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Artikel ; Online: Obesity and Type 2 Diabetes

Angelica Artasensi / Angelica Mazzolari / Alessandro Pedretti / Giulio Vistoli / Laura Fumagalli

Molecules, Vol 28, Iss 3094, p

Adiposopathy as a Triggering Factor and Therapeutic Options

2023 Band 3094

Abstract: 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 ... ...

Abstract	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.
Schlagwörter	type 2 diabetes mellitus ; diabesity ; obesity ; adiposopathy ; insulin resistance ; Organic chemistry ; QD241-441
Thema/Rubrik (Code)	616
Sprache	Englisch
Erscheinungsdatum	2023-03-01T00:00:00Z
Verlag	MDPI AG
Dokumenttyp	Artikel ; Online
Datenquelle	BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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Artikel ; Online: MetaClass, a Comprehensive Classification System for Predicting the Occurrence of Metabolic Reactions Based on the MetaQSAR Database

Angelica Mazzolari / Alice Scaccabarozzi / Giulio Vistoli / Alessandro Pedretti

Molecules, Vol 26, Iss 5857, p

2021 Band 5857

Abstract: 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 ... ...

Abstract	(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.
Schlagwörter	drug metabolism ; MetaQSAR ; metabolic reactions ; metabolism prediction ; classification algorithms ; random forest ; Organic chemistry ; QD241-441
Thema/Rubrik (Code)	570
Sprache	Englisch
Erscheinungsdatum	2021-09-01T00:00:00Z
Verlag	MDPI AG
Dokumenttyp	Artikel ; Online
Datenquelle	BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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Artikel ; Online: MetaTREE, a Novel Database Focused on Metabolic Trees, Predicts an Important Detoxification Mechanism

Angelica Mazzolari / Luca Sommaruga / Alessandro Pedretti / Giulio Vistoli

Molecules, Vol 26, Iss 2098, p

The Glutathione Conjugation

2021 Band 2098

Abstract: 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 ... ...

Abstract	(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.
Schlagwörter	drug metabolism ; glutathione conjugation ; data accuracy ; metabolic tree ; MetaQSAR ; classification algorithms ; Organic chemistry ; QD241-441
Thema/Rubrik (Code)	006
Sprache	Englisch
Erscheinungsdatum	2021-04-01T00:00:00Z
Verlag	MDPI AG
Dokumenttyp	Artikel ; Online
Datenquelle	BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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Artikel ; Online: Type 2 Diabetes Mellitus

Angelica Artasensi / Alessandro Pedretti / Giulio Vistoli / Laura Fumagalli

Molecules, Vol 25, Iss 1987, p

A Review of Multi-Target Drugs

2020 Band 1987

Abstract: 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 ... ...

Abstract	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.
Schlagwörter	diabetes mellitus ; type 2 diabetes mellitus ; multi-target compounds ; multi-target drugs ; Organic chemistry ; QD241-441
Thema/Rubrik (Code)	571
Sprache	Englisch
Erscheinungsdatum	2020-04-01T00:00:00Z
Verlag	MDPI AG
Dokumenttyp	Artikel ; Online
Datenquelle	BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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Artikel ; Online: Tree2C

Alessandro Pedretti / Angelica Mazzolari / Silvia Gervasoni / Giulio Vistoli

Applied Sciences, Vol 10, Iss 7704, p

A Flexible Tool for Enabling Model Deployment with Special Focus on Cheminformatics Applications

2020 Band 7704

Abstract: Despite the increasing role played by artificial intelligence methods (AI) in pharmaceutical sciences, model deployment remains an issue, which only can be addressed with great difficulty. This leads to a marked discrepancy between the number of ... ...

Abstract	Despite the increasing role played by artificial intelligence methods (AI) in pharmaceutical sciences, model deployment remains an issue, which only can be addressed with great difficulty. This leads to a marked discrepancy between the number of published predictive studies based on AI methods and the models, which can be used for new predictions by everyone. On these grounds, the present paper describes the Tree2C tool which automatically translates a tree-based predictive model into a source code with a view to easily generating applications which can run as a standalone software or can be inserted into an online web service. Moreover, the Tree2C tool is implemented within the VEGA environment and the generated program can include the source code to calculate the required attributes/descriptors. Tree2C supports various programming languages (i.e., C/C++, Fortran 90, Java, JavaScript, JScript, Lua, PHP, Python, REBOL and VBScript and C-Script). Along with a detailed description of the major features of this tool, the paper also describes two examples which are aimed to predict the blood–brain barrier (BBB) permeation as well as the mutagenicity. They permit a clear evaluation of the potentials of Tree2C and of its related features as implemented by the VEGA suite of programs. The Tree2C tool is available for free.
Schlagwörter	model deployment ; AI methods ; tree-based methods ; classification algorithms ; BBB prediction ; mutagenicity ; Technology ; T ; Engineering (General). Civil engineering (General) ; TA1-2040 ; Biology (General) ; QH301-705.5 ; Physics ; QC1-999 ; Chemistry ; QD1-999
Thema/Rubrik (Code)	005
Sprache	Englisch
Erscheinungsdatum	2020-10-01T00:00:00Z
Verlag	MDPI AG
Dokumenttyp	Artikel ; Online
Datenquelle	BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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Artikel ; Online: MetaSpot

Angelica Mazzolari / Pietro Perazzoni / Emanuela Sabato / Filippo Lunghini / Andrea R. Beccari / Giulio Vistoli / Alessandro Pedretti

International Journal of Molecular Sciences, Vol 24, Iss 11064, p

A General Approach for Recognizing the Reactive Atoms Undergoing Metabolic Reactions Based on the MetaQSAR Database

2023 Band 11064

Abstract: The prediction of drug metabolism is attracting great interest for the possibility of discarding molecules with unfavorable ADME/Tox profile at the early stage of the drug discovery process. In this context, artificial intelligence methods can generate ... ...

Abstract	The prediction of drug metabolism is attracting great interest for the possibility of discarding molecules with unfavorable ADME/Tox profile at the early stage of the drug discovery process. In this context, artificial intelligence methods can generate highly performing predictive models if they are trained by accurate metabolic data. MetaQSAR-based datasets were collected to predict the sites of metabolism for most metabolic reactions. The models were based on a set of structural, physicochemical, and stereo-electronic descriptors and were generated by the random forest algorithm. For each considered biotransformation, two types of models were developed: the first type involved all non-reactive atoms and included atom types among the descriptors, while the second type involved only non-reactive centers having the same atom type(s) of the reactive atoms. All the models of the first type revealed very high performances; the models of the second type show on average worst performances while being almost always able to recognize the reactive centers; only conjugations with glucuronic acid are unsatisfactorily predicted by the models of the second type. Feature evaluation confirms the major role of lipophilicity, self-polarizability, and H-bonding for almost all considered reactions. The obtained results emphasize the possibility of recognizing the sites of metabolism by classification models trained on MetaQSAR database. The two types of models can be synergistically combined since the first models identify which atoms can undergo a given metabolic reactions, while the second models detect the truly reactive centers. The generated models are available as scripts for the VEGA program.
Schlagwörter	metabolism prediction ; site of metabolism ; MetaQSAR ; random forest ; atom typing ; Biology (General) ; QH301-705.5 ; Chemistry ; QD1-999
Thema/Rubrik (Code)	540
Sprache	Englisch
Erscheinungsdatum	2023-07-01T00:00:00Z
Verlag	MDPI AG
Dokumenttyp	Artikel ; Online
Datenquelle	BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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Artikel ; Online: Target Prediction by Multiple Virtual Screenings

Silvia Gervasoni / Candida Manelfi / Sara Adobati / Carmine Talarico / Akash Deep Biswas / Alessandro Pedretti / Giulio Vistoli / Andrea R. Beccari

International Journal of Molecular Sciences, Vol 25, Iss 1, p

Analyzing the SARS-CoV-2 Phenotypic Screening by the Docking Simulations Submitted to the MEDIATE Initiative

2023 Band 450

Abstract: Phenotypic screenings are usually combined with deconvolution techniques to characterize the mechanism of action for the retrieved hits. These studies can be supported by various computational analyses, although docking simulations are rarely employed. ... ...

Abstract	Phenotypic screenings are usually combined with deconvolution techniques to characterize the mechanism of action for the retrieved hits. These studies can be supported by various computational analyses, although docking simulations are rarely employed. The present study aims to assess if multiple docking calculations can prove successful in target prediction. In detail, the docking simulations submitted to the MEDIATE initiative are utilized to predict the viral targets involved in the hits retrieved by a recently published cytopathic screening. Multiple docking results are combined by the EFO approach to develop target-specific consensus models. The combination of multiple docking simulations enhances the performances of the developed consensus models (average increases in EF1% value of 40% and 25% when combining three and two docking runs, respectively). These models are able to propose reliable targets for about half of the retrieved hits (31 out of 59). Thus, the study emphasizes that docking simulations might be effective in target identification and provide a convincing validation for the collaborative strategies that inspire the MEDIATE initiative. Disappointingly, cross-target and cross-program correlations suggest that common scoring functions are not specific enough for the simulated target.
Schlagwörter	SARS-CoV-2 ; phenotyping screening ; in silico target identification ; multiple docking simulations ; consensus strategy ; Biology (General) ; QH301-705.5 ; Chemistry ; QD1-999
Sprache	Englisch
Erscheinungsdatum	2023-12-01T00:00:00Z
Verlag	MDPI AG
Dokumenttyp	Artikel ; Online
Datenquelle	BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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Artikel ; Online: Rescoring and Linearly Combining

Alessandro Pedretti / Angelica Mazzolari / Silvia Gervasoni / Giulio Vistoli

International Journal of Molecular Sciences, Vol 20, Iss 9, p

A Highly Effective Consensus Strategy for Virtual Screening Campaigns

2019 Band 2060

Abstract: 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 ... ...

Abstract	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.
Schlagwörter	enrichment factor ; virtual screening ; molecular docking ; rescore ; consensus strategy ; Biology (General) ; QH301-705.5 ; Chemistry ; QD1-999
Sprache	Englisch
Erscheinungsdatum	2019-04-01T00:00:00Z
Verlag	MDPI AG
Dokumenttyp	Artikel ; Online
Datenquelle	BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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Artikel ; Online: Prediction of the Formation of Reactive Metabolites by A Novel Classifier Approach Based on Enrichment Factor Optimization (EFO) as Implemented in the VEGA Program

Angelica Mazzolari / Giulio Vistoli / Bernard Testa / Alessandro Pedretti

Molecules, Vol 23, Iss 11, p

2018 Band 2955

Abstract: The study is aimed at developing linear classifiers to predict the capacity of a given substrate to yield reactive metabolites. While most of the hitherto reported predictive models are based on the occurrence of known structural alerts (e.g., the ... ...

Abstract	The study is aimed at developing linear classifiers to predict the capacity of a given substrate to yield reactive metabolites. While most of the hitherto reported predictive models are based on the occurrence of known structural alerts (e.g., the presence of toxophoric groups), the present study is focused on the generation of predictive models involving linear combinations of physicochemical and stereo-electronic descriptors. The development of these models is carried out by using a novel classification approach based on enrichment factor optimization (EFO) as implemented in the VEGA suite of programs. The study took advantage of metabolic data as collected by manually curated analysis of the primary literature and published in the years 2004⁻2009. The learning set included 977 substrates among which 138 compounds yielded reactive first-generation metabolites, plus 212 substrates generating reactive metabolites in all generations (i.e., metabolic steps). The results emphasized the possibility of developing satisfactory predictive models especially when focusing on the first-generation reactive metabolites. The extensive comparison of the classifier approach presented here using a set of well-known algorithms implemented in Weka 3.8 revealed that the proposed EFO method compares with the best available approaches and offers two relevant benefits since it involves a limited number of descriptors and provides a score-based probability thus allowing a critical evaluation of the obtained results. The last analyses on non-cheminformatics UCI datasets emphasize the general applicability of the EFO approach, which conveniently performs using both balanced and unbalanced datasets.
Schlagwörter	reactive metabolite ; toxicity prediction ; machine learning ; enrichment factor ; unbalanced datasets ; Organic chemistry ; QD241-441
Thema/Rubrik (Code)	006
Sprache	Englisch
Erscheinungsdatum	2018-11-01T00:00:00Z
Verlag	MDPI AG
Dokumenttyp	Artikel ; Online
Datenquelle	BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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Artikel ; Online: Repositioning Dequalinium as Potent Muscarinic Allosteric Ligand by Combining Virtual Screening Campaigns and Experimental Binding Assays

Angelica Mazzolari / Silvia Gervasoni / Alessandro Pedretti / Laura Fumagalli / Rosanna Matucci / Giulio Vistoli

International Journal of Molecular Sciences, Vol 21, Iss 5961, p

2020 Band 5961

Abstract: Structure-based virtual screening is a truly productive repurposing approach provided that reliable target structures are available. Recent progresses in the structural resolution of the G-Protein Coupled Receptors (GPCRs) render these targets amenable ... ...

Abstract	Structure-based virtual screening is a truly productive repurposing approach provided that reliable target structures are available. Recent progresses in the structural resolution of the G-Protein Coupled Receptors (GPCRs) render these targets amenable for structure-based repurposing studies. Hence, the present study describes structure-based virtual screening campaigns with a view to repurposing known drugs as potential allosteric (and/or orthosteric) ligands for the hM 2 muscarinic subtype which was indeed resolved in complex with an allosteric modulator thus allowing a precise identification of this binding cavity. First, a docking protocol was developed and optimized based on binding space concept and enrichment factor optimization algorithm (EFO) consensus approach by using a purposely collected database including known allosteric modulators. The so-developed consensus models were then utilized to virtually screen the DrugBank database. Based on the computational results, six promising molecules were selected and experimentally tested and four of them revealed interesting affinity data; in particular, dequalinium showed a very impressive allosteric modulation for hM 2 . Based on these results, a second campaign was focused on bis-cationic derivatives and allowed the identification of other two relevant hM 2 ligands. Overall, the study enhances the understanding of the factors governing the hM 2 allosteric modulation emphasizing the key role of ligand flexibility as well as of arrangement and delocalization of the positively charged moieties.
Schlagwörter	drug repurposing ; virtual screening ; consensus function ; binding space ; muscarinic receptors ; allosteric modulators ; Biology (General) ; QH301-705.5 ; Chemistry ; QD1-999
Thema/Rubrik (Code)	540
Sprache	Englisch
Erscheinungsdatum	2020-08-01T00:00:00Z
Verlag	MDPI AG
Dokumenttyp	Artikel ; Online
Datenquelle	BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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