For optimizing the local, pulmonary targeting of inhaled medications, it is important to analyze the relationship between the physicochemical properties of small molecules and their absorption, retention and distribution in the various cell types of the airways and alveoli.
A computational, multiscale, cell-based model was constructed to facilitate analysis of pulmonary drug transport and distribution. The relationship between the physicochemical properties and pharmacokinetic profile of monobasic molecules was explored. Experimental absorption data of compounds with diverse structures were used to validate this model. Simulations were performed to evaluate the effect of active transport and organelle sequestration on the absorption kinetics of compounds.
Relating the physicochemical properties to the pharmacokinetic profiles of small molecules reveals how the absorption half-life and distribution of compounds are expected to vary in different cell types and anatomical regions of the lung. Based on logP, pKa and molecular radius, the absorption rate constants (Ka) calculated with the model were consistent with experimental measurements of pulmonary drug absorption.
The cell-based mechanistic model developed herein is an important step towards the rational design of local, lung-targeted medications, facilitating the design and interpretation of experiments aimed at optimizing drug transport properties in lung.
With a combinatorial library of bioimaging probes, it is now possible to use machine vision to analyze the contribution of different building blocks of the molecules to their cell-associated visual signals. For athis purpose, cell-permeant, fluorescent styryl molecules were synthesized by condensation of 168 aldehyde with 8 pyridinium/quinolinium building blocks. Images of cells incubated with fluorescent molecules were acquired with a high content screening instrument. Chemical and image feature analysis revealed how variation in one or the other building block of the styryl molecules led to variations in the molecules' visual signals. Across each pair of probes in the library, chemical similarity was significantly associated with spectral and total signal intensity similarity. However, chemical similarity was much less associated with similarity in subcellular probe fluorescence patterns. Quantitative analysis and visual inspection of pairs of images acquired from pairs of styryl isomers confirm that many closely-related probes exhibit different subcellular localization patterns. Therefore, idiosyncratic interactions between styryl molecules and specific cellular components greatly contribute to the subcellular distribution of the styryl probes' fluorescence signal. These results demonstrate how machine vision and cheminformatics can be combined to analyze the targeting properties of bioimaging probes, using large image data sets acquired with automated screening systems.
Cheminformatics; machine vision; bioimaging; fluorescence; styryl; high content screening; image cytometry; combinatorial chemistry
Motivation: Bioimaging techniques rapidly develop toward higher resolution and dimension. The increase in dimension is achieved by different techniques such as multitag fluorescence imaging, Matrix Assisted Laser Desorption / Ionization (MALDI) imaging or Raman imaging, which record for each pixel an N-dimensional intensity array, representing local abundances of molecules, residues or interaction patterns. The analysis of such multivariate bioimages (MBIs) calls for new approaches to support users in the analysis of both feature domains: space (i.e. sample morphology) and molecular colocation or interaction. In this article, we present our approach WHIDE (Web-based Hyperbolic Image Data Explorer) that combines principles from computational learning, dimension reduction and visualization in a free web application.
Results: We applied WHIDE to a set of MBI recorded using the multitag fluorescence imaging Toponome Imaging System. The MBI show field of view in tissue sections from a colon cancer study and we compare tissue from normal/healthy colon with tissue classified as tumor. Our results show, that WHIDE efficiently reduces the complexity of the data by mapping each of the pixels to a cluster, referred to as Molecular Co-Expression Phenotypes and provides a structural basis for a sophisticated multimodal visualization, which combines topology preserving pseudocoloring with information visualization. The wide range of WHIDE's applicability is demonstrated with examples from toponome imaging, high content screens and MALDI imaging (shown in the Supplementary Material).
Availability and implementation: The WHIDE tool can be accessed via the BioIMAX website http://ani.cebitec.uni-bielefeld.de/BioIMAX/; Login: whidetestuser; Password: whidetest.
Supplementary data are available at Bioinformatics online.
Chemical address tags can be defined as specific structural features shared by a set of bioimaging probes having a predictable influence on cell-associated visual signals obtained from these probes. Here, using a large image dataset acquired with a high content screening instrument, machine vision and cheminformatics analysis have been applied to reveal chemical address tags. With a combinatorial library of fluorescent molecules, fluorescence signal intensity, spectral, and spatial features characterizing each one of the probes' visual signals were extracted from images acquired with the three different excitation and emission channels of the imaging instrument. With multivariate regression, the additive contribution from each one of the different building blocks of the bioimaging probes towards each measured, cell-associated image-based feature was calculated. In this manner, variations in the chemical features of the molecules were associated with the resulting staining patterns, facilitating quantitative, objective analysis of chemical address tags. Hierarchical clustering and paired image-cheminformatics analysis revealed key structure-property relationships amongst many building blocks of the fluorescent molecules. The results point to different chemical modifications of the bioimaging probes that can exert similar (or different) effects on the probes' visual signals. Inspection of the clustered structures suggests intramolecular charge migration or partial charge distribution as potential mechanistic determinants of chemical address tag behavior.
Cheminformatics; machine vision; bioimaging; fluorescence; high content screening; image cytometry; combinatorial chemistry
High-throughput microscopic screening instruments can generate huge collections of images of live cells incubated with combinatorial libraries of fluorescent molecules. Organizing and visualizing these images to discern biologically important patterns that link back to chemical structure is a challenge. We present an analysis and visualization methodology - Cheminformatic Assisted Image Array (CAIA) - that greatly facilitates data mining efforts. For illustration, we considered a collection of microscopic images acquired from cells incubated with each member of a combinatorial library of styryl molecules being screened for candidate bioimaging probes. By sorting CAIAs based on quantitative image features, the relative contribution of each combinatorial building block on probe intracellular distribution could be visually discerned. The results revealed trends hidden in the dataset: most interestingly, the building blocks of the styryl molecules appeared to behave as chemical address tags, additively and independently encoding spatial patterns of intracellular fluorescence. Translated into practice, CAIA facilitated discovery of several outstanding styryl molecules for live cell nuclear imaging applications.
Cheminformatics; high content screening; combinatorial library; styryl; fluorescence; bioimaging; chemical address tags; QSAR; CAIA
The epidermal growth factor receptor (EGFR) is frequently overexpressed in many cancers, including non-small cell lung cancer (NSCLC). In silico modeling is considered to be an increasingly promising tool to add useful insights into the dynamics of the EGFR signal transduction pathway. However, most of the previous modeling work focused on the molecular or the cellular level only, neglecting the crucial feedback between these scales as well as the interaction with the heterogeneous biochemical microenvironment.
We developed a multiscale model for investigating expansion dynamics of NSCLC within a two-dimensional in silico microenvironment. At the molecular level, a specific EGFR-ERK intracellular signal transduction pathway was implemented. Dynamical alterations of these molecules were used to trigger phenotypic changes at the cellular level. Examining the relationship between extrinsic ligand concentrations, intrinsic molecular profiles and microscopic patterns, the results confirmed that increasing the amount of available growth factor leads to a spatially more aggressive cancer system. Moreover, for the cell closest to nutrient abundance, a phase-transition emerges where a minimal increase in extrinsic ligand abolishes the proliferative phenotype altogether.
Our in silico results indicate that in NSCLC, in the presence of a strong extrinsic chemotactic stimulus (and depending on the cell's location) downstream EGFR-ERK signaling may be processed more efficiently, thereby yielding a migration-dominant cell phenotype and overall, an accelerated spatio-temporal expansion rate.
Cancer invasion is one of the hallmarks of cancer and a prerequisite for cancer metastasis. However, the invasive process is very complex, depending on multiple correlated intrinsic and environmental factors, and thus is difficult to study experimentally in a fully controlled way. Therefore, there is an increased demand for interdisciplinary integrated approaches combining laboratory experiments with multiscale in silico modeling. In this review, we will summarize current computational techniques applicable to model cancer invasion in silico, with a special focus on a class of individual-cell-based models developed in our laboratories. We also discuss their integration with traditional and novel in vitro experimentation, including new invasion assays whose design was inspired by computational modeling.
computational modeling of cancer invasion; cellular assays of cancer invasion; integrative modeling
We provide a new approach for fluorescent probe design termed “PEG-fluorochrome Shielding,” where PEGylation enhances quantum yields while blocking troublesome interactions between fluorochromes and biomolecules. To demonstrate PEG-fluorochrome shielding, fluorochrome-bearing peptide probes were synthesized, three without PEG and three with a 5 kDa PEG functional group. In vitro, PEG blocked the interactions of fluorochrome-labeled peptide probes with each other (absorption spectra, self-quenching) and reduced nonspecific interactions with cells (by FACS). In vivo PEG blocked interactions with biomolecules that lead to probe retention (by surface fluorescence). Integrin targeting in vivo was obtained as the differential uptake of an 111In labeled, fluorochrome shielded, integrin binding RGD and control RAD probes. Using PEG to block fluorochrome mediated interactions, rather than synthesizing de novo fluorochromes, can yield new approaches for the design of actively or passively targeted near infrared fluorescent probes.
Protein scaffold molecules are powerful reagents for targeting various cell signal receptors, enzymes, cytokines and other cancer-related molecules. They belong to the peptide and small protein platform with distinct properties. For the purpose of development of new generation molecular probes, various protein scaffold molecules have been labeled with imaging moieties and evaluated both in vitro and in vivo. Among the evaluated probes Affibody molecules and analogs, cystine knot peptides, and nanobodies have shown especially good characteristics as protein scaffold platforms for development of in vivo molecular probes. Quantitative data obtained from positron emission tomography, single photon emission computed tomography/CT, and optical imaging together with biodistribution studies have shown high tumor uptakes and high tumor-to-blood ratios for these probes. High tumor contrast imaging has been obtained within 1 h after injection. The success of those molecular probes demonstrates the adequacy of protein scaffold strategy as a general approach in molecular probe development.
Protein scaffolds; Peptide, molecular imaging; Cancer; PET
Multiscale computational modeling of drug delivery systems (DDS) is poised to provide predictive capabilities for the rational design of targeted drug delivery systems, including multi-functional nanoparticles. Realistic, mechanistic models can provide a framework for understanding the fundamental physico-chemical interactions between drug, delivery system, and patient. Multiscale computational modeling, however, is in its infancy even for conventional drug delivery. The wide range of emerging nanotechnology systems for targeted delivery further increases the need for reliable in silico predictions. This review will present existing computational approaches at different scales in the design of traditional oral drug delivery systems. Subsequently, a multiscale framework for integrating continuum, stochastic, and computational chemistry models will be proposed and a case study will be presented for conventional DDS. The extension of this framework to emerging nanotechnology delivery systems will be discussed along with future directions. While oral delivery is the focus of the review, the outlined computational approaches can be applied to other drug delivery systems as well.
Oral drug delivery; multiscale; computational modeling; continuum; computational chemistry; stochastic
The discovery of the pharmaceutical potential of small molecule inhibitors of oncogenic protein tyrosine kinases is one of the directions in target therapy in oncology. Presently, investigations aiming at developing new therapeutically important inhibitors have to be based on a combination of computational and experimental approaches including biochitalicical, cell-based or in silico screening and the study of the three-dimensional structure of the kinase active center, in complex with an inhibitor, using crystallography and X-ray analysis or molecular modeling. This work is an example of a combination of inhibitor experimental search with the computational analysis of the potential mechanism of the inhibitors' action, which allowed to propose the 2-hydroxyphenol group as a scaffold for the design of new tyrosine kinase inhibitors.
Due to the relative transparency of its embryos and larvae, the zebrafish is an ideal model organism for bioimaging approaches in vertebrates. Novel microscope technologies allow the imaging of developmental processes in unprecedented detail, and they enable the use of complex image-based read-outs for high-throughput/high-content screening. Such applications can easily generate Terabytes of image data, the handling and analysis of which becomes a major bottleneck in extracting the targeted information. Here, we describe the current state of the art in computational image analysis in the zebrafish system. We discuss the challenges encountered when handling high-content image data, especially with regard to data quality, annotation, and storage. We survey methods for preprocessing image data for further analysis, and describe selected examples of automated image analysis, including the tracking of cells during embryogenesis, heartbeat detection, identification of dead embryos, recognition of tissues and anatomical landmarks, and quantification of behavioral patterns of adult fish. We review recent examples for applications using such methods, such as the comprehensive analysis of cell lineages during early development, the generation of a three-dimensional brain atlas of zebrafish larvae, and high-throughput drug screens based on movement patterns. Finally, we identify future challenges for the zebrafish image analysis community, notably those concerning the compatibility of algorithms and data formats for the assembly of modular analysis pipelines.
The use of nanoparticles (NPs) has increased in the past few years in various fields, including defence, aerospace, electronics, biology, medicine, and so forth. and in applications such as diagnostic technology, bioimaging, and drug/gene delivery. Thus, human exposure to NPs and nanomaterials is unavoidable and will certainly expand in the future resulting in a growing interest in nanotoxicology, the study of toxicity of nanomaterials. A number of studies have reported the effects of NPs in respect to pulmonary inflammation by investigating in vitro activation of pulmonary cells with NPs and in vivo in a variety of models in which neutrophils appear to be the predominant leukocyte cell type in lungs and in bronchoalveolar lavages following inhalation or intratracheal instillation of NPs. Despite the fact that several studies have reported an increased number of neutrophils, the literature dealing with the direct activation of neutrophils by a given NP is poorly documented. This paper will summarize the current literature in this latter area of research and will end with a perspective view in which our laboratory will be involved in the following years.
inflammation; nanotoxicology; neutrophils; nanoparticles
Binding hot spots, protein sites with high-binding affinity, can be identified using X-ray crystallography or NMR by screening libraries of small organic molecules that tend to cluster at such regions. FTMAP, a direct computational analog of the experimental screening approaches, globally samples the surface of a target protein using small organic molecules as probes, finds favorable positions, clusters the conformations and ranks the clusters on the basis of the average energy. The regions that bind several probe clusters predict the binding hot spots, in good agreement with experimental results. Small molecules discovered by fragment-based approaches to drug design also bind at the hot spot regions. To identify such molecules and their most likely bound positions, we extend the functionality of FTMAP (http://ftmap.bu.edu/param) to accept any small molecule as an additional probe. In its updated form, FTMAP identifies the hot spots based on a standard set of probes, and for each additional probe shows representative structures of nearby low energy clusters. This approach helps to predict bound poses of the user-selected molecules, detects if a compound is not likely to bind in the hot spot region, and provides input for the design of larger ligands.
Fluorescent probes, which allow visualization of cations such as Ca2+, Zn2+ etc., small biomolecules such as nitric oxide (NO) or enzyme activities in living cells by means of fluorescence microscopy, have become indispensable tools for clarifying functions in biological systems. This review deals with the general principles for the design of bioimaging fluorescent probes by modulating the fluorescence properties of fluorophores, employing mechanisms such as acceptor-excited Photoinduced electron Transfer (a-PeT), donor-excited Photoinduced electron Transfer (d-PeT), and spirocyclization, which have been established by our group. The a-PeT and d-PeT mechanisms are widely applicable for the design of bioimaging probes based on many fluorophores and the spirocyclization process is also expected to be useful as a fluorescence off/on switching mechanism. Fluorescence modulation mechanisms are essential for the rational design of novel fluorescence probes for target molecules. Based on these mechanisms, we have developed more than fifty bioimaging probes, of which fourteen are commercially available. The review also describes some applications of the probes developed by our group to in vitro and in vivo systems.
probe; bioimaging; photoinduced electron transfer; fluorescence; spirocyclization
Computational approaches are becoming increasingly popular for the discovery of drug candidates against a target of interest. Proteins have historically been the primary targets of many virtual screening efforts. While in silico screens targeting proteins has proven successful, other classes of targets, in particular DNA, remain largely unexplored using virtual screening methods. With the realization of the functional importance of many non-cannonical DNA structures such as G-quadruplexes, increased efforts are underway to discover new small molecules that can bind selectively to DNA structures. Here, we describe efforts to build an integrated in silico and in vitro platform for discovering compounds that may bind to a chosen DNA target. Millions of compounds are initially screened in silico for selective binding to a particular structure and ranked to identify several hundred best hits. An important element of our strategy is the inclusion of an array of possible competing structures in the in silico screen. The best hundred or so hits are validated experimentally for binding to the actual target structure by a high-throughput 96-well thermal denaturation assay to yield the top ten candidates. Finally, these most promising candidates are thoroughly characterized for binding to their DNA target by rigorous biophysical methods, including isothermal titration calorimetry, differential scanning calorimetry, spectroscopy and competition dialysis.This platform was validated using quadruplex DNA as a target and a newly discovered quadruplex binding compound with possible anti-cancer activity was discovered. Some considerations when embarking on virtual screening and in silico experiments are also discussed.
drug discovery; in silico screening; SURFLEX-DOCK; DNA; G-quadruplex; high-throughput screening
This article reviews the new physics and new applications of secondary ion mass spectrometry using cluster ion probes. These probes, particularly C60, exhibit enhanced molecular desorption with improved sensitivity owing to the unique nature of the energy-deposition process. In addition, these projectiles are capable of eroding molecular solids while retaining the molecular specificity of mass spectrometry. When the beams are microfocused to a spot on the sample, bioimaging experiments in two and three dimensions are feasible. We describe emerging theoretical models that allow the energy-deposition process to be understood on an atomic and molecular basis. Moreover, experiments on model systems are described that allow protocols for imaging on biological materials to be implemented. Finally, we present recent applications of imaging to biological tissue and single cells to illustrate the future directions of this methodology.
secondary ion mass spectrometry; bioimaging; molecular depth profiling; three-dimensional molecular imaging; C60; molecular dynamics
Nutrient absorption in the small intestine cannot occur until molecules are presented to the epithelial cells that line intestinal villi, finger-like protrusions under enteric control. Using a two-dimensional multiscale lattice Boltzmann model of a lid-driven cavity flow with ‘villi’ at the lower surface, we analyse the hypothesis that muscle-induced oscillatory motions of the villi generate a controlled ‘micro-mixing layer’ (MML) that couples with the macro-scale flow to enhance absorption. Nutrient molecules are modelled as passive scalar concentrations at high Schmidt number. Molecular concentration supplied at the cavity lid is advected to the lower surface by a lid-driven macro-scale eddy. We find that micro-scale eddying motions enhance the macro-scale advective flux by creating an MML that couples with the macro-scale flow to increase absorption rate. We show that the MML is modulated by its interactions with the outer flow through a diffusion-dominated layer that separates advection-dominated macro-scale and micro-scale mixed layers. The structure and strength of the MML is sensitive to villus length and oscillation frequency. Our model suggests that the classical explanation for the existence of villi—increased absorptive surface area—is probably incorrect. The model provides support for the potential importance of villus motility in the absorptive function of the small intestine.
gastrointestinal; intestine; gut; absorption; villi; lattice Boltzmann method
Imaging and computational modeling of the Arabidopsis shoot meristem epidermis suggests that biomechanical signals coordinately regulate auxin efflux carrier distribution and microtubule patterning to orchestrate the extent and directionality of growth.
Morphogenesis during multicellular development is regulated by intercellular signaling molecules as well as by the mechanical properties of individual cells. In particular, normal patterns of organogenesis in plants require coordination between growth direction and growth magnitude. How this is achieved remains unclear. Here we show that in Arabidopsis thaliana, auxin patterning and cellular growth are linked through a correlated pattern of auxin efflux carrier localization and cortical microtubule orientation. Our experiments reveal that both PIN1 localization and microtubule array orientation are likely to respond to a shared upstream regulator that appears to be biomechanical in nature. Lastly, through mathematical modeling we show that such a biophysical coupling could mediate the feedback loop between auxin and its transport that underlies plant phyllotaxis.
The proper development of plant organs such as leaves or flowers depends both on localized growth, which can be controlled by the plant hormone auxin, and directional growth, which is dependent on each cell's microtubule cytoskeleton. In this paper we show that at the shoot apex where organs initiate the orientation of the microtubule cytoskeleton is correlated with the orientation of the auxin transporter PIN1, suggesting coordination between growth patterning at the tissue level and directional growth at the cellular level. Recent work has indicated that mechanical signals play a role in orienting the plant microtubule network, and here we show that such signals can also orient PIN1. In addition, we demonstrate through mathematical modeling that an auxin transport system that is coordinated by mechanical signals akin to those we observed in vivo is sufficient to give rise to the patterns of organ outgrowth found in the plant Arabidopsis thaliana.
of reactive oxygen species (ROS) levels has been observed
in many cancer cells relative to nontransformed cells, and recent
reports have suggested that small-molecule enhancers of ROS may selectively
kill cancer cells in various in vitro and in vivo models. We used a high-throughput screening approach
to identify several hundred small-molecule enhancers of ROS in a human
osteosarcoma cell line. A minority of these compounds diminished the
viability of cancer cell lines, indicating that ROS elevation by small
molecules is insufficient to induce death of cancer cell lines. Three
chemical probes (BRD5459, BRD56491, BRD9092) are highlighted that
most strongly elevate markers of oxidative stress without causing
cell death and may be of use in a variety of cellular settings. For
example, combining nontoxic ROS-enhancing probes with nontoxic doses
of l-buthionine sulfoximine, an inhibitor of glutathione
synthesis previously studied in cancer patients, led to potent cell
death in more than 20 cases, suggesting that even nontoxic ROS-enhancing
treatments may warrant exploration in combination strategies. Additionally,
a few ROS-enhancing compounds that contain sites of electrophilicity,
including piperlongumine, show selective toxicity for transformed
cells over nontransformed cells in an engineered cell-line model of
tumorigenesis. These studies suggest that cancer cell lines are more
resilient to chemically induced increases in ROS levels than previously
thought and highlight electrophilicity as a property that may be more
closely associated with cancer-selective cell death than ROS elevation.
In this paper we show that biocompatible zinc oxide (ZnO) nanocrystals (NCs) having non-centrosymmetric structure can be used as non-resonant nonlinear optical probes for targeting in bioimaging applications in vitro by use of the second order processes of second harmonic and sum frequency generation, as well as the third order process of four wave mixing. These non-resonant processes provide advantages above and beyond traditional two-photon bioimaging: (i) the probes do not photo-bleach; (ii) the input wavelength can be judiciously selected; and (iii) no heat is dissipated into the cells, ensuring longer cell viability and ultimately longer imaging times. ZnO NCs have been synthesized in organic media by using a non-hydrolytic sol-gel process, and subsequently dispersed in aqueous media using phospholipid micelles, and incorporated with the biotargeting molecule folic acid (FA). Sum Frequency, Second Harmonic and non-resonant four wave mixing non-linear signals from this stable dispersion of ZnO NCs, targeted to the live tumor (KB) cells were used for imaging. Robust intracellular accumulation of the targeted (FA incorporated) ZnO nanocrystals could be observed, without any indication of cytotoxicity.
Binding hot spots, protein regions with high binding affinity, can be identified by using X-ray crystallography or NMR spectroscopy to screen libraries of small organic molecules that tend to cluster at such hot spots. FTMap, a direct computational analogue of the experimental screening approaches, uses 16 different probe molecules for global sampling of the surface of a target protein on a dense grid and evaluates the energy of interaction using an empirical energy function that includes a continuum electrostatic term. Energy evaluation is based on the fast Fourier transform correlation approach, which allows for the sampling of billions of probe positions. The grid sampling is followed by off-grid minimization that uses a more detailed energy expression with a continuum electrostatics term. FTMap identifies the hot spots as consensus clusters formed by overlapping clusters of several probes. The hot spots are ranked on the basis of the number of probe clusters, which predicts their binding propensity. We applied FTMap to nine structures of hen egg-white lysozyme (HEWL), whose hot spots have been extensively studied by both experimental and computational methods. FTMap found the primary hot spot in site C of all nine structures, in spite of conformational differences. In addition, secondary hot spots in sites B and D that are known to be important for the binding of polysaccharide substrates were found. The predicted probe–protein interactions agree well with those seen in the complexes of HEWL with various ligands and also agree with an NMR-based study of HEWL in aqueous solutions of eight organic solvents. We argue that FTMap provides more complete information on the HEWL binding site than previous computational methods and yields fewer false-positive binding locations than the X-ray structures of HEWL from crystals soaked in organic solvents.
Discovery of new bioactive molecules that could enter drug discovery programs or that could serve as chemical probes is a very complex and costly endeavor. Structure-based and ligand-based in silico screening approaches are nowadays extensively used to complement experimental screening approaches in order to increase the effectiveness of the process and facilitating the screening of thousands or millions of small molecules against a biomolecular target. Both in silico screening methods require as input a suitable chemical compound collection and most often the 3D structure of the small molecules has to be generated since compounds are usually delivered in 1D SMILES, CANSMILES or in 2D SDF formats.
Here, we describe the new open source program DG-AMMOS which allows the generation of the 3D conformation of small molecules using Distance Geometry and their energy minimization via Automated Molecular Mechanics Optimization. The program is validated on the Astex dataset, the ChemBridge Diversity database and on a number of small molecules with known crystal structures extracted from the Cambridge Structural Database. A comparison with the free program Balloon and the well-known commercial program Omega generating the 3D of small molecules is carried out. The results show that the new free program DG-AMMOS is a very efficient 3D structure generator engine.
DG-AMMOS provides fast, automated and reliable access to the generation of 3D conformation of small molecules and facilitates the preparation of a compound collection prior to high-throughput virtual screening computations. The validation of DG-AMMOS on several different datasets proves that generated structures are generally of equal quality or sometimes better than structures obtained by other tested methods.
A biophysical, computational model of cell pharmacokinetics (1CellPK) is being developed to enable prediction of the intracellular accumulation and transcellular transport properties of small molecules using their calculated physicochemical properties as input. To test if 1CellPK can generate accurate, quantitative hypotheses and guide experimental analysis of the transcellular transport kinetics of small molecules, epithelial cells were grown on impermeable polyester membranes with cylindrical pores and chloroquine (CQ) was used as a transport probe. The effect of the number of pores and their diameter on transcellular transport of CQ was measured in apical-to-basolateral or basolateral-to-apical directions, at pH 7.4 and 6.5 in the donor compartment. Experimental and simulation results were consistent with a phospholipid bilayer-limited, passive diffusion transport mechanism. In experiments and 1CellPK simulations, intracellular CQ mass and the net rate of mass transport varied <2-fold although total pore area per cell varied >10-fold, so by normalizing the net rate of mass transport by the pore area available for transport, cell permeability on 3µm pore diameter membranes was more than an order of magnitude less than on 0.4µm pore diameter membranes. The results of simulations of transcellular transport were accurate for the first four hours of drug exposure, but those of CQ mass accumulation were accurate only for the first five minutes. Upon prolonged incubation, changes in cellular parameters such as lysosome pH rise, lysosome volume expansion, and nuclear shrinkage were associated with excess CQ accumulation. Based on the simulations, lysosome volume expansion alone can partly account for the measured, total intracellular CQ mass increase, while adding the intracellular binding of the protonated, ionized forms of CQ (as reflected in the measured partition coefficient of CQ in detergent-permeabilized cells at physiological pH) can further improve the intracellular CQ mass accumulation prediction.
Systems Biology; Epithelial Cells; Membrane Transport; Mathematical Models; Pharmacokinetics; Cell Permeability
In vivo imaging reveals how proteins and cells function as part of complex regulatory networks in intact organisms, and thereby contributes to a systems-level understanding of biological processes. However, the development of novel in vivo imaging probes remains challenging. Most probes are directed against a limited number of pre-specified protein targets; cell-based screens for imaging probes have shown promise, but raise concerns over whether in vitro surrogate cell models recapitulate in vivo phenotypes. Here, we rapidly profile the in vitro binding of nanoparticle imaging probes in multiple samples of defined target vs. background cell types, using primary cell isolates. This approach selects for nanoparticles that show desired targeting effects across all tested members of a class of cells, and decreases the likelihood that an idiosyncratic cell line will unduly skew screening results. To adjust for multiple hypothesis testing, we use permutation methods to identify nanoparticles that best differentiate between the target and background cell classes. (This approach is conceptually analogous to one used for high-dimensionality datasets of genome-wide gene expression, e.g. to identify gene expression signatures that discriminate subclasses of cancer.) We apply this approach to the identification of nanoparticle imaging probes that bind endothelial cells, and validate our in vitro findings in human arterial samples, and by in vivo intravital microscopy in mice. Overall, this work presents a generalizable approach to the unbiased discovery of in vivo imaging probes, and may guide the further development of novel endothelial imaging probes.