Processing of NMR and MS metabolomics data using chemometrics methods: a global tool for fungi biotransformation reactions monitoring.

INTRODUCTION: Biotransformation constitutes an important aspect of the drug discovery process, to mimic human metabolism of active principal ingredient but also to generate new chemical entities. Several microorganisms such as fungi are well adapted to transform drug, whether at the stage of screening or for large-scale production. OBJECTIVES: Due to the high chemical complexity of the biotransformation media, it seems attractive to develop new analytical strategies in order to guarantee an adequate monitoring and optimize the production of targeted metabolites or drug candidates. METHODS: The model designed for this purpose concerns the biotransformation of a potential histamine H3 antagonist (S38093) in order to produce phase I metabolites. MS, NMR and chemometrics tools were used to monitor biotransformation reactions. RESULTS: First, a screening of eleven filamentous fungi was carried out by UHPLC-UV-MS and principal component analysis to select the best candidates. Subsequently, MS (tR, m/z) and NMR (1H, JRES) fingerprints associated with Consensus OPLS-DA multiblock approach were used to better understand the bioreaction mechanisms in terms of nutrient consumption and hydroxylated metabolites production. Then an experimental design was set up to optimize the production conditions (pH, kinetic) of these target metabolites. CONCLUSION: This study demonstrates how NMR and MS acquisitions combined with chemometric methods offer an innovative analytical strategy to have a grasp of functionalization mechanisms, and identify metabolites and other compounds (amino acids, nutrients, etc.) in complex biotransformation mixtures.

A Deeper Investigation of Drug Degradation Mixtures Using a Combination of MS and NMR Data: Application to Indapamide.

A global approach that is based on a combination of mass spectrometry (MS) and nuclear magnetic resonance (NMR) data has been developed for a complete and rapid understanding of drug degradation mixtures. We proposed a workflow based on a sample preparation protocol that is compatible to MS and NMR, the selection of the most appropriate experiments for each technique, and the implementation of prediction software and multivariable analysis method for a better interpretation and correlation of MS and NMR spectra. We have demonstrated the efficient quantification of the remaining active pharmaceutical ingredient (API). The unambiguous characterization of degradation products (DPs) was reached while using the potential of ion mobility-mass spectrometry (IM-MS) for fragment ions filtering (HDMSE) and the implementation of two-dimensional (2D) NMR experiments with the non-uniform sampling (NUS) method. We have demonstrated the potential of quantitative NMR (qNMR) for the estimation of low level DPs. Finally, in order to simultaneously monitor multi-samples, the contribution of partial least squares (PLS) regression was evaluated. Our methodology was tested on three indapamide forced degradation conditions (acidic, basic, and oxidative) and it could be easily transposed in the drug development field to assist in the interpretation of complex mixtures (stability studies, impurities profiling, and biotransformation screening).

Application of chemometric algorithms to MALDI mass spectrometry imaging of pharmaceutical tablets.

During drug product development, the nature and distribution of the active substance have to be controlled to ensure the correct activity and the safety of the final medication. Matrix assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI), due to its structural and spatial specificities, provides an excellent way to analyze these two critical parameters in the same acquisition. The aim of this work is to demonstrate that MALDI-MSI, coupled with four well known multivariate statistical analysis algorithms (PCA, ICA, MCR-ALS and NMF), is a powerful technique to extract spatial and spectral information about chemical compounds from known or unknown solid drug product formulations. To test this methodology, an in-house manufactured tablet and a commercialized Coversyl(®) tablet were studied. The statistical analysis was decomposed into three steps: preprocessing, estimation of the number of statistical components (manually or using singular value decomposition), and multivariate statistical analysis. The results obtained showed that while principal component analysis (PCA) was efficient in searching for sources of variation in the matrix, it was not the best technique to estimate an unmixing model of a tablet. Independent component analysis (ICA) was able to extract appropriate contributions of chemical information in homogeneous and heterogeneous datasets. Non-negative matrix factorization (NMF) and multivariate curve resolution-alternating least squares (MCR-ALS) were less accurate in obtaining the right contribution in a homogeneous sample but they were better at distinguishing the semi-quantitative information in a heterogeneous MALDI dataset.

Pyridine to aniline: an exceptional biologically driven rearrangement.

During the course of our study on the innovative ligand for nicotinic acetylcholinergic receptors, LNAChR, and in order to assess activity and toxicity profiles of the drug's metabolites, synthesis of the main metabolites was undertaken. This synthesis work was done in parallel by organic chemistry and by biotransformation of LNAChR. Filamentous fungus Aspergillus alliaceus (NRRL 315) neatly afforded three of the main metabolites, one of which arose from a very unexpected and very uncommon rearrangement.