Hologram quantitative structure activity relationship studies on 5-HT6 antagonists

Combined HQSAR method and molecular docking study on genotoxicity mechanism of quinolones with higher genotoxicity

Using the hologram quantitative structure-activity relationship (HQSAR) method, a quantitative model of the structure-activity relationship between the genotoxicity of quinolones towards gram-negative bacteria and structure of quinolones is constructed. A series of novel quinolones are designed, and 4 environmentally friendly quinolone derivatives are finally selected, because of their enhanced genotoxicity towards gram-negative/positive bacteria, decreased bioconcentration and increased photodegradability and biodegradability. The mechanisms underlying the genotoxicity of quinolones and its derivatives are analysed based on amino acid residues and molecular interactions. Three hydrophilic amino acids [arginine (ARG), asparagine (ASN) and aspartic acid (ASP)] play important roles in the antibacterial effects of quinolones. The introduction of highly hydrophilic groups into the C-7 position of amifloxacin (AMI) not only improved the stability of the AMI derivative-topoisomerase IV-DNA complex but also improved the antibacterial activities of AMI derivatives.

Interspecies quantitative structure-toxicity-toxicity (QSTTR) relationship modeling of ionic liquids. Toxicity of ionic liquids to V. fischeri, D. magna and S. vacuolatus

Considering the increasing uses of ionic liquids (ILs) in various industrial processes and chemical engineering operations, a complete assessment of their hazardous profile is essential. In the absence of adequate experimental data, in silico modeling might be helpful in filling data gaps for the toxicity of ILs towards various ecological indicator organisms. Using the rationale of taxonomic relatedness, the development of predictive quantitative structure-toxicity-toxicity relationship (QSTTR) models allows predicting the toxicity of ILs to a particular species using available experimental toxicity data towards a different species. Such studies may employ, along with the available experimental toxicity data to a species, molecular structure features and physicochemical properties of chemicals as independent variables for prediction of the toxicity profile against another closely related species. A few such interspecies toxicity correlation models have been reported in the literature for diverse chemicals in general, but this approach has been rarely applied to the class of ionic liquids. The present study involves the use of IL toxicity data towards the bacteria Vibrio fischeri along with molecular structure derived information or computational descriptors like extended topochemical atom (ETA) indices, quantum topological molecular similarity (QTMS) descriptors and computed lipophilicity measure (logk0) for the interspecies exploration of the toxicity data towards green algae S. vacuolatus and crustacea Daphnia magna, separately. This modeling study has been performed in accordance with the OECD guidelines. Finally, predictions for a true external set have been performed to fill the data gap of toxicity towards daphnids and algae using the Vibrio toxicity data and molecular structure attributes.

In silico optimization of pharmacokinetic properties and receptor binding affinity simultaneously: a 'parallel progression approach to drug design' applied to β-blockers

The present work exploits the potential of in silico approaches for minimizing attrition of leads in the later stages of drug development. We propose a theoretical approach, wherein 'parallel' information is generated to simultaneously optimize the pharmacokinetics (PK) and pharmacodynamics (PD) of lead candidates. β-blockers, though in use for many years, have suboptimal PKs; hence are an ideal test series for the 'parallel progression approach'. This approach utilizes molecular modeling tools viz. hologram quantitative structure activity relationships, homology modeling, docking, predictive metabolism, and toxicity models. Validated models have been developed for PK parameters such as volume of distribution (log Vd) and clearance (log Cl), which together influence the half-life (t1/2) of a drug. Simultaneously, models for PD in terms of inhibition constant pKi have been developed. Thus, PK and PD properties of β-blockers were concurrently analyzed and after iterative cycling, modifications were proposed that lead to compounds with optimized PK and PD. We report some of the resultant re-engineered β-blockers with improved half-lives and pKi values comparable with marketed β-blockers. These were further analyzed by the docking studies to evaluate their binding poses. Finally, metabolic and toxicological assessment of these molecules was done through in silico methods. The strategy proposed herein has potential universal applicability, and can be used in any drug discovery scenario; provided that the data used is consistent in terms of experimental conditions, endpoints, and methods employed. Thus the 'parallel progression approach' helps to simultaneously fine-tune various properties of the drug and would be an invaluable tool during the drug development process.

Chemometric design to explore pharmacophore features of BACE inhibitors for controlling Alzheimer's disease

Pharmacophoric features of potent BACE inhibitors derived from multi-chemometric studies.

Computational approaches to the design of novel 5-HT6 R ligands

5-Hydroxytryptamine (5-HT, serotonin) subtype 6 receptor (5-HT6 receptor, 5-HT6 R) belongs to a 5-HT subclass of a relatively wide G protein-coupled receptor (GPCR) family. Accumulated biological data indicate that 5-HT6 R antagonists and agonists have a great potential for the treatment of neuropathological disorders, such as Parkinson's disease, Alzheimer's disease, and schizophrenia. A number of painstaking efforts have been made toward the design of novel 5-HT6 R ligands; however, there are still no drugs that successfully passed all the clinical trials and entered the market, except for several multimodal ligands. Novel active molecules are strongly needed to progress this development forward. The in silico drug design has some benefits compared with the other rough approaches in terms of thoroughness and predictive accuracy; therefore, it can be effectively used as a solid foundation for the design of novel 5-HT6 R ligands with high potency and selectivity. Here, we provide an overview of the reported computational approaches to the design of novel 5-HT6 R ligands.

Extracting structural requirements for activity of GPR119 agonists: a hologram quantitative structure activity relationship (HQSAR) study

GPR119 is a potential target for the treatment of diabetes mellitus. GPR119 agonists minimize the side-effects observed with sulphonyl ureas and glucagon-like peptide 1 analogs. Various reported GPR119 agonists from various patents were selected for the study and a 2D-QSAR study (HQSAR) was carried out. Fifty-five molecules were selected for the study. The study was performed on a training set of 40 structurally diverse molecules with reported biological activity. The most significant HQSAR model (q2 = 0.87, r2 = 0.99) was obtained using atoms, bond, connection, and acceptor and donor as fragment distinction. The fragment size was kept at 4–7. The predictive ability of the model was evaluated by an external test set containing 15 molecules not included in the training set, and the predicted values were in good agreement with the experimental values. The important fragments determined by the study were used to design new drug candidates having increased biological activity and comparable physicochemical properties.

Quantification of contributions of different molecular fragments for antioxidant activity of coumarin derivatives based on QSAR analyses

Attempts have been made in the present work using in silico techniques for identification of essential structural features imparting antioxidant potential to naturally available coumarin molecules and their synthetic derivatives. Four different types of modeling tools have been employed for the qualitative and quantitative assessment of the molecular fragments constituting the biological pharmacophore. The descriptor-based quantitative structure–activity relationship (QSAR) and group-based QSAR (G-QSAR) models provide a quantitative estimation of the substituent requirements and the chemical nature of the parent moiety. Subsequently, 3D pharmacophore and hologram QSAR (HQSAR) models enable identification of the key molecular components necessary for the antioxidant potency to the molecules. All of the different models infer the importance of the hydrogen bond acceptor ketonic fragment for interaction of the antioxidant molecules with the neighbouring toxic radicals. Additionally, the phenyl substituent attached to the side chain and the benzene nucleus of the benzopyran moiety also constitute the response pharmacophore for the molecules under study. The models thus developed may serve as an essential query tool for screening of databases for selection of molecules bearing the essential fragments and subsequent prediction of their free radical scavenging potency.

Hologram QSAR models of 4-[(diethylamino)methyl]-phenol inhibitors of acetyl/butyrylcholinesterase enzymes as potential anti-Alzheimer agents

Hologram QSAR models were developed for a series of 36 inhibitors (29 training set and seven test set compounds) of acetyl/butyrylcholinesterase (AChE/BChE) enzymes, an attractive molecular target for Alzheimer’s disease (AD) treatment. The HQSAR models (N = 29) exhibited significant cross-validated (AChE, q² = 0.787; BChE, q² = 0. 904) and non-cross-validated (AChE, r² = 0.965; BChE, r² = 0.952) correlation coefficients. The models were used to predict the inhibitory potencies of the test set compounds, and agreement between the experimental and predicted values was verified, exhibiting a powerful predictive capability. Contribution maps show that structural fragments containing aromatic moieties and long side chains increase potency. Both the HQSAR models and the contribution maps should be useful for the further design of novel, structurally related cholinesterase inhibitors.

Fragment-based QSAR: perspectives in drug design

Drug design is a process driven by innovation and technological breakthroughs involving a combination of advanced experimental and computational methods. A broad variety of medicinal chemistry approaches can be used for the identification of hits, generation of leads, as well as to accelerate the optimization of leads into drug candidates. Quantitative structure-activity relationship (QSAR) methods are among the most important strategies that can be applied for the successful design of small molecule modulators having clinical utility. Hologram QSAR (HQSAR) is a modern 2D fragment-based QSAR method that employs specialized molecular fingerprints. HQSAR can be applied to large data sets of compounds, as well as traditional-size sets, being a versatile tool in drug design. The HQSAR approach has evolved from a classical use in the generation of standard QSAR models for data correlation and prediction into advanced drug design tools for virtual screening and pharmacokinetic property prediction. This paper provides a brief perspective on the evolution and current status of HQSAR, highlighting present challenges and new opportunities in drug design.

Selective 5-HT6 receptor ligands: progress in the development of a novel pharmacological approach to the treatment of obesity and related metabolic disorders

The increasing global prevalence of obesity unequivocally demonstrates that neither behavioural (diet and exercise) nor pharmacological approaches to this health problem are working. In this area of high unmet clinical need, the 5-HT6 receptor has generated enormous interest amongst academic and pharmaceutical industry scientists as a molecular target for the development of a new generation of safe and more effective anti-obesity drugs. In this review, we have described the major developments that have occurred in the fields of the medicinal chemistry and pharmacology of 5-HT6 ligands, with particular emphasis on their potential application as novel anti-obesity drugs. The last 5 years have witnessed an increasing understanding of the 5-HT6 receptor and its structural requirements that has produced an explosion in the number and diversity of novel, highly selective 5-HT6 receptor agonists, partial agonists and antagonists that have been designed and synthesized. In animal models, 5-HT6 receptor ligands of all functional types have been shown to decrease food intake when given acutely and chronically, to evoke profound and sustained weight-loss in obese animals, and concomitantly to improve a number of cardio-metabolic risk factors. Comparator studies in obese animal models, which are highly predictive of clinical outcomes, indicate that 5-HT6 ligands may have the potential to be more efficacious in the treatment of obesity than the current generation of anti-obesity drugs.

Structural and chemical basis for enhanced affinity and potency for a large series of estrogen receptor ligands: 2D and 3D QSAR studies

The estrogen receptor (ER) is an important drug target for the development of novel therapeutic agents for the treatment of breast cancer. Progress towards the design of more potent and selective ER modulators requires the optimization of multiple ligand-receptor interactions. Comparative molecular field analyses (CoMFA) and hologram quantitative structure-activity relationships (HQSAR) were conducted on a large set of ERalpha modulators. Two training sets containing either 127 or 69 compounds were used to generate QSAR models for in vitro binding affinity and potency, respectively. Significant correlation coefficients (affinity models, CoMFA, r(2)=0.93 and q(2)=0.79; HQSAR, r(2)=0.92 and q(2)=0.71; potency models, CoMFA, r(2)=0.94 and q(2)=0.72; HQSAR, r(2)=0.92 and q(2)=0.74) were obtained, indicating the potential of the models for untested compounds. The generated models were validated using external test sets, and the predicted values were in good agreement with the experimental results. The final QSAR models as well as the information gathered from 3D contour maps should be useful for the design of novel ERalpha modulators having improved affinity and potency.

Further studies on the binding of N1-substituted tryptamines at h5-HT6 receptors

N(1)-Arylsulfonyl-substituted analogs of N,N-dimethyltryptamine bind at 5-HT(6) receptors. Replacement of the aryl moiety with similarly hydrophobic alkyl substituents results in decreased affinity, as does replacement of a benzenesulfonyl moiety with a benzyl group. Current findings indicate that an aryl (or substituted aryl) sulfonyl (rather than alkylsulfonyl or benzyl) moiety is optimal for high-affinity binding, and further suggest that the N(1)-benzenesulfonyl- and their corresponding N(1)-benzyltryptamine counterparts bind in a different fashion.

Medicinal chemistry strategies to 5-HT6 receptor ligands as potential cognitive enhancers and antiobesity agents

Although the 5-hydroxytryptamine(6) (5-HT(6)) receptor was discovered only recently, its almost exclusive distribution in the brain makes it a promising, novel, target for central nervous system (CNS)-mediated diseases such as Alzheimer's disease (cognitive function), schizophrenia, anxiety and obesity. In the past few years a significant research interest has advanced the understanding of the functional roles and the pharmacophore requirements of this receptor. Two 5-HT(6) receptor antagonists have already entered Phase II clinical trials for the enhancement of cognitive function. Since the first discovery of selective ligands for the 5-HT(6) receptor by HTS in 1998, several medicinal-chemistry-driven approaches have delivered highly selective lead structures with well-defined functionalities, starting from either the endogenous ligand 5-HT or the chemical structures identified by HTS. The concept of 'scaffold hopping' has been employed to expand the variability of the available chemical scaffolds and to generate patentable ligands. Supported by pharmacophore models, which have been established recently, the binding and functionality (structure-activity relationships) of the lead structures have been optimized further.