Ligand-based approaches to in silico pharmacology

Identification of differential biological activity and synergy between the PARP inhibitor rucaparib and its major metabolite

The (poly)pharmacology of drug metabolites is seldom comprehensively characterized in drug discovery. However, some drug metabolites can reach high plasma concentrations and display in vivo activity. Here, we use computational and experimental methods to comprehensively characterize the kinase polypharmacology of M324, the major metabolite of the PARP1 inhibitor rucaparib. We demonstrate that M324 displays unique PLK2 inhibition at clinical concentrations. This kinase activity could have implications for the efficacy and safety of rucaparib and therefore warrants further clinical investigation. Importantly, we identify synergy between the drug and the metabolite in prostate cancer models and a complete reduction of α-synuclein accumulation in Parkinson's disease models. These activities could be harnessed in the clinic or open new drug discovery opportunities. The study reported here highlights the importance of characterizing the activity of drug metabolites to comprehensively understand drug response in the clinic and exploit our current drug arsenal in precision medicine.

Open-ComBind: harnessing unlabeled data for improved binding pose prediction

Determination of the bound pose of a ligand is a critical first step in many in silico drug discovery tasks. Molecular docking is the main tool for the prediction of non-covalent binding of a protein and ligand system. Molecular docking pipelines often only utilize the information of one ligand binding to the protein despite the commonly held hypothesis that different ligands share binding interactions when bound to the same receptor. Here we describe Open-ComBind, an easy-to-use, open-source version of the ComBind molecular docking pipeline that leverages information from multiple ligands without known bound structures to enhance pose selection. We first create distributions of feature similarities between ligand pose pairs, comparing near-native poses with all sampled docked poses. These distributions capture the likelihood of observing similar features, such as hydrogen bonds or hydrophobic contacts, in different pose configurations. These similarity distributions are then combined with a per-ligand docking score to enhance overall pose selection by 5% and 4.5% for high-affinity and congeneric series helper ligands, respectively. Open-ComBind reduces the average RMSD of ligands in our benchmark dataset by 9.0%. We provide Open-ComBind as an easy-to-use command line and Python API to increase pose prediction performance at www.github.com/drewnutt/open_combind .

A multiscale orchestrated computational framework to reveal emergent phenomena in neuroblastoma

Neuroblastoma is a complex and aggressive type of cancer that affects children. Current treatments involve a combination of surgery, chemotherapy, radiotherapy, and stem cell transplantation. However, treatment outcomes vary due to the heterogeneous nature of the disease. Computational models have been used to analyse data, simulate biological processes, and predict disease progression and treatment outcomes. While continuum cancer models capture the overall behaviour of tumours, and agent-based models represent the complex behaviour of individual cells, multiscale models represent interactions at different organisational levels, providing a more comprehensive understanding of the system. In 2018, the PRIMAGE consortium was formed to build a cloud-based decision support system for neuroblastoma, including a multi-scale model for patient-specific simulations of disease progression. In this work we have developed this multi-scale model that includes data such as patient's tumour geometry, cellularity, vascularization, genetics and type of chemotherapy treatment, and integrated it into an online platform that runs the simulations on a high-performance computation cluster using Onedata and Kubernetes technologies. This infrastructure will allow clinicians to optimise treatment regimens and reduce the number of costly and time-consuming clinical trials. This manuscript outlines the challenging framework's model architecture, data workflow, hypothesis, and resources employed in its development.

Computer-assisted drug discovery of potential natural inhibitors of the SARS-CoV-2 RNA-dependent RNA polymerase through a multi-phase in silico approach

BACKGROUND

The COVID-19 pandemic has led to significant loss of life and economic disruption worldwide. Currently, there are limited effective treatments available for this disease. SARS-CoV-2 RNA-dependent RNA polymerase (SARS-CoV-2 RdRp) has been identified as a potential target for drug development against COVID-19. Natural products have been shown to possess antiviral properties, making them a promising source for developing drugs against SARS-CoV-2.

OBJECTIVES

The objective of this study is to identify the most effective natural inhibitors of SARS-CoV-2 RdRp among a set of 4924 African natural products using a multi-phase in silico approach.

METHODS

The study utilized remdesivir (RTP), the co-crystallized ligand of RdRp, as a starting point to select compounds that have the most similar chemical structures among the examined set of compounds. Molecular fingerprints and structure similarity studies were carried out in the first part of the study. The second part of the study included molecular docking against SARS-CoV-2 RdRp (PDB ID: 7BV2) and Molecular Dynamics (MD) simulations including the calculation of RMSD, RMSF, Rg, SASA, hydrogen bonding, and PLIP. Moreover, the calculations of Molecular mechanics with generalised Born and surface area solvation (MM-GBSA) Lennard-Jones and Columbic electrostatic interaction energies have been conducted. Additionally, in silico ADMET and toxicity studies were performed to examine the drug likeness degrees of the selected compounds.

RESULTS

Eight compounds were identified as the most effective natural inhibitors of SARS-CoV-2 RdRp. These compounds are kaempferol 3-galactoside, kaempferol 3-O-β-D-glucopyranoside, mangiferin methyl ether, luteolin 7-O-β-D-glucopyranoside, quercetin-O-β-D-3-glucopyranoside, 1-methoxy-3-indolylmethyl glucosinolate, naringenin, and asphodelin A 4'-O-β-D-glucopyranoside.

CONCLUSION

The results of this study provide valuable information for the development of natural product-based drugs against COVID-19. However, the elected compounds should be further studied in vitro and in vivo to confirm their efficacy in treating COVID-19.

Predicting binding between 55 cannabinoids and 4,799 biological targets by in silico methods

Recently, there has been an increase in cannabis-derived products being marketed as foods, dietary supplements, and other consumer products. Cannabis contains over a hundred cannabinoids, many of which have unknown physiological effects. Since there are large numbers of cannabinoids, and many are not commercially available for in vitro testing, an in silico tool (Chemotargets Clarity software) was used to predict binding between 55 cannabinoids and 4,799 biological targets (enzymes, ion channels, receptors, and transporters). This tool relied on quantitative structure activity relationships (QSAR), structural similarity, and other approaches to predict binding. From this screening, 827 cannabinoid-target binding pairs were predicted, which included 143 unique targets. Many cannabinoids sharing core structures (cannabinoid "types") had similar binding profiles, whereas most cannabinoids containing carboxylic acid groups were similar without regards to their core structure. For some of the binding predictions (43), in vitro binding data were available, and they agreed well with in silico binding data (median fourfold difference in binding concentrations). Finally, clinical adverse effects associated with 22 predicted targets were identified from an online database (Clarivate Off-X), providing important insights on potential human health hazards. Overall, in silico biological target predictions are a rapid means to identify potential hazards due to cannabinoid-target interactions, and the data can be used to prioritize subsequent in vitro and in vivo testing.

Picamilon, a γ-aminobutyric acid (GABA) analogue and marketed nootropic, is inactive against 50 biological targets

Picamilon is an analogue of the neurotransmitter γ-aminobutyric acid (GABA), which is marketed as a nootropic claiming to enhance cognition. There is a lack of in silico, in vitro and in vivo data on the safety of picamilon. Therefore, to ascertain potential physiological effects of picamilon, it was screened against 50 safety-related biological targets (receptors, ion channels, enzymes and transporters) by in silico and in vitro methods. Using two in silico tools, picamilon was not predicted to bind to the targets. Similarly, picamilon exhibited weak or no binding to the targets when measured in vitro at 10 μM. Overall, this data shows that picamilon, although structurally similar to other GABA analogues, has a different biological target binding profile. Picamilon's lack of binding to the 50 targets fills important data gaps among GABA analogues, a group of structurally related substances found in drugs and other consumer products.

Environmental levels of carbaryl impair zebrafish larvae behaviour: The potential role of ADRA2B and HTR2B

The insecticide carbaryl is commonly found in indirectly exposed freshwater ecosystems at low concentrations considered safe for fish communities. In this study, we showed that after only 24 h of exposure to environmental concentrations of carbaryl (0.066-660 ng/L), zebrafish larvae exhibit impairments in essential behaviours. Interestingly, the observed behavioural effects induced by carbaryl were acetylcholinesterase-independent. To elucidate the molecular initiating event that resulted in the observed behavioural effects, in silico predictions were followed by in vitro validation. We identified two target proteins that potentially interacted with carbaryl, the α2B adrenoceptor (ADRA2B) and the serotonin 2B receptor (HTR2B). Using a pharmacological approach, we then tested the hypothesis that carbaryl had antagonistic interactions with both receptors. Similar to yohimbine and SB204741, which are prototypic antagonists of ADRA2B and HTR2B, respectively, carbaryl increased the heart rate of zebrafish larvae. When we compared the behavioural effects of a 24-h exposure to these pharmacological antagonists with those of carbaryl, a high degree of similarity was found. These results strongly suggest that antagonism of both ADRA2B and HTR2B is the molecular initiating event that leads to adverse outcomes in zebrafish larvae that have undergone 24 h of exposure to environmentally relevant levels of carbaryl.

Evolution of kinase polypharmacology across HSP90 drug discovery

Most small molecules interact with several target proteins but this polypharmacology is seldom comprehensively investigated or explicitly exploited during drug discovery. Here, we use computational and experimental methods to identify and systematically characterize the kinase cross-pharmacology of representative HSP90 inhibitors. We demonstrate that the resorcinol clinical candidates ganetespib and, to a lesser extent, luminespib, display unique off-target kinase pharmacology as compared with other HSP90 inhibitors. We also demonstrate that polypharmacology evolved during the optimization to discover luminespib and that the hit, leads, and clinical candidate all have different polypharmacological profiles. We therefore recommend the computational and experimental characterization of polypharmacology earlier in drug discovery projects to unlock new multi-target drug design opportunities.

Androgenic activation, impairment of the monoaminergic system and altered behavior in zebrafish larvae exposed to environmental concentrations of fenitrothion

Fenitrothion is an organophosphorus insecticide usually found in aquatic ecosystems at concentrations in the range of low ng/L. In this manuscript we show that 24 h exposure to environmental concentrations of fenitrothion, from ng/L to low μg/L, altered basal locomotor activity, visual-motor response and acoustic/vibrational escape response of zebrafish larvae. Furthermore, fenitrothion and expression of gap43a, gfap, atp2b1a, and mbp exhibited a significant non-monotonic concentration-response relationship. Once determined that environmental concentrations of fenitrothion were neurotoxic for zebrafish larvae, a computational analysis identified potential protein targets of this compound. Some of the predictions, including interactions with acetylcholinesterase, monoamine-oxidases and androgen receptor (AR), were experimentally validated. Binding to AR was the most suitable candidate for molecular initiating event, as indicated by both the up-regulation of cyp19a1b and sult2st3 and the non-monotonic relationship found between fenitrothion and the observed responses. Finally, when the integrity of the monoaminergic system was evaluated, altered levels of L-DOPA, DOPAC, HVA and 5-HIAA were found, as well as a significant up-regulation of slc18a2 expression at the lowest concentrations of fenitrothion. These data strongly suggest that concentrations of fenitrothion commonly found in aquatic ecosystems present a significant environmental risk for fish communities.

Mutual Support of Ligand- and Structure-Based Approaches-To What Extent We Can Optimize the Power of Predictive Model? Case Study of Opioid Receptors

The process of modern drug design would not exist in the current form without computational methods. They are part of every stage of the drug design pipeline, supporting the search and optimization of new bioactive substances. Nevertheless, despite the great help that is offered by in silico strategies, the power of computational methods strongly depends on the input data supplied at the stage of the predictive model construction. The studies on the efficiency of the computational protocols most often focus on global efficiency. They use general parameters that refer to the whole dataset, such as accuracy, precision, mean squared error, etc. In the study, we examined machine learning predictions obtained for opioid receptors (mu, kappa, delta) and focused on cases for which the predictions were the most accurate and the least accurate. Moreover, by using docking, we tried to explain prediction errors. We attempted to develop a rule of thumb, which can help in the prediction of compound activity towards opioid receptors via docking, especially those that have been incorrectly predicted by machine learning. We found out that although the combination of ligand- and structure-based path can be beneficial for the prediction accuracy, there still remain cases that cannot be reliably predicted by any available modeling method. In addition to challenging ligand- and structure-based predictions, we also examined the role of the application of machine-learning methods in comparison to simple statistical methods for both standard ligand-based representations (molecular fingerprints) and interaction fingerprints. All approaches were confronted in both classification (where compounds were assigned to the group of active and inactive group constructed on the basis of K_i values) and regression (where exact K_i value was predicted) experiments.

The kinase polypharmacology landscape of clinical PARP inhibitors

Polypharmacology plays an important role in defining response and adverse effects of drugs. For some mechanisms, experimentally mapping polypharmacology is commonplace, although this is typically done within the same protein class. Four PARP inhibitors have been approved by the FDA as cancer therapeutics, yet a precise mechanistic rationale to guide clinicians on which to choose for a particular patient is lacking. The four drugs have largely similar PARP family inhibition profiles, but several differences at the molecular and clinical level have been reported that remain poorly understood. Here, we report the first comprehensive characterization of the off-target kinase landscape of four FDA-approved PARP drugs. We demonstrate that all four PARP inhibitors have a unique polypharmacological profile across the kinome. Niraparib and rucaparib inhibit DYRK1s, CDK16 and PIM3 at clinically achievable, submicromolar concentrations. These kinases represent the most potently inhibited off-targets of PARP inhibitors identified to date and should be investigated further to clarify their potential implications for efficacy and safety in the clinic. Moreover, broad kinome profiling is recommended for the development of PARP inhibitors as PARP-kinase polypharmacology could potentially be exploited to modulate efficacy and side-effect profiles.

Closing the Gap Between Therapeutic Use and Mode of Action in Remedial Herbs

The ancient tradition of taking parts of a plant or preparing plant extracts for treating certain discomforts and maladies has long been lacking a scientific rationale to support its preparation and still widespread use in several parts of the world. In an attempt to address this challenge, we collected and integrated data connecting metabolites, plants, diseases, and proteins. A mechanistic hypothesis is generated when a metabolite is known to be present in a given plant, that plant is known to be used to treat a certain disease, that disease is known to be linked to the function of a given protein, and that protein is finally known or predicted to interact with the original metabolite. The construction of plant–protein networks from mutually connected metabolites and diseases facilitated the identification of plausible mechanisms of action for plants being used to treat analgesia, hypercholesterolemia, diarrhea, catarrh, and cough. Additional concrete examples using both experimentally known and computationally predicted, and subsequently experimentally confirmed, metabolite–protein interactions to close the connection circle between metabolites, plants, diseases, and proteins offered further proof of concept for the validity and scope of the approach to generate mode of action hypotheses for some of the therapeutic uses of remedial herbs.

The Methodological Trends of Traditional Herbal Medicine Employing Network Pharmacology

Natural products, including traditional herbal medicine (THM), are known to exert their therapeutic effects by acting on multiple targets, so researchers have employed network pharmacology methods to decipher the potential mechanisms of THM. To conduct THM-network pharmacology (THM-NP) studies, researchers have employed different tools and databases for constructing and analyzing herb–compound–target networks. In this study, we attempted to capture the methodological trends in THM-NP research. We identified the tools and databases employed to conduct THM-NP studies and visualized their combinatorial patterns. We also constructed co-author and affiliation networks to further understand how the methodologies are employed among researchers. The results showed that the number of THM-NP studies and employed databases/tools have been dramatically increased in the last decade, and there are characteristic patterns in combining methods of each analysis step in THM-NP studies. Overall, the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) was the most frequently employed network pharmacology database in THM-NP studies. Among the processes involved in THM-NP research, the methodology for constructing a compound–target network has shown the greatest change over time. In summary, our analysis describes comprehensive methodological trends and current ideas in research design for network pharmacology researchers.

Novel Computational Approach to Predict Off-Target Interactions for Small Molecules

Most small molecule drugs interact with unintended, often unknown, biological targets and these off-target interactions may lead to both preclinical and clinical toxic events. Undesired off-target interactions are often not detected using current drug discovery assays, such as experimental polypharmacological screens. Thus, improvement in the early identification of off-target interactions represents an opportunity to reduce safety-related attrition rates during preclinical and clinical development. In order to better identify potential off-target interactions that could be linked to predictable safety issues, a novel computational approach to predict safety-relevant interactions currently not covered was designed and evaluated. These analyses, termed Off-Target Safety Assessment (OTSA), cover more than 7,000 targets (~35% of the proteome) and > 2,46,704 preclinical and clinical alerts (as of January 20, 2019). The approach described herein exploits a highly curated training set of >1 million compounds (tracking >20 million compound-structure activity relationship/SAR data points) with known in vitro activities derived from patents, journals, and publicly available databases. This computational process was used to predict both the primary and secondary pharmacological activities for a selection of 857 diverse small molecule drugs for which extensive secondary pharmacology data are readily available (456 discontinued and 401 FDA approved). The OTSA process predicted a total of 7,990 interactions for these 857 molecules. Of these, 3,923 and 4,067 possible high-scoring interactions were predicted for the discontinued and approved drugs, respectively, translating to an average of 9.3 interactions per drug. The OTSA process correctly identified the known pharmacological targets for >70% of these drugs, but also predicted a significant number of off-targets that may provide additional insight into observed in vivo effects. About 51.5% (2,025) and 22% (900) of these predicted high-scoring interactions have not previously been reported for the discontinued and approved drugs, respectively, and these may have a potential for repurposing efforts. Moreover, for both drug categories, higher promiscuity was observed for compounds with a MW range of 300 to 500, TPSA of ~200, and clogP ≥7. This computation also revealed significantly lower promiscuity (i.e., number of confirmed off-targets) for compounds with MW > 700 and MW<200 for both categories. In addition, 15 internal small molecules with known off-target interactions were evaluated. For these compounds, the OTSA framework not only captured about 56.8% of in vitro confirmed off-target interactions, but also identified the right pharmacological targets for 14 compounds as one of the top scoring targets. In conclusion, the OTSA process demonstrates good predictive performance characteristics and represents an additional tool with utility during the lead optimization stage of the drug discovery process. Additionally, the computed physiochemical properties such as clogP (i.e., lipophilicity), molecular weight, pKa and logS (i.e., solubility) were found to be statistically different between the approved and discontinued drugs, but the internal compounds were close to the approved drugs space in most part.

Dual Inhibitors of PARPs and ROCKs

Recent network and system biology analyses suggest that most complex diseases are regulated by robust and highly interconnected pathways that could be better modulated by small molecules binding to multiple biological targets. These pieces of evidence recently led to devote efforts on identifying single chemical entities that bind to two different disease-relevant targets. Here, we first predicted in silico and later confirmed in vitro that UPF 1069, a known bioactive poly(ADP-ribose) polymerase-1/2 (PARP1/2) molecule, and hydroxyfasudil, a known bioactive Rho-associated protein kinase-1/2 (ROCK1/2) molecule, have low-micromolar cross-affinity for ROCK1/2 and PARP1/2, respectively. These molecules can now be regarded as chemical seeds from which pharmacological tools could be generated to study the impact of dual inhibition of PARPs and ROCKs in preclinical models of a variety of complex diseases where both targets are involved.

Artificial Intelligence in Drug Design

Artificial Intelligence (AI) plays a pivotal role in drug discovery. In particular artificial neural networks such as deep neural networks or recurrent networks drive this area. Numerous applications in property or activity predictions like physicochemical and ADMET properties have recently appeared and underpin the strength of this technology in quantitative structure-property relationships (QSPR) or quantitative structure-activity relationships (QSAR). Artificial intelligence in de novo design drives the generation of meaningful new biologically active molecules towards desired properties. Several examples establish the strength of artificial intelligence in this field. Combination with synthesis planning and ease of synthesis is feasible and more and more automated drug discovery by computers is expected in the near future.

Screening Strategies and Methods for Better Off-Target Liability Prediction and Identification of Small-Molecule Pharmaceuticals

Pharmaceutical discovery and development is a long and expensive process that, unfortunately, still results in a low success rate, with drug safety continuing to be a major impedance. Improved safety screening strategies and methods are needed to more effectively fill this critical gap. Recent advances in informatics are now making it possible to manage bigger data sets and integrate multiple sources of screening data in a manner that can potentially improve the selection of higher-quality drug candidates. Integrated screening paradigms have become the norm in Pharma, both in discovery screening and in the identification of off-target toxicity mechanisms during later-stage development. Furthermore, advances in computational methods are making in silico screens more relevant and suggest that they may represent a feasible option for augmenting the current screening paradigm. This paper outlines several fundamental methods of the current drug screening processes across Pharma and emerging techniques/technologies that promise to improve molecule selection. In addition, the authors discuss integrated screening strategies and provide examples of advanced screening paradigms.

Active learning for computational chemogenomics

Aim: Computational chemogenomics models the compound–protein interaction space, typically for drug discovery, where existing methods predominantly either incorporate increasing numbers of bioactivity samples or focus on specific subfamilies of proteins and ligands. As an alternative to modeling entire large datasets at once, active learning adaptively incorporates a minimum of informative examples for modeling, yielding compact but high quality models. Results/methodology: We assessed active learning for protein/target family-wide chemogenomic modeling by replicate experiment. Results demonstrate that small yet highly predictive models can be extracted from only 10–25% of large bioactivity datasets, irrespective of molecule descriptors used. Conclusion: Chemogenomic active learning identifies small subsets of ligand–target interactions in a large screening database that lead to knowledge discovery and highly predictive models.

Integrated in Silico Methods for the Design and Optimization of Novel Drug Candidates

Although almost fully automated, the discovery of novel, effective, and safe drugs is still a long-term and highly expensive process. Consequently, the need for fleet, rational, and cost-efficient development of novel drugs is crucial, and nowadays the advanced in silico drug design methodologies seem to effectively meet these issues. The aim of this chapter is to provide a comprehensive overview of some of the current trends and advances in the in silico design of novel drug candidates with a special emphasis on 6-fluoroquinolone (6-FQ) antibacterials as potential novel Mycobacterium tuberculosis DNA gyrase inhibitors. In particular, the chapter covers some of the recent aspects of a wide range of in silico drug discovery approaches including multidimensional machine-learning methods, ligand-based and structure-based methodologies, as well as their proficient combination and integration into an intelligent virtual screening protocol for design and optimization of novel 6-FQ analogs.

Discovery of a New Class of Cathepsin K Inhibitors in Rhizoma Drynariae as Potential Candidates for the Treatment of Osteoporosis

Rhizoma Drynariae (RD), as one of the most common clinically used folk medicines, has been reported to exert potent anti-osteoporotic activity. The bioactive ingredients and mechanisms that account for its bone protective effects are under active investigation. Here we adopt a novel in silico target fishing method to reveal the target profile of RD. Cathepsin K (Ctsk) is one of the cysteine proteases that is over-expressed in osteoclasts and accounts for the increase in bone resorption in metabolic bone disorders such as postmenopausal osteoporosis. It has been the focus of target based drug discovery in recent years. We have identified two components in RD, Kushennol F and Sophoraflavanone G, that can potentially interact with Ctsk. Biological studies were performed to verify the effects of these compounds on Ctsk and its related bone resorption process, which include the use of in vitro fluorescence-based Ctsk enzyme assay, bone resorption pit formation assay, as well as Receptor Activator of Nuclear factor κB (NF-κB) ligand (RANKL)-induced osteoclastogenesis using murine RAW264.7 cells. Finally, the binding mode and stability of these two compounds that interact with Ctsk were determined by molecular docking and dynamics methods. The results showed that the in silico target fishing method could successfully identify two components from RD that show inhibitory effects on the bone resorption process related to protease Ctsk.

The In Vitro Pharmacological Profile of Drugs as a Proxy Indicator of Potential In Vivo Organ Toxicities

The potential of a drug to cause certain organ toxicities is somehow implicitly contained in its full pharmacological profile, provided the drug reaches and accumulates at the various organs where the different interacting proteins in its profile, both targets and off-targets, are expressed. Under this assumption, a computational approach was implemented to obtain a projected anatomical profile of a drug from its in vitro pharmacological profile linked to protein expression data across 47 organs. It was observed that the anatomical profiles obtained when using only the known primary targets of the drugs reflected roughly the intended organ targets. However, when both known and predicted secondary pharmacology was considered, the projected anatomical profiles of the drugs were able to clearly highlight potential organ off-targets. Accordingly, when applied to sets of drugs known to cause cardiotoxicity and hepatotoxicity, the approach is able to identify heart and liver, respectively, as the organs where the proteins in the pharmacological profile of the corresponding drugs are specifically expressed. When applied to a set of drugs linked to a risk of Torsades de Pointes, heart is again the organ clearly standing out from the rest and a potential protein profile hazard is proposed. The approach can be used as a proxy indicator of potential in vivo organ toxicities.

Large-Scale Predictive Drug Safety: From Structural Alerts to Biological Mechanisms

The recent explosion of data linking drugs, proteins, and pathways with safety events has promoted the development of integrative systems approaches to large-scale predictive drug safety. The added value of such approaches is that, beyond the traditional identification of potentially labile chemical fragments for selected toxicity end points, they have the potential to provide mechanistic insights for a much larger and diverse set of safety events in a statistically sound nonsupervised manner, based on the similarity to drug classes, the interaction with secondary targets, and the interference with biological pathways. The combined identification of chemical and biological hazards enhances our ability to assess the safety risk of bioactive small molecules with higher confidence than that using structural alerts only. We are still a very long way from reliably predicting drug safety, but advances toward gaining a better understanding of the mechanisms leading to adverse outcomes represent a step forward in this direction.

In silico design and analysis of a new hyperglycosylated analog of erythropoietin to improve drug efficacy

Background:

The enhancement of glycosylation by applying glycoengineering approaches has become widely used to boost properties for protein therapeutics. The objective of this work was to engineer a new hyperglycosylated analog of erythropoietin (EPO) with appropriately targeted N-linked carbohydrates through bioinformatics tools.

Materials and Methods:

The EPO protein sequence was retrieved from NCBI protein sequence database. Prediction of N-glycosylation sites for the target protein was done using the prediction server, NetNGlyc. The three-dimensional model of glycoengineered EPO (named as kypoetin) was constructed using the homology modeling program. Ramchandran plot obtained from PROCHECK server was used to check stereochemical property. Meanwhile, 3D model of kypoetin with attached N-carbohydrates was built up using the GlyProt server.

Results:

In the new modified analog, three additional N-glycosylation sites at amino-acid positions 30, 34 and 86 were inserted. Ramchandran plot analysis showed 81.6% of the residues in the most favored region, 15.6% in the additional allowed, 1.4% in the generously allowed regions and 1.4% in the disallowed region. 3D structural modeling showed that attached carbohydrates were on the proper spatial position. The whole solvent accessible surface areas of kypoetin (15132.69) were higher than EPO (9938.62).

Conclusions:

Totally, various model evaluation methods indicated that the glycoengineered version of EPO had considerably good geometry and acceptable profiles for clinical studies and could be considered as the effective drug.

Chemometrics approach for the prediction of structure-activity relationship for membrane transporter bilitranslocase

Membrane transport proteins are essential for cellular uptake of numerous salts, nutrients and drugs. Bilitranslocase is a transporter, specific for water-soluble organic anions, and is the only known carrier of nucleotides and nucleotide-like compounds. Experimental data of bilitranslocase ligand specificity for 120 compounds were used to construct classification models using counter-propagation artificial neural networks (CP-ANNs) and support vector machines (SVMs). A subset of active compounds with experimentally determined transport rates was used to build predictive QSAR models for estimation of transport rates of unknown compounds. Several modelling methods and techniques were applied, i.e. CP-ANN, genetic algorithm, self-organizing mapping and multiple linear regression method. The best predictions were achieved using CP-ANN coupled with a genetic algorithm, with the external validation parameter QV(2) of 0.96. The applicability domains of the models were defined to determine the chemical space in which reliable predictions can be obtained. The models were applied for the estimation of bilitranslocase transport activity for two sets of pharmaceutically interesting compounds, antioxidants and antiprions. We found that the relative planarity and a high potential for hydrogen bond formation are the common structural features of anticipated substrates of bilitranslocase. These features may serve as guidelines in the design of new pharmaceuticals transported by bilitranslocase.

Design of a general-purpose European compound screening library for EU-OPENSCREEN

This work describes a collaborative effort to define and apply a protocol for the rational selection of a general-purpose screening library, to be used by the screening platforms affiliated with the EU-OPENSCREEN initiative. It is designed as a standard source of compounds for primary screening against novel biological targets, at the request of research partners. Given the general nature of the potential applications of this compound collection, the focus of the selection strategy lies on ensuring chemical stability, absence of reactive compounds, screening-compliant physicochemical properties, loose compliance to drug-likeness criteria (as drug design is a major, but not exclusive application), and maximal diversity/coverage of chemical space, aimed at providing hits for a wide spectrum of drugable targets. Finally, practical availability/cost issues cannot be avoided. The main goal of this publication is to inform potential future users of this library about its conception, sources, and characteristics. The outline of the selection procedure, notably of the filtering rules designed by a large committee of European medicinal chemists and chemoinformaticians, may be of general methodological interest for the screening/medicinal chemistry community. The selection task of 200K molecules out of a pre-filtered set of 1.4M candidates was shared by five independent European research groups, each picking a subset of 40K compounds according to their own in-house methodology and expertise. An in-depth analysis of chemical space coverage of the library serves not only to characterize the collection, but also to compare the various chemoinformatics-driven selection procedures of maximal diversity sets. Compound selections contributed by various participating groups were mapped onto general-purpose self-organizing maps (SOMs) built on the basis of marketed drugs and bioactive reference molecules. In this way, the occupancy of chemical space by the EU-OPENSCREEN library could be directly compared with distributions of known bioactives of various classes. This mapping highlights the relevance of the selection and shows how the consensus reached by merging the five different 40K selections contributes to achieve this relevance. The approach also allows one to readily identify subsets of target- or target-class-oriented compounds from the EU-OPENSCREEN library to suit the needs of the diverse range of potential users. The final EU-OPENSCREEN library, assembled by merging five independent selections of 40K compounds from various expert groups, represents an excellent example of a Europe-wide collaborative effort toward the common objective of building best-in-class European open screening platforms.

Predictive in silico off-target profiling in drug discovery

Drug action can be rationalized as interaction of a molecule with proteins in a regulatory network of targets from a specific biological system. Both drug and side effects are often governed by interaction of the drug molecule with many, often unrelated, targets. Accordingly, arrays of protein–ligand interaction data from numerous in vitro profiling assays today provide growing evidence of polypharmacological drug interactions, even for marketed drugs. In vitro off-target profiling has therefore become an important tool in early drug discovery to learn about potential off-target liabilities, which are sometimes beneficial, but more often safety relevant. The rapidly developing field of in silico profiling approaches is complementing in vitro profiling. These approaches capitalize from large amounts of biochemical data from multiple sources to be exploited for optimizing undesirable side effects in pharmaceutical research. Therefore, current in silico profiling models are nowadays perceived as valuable tools in drug discovery, and promise a platform to support optimally informed decisions.

Effects of BDE-209 contaminated sediments on zebrafish development and potential implications to human health

Polybrominated diphenyl ethers are compounds widely used as flame-retardants, which are of increasing environmental concern due to their persistence, and potential adverse effects. This study had two objectives. First, we assessed if BDE-209 in sediment was bioavailable and bioaccumulated into zebrafish embryos. Secondly, we assessed the potential impact on human and environmental health of bioavailable BDE-209 using human in vitro cell assays and zebrafish embryos. Zebrafish were exposed from 4h to 8days post-fertilization to sediments spiked with 12.5mg/kg of BDE-209. Zebrafish larvae accumulated ten fold more BDE-209 than controls in unspiked sediment after 8days. BDE-209 impacted expression of neurological pathways and altered behavior of larvae, although BDE-209 had no visible affect on thyroid function or motoneuron and neuromast development. Zebrafish data and in silico predictions suggested that BDE-209 would also interact with key human transcription factors and receptors. We therefore tested these predictions using mammalian in vitro assays. BDE-209 activated human aryl hydrocarbon receptor, peroxisome proliferator activating receptors, CF/b-cat, activator protein 1, Oct-MLP, and the estrogen receptor-related alpha (ERRα) receptor in cell-based assays. BDE-209 also inhibited human acetylcholinesterase activity. The observation that BDE-209 can be bioaccumulated from contaminated sediment highlights the need to consider this as a potential environmental exposure route. Once accumulated, our data also show that BDE-209 has the potential to cause impacts on both human and environmental health.

Prediction of the P. falciparum target space relevant to malaria drug discovery

Malaria is still one of the most devastating infectious diseases, affecting hundreds of millions of patients worldwide. Even though there are several established drugs in clinical use for malaria treatment, there is an urgent need for new drugs acting through novel mechanisms of action due to the rapid development of resistance. Resistance emerges when the parasite manages to mutate the sequence of the drug targets to the extent that the protein can still perform its function in the parasite but can no longer be inhibited by the drug, which then becomes almost ineffective. The design of a new generation of malaria drugs targeting multiple essential proteins would make it more difficult for the parasite to develop full resistance without lethally disrupting some of its vital functions. The challenge is then to identify which set of Plasmodium falciparum proteins, among the millions of possible combinations, can be targeted at the same time by a given chemotype. To do that, we predicted first the targets of the close to 20,000 antimalarial hits identified recently in three independent phenotypic screening campaigns. All targets predicted were then projected onto the genome of P. falciparum using orthologous relationships. A total of 226 P. falciparum proteins were predicted to be hit by at least one compound, of which 39 were found to be significantly enriched by the presence and degree of affinity of phenotypically active compounds. The analysis of the chemically compatible target combinations containing at least one of those 39 targets led to the identification of a priority set of 64 multi-target profiles that can set the ground for a new generation of more robust malaria drugs.

Argyreia nervosa (Burm. f.): receptor profiling of lysergic acid amide and other potential psychedelic LSD-like compounds by computational and binding assay approaches

ETHNOPHARMACOLOGICAL RELEVANCE

The convolvulacea Argyreia nervosa (Burm. f.) is well known as an important medical plant in the traditional Ayurvedic system of medicine and it is used in numerous diseases (e.g. nervousness, bronchitis, tuberculosis, arthritis, and diabetes). Additionally, in the Indian state of Assam and in other regions Argyreia nervosa is part of the traditional tribal medicine (e.g. the Santali people, the Lodhas, and others). In the western hemisphere, Argyreia nervosa has been brought in attention as so called "legal high". In this context, the seeds are used as source of the psychoactive ergotalkaloid lysergic acid amide (LSA), which is considered as the main active ingredient.

AIM OF THE STUDY

As the chemical structure of LSA is very similar to that of lysergic acid diethylamide (LSD), the seeds of Argyreia nervosa (Burm. f.) are often considered as natural substitute of LSD. In the present study, LSA and LSD have been compared concerning their potential pharmacological profiles based on the receptor binding affinities since our recent human study with four volunteers on p.o. application of Argyreia nervosa seeds has led to some ambiguous effects.

MATERIAL AND METHODS

In an initial step computer-aided in silico prediction models on receptor binding were employed to screen for serotonin, norepinephrine, dopamine, muscarine, and histamine receptor subtypes as potential targets for LSA. In addition, this screening was extended to accompany ergotalkaloids of Argyreia nervosa (Burm. f.). In a verification step, selected LSA screening results were confirmed by in vitro binding assays with some extensions to LSD.

RESULTS

In the in silico model LSA exhibited the highest affinity with a pKi of about 8.0 at α1A, and α1B. Clear affinity with pKi>7 was predicted for 5-HT1A, 5-HT1B, 5-HT1D, 5-HT6, 5-HT7, and D2. From these receptors the 5-HT1D subtype exhibited the highest pKi with 7.98 in the prediction model. From the other ergotalkaloids, agroclavine and festuclavine also seemed to be highly affine to the 5-HT1D-receptor with pKi>8. In general, the ergotalkaloids of Argyreia nervosa seem to prefer serotonin and dopamine receptors (pKi>7). However, with exception of ergometrine/ergometrinine only for 5-HT3A, and histamine H2 and H4 no affinities were predicted. Compared to LSD, LSA exhibited lower binding affinities in the in vitro binding assays for all tested receptor subtypes. However, with a pKi of 7.99, 7.56, and 7.21 a clear affinity for 5-HT1A, 5-HT2, and α2 could be demonstrated. For DA receptor subtypes and the α1-receptor the pKi ranged from 6.05 to 6.85.

CONCLUSION

Since the psychedelic activity of LSA in the recent human study was weak and although LSA from Argyreia nervosa is often considered as natural exchange for LSD, LSA should not be regarded as LSD-like psychedelic drug. However, vegetative side effects and psychotropic effects may be triggered by serotonin or dopamine receptor subtypes.

Chemoisosterism in the proteome

The concept of chemoisosterism of protein environments is introduced as the complementary property to bioisosterism of chemical fragments. In the same way that two chemical fragments are considered bioisosteric if they can bind to the same protein environment, two protein environments will be considered chemoisosteric if they can interact with the same chemical fragment. The basis for the identification of chemoisosteric relationships among protein environments was the increasing amount of crystal structures available currently for protein-ligand complexes. It is shown that one can recover the right location and orientation of chemical fragments constituting the native ligand in a nuclear receptor structure by using only chemoisosteric environments present in enzyme structures. Examples of the potential applicability of chemoisosterism in fragment-based drug discovery are provided.

Structural insights into the molecular basis of the ligand promiscuity

Selectivity is a key factor in drug development. In this paper, we questioned the Protein Data Bank to better understand the reasons for the promiscuity of bioactive compounds. We assembled a data set of >1000 pairs of three-dimensional structures of complexes between a "drug-like" ligand (as its physicochemical properties overlap that of approved drugs) and two distinct "druggable" protein targets (as their binding sites are likely to accommodate "drug-like" ligands). Studying the similarity between the ligand-binding sites in the different targets revealed that the lack of selectivity of a ligand can be due (i) to the fact that Nature has created the same binding pocket in different proteins, which do not necessarily have otherwise sequence or fold similarity, or (ii) to specific characteristics of the ligand itself. In particular, we demonstrated that many ligands can adapt to different protein environments by changing their conformation, by using different chemical moieties to anchor to different targets, or by adopting unusual extreme binding modes (e.g., only apolar contact between the ligand and the protein, even though polar groups are present on the ligand or at the protein surface). Lastly, we provided new elements in support to the recent studies which suggest that the promiscuity of a ligand might be inferred from its molecular complexity.

A chemocentric approach to the identification of cancer targets

A novel chemocentric approach to identifying cancer-relevant targets is introduced. Starting with a large chemical collection, the strategy uses the list of small molecule hits arising from a differential cytotoxicity screening on tumor HCT116 and normal MRC-5 cell lines to identify proteins associated with cancer emerging from a differential virtual target profiling of the most selective compounds detected in both cell lines. It is shown that this smart combination of differential in vitro and in silico screenings (DIVISS) is capable of detecting a list of proteins that are already well accepted cancer drug targets, while complementing it with additional proteins that, targeted selectively or in combination with others, could lead to synergistic benefits for cancer therapeutics. The complete list of 115 proteins identified as being hit uniquely by compounds showing selective antiproliferative effects for tumor cell lines is provided.

Combination of biological screening in a cellular model of viral latency and virtual screening identifies novel compounds that reactivate HIV-1

Although highly active antiretroviral therapy (HAART) has converted HIV into a chronic disease, a reservoir of HIV latently infected resting T cells prevents the eradication of the virus from patients. To achieve eradication, HAART must be combined with drugs that reactivate the dormant viruses. We examined this problem in an established model of HIV postintegration latency by screening a library of small molecules. Initially, we identified eight molecules that reactivated latent HIV. Using them as templates, additional hits were identified by means of similarity-based virtual screening. One of those hits, 8-methoxy-6-methylquinolin-4-ol (MMQO), proved to be useful to reactivate HIV-1 in different cellular models, especially in combination with other known reactivating agents, without causing T-cell activation and with lower toxicity than that of the initial hits. Interestingly, we have established that MMQO produces Jun N-terminal protein kinase (JNK) activation and enhances the T-cell receptor (TCR)/CD3 stimulation of HIV-1 reactivation from latency but inhibits CD3-induced interleukin-2 (IL-2) and tumor necrosis factor alpha (TNF-α) gene transcription. Moreover, MMQO prevents TCR-induced cell cycle progression and proliferation in primary T cells. The present study documents that the combination of biological screening in a cellular model of viral latency with virtual screening is useful for the identification of novel agents able to reactivate HIV-1. Moreover, we set the bases for a hypothetical therapy to reactivate latent HIV by combining MMQO with physiological or pharmacological TCR/CD3 stimulation.

Target Profile Prediction and Practical Evaluation of a Biginelli-Type Dihydropyrimidine Compound Library

We present a self-organizing map (SOM) approach to predicting macromolecular targets for combinatorial compound libraries. The aim was to study the usefulness of the SOM in combination with a topological pharmacophore representation (CATS) for selecting biologically active compounds from a virtual combinatorial compound collection, taking the multi-component Biginelli dihydropyrimidine reaction as an example. We synthesized a candidate compound from this library, for which the SOM model suggested inhibitory activity against cyclin-dependent kinase 2 (CDK2) and other kinases. The prediction was confirmed in an in vitro panel assay comprising 48 human kinases. We conclude that the computational technique may be used for ligand-based in silico pharmacology studies, off-target prediction, and drug re-purposing, thereby complementing receptor-based approaches.

Multi-targeted activity of maslinic acid as an antimalarial natural compound

Most drugs against malaria that are available or under development target a single process of the parasite infective cycle, favouring the appearance of resistant mutants which are easily spread in areas under chemotherapeutic treatments. Maslinic acid (MA) is a low toxic natural pentacyclic triterpene for which a wide variety of biological and therapeutic activities have been reported. Previous work revealed that Plasmodium falciparum erythrocytic cultures were inhibited by MA, which was able to hinder the maturation from ring to schizont stage and, as a consequence, prevent the release of merozoites and the subsequent invasion. We show here that MA effectively inhibits the proteolytic processing of the merozoite surface protein complex, probably by inhibition of PfSUB1. In addition, MA was also found to inhibit metalloproteases of the M16 family by a non-chelating mechanism, suggesting the possible hindrance of plasmodial metalloproteases belonging to that family, such as falcilysin and apicoplast peptide-processing proteases. Finally, in silico target screening was used to search for other potential binding targets that may have remained undetected. Among the targets identified, the method recovered two for which experimental activity could be confirmed, and suggested several putative new targets to which MA could have affinity. One of these unreported targets, phospholipase A2, was shown to be partially inhibited by MA. These results suggest that MA may behave as a multi-targeted drug against the intra-erythrocytic cycle of Plasmodium, providing a new tool to investigate the synergistic effect of inhibiting several unrelated processes with a single compound, a new concept in antimalarial research.