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1.  Forward Modeling of the Coumarin Antifungals; SPR/SAR Based Perspective 
Although, coumarins are a group of compounds which are naturally found in some plants, they can be synthetically produced as well. Because of their diverse derivatives, origin and properties most of them can be used for medicinal purposes. For example, they can be used against fungal diseases or in studying structure and biological properties of antifungal agents to discover new compounds with the similar activity. A Structure Property/Activity Relationship (SAR) can be utilized in prediction of biological activity of desired molecules.
In order to represent a relationship between the physicochemical properties of coumarin compounds and their biological activities, 68 coumarins and coumarin derivatives with already reported antifungal activities were selected and eleven attributes were generated. The descriptors were used to perform artificial neural network (ANN) and to build a model for predicting effectiveness of the new ones. The correlation coefficient between the experimental and the predicted MIC values pertaining to all the coumarins was 0.984. This study paves the way for further researches about antifungal activity of coumarins, and offers a powerful tool in modeling and prediction of their bioactivities.
PMCID: PMC3558124  PMID: 23407575
Antifungal activity; Coumarin; Modeling; Neural network
2.  Cross-species discovery of syncretic drug combinations that potentiate the antifungal fluconazole 
The authors screen for compounds that show synergistic antifungal activity when combined with the widely-used fungistatic drug fluconazole. Chemogenomic profiling explains the mode of action of synergistic drugs and allows the prediction of additional drug synergies.
The authors screen for compounds that show synergistic antifungal activity when combined with the widely-used fungistatic drug fluconazole. Chemogenomic profiling explains the mode of action of synergistic drugs and allows the prediction of additional drug synergies.
Chemical screens with a library enriched for known drugs identified a diverse set of 148 compounds that potentiated the action of the antifungal drug fluconazole against the fungal pathogens Cryptococcus neoformans, Cryptococcus gattii and Candida albicans, and the model yeast Saccharomyces cerevisiae, often in a species-specific manner.Chemogenomic profiles of six confirmed hits in S. cerevisiae revealed different modes of action and enabled the prediction of additional synergistic combinations; three-way synergistic interactions exhibited even stronger synergies at low doses of fluconazole.The synergistic combination of fluconazole and the antidepressant sertraline was active against fluconazole-resistant clinical fungal isolates and in an in vivo model of Cryptococcal infection.
Rising fungal infection rates, especially among immune-suppressed individuals, represent a serious clinical challenge (Gullo, 2009). Cancer, organ transplant and HIV patients, for example, often succumb to opportunistic fungal pathogens. The limited repertoire of approved antifungal agents and emerging drug resistance in the clinic further complicate the effective treatment of systemic fungal infections. At the molecular level, the paucity of fungal-specific essential targets arises from the conserved nature of cellular functions from yeast to humans, as well as from the fact that many essential yeast genes can confer viability at a fraction of wild-type dosage (Yan et al, 2009). Although only ∼1100 of the ∼6000 genes in yeast are essential, almost all genes become essential in specific genetic backgrounds in which another non-essential gene has been deleted or otherwise attenuated, an effect termed synthetic lethality (Tong et al, 2001). Genome-scale surveys suggest that over 200 000 binary synthetic lethal gene combinations dominate the yeast genetic landscape (Costanzo et al, 2010). The genetic buffering phenomenon is also manifest as a plethora of differential chemical–genetic interactions in the presence of sublethal doses of bioactive compounds (Hillenmeyer et al, 2008). These observations frame the difficulty of interdicting network functions in eukaryotic pathogens with single agent therapeutics. At the same time, however, this genetic network organization suggests that judicious combinations of small molecule inhibitors of both essential and non-essential targets may elicit additive or synergistic effects on cell growth (Sharom et al, 2004; Lehar et al, 2008). Unbiased screens for drugs that synergistically enhance a specific bioactive effect, but which are not themselves individually active—termed a syncretic combination—are one means to substantially elaborate chemical space (Keith et al, 2005). Indeed, compounds that enhance the activity of known agents in model yeast and cancer cell line systems have been identified both by focused small molecule library screens and by computational methods (Borisy et al, 2003; Lehar et al, 2007; Nelander et al, 2008; Jansen et al, 2009; Zinner et al, 2009).
To extend the stratagem of chemical synthetic lethality to clinically relevant fungal pathogens, we screened a bioactive library of known drugs for synergistic enhancers of the widely used fungistatic drug fluconazole against the clinically relevant pathogens C. albicans, C. neoformans and C. gattii, as well as the genetically tractable budding yeast S. cerevisiae. Fluconazole is an azole drug that inhibits lanosterol 14α-demethylase, the gene product of ERG11, an essential cytochrome P450 enzyme in the ergosterol biosynthetic pathway (Groll et al, 1998). We identified 148 drugs that potentiate the antifungal action of fluconazole against the four species. These syncretic compounds had not been previously recognized in the clinic as antifungal agents, and many acted in a species-specific manner, often in a potent fungicidal manner.
To understand the mechanisms of synergism, we interrogated six syncretic drugs—trifluoperazine, tamoxifen, clomiphene, sertraline, suloctidil and L-cycloserine—in genome-wide chemogenomic profiles of the S. cerevisiae deletion strain collection (Giaever et al, 1999). These profiles revealed that membrane, vesicle trafficking and lipid biosynthesis pathways are targeted by five of the synergizers, whereas the sphingolipid biosynthesis pathway is targeted by L-cycloserine. Cell biological assays confirmed the predicted membrane disruption effects of the former group of compounds, which may perturb ergosterol metabolism, impair fluconazole export by drug efflux pumps and/or affect active import of fluconazole (Kuo et al, 2010; Mansfield et al, 2010). Based on the integration of chemical–genetic and genetic interaction space, a signature set of deletion strains that are sensitive to the membrane active synergizers correctly predicted additional drug synergies with fluconazole. Similarly, the L-cycloserine chemogenomic profile correctly predicted a synergistic interaction between fluconazole and myriocin, another inhibitor of sphingolipid biosynthesis. The structure of genetic networks suggests that it should be possible to devise higher order drug combinations with even greater selectivity and potency (Sharom et al, 2004). In an initial test of this concept, we found that the combination of a non-synergistic pair drawn from the membrane active and sphingolipid target classes exhibited potent three-way synergism with a low dose of fluconazole. Finally, the combination of sertraline and fluconazole was active in a G. mellonella model of Cryptococcal infection, and was also efficacious against fluconazole-resistant clinical isolates of C. albicans and C. glabrata.
Collectively, these results demonstrate that the combinatorial redeployment of known drugs defines a powerful antifungal strategy and establish a number of potential lead combinations for future clinical assessment.
Resistance to widely used fungistatic drugs, particularly to the ergosterol biosynthesis inhibitor fluconazole, threatens millions of immunocompromised patients susceptible to invasive fungal infections. The dense network structure of synthetic lethal genetic interactions in yeast suggests that combinatorial network inhibition may afford increased drug efficacy and specificity. We carried out systematic screens with a bioactive library enriched for off-patent drugs to identify compounds that potentiate fluconazole action in pathogenic Candida and Cryptococcus strains and the model yeast Saccharomyces. Many compounds exhibited species- or genus-specific synergism, and often improved fluconazole from fungistatic to fungicidal activity. Mode of action studies revealed two classes of synergistic compound, which either perturbed membrane permeability or inhibited sphingolipid biosynthesis. Synergistic drug interactions were rationalized by global genetic interaction networks and, notably, higher order drug combinations further potentiated the activity of fluconazole. Synergistic combinations were active against fluconazole-resistant clinical isolates and an in vivo model of Cryptococcus infection. The systematic repurposing of approved drugs against a spectrum of pathogens thus identifies network vulnerabilities that may be exploited to increase the activity and repertoire of antifungal agents.
PMCID: PMC3159983  PMID: 21694716
antifungal; combination; pathogen; resistance; synergism
3.  Prediction of Force Measurements of a Microbend Sensor Based on an Artificial Neural Network 
Sensors (Basel, Switzerland)  2009;9(9):7167-7176.
Artificial neural network (ANN) based prediction of the response of a microbend fiber optic sensor is presented. To the best of our knowledge no similar work has been previously reported in the literature. Parallel corrugated plates with three deformation cycles, 6 mm thickness of the spacer material and 16 mm mechanical periodicity between deformations were used in the microbend sensor. Multilayer Perceptron (MLP) with different training algorithms, Radial Basis Function (RBF) network and General Regression Neural Network (GRNN) are used as ANN models in this work. All of these models can predict the sensor responses with considerable errors. RBF has the best performance with the smallest mean square error (MSE) values of training and test results. Among the MLP algorithms and GRNN the Levenberg-Marquardt algorithm has good results. These models successfully predict the sensor responses, hence ANNs can be used as useful tool in the design of more robust fiber optic sensors.
PMCID: PMC3290459  PMID: 22399991
artificial neural networks; fiber optic sensors; microbend sensors; multilayer perceptron; radial basis function; general regression neural network
4.  Artificial Neural Network-Based System for PET Volume Segmentation 
Tumour detection, classification, and quantification in positron emission tomography (PET) imaging at early stage of disease are important issues for clinical diagnosis, assessment of response to treatment, and radiotherapy planning. Many techniques have been proposed for segmenting medical imaging data; however, some of the approaches have poor performance, large inaccuracy, and require substantial computation time for analysing large medical volumes. Artificial intelligence (AI) approaches can provide improved accuracy and save decent amount of time. Artificial neural networks (ANNs), as one of the best AI techniques, have the capability to classify and quantify precisely lesions and model the clinical evaluation for a specific problem. This paper presents a novel application of ANNs in the wavelet domain for PET volume segmentation. ANN performance evaluation using different training algorithms in both spatial and wavelet domains with a different number of neurons in the hidden layer is also presented. The best number of neurons in the hidden layer is determined according to the experimental results, which is also stated Levenberg-Marquardt backpropagation training algorithm as the best training approach for the proposed application. The proposed intelligent system results are compared with those obtained using conventional techniques including thresholding and clustering based approaches. Experimental and Monte Carlo simulated PET phantom data sets and clinical PET volumes of nonsmall cell lung cancer patients were utilised to validate the proposed algorithm which has demonstrated promising results.
PMCID: PMC2948894  PMID: 20936152
5.  Artificial Neural Network Modelling of Photodegradation in Suspension of Manganese Doped Zinc Oxide Nanoparticles under Visible-Light Irradiation 
The Scientific World Journal  2014;2014:726101.
The artificial neural network (ANN) modeling of m-cresol photodegradation was carried out for determination of the optimum and importance values of the effective variables to achieve the maximum efficiency. The photodegradation was carried out in the suspension of synthesized manganese doped ZnO nanoparticles under visible-light irradiation. The input considered effective variables of the photodegradation were irradiation time, pH, photocatalyst amount, and concentration of m-cresol while the efficiency was the only response as output. The performed experiments were designed into three data sets such as training, testing, and validation that were randomly splitted by the software's option. To obtain the optimum topologies, ANN was trained by quick propagation (QP), Incremental Back Propagation (IBP), Batch Back Propagation (BBP), and Levenberg-Marquardt (LM) algorithms for testing data set. The topologies were determined by the indicator of minimized root mean squared error (RMSE) for each algorithm. According to the indicator, the QP-4-8-1, IBP-4-15-1, BBP-4-6-1, and LM-4-10-1 were selected as the optimized topologies. Among the topologies, QP-4-8-1 has presented the minimum RMSE and absolute average deviation as well as maximum R-squared. Therefore, QP-4-8-1 was selected as final model for validation test and navigation of the process. The model was used for determination of the optimum values of the effective variables by a few three-dimensional plots. The optimum points of the variables were confirmed by further validated experiments. Moreover, the model predicted the relative importance of the variables which showed none of them was neglectable in this work.
PMCID: PMC4236903  PMID: 25538962
6.  Comparative Analyses of Response Surface Methodology and Artificial Neural Network on Medium Optimization for Tetraselmis sp. FTC209 Grown under Mixotrophic Condition 
The Scientific World Journal  2013;2013:948940.
Mixotrophic metabolism was evaluated as an option to augment the growth and lipid production of marine microalga Tetraselmis sp. FTC 209. In this study, a five-level three-factor central composite design (CCD) was implemented in order to enrich the W-30 algal growth medium. Response surface methodology (RSM) was employed to model the effect of three medium variables, that is, glucose (organic C source), NaNO3 (primary N source), and yeast extract (supplementary N, amino acids, and vitamins) on biomass concentration, Xmax, and lipid yield, Pmax/Xmax. RSM capability was also weighed against an artificial neural network (ANN) approach for predicting a composition that would result in maximum lipid productivity, Prlipid. A quadratic regression from RSM and a Levenberg-Marquardt trained ANN network composed of 10 hidden neurons eventually produced comparable results, albeit ANN formulation was observed to yield higher values of response outputs. Finalized glucose (24.05 g/L), NaNO3 (4.70 g/L), and yeast extract (0.93 g/L) concentration, affected an increase of Xmax to 12.38 g/L and lipid a accumulation of 195.77 mg/g dcw. This contributed to a lipid productivity of 173.11 mg/L per day in the course of two-week cultivation.
PMCID: PMC3784237  PMID: 24109209
7.  Artificial Neural Network Analysis in Preclinical Breast Cancer 
Cell Journal (Yakhteh)  2013;15(4):324-331.
In this study, artificial neural network (ANN) analysis of virotherapy in preclinical breast cancer was investigated.
Materials and Methods:
In this research article, a multilayer feed-forward neural network trained with an error back-propagation algorithm was incorporated in order to develop a predictive model. The input parameters of the model were virus dose, week and tamoxifen citrate, while tumor weight was included in the output parameter. Two different training algorithms, namely quick propagation (QP) and Levenberg-Marquardt (LM), were used to train ANN.
The results showed that the LM algorithm, with 3-9-1 arrangement is more efficient compared to QP. Using LM algorithm, the coefficient of determination (R2) between the actual and predicted values was determined as 0.897118 for all data.
It can be concluded that this ANN model may provide good ability to predict the biometry information of tumor in preclinical breast cancer virotherapy. The results showed that the LM algorithm employed by Neural Power software gave the better performance compared with the QP and virus dose, and it is more important factor compared to tamoxifen and time (week).
PMCID: PMC3866536  PMID: 24381857
Neural Network Model; Breast Cancer; Virotherapy
8.  Artificial Neural Networks Based Controller for Glucose Monitoring during Clamp Test 
PLoS ONE  2012;7(8):e44587.
Insulin resistance (IR) is one of the most widespread health problems in modern times. The gold standard for quantification of IR is the hyperinsulinemic-euglycemic glucose clamp technique. During the test, a regulated glucose infusion is delivered intravenously to maintain a constant blood glucose concentration. Current control algorithms for regulating this glucose infusion are based on feedback control. These models require frequent sampling of blood, and can only partly capture the complexity associated with regulation of glucose. Here we present an improved clamp control algorithm which is motivated by the stochastic nature of glucose kinetics, while using the minimal need in blood samples required for evaluation of IR. A glucose pump control algorithm, based on artificial neural networks model was developed. The system was trained with a data base collected from 62 rat model experiments, using a back-propagation Levenberg-Marquardt optimization. Genetic algorithm was used to optimize network topology and learning features. The predictive value of the proposed algorithm during the temporal period of interest was significantly improved relative to a feedback control applied at an equivalent low sampling interval. Robustness to noise analysis demonstrates the applicability of the algorithm in realistic situations.
PMCID: PMC3432111  PMID: 22952998
9.  Comparison result of inversion of gravity data of a fault by particle swarm optimization and Levenberg-Marquardt methods 
SpringerPlus  2013;2:462.
The purpose of this study was to compare the performance of two methods for gravity inversion of a fault. First method [Particle swarm optimization (PSO)] is a heuristic global optimization method and also an optimization algorithm, which is based on swarm intelligence. It comes from the research on the bird and fish flock movement behavior. Second method [The Levenberg-Marquardt algorithm (LM)] is an approximation to the Newton method used also for training ANNs. In this paper first we discussed the gravity field of a fault, then describes the algorithms of PSO and LM And presents application of Levenberg-Marquardt algorithm, and a particle swarm algorithm in solving inverse problem of a fault. Most importantly the parameters for the algorithms are given for the individual tests. Inverse solution reveals that fault model parameters are agree quite well with the known results. A more agreement has been found between the predicted model anomaly and the observed gravity anomaly in PSO method rather than LM method.
PMCID: PMC3786064  PMID: 24083109
Particle swarm optimization; Levenberg-Marquardt method; Inversion; Gravity data; Fault
10.  A quantitative structure–activity relationship study of anti-HIV activity of substituted HEPT using nonlinear models 
Medicinal Chemistry Research  2013;22(11):5442-5452.
We performed studies on extended series of 79 HEPT ligands (1-[(2-hydroxyethoxy)methyl]-6-(phenylthio)thymine), inhibitors of HIV reverse-transcriptase with anti-HIV biological activity, using quantitative structure–activity relationship (QSAR) methods that imply analysis of correlations and representation of models. A suitable set of molecular descriptors was calculated, and the genetic algorithm was employed to select those descriptors which resulted in the best-fit models. The kernel partial least square and Levenberg–Marquardt artificial neural network were utilized to construct the nonlinear QSAR models. The proposed methods will be of great significance in this research, and would be expected to apply to other similar research fields.
PMCID: PMC3785711  PMID: 24098069
AIDS; Anti-HIV activity; HEPT ligands; QSAR; Genetic algorithm; Levenberg–Marquardt artificial neural network
11.  An Artificial Neural Network Approach for the Prediction of Absorption Measurements of an Evanescent Field Fiber Sensor 
Sensors (Basel, Switzerland)  2008;8(3):1585-1594.
This paper describes artificial neural network (ANN) based prediction of the response of a fiber optic sensor using evanescent field absorption (EFA). The sensing probe of the sensor is made up a bundle of five PCS fibers to maximize the interaction of evanescent field with the absorbing medium. Different backpropagation algorithms are used to train the multilayer perceptron ANN. The Levenberg-Marquardt algorithm, as well as the other algorithms used in this work successfully predicts the sensor responses.
PMCID: PMC3663013
Fiber optic sensor; evanescent field; absorption; artificial neural networks
12.  Bayesian Approach to Model CD137 Signaling in Human M. tuberculosis In Vitro Responses 
PLoS ONE  2013;8(2):e55987.
Immune responses are qualitatively and quantitatively influenced by a complex network of receptor-ligand interactions. Among them, the CD137:CD137L pathway is known to modulate innate and adaptive human responses against Mycobacterium tuberculosis. However, the underlying mechanisms of this regulation remain unclear. In this work, we developed a Bayesian Computational Model (BCM) of in vitro CD137 signaling, devised to fit previously gathered experimental data. The BCM is fed with the data and the prior distribution of the model parameters and it returns their posterior distribution and the model evidence, which allows comparing alternative signaling mechanisms. The BCM uses a coupled system of non-linear differential equations to describe the dynamics of Antigen Presenting Cells, Natural Killer and T Cells together with the interpheron (IFN)-γ and tumor necrosis factor (TNF)-α levels in the media culture. Fast and complete mixing of the media is assumed. The prior distribution of the parameters that describe the dynamics of the immunological response was obtained from the literature and theoretical considerations Our BCM applies successively the Levenberg-Marquardt algorithm to find the maximum a posteriori likelihood (MAP); the Metropolis Markov Chain Monte Carlo method to approximate the posterior distribution of the parameters and Thermodynamic Integration to calculate the evidence of alternative hypothesis. Bayes factors provided decisive evidence favoring direct CD137 signaling on T cells. Moreover, the posterior distribution of the parameters that describe the CD137 signaling showed that the regulation of IFN-γ levels is based more on T cells survival than on direct induction. Furthermore, the mechanisms that account for the effect of CD137 signaling on TNF-α production were based on a decrease of TNF-α production by APC and, perhaps, on the increase in APC apoptosis. BCM proved to be a useful tool to gain insight on the mechanisms of CD137 signaling during human response against Mycobacterium tuberculosis.
PMCID: PMC3577821  PMID: 23437083
13.  Complex polarization ratio to determine polarization properties of anisotropic tissue using polarization-sensitive optical coherence tomography 
Optics express  2009;17(16):13402-13417.
Complex polarization ratio (CPR) in materials with birefringence and biattenuance is shown as a logarithmic spiral in the complex plane. A multi-state Levenberg-Marquardt nonlinear fitting algorithm using the CPR trajectory collected by polarization sensitive optical coherence tomography (PS-OCT) was developed to determine polarization properties of an anisotropic scattering medium. The Levenberg-Marquardt nonlinear fitting algorithm using the CPR trajectory is verified using simulated PS-OCT data with speckle noise. Birefringence and biattenuance of a birefringent film, ex-vivo rodent tail tendon and in-vivo primate retinal nerve fiber layer were determined using measured CPR trajectories and the Levenberg-Marquardt nonlinear fitting algorithm.
PMCID: PMC2749477  PMID: 19654746
14.  Antifungal Chemical Compounds Identified Using a C. elegans Pathogenicity Assay 
PLoS Pathogens  2007;3(2):e18.
There is an urgent need for the development of new antifungal agents. A facile in vivo model that evaluates libraries of chemical compounds could solve some of the main obstacles in current antifungal discovery. We show that Candida albicans, as well as other Candida species, are ingested by Caenorhabditis elegans and establish a persistent lethal infection in the C. elegans intestinal track. Importantly, key components of Candida pathogenesis in mammals, such as filament formation, are also involved in nematode killing. We devised a Candida-mediated C. elegans assay that allows high-throughput in vivo screening of chemical libraries for antifungal activities, while synchronously screening against toxic compounds. The assay is performed in liquid media using standard 96-well plate technology and allows the study of C. albicans in non-planktonic form. A screen of 1,266 compounds with known pharmaceutical activities identified 15 (∼1.2%) that prolonged survival of C. albicans-infected nematodes and inhibited in vivo filamentation of C. albicans. Two compounds identified in the screen, caffeic acid phenethyl ester, a major active component of honeybee propolis, and the fluoroquinolone agent enoxacin exhibited antifungal activity in a murine model of candidiasis. The whole-animal C. elegans assay may help to study the molecular basis of C. albicans pathogenesis and identify antifungal compounds that most likely would not be identified by in vitro screens that target fungal growth. Compounds identified in the screen that affect the virulence of Candida in vivo can potentially be used as “probe compounds” and may have antifungal activity against other fungi.
Author Summary
Candida spp. are among the most significant causes of nosocomial infections, and disseminated candidiasis continues to have an attributable mortality rate of over 25%. For this reason, we have developed a liquid media assay using the model nematode Caenorhabditis elegans as a model organism for Candida infection. The worms are infected on solid media lawns and then moved to pathogen-free liquid media. Unless antifungal compounds are added to the wells, the majority of worms die within 3–4 d. This model is similar to the infection process in humans, in that Candida cells are able to produce filaments, which are essential for the infection process in humans. We used this pathogen model to create a semi-automated, high-throughput screen using C. elegans to evaluate the antifungal effectiveness of many types of chemical compounds. Through this process, we have identified three compounds that we show have varying degrees of antifungal activity in C. elegans, in vitro, and in mice.
PMCID: PMC1790726  PMID: 17274686
15.  Acoustic Sensor Network for Relative Positioning of Nodes 
Sensors (Basel, Switzerland)  2009;9(11):8490-8507.
In this work, an acoustic sensor network for a relative localization system is analyzed by reporting the accuracy achieved in the position estimation. The proposed system has been designed for those applications where objects are not restricted to a particular environment and thus one cannot depend on any external infrastructure to compute their positions. The objects are capable of computing spatial relations among themselves using only acoustic emissions as a ranging mechanism. The object positions are computed by a multidimensional scaling (MDS) technique and, afterwards, a least-square algorithm, based on the Levenberg-Marquardt algorithm (LMA), is applied to refine results. Regarding the position estimation, all the parameters involved in the computation of the temporary relations with the proposed ranging mechanism have been considered. The obtained results show that a fine-grained localization can be achieved considering a Gaussian distribution error in the proposed ranging mechanism. Furthermore, since acoustic sensors require a line-of-sight to properly work, the system has been tested by modeling the lost of this line-of-sight as a non-Gaussian error. A suitable position estimation has been achieved even if it is considered a bias of up to 25 of the line-of-sight measurements among a set of nodes.
PMCID: PMC3260597  PMID: 22291520
sensor networks; relative localization; remote sensing
16.  Synthesis, antifungal activity, and QSAR studies of 1,6-dihydropyrimidine derivatives 
A practical synthesis of pyrimidinone would be very helpful for chemists because pyrimidinone is found in many bioactive natural products and exhibits a wide range of biological properties. The biological significance of pyrimidine derivatives has led us to the synthesis of substituted pyrimidine.
Materials and Methods:
With the aim of developing potential antimicrobials, new series of 5-cyano-6-oxo-1,6-dihydro-pyrimidine derivatives namely 2-(5-cyano-6-oxo-4-substituted (aryl)-1,6-dihydropyrimidin-2-ylthio)-N-substituted (phenyl) acetamide (C1-C41) were synthesized and characterized by Fourier transform infrared spectroscopy (FTIR), mass analysis, and proton nuclear magnetic resonance (1H NMR). All the compounds were screened for their antifungal activity against Candida albicans (MTCC, 227).
Results and Discussion:
Quantitative structure activity relationship (QSAR) studies of a series of 1,6-dihydro-pyrimidine were carried out to study various structural requirements for fungal inhibition. Various lipophilic, electronic, geometric, and spatial descriptors were correlated with antifungal activity using genetic function approximation. Developed models were found predictive as indicated by their square of predictive regression values (r2pred) and their internal and external cross-validation. Study reveals that CHI_3_C, Molecular_SurfaceArea, and Jurs_DPSA_1 contributed significantly to the activity along with some electronic, geometric, and quantum mechanical descriptors.
A careful analysis of the antifungal activity data of synthesized compounds revealed that electron withdrawing substitution on N-phenyl acetamide ring of 1,6-dihydropyrimidine moiety possess good activity.
PMCID: PMC3831741  PMID: 24302836
1; 6-dihydro-pyrimidine; antifungal activity; genetic function approximation; lack of fit; quantitative structure activity relationship
17.  Automated NMR relaxation dispersion data analysis using NESSY 
BMC Bioinformatics  2011;12:421.
Proteins are dynamic molecules with motions ranging from picoseconds to longer than seconds. Many protein functions, however, appear to occur on the micro to millisecond timescale and therefore there has been intense research of the importance of these motions in catalysis and molecular interactions. Nuclear Magnetic Resonance (NMR) relaxation dispersion experiments are used to measure motion of discrete nuclei within the micro to millisecond timescale. Information about conformational/chemical exchange, populations of exchanging states and chemical shift differences are extracted from these experiments. To ensure these parameters are correctly extracted, accurate and careful analysis of these experiments is necessary.
The software introduced in this article is designed for the automatic analysis of relaxation dispersion data and the extraction of the parameters mentioned above. It is written in Python for multi platform use and highest performance. Experimental data can be fitted to different models using the Levenberg-Marquardt minimization algorithm and different statistical tests can be used to select the best model. To demonstrate the functionality of this program, synthetic data as well as NMR data were analyzed. Analysis of these data including the generation of plots and color coded structures can be performed with minimal user intervention and using standard procedures that are included in the program.
NESSY is easy to use open source software to analyze NMR relaxation data. The robustness and standard procedures are demonstrated in this article.
PMCID: PMC3215250  PMID: 22032230
Protein dynamics; software; cpmg; conformational/chemical exchange; μs-ms motion; van't Hoff; transition state theory
18.  Novel Algorithms for the Identification of Biologically Informative Chemical Diversity Metrics 
Despite great advances in the efficiency of analytical and synthetic chemistry, time and available starting material still limit the number of unique compounds that can be practically synthesized and evaluated as prospective therapeutics. Chemical diversity analysis (the capacity to identify finite diverse subsets that reliably represent greater manifolds of drug-like chemicals) thus remains an important resource in drug discovery. Despite an unproven track record, chemical diversity has also been used to posit, from preliminary screen hits, new compounds with similar or better activity. Identifying diversity metrics that demonstrably encode bioactivity trends is thus of substantial potential value for intelligent assembly of targeted screens. This paper reports novel algorithms designed to simultaneously reflect chemical similarity or diversity trends and apparent bioactivity in compound collections. An extensive set of descriptors are evaluated within large NCI screening data sets according to bioactivity differentiation capacities, quantified as the ability to co-localize known active species into bioactive-rich K-means clusters. One method tested for descriptor selection orders features according to relative variance across a set of training compounds, and samples increasingly finer subset meshes for descriptors whose exclusion from the model induces drastic drops in relative bioactive colocalization. This yields metrics with reasonable bioactive enrichment (greater than 50% of all bioactive compounds collected into clusters or cells with significantly enriched active/inactive rates) for each of the four data sets examined herein. A second method replaces variance by an active/inactive divergence score, achieving comparable enrichment via a much more efficient search process. Combinations of the above metrics are tested in 2D rectilinear diversity models, achieving similarly successful colocalization statistics, with metrics derived from the active/inactive divergence score typically outperforming those selected from the variance criterion and computed from the DiverseSolutions software.
PMCID: PMC2753527  PMID: 19789655
19.  A Chemically Modified Tetracycline (CMT-3) Is a New Antifungal Agent 
Several chemically modified tetracycline analogs (CMTs), which were chemically modified to eliminate their antibacterial efficacy, were unexpectedly found to have antifungal properties. Of 10 CMTs screened in vitro, all exhibited antifungal activities, although their efficacies varied. Among these compounds, CMT-315, -3, and -308 were found to be the most potent as antifungal agents. The MICs of CMT-3 against 47 strains of fungi in vitro were determined by using amphotericin B (AMB) and doxycycline as positive and negative controls, respectively. The MICs of CMT-3 were generally found to be between 0.25 and 8.00 μg/ml, a range that approximates the blood levels of this drug when administrated orally to humans. Of all the yeast species tested to date, Candida albicans showed the greatest sensitivity to CMT-3. The filamentous species most susceptible to CMT-3 were found to be Epidermophyton floccosum, Microsporum gypseum, Pseudallescheria boydii, a Penicillium sp., Scedosporium apiospermum, a Tricothecium sp., and Trichophyton rubrum. Growth inhibition of C. albicans by CMT-3, determined by a turbidity assay, indicated a 50% inhibitory concentration of 1 μg/ml. Thirty-nine strains, including 20 yeasts and 19 molds, were used to measure viability (the ability to grow after treatment with a drug) inhibition by CMT-3 and AMB. CMT-3 exhibited fungicidal activity against most of these fungi, especially the filamentous fungi. Eighty-four percent (16 of 19) of the filamentous fungi tested showed more than 90% inhibition of viability by CMT-3. In contrast, AMB showed fungicidal activity against all yeasts tested. However, most of the filamentous fungi (16 of 19) showed less than 50% inhibition of viability by AMB, indicating that AMB is fungistatic against most of these filamentous fungi. To begin to identify the sites in fungal cells affected by CMT-3, C. albicans and a Penicillium sp. were incubated with the compound at 35°C, and then the fluorescence of CMT-3 was observed by confocal laser scanning electron microscopy. CMT-3 appeared to have widespread intracellular distribution throughout C. albicans and the Penicillium sp. The mechanisms of the antifungal activity of CMT-3 are now being explored.
PMCID: PMC127171  PMID: 11959581
20.  In vivo activity of terpinen-4-ol, the main bioactive component of Melaleuca alternifolia Cheel (tea tree) oil against azole-susceptible and -resistant human pathogenic Candida species 
Recent investigations on the antifungal properties of essential oil of Melaleuca alternifolia Cheel (Tea Tree Oil, TTO) have been performed with reference to the treatment of vaginal candidiasis. However, there is a lack of in vivo data supporting in vitro results, especially regarding the antifungal properties of TTO constituents. Thus, the aim of our study was to investigate the in vitro and the in vivo anti-Candida activity of two critical bioactive constituents of TTO, terpinen-4-ol and 1,8-cineole.
Oophorectomized, pseudoestrus rats under estrogen treatment were used for experimental vaginal infection with azole (fluconazole, itraconazole) -susceptible or -resistant strains of C. albicans. All these strains were preliminarily tested for in vitro susceptibility to TTO, terpinen-4-ol and 1,8-cineole for their antifungal properties, using a modification of the CLSI (formerly NCCLS) reference M27-A2 broth micro-dilution method.
In vitro minimal inhibitory concentrations (MIC90) values were 0.06% (volume/volume) for terpinen-4-ol and 4% (volume/volume) for 1,8-cineole, regardless of susceptibility or resistance of the strains to fluconazole and itraconazole. Fungicidal concentrations of terpinen-4-ol were equivalent to the candidastatic activity. In the rat vaginal infection model, terpinen-4-ol was as active as TTO in accelerating clearance from the vagina of all Candida strains examined.
Our data suggest that terpinen-4-ol is a likely mediator of the in vitro and in vivo activity of TTO. This is the first in vivo demonstration that terpinen-4-ol could control C. albicans vaginal infections. The purified compound holds promise for the treatment of vaginal candidiasis, and particularly the azole-resistant forms.
PMCID: PMC1637110  PMID: 17083732
21.  In Vitro Antifungal Activities of a Series of Dication-Substituted Carbazoles, Furans, and Benzimidazoles 
Antimicrobial Agents and Chemotherapy  1998;42(10):2503-2510.
Aromatic dicationic compounds possess antimicrobial activity against a wide range of eucaryotic pathogens, and in the present study an examination of the structures-functions of a series of compounds against fungi was performed. Sixty-seven dicationic molecules were screened for their inhibitory and fungicidal activities against Candida albicans and Cryptococcus neoformans. The MICs of a large number of compounds were comparable to those of the standard antifungal drugs amphotericin B and fluconazole. Unlike fluconazole, potent inhibitory compounds in this series were found to have excellent fungicidal activities. The MIC of one of the most potent compounds against C. albicans was 0.39 μg/ml, and it was the most potent compound against C. neoformans (MIC, ≤0.09 μg/ml). Selected compounds were also found to be active against Aspergillus fumigatus, Fusarium solani, Candida species other than C. albicans, and fluconazole-resistant strains of C. albicans and C. neoformans. Since some of these compounds have been safely given to animals, these classes of molecules have the potential to be developed as antifungal agents.
PMCID: PMC105871  PMID: 9756748
22.  Optimisation of NMR dynamic models I. Minimisation algorithms and their performance within the model-free and Brownian rotational diffusion spaces 
Journal of Biomolecular Nmr  2007;40(2):107-119.
The key to obtaining the model-free description of the dynamics of a macromolecule is the optimisation of the model-free and Brownian rotational diffusion parameters using the collected R1, R2 and steady-state NOE relaxation data. The problem of optimising the chi-squared value is often assumed to be trivial, however, the long chain of dependencies required for its calculation complicates the model-free chi-squared space. Convolutions are induced by the Lorentzian form of the spectral density functions, the linear recombinations of certain spectral density values to obtain the relaxation rates, the calculation of the NOE using the ratio of two of these rates, and finally the quadratic form of the chi-squared equation itself. Two major topological features of the model-free space complicate optimisation. The first is a long, shallow valley which commences at infinite correlation times and gradually approaches the minimum. The most severe convolution occurs for motions on two timescales in which the minimum is often located at the end of a long, deep, curved tunnel or multidimensional valley through the space. A large number of optimisation algorithms will be investigated and their performance compared to determine which techniques are suitable for use in model-free analysis. Local optimisation algorithms will be shown to be sufficient for minimisation not only within the model-free space but also for the minimisation of the Brownian rotational diffusion tensor. In addition the performance of the programs Modelfree and Dasha are investigated. A number of model-free optimisation failures were identified: the inability to slide along the limits, the singular matrix failure of the Levenberg–Marquardt minimisation algorithm, the low precision of both programs, and a bug in Modelfree. Significantly, the singular matrix failure of the Levenberg–Marquardt algorithm occurs when internal correlation times are undefined and is greatly amplified in model-free analysis by both the grid search and constraint algorithms. The program relax ( is also presented as a new software package designed for the analysis of macromolecular dynamics through the use of NMR relaxation data and which alleviates all of the problems inherent within model-free analysis.
Electronic supplementary material
The online version of this article (doi:10.1007/s10858-007-9214-2) contains supplementary material, which is available to authorized users.
PMCID: PMC2758376  PMID: 18085410
Brownian rotational diffusion; Model-free analysis; Minimisation; Newton minimisation; Optimisation; NMR relaxation
23.  Synthesis and Antifungal Activity of Metal Complexes Containing Dichloro-Tetramorpholino-Cyclophosphazatriene 
Metal-Based Drugs  1998;5(5):287-294.
Metal complexes of dichloro-tetramorpholino-cyclophosphazatriene containing divalent cations such as Ni(II), Co(II), and Mn(II) have been prepared and characterised by standard physico-chemical procedures (elemental chemical analysis, IR and UV-VIS spectra, conductimetric measurement). The newly synthesised compounds possessed antifungal activity against Aspergillus and Candida spp., some of them showing effects comparable to ketoconazole (with minimum inhibitory concentrations in the range of 2- 30 μg/mL) but being generally less active as compared to the azole. Best activity was detected against C. albicans, and worst activity against A. niger. The mechanism of action of these compounds probably involves inhibition of ergosterol biosynthesis, and interaction with lanosterol-14-α-demethylase (CYP51A1), since reduced amounts of ergosterol were evidenced by means of HPLC in cultures of the sensitive strain A. niger treated with some of these inhibitors.
PMCID: PMC2365132  PMID: 18475860
24.  A Monte-Carlo-Based Network Method for Source Positioning in Bioluminescence Tomography 
We present an approach based on the improved Levenberg Marquardt (LM) algorithm of backpropagation (BP) neural network to estimate the light source position in bioluminescent imaging. For solving the forward problem, the table-based random sampling algorithm (TBRS), a fast Monte Carlo simulation method we developed before, is employed here. Result shows that BP is an effective method to position the light source.
PMCID: PMC2216075  PMID: 18273391
25.  Analysis of multiple compound–protein interactions reveals novel bioactive molecules 
The authors use machine learning of compound-protein interactions to explore drug polypharmacology and to efficiently identify bioactive ligands, including novel scaffold-hopping compounds for two pharmaceutically important protein families: G-protein coupled receptors and protein kinases.
We have demonstrated that machine learning of multiple compound–protein interactions is useful for efficient ligand screening and for assessing drug polypharmacology.This approach successfully identified novel scaffold-hopping compounds for two pharmaceutically important protein families: G-protein-coupled receptors and protein kinases.These bioactive compounds were not detected by existing computational ligand-screening methods in comparative studies.The results of this study indicate that data derived from chemical genomics can be highly useful for exploring chemical space, and this systems biology perspective could accelerate drug discovery processes.
The discovery of novel bioactive molecules advances our systems-level understanding of biological processes and is crucial for innovation in drug development. Perturbations of biological systems by chemical probes provide broader applications not only for analysis of complex systems but also for intentional manipulations of these systems. Nevertheless, the lack of well-characterized chemical modulators has limited their use. Recently, chemical genomics has emerged as a promising area of research applicable to the exploration of novel bioactive molecules, and researchers are currently striving toward the identification of all possible ligands for all target protein families (Wang et al, 2009). Chemical genomics studies have shown that patterns of compound–protein interactions (CPIs) are too diverse to be understood as simple one-to-one events. There is an urgent need to develop appropriate data mining methods for characterizing and visualizing the full complexity of interactions between chemical space and biological systems. However, no existing screening approach has so far succeeded in identifying novel bioactive compounds using multiple interactions among compounds and target proteins.
High-throughput screening (HTS) and computational screening have greatly aided in the identification of early lead compounds for drug discovery. However, the large number of assays required for HTS to identify drugs that target multiple proteins render this process very costly and time-consuming. Therefore, interest in using in silico strategies for screening has increased. The most common computational approaches, ligand-based virtual screening (LBVS) and structure-based virtual screening (SBVS; Oprea and Matter, 2004; Muegge and Oloff, 2006; McInnes, 2007; Figure 1A), have been used for practical drug development. LBVS aims to identify molecules that are very similar to known active molecules and generally has difficulty identifying compounds with novel structural scaffolds that differ from reference molecules. The other popular strategy, SBVS, is constrained by the number of three-dimensional crystallographic structures available. To circumvent these limitations, we have shown that a new computational screening strategy, chemical genomics-based virtual screening (CGBVS), has the potential to identify novel, scaffold-hopping compounds and assess their polypharmacology by using a machine-learning method to recognize conserved molecular patterns in comprehensive CPI data sets.
The CGBVS strategy used in this study was made up of five steps: CPI data collection, descriptor calculation, representation of interaction vectors, predictive model construction using training data sets, and predictions from test data (Figure 1A). Importantly, step 1, the construction of a data set of chemical structures and protein sequences for known CPIs, did not require the three-dimensional protein structures needed for SBVS. In step 2, compound structures and protein sequences were converted into numerical descriptors. These descriptors were used to construct chemical or biological spaces in which decreasing distance between vectors corresponded to increasing similarity of compound structures or protein sequences. In step 3, we represented multiple CPI patterns by concatenating these chemical and protein descriptors. Using these interaction vectors, we could quantify the similarity of molecular interactions for compound–protein pairs, despite the fact that the ligand and protein similarity maps differed substantially. In step 4, concatenated vectors for CPI pairs (positive samples) and non-interacting pairs (negative samples) were input into an established machine-learning method. In the final step, the classifier constructed using training sets was applied to test data.
To evaluate the predictive value of CGBVS, we first compared its performance with that of LBVS by fivefold cross-validation. CGBVS performed with considerably higher accuracy (91.9%) than did LBVS (84.4%; Figure 1B). We next compared CGBVS and SBVS in a retrospective virtual screening based on the human β2-adrenergic receptor (ADRB2). Figure 1C shows that CGBVS provided higher hit rates than did SBVS. These results suggest that CGBVS is more successful than conventional approaches for prediction of CPIs.
We then evaluated the ability of the CGBVS method to predict the polypharmacology of ADRB2 by attempting to identify novel ADRB2 ligands from a group of G-protein-coupled receptor (GPCR) ligands. We ranked the prediction scores for the interactions of 826 reported GPCR ligands with ADRB2 and then analyzed the 50 highest-ranked compounds in greater detail. Of 21 commercially available compounds, 11 showed ADRB2-binding activity and were not previously reported to be ADRB2 ligands. These compounds included ligands not only for aminergic receptors but also for neuropeptide Y-type 1 receptors (NPY1R), which have low protein homology to ADRB2. Most ligands we identified were not detected by LBVS and SBVS, which suggests that only CGBVS could identify this unexpected cross-reaction for a ligand developed as a target to a peptidergic receptor.
The true value of CGBVS in drug discovery must be tested by assessing whether this method can identify scaffold-hopping lead compounds from a set of compounds that is structurally more diverse. To assess this ability, we analyzed 11 500 commercially available compounds to predict compounds likely to bind to two GPCRs and two protein kinases. Functional assays revealed that nine ADRB2 ligands, three NPY1R ligands, five epidermal growth factor receptor (EGFR) inhibitors, and two cyclin-dependent kinase 2 (CDK2) inhibitors were concentrated in the top-ranked compounds (hit rate=30, 15, 25, and 10%, respectively). We also evaluated the extent of scaffold hopping achieved in the identification of these novel ligands. One ADRB2 ligand, two NPY1R ligands, and one CDK2 inhibitor exhibited scaffold hopping (Figure 4), indicating that CGBVS can use this characteristic to rationally predict novel lead compounds, a crucial and very difficult step in drug discovery. This feature of CGBVS is critically different from existing predictive methods, such as LBVS, which depend on similarities between test and reference ligands, and focus on a single protein or highly homologous proteins. In particular, CGBVS is useful for targets with undefined ligands because this method can use CPIs with target proteins that exhibit lower levels of homology.
In summary, we have demonstrated that data mining of multiple CPIs is of great practical value for exploration of chemical space. As a predictive model, CGBVS could provide an important step in the discovery of such multi-target drugs by identifying the group of proteins targeted by a particular ligand, leading to innovation in pharmaceutical research.
The discovery of novel bioactive molecules advances our systems-level understanding of biological processes and is crucial for innovation in drug development. For this purpose, the emerging field of chemical genomics is currently focused on accumulating large assay data sets describing compound–protein interactions (CPIs). Although new target proteins for known drugs have recently been identified through mining of CPI databases, using these resources to identify novel ligands remains unexplored. Herein, we demonstrate that machine learning of multiple CPIs can not only assess drug polypharmacology but can also efficiently identify novel bioactive scaffold-hopping compounds. Through a machine-learning technique that uses multiple CPIs, we have successfully identified novel lead compounds for two pharmaceutically important protein families, G-protein-coupled receptors and protein kinases. These novel compounds were not identified by existing computational ligand-screening methods in comparative studies. The results of this study indicate that data derived from chemical genomics can be highly useful for exploring chemical space, and this systems biology perspective could accelerate drug discovery processes.
PMCID: PMC3094066  PMID: 21364574
chemical genomics; data mining; drug discovery; ligand screening; systems chemical biology

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