Chemoinformatic Analysis of a Supertargeted Combinatorial Library of Styryl Molecules

More Is Not Always Better: Local Models Provide Accurate Predictions of Spectral Properties of Porphyrins

The development of new functional materials based on porphyrins requires fast and accurate prediction of their spectral properties. The available models in the literature for absorption wavelength and extinction coefficient of the Soret band have low accuracy for this class of compounds. We collected spectral data for porphyrins to extend the literature set and compared the performance of global and local models for their modelling using different machine learning methods. Interestingly, extension of the public database contributed models with lower accuracies compared to the models, which we built using porphyrins only. The later model calculated acceptable RMSE = 2.61 for prediction of the absorption band of 335 porphyrins synthesized in our laboratory, but had a low accuracy (RMSE = 0.52) for extinction coefficient. A development of models using only compounds from our laboratory significantly decreased errors for these compounds (RMSE = 0.5 and 0.042 for absorption band and extinction coefficient, respectively), but limited their applicability only to these homologous series. When developing models, one should clearly keep in mind their potential use and select a strategy that could contribute the most accurate predictions for the target application. The models and data are publicly available.

More Is Not Always Better: Local Models Provide Accurate Predictions of Spectral Properties of Porphyrins

The development of new functional materials based on porphyrins requires fast and accurate prediction of their spectral properties. The available models in the literature for absorption wavelength and extinction coefficient of the Soret band have low accuracy for this class of compounds. We collected spectral data for porphyrins to extend the literature set and compared the performance of global and local models for their modelling using different machine learning methods. Interestingly, extension of the public database contributed models with lower accuracies compared to the models, which we built using porphyrins only. The later model calculated acceptable RMSE = 2.61 for prediction of the absorption band of 335 porphyrins synthesized in our laboratory, but had a low accuracy (RMSE = 0.52) for extinction coefficient. A development of models using only compounds from our laboratory significantly decreased errors for these compounds (RMSE = 0.5 and 0.042 for absorption band and extinction coefficient, respectively), but limited their applicability only to these homologous series. When developing models, one should clearly keep in mind their potential use and select a strategy that could contribute the most accurate predictions for the target application. The models and data are publicly available.

More Is Not Always Better: Local Models Provide Accurate Predictions of Spectral Properties of Porphyrins

The development of new functional materials based on porphyrins requires fast and accurate prediction of their spectral properties. The available models in the literature for absorption wavelength and extinction coefficient of the Soret band have low accuracy for this class of compounds. We collected spectral data for porphyrins to extend the literature set and compared the performance of global and local models for their modelling using different machine learning methods. Interestingly, extension of the public database contributed models with lower accuracies compared to the models, which we built using porphyrins only. The later model calculated acceptable RMSE = 2.61 for prediction of the absorption band of 335 porphyrins synthesized in our laboratory, but had a low accuracy (RMSE = 0.52) for extinction coefficient. A development of models using only compounds from our laboratory significantly decreased errors for these compounds (RMSE = 0.5 and 0.042 for absorption band and extinction coefficient, respectively), but limited their applicability only to these homologous series. When developing models, one should clearly keep in mind their potential use and select a strategy that could contribute the most accurate predictions for the target application. The models and data are publicly available.

Computational approaches to analyse and predict small molecule transport and distribution at cellular and subcellular levels

Quantitative structure-activity relationship (QSAR) studies and mechanistic mathematical modeling approaches have been independently employed for analysing and predicting the transport and distribution of small molecule chemical agents in living organisms. Both of these computational approaches have been useful for interpreting experiments measuring the transport properties of small molecule chemical agents, in vitro and in vivo. Nevertheless, mechanistic cell-based pharmacokinetic models have been especially useful to guide the design of experiments probing the molecular pathways underlying small molecule transport phenomena. Unlike QSAR models, mechanistic models can be integrated from microscopic to macroscopic levels, to analyse the spatiotemporal dynamics of small molecule chemical agents from intracellular organelles to whole organs, well beyond the experiments and training data sets upon which the models are based. Based on differential equations, mechanistic models can also be integrated with other differential equations-based systems biology models of biochemical networks or signaling pathways. Although the origin and evolution of mathematical modeling approaches aimed at predicting drug transport and distribution has occurred independently from systems biology, we propose that the incorporation of mechanistic cell-based computational models of drug transport and distribution into a systems biology modeling framework is a logical next step for the advancement of systems pharmacology research.

Visualizing chemical structure-subcellular localization relationships using fluorescent small molecules as probes of cellular transport

Background

To study the chemical determinants of small molecule transport inside cells, it is crucial to visualize relationships between the chemical structure of small molecules and their associated subcellular distribution patterns. For this purpose, we experimented with cells incubated with a synthetic combinatorial library of fluorescent, membrane-permeant small molecule chemical agents. With an automated high content screening instrument, the intracellular distribution patterns of these chemical agents were microscopically captured in image data sets, and analyzed off-line with machine vision and cheminformatics algorithms. Nevertheless, it remained challenging to interpret correlations linking the structure and properties of chemical agents to their subcellular localization patterns in large numbers of cells, captured across large number of images.

Results

To address this challenge, we constructed a Multidimensional Online Virtual Image Display (MOVID) visualization platform using off-the-shelf hardware and software components. For analysis, the image data set acquired from cells incubated with a combinatorial library of fluorescent molecular probes was sorted based on quantitative relationships between the chemical structures, physicochemical properties or predicted subcellular distribution patterns. MOVID enabled visual inspection of the sorted, multidimensional image arrays: Using a multipanel desktop liquid crystal display (LCD) and an avatar as a graphical user interface, the resolution of the images was automatically adjusted to the avatar’s distance, allowing the viewer to rapidly navigate through high resolution image arrays, zooming in and out of the images to inspect and annotate individual cells exhibiting interesting staining patterns. In this manner, MOVID facilitated visualization and interpretation of quantitative structure-localization relationship studies. MOVID also facilitated direct, intuitive exploration of the relationship between the chemical structures of the probes and their microscopic, subcellular staining patterns.

Conclusion

MOVID can provide a practical, graphical user interface and computer-assisted image data visualization platform to facilitate bioimage data mining and cheminformatics analysis of high content, phenotypic screening experiments.

Chemical address tags of fluorescent bioimaging probes

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 toward 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.

Machine vision-assisted analysis of structure-localization relationships in a combinatorial library of prospective bioimaging probes

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 this 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.

Simulation-based cheminformatic analysis of organelle-targeted molecules: lysosomotropic monobasic amines

Cell-based molecular transport simulations are being developed to facilitate exploratory cheminformatic analysis of virtual libraries of small drug-like molecules. For this purpose, mathematical models of single cells are built from equations capturing the transport of small molecules across membranes. In turn, physicochemical properties of small molecules can be used as input to simulate intracellular drug distribution, through time. Here, with mathematical equations and biological parameters adjusted so as to mimic a leukocyte in the blood, simulations were performed to analyze steady state, relative accumulation of small molecules in lysosomes, mitochondria, and cytosol of this target cell, in the presence of a homogenous extracellular drug concentration. Similarly, with equations and parameters set to mimic an intestinal epithelial cell, simulations were also performed to analyze steady state, relative distribution and transcellular permeability in this non-target cell, in the presence of an apical-to-basolateral concentration gradient. With a test set of ninety-nine monobasic amines gathered from the scientific literature, simulation results helped analyze relationships between the chemical diversity of these molecules and their intracellular distributions.

Lysosomal sequestration of amine-containing drugs: analysis and therapeutic implications

Amine-containing drugs represent a very important class of therapeutic agents, with the majority of all drugs containing at least one basic nitrogen. For many decades, it has been known that weakly basic compounds can be sequestered into acidic organelles such as lysosomes. Some amines can achieve very high concentrations and induce a dramatic expansion (vacuolization) of the compartment. In the early 70s, Nobel laureate and discoverer of lysosomes, Christian de Duve et al. wrote an elegant commentary describing the theoretical basis for lysosomal sequestration of amines, referring to the process as pH-partitioning and the substrates as lysosomotropics. Recently, a resurgence of interest in the intracellular distribution of drugs has occurred considering its therapeutic importance. Specifically, lysosomal sequestration of amines has received considerable attention for reasons including its involvement in drug resistance, inducement of phospholipidosis, and its influence on whole body distribution/pharmacokinetics. Moreover, the sequestration phenomenon has been recently exploited in the development of a novel drug targeting strategy. This review will focus on these occurrences/developments and conclude with a commentary on the expected impact that knowledge regarding the intracellular distribution of drugs will likely have on future drug development processes.

A cell-based molecular transport simulator for pharmacokinetic prediction and cheminformatic exploration

In the body, cell monolayers serve as permeability barriers, determining transport of molecules from one organ or tissue compartment to another. After oral drug administration, for example, transport across the epithelial cell monolayer lining the lumen of the intestine determines the fraction of drug in the gut that is absorbed by the body. By modeling passive transcellular transport properties in the presence of an apical to basolateral concentration gradient, we demonstrate how a computational, cell-based molecular transport simulator can be used to define a physicochemical property space occupied by molecules with desirable permeability and intracellular retention characteristics. Considering extracellular domains of cell surface receptors located on the opposite side of a cell monolayer as a drug's desired site of action, simulation of transcellular transport can be used to define the physicochemical properties of molecules with maximal transcellular permeability but minimal intracellular retention. Arguably, these molecules would possess very desirable features: least likely to exhibit nonspecific toxicity, metabolism, and side effects associated with high (undesirable) intracellular accumulation; and most likely to exhibit favorable bioavailability and efficacy associated with maximal rates of transport across cells and minimal intracellular retention, resulting in (desirable) accumulation at the extracellular site of action. Simulated permeability values showed good correlations with PAMPA, Caco-2, and intestinal permeability measurements, without "training" the model and without resorting to statistical regression techniques to "fit" the data. Therefore, cell-based molecular transport simulators could be useful in silico screening tools for chemical genomics and drug discovery.

A Cheminformatic Toolkit for Mining Biomedical Knowledge

PURPOSE

Cheminformatics can be broadly defined to encompass any activity related to the application of information technology to the study of properties, effects and uses of chemical agents. One of the most important current challenges in cheminformatics is to allow researchers to search databases of biomedical knowledge, using chemical structures as input.

MATERIALS AND METHODS

An important step towards this goal was the establishment of PubChem, an open, centralized database of small molecules accessible through the World Wide Web. While PubChem is primarily intended to serve as a repository for high throughput screening data from federally-funded screening centers and academic research laboratories, the major impact of PubChem could also reside in its ability to serve as a chemical gateway to biomedical databases such as PubMed.

CONCLUSION

This article will review cheminformatic tools that can be applied to facilitate annotation of PubChem through links to the scientific literature; to integrate PubChem with transcriptomic, proteomic, and metabolomic datasets; to incorporate results of numerical simulations of physiological systems into PubChem annotation; and ultimately, to translate data of chemical genomics screening efforts into information that will benefit biomedical researchers and physician scientists across all therapeutic areas.

Prospecting for Live Cell BioImaging Probes With Cheminformatic Assisted Image Arrays (CAIA)

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.

RNA-selective, live cell imaging probes for studying nuclear structure and function

The higher-order structural organization of the cell nucleus reflects the underlying genome-wide transcriptional activity and macromolecular transport processes. To study the microscopic organization of RNA distribution within the nucleus, a combinatorial library of fluorescent styryl molecules was synthesized and screened for an in vitro RNA response and live cell nuclear imaging. Four different cell lines (HeLa, A549, 3T3, and 3T3-L1) were analyzed in terms of higher-order nuclear organization. We identified RNA-selective dyes with better imaging properties relative to commercially available SYTORNASelect dye; the selected dyes were also cell permeant, photostable, and well tolerated by the cells. Our dyes also had very good counterstain compatibility with Hoechst and DAPI, which could help to image the DNA distribution in relation to RNA distribution in live cells and therefore reveal different patterns of RNA-DNA colocalization.

The great multidrug-resistance paradox

Much of the attention devoted to the elucidation of multidrug-resistance mechanisms in tumor cells has focused on transmembrane drug transporters and their ability to pump drug molecules from the cytosol to the extracellular medium. However, intracellular drug concentrations often remain high in drug-resistant cells and therefore do not explain how drug pumping at the plasma membrane confers multidrug resistance. Recent work indicates how drug sequestration in cytoplasmic organelles can account for these paradoxical results and how cellular pharmacokinetics may be exploited to target the activity of small molecules to specific cell types.

A Novel Assay Reveals That Weakly Basic Model Compounds Concentrate in Lysosomes to an Extent Greater Than pH-Partitioning Theory Would Predict

Many weakly basic drugs incubated with cells have been shown to specifically accumulate in lysosomes. The mechanistic basis and substrate specificity for this sequestration have not been rigorously evaluated; however, conditions are favorable for a pH-partitioning type accumulation. In some circumstances, this compartmentalization can be very extensive, which can impact the therapeutic efficacy of a drug. Despite the pharmaceutical importance, direct quantitative assessments of drug accumulation in lysosomes have not been previously described. We report here a novel magnetic capture technique that allows for quick and efficient isolation of lysosomes from cultured HL-60 cells that have been preincubated with model compounds. The amount of compound associated with the isolated fraction is determined by HPLC. Extensive biochemical and morphological characterizations of isolated lysosomes, together with HPLC data, allowed for estimates to be made regarding the concentration of model compounds in lysosomes. The corresponding theoretically determined concentration values, based on pH-partitioning theory, were also calculated for comparison purposes. Interestingly, experimentally determined values were approximately 3-15 times higher than theoretically predicted values. This finding suggests that mechanisms, in addition to pH-partitioning, may play a significant role in the accumulation of drugs in lysosomes.

Mitochondria Give Cells a Tan

The discovery of melanogenin, a small molecule that induces pigmentation in melanocytes, has led to identification of a mitochondrial protein as target. The finding is relevant therapeutically, and argues for how chemical biology can be used to elucidate organelle-specific functions.

System Dynamics of Subcellular Transport

In pharmacokinetic experiments, interpretations often hinge on treating cells as a "black box": a single, lumped compartment or boundary. Here, a combinatorial library of fluorescent small molecules was used to visualize subcellular transport pathways in living cells, using a kinetic, high content imaging system to monitor spatiotemporal variations of intracellular probe distribution. Most probes accumulate in cytoplasmic vesicles and probe kinetics conform to a nested, two-compartment dynamical system. At steady state, probes preferentially partition from the extracellular medium to the cytosol, and from the cytosol to cytoplasmic vesicles, with hydrophobic molecules favoring sequestration. Altogether, these results point to a general organizing principle underlying the system dynamics of subcellular, small molecule transport. In addition to plasma membrane permeability, subcellular transport phenomena can determine the active concentration of small molecules in the cytosol and the efflux of small molecules from cells. Fundamentally, direct observation of intracellular probe distribution challenges the simple boundary model of classical pharmacokinetics, which considers cells as static permeability barriers.

Exploratory Chemoinformatic Analysis of Cell Type-Selective Anticancer Drug Targeting

In pharmaceutical development, structure-activity relationship studies aim to identify characteristics of chemical structures associated with well-defined activity end points. While this goal-driven approach is ideally suited for lead development purposes, a more exploration-driven approach is needed to discover cell type-selective drug targeting mechanisms in complex data sets. Growth inhibition profiles across different cancer cell lines are potentially informative with respect to molecular mechanisms targeting the activity of anticancer agents to specific tumor cells, yet only a small number of mechanistic associations between chemical structure and growth inhibition profiles have been discovered to date. Here, we have applied an exhaustive statistical analysis strategy to more than 10000 compounds in the NCI's anticancer agent database to identify molecular substructures associated with specific cytotoxicity signatures against a panel of human tumor-derived cancer cell lines (the Developmental Therapeutics Program 60-cell line panel). Some of the most significant substructures conferring cell type-selective cytotoxic activity include a large family of delocalized lipophilic cations; chloropurines, chloropyrimidines, and thiazoles; organosulfur chelators and organometallic complexes; and an unexpectedly related family of alkyl-lysophospholipids and phosphate prodrugs. Information from cell-based assays and gene expression measurements have been related to substructures represented in the chemical space covered by the library, yielding several candidate targeting mechanisms.