Additivity of molecular fields: CoMFA study on dual activators of PPARalpha and PPARgamma

Importance of tautomerism in drugs

Tautomerism is an important phenomenon exhibited by many drugs. As we discuss in this review, identifying the different tautomers of drugs and exploring their importance in the mechanisms of drug action are integral components of current drug discovery. Nuclear magnetic resonance (NMR), infrared (IR), ultraviolet (UV), Raman, and terahertz spectroscopic techniques, as well as X-ray diffraction, are useful for exploring drug tautomerism. Quantum chemical methods, in association with pharmacoinformatics tools, are being used to evaluate tautomeric preferences in terms of energy effects. Desmotropy (i.e., tautomeric polymorphism) of the drugs is particularly important in drug delivery studies.

Topomer-CoMFA proposed as a tool to construct dual EGFR/HER-2 models

Protein kinases (in this case, HER-2 and EGFR) are involved in cancer-related diseases. Some reports have shown unique CoMFA models using the sum of activities expressed as pIC₅₀ (-log IC₅₀), as the classical CoMFA technique would not be the best strategy to construct models for multitarget therapy considering that the molecular alignment will not be the same for different targets. An alternative for this problem is the use of Topomer-CoMFA, a variation of CoMFA, which does not require the alignment step in the generation of 3D models. In this study, we propose the combined use of the sum of activities and Topomer-CoMFA for the construction of a unique dual 3D model considering the inhibitory activities against EGFR and HER-2. For this, 88 compounds from the literature were divided into two groups: training (71) and test (17) sets. The biological activity of each compound, expressed as IC₅₀ for EGFR and HER-2, was transformed into pIC₅₀, summed, and used as the dependent variable in the Topomer-CoMFA analyses. The obtained model was considered statistically robust in the prediction of the dual activity of new compounds. Finally, based on the obtained model, we proposed structural modifications to some of the compounds used to improve the biological data. From the 3D model, we suggested new derivative compounds with improved biological activity for both targets. Therefore, the combination of the techniques proposed in this study proves to be a good strategy to construct better statistical models that can predict biological activities in multitarget systems.

Multi-objective optimization methods in novel drug design

Introduction: In multi-objective drug design, optimization gains importance, being upgraded to a discipline that attracts its own research. Current strategies are broadly classified into single - objective optimization (SOO) and multi-objective optimization (MOO).Areas covered: Starting with SOO and the ways used to incorporate multiple criteria into it, the present review focuses on MOO techniques, their comparison, advantages, and restrictions. Pareto analysis and the concept of dominance stand in the core of MOO. The Pareto front, Pareto ranking, and limitations of Pareto-based methods, due to high dimensions and data uncertainty, are outlined. Desirability functions and the weighted sum approaches are described as stand-alone techniques to transform the MOO problem to SOO or in combination with pareto analysis and evolutionary algorithms. Representative applications in different drug research areas are also discussed.Expert opinion: Despite their limitations, the use of combined MOO techniques, as well as being complementary to SOO or in conjunction with artificial intelligence, contributes dramatically to efficient drug design, assisting decisions and increasing success probabilities. For multi-target drug design, optimization is supported by network approaches, while applicability of MOO to other fields like drug technology or biological complexity opens new perspectives in the interrelated fields of medicinal chemistry and molecular biology.

Design, synthesis, and evaluation of novel l-phenylglycine derivatives as potential PPARγ lead compounds

In accordance with the structural characteristics of thiazolidinedione drugs and highly bioactive tyrosine derivatives, we tentatively designed the l-phenylglycine derivatives TM1 and TM2 based on basic principles of drug design and then synthesized them. The in vitro screening of peroxisome proliferator-activated receptor gamma (PPARγ) activated activity, α-glucosidase inhibitory and dipeptidyl peptidase-4 inhibitory activities showed that the novel molecule M5 had efficient PPAR response element (PPRE) activated activity (PPRE relative activity 105.04% at 10 μg·mL^-1 compared with the positive control pioglitazone, with 100% activity). Therefore, M5 was selected as the hit compound from which the TM3 and TM4 series of compounds were further designed and synthesized. Based on the PPRE relative activities of TM3 and TM4, we discovered another new molecule, TM4h, which had the strongest PPRE relative activity (120.42% at 10 μg·mL^-1). In addition, the concentration-dependent activity of the highly active compounds was determined by assaying their half-maximal effective concentration (EC₅₀) values. The molecular physical parameter calculation and the molecular toxicity prediction were used to theoretically evaluate the lead-likeness and safety of the active compounds. In conclusion, we identified a potential PPARγ lead molecule and developed a tangible strategy for antidiabetic drug development.

Isolated flavonoids from Ficus racemosa stem bark possess antidiabetic, hypolipidemic and protective effects in albino Wistar rats

ETHNOPHARMACOLOGICAL RELEVANCE

Ficus racemosa (FR) has been used for thousands of years in Ayurvedic system of medicine in India and is closely associated with prevention, treatment and cure of various human ailments like obesity and diabetes. It is popularly known as gular. A vast and wide range of chemical compounds like polyphenols, friedelane-type triterpenes, norfriedelane type triterpene, eudesmane-type sesquiterpene including various glycosides had been isolated from this plant. However, no detail studies related to isolation of flavonoids has been reported previously with their antidiabetic, hypolipidemic and toxicological consequences.

AIM OF THE STUDY

The present study was undertaken to evaluate antidiabetic, hypolipidemic and toxicological assessments of flavonoids isolated from Ficus racemosa (FR) stem bark.

MATERIALS AND METHODS

We isolated four flavonoids from stem bark of FR and structures were confirmed by Infrared spectroscopy (IR), Nuclear Magnetic Resonance (NMR) (both 1D and 2D), mass spectroscopy (MS). Later, these flavonoids were administered to streptozotocin (STZ) rats once in a day for a period of seven days at 100mg/kg dose. We measured blood glucose level and body weight changes at different days (1st, 3rd, 5th and 7th days). Serum lipid profiles were also estimated to investigate the hypolipidemic potential of flavonoids in the similar experiment. Various oxidative stress parameters in pancreas and liver and hepatic biomarker enzymes in plasma were also determined to investigate the toxicity potential of isolated flavonoids. Finally, we performed docking studies to find out the mechanism of action.

RESULTS

Our results collectively suggested that four flavonoids reduced blood glucose level and restored body weight, signifying antidiabetic action. There were reduction of other lipid profile parameters and increase of high density lipoprotein (HDL) during administration of flavonoids, also signifying hypolipidemic action. Various oxidative stress biomarkers and hepatic enzymes levels were also normalized with respect to diabetic control at the same time. Docking studies revealed that isolated flavonoids showed their antidiabetic potential via binding to PPARγ and GLUT1 receptors.

CONCLUSION

The isolated four flavonoids demonstrated good antidiabetic, hypolipidemic and antioxidant properties in STZ diabetic rats which supported the use of FR stem bark as useful supplementary drug for future antidiabetic therapy.

QSAR studies in the discovery of novel type-II diabetic therapies

INTRODUCTION

Type-II diabetes mellitus (T2DM) is a complex chronic disease that represents a major therapeutic challenge. Despite extensive efforts in T2DM drug development, therapies remain unsatisfactory. Currently, there are many novel and important antidiabetic drug targets under investigation by many research groups worldwide. One of the main challenges to develop effective orally active hypoglycemic agents is off-target effects. Computational tools have impacted drug discovery at many levels. One of the earliest methods is quantitative structure-activity relationship (QSAR) studies. QSAR strategies help medicinal chemists understand the relationship between hypoglycemic activity and molecular properties. Hence, QSAR may hold promise in guiding the synthesis of specifically designed novel ligands that demonstrate high potency and target selectivity.

AREAS COVERED

This review aims to provide an overview of the QSAR strategies used to model antidiabetic agents. In particular, this review focuses on drug targets that raised recent scientific interest and/or led to successful antidiabetic agents in the market. Special emphasis has been made on studies that led to the identification of novel antidiabetic scaffolds.

EXPERT OPINION

Computer-aided molecular design and discovery techniques like QSAR have a great potential in designing leads against complex diseases such as T2DM. Combined with other in silico techniques, QSAR can provide more useful and rational insights to facilitate the discovery of novel compounds. However, since T2DM is a complex disease that includes several faulty biological targets, multi-target QSAR studies are recommended in the future to achieve efficient antidiabetic therapies.

Resistance-resistant antibiotics

New antibiotics are needed because drug resistance is increasing while the introduction of new antibiotics is decreasing. We discuss here six possible approaches to develop 'resistance-resistant' antibiotics. First, multitarget inhibitors in which a single compound inhibits more than one target may be easier to develop than conventional combination therapies with two new drugs. Second, inhibiting multiple targets in the same metabolic pathway is expected to be an effective strategy owing to synergy. Third, discovering multiple-target inhibitors should be possible by using sequential virtual screening. Fourth, repurposing existing drugs can lead to combinations of multitarget therapeutics. Fifth, targets need not be proteins. Sixth, inhibiting virulence factor formation and boosting innate immunity may also lead to decreased susceptibility to resistance. Although it is not possible to eliminate resistance, the approaches reviewed here offer several possibilities for reducing the effects of mutations and, in some cases, suggest that sensitivity to existing antibiotics may be restored in otherwise drug-resistant organisms.

Identification of novel peroxisome proliferator-activated receptor-gamma (PPARγ) agonists using molecular modeling method

Peroxisome proliferator-activated receptor-gamma (PPARγ) plays a critical role in lipid and glucose homeostasis. It is the target of many drug discovery studies, because of its role in various disease states including diabetes and cancer. Thiazolidinediones, a synthetic class of agents that work by activation of PPARγ, have been used extensively as insulin-sensitizers for the management of type 2 diabetes. In this study, a combination of QSAR and docking methods were utilised to perform virtual screening of more than 25 million compounds in the ZINC library. The QSAR model was developed using 1,517 compounds and it identified 42,378 potential PPARγ agonists from the ZINC library, and 10,000 of these were selected for docking with PPARγ based on their diversity. Several steps were used to refine the docking results, and finally 30 potentially highly active ligands were identified. Four compounds were subsequently tested for their in vitro activity, and one compound was found to have a K i values of <5 μM.

SAR and Computer-Aided Drug Design Approaches in the Discovery of Peroxisome Proliferator-Activated Receptor γ Activators: A Perspective

Activators of PPARγ, Troglitazone (TGZ), Rosiglitazone (RGZ), and Pioglitazone (PGZ) were introduced for treatment of Type 2 diabetes, but TGZ and RGZ have been withdrawn from the market along with other promising leads due cardiovascular side effects and hepatotoxicity. However, the continuously improving understanding of the structure/function of PPARγ and its interactions with potential ligands maintain the importance of PPARγ as an antidiabetic target. Extensive structure activity relationship (SAR) studies have thus been performed on a variety of structural scaffolds by various research groups. Computer-aided drug discovery (CADD) approaches have also played a vital role in the search and optimization of potential lead compounds. This paper focuses on these approaches adopted for the discovery of PPARγ ligands for the treatment of Type 2 diabetes. Key concepts employed during the discovery phase, classification based on agonistic character, applications of various QSAR, pharmacophore mapping, virtual screening, molecular docking, and molecular dynamics studies are highlighted. Molecular level analysis of the dynamic nature of ligand-receptor interaction is presented for the future design of ligands with better potency and safety profiles. Recently identified mechanism of inhibition of phosphorylation of PPARγ at SER273 by ligands is reviewed as a new strategy to identify novel drug candidates.

Insights into ligand-elicited activation of human constitutive androstane receptor based on novel agonists and three-dimensional quantitative structure-activity relationship

The human constitutive androstane receptor (CAR, NR1I3) is an important regulator of xenobiotic metabolism and other physiological processes. So far, only few CAR agonists are known and no explicit mechanism has been proposed for their action. Thus, we aimed to generate a 3D QSAR model that could explain the molecular determinants of CAR agonist action. To obtain a sufficient number of agonists that cover a wide range of activity, we applied a virtual screening approach using both structure- and ligand-based methods. We identified 27 novel human CAR agonists on which a 3D QSAR model was generated. The model, complemented by coregulator recruitment and mutagenesis results, suggests a potential activation mechanism for human CAR and may serve to predict potential activation of CAR for compounds emerging from drug development projects or for chemicals undergoing toxicological risk assessment.

Quantitative structure-activity relationship models with receptor-dependent descriptors for predicting peroxisome proliferator-activated receptor activities of thiazolidinedione and oxazolidinedione derivatives

A quantitative structure-activity relationship study has been carried out, in which the relationship between the peroxisome proliferator-activated receptor alpha and the peroxisome proliferator-activated receptor gamma agonistic activities of thiazolidinedione and oxazolidinedione derivatives and quantitative descriptors, V(site) calculated in a receptor-dependent manner is modeled. These descriptors quantify the volume occupied by the optimized ligands in regions that are either common or specific to the superimposed binding sites of the targets under consideration. The quantitative structure-activity relationship models were built by forward stepwise linear regression modeling for a training set of 27 compounds and validated for a test set of seven compounds, resulting in a squared correlation coefficient value of 0.90 for peroxisome proliferator-activated receptor alpha and of 0.89 for peroxisome proliferator-activated receptor gamma. The leave-one-out cross-validation and test set predictability squared correlation coefficient values for these models were 0.85 and 0.62 for peroxisome proliferator-activated receptor alpha and 0.89 and 0.50 for peroxisome proliferator-activated receptor gamma respectively. A dual peroxisome proliferator-activated receptor model has also been developed, and it indicates the structural features required for the design of ligands with dual peroxisome proliferator-activated receptor activity. These quantitative structure-activity relationship models show the importance of the descriptors here introduced in the prediction and interpretation of the compounds affinity and selectivity.

Important pharmacophoric features of pan PPAR agonists: common chemical feature analysis and virtual screening

HipHop program was used to generate a common chemical feature hypothesis for pan Peroxisome Proliferator-Activated Receptor (PPAR) agonists. The top scoring hypothesis (hypo-1) was found to differentiate the pan agonists (actives) from subtype-specific and dual PPAR agonists (inactives). The importance of individual features in hypo-1 was assessed by deleting a particular feature to generate a new hypothesis and observing its discriminating ability between 'actives' and 'inactives'. Deletion of aromatic features AR-1 (hypo-1b), AR-2 (hypo-1e) and a Hydrophobic feature HYD-1 (hypo-1c) individually did not affect the discriminating power of the hypo-1 significantly. However, deletion of a Hydrogen Bond Acceptor (HBA) feature (hypo-1f) in the hydrophobic tail group was found to be highly detrimental for the specificity of hypo-1 leading to high hit rate of 'inactives'. Since hypo-1 did not produce any useful hits from the database search, hypo-1b, hypo-1c and hypo-1e were used for virtual screening leading to the identification of new potential pan PPAR ligands. The docking studies were used to predict the binding pose of the proposed molecules in PPARgamma active site.

New drugs targeting Th2 lymphocytes in asthma

Asthma represents a profound worldwide public health problem. The most effective anti-asthmatic drugs currently available include inhaled beta2-agonists and glucocorticoids and control asthma in about 90-95% of patients. The current asthma therapies are not cures and symptoms return soon after treatment is stopped even after long term therapy. Although glucocorticoids are highly effective in controlling the inflammatory process in asthma, they appear to have little effect on the lower airway remodelling processes that appear to play a role in the pathophysiology of asthma at currently prescribed doses. The development of novel drugs may allow resolution of these changes. In addition, severe glucocorticoid-dependent and resistant asthma presents a great clinical burden and reducing the side-effects of glucocorticoids using novel steroid-sparing agents is needed. Furthermore, the mechanisms involved in the persistence of inflammation are poorly understood and the reasons why some patients have severe life threatening asthma and others have very mild disease are still unknown. Drug development for asthma has been directed at improving currently available drugs and findings new compounds that usually target the Th2-driven airway inflammatory response. Considering the apparently central role of T lymphocytes in the pathogenesis of asthma, drugs targeting disease-inducing Th2 cells are promising therapeutic strategies. However, although animal models of asthma suggest that this is feasible, the translation of these types of studies for the treatment of human asthma remains poor due to the limitations of the models currently used. The myriad of new compounds that are in development directed to modulate Th2 cells recruitment and/or activation will clarify in the near future the relative importance of these cells and their mediators in the complex interactions with the other pro-inflammatory/anti-inflammatory cells and mediators responsible of the different asthmatic phenotypes. Some of these new Th2-oriented strategies may in the future not only control symptoms and modify the natural course of asthma, but also potentially prevent or cure the disease.

Comparative QSTR studies for predicting mutagenicity of nitro compounds

Mutagenicity and carcinogenicity are toxicological endpoints which pose a great concern being the major determinants of cancers and tumours. Nitroarenes possess genotoxic properties as they can form various electrophilic intermediates and adducts with biological systems. Different QSTR techniques were employed to develop models for the prediction of mutagenicity of nitroarenes using a diverse set of 197 nitro aromatic and hetero aromatic molecules. The 2D and 3D QSTR methods used for model development gave statistically significant results. The alignment for 3D methods was obtained by maximum common substructures (MCS) approach, by taking the most mutagenic molecule of the dataset as the template. All the QSTR models were developed with the same set of training and test set molecules. The 3D contours and 2D contribution maps along with molecular fingerprints provide useful information about the mutagenic potentials of the molecules. The GFA based model shows thermodynamic and topological descriptors play an important role in characterizing mutagenicity of nitroarenes. Atomic-level thermodynamic descriptor namely AlogP throws light on hydrophobic features and helps to understand the bilinear model. Topological aspects of these classes of compounds were depicted by the fragment fingerprints and Balaban indices obtained from HQSAR and GFA models, respectively. The predictive abilities of 2D and 3D QSTR models may be useful as a vibrant predictive tool to screen out mutagenic nitroarenes and design safer non-mutagenic nitro compounds.

CoMFA analysis of dual/multiple PPAR activators

Dual or multiple activators are agents which act at more than one biological target and produce synergistic therapeutic effect. Computational methods can be successfully employed in designing dual activators. 'Additivity of molecular fields' concept was recently introduced to help design new dual activators. This concept is employed in this work to explore the scope and limitations of the concept, with the help of reported PPARalpha/gamma/delta multiple activators. Three individual CoMFA models were first generated, followed by dual and multiple models. Dual PPARalpha/gamma CoMFA model could be developed successfully. However, dual PPARgamma/delta, dual PPARalpha/delta and multiple PPARalpha/gamma/delta CoMFA models could not be very well developed. This follows from the poor correlation observed in the PPARdelta CoMFA model.

Three-dimensional QSAR of HPPD inhibitors, PSA inhibitors, and anxiolytic agents: Effect of tautomerism on the CoMFA models

The present study was design to examine the effect of tautomerism upon the CoMFA results. Three selected data sets involving protropic tautomerism, which are 21 p-hydroxyphenylpyruvate dioxygenase (HPPD) inhibitors, 35 inhibitors of puromycin-sensitive aminopeptidase (PSA), and 67 anxiolytic agents, were used for this purpose. Atom-by-atom alignment technique was adopted to superimpose the molecules in the data sets onto a template. The structural alignments using different tautomeric forms had no significant difference except the atoms involved in tautomerism, which ensures, to a great extent, that the differences of the CoMFA results result primarily from the tautomerism. All-orientation and all-placement search (AOS-APS) based CoMFA models, in addition to the conventional ones, were derived for each system and proved to be capable of yielding much improved statistical results. In the cases of the data sets of HPPD inhibitors and PSA inhibitors, excellent AOS-APS CoMFA models (q2>0.8 with four components for the former and q2>0.7 with seven components for the latter) were obtained, and almost no significant difference in statistical quality was observed when using different tautomeric forms to derive the models. However, it was not the case when treating the data set of anxiolytic agents. The keto tautomer, which was the active form of the PBI type inhibitors, produced measurably better results (q2=0.54 with eight components) than that the enol one (q2=0.37 with five components), indicating the importance of selecting proper tautomer in the CoMFA studies. Furthermore, there existed some substantial differences of the electrostatic field contours between the two different tautomeric forms for all of the three systems considered, whereas the differences in the steric field contour maps were limited. This implies that the resulting new potent ligands may be quite different if one utilizes the CoMFA models of different tautomeric forms for guiding further structural refinements.

Design and synthesis of 6-methyl-2-oxo-1,2,3,4-tetrahydro-pyrimidine-5-carboxylic acid derivatives as PPARγ activators

The design and synthesis of novel series of 6-methyl-2-oxo-1,2,3,4-tetrahydro-pyrimidine-5-carboxylic acid (pyrimidone) derivatives that are high affinity ligands for peroxisome proliferators activated receptor gamma have been reported as a potential substitute of 2,4-thiazolidinedione head group. The FlexX docking and radioligand binding affinity of some promising compounds of this series is comparable to that of thiazolidinedione based antidiabetic drugs currently in clinical use.

3D-QSAR and molecular docking study on bisarylmaleimide series as glycogen synthase kinase 3, cyclin dependent kinase 2 and cyclin dependent kinase 4 inhibitors: An insight into the criteria for selectivity

Selective glycogen synthase kinase 3 (GSK3) inhibition over cyclin dependent kinases such as cyclin dependent kinase 2 (CDK2) and cyclin dependent kinase 4 (CDK4) is an important requirement for improved therapeutic profile of GSK3 inhibitors. The concepts of selectivity and additivity fields have been employed in developing selective CoMFA models for these related kinases. Initially, sets of three individual CoMFA models were developed, using 36 compounds of bisarylmaleimide series to correlate with the GSK3, CDK2 and CDK4 inhibitory potencies. These models showed a satisfactory statistical significance: CoMFA-GSK3 (r(2)(con), r(2)(cv): 0.931, 0.519), CoMFA-CDK2 (0.937, 0.563), and CoMFA-CDK4 (0.892, 0.725). Three different selective CoMFA models were then developed using differences in pIC(50) values. These three models showed a superior statistical significance: (i) CoMFA-Selective1 (r(2)(con), r(2)(cv): 0.969, 0.768), (ii) CoMFA-Selective 2 (0.974, 0.835) and (iii) CoMFA-Selective3 (0.963, 0.776). The selective models were found to outperform the individual models in terms of the quality of correlation and were found to be more informative in pinpointing the structural basis for the observed quantitative differences of kinase inhibition. An in-depth comparative investigation was carried out between the individual and selective models to gain an insight into the selectivity criterion. To further validate this approach, a set of new compounds were designed which show selectivity and were docked into the active site of GSK3, using FlexX based incremental construction algorithm.

Synthesis and evaluation of N-acetyl-l-tyrosine based compounds as PPARα selective activators

The development of type 2 diabetes in obese individuals is linked to lipid accumulation in non-adipose tissues. A series of N-acetyl-L-tyrosine derivatives were synthesized and evaluated for PPAR transactivation. Compounds 4d and 4f were found to show better PPARalpha transactivation as compared to PPARgamma. Molecular docking analysis was carried out to study their important interactions with the active site of PPARalpha.

2D QSAR of PPARγ agonist binding and transactivation

Multilinear QSAR models are developed for the largest and most diverse set of PPARgamma agonists treated hitherto. Binding of these small molecules to the human nuclear receptor PPARgamma is described by models that are built on simple 2D molecular descriptors and nevertheless are of good quality and predictive power (e.g., 144 compounds, 10 descriptors, r2=0.79, r2(cv)=0.76). The models presented are thoroughly validated by crossvalidation, randomization experiments, bootstrapping, and training set/test set partitioning. They may therefore be helpful in the design of new antidiabetic drug candidates. For gene transactivation, the functional activity of the agonists, a corresponding model for a similarly diverse compound set is of somewhat lower statistical quality.

Inhibition of Trypanosoma cruzi hexokinase by bisphosphonates

Hexokinase is the first enzyme involved in glycolysis in most organisms, including the etiological agents of Chagas disease (Trypanosoma cruzi) and African sleeping sickness (Trypanosoma brucei). The T. cruzi enzyme is unusual since, unlike the human enzyme, it is inhibited by inorganic diphosphate (PPi). Here, we show that non-hydrolyzable analogues of PPi, bisphosphonates, are potent inhibitors of T. cruzi hexokinase (TcHK). We determined the activity of 42 bisphosphonates against TcHK, and the IC(50) values were used to construct pharmacophore and comparative molecular similarity indices analysis (CoMSIA) models for enzyme inhibition. Both models revealed the importance of electrostatic, hydrophobic, and steric interactions, and the IC(50) values for 17 active compounds were predicted with an average error of 2.4x by using the CoMSIA models. The compound most active against T. cruzi hexokinase was found to have a 2.2 microM IC(50) versus the clinically relevant intracellular amastigote form of T. cruzi, but only a approximately 1-2 mM IC(50) versus Dictyostelium discoideum and a human cell line, indicating selective activity versus T. cruzi.