The molecular mechanism of carbofuran metabolism was investigated by molecular modeling using the energy-minimized active site of cytochrome P-450 cam . A feasible binding conformation of carbofuran was subjected to Monte Carlo (MC) conformational search and molecular dynamics (MD) simulation in the active site to obtain the global minimum of the enzyme-substrate complex. For exploring its conformational space, MC was found to be more effective than simple MD. Enzyme-substrate interactions were examined in detail in all low-energy states. Distances between the active ferryl oxygen of the central heme unit and the reactive centers of carbofuran involved in oxidative metabolism were monitored. H-bonding interaction between the carbamate group of carbofuran and the Tyr96-OH group, as well as the steric effects of Val247 and Val295, were found to be crucial for the orientation of carbofuran. The preferred formation of 3-hydroxycarbofuran, the major primer metabolite, could be rationalized by the model. The configuration at the C3 atom was predicted to be S in accordance with the stereospecificity reported for the natural substrate.

A novel chemocentric approach to identifying cancer-relevant targets is introduced. Starting with a large chemical collection, the strategy uses the list of small molecule hits arising from a differential cytotoxicity screening on tumor HCT116 and normal MRC-5 cell lines to identify proteins associated with cancer emerging from a differential virtual target profiling of the most selective compounds detected in both cell lines. It is shown that this smart combination of differential in vitro and in silico screenings (DIVISS) is capable of detecting a list of proteins that are already well accepted cancer drug targets, while complementing it with additional proteins that, targeted selectively or in combination with others, could lead to synergistic benefits for cancer therapeutics. The complete list of 115 proteins identified as being hit uniquely by compounds showing selective antiproliferative effects for tumor cell lines is provided.

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Cross-substituted derivatives of piperonyl butoxide (PBO) and MB-599 (proposed common name: verbutin) were synthesized and investigated as carbofuran and permethrin synergists against housefly, Musca domestica L. The majority of PBO and MB-599 derivatives were significantly more potent synergists for carbofuran than for permethrin. PBO, the most important representative of this series was not the most potent synergist for carbofuran or for permethrin. Cleavage of the methylenedioxy ring of methylenedioxyphenyl (MDP) polyether compounds resulted in complete loss of synergistic activity with both insecticides, but it could be restored or even improved by incorporating an alkynyl ether moiety into the molecule. The improved synergistic activity was found to be closely associated with the 2-butynyloxymethyl side-chain, suggesting that this can be regarded as a characteristic synergophore group. MB-599, one of the most promising compounds bearing this group showed considerably higher activity with carbofuran (synergist ratio, SR = 37.8) than with PBO (SR = 6.4). There was no significant difference between synergistic activities of MB-599 (SR = 4.6) and PBO (SR = 4) for permethrin.

Cytochrome P450 (CP450) catalyzed oxidative metabolism of carbofuran (1), carbaryl (2), and pirimicarb (3) has been modeled using biomimetic oxidations catalyzed by iron(III) tetraarylporphyrins. Oxidation products of 1 were identified by comparison of HPLC retention times measured under standardized conditions for metabolites synthesized and characterized by (1)H and (13)C NMR spectroscopy. Comparison of product distributions to in vivo metabolic profiles revealed that the H(2)O(2)/meso-tetrakis(pentafluorophenyl)porphyrin iron(III) chloride [Fe(TF(20)PP)] system mimics the action of insect CP450s against carbofuran. The effectiveness of this system was further demonstrated by the biomimetic oxidation of other carbamate insecticides (2 and 3) monitored by HPLC/electrospray MS. The predictive power of this biomimetic model was compared to that of knowledge-based expert systems. Although similar models were recently applied in pharmaceutical research, the usefulness of this approach has first been demonstrated for the prediction of metabolic profiles of agrochemicals.