In Silico and In Vitro Investigation into the Next Generation of New Psychoactive Substances

New Psychoactive Substances (NPS) were designed to be legal alternatives to existing established recreational drugs. They have fast become a very popular and up until 2016, NPS were legal, cheap and freely accessible via the internet and high street “head shops”. The rapid expansion in the number of these drugs has reached epidemic proportions, whereby hundreds of NPS have been developed and sold within the last five-year period. As NPS are synthesized in clandestine laboratories there is little to no control in the manufacture, dosage and packaging of these drugs. The public health risks posed by these drugs are therefore far-reaching. Fatalities and severe adverse reactions associated with these compounds have become an ongoing challenge to healthcare services, primarily because these drugs have not previously been abused and therefore there is little pharmacological information available regarding NPS. There are a number of different biological receptors that are implicated in the effects of NPS and the mechanism of action for the majority of these drugs is still largely unknown. It is of great importance to try and establish an understanding of how various classes of NPS interact on a molecular level. In this thesis, structure-based and ligand-based in Silico methodologies were employed to gain a better understanding of how NPS may interact with monoamine transporters (MAT). Key findings included both molecular docking studies and a number of robust and predictive QSAR models for the dopamine and serotonin transporters provided insight into how promiscuity of NPS between the different MAT isoforms could arise. In addition, pharmacophore models were generated to identify chemical entities that were structurally dissimilar to known existing NPS that had the potential to interact with the cannabinoid 1 receptor (CB1) and hence were hypothesised could elicit similar biological responses to known potent synthetic cannabinoids. Thirteen of these compounds were identified and carried forward for in vitro and ex vivo analyses, where preliminary results have shown that two compounds activate the CB1 receptor. Further optimisation of these compounds could yield a novel SC scaffold that was previously unseen. Additionally, the compounds identified and the methodology employed in the generation of these new chemical scaffolds could be used to guide Early Warning Systems (EWS) and facilitate law enforcement with respect to emergent NPS

Virtual screening of GPCRs: An in silico chemogenomics approach

International audienceThe G-protein coupled receptor (GPCR) superfamily is currently the largest class of therapeutic targets. In silico prediction of interactions between GPCRs and small molecules in the transmembrane ligand-binding site is therefore a crucial step in the drug discovery process, which remains a daunting task due to the difficulty to characterize the 3D structure of most GPCRs, and to the limited amount of known ligands for some members of the superfamily. Chemogenomics, which attempts to characterize interactions between all members of a target class and all small molecules simultaneously, has recently been proposed as an interesting alternative to traditional docking or ligand-based virtual screening strategies

Molecular Similarity and Xenobiotic Metabolism

MetaPrint2D, a new software tool implementing a data-mining approach for predicting sites of xenobiotic metabolism has been developed. The algorithm is based on a statistical analysis of the occurrences of atom centred circular fingerprints in both substrates and metabolites. This approach has undergone extensive evaluation and been shown to be of comparable accuracy to current best-in-class tools, but is able to make much faster predictions, for the first time enabling chemists to explore the effects of structural modifications on a compound’s metabolism in a highly responsive and interactive manner.MetaPrint2D is able to assign a confidence score to the predictions it generates, based on the availability of relevant data and the degree to which a compound is modelled by the algorithm.In the course of the evaluation of MetaPrint2D a novel metric for assessing the performance of site of metabolism predictions has been introduced. This overcomes the bias introduced by molecule size and the number of sites of metabolism inherent to the most commonly reported metrics used to evaluate site of metabolism predictions.This data mining approach to site of metabolism prediction has been augmented by a set of reaction type definitions to produce MetaPrint2D-React, enabling prediction of the types of transformations a compound is likely to undergo and the metabolites that are formed. This approach has been evaluated against both historical data and metabolic schemes reported in a number of recently published studies. Results suggest that the ability of this method to predict metabolic transformations is highly dependent on the relevance of the training set data to the query compounds.MetaPrint2D has been released as an open source software library, and both MetaPrint2D and MetaPrint2D-React are available for chemists to use through the Unilever Centre for Molecular Science Informatics website.----Boehringer-Ingelhie

Discovery of Novel Glycogen Synthase Kinase-3beta Inhibitors: Molecular Modeling, Virtual Screening, and Biological Evaluation

Glycogen synthase kinase-3 (GSK-3) is a multifunctional serine/threonine protein kinase which is engaged in a variety of signaling pathways, regulating a wide range of cellular processes. Due to its distinct regulation mechanism and unique substrate specificity in the molecular pathogenesis of human diseases, GSK-3 is one of the most attractive therapeutic targets for the unmet treatment of pathologies, including type-II diabetes, cancers, inflammation, and neurodegenerative disease. Recent advances in drug discovery targeting GSK-3 involved extensive computational modeling techniques. Both ligand/structure-based approaches have been well explored to design ATP-competitive inhibitors. Molecular modeling plus dynamics simulations can provide insight into the protein-substrate and protein-protein interactions at substrate binding pocket and C-lobe hydrophobic groove, which will benefit the discovery of non-ATP-competitive inhibitors. To identify structurally novel and diverse compounds that effectively inhibit GSK-3â, we performed virtual screening by implementing a mixed ligand/structure-based approach, which included pharmacophore modeling, diversity analysis, and ensemble docking. The sensitivities of different docking protocols to the induced-fit effects at the ATP-competitive binding pocket of GSK-3â have been explored. An enrichment study was employed to verify the robustness of ensemble docking compared to individual docking in terms of retrieving active compounds from a decoy dataset. A total of 24 structurally diverse compounds obtained from the virtual screening experiment underwent biological validation. The bioassay results shothat 15 out of the 24 hit compounds are indeed GSK-3â inhibitors, and among them, one compound exhibiting sub-micromolar inhibitory activity is a reasonable starting point for further optimization. To further identify structurally novel GSK-3â inhibitors, we performed virtual screening by implementing another mixed ligand-based/structure-based approach, which included quantitative structure-activity relationship (QSAR) analysis and docking prediction. To integrate and analyze complex data sets from multiple experimental sources, we drafted and validated hierarchical QSAR, which adopts a multi-level structure to take data heterogeneity into account. A collection of 728 GSK-3 inhibitors with diverse structural scaffolds were obtained from published papers of 7 research groups based on different experimental protocols. Support vector machines and random forests were implemented with wrapper-based feature selection algorithms in order to construct predictive learning models. The best models for each single group of compounds were then selected, based on both internal and external validation, and used to build the final hierarchical QSAR model. The predictive performance of the hierarchical QSAR model can be demonstrated by an overall R2 of 0.752 for the 141 compounds in the test set. The compounds obtained from the virtual screening experiment underwent biological validation. The bioassay results confirmed that 2 hit compounds are indeed GSK-3â inhibitors exhibiting sub-micromolar inhibitory activity, and therefore validated hierarchical QSAR as an effective approach to be used in virtual screening experiments. We have successfully implemented a variant of supervised learning algorithm, named multiple-instance learning, in order to predict bioactive conformers of a given molecule which are responsible for the observed biological activity. The implementation requires instance-based embedding, and joint feature selection and classification. The goal of the present project is to implement multiple-instance learning in drug activity prediction, and subsequently to identify the bioactive conformers for each molecule. The proposed approach was proven not to suffer from overfitting and to be highly competitive with classical predictive models, so it is very powerful for drug activity prediction. The approach was also validated as a useful method for pursuit of bioactive conformers

Structure- and Ligand-Based Design of Novel Antimicrobial Agents

The use of computer based techniques in the design of novel therapeutic agents is a rapidly emerging field. Although the drug-design techniques utilized by Computational Medicinal Chemists vary greatly, they can roughly be classified into structure-based and ligand-based approaches. Structure-based methods utilize a solved structure of the design target, protein or DNA, usually obtained by X-ray or NMR methods to design or improve compounds with activity against the target. Ligand-based methods use active compounds with known affinity for a target that may yet be unresolved. These methods include Pharmacophore-based searching for novel active compounds or Quantitative Structure-Activity Relationship (QSAR) studies. The research presented here utilized both structure and ligand-based methods against two bacterial targets: Bacillus anthracis and Mycobacterium tuberculosis. The first part of this thesis details our efforts to design novel inhibitors of the enzyme dihydropteroate synthase from B. anthracis using crystal structures with known inhibitors bound. The second part describes a QSAR study that was performed using a series of novel nitrofuranyl compounds with known, whole-cell, inhibitory activity against M. tuberculosis. Dihydropteroate synthase (DHPS) catalyzes the addition of p-amino benzoic acid (pABA) to dihydropterin pyrophosphate (DHPP) to form pteroic acid as a key step in bacterial folate biosynthesis. It is the traditional target of the sulfonamide class of antibiotics. Unfortunately, bacterial resistance and adverse effects have limited the clinical utility of the sulfonamide antibiotics. Although six bacterial crystal structures are available, the flexible loop regions that enclose pABA during binding and contain key sulfonamide resistance sites have yet to be visualized in their functional conformation. To gain a new understanding of the structural basis of sulfonamide resistance, the molecular mechanism of DHPS action, and to generate a screening structure for high-throughput virtual screening, molecular dynamics simulations were applied to model the conformations of the unresolved loops in the active site. Several series of molecular dynamics simulations were designed and performed utilizing enzyme substrates and inhibitors, a transition state analog, and a pterin-sulfamethoxazole adduct. The positions of key mutation sites conserved across several bacterial species were closely monitored during these analyses. These residues were shown to interact closely with the sulfonamide binding site. The simulations helped us gain new understanding of the positions of the flexible loops during inhibitor binding that has allowed the development of a DHPS structural model that could be used for high-through put virtual screening (HTVS). Additionally, insights gained on the location and possible function of key mutation sites on the flexible loops will facilitate the design of new, potent inhibitors of DHPS that can bypass resistance mutations that render sulfonamides inactive. Prior to performing high-throughput virtual screening, the docking and scoring functions to be used were validated using established techniques against the B. anthracis DHPS target. In this validation study, five commonly used docking programs, FlexX, Surflex, Glide, GOLD, and DOCK, as well as nine scoring functions, were evaluated for their utility in virtual screening against the novel pterin binding site. Their performance in ligand docking and virtual screening against this target was examined by their ability to reproduce a known inhibitor conformation and to correctly detect known active compounds seeded into three separate decoy sets. Enrichment was demonstrated by calculated enrichment factors at 1% and Receiver Operating Characteristic (ROC) curves. The effectiveness of post-docking relaxation prior to rescoring and consensus scoring were also evaluated. Of the docking and scoring functions evaluated, Surflex with SurflexScore and Glide with GlideScore performed best overall for virtual screening against the DHPS target. The next phase of the DHPS structure-based drug design project involved high-throughput virtual screening against the DHPS structural model previously developed and docking methodology validated against this target. Two general virtual screening methods were employed. First, large, virtual libraries were pre-filtered by 3D pharmacophore and modified Rule-of-Three fragment constraints. Nearly 5 million compounds from the ZINC databases were screened generating 3,104 unique, fragment-like hits that were subsequently docked and ranked by score. Second, fragment docking without pharmacophore filtering was performed on almost 285,000 fragment-like compounds obtained from databases of commercial vendors. Hits from both virtual screens with high predicted affinity for the pterin binding pocket, as determined by docking score, were selected for in vitro testing. Activity and structure-activity relationship of the active fragment compounds have been developed. Several compounds with micromolar activity were identified and taken to crystallographic trials. Finally, in our ligand-based research into M. tuberculosis active agents, a series of nitrofuranylamide and related aromatic compounds displaying potent activity was investigated utilizing 3-Dimensional Quantitative Structure-Activity Relationship (3D-QSAR) techniques. Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Similarity Indices Analysis (CoMSIA) methods were used to produce 3D-QSAR models that correlated the Minimum Inhibitory Concentration (MIC) values against M. tuberculosis with the molecular structures of the active compounds. A training set of 95 active compounds was used to develop the models, which were then evaluated by a series of internal and external cross-validation techniques. A test set of 15 compounds was used for the external validation. Different alignment and ionization rules were investigated as well as the effect of global molecular descriptors including lipophilicity (cLogP, LogD), Polar Surface Area (PSA), and steric bulk (CMR), on model predictivity. Models with greater than 70% predictive ability, as determined by external validation and high internal validity (cross validated r2 \u3e .5) were developed. Incorporation of lipophilicity descriptors into the models had negligible effects on model predictivity. The models developed will be used to predict the activity of proposed new structures and advance the development of next generation nitrofuranyl and related nitroaromatic anti-tuberculosis agents

Comparative analysis of molecular fingerprints in prediction of drug combination effects

bbab291Application of machine and deep learning methods in drug discovery and cancer research has gained a considerable amount of attention in the past years. As the field grows, it becomes crucial to systematically evaluate the performance of novel computational solutions in relation to established techniques. To this end, we compare rule-based and data-driven molecular representations in prediction of drug combination sensitivity and drug synergy scores using standardized results of 14 high-throughput screening studies, comprising 64 200 unique combinations of 4153 molecules tested in 112 cancer cell lines. We evaluate the clustering performance of molecular representations and quantify their similarity by adapting the Centered Kernel Alignment metric. Our work demonstrates that to identify an optimal molecular representation type, it is necessary to supplement quantitative benchmark results with qualitative considerations, such as model interpretability and robustness, which may vary between and throughout preclinical drug development projects.Peer reviewe

Optimal assignment methods for ligand-based virtual screening

<p>Abstract</p> <p>Background</p> <p>Ligand-based virtual screening experiments are an important task in the early drug discovery stage. An ambitious aim in each experiment is to disclose active structures based on new scaffolds. To perform these "scaffold-hoppings" for individual problems and targets, a plethora of different similarity methods based on diverse techniques were published in the last years. The optimal assignment approach on molecular graphs, a successful method in the field of quantitative structure-activity relationships, has not been tested as a ligand-based virtual screening method so far.</p> <p>Results</p> <p>We evaluated two already published and two new optimal assignment methods on various data sets. To emphasize the "scaffold-hopping" ability, we used the information of chemotype clustering analyses in our evaluation metrics. Comparisons with literature results show an improved early recognition performance and comparable results over the complete data set. A new method based on two different assignment steps shows an increased "scaffold-hopping" behavior together with a good early recognition performance.</p> <p>Conclusion</p> <p>The presented methods show a good combination of chemotype discovery and enrichment of active structures. Additionally, the optimal assignment on molecular graphs has the advantage to investigate and interpret the mappings, allowing precise modifications of internal parameters of the similarity measure for specific targets. All methods have low computation times which make them applicable to screen large data sets.</p

Implementation of Multiple-Instance Learning in Drug Activity Prediction

In the context of drug discovery and development, much effort has been exerted to determine which conformers of a given molecule are responsible for the observed biological activity. In this work we aimed to predict bioactive conformers using a variant of supervised learning, named multiple-instance learning. A single molecule, treated as a bag of conformers, is biologically active if and only if at least one of its conformers, treated as an instance, is responsible for the observed bioactivity; and a molecule is inactive if none of its conformers is responsible for the observed bioactivity. The implementation requires instance-based embedding, and joint feature selection and classification. The goal of the present project is to implement multiple-instance learning in drug activity prediction, and subsequently to identify the bioactive conformers for each molecule. We encoded the 3-dimensional structures using pharmacophore fingerprints which are binary strings, and accomplished instance-based embedding using calculated dissimilarity distances. Four dissimilarity measures were employed and their performances were compared. 1-norm SVM was used for joint feature selection and classification. The approach was applied to four data sets, and the best proposed model for each data set was determined by using the dissimilarity measure yielding the smallest number of selected features. The predictive abilities of the proposed approach were compared with three classical predictive models without instance-based embedding. The proposed approach produced the best predictive models for one data set and second best predictive models for the rest of the data sets, based on the external validations. To validate the ability of the proposed approach to find bioactive conformers, 12 small molecules with co-crystallized structures were seeded in one data set. 10 out of 12 co-crystallized structures were indeed identified as significant conformers using the proposed approach. The proposed approach was demonstrated to be highly competitive with classical predictive models, hence it is very powerful for drug activity prediction. The approach was also validated as a useful method for pursuit of bioactive conformers

Experimentally Validated Pharmacoinformatics Approach to Predict hERG Inhibition Potential of New Chemical Entities

The hERG (human ether-a-go-go-related gene) encoded potassium ion (K+) channel plays a major role in cardiac repolarization. Drug-induced blockade of hERG has been a major cause of potentially lethal ventricular tachycardia termed Torsades de Pointes (TdPs). Therefore, we presented a pharmacoinformatics strategy using combined ligand and structure based models for the prediction of hERG inhibition potential (IC50) of new chemical entities (NCEs) during early stages of drug design and development. Integrated GRid-INdependent Descriptor (GRIND) models, and lipophilic efficiency (LipE), ligand efficiency (LE) guided template selection for the structure based pharmacophore models have been used for virtual screening and subsequent hERG activity (pIC50) prediction of identified hits. Finally selected two hits were experimentally evaluated for hERG inhibition potential (pIC50) using whole cell patch clamp assay. Overall, our results demonstrate a difference of less than ±1.6 log unit between experimentally determined and predicted hERG inhibition potential (IC50) of the selected hits. This revealed predictive ability and robustness of our models and could help in correctly rank the potency order (lower μM to higher nM range) against hERG