Advances in cobalt complexes as anticancer agents Munteanu, Catherine R; Suntharalingam, Kogularamanan
Dalton transactions : an international journal of inorganic chemistry,
01/2015, Letnik:
44, Številka:
31
Journal Article
Recenzirano
The evolution of resistance to traditional platinum-based anticancer drugs has compelled researchers to investigate the cytostatic properties of alternative transition metal-based compounds. The ...anticancer potential of cobalt complexes has been extensively studied over the last three decades, and much time has been devoted to understanding their mechanisms of action. This perspective catalogues the development of antiproliferative cobalt complexes, and provides an in depth analysis of their mode of action. Early studies on simple cobalt coordination complexes, Schiff base complexes, and cobalt-carbonyl clusters will be documented. The physiologically relevant redox properties of cobalt will be highlighted and the role this plays in the preparation of hypoxia selective prodrugs and imaging agents will be discussed. The use of cobalt-containing cobalamin as a cancer specific delivery agent for cytotoxins will also be described. The work summarised in this perspective shows that the biochemical and biophysical properties of cobalt-containing compounds can be fine-tuned to produce new generations of anticancer agents with clinically relevant efficacies.
This perspective describes the advances in cobalt-containing compounds as anticancer agents. Cobalt, being an essential trace element, offers a less toxic alternative to traditional platinum-based anticancer drugs.
► GP based feature extraction. ► KNN performance improvement. ► Feature space reduction.
This paper applies genetic programming (GP) to perform automatic feature extraction from original feature ...database with the aim of improving the discriminatory performance of a classifier and reducing the input feature dimensionality at the same time. The tree structure of GP naturally represents the features, and a new function generated in this work automatically decides the number of the features extracted. In experiments on two common epileptic EEG detection problems, the classification accuracy on the GP-based features is significant higher than on the original features. Simultaneously, the dimension of the input features for the classifier is much smaller than that of the original features.
Gallium-68 (
Ga) is a positron-emitting isotope used for clinical PET imaging of peptide receptor expression.
Ga radiopharmaceuticals used in molecular PET imaging consist of disease-targeting ...biomolecules tethered to chelators that complex
Ga
. Ideally, the chelator will rapidly, quantitatively and stably coordinate
Ga
at room temperature, near neutral pH and low chelator concentration, allowing for simple routine radiopharmaceutical formulation. Identification of chelators that fulfil these requirements will facilitate development of kit-based
Ga radiopharmaceuticals. Herein the reaction of a range of widely used macrocyclic and acyclic chelators with
Ga
is reported. Radiochemical yields have been measured under conditions of varying chelator concentrations, pH (3.5 and 6.5) and temperature (25 and 90 °C). These chelators are: 1,4,7,10-tetraazacyclododecane-1,4,7,10-tetraacetic acid (DOTA), 1,4,7-triazacyclononane-1,4,7-triacetic acid (NOTA), 1,4,7-triazacyclononane macrocycles substituted with phosphonic (NOTP) and phosphinic (TRAP) groups at the amine, bis(2-hydroxybenzyl)ethylenediaminediacetic acid (HBED), a tris(hydroxypyridinone) containing three 1,6-dimethyl-3-hydroxypyridin-4-one groups (THP) and the hexadentate tris(hydroxamate) siderophore desferrioxamine-B (DFO). Competition studies have also been undertaken to assess relative complexation efficiencies of each chelator for
Ga
under different pH and temperature conditions. Performing radiolabelling reactions at pH 6.5, 25 °C and 5-50 μM chelator concentration resulted in near quantitative radiochemical yields for all chelators, except DOTA. Radiochemical yields either decreased or were not substantially improved when the reactions were undertaken at lower pH or at higher temperature, except in the case of DOTA. THP and DFO were the most effective
Ga
chelators at near-neutral pH and 25 °C, rapidly providing near-quantitative radiochemical yields at very low chelator concentrations. NOTP and HBED were only slightly less effective under these conditions. In competition studies with all other chelators, THP demonstrated highest reactivity for
Ga
complexation under all conditions. These data point to THP possessing ideal properties for rapid, one-step kit-based syntheses of
Ga-biomolecules for molecular PET imaging. LC-MS and
H,
C{
H} and
Ga NMR studies of HBED complexes of Ga
showed that under the analytical conditions employed in this study, multiple HBED-bound Ga complexes exist. X-ray diffraction data indicated that crystals isolated from these solutions contained octahedral Ga(HBED)(H
O), with HBED coordinated in a pentadentate N
O
mode, with only one phenolic group coordinated to Ga
, and the remaining coordination site occupied by a water molecule.
Horizontal gene transfer (HGT) is a widely acknowledged phenomenon in prokaryotes for generating genetic diversity. However, the impact of this process in eukaryotes, particularly interdomain HGT, is ...a topic of debate. Although there have been observed biases in interdomain HGT detection, little exploration has been conducted on the effects of imbalanced databases. In our study, we conducted experiments to assess how different databases affect the detection of interdomain HGT using proteomes from the Pezizomycotina fungal subphylum as our focus group. Our objective was to simulate the database imbalance commonly found in public biological databases, where bacterial and eukaryotic sequences are unevenly represented, and demonstrate that an increase in uploaded eukaryotic sequences leads to a decrease in predicted HGTs. For our experiments, four databases with varying proportions of eukaryotic sequences but consistent proportions of bacterial sequences were utilized. We observed a significant reduction in detected interdomain HGT candidates as the proportion of eukaryotes increased within the database. Our data suggest that the imbalance in databases bias the interdomain HGT detection and highlights challenges associated with confirming the presence of interdomain HGT among Pezizomycotina fungi and potentially other groups within Eukarya.
The paper aims to study a plane system with bars, with certain symmetries. Such problems can be encountered frequently in industry and civil engineering. Considerations related to the economy of the ...design process, constructive simplicity, cost and logistics make the use of identical parts a frequent procedure. The paper aims to determine the properties of the eigenvalues and eigenmodes for transverse and torsional vibrations of a mechanical system where two of the three component bars are identical. The determination of these properties allows the calculus effort and the computation time and thus increases the accuracy of the results in such matters.
The druggable proteome refers to proteins that can bind to small molecules with appropriate chemical affinity, inducing a favorable clinical response. Predicting druggable proteins through screening ...and in silico modeling is imperative for drug design. To contribute to this field, we developed an accurate predictive classifier for druggable cancer-driving proteins using amino acid composition descriptors of protein sequences and 13 machine learning linear and non-linear classifiers. The optimal classifier was achieved with the support vector machine method, utilizing 200 tri-amino acid composition descriptors. The high performance of the model is evident from an area under the receiver operating characteristics (AUROC) of 0.975 ± 0.003 and an accuracy of 0.929 ± 0.006 (threefold cross-validation). The machine learning prediction model was enhanced with multi-omics approaches, including the target-disease evidence score, the shortest pathways to cancer hallmarks, structure-based ligandability assessment, unfavorable prognostic protein analysis, and the oncogenic variome. Additionally, we performed a drug repurposing analysis to identify drugs with the highest affinity capable of targeting the best predicted proteins. As a result, we identified 79 key druggable cancer-driving proteins with the highest ligandability, and 23 of them demonstrated unfavorable prognostic significance across 16 TCGA PanCancer types: CDKN2A, BCL10, ACVR1, CASP8, JAG1, TSC1, NBN, PREX2, PPP2R1A, DNM2, VAV1, ASXL1, TPR, HRAS, BUB1B, ATG7, MARK3, SETD2, CCNE1, MUTYH, CDKN2C, RB1, and SMARCA4. Moreover, we prioritized 11 clinically relevant drugs targeting these proteins. This strategy effectively predicts and prioritizes biomarkers, therapeutic targets, and drugs for in-depth studies in clinical trials. Scripts are available at https://github.com/muntisa/machine-learning-for-druggable-proteins.
For understanding a chemical compound’s mechanism of action and its side effects, as well as for drug discovery, it is crucial to predict its possible protein targets. This study examines 15 ...developed target-centric models (TCM) employing different molecular descriptions and machine learning algorithms. They were contrasted with 17 third-party models implemented as web tools (WTCM). In both sets of models, consensus strategies were implemented as potential improvement over individual predictions. The findings indicate that TCM reach f1-score values greater than 0.8. Comparing both approaches, the best TCM achieves values of 0.75, 0.61, 0.25 and 0.38 for true positive/negative rates (TPR, TNR) and false negative/positive rates (FNR, FPR); outperforming the best WTCM. Moreover, the consensus strategy proves to have the most relevant results in the top
20
%
of target profiles. TCM consensus reach TPR and FNR values of 0.98 and 0; while on WTCM reach values of 0.75 and 0.24. The implemented computational tool with the TCM and their consensus strategy at:
https://bioquimio.udla.edu.ec/tidentification01/
. Scientific Contribution: We compare and discuss the performances of 17 public compound-target interaction prediction models and 15 new constructions. We also explore a compound-target interaction prioritization strategy using a consensus approach, and we analyzed the challenging involved in interactions modeling.
Graphical Abstract
Quantitative Structure-Activity/Property Relationships (QSAR/QSPR) models have been largely used for different kind of problems in Medicinal Chemistry and other Biosciences as well. Nevertheless, the ...applications of QSAR models have been restricted to the study of small molecules in the past. In this context, many authors use molecular graphs, atoms (nodes) connected by chemical bonds (links) to represent and numerically characterize the molecular structure. On the other hand, Complex Networks are useful in solving problems in drug research and industry, developing mathematical representations of different systems. These systems move in a wide range from relatively simple graph representations of drug molecular structures (molecular graphs used in classic QSAR) to large systems. We can cite for instance, drug-target interaction networks, protein structure networks, protein interaction networks (PINs), or drug treatment in large geographical disease spreading networks. In any case, all complex networks have essentially the same components: nodes (atoms, drugs, proteins, microorganisms and/or parasites, geographical areas, drug policy legislations, etc.) and links (chemical bonds, drug-target interactions, drug-parasite treatment, drug use, etc.). Consequently, we can use the same type of numeric parameters called Topological Indices (TIs) to describe the connectivity patterns in all these kinds of Complex Networks irrespective the nature of the object they represent and use these TIs to develop QSAR/QSPR models beyond the classic frontiers of drugs small-sized molecules. The goal of this work, in first instance, is to offer a common background to all the manuscripts presented in this special issue. In so doing, we make a review of the most used software and databases, common types of QSAR/QSPR models, and complex networks involving drugs or their targets. In addition, we review both classic TIs that have been used to describe the molecular structure of drugs and/or larger complex networks. In second instance, we use for the first time a Markov chain model to generalize Spectral moments to higher order analogues coined here as the Stochastic Spectral Moments TIs of order k (πk). Lastly, we report for the first time different QSAR/QSPR models for different classes of networks found in drug research, nature, technology, and social-legal sciences using πk values. This work updates our previous reviews Gonzalez-Diaz et al. Curr Top Med Chem. 2007; 7(10): 1015-29 and Gonzalez-Diaz et al. Curr Top Med Chem. 2008; 8(18):1676-90. It has been prepared in response to the kind invitation of the editor Prof. AB Reitz in commemoration of the 10th anniversary of this journal in 2010.
Early detection is crucial to prevent the progression of Alzheimer’s disease (AD). Thus, specialists can begin preventive treatment as soon as possible. They demand fast and precise assessment in the ...diagnosis of AD in the earliest and hardest to detect stages. The main objective of this work is to develop a system that automatically detects the presence of the disease in sagittal magnetic resonance images (MRI), which are not generally used. Sagittal MRIs from ADNI and OASIS data sets were employed. Experiments were conducted using Transfer Learning (TL) techniques in order to achieve more accurate results. There are two main conclusions to be drawn from this work: first, the damages related to AD and its stages can be distinguished in sagittal MRI and, second, the results obtained using DL models with sagittal MRIs are similar to the state-of-the-art, which uses the horizontal-plane MRI. Although sagittal-plane MRIs are not commonly used, this work proved that they were, at least, as effective as MRI from other planes at identifying AD in early stages. This could pave the way for further research. Finally, one should bear in mind that in certain fields, obtaining the examples for a data set can be very expensive. This study proved that DL models could be built in these fields, whereas TL is an essential tool for completing the task with fewer examples.
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The complex diseases in the field of Neurology, Cardiology and Oncology have the most important impact on our society. The theoretical methods are fast and they involve some efficient tools aimed at ...discovering new active drugs specially designed for these diseases. The ontology of all the items that are linked with the molecule metabolism and the treatment of these diseases gives us the possibility to correlate information from different levels and to discover new relationships between complex diseases such as common drug targets and disease patterns. This review presents the ontologies used to process drug discovery and design in the most common complex diseases.
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