Diabetes Mellitus (DM) is a multi-factorial chronic health condition that affects a large part of population and according to the World Health Organization (WHO) the number of adults living with ...diabetes is expected to increase. Since type 2 diabetes mellitus (T2DM) is suffered by the majority of diabetic patients (around 90-95%) and often the mono-target therapy fails in managing blood glucose levels and the other comorbidities, this review focuses on the potential drugs acting on multi-targets involved in the treatment of this type of diabetes. In particular, the review considers the main systems directly involved in T2DM or involved in diabetes comorbidities. Agonists acting on incretin, glucagon systems, as well as on peroxisome proliferation activated receptors are considered. Inhibitors which target either aldose reductase and tyrosine phosphatase 1B or sodium glucose transporters 1 and 2 are taken into account. Moreover, with a view at the multi-target approaches for T2DM some phytocomplexes are also discussed.
The main molecular mechanisms explaining the well-established antioxidant and reducing activity of N-acetylcysteine (NAC), the N-acetyl derivative of the natural amino acid l-cysteine, are summarised ...and critically reviewed. The antioxidant effect is due to the ability of NAC to act as a reduced glutathione (GSH) precursor; GSH is a well-known direct antioxidant and a substrate of several antioxidant enzymes. Moreover, in some conditions where a significant depletion of endogenous Cys and GSH occurs, NAC can act as a direct antioxidant for some oxidant species such as NO
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and HOX. The antioxidant activity of NAC could also be due to its effect in breaking thiolated proteins, thus releasing free thiols as well as reduced proteins, which in some cases, such as for mercaptoalbumin, have important direct antioxidant activity. As well as being involved in the antioxidant mechanism, the disulphide breaking activity of NAC also explains its mucolytic activity which is due to its effect in reducing heavily cross-linked mucus glycoproteins. Chemical features explaining the efficient disulphide breaking activity of NAC are also explained.
In this article, we offer an overview of the compared quantitative importance of biotransformation reactions in the metabolism of drugs and other xenobiotics, based on a meta-analysis of current ...research interests. Also, we assess the relative significance the enzyme (super)families or categories catalysing these reactions. We put the facts unveiled by the analysis into a drug discovery context and draw some implications. The results confirm the primary role of cytochrome P450-catalysed oxidations and UDP-glucuronosyl-catalysed glucuronidations, but they also document the marked significance of several other reactions. Thus, there is a need for several drug discovery scientists to better grasp the variety of drug metabolism reactions and enzymes and their consequences.
Which are the most important reactions and enzymes in drug metabolism? The answer depends on what ‘important’ means, but a meta-analysis unveils some surprising figures.
Obesity and type 2 diabetes (T2DM) are major public health concerns associated with serious morbidity and increased mortality. Both obesity and T2DM are strongly associated with adiposopathy, a term ...that describes the pathophysiological changes of the adipose tissue. In this review, we have highlighted adipose tissue dysfunction as a major factor in the etiology of these conditions since it promotes chronic inflammation, dysregulated glucose homeostasis, and impaired adipogenesis, leading to the accumulation of ectopic fat and insulin resistance. This dysfunctional state can be effectively ameliorated by the loss of at least 15% of body weight, that is correlated with better glycemic control, decreased likelihood of cardiometabolic disease, and an improvement in overall quality of life. Weight loss can be achieved through lifestyle modifications (healthy diet, regular physical activity) and pharmacotherapy. In this review, we summarized different effective management strategies to address weight loss, such as bariatric surgery and several classes of drugs, namely metformin, GLP-1 receptor agonists, amylin analogs, and SGLT2 inhibitors. These drugs act by targeting various mechanisms involved in the pathophysiology of obesity and T2DM, and they have been shown to induce significant weight loss and improve glycemic control in obese individuals with T2DM.
4-Hydroxynonenal (HNE), an electrophilic end-product deriving from lipid peroxidation, undergoes a heterogeneous set of biotransformations including enzymatic and non-enzymatic reactions. The former ...mostly involve red-ox reactions on the HNE oxygenated functions (phase I metabolism) and GSH conjugations (phase II) while the latter are due to the HNE capacity to spontaneously condense with nucleophilic sites within endogenous molecules such as proteins, nucleic acids and phospholipids. The overall metabolic fate of HNE has recently attracted great interest not only because it clearly determines the HNE disposal, but especially because the generated metabolites and adducts are not inactive molecules (as initially believed) but show biological activities even more pronounced than those of the parent compound as exemplified by potent pro-inflammatory stimulus induced by GSH conjugates. Similarly, several studies revealed that the non-enzymatic reactions, initially considered as damaging processes randomly involving all endogenous nucleophilic reactants, are in fact quite selective in terms of both reactivity of the nucleophilic sites and stability of the generated adducts. Even though many formed adducts retain the expected toxic consequences, some adducts exhibit well-defined beneficial roles as documented by the protective effects of sublethal concentrations of HNE against toxic concentrations of HNE. Clearly, future investigations are required to gain a more detailed understanding of the metabolic fate of HNE as well as to identify novel targets involved in the biological activity of the HNE metabolites. These studies are and will be permitted by the continuous progress in the analytical methods for the identification and quantitation of novel HNE metabolites as well as for proteomic analyses able to offer a comprehensive picture of the HNE-induced adducted targets. On these grounds, the present review will focus on the major enzymatic and non-enzymatic HNE biotransformations discussing both the molecular mechanisms involved and the biological effects elicited. The review will also describe the most important analytical enhancements that have permitted the here discussed advancements in our understanding of the HNE metabolic fate and which will permit in a near future an even better knowledge of this enigmatic molecule.
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•Recent advances of enzymatic and non-enzymatic detoxification of HNE are described.•Increasing importance in the biological activity of HNE metabolites.•Good analytical strategies are available for elucidating HNE detoxification pathways.•Critical balance between protective or damaging role of HNE.
(1) Background: Machine learning algorithms are finding fruitful applications in predicting the ADME profile of new molecules, with a particular focus on metabolism predictions. However, the ...development of comprehensive metabolism predictors is hampered by the lack of highly accurate metabolic resources. Hence, we recently proposed a manually curated metabolic database (MetaQSAR), the level of accuracy of which is well suited to the development of predictive models. (2) Methods: MetaQSAR was used to extract datasets to predict the metabolic reactions subdivided into major classes, classes and subclasses. The collected datasets comprised a total of 3788 first-generation metabolic reactions. Predictive models were developed by using standard random forest algorithms and sets of physicochemical, stereo-electronic and constitutional descriptors. (3) Results: The developed models showed satisfactory performance, especially for hydrolyses and conjugations, while redox reactions were predicted with greater difficulty, which was reasonable as they depend on many complex features that are not properly encoded by the included descriptors. (4) Conclusions: The generated models allowed a precise comparison of the propensity of each metabolic reaction to be predicted and the factors affecting their predictability were discussed in detail. Overall, the study led to the development of a freely downloadable global predictor, MetaClass, which correctly predicts 80% of the reported reactions, as assessed by an explorative validation analysis on an external dataset, with an overall MCC = 0.44.
(1) Background: Data accuracy plays a key role in determining the model performances and the field of metabolism prediction suffers from the lack of truly reliable data. To enhance the accuracy of ...metabolic data, we recently proposed a manually curated database collected by a meta-analysis of the specialized literature (MetaQSAR). Here we aim to further increase data accuracy by focusing on publications reporting exhaustive metabolic trees. This selection should indeed reduce the number of false negative data. (2) Methods: A new metabolic database (MetaTREE) was thus collected and utilized to extract a dataset for metabolic data concerning glutathione conjugation (MT-dataset). After proper pre-processing, this dataset, along with the corresponding dataset extracted from MetaQSAR (MQ-dataset), was utilized to develop binary classification models using a random forest algorithm. (3) Results: The comparison of the models generated by the two collected datasets reveals the better performances reached by the MT-dataset (MCC raised from 0.63 to 0.67, sensitivity from 0.56 to 0.58). The analysis of the applicability domain also confirms that the model based on the MT-dataset shows a more robust predictive power with a larger applicability domain. (4) Conclusions: These results confirm that focusing on metabolic trees represents a convenient approach to increase data accuracy by reducing the false negative cases. The encouraging performances shown by the models developed by the MT-dataset invites to use of MetaTREE for predictive studies in the field of xenobiotic metabolism.
The study proposes a novel consensus strategy based on linear combinations of different docking scores to be used in the evaluation of virtual screening campaigns. The consensus models are generated ...by applying the recently proposed Enrichment Factor Optimization (EFO) method, which develops the linear equations by exhaustively combining the available docking scores and by optimizing the resulting enrichment factors. The performances of such a consensus strategy were evaluated by simulating the entire Directory of Useful Decoys (DUD datasets). In detail, the poses were initially generated by the PLANTS docking program and then rescored by ReScore+ with and without the minimization of the complexes. The so calculated scores were then used to generate the mentioned consensus models including two or three different scoring functions. The reliability of the generated models was assessed by a per target validation as performed by default by the EFO approach. The encouraging performances of the here proposed consensus strategy are emphasized by the average increase of the 17% in the Top 1% enrichment factor (EF) values when comparing the single best score with the linear combination of three scores. Specifically, kinases offer a truly convincing demonstration of the efficacy of the here proposed consensus strategy since their Top 1% EF average ranges from 6.4 when using the single best performing primary score to 23.5 when linearly combining scoring functions. The beneficial effects of this consensus approach are clearly noticeable even when considering the entire DUD datasets as evidenced by the area under the curve (AUC) averages revealing a 14% increase when combining three scores. The reached AUC values compare very well with those reported in literature by an extended set of recent benchmarking studies and the three-variable models afford the highest AUC average.
In this work we present the third generation of FAst MEtabolizer (FAME 3), a collection of extra trees classifiers for the prediction of sites of metabolism (SoMs) in small molecules such as drugs, ...druglike compounds, natural products, agrochemicals, and cosmetics. FAME 3 was derived from the MetaQSAR database (Pedretti et al. J. Med. Chem. 2018, 61, 1019 ), a recently published data resource on xenobiotic metabolism that contains more than 2100 substrates annotated with more than 6300 experimentally confirmed SoMs related to redox reactions, hydrolysis and other nonredox reactions, and conjugation reactions. In tests with holdout data, FAME 3 models reached competitive performance, with Matthews correlation coefficients (MCCs) ranging from 0.50 for a global model covering phase 1 and phase 2 metabolism, to 0.75 for a focused model for phase 2 metabolism. A model focused on cytochrome P450 metabolism yielded an MCC of 0.57. Results from case studies with several synthetic compounds, natural products, and natural product derivatives demonstrate the agreement between model predictions and literature data even for molecules with structural patterns clearly distinct from those present in the training data. The applicability domains of the individual models were estimated by a new, atom-based distance measure (FAMEscore) that is based on a nearest-neighbor search in the space of atom environments. FAME 3 is available via a public web service at https://nerdd.zbh.uni-hamburg.de/ and as a self-contained Java software package, free for academic and noncommercial research.
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