Chemoinformatics and chemical genomics: potential utility of in silico methods

Understanding and targeting resistance mechanisms in cancer

Resistance to cancer therapies has been a commonly observed phenomenon in clinical practice, which is one of the major causes of treatment failure and poor patient survival. The reduced responsiveness of cancer cells is a multifaceted phenomenon that can arise from genetic, epigenetic, and microenvironmental factors. Various mechanisms have been discovered and extensively studied, including drug inactivation, reduced intracellular drug accumulation by reduced uptake or increased efflux, drug target alteration, activation of compensatory pathways for cell survival, regulation of DNA repair and cell death, tumor plasticity, and the regulation from tumor microenvironments (TMEs). To overcome cancer resistance, a variety of strategies have been proposed, which are designed to enhance the effectiveness of cancer treatment or reduce drug resistance. These include identifying biomarkers that can predict drug response and resistance, identifying new targets, developing new targeted drugs, combination therapies targeting multiple signaling pathways, and modulating the TME. The present article focuses on the different mechanisms of drug resistance in cancer and the corresponding tackling approaches with recent updates. Perspectives on polytherapy targeting multiple resistance mechanisms, novel nanoparticle delivery systems, and advanced drug design tools for overcoming resistance are also reviewed.

In silico studies on recreational drugs: 3D quantitative structure activity relationship prediction of classified and de novo designer benzodiazepines

Currently, increasing availability and popularity of designer benzodiazepines (DBZDs) constitutes a primary threat to public health. To assess this threat, the biological activity/potency of DBZDs was investigated using in silico studies. Specific Quantitative Structure Activity Relationship (QSAR) models were developed in Forge™ for the prediction of biological activity (IC50 ) on the γ-aminobutyric acid A receptor (GABA-AR) of previously identified classified and unclassified DBDZs. A set of new potential ligands resulting from scaffold hopping studies conducted with MOE® was also evaluated. Two generated QSAR models (i.e. 3D-field QSAR and RVM) returned very good performance statistics (r2  = 0.98 [both] and q2  = 0.75 and 0.72, respectively). The DBZDs predicted to be the most active were flubrotizolam, clonazolam, pynazolam and flucotizolam, consistently with what reported in literature and/or drug discussion fora. The scaffold hopping studies strongly suggest that replacement of the pendant phenyl moiety with a five-membered ring could increase biological activity and highlight the existence of a still unexplored chemical space for DBZDs. QSAR could be of use as a preliminary risk assessment model for (newly) identified DBZDs, as well as scaffold hopping for the creation of computational libraries that could be used by regulatory bodies as support tools for scheduling procedures.

Designer Benzodiazepines' Activity on Opioid Receptors: A Docking Study

BACKGROUND

Previous studies have reported that benzodiazepines (BZDs) seem to enhance euphoric and reinforcing properties of opioids in opioid users so that a direct effect on opioid receptors has been postulated, together with a possible synergistic induction of severe side effects due to co use of BDZs and opioids. This is particularly worrisome given the appearance on the market of designer benzodiazepines (DBZDs), whose activity/toxicity profiles are scarcely known.

OBJECTIVES

This study aimed to evaluate, through computational studies, the binding affinity (or lack thereof) of 101 DBZDs identified online on the kappa, mu, and delta opioid receptors (K, M, DOR); and to assess whether their mechanism of action could include activation of the latter.

METHODS

MOE® was used for the computational studies. Pharmacophore mapping based on strong opioids agonist binders' 3D chemical features was used to filter the DBZDs. Resultant DBZDs were docked into the crystallised 3D active conformation of KOR (PDB6B73), DOR (PDB6PT3) and MOR (PDB5C1M). Co-crystallised ligands and four strong agonists were used as reference compounds. A score (S, Kcal/mol) representative of the predicted binding affinity, and a description of ligand interactions were obtained from MOE®.

RESULTS

The docking results, filtered for S < -8.0 and the interaction with the Asp residue, identified five DBZDs as putative binders of the three ORs : ciclotizolam, fluloprazolam, JQ1, Ro 48-6791, and Ro 48-8684.

CONCLUSION

It may be inferred that at least some DBZDs may have the potential to activate opioid receptors. This could mediate/increase their anxiolytic, analgesic, and addiction potentials, as well as worsen the side effects associated with opioid co-use.

The Psychonauts' Benzodiazepines; Quantitative Structure-Activity Relationship (QSAR) Analysis and Docking Prediction of Their Biological Activity

Designer benzodiazepines (DBZDs) represent a serious health concern and are increasingly reported in polydrug consumption-related fatalities. When new DBZDs are identified, very limited information is available on their pharmacodynamics. Here, computational models (i.e., quantitative structure-activity relationship/QSAR and Molecular Docking) were used to analyse DBZDs identified online by an automated web crawler (NPSfinder^®) and to predict their possible activity/affinity on the gamma-aminobutyric acid A receptors (GABA-ARs). The computational software MOE was used to calculate 2D QSAR models, perform docking studies on crystallised GABA-A receptors (6HUO, 6HUP) and generate pharmacophore queries from the docking conformational results. 101 DBZDs were identified online by NPSfinder^®. The validated QSAR model predicted high biological activity values for 41% of these DBDZs. These predictions were supported by the docking studies (good binding affinity) and the pharmacophore modelling confirmed the importance of the presence and location of hydrophobic and polar functions identified by QSAR. This study confirms once again the importance of web-based analysis in the assessment of drug scenarios (DBZDs), and how computational models could be used to acquire fast and reliable information on biological activity for index novel DBZDs, as preliminary data for further investigations.

Exploring G Protein-Coupled Receptors (GPCRs) Ligand Space via Cheminformatics Approaches: Impact on Rational Drug Design

The primary goal of rational drug discovery is the identification of selective ligands which act on single or multiple drug targets to achieve the desired clinical outcome through the exploration of total chemical space. To identify such desired compounds, computational approaches are necessary in predicting their drug-like properties. G Protein-Coupled Receptors (GPCRs) represent one of the largest and most important integral membrane protein families. These receptors serve as increasingly attractive drug targets due to their relevance in the treatment of various diseases, such as inflammatory disorders, metabolic imbalances, cardiac disorders, cancer, monogenic disorders, etc. In the last decade, multitudes of three-dimensional (3D) structures were solved for diverse GPCRs, thus referring to this period as the "golden age for GPCR structural biology." Moreover, accumulation of data about the chemical properties of GPCR ligands has garnered much interest toward the exploration of GPCR chemical space. Due to the steady increase in the structural, ligand, and functional data of GPCRs, several cheminformatics approaches have been implemented in its drug discovery pipeline. In this review, we mainly focus on the cheminformatics-based paradigms in GPCR drug discovery. We provide a comprehensive view on the ligand- and structure-based cheminformatics approaches which are best illustrated via GPCR case studies. Furthermore, an appropriate combination of ligand-based knowledge with structure-based ones, i.e., integrated approach, which is emerging as a promising strategy for cheminformatics-based GPCR drug design is also discussed.

The use of a quantitative structure-activity relationship (QSAR) model to predict GABA-A receptor binding of newly emerging benzodiazepines

The illicit market for new psychoactive substances is forever expanding. Benzodiazepines and their derivatives are one of a number of groups of these substances and thus far their number has grown year upon year. For both forensic and clinical purposes it is important to be able to rapidly understand these emerging substances. However as a consequence of the illicit nature of these compounds, there is a deficiency in the pharmacological data available for these 'new' benzodiazepines. In order to further understand the pharmacology of 'new' benzodiazepines we utilised a quantitative structure-activity relationship (QSAR) approach. A set of 69 benzodiazepine-based compounds was analysed to develop a QSAR training set with respect to published binding values to GABA_A receptors. The QSAR model returned an R² value of 0.90. The most influential factors were found to be the positioning of two H-bond acceptors, two aromatic rings and a hydrophobic group. A test set of nine random compounds was then selected for internal validation to determine the predictive ability of the model and gave an R² value of 0.86 when comparing the binding values with their experimental data. The QSAR model was then used to predict the binding for 22 benzodiazepines that are classed as new psychoactive substances. This model will allow rapid prediction of the binding activity of emerging benzodiazepines in a rapid and economic way, compared with lengthy and expensive in vitro/in vivo analysis. This will enable forensic chemists and toxicologists to better understand both recently developed compounds and prediction of substances likely to emerge in the future.

Systems Biology to Support Nanomaterial Grouping

The assessment of potential health risks of engineered nanomaterials (ENMs) is a challenging task due to the high number and great variety of already existing and newly emerging ENMs. Reliable grouping or categorization of ENMs with respect to hazards could help to facilitate prioritization and decision making for regulatory purposes. The development of grouping criteria, however, requires a broad and comprehensive data basis. A promising platform addressing this challenge is the systems biology approach. The different areas of systems biology, most prominently transcriptomics, proteomics and metabolomics, each of which provide a wealth of data that can be used to reveal novel biomarkers and biological pathways involved in the mode-of-action of ENMs. Combining such data with classical toxicological data would enable a more comprehensive understanding and hence might lead to more powerful and reliable prediction models. Physico-chemical data provide crucial information on the ENMs and need to be integrated, too. Overall statistical analysis should reveal robust grouping and categorization criteria and may ultimately help to identify meaningful biomarkers and biological pathways that sufficiently characterize the corresponding ENM subgroups. This chapter aims to give an overview on the different systems biology technologies and their current applications in the field of nanotoxicology, as well as to identify the existing challenges.

Predicting cytotoxicity of complex mixtures in high cancer incidence regions of the Huai River Basin based on GC-MS spectrum with partial least squares regression

Complex mixture exposures, such as those associated with water sources, are an important issue in health risk assessment. This study assessed the cytotoxicity of chemical mixtures extracted from water sources in regions of the Huai River Basin with high cancer incidences and built statistical models of cytotoxicity based on pollution profiles that were measured with gas chromatography-mass spectrometry (GC-MS). Both surface and ground waters were collected from rural water sources of Shenqiu County, Henan Province of China from 2008 to 2011 and extracted with XAD-2 resigns. Cytotoxicity was evaluated with Chinese hamster ovary K1 (CHO-K1) cells and compared against the pollution profiles of the extracts. IC50 of water samples ranged from 0.023 to 0.338L-eq/mL. The pollutants in waters determined by GC-MS are complex and some of the compounds that contributed to cytotoxicity lack toxicity data. A partial least squares (PLS) regression model of cytotoxicity was built based on linear aggregation of predictor variables (i.e., peaks for single compounds in the gas chromatograms). The PLS model contains 2 PLS factors extracted from 141 variables. The model was validated internally with training data permutation and externally with a test sample. The model explained 92% of the cytotoxicity in the training samples and 40% in the test sample. This approach provides a general, rapid method for relating water toxicity to GC-MS chromatograms and for predicting the compounds that contribute most to toxicity.

Scaling predictive modeling in drug development with cloud computing

Growing data sets with increased time for analysis is hampering predictive modeling in drug discovery. Model building can be carried out on high-performance computer clusters, but these can be expensive to purchase and maintain. We have evaluated ligand-based modeling on cloud computing resources where computations are parallelized and run on the Amazon Elastic Cloud. We trained models on open data sets of varying sizes for the end points logP and Ames mutagenicity and compare with model building parallelized on a traditional high-performance computing cluster. We show that while high-performance computing results in faster model building, the use of cloud computing resources is feasible for large data sets and scales well within cloud instances. An additional advantage of cloud computing is that the costs of predictive models can be easily quantified, and a choice can be made between speed and economy. The easy access to computational resources with no up-front investments makes cloud computing an attractive alternative for scientists, especially for those without access to a supercomputer, and our study shows that it enables cost-efficient modeling of large data sets on demand within reasonable time.

Mammalian olfactory receptors: molecular mechanisms of odorant detection, 3D-modeling, and structure-activity relationships

This chapter describes the main characteristics of olfactory receptor (OR) genes of vertebrates, including generation of this large multigenic family and pseudogenization. OR genes are compared in relation to evolution and among species. OR gene structure and selection of a given gene for expression in an olfactory sensory neuron (OSN) are tackled. The specificities of OR proteins, their expression, and their function are presented. The expression of OR proteins in locations other than the nasal cavity is regulated by different mechanisms, and ORs display various additional functions. A conventional olfactory signal transduction cascade is observed in OSNs, but individual ORs can also mediate different signaling pathways, through the involvement of other molecular partners and depending on the odorant ligand encountered. ORs are engaged in constitutive dimers. Ligand binding induces conformational changes in the ORs that regulate their level of activity depending on odorant dose. When present, odorant binding proteins induce an allosteric modulation of OR activity. Since no 3D structure of an OR has been yet resolved, modeling has to be performed using the closest G-protein-coupled receptor 3D structures available, to facilitate virtual ligand screening using the models. The study of odorant binding modes and affinities may infer best-bet OR ligands, to be subsequently checked experimentally. The relationship between spatial and steric features of odorants and their activity in terms of perceived odor quality are also fields of research that development of computing tools may enhance.

Size does matter! Label-free detection of small molecule-protein interaction

This review is focused on methods for detecting small molecules and, in particular, the characterisation of their interaction with natural proteins (e.g. receptors, ion channels). Because there are intrinsic advantages to using label-free methods over labelled methods (e.g. fluorescence, radioactivity), this review only covers label-free techniques. We briefly discuss available techniques and their advantages and disadvantages, especially as related to investigating the interaction between small molecules and proteins. The reviewed techniques include well-known and widely used standard analytical methods (e.g. HPLC-MS, NMR, calorimetry, and X-ray diffraction), newer and more specialised analytical methods (e.g. biosensors), biological systems (e.g. cell lines and animal models), and in-silico approaches.

Methods to enable the design of bioactive small molecules targeting RNA

RNA is an immensely important target for small molecule therapeutics or chemical probes of function. However, methods that identify, annotate, and optimize RNA-small molecule interactions that could enable the design of compounds that modulate RNA function are in their infancies. This review describes recent approaches that have been developed to understand and optimize RNA motif-small molecule interactions, including structure-activity relationships through sequencing (StARTS), quantitative structure-activity relationships (QSAR), chemical similarity searching, structure-based design and docking, and molecular dynamics (MD) simulations. Case studies described include the design of small molecules targeting RNA expansions, the bacterial A-site, viral RNAs, and telomerase RNA. These approaches can be combined to afford a synergistic method to exploit the myriad of RNA targets in the transcriptome.

Integrative approaches for predicting in vivo effects of chemicals from their structural descriptors and the results of short-term biological assays

Cheminformatics approaches such as Quantitative Structure Activity Relationship (QSAR) modeling have been used traditionally for predicting chemical toxicity. In recent years, high throughput biological assays have been increasingly employed to elucidate mechanisms of chemical toxicity and predict toxic effects of chemicals in vivo. The data generated in such assays can be considered as biological descriptors of chemicals that can be combined with molecular descriptors and employed in QSAR modeling to improve the accuracy of toxicity prediction. In this review, we discuss several approaches for integrating chemical and biological data for predicting biological effects of chemicals in vivo and compare their performance across several data sets. We conclude that while no method consistently shows superior performance, the integrative approaches rank consistently among the best yet offer enriched interpretation of models over those built with either chemical or biological data alone. We discuss the outlook for such interdisciplinary methods and offer recommendations to further improve the accuracy and interpretability of computational models that predict chemical toxicity.

Assessment of the genotoxic and carcinogenic potentials of 3-aminothiophene derivatives using in vitro and in silico methodologies

Thiophene derivatives, a class of compounds widely used in products such as pharmaceuticals, agrochemicals or dyestuffs, represent chemicals of concern. Indeed, the thiophene ring is often considered as a structural moiety that may be involved in toxic effects in humans. We primarily focus on the genotoxic/mutagenic and carcinogenic potentials of the methyl 3-amino-4-methylthiophene-2-carboxylate (1), a precursor of the articaine local anesthetic (4) which falls within the scope of the European REACH (Registration, Evaluation, Authorisation and restriction of CHemicals) legislation. To discern some structure-toxicity relationships, we also studied two related compounds, namely the 3-amino 4-methylthiophene (2) and the 2-acetyl 4-chlorothiophene (3). Techniques employed to assess mutagenic and DNA-damaging effects involved the Salmonella mutagenicity assay (or Ames test) and the single-cell gel electrophoresis assay (or Comet assay). In the range of tested doses, none of these derivatives led to a positive response in the Ames tests and DNA damage was only observed in the Comet assay after high concentration exposure of 2. The study of their carcinogenic potential using the in vitro SHE (Syrian Hamster Embryo) cell transformation assay (CTA) highlighted the activity of compound 2. A combination of experimental data with in silico predictions of the reactivity of thiophene derivatives towards cytochrome P450 (CYP450), enabled us to hypothesize possible pathways leading to these toxicological profiles.

Rational design of chemical genetic probes of RNA function and lead therapeutics targeting repeating transcripts

RNA is an important yet vastly underexploited target for small molecule chemical probes or lead therapeutics. Small molecules have been used successfully to modulate the function of the bacterial ribosome, viral RNAs and riboswitches. These RNAs are either highly expressed or can be targeted using substrate mimicry, a mainstay in the design of enzyme inhibitors. However, most cellular RNAs are neither highly expressed nor have a lead small molecule inhibitor, a significant challenge for drug discovery efforts. Herein, I describe the design of small molecules targeting expanded repeating transcripts that cause myotonic muscular dystrophy (DM). These test cases illustrate the challenges of designing small molecules that target RNA and the advantages of targeting repeating transcripts. Lastly, I discuss how small molecules might be more advantageous than oligonucleotides for targeting RNA.

Integrative chemical-biological read-across approach for chemical hazard classification

Traditional read-across approaches typically rely on the chemical similarity principle to predict chemical toxicity; however, the accuracy of such predictions is often inadequate due to the underlying complex mechanisms of toxicity. Here, we report on the development of a hazard classification and visualization method that draws upon both chemical structural similarity and comparisons of biological responses to chemicals measured in multiple short-term assays ("biological" similarity). The Chemical-Biological Read-Across (CBRA) approach infers each compound's toxicity from both chemical and biological analogues whose similarities are determined by the Tanimoto coefficient. Classification accuracy of CBRA was compared to that of classical RA and other methods using chemical descriptors alone or in combination with biological data. Different types of adverse effects (hepatotoxicity, hepatocarcinogenicity, mutagenicity, and acute lethality) were classified using several biological data types (gene expression profiling and cytotoxicity screening). CBRA-based hazard classification exhibited consistently high external classification accuracy and applicability to diverse chemicals. Transparency of the CBRA approach is aided by the use of radial plots that show the relative contribution of analogous chemical and biological neighbors. Identification of both chemical and biological features that give rise to the high accuracy of CBRA-based toxicity prediction facilitates mechanistic interpretation of the models.