Two diketopiperazines, XR334 (1) and the novel compound XR330 (2), were isolated from the lyophilised biomass of an unidentified Streptomyces sp. Their structures were elucidated on the basis of spectroscopic studies and confirmed by chemical synthesis. Both compounds inhibited plasminogen activator inhibitor-1 activity in an amidolytic assay of tissue plasminogen activator mediated plasmin generation. Compound1 also enhanced fibrinolysis ex vivo and protected against thrombus formation in the rat. These diketopiperazines represent the first low molecular weight inhibitors of plasminogen activator inhibitor-1, a physiological regulator of fibrinolysis protein including a 23 amino acid signal peptide with a predicted, glycosylated mass of 52 kD3). PAI-1 inhibition of tPA is mediated through a "bait" residue (Arg 346-Met 347) which mimics the normal substrate40. The physiological importance of PAI-1 has been demonstrated in transgenic mice which express high levels of human PAI-1 and suffer severe venous thrombosis5). An increase in the plasma concentration of PAI-1 has been proposed as a risk factor in thrombotic disease6). During a screening programme for inhibitors of PAI-1 activity we discovered a series of low molecular weight, nonpeptidyl inhibitors of PAI-1 from fermentation of an unidentified Streptomyces sp. Twoexamples of this series, XR334 (1) and the novel compound XR330 (2) were purified from the mycelium7) (Fig. 1). A series of closely related metabolites has been isolated from Streptomyces thioluteus including 18). Several of these compounds were reported to show weak antibacterial activity. Synthetic analogues of 1 have also been reported previously9~11}. Related diketopiperazines with putative cytotoxic activity have also been isolated from Micromonospora neiheunsis12). Wereport here an important new biologi- Structures of 1 and 2 isolated from the culture biomass of Streptomyces sp. and the structure confirmed by synthesis. Structures for two additional minor metabolites, 3 and 4, are proposed.
Binary mixtures of model systems consisting of the antibiotic ampicillin with either Escherichia coli or Staphylococcus aureus were subjected to pyrolysis mass spectrometry (PyMS). To deconvolute the pyrolysis mass spectra, so as to obtain quantitative information on the concentration of ampicillin in the mixtures, partial least squares regression (PLS), principal components regression (PCR), and fully interconnected feedforward artificial neural networks (ANNs) were studied. In the latter case, the weights were modified using the standard backpropagation algorithm, and the nodes used a sigmoidal squashing function. It was found that each of the methods could be used to provide calibration models which gave excellent predictions for the concentrations of ampicillin in samples on which they had not been trained. Furthermore, ANNs trained to predict the amount of ampicillin in E. coli were able to generalise so as to predict the concentration of ampicillin in a S. aureus background, illustrating the robustness of ANNs to rather substantial variations in the biological background. The PyMS of the complex mixture of ampicillin in bacteria could not be expressed simply in terms of additive combinations of the spectra describing the pure components of the mixtures and their relative concentrations. Intermolecular reactions took place in the pyrolysate, leading to a lack of superposition of the spectral components and to a dependence of the normalized mass spectrum on sample size. Samples from fermentations of a single organism in a complex production medium were also analyzed quantitatively for a drug of commercial interest. The drug could also be quantified in a variety of mutant-producing strains cultivated in the same medium. The combination of PyMS and ANNs constitutes a novel, rapid, and convenient method for exploitation in strain improvement screening programs. 0 1994John Wiley & Sons, Inc.
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