Chorismate synthase: An attractive target for drug development against orphan diseases

The increase in incidence of infectious diseases worldwide, particularly in developing countries, is worrying. Each year, 14 million people are killed by infectious diseases, mainly HIV/AIDS, respiratory infections, malaria and tuberculosis. Despite the great burden in the poor countries, drug discovery to treat tropical diseases has come to a standstill. There is no interest by the pharmaceutical industry in drug development against the major diseases of the poor countries, since the financial return cannot be guaranteed. This has created an urgent need for new therapeutics to neglected diseases. A possible approach has been the exploitation of the inhibition of unique targets, vital to the pathogen such as the shikimate pathway enzymes, which are present in bacteria, fungi and apicomplexan parasites but are absent in mammals. The chorismate synthase (CS) catalyses the seventh step in this pathway, the conversion of 5-enolpyruvylshikimate-3-phosphate to chorismate. The strict requirement for a reduced flavin mononucleotide and the anti 1,4 elimination are both unusual aspects which make CS reaction unique among flavin-dependent enzymes, representing an important target for the chemotherapeutic agents development. In this review we present the main biochemical features of CS from bacterial and fungal sources and their difference from the apicomplexan CS. The CS mechanisms proposed are discussed and compared with structural data. The CS structures of some organisms are compared and their distinct features analyzed. Some known CS inhibitors are presented and the main characteristics are discussed. The structural and kinetics data reviewed here can be useful for the design of inhibitors. © 2007 Bentham Science Publishers Ltd.

Advances in sickle cell disease treatment: From drug discovery until the patient monitoring

Sickle Cell Disease (SCD) is one of the most prevalent hematological diseases in the world. Despite the immense progress in molecular knowledge about SCD in last years few therapeutical sources are currently available. Nowadays the treatment is performed mainly with drugs such as hydroxyurea or other fetal hemoglobin inducers and chelating agents. This review summarizes current knowledge about the treatment and the advancements in drug design in order to discover more effective and safe drugs. Patient monitoring methods in SCD are also discussed. © 2011 Bentham Science Publishers Ltd.

Design, synthesis and biological evaluation of new aryl thiosemicarbazone as antichagasic candidates

The present work reports on the synthesis, biological assaying and docking studies of a series of 12 aryl thiosemicarbazones, which were planned to act over two main enzymes, cruzain and trypanothione reductase. These enzymes are used as targets of trypanocidal activity in Chagas disease control with a minimal mutagenic profile. Three p-nitroaromatic thiosemicarbazones showed high activity against Trypanosoma cruzi in in vitro assays (IC50 < 57 μM), and no mutagenic profile was observed in micronucleous tests. Although the in vitro inhibition test showed that 10-μM doses of eight compounds inhibited cruzain activity, no correlation was found between cruzain inhibition and trypanocidal activity. © 2013 Elsevier Masson SAS. All rights reserved.

Automatic design of decision-tree induction algorithms tailored to flexible-receptor docking data

Background: This paper addresses the prediction of the free energy of binding of a drug candidate with enzyme InhA associated with Mycobacterium tuberculosis. This problem is found within rational drug design, where interactions between drug candidates and target proteins are verified through molecular docking simulations. In this application, it is important not only to correctly predict the free energy of binding, but also to provide a comprehensible model that could be validated by a domain specialist. Decision-tree induction algorithms have been successfully used in drug-design related applications, specially considering that decision trees are simple to understand, interpret, and validate. There are several decision-tree induction algorithms available for general-use, but each one has a bias that makes it more suitable for a particular data distribution. In this article, we propose and investigate the automatic design of decision-tree induction algorithms tailored to particular drug-enzyme binding data sets. We investigate the performance of our new method for evaluating binding conformations of different drug candidates to InhA, and we analyze our findings with respect to decision tree accuracy, comprehensibility, and biological relevance. Results: The empirical analysis indicates that our method is capable of automatically generating decision-tree induction algorithms that significantly outperform the traditional C4.5 algorithm with respect to both accuracy and comprehensibility. In addition, we provide the biological interpretation of the rules generated by our approach, reinforcing the importance of comprehensible predictive models in this particular bioinformatics application. Conclusions: We conclude that automatically designing a decision-tree algorithm tailored to molecular docking data is a promising alternative for the prediction of the free energy from the binding of a drug candidate with a flexible-receptor.

A Combined Study Using Ligand-Based Design, Synthesis, and Pharmacological Evaluation of Analogues of the Acetaminophen Ortho-Regioisomer with Potent Analgesic Activity

A ligand-based drug design study was performed to acetaminophen regioisomers as analgesic candidates employing quantum chemical calculations at the DFT/B3LYP level of theory and the 6-31G* basis set. To do so, many molecular descriptors were used such as highest occupied molecular orbital, ionization potential, HO bond dissociation energies, and spin densities, which might be related to quench reactivity of the tyrosyl radical to give N-acetyl-p-benzosemiquinone-imine through an initial electron withdrawing or hydrogen atom abstraction. Based on this in silico work, the most promising molecule, orthobenzamol, was synthesized and tested. The results expected from the theoretical prediction were confirmed in vivo using mouse models of nociception such as writhing, paw licking, and hot plate tests. All biological results suggested an antinociceptive activity mediated by opioid receptors. Furthermore, at 90 and 120 min, this new compound had an effect that was comparable to morphine, the standard drug for this test. Finally, the pharmacophore model is discussed according to the electronic properties derived from quantum chemistry calculations.

Pharmacokinetic properties and in silico ADME modeling in drug discovery

The discovery and development of a new drug are time-consuming, difficult and expensive. This complex process has evolved from classical methods into an integration of modern technologies and innovative strategies addressed to the design of new chemical entities to treat a variety of diseases. The development of new drug candidates is often limited by initial compounds lacking reasonable chemical and biological properties for further lead optimization. Huge libraries of compounds are frequently selected for biological screening using a variety of techniques and standard models to assess potency, affinity and selectivity. In this context, it is very important to study the pharmacokinetic profile of the compounds under investigation. Recent advances have been made in the collection of data and the development of models to assess and predict pharmacokinetic properties (ADME - absorption, distribution, metabolism and excretion) of bioactive compounds in the early stages of drug discovery projects. This paper provides a brief perspective on the evolution of in silico ADME tools, addressing challenges, limitations, and opportunities in medicinal chemistry.

Advanced Protein Modeling Method: Benchmarking and Applications in Computer-Aided Drug Discovery

Development of homology modeling methods will remain an area of active research. These methods aim to develop and model increasingly accurate three-dimensional structures of yet uncrystallized therapeutically relevant proteins e.g. Class A G-Protein Coupled Receptors. Incorporating protein flexibility is one way to achieve this goal. Here, I will discuss the enhancement and validation of the ligand-steered modeling, originally developed by Dr. Claudio Cavasotto, via cross modeling of the newly crystallized GPCR structures. This method uses known ligands and known experimental information to optimize relevant protein binding sites by incorporating protein flexibility. The ligand-steered models were able to model, reasonably reproduce binding sites and the co-crystallized native ligand poses of the β2 adrenergic and Adenosine 2A receptors using a single template structure. They also performed better than the choice of template, and crude models in a small scale high-throughput docking experiments and compound selectivity studies. Next, the application of this method to develop high-quality homology models of Cannabinoid Receptor 2, an emerging non-psychotic pain management target, is discussed. These models were validated by their ability to rationalize structure activity relationship data of two, inverse agonist and agonist, series of compounds. The method was also applied to improve the virtual screening performance of the β2 adrenergic crystal structure by optimizing the binding site using β2 specific compounds. These results show the feasibility of optimizing only the pharmacologically relevant protein binding sites and applicability to structure-based drug design projects.

Molecular modelling assisted design and synthesis of cyclothialidine derivatives as DNA gyrase inhibitors

Since cyclothialidine was discovered as the most active DNA gyrase inhibitor in 1994, enormous efforts have been devoted to make it into a commercial medicine by a number of pharmaceutical companies and research groups worldwide. However, no serious breakthrough has been made up to now. An essential problem involved with cyclothialidine is that though it demonstrated the potent inhibition of DNA gyrase, it showed little activity against bacteria. This probably is attributable to its inability to penetrate bacterial cell walls and membranes. We applied the TSAR programme to generate a QSAR equation to the gram-negative organisms. In that equation, LogP is profoundly indicated as the key factor influencing the cyclothialidine activity against bacteria. However, the synthesized new analogues have failed to prove that. In the structure based drug design stage, we designed a group of open chain cyclothialidine derivatives by applying the SPROUT programme and completed the syntheses. Improved activity is found in a few analogues and a 3D pharmacophore of the DNA gyrase B is proposed to lead to synthesis of the new derivatives for development of potent antibiotics.

Design and synthesis of potential antimicrobial agents

Tuberculosis is one of the most devastating diseases in the world primarily due to several decades of neglect and an emergence of multidrug-resitance strains (MDR) of M. tuberculosis together with the increased incidence of disseminated infections produced by other mycobacterium in AIDS patients. This has prompted the search for new antimycobacterial drugs. A series of pyridine-2-, pyridine-3-, pyridine-4-, pyrazine and quinoline-2-carboxamidrazone derivatives and new classes of carboxamidrazone were prepared in an automated fashion and by traditional synthesis. Over nine hundred synthesized compounds were screened for their anti mycobacterial activity against M. fortutium (NGTG 10394) as a surrogate for M. tuberculosis. The new classes of amidrazones were also screened against tuberculosis H37 Rv and antimicrobial activities against various bacteria. Fifteen tested compounds were found to provide 90-100% inhibition of mycobacterium growth of M. tuberculosis H37 Rv in the primary screen at 6.25 μg mL-1. The most active compound in the carboxamidrazone amide series had an MIG value of 0.1-2 μg mL-1 against M. fortutium. The enzyme dihydrofolate reductase (DHFR) has been a drug-design target for decades. Blocking of the enzymatic activity of DHFR is a key element in the treatment of many diseases, including cancer, bacterial and protozoal infection. The x-ray structure of DHFR from M. tuberculosis and human DHFR were found to have differences in substrate binding site. The presence of glycerol molecule in the Xray structure from M. tuberculosis DHFR provided opportunity to design new antifolates. The new antifolates described herein were designed to retain the pharmcophore of pyrimethamine (2,4- diamino-5(4-chlorophenyl)-6-ethylpyrimidine), but encompassing a range of polar groups that might interact with the M. tuberculosis DHFR glycerol binding pockets. Finally, the research described in this thesis contributes to the preparation of molecularly imprinted polymers for the recognition of 2,4-diaminopyrimidine for the binding the target. The formation of hydrogen bonding between the model functional monomer 5-(4-tert-butyl-benzylidene)-pyrimidine-2,4,6-trione and 2,4-diaminopyrimidine in the pre-polymerisation stage was verified by 1H-NMR studies. Having proven that 2,4-diaminopyrimidine interacts strongly with the model 5-(4-tert-butylbenzylidene)- pyrimidine-2,4,6-trione, 2,4-diaminopyrimidine-imprinted polymers were prepared using a novel cyclobarbital derived functional monomer, acrylic acid 4-(2,4,6-trioxo-tetrahydro-pyrimidin-5- ylidenemethyl)phenyl ester, capable of multiple hydrogen bond formation with the 2,4- diaminopyrimidine. The recognition property of the respective polymers toward the template and other test compounds was evaluated by fluorescence. The results demonstrate that the polymers showed dose dependent enhancement of fluorescence emissions. In addition, the results also indicate that synthesized MIPs have higher 2,4-diaminopyrimidine binding ability as compared with corresponding non-imprinting polymers.

The design of novel antifolates against pneumocystis carinii

The pneumonia caused by Pneumocystis carinii is ultimately responsible for the death of many acquired immunodeficiency syndrome (AIDS) patients. Large doses of trimethoprim and pyrimethamine in combination with a sulphonamide and/or pentamidine suppress the infection but produce serious side-effects and seldom prevent recurrence after treatment withdrawal. However, the partial success of the aforementioned antifolates, and also trimetrexate used alone, does suggest dihydrofolate reductase (DHFR) as a target for the development of antipneumocystis agents. From the DHFR inhibitory activities of 3'-substituted pyrimethamine analogues it was suggested that the 3'-(3'',3''-dimethyltriazen-1''-yl) substituent may be responsible for the greater activity for the P.carinii over the mammalian enzyme. Crystallographic and molecular modeling studies revealed considerable geometrical and electronic differences between the triazene and the chemically related formamidine functions that may account for the differences in DHFR inhibitory profiles. Structural and electronic parameters calculated for a series of 3'-(3'',3''-disubstitutedtriazen-1''-yl) pyrimethamine analogues did not correlate with the DHFR inhibitory activities. However, the in vitro screening against P.carinii DHFR revealed that the 3''-hydroxyethyl-3''-benzyl analogue was the most active and selective. Models of the active sites of human and P.carinii DHFRs were constructed using DHFR sequence and structural homology data which had identified key residues involved in substrate and cofactor binding. Low energy conformations of the 3'',3''-dimethyl and 3''-hydroxyethyl-3''-benzyle analogues, determined from nuclear magnetic resonance studies and theoretical calculations, were docked by superimposing the diaminopyrimidine fragment onto a previously docked pyrimethamine analogue. Enzyme kinetic data supported the 3''-hydroxyethyl-3''-benzyl moiety being located in the NADPH binding groove. The 3''-benzyl substituent was able to locate to within 3 AA of a valine residue in the active site of P.carinii DHFR thereby producing a hydrophobic contact. The equivalent residue in human DHFR is threonine, more hydrophilic and less likely to be involved in such a contact. This difference may account for the greater inhibitory activity this analogue has for P.carinii DHFR and provide a basis for future drug design. From an in vivo model of PCP in immunosuppressed rats it was established that the 3"-hydroxyethyl-3"-benzyl analogue was able to reduce the.P.carinii burden more effectively with increasing doses, without causmg any visible signs of toxicity. However, equivalent doses were not as effective as pentamidine, a current treatment of choice for Pneumocystis carinii pneumonia.

Relieving the first bottleneck in the drug discovery pipeline:using array technologies to rationalize membrane protein production

The slow down in the drug discovery pipeline is, in part, owing to a lack of structural and functional information available for new drug targets. Membrane proteins, the targets of well over 50% of marketed pharmaceuticals, present a particular challenge. As they are not naturally abundant, they must be produced recombinantly for the structural biology that is a prerequisite to structure-based drug design. Unfortunately, however, obtaining high yields of functional, recombinant membrane proteins remains a major bottleneck in contemporary bioscience. While repeated rounds of trial-and-error optimization have not (and cannot) reveal mechanistic details of the biology of recombinant protein production, examination of the host response has provided new insights. To this end, we published an early transcriptome analysis that identified genes implicated in high-yielding yeast cell factories, which has enabled the engineering of improved production strains. These advances offer hope that the bottleneck of membrane protein production can be relieved rationally.

Improving drug discovery using hybrid softcomputing methods

Virtual screening (VS) methods can considerably aid clinical research, predicting how ligands interact with drug targets. Most VS methods suppose a unique binding site for the target, but it has been demonstrated that diverse ligands interact with unrelated parts of the target and many VS methods do not take into account this relevant fact. This problem is circumvented by a novel VS methodology named BINDSURF that scans the whole protein surface in order to find new hotspots, where ligands might potentially interact with, and which is implemented in last generation massively parallel GPU hardware, allowing fast processing of large ligand databases. BINDSURF can thus be used in drug discovery, drug design, drug repurposing and therefore helps considerably in clinical research. However, the accuracy of most VS methods and concretely BINDSURF is constrained by limitations in the scoring function that describes biomolecular interactions, and even nowadays these uncertainties are not completely understood. In order to improve accuracy of the scoring functions used in BINDSURF we propose a hybrid novel approach where neural networks (NNET) and support vector machines (SVM) methods are trained with databases of known active (drugs) and inactive compounds, being this information exploited afterwards to improve BINDSURF VS predictions.

Improving drug discovery using a neural networks based parallel scoring function

Virtual Screening (VS) methods can considerably aid clinical research, predicting how ligands interact with drug targets. Most VS methods suppose a unique binding site for the target, but it has been demonstrated that diverse ligands interact with unrelated parts of the target and many VS methods do not take into account this relevant fact. This problem is circumvented by a novel VS methodology named BINDSURF that scans the whole protein surface to find new hotspots, where ligands might potentially interact with, and which is implemented in massively parallel Graphics Processing Units, allowing fast processing of large ligand databases. BINDSURF can thus be used in drug discovery, drug design, drug repurposing and therefore helps considerably in clinical research. However, the accuracy of most VS methods is constrained by limitations in the scoring function that describes biomolecular interactions, and even nowadays these uncertainties are not completely understood. In order to solve this problem, we propose a novel approach where neural networks are trained with databases of known active (drugs) and inactive compounds, and later used to improve VS predictions.

Design and validation of a novel generic platform for the production of tetravalent IgG1-like bispecific antibodies

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