descriptors such as molecular connectivity indices or atom pair descriptors, which are derived from two-dimensional molecular topology. QSAR models with high internal accuracy were generated, with leave-one-out cross-validated R(2) (q(2)) values ranging between 0.6 and 0.8. The q(2) values for the actual dataset were significantly higher than those obtained for the same dataset with randomly shuffled activity values, indicating that models were statistically significant. The original dataset was further divided into several training and test sets, with highly predictive models providing q(2) values greater than 0.5 for the training sets and R(2) values greater than 0.6 for the test sets. These models were capable of predicting with reasonable accuracy the activity of 13 novel compounds not included in the original dataset. The successful development of highly predictive QSAR models affords further design and discovery of novel anticonvulsant agents.