Discovery of 2-(2-benzoxazoyl amino)-4-aryl-5-cyanopyrimidine as negative allosteric modulators (NAMs) of metabotropic glutamate receptor 5 (mGlu₅): from an artificial neural network virtual screen to an in vivo tool compound

Review of Personalized Medicine and Pharmacogenomics of Anti-Cancer Compounds and Natural Products

In recent years, the FDA has approved numerous anti-cancer drugs that are mutation-based for clinical use. These drugs have improved the precision of treatment and reduced adverse effects and side effects. Personalized therapy is a prominent and hot topic of current medicine and also represents the future direction of development. With the continuous advancements in gene sequencing and high-throughput screening, research and development strategies for personalized clinical drugs have developed rapidly. This review elaborates the recent personalized treatment strategies, which include artificial intelligence, multi-omics analysis, chemical proteomics, and computation-aided drug design. These technologies rely on the molecular classification of diseases, the global signaling network within organisms, and new models for all targets, which significantly support the development of personalized medicine. Meanwhile, we summarize chemical drugs, such as lorlatinib, osimertinib, and other natural products, that deliver personalized therapeutic effects based on genetic mutations. This review also highlights potential challenges in interpreting genetic mutations and combining drugs, while providing new ideas for the development of personalized medicine and pharmacogenomics in cancer study.

Biotechnological Innovations from Ocean: Transpiring Role of Marine Drugs in Management of Chronic Disorders

Marine drugs are abundant in number, comprise of a diverse range of structures with corresponding mechanisms of action, and hold promise for the discovery of new and better treatment approaches for the management of several chronic diseases. There are huge reserves of natural marine biological compounds, as 70 percent of the Earth is covered with oceans, indicating a diversity of chemical entities on the planet. The marine ecosystems are a rich source of bioactive products and have been explored for lead drug molecules that have proven to be novel therapeutic targets. Over the last 70 years, many structurally diverse drug products and their secondary metabolites have been isolated from marine sources. The drugs obtained from marine sources have displayed an exceptional potential in the management of a wide array of diseases, ranging from acute to chronic conditions. A beneficial role of marine drugs in human health has been recently proposed. The current review highlights various marine drugs and their compounds and role in the management of chronic diseases such as cancer, diabetes, neurodegenerative diseases, and cardiovascular disorders, which has led to the development of new drug treatment approaches.

A computer-aided drug design approach to discover tumour suppressor p53 protein activators for colorectal cancer therapy

Colorectal cancer (CRC) is the third most detected cancer and the second foremost cause of cancer deaths in the world. Intervention targeting p53 provides potential therapeutic strategies, but thus far no p53-based therapy has been successfully translated into clinical cancer treatment. Here we developed a Quantitative Structure-Activity Relationships (QSAR) classification models using empirical molecular descriptors and fingerprints to predict the activity against the p53 protein, using the potency value with the active or inactive label, were developed. These models were built using in total 10,505 molecules that were extracted from the ChEMBL, ZINC and Reaxys® databases, and recent literature. Three machine learning (ML) techniques e.g., Random Forest, Support Vector Machine, Convolutional Neural Network were explored to build models for p53 inhibitor prediction. The performances of the models were successfully evaluated by internal and external validation. Moreover, based on the best in silico p53 model, a virtual screening campaign was carried out using 1443 FDA-approved drugs that were extracted from the ZINC database. A list of virtual screening hits was assented on base of some limits established in this approach, such as: (1) probability of being active against p53; (2) applicability domain; (3) prediction of the affinity between the p53, and ligands, through molecular docking. The most promising according to the limits established above was dihydroergocristine. This compound revealed cytotoxic activity against a p53-expressing CRC cell line with an IC₅₀ of 56.8 µM. This study demonstrated that the computer-aided drug design approach can be used to identify previously unknown molecules for targeting p53 protein with anti-cancer activity and thus pave the way for the study of a therapeutic solution for CRC.

Identification of Novel HBV/HDV Entry Inhibitors by Pharmacophore- and QSAR-Guided Virtual Screening

The hepatic bile acid transporter Na⁺/taurocholate co-transporting polypeptide (NTCP) was identified in 2012 as the high-affinity hepatic receptor for the hepatitis B and D viruses (HBV/HDV). Since then, this carrier has emerged as promising drug target for HBV/HDV virus entry inhibitors, but the synthetic peptide Hepcludex^® of high molecular weight is the only approved HDV entry inhibitor so far. The present study aimed to identify small molecules as novel NTCP inhibitors with anti-viral activity. A ligand-based bioinformatic approach was used to generate and validate appropriate pharmacophore and QSAR (quantitative structure–activity relationship) models. Half-maximal inhibitory concentrations (IC₅₀) for binding inhibition of the HBV/HDV-derived preS1 peptide (as surrogate parameter for virus binding to NTCP) were determined in NTCP-expressing HEK293 cells for 150 compounds of different chemical classes. IC₅₀ values ranged from 2 µM up to >1000 µM. The generated pharmacophore and QSAR models were used for virtual screening of drug-like chemicals from the ZINC¹⁵ database (~11 million compounds). The 20 best-performing compounds were then experimentally tested for preS1-peptide binding inhibition in NTCP-HEK293 cells. Among them, four compounds were active and revealed experimental IC₅₀ values for preS1-peptide binding inhibition of 9, 19, 20, and 35 µM, which were comparable to the QSAR-based predictions. All these compounds also significantly inhibited in vitro HDV infection of NTCP-HepG2 cells, without showing any cytotoxicity. The best-performing compound in all assays was ZINC000253533654. In conclusion, the present study demonstrates that virtual compound screening based on NTCP-specific pharmacophore and QSAR models can predict novel active hit compounds for the development of HBV/HDV entry inhibitors.

Hepatitis D Virus Entry Inhibitors Based on Repurposing Intestinal Bile Acid Reabsorption Inhibitors

Identification of Na⁺/taurocholate co-transporting polypeptide (NTCP) as high-affinity hepatic entry receptor for the Hepatitis B and D viruses (HBV/HDV) opened the field for target-based development of cell-entry inhibitors. However, most of the HBV/HDV entry inhibitors identified so far also interfere with the physiological bile acid transporter function of NTCP. The present study aimed to identify more virus-selective inhibitors of NTCP by screening of 87 propanolamine derivatives from the former development of intestinal bile acid reabsorption inhibitors (BARIs), which interact with the NTCP-homologous intestinal apical sodium-dependent bile acid transporter (ASBT). In NTCP-HEK293 cells, the ability of these compounds to block the HBV/HDV-derived preS1-peptide binding to NTCP (virus receptor function) as well as the taurocholic acid transport via NTCP (bile acid transporter function) were analyzed in parallel. Hits were subsequently validated by performing in vitro HDV infection experiments in NTCP-HepG2 cells. The most potent compounds S985852, A000295231, and S973509 showed in vitro anti-HDV activities with IC₅₀ values of 15, 40, and 70 µM, respectively, while the taurocholic acid uptake inhibition occurred at much higher IC₅₀ values of 24, 780, and 490 µM, respectively. In conclusion, repurposing of compounds from the BARI class as novel HBV/HDV entry inhibitors seems possible and even enables certain virus selectivity based on structure-activity relationships.

Fine Tuning Muscarinic Acetylcholine Receptor Signaling Through Allostery and Bias

The M1 and M4 muscarinic acetylcholine receptors (mAChRs) are highly pursued drug targets for neurological diseases, in particular for Alzheimer's disease and schizophrenia. Due to high sequence homology, selective targeting of any of the M1-M5 mAChRs through the endogenous ligand binding site has been notoriously difficult to achieve. With the discovery of highly subtype selective mAChR positive allosteric modulators in the new millennium, selectivity through targeting an allosteric binding site has opened new avenues for drug discovery programs. However, some hurdles remain to be overcome for these promising new drug candidates to progress into the clinic. One challenge is the potential for on-target side effects, such as for the M1 mAChR where over-activation of the receptor by orthosteric or allosteric ligands can be detrimental. Therefore, in addition to receptor subtype selectivity, a drug candidate may need to exhibit a biased signaling profile to avoid such on-target adverse effects. Indeed, recent studies in mice suggest that allosteric modulators for the M1 mAChR that bias signaling toward specific pathways may be therapeutically important. This review brings together details on the signaling pathways activated by the M1 and M4 mAChRs, evidence of biased agonism at these receptors, and highlights pathways that may be important for developing new subtype selective allosteric ligands to achieve therapeutic benefit.

Structure-based discovery and development of metabotropic glutamate receptor 5 negative allosteric modulators

The metabotropic glutamate (mGlu) receptors are a family of eight class C G protein-coupled receptors (GPCRs) which modulate cell signaling and synaptic transmission to the major excitatory neurotransmitter l-glutamate (l-glutamic acid). Due to their role in modulating glutamate response, their widespread distribution in the central nervous system (CNS) and some evidence of dysregulation in disease, the mGlu receptors have become attractive pharmacological targets. As the orthosteric (glutamate) binding site is highly conserved across the eight mGlu receptors, it is difficult not only to generate ligands with subtype selectivity but, due to the nature of the binding site, with suitable drug-like properties to allow oral bioavailability and CNS penetration. Selective pharmacological targeting of a single receptor subtype can be achieved by targeting alternative (allosteric) binding sites. The nature of the allosteric binding pockets allows ligands to be developed that have good physical chemical properties as evidenced by several allosteric modulators of mGlu receptors entering clinical trials. The first negative allosteric modulators of the metabotropic glutamate 5 (mGlu₅) receptor were discovered from high throughput screening activities. An alternative approach to drug discovery is to use structural knowledge to enable structure-based drug design (SBDD), which allows the design of molecules in a more rational, rather than empirical, fashion. Here we will describe the process of SBDD in the discovery of the mGlu₅ negative allosteric modulator HTL0014242 and describe how knowledge of receptor structure can also be used to gain insights into the receptor activation mechanisms.

Identification of Novel Allosteric Modulators of Metabotropic Glutamate Receptor Subtype 5 Acting at Site Distinct from 2-Methyl-6-(phenylethynyl)-pyridine Binding

As part of the G-protein coupled receptor (GPCR) family, metabotropic glutamate (mGlu) receptors play an important role as drug targets of cognitive diseases. Selective allosteric modulators of mGlu subtype 5 (mGlu₅) have the potential to alleviate symptoms of numerous central nervous system disorders such as schizophrenia in a more targeted fashion. Multiple mGlu₅ positive allosteric modulators (PAMs), such as 1-(3-fluorophenyl)-N-((3-fluorophenyl)-methylideneamino)-methanimine (DFB), 3-cyano-N-(1,3-diphenyl-1H-pyrazol-5-yl)-benzamide (CDPPB), and 4-nitro-N-(1,3-diphenyl-1H-pyrazol-5-yl)-benzamide (VU-29), exert their actions by binding to a defined allosteric site on mGlu₅ located in the seven-transmembrane domain (7TM) and shared by mGlu₅ negative allosteric modulator (NAM) 2-methyl-6-(phenylethynyl)-pyridine (MPEP). Actions of the PAM N-{4-chloro-2-[(1,3-dioxo-1,3-dihydro-2H-isoindol-2-yl)methyl]phenyl}-2-hydroxybenzamide (CPPHA) are mediated by a distinct allosteric site in the 7TM domain different from the MPEP binding site. Experimental evidence confirms these findings through mutagenesis experiments involving residues F585 (TM1) and A809 (TM7). In an effort to investigate mGlu₅ PAM selectivity for this alternative allosteric site distinct from MPEP binding, we employed in silico quantitative structure-activity relationship (QSAR) modeling. Subsequent ligand-based virtual screening prioritized a set of 63 candidate compounds predicted from a library of over 4 million commercially available compounds to bind exclusively to this novel site. Experimental validation verified the biological activity for seven of 63 selected candidates. Further, medicinal chemistry optimizations based on these molecules revealed compound VU6003586 with an experimentally validated potency of 174 nM. Radioligand binding experiments showed only partial inhibition at very high concentrations, most likely indicative of binding at a non-MPEP site. Selective positive allosteric modulators for mGlu₅ have the potential for tremendous impact concerning devastating neurological disorders such as schizophrenia and Huntington's disease. These identified and validated novel selective compounds can serve as starting points for more specifically tailored lead and probe molecules and thus help the development of potential therapeutic agents with reduced adverse effects.

Computational Drug Design Applied to the Study of Metabotropic Glutamate Receptors

Metabotropic glutamate (mGlu) receptors are a family of eight GPCRs that are attractive drug discovery targets to modulate glutamate action and response. Here we review the application of computational methods to the study of this family of receptors. X-ray structures of the extracellular and 7-transmembrane domains have played an important role to enable structure-based modeling approaches, whilst we also discuss the successful application of ligand-based methods. We summarize the literature and highlight the areas where modeling and experiment have delivered important understanding for mGlu receptor drug discovery. Finally, we offer suggestions of future areas of opportunity for computational work.

Kinetic and system bias as drivers of metabotropic glutamate receptor 5 allosteric modulator pharmacology

Allosteric modulators of the metabotropic glutamate receptor subtype 5 (mGlu₅) have been proposed as potential therapies for various CNS disorders. These ligands bind to sites distinct from the orthosteric (or endogenous) ligand, often with improved subtype selectivity and spatio-temporal control over receptor responses. We recently revealed that mGlu₅ allosteric agonists and positive allosteric modulators exhibit biased agonism and/or modulation. To establish whether negative allosteric modulators (NAMs) engender similar bias, we rigorously characterized the pharmacology of eight diverse mGlu₅ NAMs. Radioligand inhibition binding studies revealed novel modes of interaction with mGlu₅ for select NAMs, with biphasic or incomplete inhibition of the radiolabeled NAM, [³H]methoxy-PEPy. We assessed mGlu₅-mediated intracellular Ca²⁺ (iCa²⁺) mobilization and inositol phosphate (IP₁) accumulation in HEK293A cells stably expressing low levels of mGlu₅ (HEK293A-rat mGlu₅-low) and mouse embryonic cortical neurons. The apparent affinity of acetylenic NAMs, MPEP, MTEP and dipraglurant, was dependent on the signaling pathway measured, agonist used, and cell type (HEK293A-rat mGlu₅-low versus mouse cortical neurons). In contrast, the acetylenic partial NAM, M-5MPEP, and structurally distinct NAMs (VU0366248, VU0366058, fenobam), had similar affinity estimates irrespective of the assay or cellular background. Biased modulation was evident for VU0366248 in mouse cortical neurons where it was a NAM for DHPG-mediated iCa²⁺ mobilization, but neutral with DHPG in IP₁ accumulation assays. Overall, this study highlights the inherent complexity in mGlu₅ NAM pharmacology that we hypothesize may influence interpretation when translating into preclinical models and beyond in the design and development of novel therapeutics for neuropsychiatric and neurological disorders.

QSAR-Based Virtual Screening: Advances and Applications in Drug Discovery

Virtual screening (VS) has emerged in drug discovery as a powerful computational approach to screen large libraries of small molecules for new hits with desired properties that can then be tested experimentally. Similar to other computational approaches, VS intention is not to replace in vitro or in vivo assays, but to speed up the discovery process, to reduce the number of candidates to be tested experimentally, and to rationalize their choice. Moreover, VS has become very popular in pharmaceutical companies and academic organizations due to its time-, cost-, resources-, and labor-saving. Among the VS approaches, quantitative structure–activity relationship (QSAR) analysis is the most powerful method due to its high and fast throughput and good hit rate. As the first preliminary step of a QSAR model development, relevant chemogenomics data are collected from databases and the literature. Then, chemical descriptors are calculated on different levels of representation of molecular structure, ranging from 1D to nD, and then correlated with the biological property using machine learning techniques. Once developed and validated, QSAR models are applied to predict the biological property of novel compounds. Although the experimental testing of computational hits is not an inherent part of QSAR methodology, it is highly desired and should be performed as an ultimate validation of developed models. In this mini-review, we summarize and critically analyze the recent trends of QSAR-based VS in drug discovery and demonstrate successful applications in identifying perspective compounds with desired properties. Moreover, we provide some recommendations about the best practices for QSAR-based VS along with the future perspectives of this approach.

In Silico Studies in Drug Research Against Neurodegenerative Diseases

Background

Neurodegenerative diseases such as Alzheimer's disease (AD), amyotrophic lateral sclerosis, Parkinson's disease (PD), spinal cerebellar ataxias, and spinal and bulbar muscular atrophy are described by slow and selective degeneration of neurons and axons in the central nervous system (CNS) and constitute one of the major challenges of modern medicine. Computer-aided or in silico drug design methods have matured into powerful tools for reducing the number of ligands that should be screened in experimental assays.

Methods

In the present review, the authors provide a basic background about neurodegenerative diseases and in silico techniques in the drug research. Furthermore, they review the various in silico studies reported against various targets in neurodegenerative diseases, including homology modeling, molecular docking, virtual high-throughput screening, quantitative structure activity relationship (QSAR), hologram quantitative structure activity relationship (HQSAR), 3D pharmacophore mapping, proteochemometrics modeling (PCM), fingerprints, fragment-based drug discovery, Monte Carlo simulation, molecular dynamic (MD) simulation, quantum-mechanical methods for drug design, support vector machines, and machine learning approaches.

Results

Detailed analysis of the recently reported case studies revealed that the majority of them use a sequential combination of ligand and structure-based virtual screening techniques, with particular focus on pharmacophore models and the docking approach.

Conclusion

Neurodegenerative diseases have a multifactorial pathoetiological origin, so scientists have become persuaded that a multi-target therapeutic strategy aimed at the simultaneous targeting of multiple proteins (and therefore etiologies) involved in the development of a disease is recommended in future.

In Silico HCT116 Human Colon Cancer Cell-Based Models En Route to the Discovery of Lead-Like Anticancer Drugs

To discover new inhibitors against the human colon carcinoma HCT116 cell line, two quantitative structure–activity relationship (QSAR) studies using molecular and nuclear magnetic resonance (NMR) descriptors were developed through exploration of machine learning techniques and using the value of half maximal inhibitory concentration (IC₅₀). In the first approach, A, regression models were developed using a total of 7339 molecules that were extracted from the ChEMBL and ZINC databases and recent literature. The performance of the regression models was successfully evaluated by internal and external validations, the best model achieved R² of 0.75 and 0.73 and root mean square error (RMSE) of 0.66 and 0.69 for the training and test sets, respectively. With the inherent time-consuming efforts of working with natural products (NPs), we conceived a new NP drug hit discovery strategy that consists in frontloading samples with 1D NMR descriptors to predict compounds with anticancer activity prior to bioactivity screening for NPs discovery, approach B. The NMR QSAR classification models were built using 1D NMR data (¹H and ¹³C) as descriptors, from 50 crude extracts, 55 fractions and five pure compounds obtained from actinobacteria isolated from marine sediments collected off the Madeira Archipelago. The overall predictability accuracies of the best model exceeded 63% for both training and test sets.

Computational Methodologies in the Exploration of Marine Natural Product Leads

Computational methodologies are assisting the exploration of marine natural products (MNPs) to make the discovery of new leads more efficient, to repurpose known MNPs, to target new metabolites on the basis of genome analysis, to reveal mechanisms of action, and to optimize leads. In silico efforts in drug discovery of NPs have mainly focused on two tasks: dereplication and prediction of bioactivities. The exploration of new chemical spaces and the application of predicted spectral data must be included in new approaches to select species, extracts, and growth conditions with maximum probabilities of medicinal chemistry novelty. In this review, the most relevant current computational dereplication methodologies are highlighted. Structure-based (SB) and ligand-based (LB) chemoinformatics approaches have become essential tools for the virtual screening of NPs either in small datasets of isolated compounds or in large-scale databases. The most common LB techniques include Quantitative Structure–Activity Relationships (QSAR), estimation of drug likeness, prediction of adsorption, distribution, metabolism, excretion, and toxicity (ADMET) properties, similarity searching, and pharmacophore identification. Analogously, molecular dynamics, docking and binding cavity analysis have been used in SB approaches. Their significance and achievements are the main focus of this review.

Opportunities and Challenges in the Discovery of Allosteric Modulators of GPCRs

From the pharmacological point of view, allosteric modulators may present numerous advantages over orthosteric ligands. Growing availability of novel tools and experimental data provides a tempting opportunity to apply computational methods to improve known modulators and design novel ones. However, recent progress in understanding of complexity of allostery increases awareness of problems involved in design of modulators with desired properties. Deeper insight into phenomena such as probe dependence, altering signaling bias with minor changes in ligand structure, as well as influence of subtle endogenous allosteric factors turns out to be fundamental. These effects make the design of a modulator with precise pharmacological outcome a very challenging task, and need to be taken into consideration throughout the design process. In this chapter, we focus on nuances of targeting GPCR allosteric sites in computational drug design efforts, in particular with application of docking, virtual screening, and molecular dynamics.

Discovery and Characterization of (R)-6-Neopentyl-2-(pyridin-2-ylmethoxy)-6,7-dihydropyrimido[2,1-c][1,4]oxazin-4(9H)-one (PF-06462894), an Alkyne-Lacking Metabotropic Glutamate Receptor 5 Negative Allosteric Modulator Profiled in both Rat and Nonhuman Primates

We previously observed a cutaneous type IV immune response in nonhuman primates (NHP) with the mGlu₅ negative allosteric modulator (NAM) 7. To determine if this adverse event was chemotype- or mechanism-based, we evaluated a distinct series of mGlu₅ NAMs. Increasing the sp³ character of high-throughput screening hit 40 afforded a novel morpholinopyrimidone mGlu₅ NAM series. Its prototype, (R)-6-neopentyl-2-(pyridin-2-ylmethoxy)-6,7-dihydropyrimido[2,1-c][1,4]oxazin-4(9H)-one (PF-06462894, 8), possessed favorable properties and a predicted low clinical dose (2 mg twice daily). Compound 8 did not show any evidence of immune activation in a mouse drug allergy model. Additionally, plasma samples from toxicology studies confirmed that 8 did not form any reactive metabolites. However, 8 caused the identical microscopic skin lesions in NHPs found with 7, albeit with lower severity. Holistically, this work supports the hypothesis that this unique toxicity may be mechanism-based although additional work is required to confirm this and determine clinical relevance.

Cannabinoid Receptor 1 and Fatty Acid Amide Hydrolase Contribute to Operant Sensation Seeking in Mice

A large body of evidence in humans and preclinical models supports a role for the endocannabinoid system in the proper execution of motivated or goal-directed behaviors. Operant sensation seeking (OSS) is a task that uses varied sensory stimuli as a reinforcer to maintain operant responding in mice. The purpose of the studies in this report was to begin to explore the role of endocannabinoid signaling in OSS utilizing cannabinoid receptor 1 (CB1R) and fatty acid amide hydrolase (FAAH) knock out mice. Compared to wild type littermate controls, CB1R knock out mice exhibited significantly fewer active responses and earned significantly fewer reinforcers in fixed ratio and progressive ratio schedules. On the other hand, FAAH knock out mice exhibited increased active responses and earned more reinforcers than wild type littermates in fixed ratio but not progressive ratio schedules. These findings support the role of endocannabinoid signaling in motivated behaviors and also expand our understanding of the signaling processes involved in OSS.

Ligand-based virtual screen for the discovery of novel M5 inhibitor chemotypes

This Letter describes a ligand-based virtual screening campaign utilizing SAR data around the M5 NAMs, ML375 and VU6000181. Both QSAR and shape scores were employed to virtually screen a 98,000-member compound library. Neither approach alone proved productive, but a consensus score of the two models identified a novel scaffold which proved to be a modestly selective, but weak inhibitor (VU0549108) of the M5 mAChR (M5 IC50=6.2μM, M1-4 IC50s>10μM) based on an unusual 8-((1,3,5-trimethyl-1H-pyrazol-4-yl)sulfonyl)-1-oxa-4-thia-8-azaspiro[4,5]decane scaffold. [(3)H]-NMS binding studies showed that VU0549108 interacts with the orthosteric site (Ki of 2.7μM), but it is not clear if this is negative cooperativity or orthosteric binding. Interestingly, analogs synthesized around VU0549108 proved weak, and SAR was very steep. However, this campaign validated the approach and warranted further expansion to identify additional novel chemotypes.

Have artificial neural networks met expectations in drug discovery as implemented in QSAR framework?

INTRODUCTION

Artificial neural networks (ANNs) are highly adaptive nonlinear optimization algorithms that have been applied in many diverse scientific endeavors, ranging from economics, engineering, physics, and chemistry to medical science. Notably, in the past two decades, ANNs have been used widely in the process of drug discovery.

AREAS COVERED

In this review, the authors discuss advantages and disadvantages of ANNs in drug discovery as incorporated into the quantitative structure-activity relationships (QSAR) framework. Furthermore, the authors examine the recent studies, which span over a broad area with various diseases in drug discovery. In addition, the authors attempt to answer the question about the expectations of the ANNs in drug discovery and discuss the trends in this field.

EXPERT OPINION

The old pitfalls of overtraining and interpretability are still present with ANNs. However, despite these pitfalls, the authors believe that ANNs have likely met many of the expectations of researchers and are still considered as excellent tools for nonlinear data modeling in QSAR. It is likely that ANNs will continue to be used in drug development in the future.

N-Alkylpyrido[1',2':1,5]pyrazolo-[4,3-d]pyrimidin-4-amines: A new series of negative allosteric modulators of mGlu1/5 with CNS exposure in rodents

Selective negative allosteric modulators (NAMs) of each of the group I metabotropic glutamate receptors (mGlu1 and mGlu5) have been well characterized in the literature and offer potential as therapeutics in several disorders of the central nervous system (CNS). Still, compounds that are potent mGlu1/5 NAMs with selectivity versus the other six members of the mGlu family as well as the balance of properties required for use in vivo are lacking. A medicinal chemistry effort centered on the identification of a lead series with the potential of delivering such compounds is described in this Letter. Specifically, a new class of pyrido[1',2':1,5]pyrazolo[4,3-d]pyrimidin-4-amines was designed as a novel isosteric replacement for 4-aminoquinazolines, and compounds from within this chemotype exhibited dual NAM activity at both group I mGlus. One compound, VU0467558 (29), demonstrated near equipotent activity at both receptors, selectivity versus other mGlus, a favorable ancillary pharmacology profile, and CNS exposure in rodents.

Identification of a Selective G1-Phase Benzimidazolone Inhibitor by a Senescence-Targeted Virtual Screen Using Artificial Neural Networks

Cellular senescence is a barrier to tumorigenesis in normal cells, and tumor cells undergo senescence responses to genotoxic stimuli, which is a potential target phenotype for cancer therapy. However, in this setting, mixed-mode responses are common with apoptosis the dominant effect. Hence, more selective senescence inducers are required. Here we report a machine learning-based in silico screen to identify potential senescence agonists. We built profiles of differentially affected biological process networks from expression data obtained under induced telomere dysfunction conditions in colorectal cancer cells and matched these to a panel of 17 protein targets with confirmatory screening data in PubChem. We trained a neural network using 3517 compounds identified as active or inactive against these targets. The resulting classification model was used to screen a virtual library of ~2M lead-like compounds. One hundred and forty-seven virtual hits were acquired for validation in growth inhibition and senescence-associated β-galactosidase assays. Among the found hits, a benzimidazolone compound, CB-20903630, had low micromolar IC50 for growth inhibition of HCT116 cells and selectively induced senescence-associated β-galactosidase activity in the entire treated cell population without cytotoxicity or apoptosis induction. Growth suppression was mediated by G1 blockade involving increased p21 expression and suppressed cyclin B1, CDK1, and CDC25C. In addition, the compound inhibited growth of multicellular spheroids and caused severe retardation of population kinetics in long-term treatments. Preliminary structure-activity and structure clustering analyses are reported, and expression analysis of CB-20903630 against other cell cycle suppressor compounds suggested a PI3K/AKT-inhibitor-like profile in normal cells, with different pathways affected in cancer cells.

Fragment and Structure-Based Drug Discovery for a Class C GPCR: Discovery of the mGlu5 Negative Allosteric Modulator HTL14242 (3-Chloro-5-[6-(5-fluoropyridin-2-yl)pyrimidin-4-yl]benzonitrile)

Fragment screening of a thermostabilized mGlu5 receptor using a high-concentration radioligand binding assay enabled the identification of moderate affinity, high ligand efficiency (LE) pyrimidine hit 5. Subsequent optimization using structure-based drug discovery methods led to the selection of 25, HTL14242, as an advanced lead compound for further development. Structures of the stabilized mGlu5 receptor complexed with 25 and another molecule in the series, 14, were determined at resolutions of 2.6 and 3.1 Å, respectively.

Discovery of VU0431316: a negative allosteric modulator of mGlu5 with activity in a mouse model of anxiety

Development of SAR in an aryl ether series of mGlu5 NAMs leading to the identification of pyrazine analog VU0431316 is described in this Letter. VU0431316 is a potent and selective non-competitive antagonist of mGlu5 that binds at a known allosteric binding site. VU0431316 demonstrates an attractive DMPK profile, including moderate clearance and good bioavailability in rats. Intraperitoneal (IP) dosing of VU0431316 in a mouse marble burying model of anxiety, an assay known to be sensitive to mGlu5 antagonists and other anxiolytics, produced dose proportional effects.

Identification of specific ligand-receptor interactions that govern binding and cooperativity of diverse modulators to a common metabotropic glutamate receptor 5 allosteric site

A common metabotropic glutamate receptor 5 (mGlu5) allosteric site is known to accommodate diverse chemotypes. However, the structural relationship between compounds from different scaffolds and mGlu5 is not well understood. In an effort to better understand the molecular determinants that govern allosteric modulator interactions with mGlu5, we employed a combination of site-directed mutagenesis and computational modeling. With few exceptions, six residues (P654, Y658, T780, W784, S808, and A809) were identified as key affinity determinants across all seven allosteric modulator scaffolds. To improve our interpretation of how diverse allosteric modulators occupy the common allosteric site, we sampled the wealth of mGlu5 structure-activity relationship (SAR) data available by docking 60 ligands (actives and inactives) representing seven chemical scaffolds into our mGlu5 comparative model. To spatially and chemically compare binding modes of ligands from diverse scaffolds, the ChargeRMSD measure was developed. We found a common binding mode for the modulators that placed the long axes of the ligands parallel to the transmembrane helices 3 and 7. W784 in TM6 not only was identified as a key NAM cooperativity determinant across multiple scaffolds, but also caused a NAM to PAM switch for two different scaffolds. Moreover, a single point mutation in TM5, G747V, altered the architecture of the common allosteric site such that 4-nitro-N-(1,3-diphenyl-1H-pyrazol-5-yl)benzamide (VU29) was noncompetitive with the common allosteric site. Our findings highlight the subtleties of allosteric modulator binding to mGlu5 and demonstrate the utility in incorporating SAR information to strengthen the interpretation and analyses of docking and mutational data.

Computational methods in drug discovery

Computer-aided drug discovery/design methods have played a major role in the development of therapeutically important small molecules for over three decades. These methods are broadly classified as either structure-based or ligand-based methods. Structure-based methods are in principle analogous to high-throughput screening in that both target and ligand structure information is imperative. Structure-based approaches include ligand docking, pharmacophore, and ligand design methods. The article discusses theory behind the most important methods and recent successful applications. Ligand-based methods use only ligand information for predicting activity depending on its similarity/dissimilarity to previously known active ligands. We review widely used ligand-based methods such as ligand-based pharmacophores, molecular descriptors, and quantitative structure-activity relationships. In addition, important tools such as target/ligand data bases, homology modeling, ligand fingerprint methods, etc., necessary for successful implementation of various computer-aided drug discovery/design methods in a drug discovery campaign are discussed. Finally, computational methods for toxicity prediction and optimization for favorable physiologic properties are discussed with successful examples from literature.

New technologies enabling the industrialization of allosteric modulator discovery

Allosteric modulators (AMs) are a promising avenue towards safe and selective drugs. AMs can interact selectively with unique domains distinct from the endogenous ligand binding site of receptors, up- or downregulating the response to receptor activation. Emphasis is placed in this article on the latest development in high-sensitivity technologies designed to identify AMs of G-protein coupled receptors. In addition to new pharmacological approaches, encouraging results in the crystal resolution of these targets enable use of more rational approaches to identification and optimization of AMs.

Discovery of VU0409106: A negative allosteric modulator of mGlu5 with activity in a mouse model of anxiety

Development of SAR in an aryl ether series of mGlu5 NAMs leading to the identification of tool compound VU0409106 is described in this Letter. VU0409106 is a potent and selective negative allosteric modulator of mGlu5 that binds at the known allosteric binding site and demonstrates good CNS exposure following intraperitoneal dosing in mice. VU0409106 also proved efficacious in a mouse marble burying model of anxiety, an assay known to be sensitive to mGlu5 antagonists as well as clinically efficacious anxiolytics.

Substituted 1-Phenyl-3-(pyridin-2-yl)urea negative allosteric modulators of mGlu5: discovery of a new tool compound VU0463841 with activity in rat models of cocaine addiction

Cocaine is a powerful and highly addictive stimulant that disrupts the normal reward circuitry in the central nervous system (CNS), producing euphoric effects. Cocaine use can lead to acute and life threatening emergencies, and abuse is associated with increased risk for contracting infectious diseases. Though certain types of behavioral therapy have proven effective for treatment of cocaine addiction, relapse remains high, and there are currently no approved medications for the treatment of cocaine abuse. Evidence has continued to accumulate that indicates a critical role for the metabotropic glutamate receptor subtype 5 (mGlu5) in the modulation of neural circuitry associated with the addictive properties of cocaine. While the small molecule mGlu5 negative allosteric modulator (NAM) field is relatively advanced, investigation into the potential of small molecule mGlu5 NAMs for the treatment of cocaine addiction remains an area of high interest. Herein we describe the discovery and characterization of a potent and selective compound 29 (VU0463841) with good CNS exposure in rats. The utility of 29 (VU0463841) was demonstrated by its ability to attenuate drug seeking behaviors in relevant rat models of cocaine addiction.

Allosteric transition: a comparison of two models

Introduction

Two recent models are in use for analysis of allosteric drug action at receptor sites remote from orthosteric binding sites. One is an allosteric two-state mechanical model derived in 2000 by David Hall. The other is an extended operational model developed in 2007 by Arthur Christopoulos’s group. The models are valid in pharmacology, enzymology, transportology as well as several other fields of biology involving allosteric concentration effects.

Results

I show here that Hall’s model for interactions between an orthoster, an alloster, and a receptive unit is the best choice of model both for simulation and analysis of allosteric concentration-responses at equilibrium or steady-state.

Conclusions

As detailed knowledge of receptors systems becomes available, systems with several pathways and states and/ or more than two binding sites should be analysed by extended forms of the Hall model rather than for instance a Hill type exponentiation of terms as introduced in non-mechanistic (operational) model approaches; yielding semi-quantitative estimates of actual system parameters based on Hill’s unlikely simultaneity model for G protein-coupled receptors.

Investigating metabotropic glutamate receptor 5 allosteric modulator cooperativity, affinity, and agonism: enriching structure-function studies and structure-activity relationships

Drug discovery programs increasingly are focusing on allosteric modulators as a means to modify the activity of G protein-coupled receptor (GPCR) targets. Allosteric binding sites are topographically distinct from the endogenous ligand (orthosteric) binding site, which allows for co-occupation of a single receptor with the endogenous ligand and an allosteric modulator that can alter receptor pharmacological characteristics. Negative allosteric modulators (NAMs) inhibit and positive allosteric modulators (PAMs) enhance the affinity and/or efficacy of orthosteric agonists. Established approaches for estimation of affinity and efficacy values for orthosteric ligands are not appropriate for allosteric modulators, and this presents challenges for fully understanding the actions of novel modulators of GPCRs. Metabotropic glutamate receptor 5 (mGlu(5)) is a family C GPCR for which a large array of allosteric modulators have been identified. We took advantage of the many tools for probing allosteric sites on mGlu(5) to validate an operational model of allosterism that allows quantitative estimation of modulator affinity and cooperativity values. Affinity estimates derived from functional assays fit well with affinities measured in radioligand binding experiments for both PAMs and NAMs with diverse chemical scaffolds and varying degrees of cooperativity. We observed modulation bias for PAMs when we compared mGlu(5)-mediated Ca(2+) mobilization and extracellular signal-regulated kinase 1/2 phosphorylation data. Furthermore, we used this model to quantify the effects of mutations that reduce binding or potentiation by PAMs. This model can be applied to PAM and NAM potency curves in combination with maximal fold-shift data to derive reliable estimates of modulator affinities.