In Silico Receptorome Screening of Antipsychotic Drugs

Artificial Intelligence/Machine Learning-Driven Small Molecule Repurposing via Off-Target Prediction and Transcriptomics

The process of discovering small molecule drugs involves screening numerous compounds and optimizing the most promising ones, both in vitro and in vivo. However, approximately 90% of these optimized candidates fail during trials due to unexpected toxicity or insufficient efficacy. Current concepts with respect to drug-protein interactions suggest that each small molecule interacts with an average of 6-11 targets. This implies that approved drugs and even discontinued compounds could be repurposed by leveraging their interactions with unintended targets. Therefore, we developed a computational repurposing framework for small molecules, which combines artificial intelligence/machine learning (AI/ML)-based and chemical similarity-based target prediction methods with cross-species transcriptomics information. This repurposing methodology incorporates eight distinct target prediction methods, including three machine learning methods. By using multiple orthogonal methods for a "dataset" composed of 2766 FDA-approved drugs targeting multiple therapeutic target classes, we identified 27,371 off-target interactions involving 2013 protein targets (i.e., an average of around 10 interactions per drug). Relative to the drugs in the dataset, we identified 150,620 structurally similar compounds. The highest number of predicted interactions were for drugs targeting G protein-coupled receptors (GPCRs), enzymes, and kinases with 10,648, 4081, and 3678 interactions, respectively. Notably, 17,283 (63%) of the off-target interactions have been confirmed in vitro. Approximately 4000 interactions had an IC50 of <100 nM for 1105 FDA-approved drugs and 1661 interactions had an IC50 of <10 nM for 696 FDA-approved drugs. Together, the confirmation of numerous predicted interactions and the exploration of tissue-specific expression patterns in human and animal tissues offer insights into potential drug repurposing for new therapeutic applications.

Trips and neurotransmitters: Discovering principled patterns across 6850 hallucinogenic experiences

Psychedelics probably alter states of consciousness by disrupting how the higher association cortex governs bottom-up sensory signals. Individual hallucinogenic drugs are usually studied in participants in controlled laboratory settings. Here, we have explored word usage in 6850 free-form testimonials about 27 drugs through the prism of 40 neurotransmitter receptor subtypes, which were then mapped to three-dimensional coordinates in the brain via their gene transcription levels from invasive tissue probes. Despite high interindividual variability, our pattern-learning approach delineated how drug-induced changes of conscious awareness are linked to cortex-wide anatomical distributions of receptor density proxies. Each discovered receptor-experience factor spanned between a higher-level association pole and a sensory input pole, which may relate to the previously reported collapse of hierarchical order among large-scale networks. Coanalyzing many psychoactive molecules and thousands of natural language descriptions of drug experiences, our analytical framework finds the underlying semantic structure and maps it directly to the brain.

A new strategy for discovering effective substances and mechanisms of traditional Chinese medicine based on standardized drug containing plasma and the absorbed ingredients composition, a case study of Xian-Ling-Gu-Bao capsules

ETHNOPHARMACOLOGICAL RELEVANCE

The overall therapeutic effect of traditional Chinese medicine formulae (TCMF) was achieved by the interactions of multiple components with multiple targets. However, current pharmacology research strategies have struggled to identify effective substance groups and encountered challenges in elucidating the underlying mechanisms of TCMF.

AIM

In this study, a comprehensive strategy was proposed and applied to elucidate the interactions of the multiple components that underlie the functions of the famous TCMF: Xian-Ling-Gu-Bao (XLGB) capsule on bone metabolism in vivo and to elucidate the molecular mechanisms underlying the effects of XLGB on bone cells, especially on osteoblasts.

METHODS

The efficacy of XLGB in the protection against bones loss in ovariectomized (OVX) rats was confirmed by Micro-CT analysis. The anti-osteoporosis mechanism involved in the systemic regulatory actions of XLGB was elucidated by transcriptome sequencing analysis on bone marrow mesenchymal stem cells isolated from OVX rats. Moreover, the components absorbed in XLGB-treated plasma were characterized by mass spectrometry analysis, and subsequently, a standardized preparation process of drug-containing plasma was established. The synergistic osteogenic effect of the multiple components in plasma was investigated by a combination and then knockout of components using pre-osteoblast MC3T3-E1 cells. In order to decipher the underlying mechanism of XLGB, the targets of the absorbed components on bone were predicted by target prediction and network pharmacology analysis, then several interactions were validated by biochemical and cell-based assay.

RESULTS

A total of 18 genes, including HDC, CXCL1/2, TNF, IL6 and Il1b, were newly found to be the major target genes regulated by XLGB. Interestingly, we found that a combination of the three absorbed components, i.e. MSP, rather than their single form at the same concentration, stimulated the formation of calcified nodules in MC3T3-E1 cells, suggesting a synergistic effect of these components. Besides, target prediction and experimental validation confirmed the binding affinity of corylin and icaritin for estrogen receptor α and β, the inhibitory activity of isobavachin and isobavachalcone on glycogen synthase kinase-3β, and the inhibitory activity of isobavachalcone on cathepsin K. The cell-based assay further confirmed the result of the biochemical assay. A network that integrated absorbed components of XLGB-targets-perturbation genes-pathways against osteoporosis was established.

CONCLUSION

Our current study provides a new systemic strategy for discovering active ingredient groups of TCM formulae and understanding their underlying mechanisms.

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.

Evaluating kratom alkaloids using PHASE

Kratom is a botanical substance that is marketed and promoted in the US for pharmaceutical opioid indications despite having no US Food and Drug Administration approved uses. Kratom contains over forty alkaloids including two partial agonists at the mu opioid receptor, mitragynine and 7-hydroxymitragynine, that have been subjected to the FDA's scientific and medical evaluation. However, pharmacological and toxicological data for the remaining alkaloids are limited. Therefore, we applied the Public Health Assessment via Structural Evaluation (PHASE) protocol to generate in silico binding profiles for 25 kratom alkaloids to facilitate the risk evaluation of kratom. PHASE demonstrates that kratom alkaloids share structural features with controlled opioids, indicates that several alkaloids bind to the opioid, adrenergic, and serotonin receptors, and suggests that mitragynine and 7-hydroxymitragynine are the strongest binders at the mu opioid receptor. Subsequently, the in silico binding profiles of a subset of the alkaloids were experimentally verified at the opioid, adrenergic, and serotonin receptors using radioligand binding assays. The verified binding profiles demonstrate the ability of PHASE to identify potential safety signals and provide a tool for prioritizing experimental evaluation of high-risk compounds.

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.

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.

The Varieties of the Psychedelic Experience: A Preliminary Study of the Association Between the Reported Subjective Effects and the Binding Affinity Profiles of Substituted Phenethylamines and Tryptamines

Classic psychedelics are substances of paramount cultural and neuroscientific importance. A distinctive feature of psychedelic drugs is the wide range of potential subjective effects they can elicit, known to be deeply influenced by the internal state of the user ("set") and the surroundings ("setting"). The observation of cross-tolerance and a series of empirical studies in humans and animal models support agonism at the serotonin (5-HT)2A receptor as a common mechanism for the action of psychedelics. The diversity of subjective effects elicited by different compounds has been attributed to the variables of "set" and "setting," to the binding affinities for other 5-HT receptor subtypes, and to the heterogeneity of transduction pathways initiated by conformational receptor states as they interact with different ligands ("functional selectivity"). Here we investigate the complementary (i.e., not mutually exclusive) possibility that such variety is also related to the binding affinity for a range of neurotransmitters and monoamine transporters including (but not limited to) 5-HT receptors. Building on two independent binding affinity datasets (compared to "in silico" estimates) in combination with natural language processing tools applied to a large repository of reports of psychedelic experiences (Erowid's Experience Vaults), we obtained preliminary evidence supporting that the similarity between the binding affinity profiles of psychoactive substituted phenethylamines and tryptamines is correlated with the semantic similarity of the associated reports. We also showed that the highest correlation was achieved by considering the combined binding affinity for the 5-HT, dopamine (DA), glutamate, muscarinic and opioid receptors and for the Ca+ channel. Applying dimensionality reduction techniques to the reports, we linked the compounds, receptors, transporters and the Ca+ channel to distinct fingerprints of the reported subjective effects. To the extent that the existing binding affinity data is based on a low number of displacement curves that requires further replication, our analysis produced preliminary evidence consistent with the involvement of different binding sites in the reported subjective effects elicited by psychedelics. Beyond the study of this particular class of drugs, we provide a methodological framework to explore the relationship between the binding affinity profiles and the reported subjective effects of other psychoactive compounds.

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.

OCEAN: Optimized Cross rEActivity estimatioN

The prediction of molecular targets is highly beneficial during the drug discovery process, be it for off-target elucidation or deconvolution of phenotypic screens. Here, we present OCEAN, a target prediction tool exclusively utilizing publically available ChEMBL data. OCEAN uses a heuristics approach based on a validation set containing almost 1000 drug ← → target relationships. New ChEMBL data (ChEMBL20 as well as ChEMBL21) released after the validation was used for a prospective OCEAN performance check. The success rates of OCEAN to predict correctly the targets within the TOP10 ranks are 77% for recently marketed drugs and 62% for all new ChEMBL20 compounds and 51% for all new ChEMBL21 compounds. OCEAN is also capable of identifying polypharmacological compounds; the success rate for molecules simultaneously hitting at least two targets is 64% to be correctly predicted within the TOP10 ranks. The source code of OCEAN can be found at http://www.github.com/rdkit/OCEAN.

Accurate and efficient target prediction using a potency-sensitive influence-relevance voter

BACKGROUND

A number of algorithms have been proposed to predict the biological targets of diverse molecules. Some are structure-based, but the most common are ligand-based and use chemical fingerprints and the notion of chemical similarity. These methods tend to be computationally faster than others, making them particularly attractive tools as the amount of available data grows.

RESULTS

Using a ChEMBL-derived database covering 490,760 molecule-protein interactions and 3236 protein targets, we conduct a large-scale assessment of the performance of several target-prediction algorithms at predicting drug-target activity. We assess algorithm performance using three validation procedures: standard tenfold cross-validation, tenfold cross-validation in a simulated screen that includes random inactive molecules, and validation on an external test set composed of molecules not present in our database.

CONCLUSIONS

We present two improvements over current practice. First, using a modified version of the influence-relevance voter (IRV), we show that using molecule potency data can improve target prediction. Second, we demonstrate that random inactive molecules added during training can boost the accuracy of several algorithms in realistic target-prediction experiments. Our potency-sensitive version of the IRV (PS-IRV) obtains the best results on large test sets in most of the experiments. Models and software are publicly accessible through the chemoinformatics portal at http://chemdb.ics.uci.edu/.

Using quantitative systems pharmacology for novel drug discovery

INTRODUCTION

Over the past three decades, the predominant paradigm in drug discovery was designing selective ligands for a specific target to avoid unwanted side effects. However, in the last 5 years, the aim has shifted to take into account the biological network in which they interact. Quantitative and Systems Pharmacology (QSP) is a new paradigm that aims to understand how drugs modulate cellular networks in space and time, in order to predict drug targets and their role in human pathophysiology.

AREAS COVERED

This review discusses existing computational and experimental QSP approaches such as polypharmacology techniques combined with systems biology information and considers the use of new tools and ideas in a wider 'systems-level' context in order to design new drugs with improved efficacy and fewer unwanted off-target effects.

EXPERT OPINION

The use of network biology produces valuable information such as new indications for approved drugs, drug-drug interactions, proteins-drug side effects and pathways-gene associations. However, we are still far from the aim of QSP, both because of the huge effort needed to model precisely biological network models and the limited accuracy that we are able to reach with those. Hence, moving from 'one molecule for one target to give one therapeutic effect' to the 'big systems-based picture' seems obvious moving forward although whether our current tools are sufficient for such a step is still under debate.

Comparison of ultra-fast 2D and 3D ligand and target descriptors for side effect prediction and network analysis in polypharmacology

BACKGROUND AND PURPOSE

Some existing computational methods are used to infer protein targets of small molecules and can therefore be used to find new targets for existing drugs, with the goals of re-directing the molecule towards a different therapeutic purpose or explaining off-target effects due to multiple targeting. Inherent limitations, however, arise from the fact that chemical analogy is calculated on the basis of common frameworks or scaffolds and also because target information is neglected. The method we present addresses these issues by taking into account 3D information from both the ligand and the target.

EXPERIMENTAL APPROACH

ElectroShape is an established method for ultra-fast comparison of the shapes and charge distributions of ligands that is validated here for prediction of on-target activities, off-target profiles and adverse effects of drugs and drug-like molecules taken from the DrugBank database.

KEY RESULTS

The method is shown to predict polypharmacology profiles and relate targets from two complementary viewpoints (ligand- and target-based networks).

CONCLUSIONS AND IMPLICATIONS

The open-access web tool presented here (http://ub.cbm.uam.es/chemogenomics/) allows interactive navigation in a unified 'pharmacological space' from the viewpoints of both ligands and targets. It also enables prediction of pharmacological profiles, including likely side effects, for new compounds. We hope this web interface will help many pharmacologists to become aware of this new paradigm (up to now mostly used in the realm of the so-called 'chemical biology') and encourage its use with a view to revealing 'hidden' relationships between new and existing compounds and pharmacologically relevant targets.

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.

Comparative mode-of-action analysis following manual and automated phenotype detection in Xenopus laevis

Given the increasing utilization of phenotypic screens in drug discovery also the subsequent mechanism-of-action analysis gains increased attention.

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.

Computational profiling of bioactive compounds using a target-dependent composite workflow

Computational target fishing is a chemoinformatic method aimed at determining main and secondary targets of bioactive compounds in order to explain their mechanism of action, anticipate potential side effects, or repurpose existing drugs for novel therapeutic indications. Many existing successes in this area have been based on a use of a single computational method to estimate potentially new target-ligand associations. We herewith present an automated workflow using several methods to optimally browse target-ligand space according to existing knowledge on either ligand and target space under investigation. The protocol uses four ligand-based (SVM classification, SVR affinity prediction, nearest neighbors interpolation, shape similarity) and two structure-based approaches (docking, protein-ligand pharmacophore match) in series, according to well-defined ligand and target property checks. The workflow was remarkably accurate (72%) in identifying the main target of 189 clinical candidates and proposed two novel off-targets which could be experimentally validated. Rolofylline, an adenosine A1 receptor antagonist, was confirmed to inhibit phosphodiesterase 5 with a moderate affinity (IC50 = 13.8 μM). More interestingly, we describe a strong binding (IC50 = 142 nM) of a claimed selective phosphodiesterase 10 A inhibitor (PF-2545920) with the cysteinyl leukotriene type 1 G protein-coupled receptor.

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.

Identification of pim kinases as novel targets for PJ34 with confounding effects in PARP biology

Small molecules are widely used in chemical biology without complete knowledge of their target profile, at risk of deriving conclusions that ignore potential confounding effects from unknown off-target interactions. The prediction and further experimental confirmation of novel affinities for PJ34 on Pim1 (IC(50) = 3.7 μM) and Pim2 (IC(50) = 16 μM) serine/threonine kinases, together with their involvement in many of the processes relevant to PARP biology, questions the appropriateness of using PJ34 as a chemical tool to probe the biological role of PARP1 and PARP2 at the high micromolar concentrations applied in most studies.

Automated design of ligands to polypharmacological profiles

The clinical efficacy and safety of a drug is determined by its activity profile across many proteins in the proteome. However, designing drugs with a specific multi-target profile is both complex and difficult. Therefore methods to design drugs rationally a priori against profiles of several proteins would have immense value in drug discovery. Here we describe a new approach for the automated design of ligands against profiles of multiple drug targets. The method is demonstrated by the evolution of an approved acetylcholinesterase inhibitor drug into brain-penetrable ligands with either specific polypharmacology or exquisite selectivity profiles for G-protein-coupled receptors. Overall, 800 ligand-target predictions of prospectively designed ligands were tested experimentally, of which 75% were confirmed to be correct. We also demonstrate target engagement in vivo. The approach can be a useful source of drug leads when multi-target profiles are required to achieve either selectivity over other drug targets or a desired polypharmacology.

Identification of small molecule inhibitors of amyloid β-induced neuronal apoptosis acting through the imidazoline I(2) receptor

Aberrant activation of signaling pathways plays a pivotal role in central nervous system disorders, such as Alzheimer's disease (AD). Using a combination of virtual screening and experimental testing, novel small molecule inhibitors of tPA-mediated extracellular signal-regulated kinase (Erk)1/2 activation were identified that provide higher levels of neuroprotection from Aβ-induced apoptosis than Memantine, the most recently FDA-approved drug for AD treatment. Subsequent target deconvolution efforts revealed that they all share low micromolar affinity for the imidazoline I(2) receptor, while being devoid of any significant affinity to a list of AD-relevant targets, including the N-methyl-d-aspartate receptor (NMDAR), acetylcholinesterase (AChE), and monoamine oxidase B (MAO-B). Targeting the imidazoline I(2) receptor emerges as a new mechanism of action to inhibit tPA-induced signaling in neurons for the treatment of AD and other neurodegenerative diseases.

High-Throughput parallel blind Virtual Screening using BINDSURF

Background

Virtual Screening (VS) methods can considerably aid clinical research, predicting how ligands interact with drug targets. Most VS methods suppose a unique binding site for the target, usually derived from the interpretation of the protein crystal structure. However, it has been demonstrated that in many cases, diverse ligands interact with unrelated parts of the target and many VS methods do not take into account this relevant fact.

Results

We present BINDSURF, a novel VS methodology that scans the whole protein surface in order to find new hotspots, where ligands might potentially interact with, and which is implemented in last generation massively parallel GPU hardware, allowing fast processing of large ligand databases.

Conclusions

BINDSURF is an efficient and fast blind methodology for the determination of protein binding sites depending on the ligand, that uses the massively parallel architecture of GPUs for fast pre-screening of large ligand databases. Its results can also guide posterior application of more detailed VS methods in concrete binding sites of proteins, and its utilization can aid in drug discovery, design, repurposing and therefore help considerably in clinical research.

Large-Scale QSAR in Target Prediction and Phenotypic HTS Assessment

The advent of in silico compound target prediction offers a potential paradigm shift in how large compound collections are understood and used strategically in high-throughput screens (HTS). Specifically, phenotypic HTS hits may be annotated both with known targets and predicted targets using large-scale QSAR models, enabling a more sophisticated hit assessment. Efforts in massive bioactivity data integration and standardization is empowering such compound-target annotations. These approaches differ fundamentally from the traditional role of QSAR in lead optimization and binding affinity predictions to global, probabilistic target predictions for thousands of human proteins.

Assessing drug target association using semantic linked data

The rapidly increasing amount of public data in chemistry and biology provides new opportunities for large-scale data mining for drug discovery. Systematic integration of these heterogeneous sets and provision of algorithms to data mine the integrated sets would permit investigation of complex mechanisms of action of drugs. In this work we integrated and annotated data from public datasets relating to drugs, chemical compounds, protein targets, diseases, side effects and pathways, building a semantic linked network consisting of over 290,000 nodes and 720,000 edges. We developed a statistical model to assess the association of drug target pairs based on their relation with other linked objects. Validation experiments demonstrate the model can correctly identify known direct drug target pairs with high precision. Indirect drug target pairs (for example drugs which change gene expression level) are also identified but not as strongly as direct pairs. We further calculated the association scores for 157 drugs from 10 disease areas against 1683 human targets, and measured their similarity using a score matrix. The similarity network indicates that drugs from the same disease area tend to cluster together in ways that are not captured by structural similarity, with several potential new drug pairings being identified. This work thus provides a novel, validated alternative to existing drug target prediction algorithms. The web service is freely available at: http://chem2bio2rdf.org/slap.

Modern drug discovery requires the understanding of chemogenomics, the complex interaction of chemical compounds and drugs with a wide variety of protein target and genes in the body. A large amount of data pertaining to such relationships exists in publicly-accessible datasets but it is siloed and thus impossible to use in an integrated fashion. In this work we have integrated and semantically annotated a large amount of public data from a wide range of databases, including compound-gene, drug-drug, protein-protein, drug-side effects and so on, to create a complex network of interactions relating to compounds and protein targets. We developed a statistical algorithm called Semantic Link Association Prediction (SLAP) for predicting “missing links” in this data network: i.e. compound-target interactions for which there is no experimental data but which are statistically probable given the other relationships that exist in this set. We present validation experiments which show this method works with a high degree of accuracy, and also demonstrate how it can be used to create a drug similarity network to make predictions of new indications for existing drugs.

New chromene scaffolds for adenosine A(2A) receptors: synthesis, pharmacology and structure-activity relationships

In silico screening of a collection of 1584 academic compounds identified a small molecule hit for the human adenosine A(2A) receptor (pK(i) = 6.2) containing a novel chromene scaffold (3a). To explore the structure-activity relationships of this new chemical series for adenosine receptors, a focused library of 43 2H-chromene-3-carboxamide derivatives was synthesized and tested in radioligand binding assays at human adenosine A(1), A(2A), A(2B) and A(3) receptors. The series was found to be enriched with bioactive compounds for adenosine receptors, with 14 molecules showing submicromolar affinity (pK(i) ≥ 6.0) for at least one adenosine receptor subtype. These results provide evidence that the chromene scaffold, a core structure present in natural products from a wide variety of plants, vegetables, and fruits, constitutes a valuable source for novel therapeutic agents.

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.

Historical Strategies for Lead Generation

An increasing number of lead generation approaches are being applied to multi-target drug discovery (MTDD). Historically, focussed screening and a knowledge-based approach called framework combination have been most widely used – with varying degrees of success. More recently, alternative screening approaches such as HTS, fragment-based screening and in silico screening are being used alongside the traditional approaches in order to discover novel hits with attractive physicochemical and oral pharmacokinetic properties. Factors influencing the feasibility of discovering DMLs for particular combinations are discussed in this chapter. The role of natural products and the discovery of probes for chemical biology are also highlighted.

Identifying and characterizing promiscuous targets: implications for virtual screening

INTRODUCTION

Ligand-based shape matching approaches have become established as important and popular virtual screening (VS) techniques. However, despite their relative success, the question of how to best choose the initial query compounds and their conformations remains largely unsolved. This issue gains importance when dealing with promiscuous targets, that is, proteins that bind multiple ligand scaffold families in one or more binding site. Conventional shape matching VS approaches assume that there is only one binding mode for a given protein target. This may be true for some targets, but it is certainly not true in all cases. Several recent studies have shown that some protein targets bind to different ligands in different ways.

AREAS COVERED

The authors discuss the concept of promiscuity in the context of virtual drug screening, and present and analyze several examples of promiscuous targets. The article also reports on the impact of the query conformation on the performance of shape-based VS and the potential to improve VS performance by using consensus shape clustering techniques.

EXPERT OPINION

The notion of polypharmacology is becoming highly relevant in drug discovery. Understanding and exploiting promiscuity present challenges and opportunities for drug discovery endeavors. The examples of promiscuity presented here suggest that promiscuous targets and ligands are much more common than previously assumed, and this should be taken into account in practical VS protocols. Although some progress has been made, there is a need to develop more sophisticated computational techniques and protocols that can identify and characterize promiscuous targets on a genomic scale.

Linking pharmacology to clinical reports: cyclobenzaprine and its possible association with serotonin syndrome

The link between cyclobenzaprine (Flexeril) administration and serotonin syndrome (SS) is subject to debate. Establishing such a connection is difficult because of the limited number of case reports available and the almost complete ignorance of its preclinical pharmacology. In this context, evidence is provided here that cyclobenzaprine blocks the serotonin and norepinephrine transporters and binds to another set of five serotonin receptors. SS should be considered when indicative signs occur in the context of cyclobenzaprine use.

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.

From in silico target prediction to multi-target drug design: current databases, methods and applications

Given the tremendous growth of bioactivity databases, the use of computational tools to predict protein targets of small molecules has been gaining importance in recent years. Applications span a wide range, from the 'designed polypharmacology' of compounds to mode-of-action analysis. In this review, we firstly survey databases that can be used for ligand-based target prediction and which have grown tremendously in size in the past. We furthermore outline methods for target prediction that exist, both based on the knowledge of bioactivities from the ligand side and methods that can be applied in situations when a protein structure is known. Applications of successful in silico target identification attempts are discussed in detail, which were based partly or in whole on computational target predictions in the first instance. This includes the authors' own experience using target prediction tools, in this case considering phenotypic antibacterial screens and the analysis of high-throughput screening data. Finally, we will conclude with the prospective application of databases to not only predict, retrospectively, the protein targets of a small molecule, but also how to design ligands with desired polypharmacology in a prospective manner.

Missing Value Estimation for Compound-Target Activity Data

Relationships between drug targets and associated diseases have traditionally been investigated by means of sequence similarity, comparative protein modeling, and pathway analysis. Recently, a complementary paradigm has emerged to link targets and drugs via biological responses within activity data and visualize findings in networks. It has been indicated that one of the obstacles towards the identification of novel interactions is the sparsity of available data. In this article, we provide a survey of estimation methods that address the challenge of data sparsity. Each method is described in terms of its advantages and limitations, and an exemplary application on compound-target activity data is demonstrated. With such imputation methods in-hand, the opportunity to combine efforts in molecular informatics can be realized, yielding novel insights into ligand-target space.