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.
Antifungal activity; Coumarin; Modeling; Neural network
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.
artificial neural networks; fiber optic sensors; microbend sensors; multilayer perceptron; radial basis function; general regression neural network
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.
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.
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.
sensor networks; relative localization; remote sensing
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.
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.
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.
Protein dynamics; software; cpmg; conformational/chemical exchange; μs-ms motion; van't Hoff; transition state theory
Naturally occurring antimicrobial peptides hold promise as therapeutic agents against oral pathogens such as Candida albicans, however numerous difficulties have slowed their development. Synthetic, non-peptidic analogs that mimic the properties of these peptides have many advantages and exhibit potent, selective antimicrobial activity. Several series of mimetics (MW <1,000) were developed and screened against oral Candida strains as a proof-of-principle for their antifungal properties. One phenylalkyne and several arylamide compounds with reduced mammalian cytotoxicities were found to be active against C. albicans. These compounds demonstrated rapid fungicidal activity in liquid culture even in the presence of saliva, and demonstrated synergy with standard antifungal agents. When assayed against biofilms grown on denture acrylic, the compounds exhibited potent fungicidal activity as measured by metabolic and fluorescent viability assays. Repeated passages in sub-MIC levels did not lead to resistant Candida in contrast to fluconazole. Our results demonstrate the proof-of principle for the use of these compounds as anti-Candida agents, and their further testing is warranted as novel anti-Candida therapies.
antifungal; denture; fungicide; defensin; resistance
Candida albicans, the most common human pathogenic fungus, can establish a persistent lethal infection in the intestine of the microscopic nematode Caenorhabditis elegans. The C. elegans–C. albicans infection model was previously adapted to screen for antifungal compounds. Modifications to this screen have been made to facilitate a high-throughput assay including co-inoculation of nematodes with C. albicans and instrumentation allowing precise dispensing of worms into assay wells, eliminating two labor-intensive steps. This high-throughput method was utilized to screen a library of 3,228 compounds represented by 1,948 bioactive compounds and 1,280 small molecules derived via diversity-oriented synthesis. Nineteen compounds were identified that conferred an increase in C. elegans survival, including most known antifungal compounds within the chemical library. In addition to seven clinically used antifungal compounds, twelve compounds were identified which are not primarily used as antifungal agents, including three immunosuppressive drugs. This assay also allowed the assessment of the relative minimal inhibitory concentration, the effective concentration in vivo, and the toxicity of the compound in a single assay.
Reaction kinetics for complex, highly-interconnected kinetic schemes are modeled using analytical solutions to a system of ordinary differential equations. The algorithm employs standard linear algebra methods that are implemented using MatLab functions in a Visual Basic interface. A graphical user interface for simple entry of reaction schemes facilitates comparison of a variety of reaction schemes. To ensure microscopic balance, graph theory algorithms are used to determine violations of thermodynamic cycle constraints. Analytical solutions based on linear differential equations result in fast comparisons of first order kinetic rates and amplitudes as a function of changing ligand concentrations. For analysis of higher order kinetics, we also implemented a solution using numerical integration. In order to determine rate constants from experimental data, fitting algorithms using the Levenberg-Marquardt algorithm or using Broyden-Fletcher-Goldfarb-Shanno (BFGS) methods were implemented that adjust rate constants to fit the model to imported data. We have included the ability to carry out global fitting of data sets obtained at varying ligand concentrations. These tools are combined in a single package, which we have dubbed VisKin, to guide and analyze kinetic experiments. The software is available online for use on PCs.
Poly- and mononuclear metal complexes of 2,3,11,12-bis[4-(10-aminodecylcarbonyl)]benzo-18-
crown-6 (L) and Cu(II); Ni(II); Co(II) and Cr(III) have been synthesized and characterized by standard
physico-chemical procedures. In the newly prepared complexes the crown moiety oxygen atoms of the
macrocyclic host did not generally interact with metal ions, whereas the two amino groups of the ligand always did. Several of the newly synthesized compounds act as effective antifungal agents against
Aspergillus and Candida spp., some of them showing activities comparable to
ketoconazole, with minimum inhibitory concentrations
in the range of 0.3−0.5 μg/mL. The mechanism of antifungal action of these
coordination compounds is probably connected to an inhibition of lanosterol-14-α-demethylase, a metallo-enzyme
playing a key role in sterol biosynthesis in fungi, bacteria and eukariotes.
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.
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.
Microwave imaging for medical applications is attractive because the range of dielectric properties of different soft tissues can be substantial. Breast cancer detection and monitoring of treatment response are areas where this technology could be important because of the contrast between normal and malignant tissue. Unfortunately, the technique is unable to achieve the high spatial resolution at depth in tissue which is available from other conventional modalities such as x-ray computed tomography (CT) or magnetic resonance imaging (MRI). We have incorporated a soft-prior regularization strategy within our microwave reconstruction algorithm and compared it with the images obtained with traditional no-prior (Levenberg-Marquardt) regularization. Initial simulation and phantom results show a significant improvement of the recovered electrical properties. Specifically, errors in the microwave property estimates were improved by as much as 95%. The effects of a false-inclusion region were also evaluated and the results show that a small residual property bias of 6% in permittivity and 15% in conductivity can occur that does not otherwise degrade the property recovery accuracy of inclusions that actually exist. The work sets the stage for integrating microwave imaging with MR for improved resolution and functional imaging of the breast in the future.
Breast cancer detection; image reconstruction algorithms; microwave imaging; soft-prior regularization; spatial priors
Search for naturally occurring compounds with antifungal activity has become quite intense due to the side effects of synthetic fungicides and the development of pathogens against such fungicides. Hence screening of various Siddha drugs for their antifungal activity against various strains of Candida albicans was considered worthwhile. Seven such Siddha drugs were screened for their antifungal activity against fourteen strains of Candida albicans. The results indicate that the drugs Nandhi mezhugu, Vaan mezhugu, Erasa Kenthi mezhugu and Parangi pattai choornam possessed significant antifungal activity against various strains of C.albicans.
A detection and tracking algorithm for ferromagnetic objects based on a two stage Levenberg Marquardt Algorithm (LMA) is presented. The procedure is applied to localization and magnetic moment estimation of ferromagnetic objects moving in the vicinity of an array of two to four 3-axis magnetometers arranged as a check point configuration. The algorithms first stage provides an estimation of the target trajectory and moment that are further refined using a second iteration where only the position vector is taken as unknown. The whole procedure is fast enough to provide satisfactory results within a few seconds after the target has been detected. Tests were conducted in Soreq NRC assessing various check point scenarios and targets. The results obtained from this experiment show good localization performance and good convivial with “noisy” environment. Small targets can be localized with good accuracy using either a vertical “doorway” two to four sensors configuration or ground level two to four sensors configuration. The calculated trajectory was not affected by nearby magnetic interference such as moving vehicles or a combat soldier inspecting the gateway.
magnetic moment localization; magnetic sensors; Levenberg Marquardt Algorithm
Motivation: Recognition of poly(A) signals in mRNA is relatively straightforward due to the presence of easily recognizable polyadenylic acid tail. However, the task of identifying poly(A) motifs in the primary genomic DNA sequence that correspond to poly(A) signals in mRNA is a far more challenging problem. Recognition of poly(A) signals is important for better gene annotation and understanding of the gene regulation mechanisms. In this work, we present one such poly(A) motif prediction method based on properties of human genomic DNA sequence surrounding a poly(A) motif. These properties include thermodynamic, physico-chemical and statistical characteristics. For predictions, we developed Artificial Neural Network and Random Forest models. These models are trained to recognize 12 most common poly(A) motifs in human DNA. Our predictors are available as a free web-based tool accessible at http://cbrc.kaust.edu.sa/dps. Compared with other reported predictors, our models achieve higher sensitivity and specificity and furthermore provide a consistent level of accuracy for 12 poly(A) motif variants.
Supplementary information: Supplementary data are available at Bioinformatics online.
One of the major obstacles in computational modeling of a biological system is to determine a large number of parameters in the mathematical equations representing biological properties of the system. To tackle this problem, we have developed a global optimization method, called Discrete Selection Levenberg-Marquardt (DSLM), for parameter estimation. For fast computational convergence, DSLM suggests a new approach for the selection of optimal parameters in the discrete spaces, while other global optimization methods such as genetic algorithm and simulated annealing use heuristic approaches that do not guarantee the convergence. As a specific application example, we have targeted understanding phagocyte transmigration which is involved in the fibrosis process for biomedical device implantation. The goal of computational modeling is to construct an analyzer to understand the nature of the system. Also, the simulation by computational modeling for phagocyte transmigration provides critical clues to recognize current knowledge of the system and to predict yet-to-be observed biological phenomenon.
biological system modeling; nonlinear estimation; parameter estimation; reverse engineering
A common registration problem for the application of consumer device is to align all the acquired image sequences into a complete scene. Image alignment requires a registration algorithm that will compensate as much as possible for geometric variability among images. However, images captured views from a real scene usually produce different distortions. Some are derived from the optic characteristics of image sensors, and others are caused by the specific scenes and objects.
An image registration algorithm considering the perspective projection is proposed for the application of consumer devices in this study. It exploits a multiresolution wavelet-based method to extract significant features. An analytic differential approach is then proposed to achieve fast convergence of point matching. Finally, the registration accuracy is further refined to obtain subpixel precision by a feature-based modified Levenberg-Marquardt method. Due to its feature-based and nonlinear characteristic, it converges considerably faster than most other methods. In addition, vignette compensation and color difference adjustment are also performed to further improve the quality of registration results.
The performance of the proposed method is evaluated by testing the synthetic and real images acquired by a hand-held digital still camera and in comparison with two registration techniques in terms of the squared sum of intensity differences (SSD) and correlation coefficient (CC). The results indicate that the proposed method is promising in registration accuracy and quality, which are statistically significantly better than other two approaches.
Gielis curves and surfaces can describe a wide range of natural shapes and they have been used in various studies in biology and physics as descriptive tool. This has stimulated the generalization of widely used computational methods. Here we show that proper normalization of the Levenberg-Marquardt algorithm allows for efficient and robust reconstruction of Gielis curves, including self-intersecting and asymmetric curves, without increasing the overall complexity of the algorithm. Then, we show how complex curves of k-type can be constructed and how solutions to the Dirichlet problem for the Laplace equation on these complex domains can be derived using a semi-Fourier method. In all three methods, descriptive and computational power and efficiency is obtained in a surprisingly simple way.
The prediction of biological activity of a chemical compound from its structural features plays an important role in drug design. In this paper, we discuss the quantitative structure activity relationship (QSAR) prediction models developed on a dataset of 170 HIV protease enzyme inhibitors. Various chemical descriptors that encode hydrophobic, topological, geometrical and electronic properties are calculated to represent the structures of the molecules in the dataset. We use the hybrid-GA (genetic algorithm) optimization technique for descriptor space reduction. The linear multiple regression analysis (MLR), correlation-based feature selection (CFS), non-linear decision tree (DT), and artificial neural network (ANN) approaches are used as fitness functions. The selected descriptors represent the overall descriptor space and account well for the binding nature of the considered dataset. These selected features are also human interpretable and can be used to explain the interactions between a drug molecule and its receptor protein (HIV protease). The selected descriptors are then used for developing the QSAR prediction models by using the MLR, DT and ANN approaches. These models are discussed, analyzed and compared to validate and test their performance for this dataset. All the three approaches yield the QSAR models with good prediction performance. The models developed by DT and ANN are comparable and have better prediction than the MLR model. For ANN model, weight analysis is carried out to analyze the role of various descriptors in activity prediction. All the prediction models point towards the involvement of hydrophobic interactions. These models can be useful for predicting the biological activity of new untested HIV protease inhibitors and virtual screening for identifying new lead compounds.
Artificial Neural Networks; Genetic Algorithm; Regression Analysis; Decision Trees; HIV-1 Protease inhibitor
Adherence to vascular endothelium is considered an essential step in the pathogenesis of hematogenously disseminated candidiasis. Platelets have been shown to promote Candida adherence to vascular endothelium in vitro. In contrast, recent studies indicate that platelets may also play a role in the primary host defense against endovascular infection by secretion of alpha granule-derived platelet microbicidal protein (PMP), which possesses both bactericidal and fungicidal activities as well as antiadherence properties. We examined the influences of PMP and the antifungal agent fluconazole on the adherence of Candida albicans to rabbit platelets, as measured by quantitative flow cytometry. In the absence of PMP and fluconazole, adherence of C. albicans to platelets was rapid (complete within 1 min), saturable, and reversible. Following 2 h of exposure to fluconazole at 10x the MIC, platelet binding of C. albicans was substantially reduced (mean reduction, 32.1%; P = 0.08). Similarly, exposure of C. albicans to PMP (range, 0.5 to 5 micrograms/ml) for 2 h (but not 30 min) significantly reduced candidal adherence to platelets 43.1 to 62.1%; (reduction range, P < 0.05). Moreover, exposure of C. albicans to PMP (5 micrograms/ml for 30 min) and then fluconazole (10x the MIC for 2 h) further decreased candidal adherence to platelets in comparison with the adherence after exposure to either agent alone (mean reduction, 57.2%; P = 0.02 and 0.05, respectively). These data demonstrate that PMP and fluconazole individually reduce the ability of C. albicans to bind to platelets in vitro and that the antiadherence activities of fluconazole are augmented by PMP.
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.
Seven 6-alkyl-2,3,4,5-tetrahydropyridines (5a–5g) that mimic the natural piperideines that were recently identified in the fire ant venom have been synthesized. Compounds 5c–5g with the C-6 alkyl chain lengths from C14 to C18 showed varying degrees of antifungal activities, with 5e (6-hexadecyl-2,3,4,5-tetrahydropyridine) and 5f (6-heptadecyl-2,3,4,5-tetrahydropyridine) being the most active. Compound 5e exhibited minimum fungicidal concentrations (MFCs) of 3.8, 15.0, 7.5, and 7.5 μg/mL against Cryptococcus neoformans, Candida albicans, Candida glabrata, and Candida krusei, respectively. The antifungal activities of these compounds appear to be associated with the C-6 side chain length. This study represents the first effort to evaluate antifungal activities of synthetic analogs of the newly identified fire ant venom alkaloids.
The echinocandin MK-0991, formerly L-743,872, is a water-soluble lipopeptide that has been demonstrated in preclinical studies to have potent activity against Candida spp., Aspergillus fumigatus, and Pneumocystis carinii. An extensive in vitro biological evaluation of MK-0991 was performed to better define the potential activities of this novel compound. Susceptibility testing with MK-0991 against approximately 200 clinical isolates of Candida, Cryptococcus neoformans, and Aspergillus isolates was conducted to determine MICs and minimum fungicidal concentrations MF(s). The MFC at which 90% of isolates are inhibited for 40 C. albicans clinical isolates was 0.5 microg/ml. Susceptibility testing with panels of antifungal agent-resistant species of Candida and C. neoformans isolates indicated that the MK-0991 MFCs for these isolates are comparable to those obtained for susceptible isolates. Growth kinetic studies of MK-0991 against Candida albicans and Candida tropicalis isolates showed that the compound exhibited fungicidal activity (i.e., a 99% reduction in viability) within 3 to 7 h at concentrations ranging from 0.06 to 1 microg/ml (0.25 to 4 times the MIC). Drug combination studies with MK-0991 plus amphotericin B found that this combination was not antagonistic against C. albicans, C. neoformans, or A. fumigatus in vitro. Studies with 0 to 50% pooled human or mouse serum established that fungal susceptibility to MK-0991 was not significantly influenced by the presence of human or mouse serum. Results from resistance induction studies suggested that the susceptibility of C. albicans was not altered by repeated exposure (40 passages) to MK-0991. Erythrocyte hemolysis studies with MK-0991 with washed and unwashed human or mouse erythrocytes indicated minimal hemolytic potential with this compound. These favorable results of preclinical studies support further studies with MK-0991 with humans.