Author Archives: Vera

Identification of metabolic biotransformations can significantly affect the drug discovery process. Since bioavailability, activity, toxicity, distribution, and final elimination all depend on metabolic biotransformations, it would be extremely advantageous if this information could be produced early in the discovery phase. Once obtained, this information can help chemists to judge whether a potential candidate should be eliminated from the pipeline or modified to improve chemical stability or safety of new compounds. The use of in silico methods to predict the site of metabolism in phase I cytochrome-mediated reactions is a starting point in any metabolic pathway prediction.

This paper presents a new method, specifically designed for chemists, that provides the cytochrome involved and the site of metabolism for any human cytochrome P450 (CYP) mediated reaction acting on new substrates. The methodology can be applied automatically to all the cytochromes for which 3D structure is known and can be used by chemists to detect positions that should be protected in order to avoid metabolic degradation or to check the suitability of a new scaffold or prodrug.

The fully automated procedure is also a valuable new tool in early ADME-Tox assays (absorption, distribution, metabolism, and excretion toxicity assays), where drug safety and metabolic profile patterns must be evaluated as soon, and as early, as possible.

Predictions of the metabolic sites for new chemical entities, synthesized or only virtual, are important in the early phase of drug discovery to guide chemistry efforts in the synthesis of new compounds with reduced metabolic liability. This information can now be obtained from in silico predictions, and therefore, a thorough and unbiased evaluation of the computational techniques available is needed. Several computational methods to predict the metabolic hot spots are emerging.

In this study, metabolite identification using MetaSite and a docking methodology, GLUE, were compared. Moreover, the published CYP3A4 crystal structure and computed CYP3A4 homology models were compared for their usefulness in predicting metabolic sites. A total of 227 known CYP3A4 substrates reported to have one or more metabolites adding up to 325 metabolic pathways were analyzed. Distance-based fingerprints and four-point pharmacophore derived from GRID molecular interaction fields were used to characterize the substrate and protein in MetaSite and the docking methodology, respectively. The CYP3A4 crystal structure and homology model with the reactivity factor enabled achieved a similar prediction success (78%) using the MetaSite method.

The docking method had a relatively lower prediction success (∼57% for the homology model), although it still may provide useful insights for interactions between ligand and protein, especially for uncommon reactions. The MetaSite methodology is automated, rapid, and has relatively accurate predictions compared with the docking methodology used in this study.

The cytochrome P450 (CYP) family is composed of a large group of monooxygenases that mediate the metabolism of xenobiotics and endogenous compounds. CYP2C9, one of the major isoforms of the CYP family, is responsible for the phase I metabolism of a variety of drugs. The aim of the present investigation is to use rational design together with MetaSite, a metabolism site prediction program, to synthesize compounds that retain their pharmacological effects but that are metabolically more stable in the presence of CYP2C9.

The model compound for the study is the nonsteroidal anti-inflammatory drug celecoxib, a COX-2 selective inhibitor and known CYP2C9 substrate. Thirteen analogs of celecoxib were designed, synthesized, and evaluated with regard to their metabolic properties and pharmacologic effects. The docking solutions and the predictions from MetaSite gave useful information leading to the design of new compounds with improved metabolic properties.

Chemical modifications of drugs induced by phase I biotransformations significantly affect their pharmacokinetic properties. Because the metabolites produced can themselves have a pharmacological effect and an intrinsic toxicity, medicinal chemists need to accurately predict the sites of metabolism (SoM) of drugs as early as possible.

However, site of metabolism prediction is rarely accompanied by a prediction of the relative abundance of the various metabolites. Such a prediction would be a great help in the study of drug–drug interactions and in the process of reducing the toxicity of potential drug candidates. The aim of this paper is to present recent developments in the prediction of xenobiotic metabolism and to use concrete examples to explain the computational mechanism employed.

Microalgae have recently become a popular functional food due to their health benefits. Sulfolipids, a class of substances abundant in this matrix, have been reported to have interesting bioactivities, such as anti-carcinogenic activity. However, despite the potential interest in sulfolipids, a dedicated analytical method for their characterization is currently lacking but would significantly increase the coverage of sulfolipids with respect to the direct lipidomic analysis.

To achieve this goal, in this work a procedure, based on graphitized carbon black solid phase extraction, was developed for clean-up and enrichment of sulfolipids (sulfoquinovosyldiacylglycerols and sulfoquinovosylmonoacylglycerols) and it was applied to spirulina (Arthrospira platensis) microalgae. A careful study of the solid phase extraction conditions was performed, first to maximize the recovery of reference standards, then to increase the total number of identified sulfolipids from the spirulina lipid extract. All samples were analysed by ultra-high performance liquid chromatography coupled to high resolution mass spectrometry and lipids were tentatively identified by Lipostar, for a reliable lipid structure assignment. The developed method was compared to the direct lipidomic analysis without enrichment, to establish the enrichment efficiency in terms of number of identifications.

From the comparison, the enrichment procedure proved better and allowed the tentative identification of 199 sulfolipids, which is the largest number reported so far for the Arthrospira platensis species. The described method was validated in terms of precision, accuracy, recovery, limit of quantitation and detection for two sulfolipids. Finally, a relative lipid quantitation based on peak area was carried out on the microalgae sample, which indicated nine abundant sulfolipids as representing ca. 60% of sulfolipids in spirulina microalgae.