Calculating similarities between biological activities in the MDL Drug Data Report database

Unlocking the Reverse Targeting Mechanisms of Cannabidiol: Unveiling New Therapeutic Avenues

Cannabidiol (CBD) and Δ9-tetrahydrocannabinol (THC), the main components of Cannabis sativa plants, have attracted a significant amount of attention due to their biological activities. This study identified GPR18 as the target of partial agonist CBD activating the p42/p44 MAPK pathway leading to migration of endometrial epithelial cells. Induced fit docking (IFD) showed that the affinity of THC for GPR18 is higher than that of CBD, and molecular dynamics (MD) simulations showed that CBD-GPR18 complexes at 130/200 ns might have stable conformations, potentially activating GPR18 by changing the distances of key residues in its active pocket. In contrast, THC maintains "metastable" conformations, generating a "shrinking space" leading to full agonism of THC by adding mechanical constraints in GPR18's active pocket. Steered molecular dynamics (SMD) revealed GPR18's active pocket was influenced more by CBD's partial agonism compared with THC. This combined IFD-MD-SMD method may be used to explain the mechanism of activation of partial or full agonists of GPR18.

Novel hydroxamic acid derivative induces apoptosis and constrains autophagy in leukemic cells

INTRODUCTION

Posttranslational modification of proteins by reversible acetylation regulates key biological processes. Histone deacetylases (HDACs) catalyze protein deacetylation and are frequently dysregulated in tumors. This has spurred the development of HDAC inhibitors (HDACi). Such epigenetic drugs modulate protein acetylation, eliminate tumor cells, and are approved for the treatment of blood cancers.

OBJECTIVES

We aimed to identify novel, nanomolar HDACi with increased potency over existing agents and selectivity for the cancer-relevant class I HDACs (HDAC1,-2,-3,-8). Moreover, we wanted to define how such drugs control the apoptosis-autophagy interplay. As test systems, we used human leukemic cells and embryonic kidney-derived cells.

METHODS

We synthesized novel pyrimidine-hydroxamic acid HDACi (KH9/KH16/KH29) and performed in vitro activity assays and molecular modeling of their direct binding to HDACs. We analyzed how these HDACi affect leukemic cell fate, acetylation, and protein expression with flow cytometry and immunoblot. The publicly available DepMap database of CRISPR-Cas9 screenings was used to determine sensitivity factors across human leukemic cells.

RESULTS

Novel HDACi show nanomolar activity against class I HDACs. These agents are superior to the clinically used hydroxamic acid HDACi SAHA (vorinostat). Within the KH-series of compounds, KH16 (yanostat) is the most effective inhibitor of HDAC3 (IC50 = 6 nM) and the most potent inducer of apoptosis (IC50 = 110 nM; p < 0.0001) in leukemic cells. KH16 though spares embryonic kidney-derived cells. Global data analyses of knockout screenings verify that HDAC3 is a dependency factor in 115 human blood cancer cells of different lineages, independent of mutations in the tumor suppressor p53. KH16 alters pro- and anti-apoptotic protein expression, stalls cell cycle progression, and induces caspase-dependent processing of the autophagy proteins ULK1 and p62.

CONCLUSION

These data reveal that HDACs are required to stabilize autophagy proteins through suppression of apoptosis in leukemic cells. HDAC3 appears as a valid anti-cancer target for pharmacological intervention.

Novel mutual prodrug of 5-fluorouracil and heme oxygenase-1 inhibitor (5-FU/HO-1 hybrid): design and preliminary in vitro evaluation

In this work, the first mutual prodrug of 5-fluorouracil and heme oxygenase1 inhibitor (5-FU/HO-1 hybrid) has been designed, synthesised, and evaluated for its in vitro chemical and enzymatic hydrolysis stability. Predicted in silico physicochemical properties of the newly synthesised hybrid (3) demonstrated a drug-like profile with suitable Absorption, Distribution, Metabolism, and Excretion (ADME) properties and low toxic liabilities. Preliminary cytotoxicity evaluation towards human prostate (DU145) and lung (A549) cancer cell lines demonstrated that 3 exerted a similar effect on cell viability to that produced by the reference drug 5-FU. Among the two tested cancer cell lines, the A549 cells were more susceptible for 3. Of note, hybrid 3 also had a significantly lower cytotoxic effect on healthy human lung epithelial cells (BEAS-2B) than 5-FU. Altogether our results served as an initial proof-of-concept to develop 5-FU/HO-1 mutual prodrugs as potential novel anticancer agents.

Drug Repurposing: Claiming the Full Benefit from Drug Development

PURPOSE OF REVIEW

Drug development has evolved over the years from being one-at-a-time to be massive screens in an industrial manner. Bringing a new therapeutic agent from concept to bedside can take a decade and cost billions of dollars-with most concepts failing along the way. Of the few compounds that make it to clinical testing, less than one out of eight make it to approval. This traditional drug development pipeline is challenging for prevalent diseases and makes the development of new therapeutics for rare diseases financially intractable.

RECENT FINDINGS

Repurposing of drugs is an alternative to identify new applications for the thousands of compounds that have already been approved for clinical use. There is now a range of strategies for such efforts that leverage clinical data, pharmacologic data, and/or genomic or transcriptomic data. These strategies, together with examples, are detailed in this review. Drug repurposing bypasses the pre-clinical work and thereby opens up the opportunity to provide targeted treatment at a fraction of the cost that is accompanied with the development from ideation to full approval. Such an approach makes drug discovery for any disease process more efficient but holds particular promise for rare diseases for which there is little to no other viable drug development channel.

Understanding the binding conformation of ceftolozane/tazobactam with Metallo-β-lactamases VIM-5 and IMP-7 of Pseudomonas aeruginosa: A molecular docking and virtual screening process

Pseudomonas aeruginosa (P. aeruginosa) is one of the community-acquired and healthcare-associated infections causing organisms. It has become resistant to most of the available antibiotics and is termed multi-drug resistance (MDR). There are a limited number of antibiotics are available to treat such MDR organism causing infections. The ceftolozane/tazobactam is one among the combination drug therapy (CDT) prescribed for the treatment of MDR causing infections. The resistance for the same CDT was observed in the MDR P. aeruginosa harboring VIM-5 and IMP-7 Metallo beta (β)-lactamases (MBLs). To explore the resistance mechanism at the molecular level, docking studies were carried out for antibiotics against VIM-5 and IMP-7 MBLs. The Zn2 metal ions carry out the nucleophile attack on the carbonyl carbon of the β-lactam ring along with conserved water molecules. To find lead compounds against the MBLs, a virtual screening process was carried out. We have employed MODELLER for structure modeling, AutoDock for molecular docking and AutoDock Vina, Molinspiration, PASS prediction & admetSAR in virtual screening. The search of low binding energy ceftolozane analogs against VIM-5 and IMP-7 MBLs has resulted in the ZINC000029060075 and ZINC000009163636 analogs. Similarly, the screening of high binding energy inhibitors against VIM-5 and IMP-7 MBLs has resulted in ZINC000003831503 and ZINC000000897247 tazobactam analogs respectively. The ADMET prediction results in the non-toxicity of the lead compounds. Our study may provide new insights for the scientist who are designing novel drugs against MDR P. aeruginosa causing infections.

Novel ketamine analogues cause a false positive phencyclidine immunoassay

Background

Immunoassays are commonly used to test for drugs of abuse in patients in a variety of settings. The increasing prevalence of ‘designer’ drugs causes difficulties for the toxicology laboratory and may result in unexpected false positives and identification of unfamiliar compounds. Within the past decade, there have been a variety of ketamine and phencyclidine analogues identified, particularly as drugs of abuse.

Method

We present a case of intoxication with a novel ketamine analogue, deschloro-N-ethyl-ketamine, causing a false positive phencyclidine immunoassay. Additionally, we performed spiking studies and 2D molecular similarity calculations for deschloro-N-ethyl-ketamine, ketamine and three other analogues on the Siemens Viva-E EMIT-II phencyclidine assay to assess their cross-reactivity.

Results

Four of the tested compounds (deschloro-N-ethyl-ketamine, 3-methoxy-phencyclidine, 3-methoxy-eticyclidine and methoxetamine) cause false positive phencyclidine immunoassay results, while ketamine gives a negative result. The cross-reactivity data are in accord with the similarity calculations of these molecules, further validating the ability of 2D molecular similarity analysis to predict the molecular cross-reactivity in immunoassays.

Conclusions

The cross-reactivity data of phencyclidine and ketamine analogues presented in this study could help toxicology laboratories and clinicians in evaluating unexpected results, particularly when novel PCP and ketamine analogues are being considered.

In Silico Drug-Target Profiling

Pharmacological science is trying to establish the link between chemicals, targets, and disease-related phenotypes. A plethora of chemical proteomics and structural data have been generated, thanks to the target-based approach that has dominated drug discovery at the turn of the century. There is an invaluable source of information for in silico target profiling. Prediction is based on the principle of chemical similarity (similar drugs bind similar targets) or on first principles from the biophysics of molecular interactions. In the first case, compound comparison is made through ligand-based chemical similarity search or through classifier-based machine learning approach. The 3D techniques are based on 3D structural descriptors or energy-based scoring scheme to infer a binding affinity of a compound with its putative target. More recently, a new approach based on compound set metric has been proposed in which a query compound is compared with a whole of compounds associated with a target or a family of targets. This chapter reviews the different techniques of in silico target profiling and their main applications such as inference of unwanted targets, drug repurposing, or compound prioritization after phenotypic-based screening campaigns.

Novel selective, potent naphthyl TRPM8 antagonists identified through a combined ligand- and structure-based virtual screening approach

Transient receptor potential melastatin 8 (TRPM8), a nonselective cation channel, is the predominant mammalian cold temperature thermosensor and it is activated by cold temperatures and cooling compounds, such as menthol and icilin. Because of its role in cold allodynia, cold hyperalgesia and painful syndromes TRPM8 antagonists are currently being pursued as potential therapeutic agents for the treatment of pain hypersensitivity. Recently TRPM8 has been found in subsets of bladder sensory nerve fibres, providing an opportunity to understand and treat chronic hypersensitivity. However, most of the known TRPM8 inhibitors lack selectivity, and only three selective compounds have reached clinical trials to date. Here, we applied two virtual screening strategies to find new, clinics suitable, TRPM8 inhibitors. This strategy enabled us to identify naphthyl derivatives as a novel class of potent and selective TRPM8 inhibitors. Further characterization of the pharmacologic properties of the most potent compound identified, compound 1, confirmed that it is a selective, competitive antagonist inhibitor of TRPM8. Compound 1 also proved itself active in a overreactive bladder model in vivo. Thus, the novel naphthyl derivative compound identified here could be optimized for clinical treatment of pain hypersensitivity in bladder disorders but also in different other pathologies.

Understanding the diverse functions of Huatan Tongluo Fang on rheumatoid arthritis from a pharmacological perspective

Huatan Tongluo Fang (HTTLF) is a traditional herbal formula that can resolve phlegm and dredge collaterals. HTTLF has also been used to treat rheumatoid arthritis (RA); however, the mechanism underlying the therapeutic effects of HTTLF on RA has not been clearly elucidated at the molecular level. In the present study, an integrated model of system pharmacology containing chemical space analysis, potential active compound prediction and compound-target-disease network was constructed to investigate the molecular mechanisms of HTTLF. The compounds from HTTLF dispersed well in the chemical space. Most of the compounds from HTTLF had similar chemical spaces to drug/drug-like compounds associated with RA, according to the MDL Drug Data Report. A total of 127 potentially active compounds and 17 targets of RA were identified. Among them, 50 compounds interacted with ≥2 targets, while 77 compounds interacted with only one target. In addition, 17 targets were associated with 82 diseases that belonged to 26 categories. These results indicate that HTTLF has diverse chemical spaces and polypharmacology with regards to the treatment of RA. In addition, HTTLF demonstrated therapeutic potential against diverse diseases other than RA, including osteoarthritis, atherosclerosis and brain cancer. This study provides a novel platform for understanding how HTTLF treats RA; this is beneficial for explaining the diverse functions of HTTLF with regards to RA, and may help develop novel compounds with desirable therapeutic targets to treat RA.

Network pharmacology strategies toward multi-target anticancer therapies: from computational models to experimental design principles

Polypharmacology has emerged as novel means in drug discovery for improving treatment response in clinical use. However, to really capitalize on the polypharmacological effects of drugs, there is a critical need to better model and understand how the complex interactions between drugs and their cellular targets contribute to drug efficacy and possible side effects. Network graphs provide a convenient modeling framework for dealing with the fact that most drugs act on cellular systems through targeting multiple proteins both through on-target and off-target binding. Network pharmacology models aim at addressing questions such as how and where in the disease network should one target to inhibit disease phenotypes, such as cancer growth, ideally leading to therapies that are less vulnerable to drug resistance and side effects by means of attacking the disease network at the systems level through synergistic and synthetic lethal interactions. Since the exponentially increasing number of potential drug target combinations makes pure experimental approach quickly unfeasible, this review depicts a number of computational models and algorithms that can effectively reduce the search space for determining the most promising combinations for experimental evaluation. Such computational-experimental strategies are geared toward realizing the full potential of multi-target treatments in different disease phenotypes. Our specific focus is on system-level network approaches to polypharmacology designs in anticancer drug discovery, where we give representative examples of how network-centric modeling may offer systematic strategies toward better understanding and even predicting the phenotypic responses to multi-target therapies.

Towards a gold standard: regarding quality in public domain chemistry databases and approaches to improving the situation

In recent years there has been a dramatic increase in the number of freely accessible online databases serving the chemistry community. The internet provides chemistry data that can be used for data-mining, for computer models, and integration into systems to aid drug discovery. There is however a responsibility to ensure that the data are high quality to ensure that time is not wasted in erroneous searches, that models are underpinned by accurate data and that improved discoverability of online resources is not marred by incorrect data. In this article we provide an overview of some of the experiences of the authors using online chemical compound databases, critique the approaches taken to assemble data and we suggest approaches to deliver definitive reference data sources.

Informatics, machine learning and computational medicinal chemistry

This article reviews the use of informatics and computational chemistry methods in medicinal chemistry, with special consideration of how computational techniques can be adapted and extended to obtain more and higher-quality information. Special consideration is given to the computation of protein–ligand binding affinities, to the prediction of off-target bioactivities, bioactivity spectra and computational toxicology, and also to calculating absorption-, distribution-, metabolism- and excretion-relevant properties, such as solubility.

Cross-pharmacology analysis of G protein-coupled receptors

The degree of applicability of chemogenomic approaches to protein families depends on the accuracy and completeness of pharmacological data and the corresponding level of pharmacological similarity observed among their protein members. The recent public domain availability of pharmacological data for thousands of small molecules on 204 G protein-coupled receptors (GPCRs) provides a firm basis for an in-depth cross-pharmacology analysis of this superfamily. The number of protein targets included in the cross-pharmacology profile of the different GPCRs changes significantly upon varying the ligand similarity and binding affinity criteria. However, with the exception of muscarinic receptors, aminergic GPCRs distinguish themselves from the rest of the members in the family by their remarkably high levels of pharmacological similarity among them. Clusters of non-GPCR targets related by cross-pharmacology with particular GPCRs are identified and the implications for unwanted side-effects, as well as for repurposing opportunities, discussed.

VnD: a structure-centric database of disease-related SNPs and drugs

Numerous genetic variations have been found to be related to human diseases. Significant portion of those affect the drug response as well by changing the protein structure and function. Therefore, it is crucial to understand the trilateral relationship among genomic variations, diseases and drugs. We present the variations and drugs (VnD), a consolidated database containing information on diseases, related genes and genetic variations, protein structures and drug information. VnD was built in three steps. First, we integrated various resources systematically to deduce catalogs of disease-related genes, single nucleotide polymorphisms (SNPs), protein mutations and relevant drugs. VnD contains 137,195 disease-related gene records (13,940 distinct genes) and 16,586 genetic variation records (1790 distinct variations). Next, we carried out structure modeling and docking simulation for wild-type and mutant proteins to examine the structural and functional consequences of non-synonymous SNPs in the drug-related genes. Conformational changes in 590 wild-type and 4437 mutant proteins from drug-related genes were included in our database. Finally, we investigated the structural and biochemical properties relevant to drug binding such as the distribution of SNPs in proximal protein pockets, thermo-chemical stability, interactions with drugs and physico-chemical properties. The VnD database, available at http://vnd.kobic.re.kr:8080/VnD/ or vandd.org, would be a useful platform for researchers studying the underlying mechanism for association among genetic variations, diseases and drugs.

On the industrial applications of MCRs: molecular diversity in drug discovery and generic drug synthesis

During the last decades, multicomponent chemistry has gained much attention in pharmaceutical research, especially in the context of lead finding and optimization. Here, in particular, the main advantages of multicomponent reactions (MCRs) like ease of automation and high diversity generation were utilized. In consequence of these beneficial properties, a plethora of new MCRs combined with appropriate classical reaction sequences have been published, the accessible chemical space was extended steadily. In the meantime, the desired high diversity became a challenge itself, because by now the systematic use of this huge and unmanageable space for drug discovery was limited by the lack of suitable computational tools. Therefore, this article provides an insight for the rational use of this enormous chemical space in drug discovery and generic drug synthesis. In this context, a short overview of the applied chemo informatics, necessary for the virtual screening of the biggest available chemical space, is given. Furthermore, some examples for recently developed multicomponent sequences are presented.

ScafBank: a public comprehensive Scaffold database to support molecular hopping

AIM

The search for molecules whose bioactivities are similar to those of given compounds or to optimize the initial lead compounds from high throughput screening has attracted increasing interest in recent years. Our goal is to provide a publically searchable database of scaffolds out from a large collection of existing chemical molecules.

RESULTS

Although a number of in silico methods have emerged to facilitate this process, which has become known as "scaffold hopping" or "molecular hopping", there is an urgent need for a database system to provide such valuable data in the drug design field. Here we have systematically analyzed a collection of commercially available small molecule databases and a bioactive compound database to identify unique scaffolds and we have built a publically searchable database. The analysis of approximately 4,800,000 of these compounds identified 241,824 unique scaffolds, which are stored in a relational database (http://202.127.30.184:8080/db.html). Each entry in the database is associated with a molecular occurrence and includes its distribution of molecular properties, such as molecular weight, logP, hydrogen bond acceptor number, hydrogen bond donor number, rotatable bond number and ring number. More importantly, for scaffolds derived from the bioactive compounds database, it also contains the original compounds and their target information.

CONCLUSION

This Web-based database system could help researchers in the fields of medicinal and organic chemistry to design novel molecules with properties similar to the original compounds, but built on novel scaffolds.

Isoquinolin-1-one inhibitors of the MDM2-p53 interaction

p53 has been at the centre of attention for drug design since the discovery of its growth-suppressive and pro-apoptotic activity. Herein we report the design and characterisation of a new class of isoquinolinone inhibitors of the MDM2-p53 interaction. Our identification of druglike and selective inhibitors of this protein-protein interaction included a straightforward in silico compound-selection process, a recently reported NMR spectroscopic approach for studying the MDM2-p53 interaction, and selectivity screening assays using cells with the same genetic background. The selected inhibitors were all able to induce apoptosis and the expression of p53-related genes, but only the isoquinolin-1-one-based inhibitors stabilised p53. Our NMR experiments give a persuading explanation for these results, showing that isoquinolin-1-one derivates are able to dissociate the preformed MDM2-p53 complex in vitro, releasing a folded and soluble p53. The joint application of these methods provides a framework for the discovery of protein interaction inhibitors as a promising starting point for further drug design.

Quantifying the relationships among drug classes

The similarity of drug targets is typically measured using sequence or structural information. Here, we consider chemo-centric approaches that measure target similarity on the basis of their ligands, asking how chemoinformatics similarities differ from those derived bioinformatically, how stable the ligand networks are to changes in chemoinformatics metrics, and which network is the most reliable for prediction of pharmacology. We calculated the similarities between hundreds of drug targets and their ligands and mapped the relationship between them in a formal network. Bioinformatics networks were based on the BLAST similarity between sequences, while chemoinformatics networks were based on the ligand-set similarities calculated with either the Similarity Ensemble Approach (SEA) or a method derived from Bayesian statistics. By multiple criteria, bioinformatics and chemoinformatics networks differed substantially, and only occasionally did a high sequence similarity correspond to a high ligand-set similarity. In contrast, the chemoinformatics networks were stable to the method used to calculate the ligand-set similarities and to the chemical representation of the ligands. Also, the chemoinformatics networks were more natural and more organized, by network theory, than their bioinformatics counterparts: ligand-based networks were found to be small-world and broad-scale.

Cheminformatics analysis and learning in a data pipelining environment

Workflow technology is being increasingly applied in discovery information to organize and analyze data. SciTegic's Pipeline Pilot is a chemically intelligent implementation of a workflow technology known as data pipelining. It allows scientists to construct and execute workflows using components that encapsulate many cheminformatics based algorithms. In this paper we review SciTegic's methodology for molecular fingerprints, molecular similarity, molecular clustering, maximal common subgraph search and Bayesian learning. Case studies are described showing the application of these methods to the analysis of discovery data such as chemical series and high throughput screening results. The paper demonstrates that the methods are well suited to a wide variety of tasks such as building and applying predictive models of screening data, identifying molecules for lead optimization and the organization of molecules into families with structural commonality.

Streamlining lead discovery by aligning in silico and high-throughput screening

Lead discovery in the pharmaceutical environment is largely an industrial-scale process in which it is typical to screen 1-5 million compounds in a matter of weeks using High Throughput Screening (HTS). This process is a very costly endeavor. Typically a HTS campaign of 1 million compounds will cost anywhere from $500000 to $1000000. There is consequently a great deal of pressure to maximize the return on investment by finding fast and more effective ways to screen. A panacea that has emerged over the past few years to help address this issue is in silico screening. In silico screening is now incorporated in all areas of lead discovery; from target identification and library design, to hit analysis and compound profiling. However, as lead discovery has evolved over the past few years, so has the role of in silico screening.

Classification and comparison of ligand-binding sites derived from grid-mapped knowledge-based potentials

We describe the application of knowledge-based potentials implemented in the MOE program to compare the ligand-binding sites of several proteins. The binding probabilities for a polar and a hydrophobic probe are calculated on a grid to allow easy comparison of binding sites of superimposed related proteins. The method is fast and simple enough to simultaneously use structural information of multiple proteins of a target family. The method can be used to rapidly cluster proteins into subfamilies according to the similarity of hydrophobic and polar fields of their ligand-binding sites. Regions of the binding site which are common within a protein family can be identified and analysed for the design of family-targeted libraries or those which differ for improvement of ligand selectivity. The field-based hierarchical clustering is demonstrated for three protein families: the ligand-binding domains of nuclear receptors, the ATP-binding sites of protein kinases and the substrate binding sites of proteases. More detailed comparisons are presented for serine proteases of the chymotrypsin family, for the peroxisome proliferator-activated receptor subfamily of nuclear receptors and for progesterone and androgen receptor. The results are in good accordance with structure-based analysis and highlight important differences of the binding sites, which have been also described in the literature.

Hierarchical docking of databases of multiple ligand conformations

Ligand flexibility is an important problem in molecular docking and virtual screening. To address this challenge, we investigate a hierarchical pre-organization of multiple conformations of small molecules. Such organization of pre-calculated conformations removes the exploration of ligand conformational space from the docking calculation and allows for concise representation of what can be thousands of conformations. The hierarchy also recognizes and prunes incompatible conformations early in the calculation, eliminating redundant calculations of fit. We investigate the method by docking the MDL Drug Data Report (MDDR), an annotated database of 100,000 molecules, into apo and holo forms of seven unrelated targets. This annotated database allows us to track the ranking of tens to hundreds of annotated ligands in each of the docking systems. The binding sites and database are prepared in an automated fashion in an attempt to remove some human bias from the calculations. Many thousands of explicit and implicit ligand conformations may be docked in calculations not much longer than required for single conformer docking. As long as internal energies are not considered, recombination with the hierarchy is additive as the number of degrees of freedom is increased. Molecules with even millions of conformations can be docked in a few minutes on a single desktop computer.

Design, synthesis, and application of azabicyclo[X.Y.0]alkanone amino acids as constrained dipeptide surrogates and peptide mimics

Azabicyclo[X.Y.0]alkanone amino acids are challenging synthetic targets and useful tools for studying structure-activity relationships of native peptide ligands. They have been employed to increase potency and stability in conformationally rigid enzyme inhibitors and receptor ligands. Since last reviewed in 1997, activity in their synthesis and application has increased significantly and access is now available to a wider diversity of these peptide mimics. This review focuses on recent syntheses of these heterocyclic amino acids and their application in the investigation of biologically active peptides and peptide mimics.