In vitro Metabolic Stability of Drugs and Applications of LC-MS in Metabolite Profiling

Metabolic stability of a compound is an important factor to be considered during the early stages of drug discovery. If the compound has poor metabolic stability, it never becomes a drug even though it has promising pharmacological characteristics. For example, a drug is quickly metabolized in the body; it does not have sufficient in vivo exposure levels and leads to the production of toxic, non-active or active metabolites. A drug is slowly metabolized in the body it could remain longer periods in the body and lead to unwanted adverse reactions, toxicity or may cause drug interactions. Metabolic stability assay is performed to understand the susceptibility of the compound to undergo biotransformation in the body. Intrinsic clearance of the compound is measured by metabolic stability assays. Different in vitro test systems including liver microsomes, hepatocytes, S9 fractions, cytosol, recombinant expressed enzymes, and cell lines are used to investigate the metabolic stability of drugs. Metabolite profiling is a vital part of the drug discovery process and LC–MS plays a vital role. The development of high-resolution (HR) MS technologies with improved mass accuracy, in conjunction with novel data processing techniques, has significantly improved the metabolite detection and identification process. HR-MS based data acquisition (ion intensity-dependent acquisition, accurate-mass inclusion list-dependent acquisition, isotope pattern-dependent acquisition, pseudo neutral loss-dependent acquisition, and mass defect-dependent acquisition) and data mining techniques (extracted ion chromatogram, product ion filter, mass defect filter, isotope pattern filter, neutral loss filter, background subtraction, and control sample comparison) facilitate the drug metabolite identification process.

Thermodynamic Definitions of Temperature and Kappa and Introduction of the Entropy Defect

This paper develops explicit and consistent definitions of the independent thermodynamic properties of temperature and the kappa index within the framework of nonextensive statistical mechanics and shows their connection with the formalism of kappa distributions. By defining the “entropy defect” in the composition of a system, we show how the nonextensive entropy of systems with correlations differs from the sum of the entropies of their constituents of these systems. A system is composed extensively when its elementary subsystems are independent, interacting with no correlations; this leads to an extensive system entropy, which is simply the sum of the subsystem entropies. In contrast, a system is composed nonextensively when its elementary subsystems are connected through long-range interactions that produce correlations. This leads to an entropy defect that quantifies the missing entropy, analogous to the mass defect that quantifies the mass (energy) associated with assembling subatomic particles. We develop thermodynamic definitions of kappa and temperature that connect with the corresponding kinetic definitions originated from kappa distributions. Finally, we show that the entropy of a system, composed by a number of subsystems with correlations, is determined using both discrete and continuous descriptions, and find: (i) the resulted entropic form expressed in terms of thermodynamic parameters; (ii) an optimal relationship between kappa and temperature; and (iii) the correlation coefficient to be inversely proportional to the temperature logarithm.

Uncovering Xenobiotics in the Dark Metabolome using Ion Mobility Spectrometry, Mass Defect Analysis and Machine Learning

The identification of xenobiotics in nontargeted metabolomic analyses is a vital step in understanding human exposure. Xenobiotic metabolism, excretion, and co-existence with other endogenous molecules however greatly complicate nontargeted studies. While mass spectrometry (MS)-based platforms are commonly used in metabolomic measurements, deconvoluting endogenous metabolites and xenobiotics is often challenged by the lack of xenobiotic parent and metabolite standards as well as the numerous isomers possible for each small molecule m/z feature. Here, we evaluate the use of ion mobility spectrometry coupled with MS (IMS-MS) and mass defect filtering in a xenobiotic structural annotation workflow to reduce large metabolomic feature lists and uncover potential xenobiotic classes and species detected in the metabolomic studies. To evaluate the workflow, xenobiotics having known high toxicities including per- and polyfluoroalkyl substances (PFAS), polycyclic aromatic hydrocarbons (PAHs), polychlorinated biphenyls (PCBs) and polybrominated diphenyl ethers (PBDEs) were examined. Initially, to address the lack of available IMS collision cross section (CCS) values for per- and polyfluoroalkyl substances (PFAS), 88 PFAS standards were evaluated with IMS-MS to both develop a targeted PFAS CCS library and for use in machine learning predictions. The CCS values for biomolecules and xenobiotics were then plotted versus m/z, clearly distinguishing the biomolecules and halogenated xenobiotics. The xenobiotic structural annotation workflow was then used to annotate potential PFAS features in NIST human serum. The workflow reduced the 2,423 detected LC-IMS-MS features to 80 possible PFAS with 17 confidently identified through targeted analyses and 48 additional features correlating with possible CompTox entries.

The mass-charge binding energy as an explanation for the superfine adjustment of the hadron and baryon masses

Quantum chromodynamics (QCD) describes how mass is created at the quark level. This mechanism is special, because the binding of quarks does not result in a mass loss or a release of energy, as in the case of the nuclear mass defect. Rather, mass is created by the binding of quarks. To achieve this binding, energy must be expended. At the same time, however, quarks are firmly bound to each other. Several authors have shown that the masses of the elementary particles as determined by means of QCD agree quite well with the experimentally determined values. In the following, superfine adjustment of the masses of the charged elementary particles is shown to be possible by considering the mass defect in terms of the mass-charge binding energy.