Drug Discovery Methods Research Articles

Muscle to Brain Partitioning as Measure of Transporter-Mediated Efflux at the Rat Blood-Brain Barrier and Its Implementation into Compound Optimization in Drug Discovery.

Movement of xenobiotic substances across the blood–brain barrier (BBB) is tightly regulated by various transporter proteins, especially the efflux transporters P-glycoprotein (P-gp/MDR1) and breast cancer resistance protein (BCRP). Avoiding drug efflux at the BBB is a unique challenge for the development of new central nervous system (CNS) drugs. Drug efflux at the BBB is described by the partition coefficient of unbound drug between brain and plasma (Kp,uu,brain) which is typically obtained from in vivo and often additionally in vitro measurements. Here, we describe a new method for the rapid estimation of the in vivo drug efflux at the BBB of rats: the measurement of the partition coefficient of a drug between brain and skeletal muscle (Kp,brain/muscle). Assuming a closely similar distribution of drugs into the brain and muscle and that the efflux transporters are only expressed in the brain, Kp,brain/muscle, similar to Kp,uu,brain, reflects the efflux at the BBB. The new method requires a single in vivo experiment. For 64 compounds from different research programs, we show the comparability to other approaches used to obtain Kp,uu,brain. P-gp- and BCRP-overexpressing cell systems are valuable in vitro tools for prescreening. Drug efflux at the BBB can be most accurately predicted based on a simple algorithm incorporating data from both in vitro assays. In conclusion, the combined use of our new in vivo method and the in vitro tools allows an efficient screening method in drug discovery with respect to efflux at the BBB.

Ligand based virtual screening using SVM on GPU

In silico methods play an essential role in modern drug discovery methods. Virtual screening, an in silico method, is used to filter out the chemical space on which actual wet lab experiments are need to be conducted. Ligand based virtual screening is a computational strategy using which one can build a model of the target protein based on the knowledge of the ligands that bind successfully to the target. This model is then used to predict if the new molecule is likely to bind to the target. Support vector machine, a supervised learning algorithm used for classification, can be utilized for virtual screening the ligand data. When used for virtual screening purpose, SVM could produce interesting results. But since we have a huge ligand data, the time taken for training the SVM model is quite high compared to other learning algorithms. By parallelizing these algorithms on multi-core processors, one can easily expedite these discoveries. In this paper, a GPU based ligand based virtual screening tool (GpuSVMScreen) which uses SVM have been proposed and bench-marked. This data parallel virtual screening tool provides high throughput by running in short time. The proposed GpuSVMScreen can successfully screen large number of molecules (billions) also. The source code of this tool is available at http://ccc.nitc.ac.in/project/GPUSVMSCREEN.

Cellular Stress-Modulating Drugs Can Potentially Be Identified by in Silico Screening with Connectivity Map (CMap).

Accompanied by increased life span, aging-associated diseases, such as metabolic diseases and cancers, have become serious health threats. Recent studies have documented that aging-associated diseases are caused by prolonged cellular stresses such as endoplasmic reticulum (ER) stress, mitochondrial stress, and oxidative stress. Thus, ameliorating cellular stresses could be an effective approach to treat aging-associated diseases and, more importantly, to prevent such diseases from happening. However, cellular stresses and their molecular responses within the cell are typically mediated by a variety of factors encompassing different signaling pathways. Therefore, a target-based drug discovery method currently being used widely (reverse pharmacology) may not be adequate to uncover novel drugs targeting cellular stresses and related diseases. The connectivity map (CMap) is an online pharmacogenomic database cataloging gene expression data from cultured cells treated individually with various chemicals, including a variety of phytochemicals. Moreover, by querying through CMap, researchers may screen registered chemicals in silico and obtain the likelihood of drugs showing a similar gene expression profile with desired and chemopreventive conditions. Thus, CMap is an effective genome-based tool to discover novel chemopreventive drugs.

Recent Advances in Drug Repurposing for Parkinson's Disease.

As a long-term degenerative disorder of the central nervous system that mostly affects older people, Parkinson's disease is a growing health threat to our ever-aging population. Despite remarkable advances in our understanding of this disease, all therapeutics currently available only act to improve symptoms but cannot stop the disease progression. Therefore, it is essential that more effective drug discovery methods and approaches are developed, validated, and used for the discovery of disease-modifying treatments for Parkinson's disease. Drug repurposing, also known as drug repositioning, or the process of finding new uses for existing or abandoned pharmaceuticals, has been recognized as a cost-effective and timeefficient way to develop new drugs, being equally promising as de novo drug discovery in the field of neurodegeneration and, more specifically for Parkinson's disease. The availability of several established libraries of clinical drugs and fast evolvement in disease biology, genomics and bioinformatics has stimulated the momentums of both in silico and activity-based drug repurposing. With the successful clinical introduction of several repurposed drugs for Parkinson's disease, drug repurposing has now become a robust alternative approach to the discovery and development of novel drugs for this disease. In this review, recent advances in drug repurposing for Parkinson's disease will be discussed.

Progress in regulatory peptide research.

The field of regulatory peptide research has developed significant momentum owing to several recent converging trends. Dozens of peptide-based drugs have been approved by the U.S. Food and Drug Administration in the past decade, the majority for the treatment of metabolic disorders, including diabetes. These are the "tip of the spear" for peptide therapeutics, revealing that impediments of delivery, stability, and bioavailability inherent in peptide drugs have in many cases been overcome. While most are orally available, and directed at peripheral targets, pharmaceutical delivery of peptides to the central nervous system through nasal mucosal routes has also seen much progress. Cell-based high-throughput drug discovery methods, the X-ray crystallographic structural definition of G protein-coupled receptors, and deorphanization of peptide-liganded receptors have contributed to the emergence of new targets for pharmacological intervention and accelerated the development of peptide-based as well as nonpeptide congeners for existing ones. Finally, the recognition that peptides act at their receptors, in a cellular context, in conjunction with other peptides and other first messengers, including neurotransmitters, hormones, and autocrine and paracrine factors, has led to an increased appreciation for the combinatorial possibilities of regulatory peptide action, now penetrating to drug design and discovery efforts. The fifteen reviews, reports, and perspectives collected in this special issue of Annals of the New York Academy of Sciences provide a snapshot of the frontiers of the field of regulatory peptide research as they expand physiologically, pharmacologically, and therapeutically.

An in silico Approach for Integrating Phenotypic and Target-based Approaches in Drug Discovery.

Phenotypic and target‐based approaches are useful methods in drug discovery. The phenotypic approach is an experimental approach for evaluating the phenotypic response. The target‐based approach is a rational approach for screening drug candidates targeting a biomolecule that causes diseases. These approaches are widely used for drug discovery. However, two serious problems of target deconvolution and polypharmacology are encountered in these conventional experimental approaches. To overcome these two problems, we developed a new in silico method using a probabilistic framework. This method integrates both the phenotypic and target‐based approaches to estimate a relevant network from compound to phenotype. Our method can computationally execute target deconvolution considering polypharmacology and can provide keys for understanding the pathway and mechanism from compound to phenotype, thereby promoting drug discovery.

Computer-Aided Discovery of Small Molecule Inhibitors of Thymocyte Selection-Associated High Mobility Group Box Protein (TOX) as Potential Therapeutics for Cutaneous T-Cell Lymphomas.

Cutaneous T-cell lymphomas (CTCL) are the most common primary lymphomas of the skin. We have previously identified thymocyte selection-associated high mobility group (HMG) box protein (TOX) as a promising drug target in CTCL; however, there are currently no small molecules able to directly inhibit TOX. We aimed to address this unmet opportunity by developing anti-TOX therapeutics with the use of computer-aided drug discovery methods. The available NMR-resolved structure of the TOX protein was used to model its DNA-binding HMG-box domain. To investigate the druggability of the corresponding protein–DNA interface on TOX, we performed a pilot virtual screening of 200,000 small molecules using in silico docking and identified ‘hot spots’ for drug-binding on the HMG-box domain. We then performed a large-scale virtual screening of 7.6 million drug-like compounds that were available from the ZINC15 database. As a result, a total of 140 top candidate compounds were selected for subsequent in vitro validation. Of those, 18 small molecules have been characterized as selective TOX inhibitors.

High-throughput method for in process monitoring of 3-O-sulfotransferase catalyzed sulfonation in bioengineered heparin synthesis

Bioengineered heparin (BEH) offers a potential alternative for the preparation of a safer pharmacological heparin. Construction of in-process control assays for tracking each enzymatic step during bioengineered heparin synthesis remains a challenge. Here, we report a high-throughput sensing platform based on enzyme-linked immunosorbent assay (ELISA) and enzymatic signal amplification that allows the rapid and accurate monitoring of the 3-OST sulfonation in BEH synthesis process. The anticoagulant activity of target BEH was measured to reflect the degree of sulfonation by testing its competitive antithrombin (AT) binding ability. BEH samples with different sulfonation degrees show different AT protein binding capacity and thus changes the UV response to a different extent. This BEH-induced signal can be conveniently and sensitively monitored by the plate sensing system, which benefits from its high sensitivity brought in by the enzymatic signal amplification. Furthermore, modification convenience and mechanical robustness also ensure the stability of the test platform. This proposed strategy exhibits excellent analytical performance in both BEH activity analysis and 3-OST sulfonation evaluation. The simple and sensitive plate system shows great potential in developing on-chip, high-throughput methods for fundamental biochemical process research, drug discovery, and clinic diagnostics.

P13.06 Transcriptome-wide Mendelian randomization study to identify brain-specific causal genes influencing glioma

Abstract BACKGROUND The drug treatment regimen for glioma has remained relatively static since the introduction of temozolomide, although new drugs and drug combinations are being trialled. The human transcriptome can provide promising insights into causal genes as potential drug interventions for glioma treatment and may guide drug discovery methods. MATERIAL AND METHODS We apply two sample Mendelian randomisation and colocalisation to explore the influence of genetically-predicted gene expression across 12 tissue types located in the brain (4,554 genes from GTEx) and whole blood (16,112 genes from eQTLGen) on glioma risk (5,739 cases, 5,501 controls from a meta-analysis of GICC and MDA glioma GWAS). We used the MR-Base R package to conduct these analyses. RESULTS We identify 9 genes whose genetically-predicted expression was strongly associated with glioma risk. Of these genes, 7/9 are shown to have tissue-specific expression while the other two genes showed an association with glioma across multiple brain tissues and whole blood. For example, JAK1, involved in the well-known JAK-STAT pathway, is found in the frontal cortex (OR=1.49 for glioma per standard deviation increase in gene expression; 95% CI: 1.28 to 1.73; P=1.79 × 10−7), the cerebellar hemisphere (OR=1.32; 95% CI: 1.19 to 1.47; P=2.64 × 10−7), the cerebellum (OR=1.27; 95% CI: 1.16 to 1.39; P=2.64 × 10−7) and the cortex (OR=1.38; 95% CI: 1.22 to 1.57; P=2.64 × 10−7). This pathway has been highlighted in previous research as a potential intervention target for glioma therapies. We found that 5/9 of the genes from the MR analysis are expressed in the cerebellum. However malignant cerebellar glioma is a rare tumour (~3% of all malignant gliomas). This suggests that tumourigenesis elsewhere in the brain may be affected by other tissue-specific genes, specifically in the cerebellum, though this will require further research to elucidate. We further triangulate the MR findings with evidence from the OpenTargets platform to strengthen the putative causal associations. OpenTargets aims to “generate evidence on the validity of therapeutic targets based on genome-scale experiments and analysis”. For example, JAK1 receives an overall OpenTargets score of 0.89 out of 1, with most of the evidence for this JAK1-glioma association coming from affected pathways data. CONCLUSION This study has combined genetic epidemiological approaches to the analysis of the human transcriptome on glioma incidence. We provide evidence that these genes may inform putative drug targets for tertiary treatment of glioma. Future research specifically towards this aim will be required to fully elucidate intervention targets.

Computational Drug Repurposing Algorithm Targeting TRPA1 Calcium Channel as a Potential Therapeutic Solution for Multiple Sclerosis.

Multiple sclerosis (MS) is a chronic autoimmune disease affecting the central nervous system (CNS) through neurodegeneration and demyelination, leading to physical/cognitive disability and neurological defects. A viable target for treating MS appears to be the Transient Receptor Potential Ankyrin 1 (TRPA1) calcium channel, whose inhibition has been shown to have beneficial effects on neuroglial cells and protect against demyelination. Using computational drug discovery and data mining methods, we performed an in silico screening study combining chemical graph mining, quantitative structure–activity relationship (QSAR) modeling, and molecular docking techniques in a global prediction model in order to identify repurposable drugs as potent TRPA1 antagonists that may serve as potential treatments for MS patients. After screening the DrugBank database with the combined generated algorithm, 903 repurposable structures were selected, with 97 displaying satisfactory inhibition probabilities and pharmacokinetics. Among the top 10 most probable inhibitors of TRPA1 with good blood brain barrier (BBB) permeability, desvenlafaxine, paliperidone, and febuxostat emerged as the most promising repurposable agents for treating MS. Molecular docking studies indicated that desvenlafaxine, paliperidone, and febuxostat are likely to induce allosteric TRPA1 channel inhibition. Future in vitro and in vivo studies are needed to confirm the biological activity of the selected hit molecules.

Artificial Intelligence-based Drug Discovery and Drug Repositioning

The methodologies of computational drug discovery and drug repositioning (DR) based on biomolecular profile information are reviewed systematically. For big data drug discovery and DR, 1) methods of comparing gene expression profiles of the diseased state and drug-administered state to predict the effect and toxicity of the drug, 2) DR methods based on the disease network, and 3) prediction of drug effect based on the biological network analysis are described. For methods of AI-based drug discovery, virtual screening using AI and AI-based drug target exploration are reviewed.

Bridging Crystal Engineering and Drug Discovery by Utilizing Intermolecular Interactions and Molecular Shapes in Crystals

AbstractMost structure‐based drug discovery methods utilize crystal structures of receptor proteins. Crystal engineering, on the other hand, utilizes the wealth of chemical information inherent in small‐molecule crystal structures in the Cambridge Structural Database (CSD). We show that the interaction surfaces and shapes of molecules in experimentally determined small‐molecule crystal structures can serve as effective tools in drug discovery. Our description of the shape and interaction propensities of molecules in their crystal structures can be used to screen them for specific binding compatibility with protein targets, as demonstrated through the high‐throughput profiling of around 138 000 small‐molecule structures in the CSD and a series of drug–protein crystal structures. Electron‐density‐based intermolecular boundary surfaces in small‐molecule crystal structures and in target‐protein pockets are utilized to identify potential ligand molecules from the CSD based on 3D shape and intermolecular interaction matching.

Bridging Crystal Engineering and Drug Discovery by Utilizing Intermolecular Interactions and Molecular Shapes in Crystals.

Most structure-based drug discovery methods utilize crystal structures of receptor proteins. Crystal engineering, on the other hand, utilizes the wealth of chemical information inherent in small-molecule crystal structures in the Cambridge Structural Database (CSD). We show that the interaction surfaces and shapes of molecules in experimentally determined small-molecule crystal structures can serve as effective tools in drug discovery. Our description of the shape and interaction propensities of molecules in their crystal structures can be used to screen them for specific binding compatibility with protein targets, as demonstrated through the high-throughput profiling of around 138 000 small-molecule structures in the CSD and a series of drug-protein crystal structures. Electron-density-based intermolecular boundary surfaces in small-molecule crystal structures and in target-protein pockets are utilized to identify potential ligand molecules from the CSD based on 3D shape and intermolecular interaction matching.

Discovery of potent p38α MAPK inhibitors through a funnel like workflow combining in silico screening and in vitro validation

This work describes the rational discovery of novel chemotypes of p38α MAPK inhibitors using a funnel approach consisting of several computer-aided drug discovery methods and biological experiments. Among the identified hits, four compounds belonging to different chemical families showed IC50 values lower than 10 μM. In particular, the 1,4-benzodioxane derivative 5 turned out to be a potent and efficient p38α MAPK inhibitor having IC50 = 0.07 μM, and LEexp and LipE values of 0.38 and 4.8, respectively; noteworthy, the compound had also a promising kinase selectivity profile and the capability to suppress p38α MAPK effects in human immune cells.Overall, the collected findings highlight that the applied strategy has been successful in generating chemical novelty in the inhibitor kinase field, providing suitable chemical candidates for further inhibitor optimization.

Trophoblastic cell invasion is induced by leptin involving b-Catenin/Wnt pathway

The Wnt/β-catenin pathway has a crucial role in the proliferation and differentiation of normal cells as well as the self-renewal and pluripotency of stem cells, including cancer stem cells (CSCs). Targeting this pathway with small-molecule chemotherapeutics, discovered via conventional efforts, has proved difficult. Recently, computer-aided drug discovery efforts have produced promising chemotherapeutics. A concerted effort to develop inhibitors of this pathway through more efficient and cost-effective drug discovery methods could lead to a significant increase in clinically relevant therapeutics. Herein, patents from 2010 to 2020 are reviewed to identify those that have disclosed composition of matter for small-molecule inhibitors of the Wnt/ β-catenin pathway for cancer. We believe that such efforts will provide insights for future therapeutic candidate discovery and development in this field.

Attractor - a new turning point in drug discovery.

Drug discovery for complex diseases can be viewed as a challenging problem in which the influence of compounds on dynamic features of disease system should be considered, especially the strategies escaping from the disease attractors. Moreover, escaping from the disease-related attractors has been proved to be a cue for the treatment of the complex diseases. The drug discovery methodology based on the attractor theory indicates new solutions for target identification, drug discovery and drug combination design. The methodology is based on the holism level of the organism and the features of system dynamics, so it has advantages for the classification of complex diseases and drug discovery. Currently, research results of this method have increased, which expand the insight scope for drug discovery. This article introduces the major drug discovery methods in the history of pharmacy development and their characteristics, so as to illustrate the reasons and inevitability of the appearance of attractor method, its position in the history of pharmacy development, and its advantages for drug discovery and design, thereby to prove that the attractor method can indeed become the next major drug development method. In addition, it provides a comprehensive description about the concept of attractor, the pipeline of attractor analysis, the common methods of each process and its research progress, so as to provide a macroscopic framework and optional methods and tools for the follow-up researchers.

Differential Susceptibility of Mycoplasma and Ureaplasma Species to Compound-Enhanced Copper Toxicity.

RationaleMycoplasmas represent important etiologic agents of many human diseases. Due to increasing antimicrobial resistance and slow rate of novel discovery, unconventional methods of drug discovery are necessary. Copper ions are utilized in host microbial killing, and bacteria must regulate intracellular Cu concentrations to avoid toxicity. We hypothesized that human mollicutes may have susceptibility to Cu-induced toxicity, and compounds that augment copper-dependent killing.MethodsMycoplasma pneumoniae (Mpn), Ureaplasma parvum (Up), Ureaplasma urealyticum (Uu), and Mycoplasma hominis (Mh) were exposed to CuSO4 to determine minimal inhibitory concentrations (MICs). Once inhibitory concentrations had been determined, bacteria were treated with an FDA-approved drug disulfiram (DSF), glyoxal bis(4-methyl-3-thiosemicarbazone) (GTSM), and 2,9-dimethyl-1,10-phenanthroline (neocuproine), with or without Cu2+, to determine compound MICs.ResultsUreaplasma species and Mh were able to tolerate 30–60 μM CuSO4, while Mpn tolerated over 10-fold higher concentrations (>1 mM). GTSM inhibited growth of all four organisms, but was unaffected by Cu2+ addition. Inhibition by GTSM was reduced by addition of the cell-impermeant Cu chelator, bathocuproine disulfonate (BCS). Neocuproine exhibited Cu-dependent growth inhibition of all organisms. DSF exhibited Cu-dependent growth inhibition against Mh at low micromolar concentrations, and at intermediate concentrations for Mpn.ConclusionMICs for CuSO4 differ widely among human mollicutes, with higher MICs for Mpn compared to Mh, Uu, and Up. DSF and Neocuproine exhibit Cu-dependent inhibition of mollicutes with copper concentrations between 25 and 50 μM. GTSM has copper-dependent anti-microbial activity at low levels of copper. Drug enhanced copper toxicity is a promising avenue for novel therapeutic development research with Mycoplasma and Ureaplasma species.

Quantitative Structure Activity Relationship of New Isatin Analogues for Design New Compounds as Anti-breast Cancer

Breast cancer is the most common diagnosed cancer and the leading cause of related death in woman across the world. Nowadays, there are many effective chemotherapeutic agents used in the treatment of breast cancer, however due to the high side effects of these drugs, there is still an urgent need to develop new drugs for battle the disease. Computational chemistry is unique method in drug discovery which reduce cost. In this study 105 molecules were subjected to quantitative structure-activity relationship analysis to find the structure requirements for ligand binding. Then their structures were drowning in Hyperchem and also optimized, the structural invariants used in this study were those obtained from whole molecular structures: by both hyperchem and dragon. Four chemometrics method including MLR, FA-MLR, PCR and GA-PLS were employed to make connection between structural parameters and cytotoxic effects. GA-PLS showed Chemical, Topological, Randic molecular, Charge, 3D-Morse, Functional, Atom-centeredindices to be the most significant parameters on cytotoxic activity. The result of FA-MLR analysis revealed the effects of Chemical, Atom-centered, Galvez and Functional on the cytotoxic activity too. A comparison between the different statistical methods employed indicated that GA-PLS represented superior results and it could explain and predict 72% and 80% variances in the PIC50 data, respectively.

Eschatological scrutiny of unprofessional usage of molecular docking; how unreliability in computational methods arises from amateurish mistakes

Abstract The impact of computational methods in drug design and discovery is prevailing in both academic and industrial scales. Molecular docking is one of the favorite tools for assessment of the interactions between a ligand and its congener macromolecule. In silico approaches and especially molecular docking are gaining much attention in recent years due to their cost-effective nature. In this letter to editor, we want to briefly describe how an undisciplined and unorganized research with molecular docking can result in highly equivocal results. This discussion can be useful for other scientist to avoid these pitfalls. This paper addresses the article by V. Suganya and V. Anuradha entitled “In silico molecular docking of astaxanthin and sorafenib with different apoptotic proteins involved in hepatocellular carcinoma” published in 6th of March 2019 on Biocatalysis and Agricultural Biotechnology.

CDK2 and Bcl-xL inhibitory mechanisms by docking simulations and anti-tumor activity from piperine enriched supercritical extract

Supercritical fluid technologies offer an innovative method for food industry and drug discovery from natural sources. The aim of the study is to investigate the anti-tumor activity of piperine rich extract by supercritical fluid (SFE) from black pepper (Piper nigrum). In silico docking simulations predicted anti-tumor molecular mechanism and protein-piperine hydrophobic interactions, showing hydrogen bonds between piperine and residue Ser5 inside the ATP binding site in CDK2. Moreover, piperine interacts with peptide substrate residue Lys8 inside its binding site in Cyclin A molecule. Other predicted interaction showed piperine inside the hydrophobic groove of Bcl-xL. Confirming the docking simulation, in vitro assays with SFE (40 °C/30 MPa) showed cytotoxicity to MCF-7 cells (IC50 = 27.8 ± 6.8 μg/ml) correlated to increased apoptosis. Balb/c mice-bearing Ehrlich Ascites Carcinoma (EAC) group that received the SFE (100 mg/kg/day) showed tumor growth inhibition (60%) and increased mice survival (50%), probably related to cell cycle arrest (G2/M) and increased apoptosis. In vivo treatments with SFE increased the expression of pro-apoptotic proteins (p53 and Bax), inhibited cell cycle proteins (CDK2, Cyclin A) and anti-apoptotic protein (Bcl-xL). Thus, confirming in silico predicted inhibitory interactions. These results clearly showed promising performance of the piperine-rich fraction recovered from black pepper, drawing attention to its use as complementary therapy for cancer.