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Cytochrome P450 Binding and Bioactivation of Tumor-targeted Duocarmycin Agents
No full textNoDuocarmycin natural products are promising anti-cancer cytotoxins but too potent for systemic use. Re-engineering of the duocarmycin scaffold has enabled the discovery of prodrugs designed for bioactivation by tissue-specific cytochrome P450 enzymes. Lead prodrugs bioactivated by both P450 isoforms CYP1A1 and CYP2W1 have shown promising results in xenograft studies, however to fully understand the potential of these agents it is desirable to compare dual-targeting compounds with isoform-selective analogs. Such redesign requires insight into the molecular interactions with these P450 enzymes. Herein binding and metabolism of the individual stereoisomers of the indole-based duocarmycin prodrug ICT2700 and a nontoxic benzofuran analog ICT2726 were evaluated with CYP1A1 and CYP2W1, revealing differences exploitable for drug design. While enantiomers of both compounds bound to and were metabolized by CYP1A1, the stereochemistry of the chloromethyl fragment was critical for CYP2W1 interactions. CYP2W1 differentially binds the S enantiomer of ICT2726 and its metabolite profile could potentially be used as a biomarker to identify CYP2W1 functional activity. In contrast to benzofuran-based ICT2726, CYP2W1 differentially binds the R isomer of the indole-based ICT2700 over the S stereoisomer. Thus the ICT2700 R configuration warrants further investigation as a scaffold to favor CYP2W1-selective bioactivation. Furthermore, structures of both duocarmycin S enantiomers with CYP1A1 reveal orientations correlating with nontoxic metabolites and further drug design optimization could lead to a decrease of CYP1A1 bioactivation. Overall, distinctive structural features present in the two P450 active sites can be useful for improving P450-and thus tissue-selective-bioactivation. Significance Statement Prodrug versions of the natural product duocarmycin can be metabolized by human tissue-specific cytochrome P450 enzymes 1A1 and 2W1 to form an ultrapotent cytotoxin and/or high affinity 2W1 substrates to potentially probe functional activity in situ The current work defines the binding and metabolism by both P450 enzymes to support the design of duocarmycins selectively activated by only one human P450 enzyme.National Institutes of Health and Yorkshire Cancer Research Program Grant (B381PA
Design of a graphene oxide-BODIPY conjugate for glutathione depletion and photodynamic therapy
Get PDFYesBoron dipyrromethene derivates (BODIPYs) are promising photosensitisers (PSs) for cancer treatment using photodynamic therapy (PDT). This study investigates the functionalisation of graphene oxide (GO) with a BODIPY derivate for glutathione (GSH) depletion and PDT. The functionalisation of GO with a 3,5-dichloro-8-(4-boronophenyl) BODIPY via a diol derivatisation with the phenyl boronic acid moiety at the meso position of the BODIPY core, allowed to preserve the intrinsic properties of GO. We demonstrated that both chlorine atoms were substituted by GSH in the presence of glutathione transferase (GST), inducing a relevant bathochromic shift in the absorption/emission features and thus generating the active PS. Ex vitro assessment using cell lysates containing cytoplasmatic GST revealed the intracellular catalytic mechanism for the nucleophilic substitution of the GO-BODIPY adduct with GSH. Confocal microscopy studies showed important differences in the cellular uptake of free BODIPY and GO-BODIPY and revealed the coexistence of GO-BODIPY, GO-BODIPY-GS, and GO-BODIPY-GS2 species inside vesicles and in the cytoplasm of the cells after 24 h of incubation. In vitro biocompatibility and safety of GO and GO-BODIPY were evaluated in 2D and 3D models of prostate adenocarcinoma cells (PC-3), where no toxicity was observed up to 100 µg ml−1 of GO/GO-BODIPY in all treated groups 24 h post-treatment (cell viability > 90%). Only a slight decrease to 80% at 100 µg ml−1 was observed after 48 h of incubation. We demonstrated the efficacy of a GO adduct containing an α-chlorine-substituted BODIPY for the simultaneous depletion of intracellular GSH and the photogeneration of reactive oxygen species using a halogen white light source (5.4 mW cm−2) with a maximum in the range of 500–800 nm, which significantly reduced cell viability (<50%) after irradiation. Our study provides a new vision on how to apply BODIPY derivates and potentiate the toxicity of PDT in prostate and other types of cancer
Automated Prediction of Solar Flares Using SDO Data. The Development of An Automated Computer System for Predicting Solar Flares Based on SDO Satellite Data Using HMI Images Analysis, Visualisation, and Deep Learning Technologies
No full textNowadays, space weather has become an international issue to the world's countries
because of its catastrophic effect on space-borne and ground-based systems, and
industries, impacting our lives. One of the main solar activities that is considered as a
major driver of space weather is solar flares. Solar flares can be defined as an enormous
eruption in the sun's atmosphere. This phenomenon happens when magnetic energy stored
in twisted magnetic fields, usually near sunspots, is suddenly released. Yet, their
occurrence is not fully understood. These flares can affect the Earth by the release of
massive quantities of charged particles and electromagnetic radiation. Investigating the
associations between solar flares and sunspot groups is helpful in comprehending the
possible cause and effect relationships among solar flares and sunspot features. 01 This
thesis proposes a new approach developed by integrating advances in image processing,
machine learning, and deep learning with advances in solar physics to extract valuable
knowledge from historical solar data related to sunspot regions and flares.
This dissertation aims to achieve the following:
1) We developed a new prediction algorithm based on the Automated Solar Activity
Prediction system (ASAP) system. The proposed algorithm updates the ASAP system
by extending the training process and optimizing the learning rules to the optimize
performance better. Two neural networks are used in the proposed approach. The first
neural network is used to predict whether a specific sunspot class at a particular time
is likely to produce a significant flare or not. The second neural network is used to
predict the type of this flare, X or M-class.
2) We proposed a new system called the ASAP_Deep system built on top of the ASAP
system introduced in [6] but improves the system with an updated deep learning-based
prediction capability. In addition, we successfully apply Convolutional Neural
Network (CNN) to the sunspot group image without any pr-eprocessing or feature
extraction. Moreover, our system results are considerably better, especially for the
false alarm ratio (FAR); this reduces the losses resulting from the protection measures
applied by companies. In addition, the proposed system achieves a relatively high
score of True Skill Statistic (TSS) and Heidke Skill Score (HSS).
3) We presented a novel system that used the Deep Belief Networks (DBNs) to predict
the solar flares occurrence. The input data are SDO/HMI Intensitygram and
Magnetogram images. The model outputs are "Flare or No-Flare" of significant flare
occurrence (M and X-class flares). In addition, we created a dataset from the sunspots
groups extracted from SDO HMI Intensitygram images. We compared the results
obtained from the complete suggested system with those of three previous flare forecast models using several statistical metrics.
In our view, these developed methods and results represent an excellent initial
step toward enhancing the accuracy of flare forecasting, enhance our understanding of flare occurrence, and develop efficient flare prediction systems. The systems, implementation, results, and future work are explained in this dissertation
Use of gene-expression programming to estimate Manning's roughness coefficient for a low flow stream
Get PDFYesManning’s roughness coefficient (n) has been widely used to estimate flood discharges and flow depths in natural channels. Therefore, although extensive guidelines are available, the selection of the appropriate n value is of great importance to hydraulic engineers and hydrologists. Generally, the largest source of error in post-flood estimates is caused by the estimation of n values, particularly when there has been minimal field verification of flow resistance. This emphasizes the need to improve methods for evaluating the roughness coefficients. Trinidad and Tobago currently does not have any set method or standardised procedure that they use to determine the n value. Therefore, the objective of this study was to develop a soft computing model in the calculation of the roughness coefficient values using low flow discharge measurements for a stream. This study presents Gene-Expression Programming (GEP), as an improved approach to compute Manning’s Roughness Coefficient. The GEP model was found to be accurate, producing a coefficient of determination (R2) of 0.94 and Root Mean Square Error (RSME) of 0.0024
Velocity Profile and Turbulence Structure Measurement Corrections for Sediment Transport-Induced Water-Worked Bed
Get PDFYesWhen using point measurement for environmental or sediment laden flows, there is well-recognised risk for not having aligned measurements that causes misinterpretation of the measured velocity data. In reality, these kinds of mismeasurement mainly happen due to the misinterpretation of bed orientation caused by the complexity of its determination in natural flows, especially in bedload laden or rough bed flows. This study proposes a novel bed realignment method to improve the measured data benchmarking by three-dimensional (3D) bed profile orientation and implemented it into different sets of experimental data. More specifically, the effects of realignment on velocity profile and streamwise turbulence structure measurements were investigated. The proposed technique was tested against experimental data collected over a water-worked and an experimentally arranged well-packed beds. Different from the well-packed rough bed, the water-worked bed has been generated after long sediment transport and settling and hence can be used to verify the proposed bed-alignment technique thoroughly. During the flow analysis, the corrected velocity, turbulence intensity and Reynolds stress profiles were compared to the theoretical logarithmic law, exponential law and linear gravity (universal Reynolds stress distribution) profiles, respectively. It has been observed that the proposed method has improved the agreement of the measured velocity and turbulence structure data with their actual theoretical profiles, particularly in the near-bed region (where the ratio of the flow measurement vertical distance to the total water depth, z/h, is limited to ≤0.4)
Integration of Hidden Markov Modelling and Bayesian Network for Fault Detection and Prediction of Complex Engineered Systems
Get PDFyesThis paper presents a methodology for fault detection, fault prediction and fault isolation based on the
integration of hidden Markov modelling (HMM) and Bayesian networks (BN). This addresses the nonlinear
and non-Gaussian data characteristics to support fault detection and prediction, within an explainable hybrid
framework that captures causality in the complex engineered system. The proposed methodology is based
on the analysis of the pattern of similarity in the log-likelihood (LL) sequences against the training data for
the mixture of Gaussians HMM (MoG-HMM). The BN model identifies the root cause of
detected/predicted faults, using the information propagated from the HMM model as empirical evidence.
The feasibility and effectiveness of the presented approach are discussed in conjunction with the application
to a real-world case study of an automotive exhaust gas Aftertreatment system. The paper details the
implementation of the methodology to this case study, with data available from real-world usage of the
system. The results show that the proposed methodology identifies the fault faster and attributes the fault
to the correct root cause. While the proposed methodology is illustrated with an automotive case study, its
applicability is much wider to the fault detection and prediction problem of any similar complex engineered
system
Indole-containing arene-ruthenium complexes with broad spectrum activity against antibiotic-resistant bacteria
No full textYesAntimicrobial resistant (AMR) bacteria are emerging and spreading globally, threatening our ability to treat common infectious diseases. The development of new classes of antibiotics able to kill or inhibit the growth of such AMR bacteria through novel mechanisms of action is therefore urgently needed. Here, a new family of indole-containing arene ruthenium organometallic compounds are screened against several bacterial species and drug resistant strains. The most active complex [(p-cym)Ru(O-cyclohexyl-1H-indole-2-carbothioate)Cl] (3) shows growth inhibition and bactericidal activity against different organisms (Acinetobacter baumannii, Mycobacterium abscessus, Mycobacterium tuberculosis, Staphylococcus aureus, Salmonella enterica serovar Typhi and Escherichia coli), demonstrating broad-spectrum inhibitory activity. Importantly, this compound series exhibits low toxicity against human cells. Owing to the novelty of the antibiotic family, their moderate cytotoxicity, and their inhibitory activity against Gram positive, Gram negative and acid-fast, antibiotic resistant microorganisms, this series shows significant promise for further development
Phenotypic differences between microvascular and macrovascular smooth muscle cells and their contribution to coronary microvascular dysfunction
No full textYesCoronary microvascular dysfunction (CMD) is an under-diagnosed condition characterized by functional alteration of the small coronary arterioles and the cardiac capillary bed. The vessels do not dilate appropriately in response to changes in cardiac oxygen demand, leading to chest pain and symptoms of angina. These blood vessels contain two major cell types: the endothelial cells, which line the blood vessels and detect changes in oxygen demand, and smooth muscle cells (SMC) which respond to these changes by contracting or relaxing to provide an optimal blood supply to the cardiac tissue. Many CMD studies have focused on the endothelial cells as these cells secrete vasorelaxants and vasoconstrictors. However, comparably fewer studies have examined SMC despite their functional role in contracting and relaxing. A variety of health conditions and lifestyle choices, such as diabetes, hypertension and cigarette smoking, can promote the development of both CMD and macrovascular coronary artery disease; a condition where SMC have been studied extensively. This review article will consider the influence of CMD on SMC phenotype. It will discuss the structural, cellular and molecular changes in CMD, and will summarise how co-morbidities can have differing effects on micro- and macro-vascular SMC phenotype and function, which complicates the development of new therapeutic avenues for CMD
Artemisinin-acetylenedicarboxylic acid cocrystal: Screening, structure determination, and physicochemical property characterisation
No full textYesArtemisinin is used to treat multi-drug resistant strains of malaria and is also in the early stages of development as an anti-cancer drug. However, the usage of artemisinin is limited due to its low aqueous solubility. Herein a large scale cocrystal screening of artemisinin was conducted using both computational and experimental approaches, leading to a new 2 : 1 artemisinin and acetylenedicarboxylic acid (ART2-ACA) cocrystal. ART2-ACA crystallises in the P212121 space group of the orthorhombic system with cell parameters a = 10.5089 Å, b = 24.083 Å, and c = 6.4952 Å. The asymmetric unit of the cocrystal contains two ART molecules and a single ACA molecule, assembled into discrete trimeric units held together by two supramolecular heterosynthons. It was shown that ART2-ACA cocrystals have higher solubility and a faster dissolution rate compared to the parent drug artemisinin. This journal isUK Engineering and Physical Sciences Research Council (EPSRC, EP/R021198/1
Active Distribution Networks Planning Considering Multi-DG Configurations and Contingency Analysis
No full textYesThis paper proposes a novel method for planning active distribution networks (ADNs) with the integration of an active network management (ANM) scheme using coordinated voltage control (CVC) through on-load tap changer (OLTC) transformers. The method was formulated as a security-constrained optimal power flow (SCOPF) problem to minimize total operational costs, which maximizes the utilization of renewable distributed generators (DGs) over a planning horizon. The ANM scheme was applied using OLTC to ensure safe operation and reduce voltage violations in the network. To analyse the impact of ANM, the planning problem was examined both with and without the ANM scheme. Moreover, SCOPF, considering the N-1 line contingency analysis and multi-DG configuration, was implemented to analyse the feasibility of the proposed method and the advantages of ANM under contingency situations. The method was validated on a weakly-meshed 16-bus UK generic distribution system (UKGDS). The results showed that ANM can lower operational costs and maintain network voltage for operation in feasible conditions even in the case of a contingency. Moreover, the ANM scheme mitigated the voltage rise effect caused by DGs and maximized their utilization