Current Topics in Medicinal Chemistry - Volume 13, Issue 9, 2013

Computer-aided drug design (CADD) is a critical initiating step of drug development, but a single model capable of covering all designing aspects remains to be elucidated. Hence, we developed a drug design modeling framework that integrates multiple approaches, including machine learning based quantitative structure-activity relationship (QSAR) analysis, 3D-QSAR, Bayesian network, pharmacophore modeling, and structure-based docking algorithm. Restrictions for each model were defined for improved individual and overall accuracy. An integration method was applied to join the results from each model to minimize bias and errors. In addition, the integrated model adopts both static and dynamic analysis to validate the intermolecular stabilities of the receptor-ligand conformation. The proposed protocol was applied to identifying HER2 inhibitors from traditional Chinese medicine (TCM) as an example for validating our new protocol. Eight potent leads were identified from six TCM sources. A joint validation system comprised of comparative molecular field analysis, comparative molecular similarity indices analysis, and molecular dynamics simulation further characterized the candidates into three potential binding conformations and validated the binding stability of each protein-ligand complex. The ligand pathway was also performed to predict the ligand “in” and “exit” from the binding site. In summary, we propose a novel systematic CADD methodology for the identification, analysis, and characterization of drug-like candidates.

Protein:protein interactions are becoming increasingly significant as potential drug targets; however, the rational identification of small molecule inhibitors of such interactions remains a challenge. Pharmacophore modelling is a popular tool for virtual screening of compound libraries, and has previously been successfully applied to the discovery of enzymatic inhibitors. However, the application of pharmacophore modelling in the field of protein:protein interaction inhibitors has historically been considered more of a challenge and remains limited. In this review, we explore the interaction mimicry by known inhibitors that originate from in vitro screening, demonstrating the validity of pharmacophore mapping in the generation of queries for virtual screening. We discuss the pharmacophore mapping methods that have been successfully employed in the discovery of first-in-class inhibitors. These successful cases demonstrate the usefulness of a “tool kit” of diverse strategies for application across a range of situations depending on the available structural information.

The pharmacophore modeling in modern drug research has been applied for both bioactivity profiling and early stage of risk assessment of potential side effects and toxicity due to interactions of drug candidates with antitargets namely P-glycoprotein, hERG, cytochrome P450 and pregnane X-receptor. In this article, an existing state concerning with pharmacophore modeling applied for promiscuous proteins in drug research were updated and reviewed. In an attempt to create new safe medicines faster, the partial overlap of substrate properties of hERG, P-glycoprotein, pregnane X-receptor and cytochrome P450 has to be considered and drug safety has to be dealt on a system level on the off-targets.

p38 mitogen-activated protein (MAP) kinases are the serine/threonine protein kinases, which play a vital role in cellular responses to external stress signals. p38 MAP kinase inhibitors have shown anti-inflammatory effects in the preclinical disease models, primarily through inhibition of the expression of inflammatory mediators. A number of structurally diverse p38 MAP kinase inhibitors have been developed as potential anti-inflammatory agents. Most of the inhibitors have failed in the clinical trials either due to poor pharmacokinetic profile or selectivity issue, which makes p38 MAP kinase a promising target for molecular modelling studies. Several quantitative structure activity relationships (QSAR) and pharmacophore models have been developed to identify the structural requirements essential for p38 MAP kinase inhibitory activity. In this review, we provide an overview of the presently known p38 MAP kinase inhibitors and how QSAR analyses among series of compounds have led to the development of molecular models and pharmacophores, allowing the design of novel inhibitors.

A pharmacophore model does not describe a real molecule or a real association of functional groups but illustrates a molecular recognition of a biological target shared by a group of compounds. Pharmacophores also represent the spatial arrangement of essential interactions in a receptor-binding pocket. Structure based pharmacophores (SBPs) can work both with a free (apo) structure or a macromolecule-ligand complex (holo) structure. The SBP methods that derive pharmacophore from protein-ligand complexes use the potential interactions observed between ligand and protein, whereas, the SBP method that aims to derive pharmacophore from ligand free protein, uses only protein active site information. Therefore SBPs do not encounter to challenging problems such as ligand flexibility, molecular alignment as well as proper selection of training set compounds in ligand based pharmacophore modeling. The current review deals with ‘Hot Spot’ analysis of binding site to feature generation, several approaches to feature reduction, and considers shape and excluded volumes to SBP model building. This review continues to represent several applications of SBPs in virtual screening especially in parallel screening approach and multi-target drug design. Also it reports the applications of SBPs in QSAR. This review emphasizes that SBPs are valuable tools for hit to lead optimization, virtual screening, scaffold hopping, and multi-target drug design.

Adenosine receptors (ARs) are signaling molecules ubiquitously expressed in a wide variety of tissues in the human body. ARs mediate physiological functions by interacting with four subtypes of G-protein-coupled receptors, namely A1, A2A, A2B and A3. The A3 AR, probably the most studied subtype, is also ubiquitously expressed, with high levels in peripheral organs and low levels in the brain. This type of AR is involved in a variety of important pathophysiological processes, ranging from modulation of cerebral and cardiac ischemic damage to regulation of immunosuppression and inflammation. Consequently, the development of potent and selective A3 AR ligands as promising therapeutic options for a variety of diseases has been a prime subject of medicinal chemistry research for more than two decades. Among the plethora of approaches applied quantitative structure activity relationships (QSAR) stands out for being largely employed due to their potential to increase the efficiency at initial stages of the drug discovery process. So, we provide a review of the main QSAR studies devoted to the design, discovery and development of agonist and antagonist A3 adenosine receptor ligands. Common pitfalls of these QSAR applications and the current trends in this area are also analyzed.

Extending virtual screening approaches to deal with multi-target drug design and polypharmacology is an increasingly important aspect in drug design. In light of this, the concept of accessible chemical space and its exploration should be reviewed. The great advantages of re-using drugs with safe pharmacological profiles with favourable pharmacokinetic properties highlights drug repositioning as a valid alternative to rational drug design, massive drug development efforts, and high-throughput screening, especially when supported by in silico techniques. Here, we discuss some of the advantages of multi-target approaches, and we review some significant examples of their application in the last decade to that well known class of pharmaceutical targets, the G-protein coupled receptors.

In recent years, numerous changes have been made in the field of cancer research with the progresses of molecular biology, chemoinformatics and chemogenomics. Several new biomolecular targets have been identified, and investigated for new drug discovery. In the current article, we discuss the role of pharmacophore mapping and pharmacophorebased virtual screening (PBVS) approaches for identification of novel anticancer hits. It showed that pharmacophorebased studies were performed for almost every type of anticancer agents. However, such applications are clustered on finding novel hits for a few targets like cancer-related hormones, kinase enzymes and other less investigated targets. Some reports were found with virtual hits experimentally validated against respective targets. These were thoroughly described and the novel hits were pointed out. Others with PBVS of anticancer targets were also discussed and the identified features were highlighted. Present review showed that PBVS may serve as a true lead generator if it is performed in a unified fashion that combines in silico techniques with experimental validation. With enormous progresses in computational methods as well as molecular biology, it is expected that pharmacophore-based drug discovery strategy will aid in significant upsurge in the field of cancer chemotherapy in near future.

Pharmacophore approaches have evolved to be one of the most successful tools in drug discovery, especially since the past two decades. 3D pharmacophore methods are now commonly used as part of more complex workflows in drug discovery campaigns, and have been successfully and extensively applied in virtual screening (VS) approaches. This review provides a perspective of how to assess the performance of 3D pharmacophore models to be used in VS. Since 3D VS protocols are in general assessed by their ability to discriminate between active and inactive compounds, we summarize the impact of the composition and preparation of modeling and external sets on the outcome of evaluations. Moreover, we highlight the significance of both classic enrichment parameters and advanced descriptors for the performance of 3D pharmacophore-based virtual screening methods.