Rational drug design

A selective method for optimizing ensemble docking-based experiments on an InhA Fully-Flexible receptor model

BACKGROUND

In the rational drug design process, an ensemble of conformations obtained from a molecular dynamics simulation plays a crucial role in docking experiments. Some studies have found that Fully-Flexible Receptor (FFR) models predict realistic binding energy accurately and improve scoring to enhance selectiveness. At the same time, methods have been proposed to reduce the high computational costs involved in considering the explicit flexibility of proteins in receptor-ligand docking. This study introduces a novel method to optimize ensemble docking-based experiments by reducing the size of an InhA FFR model at docking runtime and scaling docking workflow invocations on cloud virtual machines.

RESULTS

First, in order to find the most affordable cost-benefit pool of virtual machines, we evaluated the performance of the docking workflow invocations in different configurations of Azure instances. Second, we validated the gains obtained by the proposed method based on the quality of the Reduced Fully-Flexible Receptor (RFFR) models produced using AutoDock4.2. The analyses show that the proposed method reduced the model size by approximately 50% while covering at least 86% of the best docking results from the 74 ligands tested. Third, we tested our novel method using AutoDock Vina, a different docking software, and showed the positive accuracy achieved in the resulting RFFR models. Finally, our results demonstrated that the method proposed optimized ensemble docking experiments and is applicable to different docking software. In addition, it detected new binding modes, which would be unreachable if employing only the rigid structure used to generate the InhA FFR model.

CONCLUSIONS

Our results showed that the selective method is a valuable strategy for optimizing ensemble docking-based experiments using different docking software. The RFFR models produced by discarding non-promising snapshots from the original model are accurately shaped for a larger number of ligands, and the elapsed time spent in the ensemble docking experiments are considerably reduced.

Rational Discovery of (+) (S) Abscisic Acid as a Potential Antifungal Agent: a Repurposing Approach

Fungal infections are spreading widely worldwide, and the types of treatment are limited due to the lack of diverse therapeutic agents and their associated side effects and toxicity. The discovery of new antifungal classes is vital and critical. We discovered the antifungal activity of abscisic acid through a rational drug design methodology that included the building of homology models for fungal chorismate mutases and a pharmacophore model derived from a transition state inhibitor. Ligand-based virtual screening resulted in some hits that were filtered using molecular docking and molecular dynamic simulations studies. Both in silico methods and in vitro antifungal assays were used as tools to select and validate the abscisic acid repurposing. Abscisic acid inhibition assays confirmed the inhibitory effect of abscisic acid on chorismate mutase through the inhibition of phenylpyruvate production. The repositioning of abscisic acid, the well-known and naturally occurring plant growth regulator, as a potential antifungal agent because of its suggested action as an inhibitor to several fungal chorismate mutases was the main result of this work.

An overview of recent molecular dynamics applications as medicinal chemistry tools for the undruggable site challenge

Molecular dynamics (MD) has become increasingly popular due to the development of hardware and software solutions and the improvement in algorithms, which allowed researchers to scale up calculations in order to speed them up. MD simulations are usually used to address protein folding issues or protein-ligand complex stability through energy profile analysis over time. In recent years, the development of new tools able to deeply explore a potential energy surface (PES) has allowed researchers to focus on the dynamic nature of the binding recognition process and binding-induced protein conformational changes. Moreover, modern approaches have been demonstrated to be effective and reliable in calculating some kinetic and thermodynamic parameters behind the host-guest recognition process. Starting from all of these considerations, several efforts have been made in order to integrate MD within the virtual screening process in drug discovery. Knowledge retrieved from MD can, in fact, be exploited as a starting point to build pharmacophores or docking constraints in the early stage of the screening campaign as well as to define key features, in order to unravel hidden binding modes and help the optimisation of the molecular structure of a lead compound. Based on these outcomes, researchers are nowadays using MD as an invaluable tool to discover and target previously considered undruggable binding sites, including protein-protein interactions and allosteric sites on a protein surface. As a matter of fact, the use of MD has been recognised as vital to the discovery of selective protein-protein interaction modulators. The use of a dynamic overview on how the host-guest recognition occurs and of the relative conformational modifications induced allows researchers to optimise small molecules and small peptides capable of tightly interacting within the cleft between two proteins. In this review, we aim to present the most recent applications of MD as an integrated tool to be used in the rational design of small molecules or small peptides able to modulate undruggable targets, such as allosteric sites and protein-protein interactions.

Bioinformatics-based tools in drug discovery: the cartography from single gene to integrative biological networks

Originally developed for the analysis of biological sequences, bioinformatics has advanced into one of the most widely recognized domains in the scientific community. Despite this technological evolution, there is still an urgent need for nontoxic and efficient drugs. The onus now falls on the 'omics domain to meet this need by implementing bioinformatics techniques that will allow for the introduction of pioneering approaches in the rational drug design process. Here, we categorize an updated list of informatics tools and explore the capabilities of integrative bioinformatics in disease control. We believe that our review will serve as a comprehensive guide toward bioinformatics-oriented disease and drug discovery research.

Design of Drugs by Filtering Through ADMET, Physicochemical and Ligand-Target Flexibility Properties

There is a synergistic interaction between medicinal chemistry, chemoinformatics, and bioinformatics. The last one includes analyses of sequences as well as structural analysis which employ computational techniques such as docking studies and molecular dynamics (MD) simulations. Over the last years these techniques have allowed the development of new accurate computational tools for drug design. As a result, there have been an increased number of publications where computational methods such as pharmacophore modeling, de novo drug design, evaluation of physicochemical properties, and analysis of ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties have been quite useful for eliminating the compounds with poor physicochemical or toxicological properties. Furthermore, using MD simulations and docking analysis, it is possible to estimate the binding energy of the protein-ligand complexes by using scoring functions, as well as to structurally depict the binding pose of the compounds on proteins, in order to select the best evaluated compounds for subsequent synthetizing and evaluation through biological assays. In this work, we describe some computational tools that have been used for structure-based drug design of new compounds that target histone deacetylases (HDACs), which are known to be potential targets in cancer and parasitic diseases.

Selective ATP competitive leads of CDK4: Discovery by 3D-QSAR pharmacophore mapping and molecular docking approach

The discovery of ATP competitive CDK4 inhibitors is an on-going challenging task in cancer therapy. Here, an attempt has been made to develop new leads targeting ATP binding site of CDK4 by applying 3D-QSAR pharmacophore mapping and molecular docking methods The outcome of 6 leads offers a significant contribution for selective CDK4 inhibition, since they show potential binding interactions with Val⁹⁶, Arg¹⁰¹, and Glu¹⁴⁴ residues of CDK4, that are unique and from other kinases. It is worth noting that there is a striking similarity in binding interactions of the leads and known CDK4 inhibitors, namely Abemaciclib, Palbociclib and Ribociclib. Further key features, including high dock score value, good predicted activity, scaffold diversity, and the acceptable ADME profile of leads, provide a great opportunity for the development of highly potent and selective ATP competitive inhibitors of CDK4.

Advantages of Structure-Based Drug Design Approaches in Neurological Disorders

OBJECTIVE

The purpose of the review is to portray the theoretical concept on neurological disorders from research data.

BACKGROUND

The freak changes in chemical response of nerve impulse causes neurological disorders. The research evidence of the effort done in the older history suggests that the biological drug targets and their effective feature with responsive drugs could be valuable in promoting the future development of health statistics structure for improved treatment for curing the nervous disorders.

METHODS

In this review, we summarized the most iterative theoretical concept of structure based drug design approaches in various neurological disorders to unfathomable understanding of reported information for future drug design and development.

RESULTS

On the premise of reported information we analyzed the model of theoretical drug designing process for understanding the mechanism and pathology of the neurological diseases which covers the development of potentially effective inhibitors against the biological drug targets. Finally, it also suggests the management and implementation of the current treatment in improving the human health system behaviors.

CONCLUSION

With the survey of reported information we concluded the development strategies of diagnosis and treatment against neurological diseases which leads to supportive progress in the drug discovery.

Quantum mechanics implementation in drug-design workflows: does it really help?

The pharmaceutical industry is progressively operating in an era where development costs are constantly under pressure, higher percentages of drugs are demanded, and the drug-discovery process is a trial-and-error run. The profit that flows in with the discovery of new drugs has always been the motivation for the industry to keep up the pace and keep abreast with the endless demand for medicines. The process of finding a molecule that binds to the target protein using in silico tools has made computational chemistry a valuable tool in drug discovery in both academic research and pharmaceutical industry. However, the complexity of many protein-ligand interactions challenges the accuracy and efficiency of the commonly used empirical methods. The usefulness of quantum mechanics (QM) in drug-protein interaction cannot be overemphasized; however, this approach has little significance in some empirical methods. In this review, we discuss recent developments in, and application of, QM to medically relevant biomolecules. We critically discuss the different types of QM-based methods and their proposed application to incorporating them into drug-design and -discovery workflows while trying to answer a critical question: are QM-based methods of real help in drug-design and -discovery research and industry?

Aromatase inhibitory activity of 1,4-naphthoquinone derivatives and QSAR study

A series of 2-amino(chloro)-3-chloro-1,4-naphthoquinone derivatives (1-11) were investigated for their aromatase inhibitory activities. 1,4-Naphthoquinones 1 and 4 were found to be the most potent compounds affording IC₅₀ values 5.2 times lower than the reference drug, ketoconazole. A quantitative structure-activity relationship (QSAR) model provided good predictive performance (R²_CV = 0.9783 and RMSE_CV = 0.0748) and indicated mass (Mor04m and H8m), electronegativity (Mor08e), van der Waals volume (G1v) and structural information content index (SIC2) descriptors as key descriptors governing the activity. To investigate the effects of structural modifications on aromatase inhibitory activity, the model was employed to predict the activities of an additional set of 39 structurally modified compounds constructed in silico. The prediction suggested that the 2,3-disubstitution of 1,4-naphthoquinone ring with halogen atoms (i.e., Br, I and F) is the most effective modification for potent activity (1a, 1b and 1c). Importantly, compound 1b was predicted to be more potent than its parent compound 1 (11.90-fold) and the reference drug, letrozole (1.03-fold). The study suggests the 1,4-naphthoquinone derivatives as promising compounds to be further developed as a novel class of aromatase inhibitors.

Rational Drug Design Rational Drug Design

For the past three decades rationale drug design (RDD) has been developing as an innovative, rapid and successful way to discover new drug candidates. Many strategies have been followed and several targets with diverse structures and different biological roles have been investigated. Despite the variety of computational tools available, one can broadly divide them into two major classes that can be adopted either separately or in combination. The first class involves structure-based drug design, when the target's 3-dimensional structure is available or it can be computationally generated using homology modeling. On the other hand, when only a set of active molecules is available, and the structure of the target is unknown, ligand-based drug design tools are usually used. This review describes some recent advances in rational drug design, summarizes a number of their practical applications, and discusses both the advantages and shortcomings of the various techniques used.

Molecular Docking at a Glance

The current chapter introduces different aspects of molecular docking technique in order to give an overview to the readers about the topics which will be dealt with throughout this volume. Like many other fields of science, molecular docking studies has experienced a lagging period of slow and steady increase in terms of acquiring attention of scientific community as well as its frequency of application, followed by a pronounced era of exponential expansion in theory, methodology, areas of application and performance due to developments in related technologies such as computational resources and theoretical as well as experimental biophysical methods. In the following sections the evolution of molecular docking will be reviewed and its different components including methods, search algorithms, scoring functions, validation of the methods, and area of applications plus few case studies will be touched briefly.

Structural features of the carbon-sulfur chemical bond: a semi-experimental perspective

In this work semi-experimental and theoretical equilibrium geometries of 10 sulfur-containing organic molecules, as well as 4 oxygenated ones, are determined by means of a computational protocol based on density functional theory. The results collected in the present paper further enhance our online database of accurate semi-experimental equilibrium molecular geometries, adding 13 new molecules containing up to 8 atoms, for 12 of which the first semi-experimental equilibrium structure is reported, to the best of our knowledge. We focus in particular on sulfur-containing compounds, aiming both to provide new accurate data on some rather important chemical moieties, only marginally represented in the literature of the field, and to examine the structural features of carbon-sulfur bonds in the light of the previously presented linear regression approach. The structural changes issuing from substitution of oxygen by sulfur are discussed to get deeper insights on how modifications in electronic structure and nuclear potential can affect equilibrium geometries. With respect to our previous works, we perform non-linear constrained optimizations of equilibrium SE structures with a new general and user-friendly software under development in our group with updated definition of useful statistical indicators.

bSiteFinder, an improved protein-binding sites prediction server based on structural alignment: more accurate and less time-consuming

MOTIVATION

Protein-binding sites prediction lays a foundation for functional annotation of protein and structure-based drug design. As the number of available protein structures increases, structural alignment based algorithm becomes the dominant approach for protein-binding sites prediction. However, the present algorithms underutilize the ever increasing numbers of three-dimensional protein-ligand complex structures (bound protein), and it could be improved on the process of alignment, selection of templates and clustering of template. Herein, we built so far the largest database of bound templates with stringent quality control. And on this basis, bSiteFinder as a protein-binding sites prediction server was developed.

RESULTS

By introducing Homology Indexing, Chain Length Indexing, Stability of Complex and Optimized Multiple-Templates Clustering into our algorithm, the efficiency of our server has been significantly improved. Further, the accuracy was approximately 2-10 % higher than that of other algorithms for the test with either bound dataset or unbound dataset. For 210 bound dataset, bSiteFinder achieved high accuracies up to 94.8 % (MCC 0.95). For another 48 bound/unbound dataset, bSiteFinder achieved high accuracies up to 93.8 % for bound proteins (MCC 0.95) and 85.4 % for unbound proteins (MCC 0.72). Our bSiteFinder server is freely available at http://binfo.shmtu.edu.cn/bsitefinder/, and the source code is provided at the methods page.

CONCLUSION

An online bSiteFinder server is freely available at http://binfo.shmtu.edu.cn/bsitefinder/. Our work lays a foundation for functional annotation of protein and structure-based drug design. With ever increasing numbers of three-dimensional protein-ligand complex structures, our server should be more accurate and less time-consuming.Graphical Abstract bSiteFinder (http://binfo.shmtu.edu.cn/bsitefinder/) as a protein-binding sites prediction server was developed based on the largest database of bound templates so far with stringent quality control. By introducing Homology Indexing, Chain Length Indexing, Stability of Complex and Optimized Multiple-Templates Clustering into our algorithm, the efficiency of our server have been significantly improved. What's more, the accuracy was approximately 2-10 % higher than that of other algorithms for the test with either bound dataset or unbound dataset.

Carbonic anhydrase inhibitors: a review on the progress of patent literature (2011-2016)

INTRODUCTION

A large area of carbonic anhydrase (CA) research focuses on the inhibition of human CA IX and CA XII, as these isoforms have been designated as biomarkers and therapeutic targets for various cancer types.

AREAS COVERED

Recently, the majority of CA inhibitor (CAI) patents cover compound design, synthesis, and delivery methods for the treatment of glaucoma and cancer. The analysis of included patents highlights the need for isoform specific inhibitors. This review covers the patents of medically relevant carbonic anhydrase inhibitors between 2011-2016.

EXPERT OPINION

The improvement of structure-based drug design methods and access to the crystal structures of human CA isoforms have improved inhibitor development. This progress can be observed in relation to the selective inhibition of CA IX for cancer treatments, with one inhibitor in clinical trials. However, the design of nonclassical CAIs is essential to further improve isoform specificity and prevent sulfur allergies.

Potentiators exert distinct effects on human, murine, and Xenopus CFTR

VX-770 (Ivacaftor) has been approved for clinical usage in cystic fibrosis patients with several CFTR mutations. Yet the binding site(s) on CFTR for this compound and other small molecule potentiators are unknown. We hypothesize that insight into this question could be gained by comparing the effect of potentiators on CFTR channels from different origins, e.g., human, mouse, and Xenopus (frog). In the present study, we combined this comparative molecular pharmacology approach with that of computer-aided drug discovery to identify and characterize new potentiators of CFTR and to explore possible mechanism of action. Our results demonstrate that 1) VX-770, NPPB, GlyH-101, P1, P2, and P3 all exhibited ortholog-specific behavior in that they potentiated hCFTR, mCFTR, and xCFTR with different efficacies; 2) P1, P2, and P3 potentiated hCFTR in excised macropatches in a manner dependent on the degree of PKA-mediated stimulation; 3) P1 and P2 did not have additive effects, suggesting that these compounds might share binding sites. Also 4) using a pharmacophore modeling approach, we identified three new potentiators (IOWH-032, OSSK-2, and OSSK-3) that have structures similar to GlyH-101 and that also exhibit ortholog-specific potentiation of CFTR. These could potentially serve as lead compounds for development of new drugs for the treatment of cystic fibrosis. The ortholog-specific behavior of these compounds suggest that a comparative pharmacology approach, using cross-ortholog chimeras, may be useful for identification of binding sites on human CFTR.

Project Management between Will and Representation

This article challenges some deep-rooted assumptions of project management. Inspired by the work of the German philosopher, Arthur Schopenhauer, it calls for looking at projects through two complementary lenses: one that accounts for cognitive and representational aspects and one that accounts for material and volitional aspects. Understanding the many ways in which these aspects transpire and interact in projects sheds new light on project organizations, as imperfect and fragile representations that chase a shifting nexus of intractable human, social, technical, and material processes. This, in turn, can bring about a new grasp of notions such as value, knowledge, complexity, and risk.

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.

Computational Methods Applied to Rational Drug Design

Due to the synergic relationship between medical chemistry, bioinformatics and molecular simulation, the development of new accurate computational tools for small molecules drug design has been rising over the last years. The main result is the increased number of publications where computational techniques such as molecular docking, de novo design as well as virtual screening have been used to estimate the binding mode, site and energy of novel small molecules. In this work I review some tools, which enable the study of biological systems at the atomistic level, providing relevant information and thereby, enhancing the process of rational drug design.

Linear and Branched PEIs (Polyethylenimines) and Their Property Space

A chemical property space defines the adaptability of a molecule to changing conditions and its interaction with other molecular systems determining a pharmacological response. Within a congeneric molecular series (compounds with the same derivatization algorithm and thus the same brute formula) the chemical properties vary in a monotonic manner, i.e., congeneric compounds share the same chemical property space. The chemical property space is a key component in molecular design, where some building blocks are functionalized, i.e., derivatized, and eventually self-assembled in more complex systems, such as enzyme-ligand systems, of which (physico-chemical) properties/bioactivity may be predicted by QSPR/QSAR (quantitative structure-property/activity relationship) studies. The system structure is determined by the binding type (temporal/permanent; electrostatic/covalent) and is reflected in its local electronic (and/or magnetic) properties. Such nano-systems play the role of molecular devices, important in nano-medicine. In the present article, the behavior of polyethylenimine (PEI) macromolecules (linear LPEI and branched BPEI, respectively) with respect to the glucose oxidase enzyme GOx is described in terms of their (interacting) energy, geometry and topology, in an attempt to find the best shape and size of PEIs to be useful for a chosen (nanochemistry) purpose.

Zika virus drug targets: a missing link in drug design and discovery – a route map to fill the gap

This review depicts anin silicoroute map for ZIKV drug discovery, thus revealing novel potential inhibitors of viral replication.

Antileishmanial Activity of Compounds Derived from the Medicines for Malaria Venture Open Access Box Against Intracellular Leishmania major Amastigotes

Leishmaniasis is a complex tropical disease caused by kinetoplastid parasitic protozoa of the genus Leishmania and is transmitted by the sand fly insect vector. Cutaneous leishmaniasis (CL) is the most common form of this disease, and CL infections often result in serious skin lesions and scars. CL remains a public health problem in many endemic countries worldwide because of the absence of effective, safe, and cost-effective drugs for treatment. One of the strategies we chose to use to find novel chemical entities worthy of further development as antileishmanials involved screening synthetic and natural products libraries. In our study, we developed a Leishmania major intracellular amastigote assay that uses the activity of luciferase as a measure of parasite proliferation and used this assay to screen a collection of 400 compounds obtained from Medicines for Malaria Venture (MMV) for their antileishmanial activity. Our results showed that 14 compounds identified by MMV as antimalarial drugs have antileishmanial activity and can potentially be optimized for CL drug development.

Synthesis and Antimicrobial Activity of the Hybrid Molecules between Sulfonamides and Active Antimicrobial Pleuromutilin Derivative

A series of novel hybrid molecules between sulfonamides and active antimicrobial 14-o-(3-carboxy-phenylsulfide)-mutilin were synthesized, and their in vitro antibacterial activities were evaluated by the broth microdilution. Results indicated that these compounds displayed potent antimicrobial activities in vitro against various drug-susceptible and drug-resistant Gram-positive bacteria such as Staphylococci and streptococci, including methicillin-resistant Staphylococcus aureus, and mycoplasma. In particular, sulfapyridine analog (6c) exhibited more potent inhibitory activity against Gram-positive bacteria and mycoplasma, including Staphylococcus aureus (MIC = 0.016-0.063 μg/mL), methicillin-resistant Staphylococcus aureus (MIC = 0.016 μg/mL), Streptococcus pneumoniae (MIC = 0.032-0.063 μg/mL), Mycoplasma gallisepticum (MIC = 0.004 μg/mL), with respect to other synthesized compounds and reference drugs sulfonamide (MIC = 8-128 μg/mL) and valnemulin (MIC = 0.004-0.5 μg/mL). Furthermore, comparison between MIC values of pleuromutilin-sulfonamide hybrids 6a-f with pleuromutilin parent compound 3 revealed that these modifications at 14 position side chain of the pleuromutilin with benzene sulfonamide could greatly improve the antibacterial activity especially against Gram-positives.

Molecular docking and structure-based drug design strategies

Pharmaceutical research has successfully incorporated a wealth of molecular modeling methods, within a variety of drug discovery programs, to study complex biological and chemical systems. The integration of computational and experimental strategies has been of great value in the identification and development of novel promising compounds. Broadly used in modern drug design, molecular docking methods explore the ligand conformations adopted within the binding sites of macromolecular targets. This approach also estimates the ligand-receptor binding free energy by evaluating critical phenomena involved in the intermolecular recognition process. Today, as a variety of docking algorithms are available, an understanding of the advantages and limitations of each method is of fundamental importance in the development of effective strategies and the generation of relevant results. The purpose of this review is to examine current molecular docking strategies used in drug discovery and medicinal chemistry, exploring the advances in the field and the role played by the integration of structure- and ligand-based methods.

Fast and continuous-flow detection and separation of DNA complexes and DNA in nanofluidic chip format

Fast separation of DNA and detection of protein/DNA complexes are important in many state-of-the-art molecular medicine technologies, like the production of gene vaccines or medical diagnostics. Here, we describe a nanofluidic chip-based technique for fast, efficient, and virtually label-free detection and separation of protein/DNA and drug/DNA complexes and topological DNA variants. The mechanism is based on a continuous-flow dielectrophoresis at a nanoslit and allows efficient separation of small DNA fragments (<7,000 base pairs) and fast detection of DNA complexes within 1 min.

De-repressing LncRNA-Targeted Genes to Upregulate Gene Expression: Focus on Small Molecule Therapeutics

Non-protein coding RNAs (ncRNAs) make up the overwhelming majority of transcripts in the genome and have recently gained attention for their complex regulatory role in cells, including the regulation of protein-coding genes. Furthermore, ncRNAs play an important role in normal development and their expression levels are dysregulated in several diseases. Recently, several long noncoding RNAs (lncRNAs) have been shown to alter the epigenetic status of genomic loci and suppress the expression of target genes. This review will present examples of such a mechanism and focus on the potential to target lncRNAs for achieving therapeutic gene upregulation by de-repressing genes that are epigenetically silenced in various diseases. Finally, the potential to target lncRNAs, through their interactions with epigenetic enzymes, using various tools, such as small molecules, viral vectors and antisense oligonucleotides, will be discussed. We suggest that small molecule modulators of a novel class of drug targets, lncRNA-protein interactions, have great potential to treat some cancers, cardiovascular disease, and neurological disorders.

Small molecule growth inhibitors of human oncogenic gammaherpesvirus infected B-cells

Epstein-Barr virus (EBV) and Kaposi's sarcoma-associated herpesvirus (KSHV) are two human gammaherpesviruses associated with a broad spectrum of B-cell lymphomas, most acutely in immuno-compromised populations. However, there are no drugs which specifically target KSHV or EBV-associated lymphomas. To identify small molecules which selectively inhibit the growth of EBV or KSHV-associated B-cell lines, we performed a fluorescence based high-throughput screen on multiple stable GFP expressing virus-infected or uninfected B-cell lines. We identified 40 initial compounds with selective growth inhibition and subsequently determined the 50% growth inhibitory concentrations (GI50) for each drug. We further examined compounds with higher specificity to explore the underlying molecular mechanisms using transcription factor analysis, as well as a shRNA based knockdown strategy. Our data identified ten compounds with relatively high efficacy for growth inhibition. Two novel small molecules, NSC#10010 and NSC#65381 were potent growth inhibitors for gammaherpesvirus-associated B-lymphomas through activation of both the NF-κB and c-Myc-mediated signaling pathways. These drugs can serve as potential lead compounds to expand the current therapeutic window against EBV or KSHV-associated human B-cell malignancies.

How to design a potent, specific, and stable angiotensin-converting enzyme inhibitor

Angiotensin-converting enzyme inhibitors (ACE-Is) are a valuable class of antihypertensive drugs used in the treatment of cardiovascular system-related diseases. Hence, constant research into, and the development of, such compounds remain within the priorities of modern medical sciences. In this respect, a thorough understanding of their chemistry and biology is an important aspect of drug design; therefore, we present here available data on the pharmaceutical properties of ACE-Is. We also review the structural and biochemical features of the molecular target of ACE-Is and demonstrate several known enzyme-inhibitor complexes. Finally, we attempt to create a mathematical model describing the relation between the potency and/or stability of ACE-Is and their structural characteristics using quantitative structure-activity relation (QSAR), and quantitative structure-property relation (QSPR) techniques.

Molecular dynamics studies of the protein-protein interactions in inhibitor of κB kinase-β

Activation of the inhibitor of κB kinase subunit β (IKKβ) oligomer initiates a cascade that results in the translocation of transcription factors involved in mediating immune responses. Dimerization of IKKβ is required for its activation. Coarse-grained and atomistic molecular dynamics simulations were used to investigate the conformation-activity and structure-activity relationships within the oligomer assembly of IKKβ that are impacted upon activation, mutation, and binding of ATP. Intermolecular interactions, free energies, and conformational changes were compared among several conformations, including a monomer, two different dimers, and the tetramer. Modifications to the activation segment induce conformational changes that disrupt dimerization and suggest that the multimeric assembly mediates a global stability for the enzyme that influences the activity of IKKβ.

Manipulating In-House Designed Drug Databases For The Prediction of pH-Dependent Aqueous Drug Solubility

Chemical, pharmacokinetic, and pharmacodynamics properties are available in the package inserts of every Food and Drug Administration (FDA) approved prescription drug, including all available chemotherapy drugs. These inserts follow a specific format imposed by the FDA. Whether chemotherapy drugs are administered via the parenteral route or alimentary tract, a significant factor affecting their bioavailability, elimination and consequently the drug's effectiveness and potency, is its state of aqueous solubility. Water solubility has always lent itself poorly to the different predictive and experimental measures employed in the determination of a useful quantitative assessment. In this project, we first built a chemical structure based searchable database for 85 FDA approved chemotherapy drugs and then used Bio-Rad's KnowItAll^® Informatics suite to focus on the drugs pH-dependent water solubility prediction. We compared the predicted values for water solubility to the available values reported in the drug inserts, testing the practical utility and the predictive ability of our model in reporting such a clinically relevant, underreported pharmacokinetic parameter. A relational cancer drug database (MySQL) was created to further facilitate analysis and/or prediction of a chemotherapy compound's missing pharmacokinetic properties.

Molecular basis of cancer-therapy-induced cardiotoxicity: introducing microRNA biomarkers for early assessment of subclinical myocardial injury

Development of reliable biomarkers for early clinical assessment of drug-induced cardiotoxicity could allow the detection of subclinical cardiac injury risk in vulnerable patients before irreversible damage occurs. Currently, it is difficult to predict who will develop drug-induced cardiotoxicity owing to lack of sensitivity and/or specificity of currently used diagnostics. miRNAs are mRNA regulators and they are currently being extensively profiled for use as biomarkers due to their specific tissue and disease expression signature profiles. Identification of cardiotoxicity-specific miRNA biomarkers could provide clinicians with a valuable tool to allow prognosis of patients at risk of cardiovascular injury, alteration of a treatment regime or the introduction of an adjunct therapy in order to increase the long-term survival rate of patients treated with cardiotoxic drugs.

Exploring the potential of endophytes from medicinal plants as sources of antimycobacterial compounds

Natural product drug discovery has regained interest due to low production costs, structural diversity, and multiple uses of active compounds to treat various diseases. Attention has been directed towards medicinal plants as these plants have been traditionally used for generations to treat symptoms of numerous diseases.

It is established that plants harbour microorganisms, collectively known as endophytes. Exploring the as-yet untapped natural products from the endophytes increases the chances of finding novel compounds. The concept of natural products targeting microbial pathogens has been applied to isolate novel antimycobacterial compounds, and the rapid development of drug-resistant Mycobacterium tuberculosis has significantly increased the need for new treatments against this pathogen. It remains important to continuously screen for novel compounds from natural sources, particularly from rarely encountered microorganisms, such as the endophytes.

This review focuses on bioprospecting for polyketides and small peptides exhibiting antituberculosis activity, although current treatments against tuberculosis are described. It is established that natural products from these structure classes are often biosynthesised by microorganisms. Therefore it is hypothesised that some bioactive polyketides and peptides originally isolated from plants are in fact produced by their endophytes. This is of interest for further endophyte natural product investigations.

Computer-aided drug designing

Computer-aided drug designing has emerged as a cost-effective and rapid tool for the discovery of newer therapeutic agents. Several algorithms have been developed to analyze protein structure and function, to identify interacting ligands, active site residues, and to study protein-ligand interactions, which can eventually lead to the identification of new drugs. In silico drug designing involves identification of the target protein which is responsible for the development of the disease under study. The three-dimensional structure of the protein can be predicted using homology modeling, while molecular docking is applied to study the interaction of a drug molecule with the protein. The best orientation of the ligand-protein docked structure which has overall minimum energy needs to be obtained. In silico methods can be used to identify potential drugs for various diseases. Thus, computer-aided drug designing has become an indispensible and integral part of the drug discovery process.

Rational Drug Design

For the past three decades rationale drug design (RDD) has been developing as an innovative, rapid and successful way to discover new drug candidates. Many strategies have been followed and several targets with diverse structures and different biological roles have been investigated. Despite the variety of computational tools available, one can broadly divide them into two major classes that can be adopted either separately or in combination. The first class involves structure-based drug design, when the target's 3-dimensional structure is available or it can be computationally generated using homology modeling. On the other hand, when only a set of active molecules is available, and the structure of the target is unknown, ligand-based drug design tools are usually used. This review describes some recent advances in rational drug design, summarizes a number of their practical applications, and discusses both the advantages and shortcomings of the various techniques used.

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.

Novel approaches in antimalarial drug discovery

INTRODUCTION

The development of new antimalarial drugs remains of the utmost importance, since Plasmodium falciparum has developed resistance against nearly all chemotherapeutics in clinical use. In an effort to contain the resistance of P. falciparum against artemisinins and to further eradication efforts, studies are ongoing to identify novel and more efficacious approaches to develop antimalarials.

AREAS COVERED

The authors review the classical and new approaches to antimalarial drug discovery, with a special emphasis on the various stages of the parasite's life cycle and the different Plasmodium species. The authors discuss the methodologies and strategies for early efficacy testing that aim to narrow down the portfolio of promising compounds.

EXPERT OPINION

The increased efforts in the discovery and development of new antimalarial compounds have led to the recognition of new promising hits. However, there is still major roadblock of selecting the most promising compounds and then further testing them in early clinical trials, especially in the current restricted economy. Controlled human malaria infection has much potential for speeding-up the early development process of many drug candidates including those which target the pre-erythrocytic stages.

Structural characterization of some N-phenyl-4-oxo-4H-2-chromone carboxamides

N-phenyl-4-oxo-4H-2-chromone carboxamides were found to be inactive as MAO inhibitors in contrast with their N-phenyl-4-oxo-4H-3-chromone carboxamide isomers. In order to obtain a close insight into the docking mechanism for this family of compounds, the molecular and supramolecular structures of nine N-phenyl-4-oxo-4H-2-chromone carboxamides were determined. It was found that, in most of the secondary structures, the N(amido) and the O(carboxyl) of the carboxamide residue participate in strong intramolecular interactions, with the O atom of the chromene ring and with the H(ortho)-C (phenyl), respectively. When the phenyl ring had accessible acceptors as substituents a third intramolecular hydrogen bond was also observed. As a consequence, rotations of the chromone and phenyl rings around the N-C(alpha) and C(alpha')-C=O are constrained and the compounds were found to be more planar than would otherwise be expected. The deviation from planarity of the whole molecule can be quantified by the dihedral angles between mean planes of the aromatic rings and it was found that they were mainly affected by the degree of torsion of the phenyl ring with respect to the amide residue. The molecular conformations assumed by the secondary amides clearly contrast with that of a related tertiary amide that was also determined in this study. The unavailability of the N in this compound as a donor strongly influences the molecular isomerism and conformation. This analysis demonstrates that the molecules can be classified into four groups depending on the types of interactions formed as described above. If the secondary N(amido) of the carboximide is involved in two intramolecular interactions then this atom does not form any intermolecular contacts. In all other cases it does and the supramolecular structure formed is in most cases supplemented by weak C-H···O interactions.

Use of allosteric targets in the discovery of safer drugs

The need for drugs with fewer side effects cannot be overemphasized. Today, most drugs modify the actions of enzymes, receptors, transporters and other molecules by directly binding to their active (orthosteric) sites. However, orthosteric site configuration is similar in several proteins performing related functions and this leads to a lower specificity of a drug for the desired protein. Consequently, such drugs may have adverse side effects. A new basis of drug discovery is emerging based on the binding of the drug molecules to sites away (allosteric) from the orthosteric sites. It is possible to find allosteric sites which are unique and hence more specific as targets for drug discovery. Of many available examples, two are highlighted here. The first is caloxins - a new class of highly specific inhibitors of plasma membrane Ca²⁺ pumps. The second concerns the modulation of receptors for the neurotransmitter acetylcholine, which binds to 12 types of receptors. Exploitation of allosteric sites has led to the discovery of drugs which can selectively modulate the activation of only 1 (M1 muscarinic) out of the 12 different types of acetylcholine receptors. These drugs are being tested for schizophrenia treatment. It is anticipated that the drug discovery exploiting allosteric sites will lead to more effective therapeutic agents with fewer side effects.

Architecture and conservation of the bacterial DNA replication machinery, an underexploited drug target

New antibiotics with novel modes of action are required to combat the growing threat posed by multi-drug resistant bacteria. Over the last decade, genome sequencing and other high-throughput techniques have provided tremendous insight into the molecular processes underlying cellular functions in a wide range of bacterial species. We can now use these data to assess the degree of conservation of certain aspects of bacterial physiology, to help choose the best cellular targets for development of new broad-spectrum antibacterials.

DNA replication is a conserved and essential process, and the large number of proteins that interact to replicate DNA in bacteria are distinct from those in eukaryotes and archaea; yet none of the antibiotics in current clinical use acts directly on the replication machinery. Bacterial DNA synthesis thus appears to be an underexploited drug target. However, before this system can be targeted for drug design, it is important to understand which parts are conserved and which are not, as this will have implications for the spectrum of activity of any new inhibitors against bacterial species, as well as the potential for development of drug resistance. In this review we assess similarities and differences in replication components and mechanisms across the bacteria, highlight current progress towards the discovery of novel replication inhibitors, and suggest those aspects of the replication machinery that have the greatest potential as drug targets.