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1.  A Cell-based Computational Modeling Approach for Developing Site-Directed Molecular Probes 
PLoS Computational Biology  2012;8(2):e1002378.
Modeling the local absorption and retention patterns of membrane-permeant small molecules in a cellular context could facilitate development of site-directed chemical agents for bioimaging or therapeutic applications. Here, we present an integrative approach to this problem, combining in silico computational models, in vitro cell based assays and in vivo biodistribution studies. To target small molecule probes to the epithelial cells of the upper airways, a multiscale computational model of the lung was first used as a screening tool, in silico. Following virtual screening, cell monolayers differentiated on microfabricated pore arrays and multilayer cultures of primary human bronchial epithelial cells differentiated in an air-liquid interface were used to test the local absorption and intracellular retention patterns of selected probes, in vitro. Lastly, experiments involving visualization of bioimaging probe distribution in the lungs after local and systemic administration were used to test the relevance of computational models and cell-based assays, in vivo. The results of in vivo experiments were consistent with the results of in silico simulations, indicating that mitochondrial accumulation of membrane permeant, hydrophilic cations can be used to maximize local exposure and retention, specifically in the upper airways after intratracheal administration.
Author Summary
We have developed an integrative, cell-based modeling approach to facilitate the design and discovery of chemical agents directed to specific sites of action within a living organism. Here, a computational, multiscale transport model of the lung was adapted to enable virtual screening of small molecules targeting the epithelial cells of the upper airways. In turn, the transport behaviors of selected candidate probes were evaluated to establish their degree of retention at a site of absorption, using computational simulations as well as two in vitro cell-based assay systems. Lastly, bioimaging experiments were performed to examine candidate molecules' distribution in the lungs of mice after local and systemic administration. Based on computational simulations, the higher mitochondrial density per unit absorption surface area is the key parameter determining the higher retention of small molecule hydrophilic cations in the upper airways, relative to lipophilic weak bases, specifically after intratracheal administration.
PMCID: PMC3285574  PMID: 22383866
2.  Predicting Binding to P-Glycoprotein by Flexible Receptor Docking 
PLoS Computational Biology  2011;7(6):e1002083.
P-glycoprotein (P-gp) is an ATP-dependent transport protein that is selectively expressed at entry points of xenobiotics where, acting as an efflux pump, it prevents their entering sensitive organs. The protein also plays a key role in the absorption and blood-brain barrier penetration of many drugs, while its overexpression in cancer cells has been linked to multidrug resistance in tumors. The recent publication of the mouse P-gp crystal structure revealed a large and hydrophobic binding cavity with no clearly defined sub-sites that supports an “induced-fit” ligand binding model. We employed flexible receptor docking to develop a new prediction algorithm for P-gp binding specificity. We tested the ability of this method to differentiate between binders and nonbinders of P-gp using consistently measured experimental data from P-gp efflux and calcein-inhibition assays. We also subjected the model to a blind test on a series of peptidic cysteine protease inhibitors, confirming the ability to predict compounds more likely to be P-gp substrates. Finally, we used the method to predict cellular metabolites that may be P-gp substrates. Overall, our results suggest that many P-gp substrates bind deeper in the cavity than the cyclic peptide in the crystal structure and that specificity in P-gp is better understood in terms of physicochemical properties of the ligands (and the binding site), rather than being defined by specific sub-sites.
Author Summary
With many drugs failing in the preclinical stages of drug discovery due to undesirable ADMETox (absorption, distribution, metabolism, excretion and toxicity) properties, improvement of these properties early on in the process, alongside the optimization of the compound activity, is emerging as a new focus in the pharmaceutical field. One of the key players affecting pharmacokinetic profiles of many clinically relevant compounds is an active efflux transporter, P-glycoprotein. Expressed predominantly at various physiological barriers, it can influence drug absorption (intestinal epithelium, colon), drug elimination (kidney proximal tubules) and drug penetration of the blood-brain barrier (endothelial brain cells). Moreover, its increased expression in cancer cells has been linked to resistance to multiple drugs in tumors. In this study we describe a computational approach that allows prediction of which compounds are more likely to interact with P-gp. We have tested the ability of this method to differentiate between binders and nonbinders of P-gp by using consistently measured in vitro experimental data. We also implemented a blind test on a series of peptidic cysteine protease inhibitors with encouraging outcome. Overall, our results suggest that this method provides a qualitative, quick, and inexpensive way of evaluating potential drug efflux problem at the early stages of drug development.
PMCID: PMC3121697  PMID: 21731480
3.  Multiscale image-based modeling and simulation of gas flow and particle transport in the human lungs 
Improved understanding of structure and function relationships in the human lungs in individuals and sub-populations is fundamentally important to the future of pulmonary medicine. Image-based measures of the lungs can provide sensitive indicators of localized features, however to provide a better prediction of lung response to disease, treatment and environment, it is desirable to integrate quantifiable regional features from imaging with associated value-added high-level modeling. With this objective in mind, recent advances in computational fluid dynamics (CFD) of the bronchial airways - from a single bifurcation symmetric model to a multiscale image-based subject-specific lung model - will be reviewed. The interaction of CFD models with local parenchymal tissue expansion - assessed by image registration - allows new understanding of the interplay between environment, hot spots where inhaled aerosols could accumulate, and inflammation. To bridge ventilation function with image-derived central airway structure in CFD, an airway geometrical modeling method that spans from the model ‘entrance’ to the terminal bronchioles will be introduced. Finally, the effects of turbulent flows and CFD turbulence models on aerosol transport and deposition will be discussed.
CFD simulation of airflow and particle transport in the human lung has been pursued by a number of research groups, whose interest has been in studying flow physics and airways resistance, improving drug delivery, or investigating which populations are most susceptible to inhaled pollutants. The three most important factors that need to be considered in airway CFD studies are lung structure, regional lung function, and flow characteristics. Their correct treatment is important because the transport of therapeutic or pollutant particles is dependent on the characteristics of the flow by which they are transported; and the airflow in the lungs is dependent on the geometry of the airways and how ventilation is distributed to the peripheral tissue. The human airway structure spans more than 20 generations, beginning with the extra-thoracic airways (oral or nasal cavity, and through the pharynx and larynx to the trachea), then the conducting airways, the respiratory airways, and to the alveoli. The airways in individuals and sub-populations (by gender, age, ethnicity, and normal vs. diseased states) may exhibit different dimensions, branching patterns and angles, and thickness and rigidity. At the local level, one would like to capture detailed flow characteristics, e.g. local velocity profiles, shear stress, and pressure, for prediction of particle transport in an airway (lung structure) model that is specific to the geometry of an individual, to understand how inter-subject variation in airway geometry (normal or pathological) influences the transport and deposition of particles. In a systems biology – or multiscale modeling – approach, these local flow characteristics can be further integrated with epithelial cell models for the study of mechanotransduction. At the global (organ) level, one would like to match regional ventilation (lung function) that is specific to the individual, thus ensuring that the flow that transports inhaled particles is appropriately distributed throughout the lung model. Computational models that do not account for realistic distribution of ventilation are not capable of predicting realistic particle distribution or targeted drug deposition. Furthermore, the flow in the human lung can be transitional or turbulent in the upper and proximal airways, and becomes laminar in the distal airways. The flows in the laminar, transitional and turbulent regimes have different temporal and spatial scales. Therefore, modeling airway structure and predicting gas flow and particle transport at both local and global levels require image-guided multiscale modeling strategies.
In this article, we will review the aforementioned three key aspects of CFD studies of the human lungs: airway structure (conducting airways), lung function (regional ventilation and boundary conditions), and flow characteristics (modeling of turbulent flow and its effect on particle transport). For modeling airway structure, we will focus on the conducting airways, and review both symmetric vs. asymmetric airway models, idealized vs. CT-based airway models, and multiscale subject-specific airway models. Imposition of physiological subject-specific boundary conditions (BCs) in CFD is essential to match regional ventilation in individuals, which is also critical in studying preferential deposition of inhaled aerosols in sub-populations, e.g. normals vs. asthmatics that may exhibit different ventilation patterns. Subject-specific regional ventilation defines flow distributions and characteristics in airway segments and bifurcations, which subsequently determines the transport and deposition of aerosols in the entire lungs. Turbulence models are needed to capture the transient and turbulent nature of the gas flow in the human lungs. Thus, the advantages and disadvantages of different turbulence models as well as their effects on particle transport will be discussed. The ultimate goal of the development is to identify sensitive structural and functional variables in sub-populations of normal and diseased lungs for potential clinical applications.
PMCID: PMC3763693  PMID: 23843310
4.  FAF-Drugs2: Free ADME/tox filtering tool to assist drug discovery and chemical biology projects 
BMC Bioinformatics  2008;9:396.
Drug discovery and chemical biology are exceedingly complex and demanding enterprises. In recent years there are been increasing awareness about the importance of predicting/optimizing the absorption, distribution, metabolism, excretion and toxicity (ADMET) properties of small chemical compounds along the search process rather than at the final stages. Fast methods for evaluating ADMET properties of small molecules often involve applying a set of simple empirical rules (educated guesses) and as such, compound collections' property profiling can be performed in silico. Clearly, these rules cannot assess the full complexity of the human body but can provide valuable information and assist decision-making.
This paper presents FAF-Drugs2, a free adaptable tool for ADMET filtering of electronic compound collections. FAF-Drugs2 is a command line utility program (e.g., written in Python) based on the open source chemistry toolkit OpenBabel, which performs various physicochemical calculations, identifies key functional groups, some toxic and unstable molecules/functional groups. In addition to filtered collections, FAF-Drugs2 can provide, via Gnuplot, several distribution diagrams of major physicochemical properties of the screened compound libraries.
We have developed FAF-Drugs2 to facilitate compound collection preparation, prior to (or after) experimental screening or virtual screening computations. Users can select to apply various filtering thresholds and add rules as needed for a given project. As it stands, FAF-Drugs2 implements numerous filtering rules (23 physicochemical rules and 204 substructure searching rules) that can be easily tuned.
PMCID: PMC2561050  PMID: 18816385
5.  SCYX-7158, an Orally-Active Benzoxaborole for the Treatment of Stage 2 Human African Trypanosomiasis 
Human African trypanosomiasis (HAT) is an important public health problem in sub-Saharan Africa, affecting hundreds of thousands of individuals. An urgent need exists for the discovery and development of new, safe, and effective drugs to treat HAT, as existing therapies suffer from poor safety profiles, difficult treatment regimens, limited effectiveness, and a high cost of goods. We have discovered and optimized a novel class of small-molecule boron-containing compounds, benzoxaboroles, to identify SCYX-7158 as an effective, safe and orally active treatment for HAT.
Methodology/Principal Findings
A drug discovery project employing integrated biological screening, medicinal chemistry and pharmacokinetic characterization identified SCYX-7158 as an optimized analog, as it is active in vitro against relevant strains of Trypanosoma brucei, including T. b. rhodesiense and T. b. gambiense, is efficacious in both stage 1 and stage 2 murine HAT models and has physicochemical and in vitro absorption, distribution, metabolism, elimination and toxicology (ADMET) properties consistent with the compound being orally available, metabolically stable and CNS permeable. In a murine stage 2 study, SCYX-7158 is effective orally at doses as low as 12.5 mg/kg (QD×7 days). In vivo pharmacokinetic characterization of SCYX-7158 demonstrates that the compound is highly bioavailable in rodents and non-human primates, has low intravenous plasma clearance and has a 24-h elimination half-life and a volume of distribution that indicate good tissue distribution. Most importantly, in rodents brain exposure of SCYX-7158 is high, with Cmax >10 µg/mL and AUC0–24 hr >100 µg*h/mL following a 25 mg/kg oral dose. Furthermore, SCYX-7158 readily distributes into cerebrospinal fluid to achieve therapeutically relevant concentrations in this compartment.
The biological and pharmacokinetic properties of SCYX-7158 suggest that this compound will be efficacious and safe to treat stage 2 HAT. SCYX-7158 has been selected to enter preclinical studies, with expected progression to phase 1 clinical trials in 2011.
Author Summary
Human African trypanosomiasis (HAT) is caused by infection with the parasite Trypanosoma brucei and is an important public health problem in sub-Saharan Africa. New, safe, and effective drugs are urgently needed to treat HAT, particularly stage 2 disease where the parasite infects the brain. Existing therapies for HAT have poor safety profiles, difficult treatment regimens, limited effectiveness, and a high cost of goods. Through an integrated drug discovery project, we have discovered and optimized a novel class of boron-containing small molecules, benzoxaboroles, to deliver SCYX-7158, an orally active preclinical drug candidate. SCYX-7158 cured mice infected with T. brucei, both in the blood and in the brain. Extensive pharmacokinetic characterization of SCYX-7158 in rodents and non-human primates supports the potential of this drug candidate for progression to IND-enabling studies in advance of clinical trials for stage 2 HAT.
PMCID: PMC3125149  PMID: 21738803
6.  Predicting substrates of the human breast cancer resistance protein using a support vector machine method 
BMC Bioinformatics  2013;14:130.
Human breast cancer resistance protein (BCRP) is an ATP-binding cassette (ABC) efflux transporter that confers multidrug resistance in cancers and also plays an important role in the absorption, distribution and elimination of drugs. Prediction as to if drugs or new molecular entities are BCRP substrates should afford a cost-effective means that can help evaluate the pharmacokinetic properties, efficacy, and safety of these drugs or drug candidates. At present, limited studies have been done to develop in silico prediction models for BCRP substrates.
In this study, we developed support vector machine (SVM) models to predict wild-type BCRP substrates based on a total of 263 known BCRP substrates and non-substrates collected from literature. The final SVM model was integrated to a free web server.
We showed that the final SVM model had an overall prediction accuracy of ~73% for an independent external validation data set of 40 compounds. The prediction accuracy for wild-type BCRP substrates was ~76%, which is higher than that for non-substrates. The free web server ( allows the users to predict whether a query compound is a wild-type BCRP substrate and calculate its physicochemical properties such as molecular weight, logP value, and polarizability.
We have developed an SVM prediction model for wild-type BCRP substrates based on a relatively large number of known wild-type BCRP substrates and non-substrates. This model may prove valuable for screening substrates and non-substrates of BCRP, a clinically important ABC efflux drug transporter.
PMCID: PMC3641962  PMID: 23586520
Breast cancer resistance protein; Support vector machine; SVM; ATP-binding cassette; ABC transporter; in silico prediction; Substrate; BCRP; ABCG2
7.  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.
Electronic supplementary material
The online version of this article (doi:10.1007/s10822-008-9194-7) contains supplementary material, which is available to authorized users.
PMCID: PMC2516532  PMID: 18338229
Cheminformatics; Lysosomotropic; Cellular pharmacokinetics; Drug transport; Small molecule permeability; Subcellular localization; Simulation; Rational drug design
8.  A Case Study of In Silico Modelling of Ciprofloxacin Hydrochloride/Metallic Compound Interactions 
AAPS PharmSciTech  2013;15(2):270-278.
With the development of physiologically based absorption models, there is an increased scientific and regulatory interest in in silico modelling and simulation of drug–drug and drug–food interactions. Clinically significant interactions between ciprofloxacin and metallic compounds are widely documented. In the current study, a previously developed ciprofloxacin-specific in silico absorption model was employed in order to simulate ciprofloxacin/metallic compound interaction observed in vivo. Commercially available software GastroPlus™ (Simulations Plus Inc., USA) based on the ACAT model was used for gastrointestinal (GI) simulations. The required input parameters, relating to ciprofloxacin hydrochloride physicochemical and pharmacokinetic characteristics, were experimentally determined, taken from the literature or estimated by GastroPlus™. Parameter sensitivity analysis (PSA) was used to assess the importance of selected input parameters (solubility, permeability, stomach and small intestine transit time) in predicting percent drug absorbed. PSA identified solubility and permeability as critical parameters affecting the rate and extent of ciprofloxacin absorption. Using the selected input parameters, it was possible to generate a ciprofloxacin absorption model, without/with metal cation containing preparations co-administration, which matched well the in vivo data available. It was found that reduced ciprofloxacin absorption in the presence of aluminium hydroxide, calcium carbonate or multivitamins/zinc was accounted for by reduced drug solubility. The impact of solubility–permeability interplay on ciprofloxacin absorption can be observed in the ciprofloxacin–aluminium interaction, while in ciprofloxacin–calcium and ciprofloxacin–zinc interactions, effect of solubility was more pronounced. The results obtained indicate that in silico model developed can be successfully used to complement relevant in vitro studies in the simulation of physicochemical ciprofloxacin/metallic compound interactions.
PMCID: PMC3969494  PMID: 24306676
absorption profile; drug interaction; GastroPlus; permeability; solubility
9.  Case Studies for Practical Food Effect Assessments across BCS/BDDCS Class Compounds using In Silico, In Vitro, and Preclinical In Vivo Data 
The AAPS Journal  2012;15(1):143-158.
Practical food effect predictions and assessments were described using in silico, in vitro, and/or in vivo preclinical data to anticipate food effects and Biopharmaceutics Classification System (BCS)/Biopharmaceutics Drug Disposition Classification System (BDDCS) class across drug development stages depending on available data: (1) limited in silico and in vitro data in early discovery; (2) preclinical in vivo pharmacokinetic, absorption, and metabolism data at candidate selection; and (3) physiologically based absorption modeling using biorelevant solubility and precipitation data to quantitatively predict human food effects, oral absorption, and pharmacokinetic profiles for early clinical studies. Early food effect predictions used calculated or measured physicochemical properties to establish a preliminary BCS/BDDCS class. A rat-based preclinical BCS/BDDCS classification used rat in vivo fraction absorbed and metabolism data. Biorelevant solubility and precipitation kinetic data were generated via animal pharmacokinetic studies using advanced compartmental absorption and transit (ACAT) models or in vitro methods. Predicted human plasma concentration–time profiles and the magnitude of the food effects were compared with observed clinical data for assessment of simulation accuracy. Simulations and analyses successfully identified potential food effects across BCS/BDDCS classes 1–4 compounds with an average fold error less than 1.6 in most cases. ACAT physiological absorption models accurately predicted positive food effects in human for poorly soluble bases after oral dosage forms. Integration of solubility, precipitation time, and metabolism data allowed confident identification of a compound’s BCS/BDDCS class, its likely food effects, along with prediction of human exposure profiles under fast and fed conditions.
Electronic supplementary material
The online version of this article (doi:10.1208/s12248-012-9419-5) contains supplementary material, which is available to authorized users.
PMCID: PMC3535115  PMID: 23139017
absorption modeling; BCS/BDDCS; food effect prediction; human PBPK model; oral bioavailability
10.  Predicting Pharmacokinetics of Drugs Using Physiologically Based Modeling—Application to Food Effects 
The AAPS Journal  2009;11(1):45-53.
Our knowledge of the major mechanisms underlying the effect of food on drug absorption allows reliable qualitative prediction based on biopharmaceutical properties, which can be assessed during the pre-clinical phase of drug discovery. Furthermore, several recent examples have shown that physiologically based absorption models incorporating biorelevant drug solubility measurements can provide quite accurate quantitative prediction of food effect. However, many molecules currently in development have distinctly sub-optimal biopharmaceutical properties, making the quantitative prediction of food effect for different formulations from in vitro data very challenging. If such drugs reach clinical development and show undesirable variability when dosed with food, improved formulation can help to reduce the food effect and carefully designed in vivo studies in dogs can be a useful guide to clinical formulation development. Even so, such in vivo studies provide limited throughput for screening, and food effects seen in dog cannot always be directly translated to human. This paper describes how physiologically based absorption modeling can play a role in the prediction of food effect by integrating the data generated during pre-clinical and clinical research and development. Such data include physicochemical and in vitro drug properties, biorelevant solubility and dissolution, and in vivo pre-clinical and clinical pharmacokinetic data. Some background to current physiological absorption models of human and dog is given, and refinements to models of in vivo drug solubility and dissolution are described. These are illustrated with examples using GastroPlus™ to simulate the food effect in dog and human for different formulations of two marketed drugs.
PMCID: PMC2664880  PMID: 19184451
food effect; formulation; GastroPlus; pharmacokinetics; physiologically based absorption model
11.  Physiochemical property space distribution among human metabolites, drugs and toxins 
BMC Bioinformatics  2009;10(Suppl 15):S10.
The current approach to screen for drug-like molecules is to sieve for molecules with biochemical properties suitable for desirable pharmacokinetics and reduced toxicity, using predominantly biophysical properties of chemical compounds, based on empirical rules such as Lipinski's "rule of five" (Ro5). For over a decade, Ro5 has been applied to combinatorial compounds, drugs and ligands, in the search for suitable lead compounds. Unfortunately, till date, a clear distinction between drugs and non-drugs has not been achieved. The current trend is to seek out drugs which show metabolite-likeness. In identifying similar physicochemical characteristics, compounds have usually been clustered based on some characteristic, to reduce the search space presented by large molecular datasets. This paper examines the similarity of current drug molecules with human metabolites and toxins, using a range of computed molecular descriptors as well as the effect of comparison to clustered data compared to searches against complete datasets.
We have carried out statistical and substructure functional group analyses of three datasets, namely human metabolites, drugs and toxin molecules. The distributions of various molecular descriptors were investigated. Our analyses show that, although the three groups are distinct, present-day drugs are closer to toxin molecules than to metabolites. Furthermore, these distributions are quite similar for both clustered data as well as complete or unclustered datasets.
The property space occupied by metabolites is dissimilar to that of drugs or toxin molecules, with current drugs showing greater similarity to toxins than to metabolites. Additionally, empirical rules like Ro5 can be refined to identify drugs or drug-like molecules that are clearly distinct from toxic compounds and more metabolite-like. The inclusion of human metabolites in this study provides a deeper insight into metabolite/drug/toxin-like properties and will also prove to be valuable in the prediction or optimization of small molecules as ligands for therapeutic applications.
PMCID: PMC2788350  PMID: 19958509
12.  Cells on Pores: A Simulation-Driven Analysis of Transcellular Small Molecule Transport 
Molecular pharmaceutics  2010;7(2):456-467.
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.
PMCID: PMC2920490  PMID: 20025248
Systems Biology; Epithelial Cells; Membrane Transport; Mathematical Models; Pharmacokinetics; Cell Permeability
13.  Metabolic network reconstruction of Chlamydomonas offers insight into light-driven algal metabolism 
A comprehensive genome-scale metabolic network of Chlamydomonas reinhardtii, including a detailed account of light-driven metabolism, is reconstructed and validated. The model provides a new resource for research of C. reinhardtii metabolism and in algal biotechnology.
The genome-scale metabolic network of Chlamydomonas reinhardtii (iRC1080) was reconstructed, accounting for >32% of the estimated metabolic genes encoded in the genome, and including extensive details of lipid metabolic pathways.This is the first metabolic network to explicitly account for stoichiometry and wavelengths of metabolic photon usage, providing a new resource for research of C. reinhardtii metabolism and developments in algal biotechnology.Metabolic functional annotation and the largest transcript verification of a metabolic network to date was performed, at least partially verifying >90% of the transcripts accounted for in iRC1080. Analysis of the network supports hypotheses concerning the evolution of latent lipid pathways in C. reinhardtii, including very long-chain polyunsaturated fatty acid and ceramide synthesis pathways.A novel approach for modeling light-driven metabolism was developed that accounts for both light source intensity and spectral quality of emitted light. The constructs resulting from this approach, termed prism reactions, were shown to significantly improve the accuracy of model predictions, and their use was demonstrated for evaluation of light source efficiency and design.
Algae have garnered significant interest in recent years, especially for their potential application in biofuel production. The hallmark, model eukaryotic microalgae Chlamydomonas reinhardtii has been widely used to study photosynthesis, cell motility and phototaxis, cell wall biogenesis, and other fundamental cellular processes (Harris, 2001). Characterizing algal metabolism is key to engineering production strains and understanding photobiological phenomena. Based on extensive literature on C. reinhardtii metabolism, its genome sequence (Merchant et al, 2007), and gene functional annotation, we have reconstructed and experimentally validated the genome-scale metabolic network for this alga, iRC1080, the first network to account for detailed photon absorption permitting growth simulations under different light sources. iRC1080 accounts for 1080 genes, associated with 2190 reactions and 1068 unique metabolites and encompasses 83 subsystems distributed across 10 cellular compartments (Figure 1A). Its >32% coverage of estimated metabolic genes is a tremendous expansion over previous algal reconstructions (Boyle and Morgan, 2009; Manichaikul et al, 2009). The lipid metabolic pathways of iRC1080 are considerably expanded relative to existing networks, and chemical properties of all metabolites in these pathways are accounted for explicitly, providing sufficient detail to completely specify all individual molecular species: backbone molecule and stereochemical numbering of acyl-chain positions; acyl-chain length; and number, position, and cis–trans stereoisomerism of carbon–carbon double bonds. Such detail in lipid metabolism will be critical for model-driven metabolic engineering efforts.
We experimentally verified transcripts accounted for in the network under permissive growth conditions, detecting >90% of tested transcript models (Figure 1B) and providing validating evidence for the contents of iRC1080. We also analyzed the extent of transcript verification by specific metabolic subsystems. Some subsystems stood out as more poorly verified, including chloroplast and mitochondrial transport systems and sphingolipid metabolism, all of which exhibited <80% of transcripts detected, reflecting incomplete characterization of compartmental transporters and supporting a hypothesis of latent pathway evolution for ceramide synthesis in C. reinhardtii. Additional lines of evidence from the reconstruction effort similarly support this hypothesis including lack of ceramide synthetase and other annotation gaps downstream in sphingolipid metabolism. A similar hypothesis of latent pathway evolution was established for very long-chain fatty acids (VLCFAs) and their polyunsaturated analogs (VLCPUFAs) (Figure 1C), owing to the absence of this class of lipids in previous experimental measurements, lack of a candidate VLCFA elongase in the functional annotation, and additional downstream annotation gaps in arachidonic acid metabolism.
The network provides a detailed account of metabolic photon absorption by light-driven reactions, including photosystems I and II, light-dependent protochlorophyllide oxidoreductase, provitamin D3 photoconversion to vitamin D3, and rhodopsin photoisomerase; this network accounting permits the precise modeling of light-dependent metabolism. iRC1080 accounts for effective light spectral ranges through analysis of biochemical activity spectra (Figure 3A), either reaction activity or absorbance at varying light wavelengths. Defining effective spectral ranges associated with each photon-utilizing reaction enabled our network to model growth under different light sources via stoichiometric representation of the spectral composition of emitted light, termed prism reactions. Coefficients for different photon wavelengths in a prism reaction correspond to the ratios of photon flux in the defined effective spectral ranges to the total emitted photon flux from a given light source (Figure 3B). This approach distinguishes the amount of emitted photons that drive different metabolic reactions. We created prism reactions for most light sources that have been used in published studies for algal and plant growth including solar light, various light bulbs, and LEDs. We also included regulatory effects, resulting from lighting conditions insofar as published studies enabled. Light and dark conditions have been shown to affect metabolic enzyme activity in C. reinhardtii on multiple levels: transcriptional regulation, chloroplast RNA degradation, translational regulation, and thioredoxin-mediated enzyme regulation. Through application of our light model and prism reactions, we were able to closely recapitulate experimental growth measurements under solar, incandescent, and red LED lights. Through unbiased sampling, we were able to establish the tremendous statistical significance of the accuracy of growth predictions achievable through implementation of prism reactions. Finally, application of the photosynthetic model was demonstrated prospectively to evaluate light utilization efficiency under different light sources. The results suggest that, of the existing light sources, red LEDs provide the greatest efficiency, about three times as efficient as sunlight. Extending this analysis, the model was applied to design a maximally efficient LED spectrum for algal growth. The result was a 677-nm peak LED spectrum with a total incident photon flux of 360 μE/m2/s, suggesting that for the simple objective of maximizing growth efficiency, LED technology has already reached an effective theoretical optimum.
In summary, the C. reinhardtii metabolic network iRC1080 that we have reconstructed offers insight into the basic biology of this species and may be employed prospectively for genetic engineering design and light source design relevant to algal biotechnology. iRC1080 was used to analyze lipid metabolism and generate novel hypotheses about the evolution of latent pathways. The predictive capacity of metabolic models developed from iRC1080 was demonstrated in simulating mutant phenotypes and in evaluation of light source efficiency. Our network provides a broad knowledgebase of the biochemistry and genomics underlying global metabolism of a photoautotroph, and our modeling approach for light-driven metabolism exemplifies how integration of largely unvisited data types, such as physicochemical environmental parameters, can expand the diversity of applications of metabolic networks.
Metabolic network reconstruction encompasses existing knowledge about an organism's metabolism and genome annotation, providing a platform for omics data analysis and phenotype prediction. The model alga Chlamydomonas reinhardtii is employed to study diverse biological processes from photosynthesis to phototaxis. Recent heightened interest in this species results from an international movement to develop algal biofuels. Integrating biological and optical data, we reconstructed a genome-scale metabolic network for this alga and devised a novel light-modeling approach that enables quantitative growth prediction for a given light source, resolving wavelength and photon flux. We experimentally verified transcripts accounted for in the network and physiologically validated model function through simulation and generation of new experimental growth data, providing high confidence in network contents and predictive applications. The network offers insight into algal metabolism and potential for genetic engineering and efficient light source design, a pioneering resource for studying light-driven metabolism and quantitative systems biology.
PMCID: PMC3202792  PMID: 21811229
Chlamydomonas reinhardtii; lipid metabolism; metabolic engineering; photobioreactor
Molecular pharmaceutics  2006;3(6):704-716.
In the body, cell monolayers serve as permeability barriers, determining transport of drug molecules from one organ or tissue compartment to another. After oral administration, for example, drug 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 non-specific 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. Calculated permeability predictions showed good correlations with PAMPA, Caco2, 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.
PMCID: PMC2710883  PMID: 17140258
Metoprolol; permeability; chemical space; computer aided drug design; virtual screening; chemical genomics; cellular pharmacokinetics; cheminformatics; drug transport; PAMPA; Biopharmaceutics Classification System
15.  Prediction of In-silico ADME Properties of 1,2-O-Isopropylidene Aldohexose Derivatives  
Retention behaviour of molecules mostly depends on their chemical structure. Retention data of biologically active molecules could be an indirect relationship between their structure and biological or pharmacological activity, since the molecular structure affects their behaviour in all pharmacokinetic stages. In the present paper, retention parameters (RM0) of biologically active 1,2-O-isopropylidene aldohexose derivatives, obtained by normal-phase thin-layer chromatography (NP TLC), were correlated with selected physicochemical properties relevant to pharmacokinetics, i.e. absorption, distribution, metabolism, and elimination (ADME) properties. Conducted correlation analysis showed high dependence between RM0 and blood brain barrier penetration, skin permeability, enzyme inhibition, binding affinity to nuclear receptor ligand and G protein-coupled receptors, as well as lipophilicity (expressed as Hansh-Leo’s parameter Clog P). The statistical validity of the established polynomial dependence of the second degree between RM0 and mentioned ADME properties was confirmed by standard statistical measures and leave-one-out cross-validation method. On the basis of in-silico calculated ADME properties and retention data, the similarity between studied molecules was examined using principal component analysis (PCA). The obtained results indicate the possibility of predicting ADME properties of studied compounds on the basis of their retention data (RM0). These preliminary results could be treated as guideline for selecting new 1,2-O-isopropylidene aldohexose derivatives as drug candidates.
PMCID: PMC4177650  PMID: 25276190
1; 2-O-isopropylidene derivatives of aldohexoses; In-silico; ADME; PCA; Polynomial regression
16.  Simulations of the Nonlinear Dose Dependence for Substrates of Influx and Efflux Transporters in the Human Intestine 
The AAPS Journal  2009;11(2):353-363.
The purpose of this study was to develop simulation and modeling methods for the evaluation of pharmacokinetics when intestinal influx and efflux transporters are involved in gastrointestinal absorption. The advanced compartmental absorption and transit (ACAT) model as part of the computer program GastroPlus™ was used to simulate the absorption and pharmacokinetics of valacyclovir, gabapentin, and talinolol. Each of these drugs is a substrate for an influx or efflux transporter and all show nonlinear dose dependence within the normal therapeutic range. These simulations incorporated the experimentally derived gastrointestinal distributions of transporter expression levels for oligopeptide transporters PepT1 and HPT1 (valacyclovir); System L-amino acid transporter LAT2 and organic cation transporter OCTN1 (gabapentin); and organic anion transporter (OATP1A2) and P-glycoprotein (talinolol). By assuming a uniform distribution of oligopeptide transporter and by application of the in vitro Km value for valacyclovir, the simulations accurately reproduced the experimental nonlinear dose dependence. For gabapentin, LAT2 distribution produced simulation results that were much more accurate than OCTN1 distributions. For talinolol, an influx transporter distribution for OATP1A2 and the efflux transporter P-glycoprotein distributed with increasing expression in the distal small intestine produced the best results. The physiological characteristics of the small and large intestines used in the ACAT model were able to accurately account for the positional and temporal changes in concentration and carrier-mediated transport of the three drugs included in this study. The ACAT model reproduced the nonlinear dose dependence for each of these drugs.
PMCID: PMC2691471  PMID: 19434502
expression; intestine; saturation; simulation; transporter
17.  Fluticasone Furoate, a Novel Inhaled Corticosteroid, Demonstrates Prolonged Lung Absorption Kinetics in Man Compared with Inhaled Fluticasone Propionate 
Clinical Pharmacokinetics  2012;52(1):37-42.
Fluticasone furoate (FF; GW685698) is a novel inhaled corticosteroid that is active at 24 h and under development for once-daily administration in combination with the long-acting β2-adrenoceptor agonist vilanterol (GW642444) for chronic obstructive pulmonary disease and asthma. In vitro studies examining the respiratory tissue-binding properties of corticosteroids showed FF to have the largest cellular accumulation and slowest rate of efflux compared with other clinically used inhaled corticosteroids, consistent with greater tissue retention. The enhanced affinity of the glucocorticoid receptor binding of FF, coupled with its extended tissue association, may be expected to lead to greater and more prolonged anti-inflammatory effects and should provide relevant once-daily efficacy.
The aim of this study was to assess the rate and extent of systemic absorption of FF from the lung following inhaled administration of FF from three exploratory dry powder formulations (via DISKHALER®) compared with inhaled fluticasone propionate (FP) [via DISKHALER®] using deconvolution analysis.
This open-label, part-randomized, six-way crossover study evaluated three early development dry powder inhaled formulations of FF administered as single doses via DISKHALER®. Healthy male subjects (n = 24) each received FF (2,000 μg; three formulations), inhaled FP (1,000 μg; via DISKHALER®) and 250 μg of each molecule by intravenous infusion. The bioavailability of both inhaled FF and FP represents absorption from the lung as the oral bioavailability from the swallowed portion of the inhaled dose is negligible (<1.5 %). To investigate the absorption kinetics from the lung, the inhaled concentration–time data were subjected to deconvolution analysis using derived pharmacokinetic parameters from fitting of the intravenous concentration–time data.
The terminal elimination half-life (t½β) for inhaled FF was considerably longer (range 17–24 h) than the t½β estimated for intravenous FF (14 h), whereas t½β for FP was similar whether inhaled or given intravenously (11 and 14 h, respectively). This would suggest that FF is exhibiting absorption rate-limited pharmacokinetics following inhaled FF dosing and that the apparent t½β is an estimate of absorption rate. The lung mean absorption time for FF was approximately 7 h irrespective of formulation, which was considerably longer than FP (2.1 h). The time for 90 % absorption from the lung was significantly longer for FF (20–30 h) than for FP (8 h), indicating a significantly longer lung retention time for FF.
In comparison with inhaled FP, inhaled FF (independent of formulation) demonstrated prolonged absorption from the lung into the systemic circulation, indicating a longer lung retention time and suggesting the potential for maintained efficacy with once-daily administration.
PMCID: PMC3693428  PMID: 23184737
18.  Automated selection of compounds with physicochemical properties to maximize bioavailability and druglikeness 
Adequate bioavailability is one of the essential properties for an orally administered drug. Lipinski and others have formulated simplified rules in which compounds that satisfy selected physiochemical properties, for example, molecular weight (MW)≤500, or logarithm of octanol-water partition coefficient logP(o/w)<5, are anticipated to likely have pharmacokinetic properties appropriate for oral administration. However, these schemes do not simultaneously consider the combination of the physiochemical properties, complicating their application in a more automated fashion. To overcome this we present a novel method to select compounds with a combination of physicochemical properties that maximize bioavailability and druglikeness, based on compounds in the World Drug Index (WDI) database. In the study four properties, MW, logP(o/w), number of hydrogen bond donors and number of hydrogen acceptors were combined into a 4-D histogram, from which a scoring function was defined based on a 4D dependent multivariate Gaussian model. The resulting equation allows for assigning compounds a bioavailability score, termed 4D-BA, such that chemicals with higher 4D-BA scores are more likely to have oral drug-like characteristics. The descriptor is validated by applying the function to drugs previously categorized in the Biopharmaceutics Classification System and examples of application of the descriptor are given in the context of previously published studies targeting heme oxygenase and SHP2 phosphatase. The approach is anticipated to be useful in early lead identification studies in combination with clustering methods to maximize chemical and structural diversity when selecting compounds for biological assays from large database screens. It may also be applied to prioritize synthetically feasible chemical modifications during lead compound optimization.
PMCID: PMC3160130  PMID: 21142079
19.  The composite solubility versus pH profile and its role in intestinal absorption prediction 
AAPS PharmSci  2003;5(1):35-49.
The purpose of this study was to examine absorption of basic drugs as a function of the composite solubility curve and intestinally relevant pH by using a gastrointestinal tract (GIT) absorption simulation based on the advanced compartmental absorption and transit model. Absorption simulations were carried out for virtual monobasic drugs having a range of pKa, log D, and dose values as a function of presumed solubility and permeability. Results were normally expressed as the combination that resulted in 25% absorption. Absorption of basic drugs was found to be a function of the whole solubility/pH relationship rather than a single solubility value at pH 7. In addition, the parameter spaces of greatest sensitivity were identified. We compared 3 theoretical scenarios: the GIT pH range overlapping (1) only the salt solubility curve, (2) the salt and base solubility curves, or (3) only the base curve. Experimental solubilities of 32 compounds were determined at pHs of 2.2 and 7.4, and they nearly all fitted into 2 of the postulated scenarios. Typically, base solubilities can be simulated in silico, but salt solubilities at low pH can only be measured. We concluded that quality absorption simulations of candidate drugs in most cases require experimental solubility determination at 2 pHs, to permit calculation of the whole solubility/pH profile.
PMCID: PMC2751472  PMID: 12713276
GIT; absorption simulation; pH solubility curve; BCS; solid-state properties; solubility screening
20.  In silico structure-based screening of versatile P-glycoprotein inhibitors using polynomial empirical scoring functions 
P-glycoprotein (P-gp) is an ATP (adenosine triphosphate)-binding cassette transporter that causes multidrug resistance of various chemotherapeutic substances by active efflux from mammalian cells. P-gp plays a pivotal role in limiting drug absorption and distribution in different organs, including the intestines and brain. Thus, the prediction of P-gp–drug interactions is of vital importance in assessing drug pharmacokinetic and pharmacodynamic properties. To find the strongest P-gp blockers, we performed an in silico structure-based screening of P-gp inhibitor library (1,300 molecules) by the gradient optimization method, using polynomial empirical scoring (POLSCORE) functions. We report a strong correlation (r2=0.80, F=16.27, n=6, P<0.0157) of inhibition constants (Kiexp or pKiexp; experimental Ki or negative decimal logarithm of Kiexp) converted from experimental IC50 (half maximal inhibitory concentration) values with POLSCORE-predicted constants (KiPOLSCORE or pKiPOLSCORE), using a linear regression fitting technique. The hydrophobic interactions between P-gp and selected drug substances were detected as the main forces responsible for the inhibition effect. The results showed that this scoring technique might be useful in the virtual screening and filtering of databases of drug-like compounds at the early stage of drug development processes.
PMCID: PMC3969253  PMID: 24711707
ATP-binding cassette transporter; P-gp inhibitors; multidrug resistance; molecular docking; POLSCORE
21.  Oral Administration of the Pimelic Diphenylamide HDAC Inhibitor HDACi 4b Is Unsuitable for Chronic Inhibition of HDAC Activity in the CNS In Vivo 
PLoS ONE  2012;7(9):e44498.
Histone deacetylase (HDAC) inhibitors have received considerable attention as potential therapeutics for a variety of cancers and neurological disorders. Recent publications on a class of pimelic diphenylamide HDAC inhibitors have highlighted their promise in the treatment of the neurodegenerative diseases Friedreich’s ataxia and Huntington’s disease, based on efficacy in cell and mouse models. These studies’ authors have proposed that the unique action of these compounds compared to hydroxamic acid-based HDAC inhibitors results from their unusual slow-on/slow-off kinetics of binding, preferentially to HDAC3, resulting in a distinctive pharmacological profile and reduced toxicity. Here, we evaluate the HDAC subtype selectivity, cellular activity, absorption, distribution, metabolism and excretion (ADME) properties, as well as the central pharmacodynamic profile of one such compound, HDACi 4b, previously described to show efficacy in vivo in the R6/2 mouse model of Huntington’s disease. Based on our data reported here, we conclude that while the in vitro selectivity and binding mode are largely in agreement with previous reports, the physicochemical properties, metabolic and p-glycoprotein (Pgp) substrate liability of HDACi 4b render this compound suboptimal to investigate central Class I HDAC inhibition in vivo in mouse per oral administration. A drug administration regimen using HDACi 4b dissolved in drinking water was used in the previous proof of concept study, casting doubt on the validation of CNS HDAC3 inhibition as a target for the treatment of Huntington’s disease. We highlight physicochemical stability and metabolic issues with 4b that are likely intrinsic liabilities of the benzamide chemotype in general.
PMCID: PMC3433414  PMID: 22973455
22.  Initial Observations of Cell-Mediated Drug Delivery to the Deep Lung 
Cell Transplantation  2010;20(5):609-618.
Using current methodologies, drug delivery to small airways, terminal bronchioles, and alveoli (deep lung) is inefficient, especially to the lower lungs. Urgent lung pathologies such as acute respiratory distress syndrome (ARDS) and post-lung transplantation complications are difficult to treat, in part due to the methodological limitations in targeting the deep lung with high efficiency drug distribution to the site of pathology. To overcome drug delivery limitations inhibiting the optimization of deep lung therapy, isolated rat Sertoli cells preloaded with chitosan nanoparticles were use to obtain a high-density distribution and concentration (92%) of the nanoparticles in the lungs of mice by way of the peripheral venous vasculature rather than the more commonly used pulmonary route. Additionally, Sertoli cells were preloaded with chitosan nanoparticles coupled with the anti-inflammatory compound curcumin and then injected intravenously into control or experimental mice with deep lung inflammation. By 24 h postinjection, most of the curcumin load (~90%) delivered in the injected Sertoli cells was present and distributed throughout the lungs, including the perialveloar sac area in the lower lungs. This was based on the high-density, positive quantification of both nanoparticles and curcumin in the lungs. There was a marked positive therapeutic effect achieved 24 h following curcumin treatment delivered by this Sertoli cell nanoparticle protocol (SNAP). Results identify a novel and efficient protocol for targeted delivery of drugs to the deep lung mediated by extratesticular Sertoli cells. Utilization of SNAP delivery may optimize drug therapy for conditions such as ARDS, status asthmaticus, pulmonary hypertension, lung cancer, and complications following lung transplantation where the use of high concentrations of anti-inflammatory drugs is desirable, but often limited by risks of systemic drug toxicity.
PMCID: PMC3324823  PMID: 21054942
Sertoli cells; Drug delivery; Deep lung
23.  Systems Biological Approach of Molecular Descriptors Connectivity: Optimal Descriptors for Oral Bioavailability Prediction 
PLoS ONE  2012;7(7):e40654.
Poor oral bioavailability is an important parameter accounting for the failure of the drug candidates. Approximately, 50% of developing drugs fail because of unfavorable oral bioavailability. In silico prediction of oral bioavailability (%F) based on physiochemical properties are highly needed. Although many computational models have been developed to predict oral bioavailability, their accuracy remains low with a significant number of false positives. In this study, we present an oral bioavailability model based on systems biological approach, using a machine learning algorithm coupled with an optimal discriminative set of physiochemical properties.
The models were developed based on computationally derived 247 physicochemical descriptors from 2279 molecules, among which 969, 605 and 705 molecules were corresponds to oral bioavailability, intestinal absorption (HIA) and caco-2 permeability data set, respectively. The partial least squares discriminate analysis showed 49 descriptors of HIA and 50 descriptors of caco-2 are the major contributing descriptors in classifying into groups. Of these descriptors, 47 descriptors were commonly associated to HIA and caco-2, which suggests to play a vital role in classifying oral bioavailability. To determine the best machine learning algorithm, 21 classifiers were compared using a bioavailability data set of 969 molecules with 47 descriptors. Each molecule in the data set was represented by a set of 47 physiochemical properties with the functional relevance labeled as (+bioavailability/−bioavailability) to indicate good-bioavailability/poor-bioavailability molecules. The best-performing algorithm was the logistic algorithm. The correlation based feature selection (CFS) algorithm was implemented, which confirms that these 47 descriptors are the fundamental descriptors for oral bioavailability prediction.
The logistic algorithm with 47 selected descriptors correctly predicted the oral bioavailability, with a predictive accuracy of more than 71%. Overall, the method captures the fundamental molecular descriptors, that can be used as an entity to facilitate prediction of oral bioavailability.
PMCID: PMC3398012  PMID: 22815781
24.  An in silico evaluation of the ADMET profile of the StreptomeDB database 
SpringerPlus  2013;2:353.
Computer-aided drug design (CADD) often involves virtual screening (VS) of large compound datasets and the availability of such is vital for drug discovery protocols. This paper presents an assessment of the “drug-likeness” and pharmacokinetic profile of > 2,400 compounds of natural origin, currently available in the recently published StreptomeDB database.
The evaluation of “drug-likeness” was performed on the basis of Lipinski’s “Rule of Five”, while 46 computed physicochemical properties or molecular descriptors were used to predict the absorption, distribution, metabolism, elimination and toxicity (ADMET) of the compounds.
This survey demonstrated that, of the computed molecular descriptors, about 28% of the compounds within the StreptomeDB database were compliant, having properties which fell within the range of ADMET properties of 95% of currently known drugs, while about 44% of the compounds had ≤ 2 violations. Moreover, about 50% of the compounds within the corresponding “drug-like” subset showed compliance, while >83% of the “drug-like” compounds had ≤ 2 violations.
In addition to the previously verified range of measured biological activities, the compounds in the StreptomeDB database show interesting DMPK profiles and hence could represent an important starting point for hit/lead discovery from natural sources. The generated data are available and could be highly useful for natural product lead generation programs.
Electronic supplementary material
The online version of this article (doi:10.1186/2193-1801-2-353) contains supplementary material, which is available to authorized users.
PMCID: PMC3736076  PMID: 23961417
3D structures; Streptomyces; Database; In silico; Natural products; Virtual screening
25.  RP-HPTLC Retention Data in Correlation with the In-silico ADME Properties of a Series of s-triazine Derivatives 
The properties relevant to pharmacokinetics and pharmacodynamics of four series of synthesized s-triazine derivatives have been studied by Quantitative structure-retention relationship (QSRR) approach. The chromatographic behavior of these compounds was investigated by using reversed-phase high performance thin-layer chromatography (RP-HPTLC). Chromatographic retention (RM0) was correlated with selected physicochemical parameters relevant to pharmacokinetics, i.e. ADME (absorption, distribution, metabolism and excretion). In addition, the ability to act as kinase inhibitors and protease inhibitors was predicted for all investigated triazine classes. Also, in order to confirm similarities/dissimilarities between series of examined compounds, principal component analysis (PCA) based on calculated ADME properties was conducted. The RM0 values of the s-triazine derivatives have been recommended for description and evaluation of pharmacokinetic properties. According to results of this study, the synthesized s-triazine derivatives meet pharmacokinetic criteria of preselection for drug candidates.
PMCID: PMC4232785  PMID: 25587308
s-triazine derivatives; In-silico; ADME; PCA; Polynomial regression

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