Exploring possible mechanisms of action for the nanotoxicity and protein binding of decorated nanotubes: interpretation of physicochemical properties from optimal QSAR models.

Carbon nanotubes have become widely used in a variety of applications including biosensors and drug carriers. Therefore, the issue of carbon nanotube toxicity is increasingly an area of focus and concern. While previous studies have focused on the gross mechanisms of action relating to nanomaterials interacting with biological entities, this study proposes detailed mechanisms of action, relating to nanotoxicity, for a series of decorated (functionalized) carbon nanotube complexes based on previously reported QSAR models. Possible mechanisms of nanotoxicity for six endpoints (bovine serum albumin, carbonic anhydrase, chymotrypsin, hemoglobin along with cell viability and nitrogen oxide production) have been extracted from the corresponding optimized QSAR models. The molecular features relevant to each of the endpoint respective mechanism of action for the decorated nanotubes are also discussed. Based on the molecular information contained within the optimal QSAR models for each nanotoxicity endpoint, either the decorator attached to the nanotube is directly responsible for the expression of a particular activity, irrespective of the decorator's 3D-geometry and independent of the nanotube, or those decorators having structures that place the functional groups of the decorators as far as possible from the nanotube surface most strongly influence the biological activity. These molecular descriptors are further used to hypothesize specific interactions involved in the expression of each of the six biological endpoints.

Experimental and computational studies of physicochemical properties influence NSAID-cyclodextrin complexation.

The objective of this research was to investigate physicochemical properties of an active pharmaceutical ingredient (API) that influence cyclodextrin complexation through experimental and computational studies. Native ß-cyclodextrin (B-CD) and two hydroxypropyl derivatives were first evaluated by conventional phase solubility experiments for their ability to complex four poorly water-soluble nonsteroidal anti-inflammatory drugs (NSAIDs). Differential scanning calorimetry was used to confirm complexation. Secondly, molecular modeling was used to estimate Log P and aqueous solubility (S o) of the NSAIDs. Molecular dynamics simulations (MDS) were used to investigate the thermodynamics and geometry of drug-CD cavity docking. NSAID solubility increased linearly with increasing CD concentration for the two CD derivatives (displaying an AL profile), whereas increases in drug solubility were low and plateaued in the B-CD solutions (type B profile). The calculated Log P and S o of the NSAIDs were in good concordance with experimental values reported in the literature. Side chain substitutions on the B-CD moiety did not significantly influence complexation. Explicitly, complexation and the associated solubility increase were mainly dependent on the chemical structure of the NSAID. MDS indicated that each NSAID-CD complex had a distinct geometry. Moreover, complexing energy had a large, stabilizing, and fairly constant hydrophobic component for a given CD across the NSAIDs, while electrostatic and solvation interaction complex energies were quite variable but smaller in magnitude.

Dependence of QSAR models on the selection of trial descriptor sets: a demonstration using nanotoxicity endpoints of decorated nanotubes.

Little attention has been given to the selection of trial descriptor sets when designing a QSAR analysis even though a great number of descriptor classes, and often a greater number of descriptors within a given class, are now available. This paper reports an effort to explore interrelationships between QSAR models and descriptor sets. Zhou and co-workers (Zhou et al., Nano Lett. 2008, 8 (3), 859-865) designed, synthesized, and tested a combinatorial library of 80 surface modified, that is decorated, multi-walled carbon nanotubes for their composite nanotoxicity using six endpoints all based on a common 0 to 100 activity scale. Each of the six endpoints for the 29 most nanotoxic decorated nanotubes were incorporated as the training set for this study. The study reported here includes trial descriptor sets for all possible combinations of MOE, VolSurf, and 4D-fingerprints (FP) descriptor classes, as well as including and excluding explicit spatial contributions from the nanotube. Optimized QSAR models were constructed from these multiple trial descriptor sets. It was found that (a) both the form and quality of the best QSAR models for each of the endpoints are distinct and (b) some endpoints are quite dependent upon 4D-FP descriptors of the entire nanotube-decorator complex. However, other endpoints yielded equally good models only using decorator descriptors with and without the decorator-only 4D-FP descriptors. Lastly, and most importantly, the quality, significance, and interpretation of a QSAR model were found to be critically dependent on the trial descriptor sets used within a given QSAR endpoint study.

Receptor-dependent 4D-QSAR analysis of peptidemimetic inhibitors of Trypanosoma cruzi trypanothione reductase with receptor-based alignment.

Receptor-dependent four-dimensional quantitative structure-activity relationship (RD-4D-QSAR) studies were applied on a series of 21 peptides reversible inhibitors of Trypanosoma cruzi trypanothione reductase (TR) (Amino Acids, 20, 2001, 145). The RD-4D-QSAR (J Chem Inform Comp Sci, 43, 2003, 1591) approach can evaluate multiple conformations from molecular dynamics simulation and several superposition structure alignments inside a box composed by unitary cubic cells. The descriptors are the occupancy frequency of the atoms types inside the grid cells. We could develop 3D-QSAR models that were highly predictive (q(2) above 0.71). The 3D-QSAR models can be visualized as a spatial map of atom types that are important on the comprehension of the ligand-enzyme interaction mechanism, pointing main pharmacophoric groups and TR subsites described in the literature. We were able also to identify some TR subsites for further development in the drug discovery process against tropical diseases not yet studied.

The great descriptor melting pot: mixing descriptors for the common good of QSAR models.

The usefulness and utility of QSAR modeling depends heavily on the ability to estimate the values of molecular descriptors relevant to the endpoints of interest followed by an optimized selection of descriptors to form the best QSAR models from a representative set of the endpoints of interest. The performance of a QSAR model is directly related to its molecular descriptors. QSAR modeling, specifically model construction and optimization, has benefited from its ability to borrow from other unrelated fields, yet the molecular descriptors that form QSAR models have remained basically unchanged in both form and preferred usage. There are many types of endpoints that require multiple classes of descriptors (descriptors that encode 1D through multi-dimensional, 4D and above, content) needed to most fully capture the molecular features and interactions that contribute to the endpoint. The advantages of QSAR models constructed from multiple, and different, descriptor classes have been demonstrated in the exploration of markedly different, and principally biological systems and endpoints. Multiple examples of such QSAR applications using different descriptor sets are described and that examined. The take-home-message is that a major part of the future of QSAR analysis, and its application to modeling biological potency, ADME-Tox properties, general use in virtual screening applications, as well as its expanding use into new fields for building QSPR models, lies in developing strategies that combine and use 1D through nD molecular descriptors.

A comprehensive support vector machine binary hERG classification model based on extensive but biased end point hERG data sets.

The human ether-a-go-go related gene (hERG) potassium ion channel plays a key role in cardiotoxicity and is therefore a key target as part of preclinical drug discovery toxicity screening. The PubChem hERG Bioassay data set, composed of 1668 compounds, was used to construct an in silico screening model. The corresponding trial models were constructed from a descriptor pool composed of 4D fingerprints (4D-FP) and traditional 2D and 3D VolSurf-like molecular descriptors. A final binary classification model was constructed via a support vector machine (SVM). The resultant model was then validated using the PubChem hERG Bioassay data set (AID 376) and an external hERG data set by evaluating the model's ability to determine hERG blockers from nonblockers. The external data set (the test set) consisted of 356 compounds collected from available literature data and consisting of 287 actives and 69 inactives. Four different sampling protocols and a 10-fold cross-correlation analysis--used in the validation process to evaluate classification models--explored the impact of the active--inactive data imbalance distribution of the PubChem high-throughput data set. Four different data sets were explored, and the one employing Lipinski's rule-of-five coupled with measures of relative molecular lipophilicity performed the best in the 10-fold cross-correlation validation of the training data set as well as overall prediction accuracy of the external test sets. The linear SVM binary classification model building strategy was applied to different combinations of MOE (traditional 2D, "21/2D", and 3D VolSurf-like) and 4D-FP molecular descriptors to further explore and refine previously proposed key descriptors, identify new significant features that contribute to the prediction of hERG toxicity, and construct the optimal SVM binary classification model from a shrunken descriptor pool. The accuracy, sensitivity, and specificity of the best model determined from 10-fold cross-validation are 95, 90, and 96%, respectively; the overall accuracy is near 87% for the external set. The models constructed in this study demonstrate the following: (i) robustness based upon performance in accuracy across the structural diversity of the training set, (ii) ability to predict a compound's "predisposition" to block hERG ion channels, and (iii) define and illustrate structural features that can be overlaid onto the chemical structures to aid in the 3D structure-activity interpretation of the hERG blocking effect.

The discovery of new anesthetics by targeting GABA(A) receptors.

INTRODUCTION: Many of the general anesthetics, currently used in clinical practice, work through interactions with GABA(A) receptors. The last 2 decades has witnessed substantial progress in defining the molecular mechanisms by which general anesthetics interact with GABA(A) receptor sites. However, despite progress in the basic scientific understanding of the mechanism of action of general anesthetics, introduction of novel general anesthetic agents into clinical practice has proven quite challenging. AREAS COVERED: The focus of this review is on the potential for translating basic science advances into the design of new and improved anesthetics. The authors review general anesthetics in current practice as well as anesthetic drug candidates in development and discuss the potential for novel anesthetic drug development. EXPERT OPINION: Opportunities for the discovery of new anesthetics include: computational-based ligand-design, structure-based design, re-exploration of old structure-activity data, absorption, distribution, metabolism, excretion and toxicity predictions and high-throughput screening. The authors believe a lack of high-resolution three-dimensional structures of mammalian GABA(A) receptors remains a significant limiting factor in structure-based anesthetic drug design.

4D-QSAR: perspectives in drug design.

Drug design is a process driven by innovation and technological breakthroughs involving a combination of advanced experimental and computational methods. A broad variety of medicinal chemistry approaches can be used for the identification of hits, generation of leads, as well as to accelerate the optimization of leads into drug candidates. The quantitative structure-activity relationship (QSAR) formalisms are among the most important strategies that can be applied for the successful design new molecules. This review provides a comprehensive review on the evolution and current status of 4D-QSAR, highlighting present challenges and new opportunities in drug design.

In silico binary classification QSAR models based on 4D-fingerprints and MOE descriptors for prediction of hERG blockage.

Blockage of the human ether-a-go-go related gene (hERG) potassium ion channel is a major factor related to cardiotoxicity. Hence, drugs binding to this channel have become an important biological end point in side effects screening. A set of 250 structurally diverse compounds screened for hERG activity from the literature was assembled using a set of reliability filters. This data set was used to construct a set of two-state hERG QSAR models. The descriptor pool used to construct the models consisted of 4D-fingerprints generated from the thermodynamic distribution of conformer states available to a molecule, 204 traditional 2D descriptors and 76 3D VolSurf-like descriptors computed using the Molecular Operating Environment (MOE) software. One model is a continuous partial least-squares (PLS) QSAR hERG binding model. Another related model is an optimized binary classification QSAR model that classifies compounds as active or inactive. This binary model achieves 91% accuracy over a large range of molecular diversity spanning the training set. Two external test sets were constructed. One test set is the condensed PubChem bioassay database containing 876 compounds, and the other test set consists of 106 additional compounds found in the literature. Both of the test sets were used to validate the binary QSAR model. The binary QSAR model permits a structural interpretation of possible sources for hERG activity. In particular, the presence of a polar negative group at a distance of 6-8 A from a hydrogen bond donor in a compound is predicted to be a quite structure-specific pharmacophore that increases hERG blockage. Since a data set of high chemical diversity was used to construct the binary model, it is applicable for performing general virtual hERG screening.

3D-Pharmacophore mapping of thymidine-based inhibitors of TMPK as potential antituberculosis agents.

Tuberculosis (TB) is the primary cause of mortality among infectious diseases. Mycobacterium tuberculosis monophosphate kinase (TMPKmt) is essential to DNA replication. Thus, this enzyme represents a promising target for developing new drugs against TB. In the present study, the receptor-independent, RI, 4D-QSAR method has been used to develop QSAR models and corresponding 3D-pharmacophores for a set of 81 thymidine analogues, and two corresponding subsets, reported as inhibitors of TMPKmt. The resulting optimized models are not only statistically significant with r(2) ranging from 0.83 to 0.92 and q(2) from 0.78 to 0.88, but also are robustly predictive based on test set predictions. The most and the least potent inhibitors in their respective postulated active conformations, derived from each of the models, were docked in the active site of the TMPKmt crystal structure. There is a solid consistency between the 3D-pharmacophore sites defined by the QSAR models and interactions with binding site residues. Moreover, the QSAR models provide insights regarding a probable mechanism of action of the analogues.

The receptor-dependent QSAR paradigm: an overview of the current state of the art.

The original quantitative structure-activity relationship (QSAR) formulation was proposed by Hansch and Fujita in the 1960's and, since then QSAR analysis has evolved as a mature science, due mainly to the advances that occurred in the past two decades in the fields of molecular modeling, data analysis algorithms, chemoinformatics, and the application of graph theory in chemistry. Moreover, it is also worthy of note the exponential progress that have occurred in software and hardware development. In this context, a myriad of QSAR methods exist; from the considered "classical" approaches (known as two-dimensional (2D) QSAR), to three-dimensional (3D) and multidimensional (nD) QSAR ones. A distinct QSAR approach has been recently proposed, the receptor-dependent-QSAR, where explicit information regarding the receptor structure (usually a protein) is extensively used during modeling process. Indeed, a limited, but growing number of receptor-dependent QSAR methods are reported in the literature. With no intention to be comprehensive, an overview of receptor-dependent QSAR methods will be discussed along with an in-depth examination of their applications in drug design, virtual screen, and ADMET modeling in silico.

Rational design and 3D-pharmacophore mapping of 5'-thiourea-substituted alpha-thymidine analogues as mycobacterial TMPK inhibitors.

Thymidine monophosphate kinase (TMPK) has emerged as an attractive target for developing inhibitors of Mycobacterium tuberculosis growth. In this study the receptor-independent (RI) 4D-QSAR formalism has been used to develop QSAR models and corresponding 3D-pharmacophores for a set of 5'-thiourea-substituted alpha-thymidine inhibitors. Models were developed for the entire training set and for a subset of the training set consisting of the most potent inhibitors. The optimized (RI) 4D-QSAR models are statistically significant (r(2) = 0.90, q(2) = 0.83 entire set, r(2) = 0.86, q(2) = 0.80 high potency subset) and also possess good predictivity based on test set predictions. The most and least potent inhibitors, in their respective postulated active conformations derived from the models, were docked in the active site of the TMPK crystallographic structure. There is a solid consistency between the 3D-pharmacophore sites defined by the QSAR models and interactions with binding site residues. This model identifies new regions of the inhibitors that contain pharmacophore sites, such as the sugar-pyrimidine ring structure and the region of the 5'-arylthiourea moiety. These new regions of the ligands can be further explored and possibly exploited to identify new, novel, and, perhaps, better antituberculosis inhibitors of TMPKmt. Furthermore, the 3D-pharmacophores defined by these models can be used as a starting point for future receptor-dependent antituberculosis drug design as well as to elucidate candidate sites for substituent addition to optimize ADMET properties of analog inhibitors.

Affinity of drugs and small biologically active molecules to carbon nanotubes: a pharmacodynamics and nanotoxicity factor?

The MM-PBSA MD method was used to estimate the affinity, as represented by log k(b), of each of a variety of biologically active molecules to a carbon nanotube in an aqueous environment. These ligand-receptor binding simulations were calibrated by first estimating the log k(b) values for eight ligands to human serum albumin, HSA, whose log k(b) values have been observed. A validation linear correlation equation was established [R(2) = 0.888, Q(2) = 0.603] between the observed and estimated log k(b) values to HSA. This correlation equation was then used to rescale all MM-PBSA MD log k(b) values using a carbon nanotube as the receptor. The log k(b) of the eight HSA ligands, nine polar and/or rigid ligands and six nonpolar and/or flexible ligands to a carbon nanotube were estimated. The range in rescaled log k(b) values across this set of 23 ligands is 0.25 to 7.14, essentially 7 orders of magnitude. Some ligands, like PGI2, bind in the log k(b) = 7 range which corresponds to the lower limits of known drugs. Thus, such significant levels of binding of biologically relevant compounds to carbon nanotubes might lead to alterations in the normal pharmacodynamic profiles of these compounds and be a source of toxicity. Ligand binding potency to a carbon nanotube is largely controlled by the shape, polarity/nonpolarity distribution and flexibility of the ligand. HSA ligands exhibit the most limited binding to a carbon nanotube, and they are relatively rigid and of generally spherical shape. Polar and/or rigid ligands bind less strongly to the carbon nanotube, on average, than nonpolar and/or flexible ligands even though the chosen members of both classes of ligands in this study have chainlike shapes that facilitate binding. The introduction of only a few strategically spaced single bonds in the polar and/or rigid ligands markedly increases their binding to a carbon nanotube.

Semi-synthetic ecdysteroids as gene-switch actuators: synthesis, structure-activity relationships, and prospective ADME properties.

The ligand-inducible, ecdysteroid receptor (EcR) gene-expression system can add critical control features to protein expression in cell and gene therapy. However, potent natural ecdysteroids possess absorption, distribution, metabolism and excretion (ADME) properties that have not been optimised for use as gene-switch actuators in vivo. Herein we report the first systematic synthetic exploration of ecdysteroids toward modulation of gene-switch potency. Twenty-three semi-synthetic O-alkyl ecdysteroids were assayed in both a natural insect system (Drosophila B(II) cells) and engineered gene-switch systems in mammalian cells using Drosophila melanogaster, Choristoneura fumiferana, and Aedes aegypti EcRs. Gene-switch potency is maintained, or even enhanced, for ecdysteroids methylated at the 22-position in favourable cases. Furthermore, trends toward lower solubility, higher permeability, and higher blood-brain barrier penetration are supported by predicted ADME properties, calculated using the membrane-interaction (MI)-QSAR methodology. The structure-activity relationship (SAR) of alkylated ecdysteroids indicates that 22-OH is an H-bond acceptor, 25-OH is most likely an H-bond donor, and 2-OH and 3-OH are donors and/or acceptors in network with each other, and with the EcR. The strategy of alkylation points the way to improved ecdysteroidal actuators for switch-activated gene therapy.

Categorical QSAR models for skin sensitization based on local lymph node assay measures and both ground and excited state 4D-fingerprint descriptors.

In previous studies we have developed categorical QSAR models for predicting skin-sensitization potency based on 4D-fingerprint (4D-FP) descriptors and in vivo murine local lymph node assay (LLNA) measures. Only 4D-FP derived from the ground state (GMAX) structures of the molecules were used to build the QSAR models. In this study we have generated 4D-FP descriptors from the first excited state (EMAX) structures of the molecules. The GMAX, EMAX and the combined ground and excited state 4D-FP descriptors (GEMAX) were employed in building categorical QSAR models. Logistic regression (LR) and partial least square coupled logistic regression (PLS-CLR), found to be effective model building for the LLNA skin-sensitization measures in our previous studies, were used again in this study. This also permitted comparison of the prior ground state models to those involving first excited state 4D-FP descriptors. Three types of categorical QSAR models were constructed for each of the GMAX, EMAX and GEMAX datasets: a binary model (2-state), an ordinal model (3-state) and a binary-binary model (two-2-state). No significant differences exist among the LR 2-state model constructed for each of the three datasets. However, the PLS-CLR 3-state and 2-state models based on the EMAX and GEMAX datasets have higher predictivity than those constructed using only the GMAX dataset. These EMAX and GMAX categorical models are also more significant and predictive than corresponding models built in our previous QSAR studies of LLNA skin-sensitization measures.

Identification of possible sources of nanotoxicity from carbon nanotubes inserted into membrane bilayers using membrane interaction quantitative structure--activity relationship analysis.

Four possible sources of cellular toxicity due to the insertion of a carbon nanotube into a dimyristoylphosphatidylcholine (DMPC) membrane bilayer were explored using the membrane interaction quantitative structure-activity relationship methodology. Comparisons of (i) the structural organization of the membrane bilayer, (ii) dynamical features of the membrane bilayer, and (iii) transport of small polar molecules across the membrane bilayer were carried out with, and without, a carbon nanotube inserted into the bilayer. A fourth study was performed to determine how the transport of solvated ions through the inserted nanotube might alter the structure of the membrane bilayer. Two large changes in the bilayer occur due to insertion of the carbon nanotube. First, there is an alteration in the packing of the DMPC bilayer molecules, which extends at least 18 A from the nanotube, and includes the creation of a relatively open, unoccupied cylindrical ring of 2-4 A thickness directly around the nanotube. Second, the same bilayer structure, which undergoes the change in structural organization, also becomes much more rigid than when the nanotube is not inserted. Solvated calcium ions are predicted to preferentially transport through the inserted nanotube as compared to hydrated sodium ions, but the solvated calcium ion also produces an alteration in the local bilayer structure as it passes through the nanotube. The total diffusion coefficient of ethanol through the membrane bilayer increases by about 35% in the presence of the inserted nanotube. Urea and caffeine also undergo increases in their diffusion coefficients for transport through the bilayer, due to the inserted nanotube, but these increases are less than that of ethanol. Each of the three penetrants also diffuses more directly through the membrane bilayer in the presence of the nanotube, especially caffeine and urea.

Combined 4D-fingerprint and clustering based membrane-interaction QSAR analyses for constructing consensus Caco-2 cell permeation virtual screens.

A set of 30 structurally diverse molecules, for which Caco-2 cell permeation coefficients were determined, formed the training set for construction of Caco-2 cell permeation models based upon membrane-interaction (MI) QSAR analysis and a new QSAR method called 4D-fingerprint QSAR analysis. The descriptor terms of the 4D-fingerprints equation are molecular similarity eigenvalues, and this set of descriptors is being evaluated as a potential "universal" QSAR descriptor set. The 4D-fingerprint model suggests that Caco-2 cell permeation is governed by the spatial distribution of hydrogen bonding and nonpolar groups over the molecular shape of a molecule. Moreover, a complementary resampling of the original Caco-2 cell permeation training set, followed by the construction of several "clustered" MI-QSAR models, led to a consensus model consistent in interpretation with the 4D-fingerprint model.

Categorical QSAR Models for skin sensitization based upon local lymph node assay classification measures part 2: 4D-fingerprint three-state and two-2-state logistic regression models.

Three and four state categorical quantitative structure-activity relationship (QSAR) models for skin sensitization have been constructed using data from the murine Local Lymph Node Assay studies. These are the same data we previously used to build two-state (sensitizer, nonsensitizer) QSAR models (Li et al., 2007, Chem. Res. Toxicol. 20, 114-128). 4D-fingerprint descriptors derived from the 4D-molecular similarity paradigm are used to generate these models. A training set of 196 and a test set of 22 structurally diverse compounds were used in this study. Logistic regression, and partial least square coupled logistic regression were used to build the models. The three-state QSAR model gives a classification accuracy of 73.4% for the training set and 63.6% for the test set, while the random average value of classification accuracy for any three-state data set is 33.3%. The two-2-state [four categories in total] QSAR model gives a classification accuracy of 83.2% for the training set and 54.6% for the test set, while the random average value of classification accuracy for any two-2-state data set is 25%. An analysis of the skin-sensitization models developed in this study, as well as the two-state QSAR models developed in our previous analysis, suggests that the "moderate" sensitizers may be the main source of limited model accuracy.

4D-fingerprint categorical QSAR models for skin sensitization based on the classification of local lymph node assay measures.

Currently, the only validated methods to identify skin sensitization effects are in vivo models, such as the local lymph node assay (LLNA) and guinea pig studies. There is a tremendous need, in particular due to novel legislation, to develop animal alternatives, for eaxample, quantitative structure-activity relationship (QSAR) models. Here, QSAR models for skin sensitization using LLNA data have been constructed. The descriptors used to generate these models are derived from the 4D-molecular similarity paradigm and are referred to as universal 4D-fingerprints. A training set of 132 structurally diverse compounds and a test set of 15 structurally diverse compounds were used in this study. The statistical methodologies used to build the models are logistic regression (LR) and partial least-square coupled logistic regression (PLS-LR), which prove to be effective tools for studying skin sensitization measures expressed in the two categorical terms of sensitizer and non-sensitizer. QSAR models with low values of the Hosmer-Lemeshow goodness-of-fit statistic, X(2)HL, are significant and predictive. For the training set, the cross-validated prediction accuracy of the logistic regression models ranges from 77.3% to 78.0%, whereas that of the PLS-logistic regression models ranges from 87.1% to 89.4%. For the test set, the prediction accuracy of logistic regression models ranges from 80.0% to 86.7%, whereas that of the PLS-logistic regression models ranges from 73.3% to 80.0%. The QSAR models are made up of 4D-fingerprints related to aromatic atoms, hydrogen bond acceptors, and negatively partially charged atoms.