Current Drug Targets - Volume 18, Issue 5, 2017

Dicoumarol, a symmetrical biscoumarin can be considered as the “parent” of the widely used anticoagulant drug, warfarin. The discovery of dicoumarol’s bioactive properties resulted from an investigation into a mysterious cattle disease in the 1940s. It was then developed as a pharmaceutical, but was superseded in the 1950s by warfarin. Both dicoumarol and warfarin antagonise the blood clotting process through inhibition of vitamin K epoxide reductase (VKOR). This blocks the recycling of vitamin K and prevents the γ-carboxylation of glutamate residues in clotting factors. VKOR is an integral membrane protein and our understanding of the molecular mechanism of action of dicoumarol and warfarin is hampered by the lack of a three dimensional structure. There is consequent controversy about the membrane topology of VKOR, the location of the binding site for coumarin inhibitors and the mechanism of inhibition by these compounds. Dicoumarol (and warfarin) also inhibit a second enzyme, NAD(P)H quinone oxidoreductase 1 (NQO1). This soluble, cytoplasmic enzyme may also play a minor role in the recycling of vitamin K. However, its main cellular roles as an enzyme appear to be detoxification and the prevention of the build-up of reactive oxygen species. NQO1 is well characterised biochemically and structurally. Consequently, structure-based drug design has identified NQO1 inhibitors which have potential for the development of anti-cancer drugs. Many of these compounds are structurally related to dicoumarol and some have reduced “off target” effects. Therefore, it is possible that dicoumarol will become the “parent” of a second group of drugs.

Hansch’s model is a classic approach to Quantitative Structure-Binding Relationships (QSBR) problems in Pharmacology and Medicinal Chemistry. Hansch QSAR equations are used as input parameters of electronic structure and lipophilicity. In this work, we perform a review on Hansch’s analysis. We also developed a new type of PT-QSBR Hansch’s model based on Perturbation Theory (PT) and QSBR approach for a large number of drugs reported in CheMBL. The targets are proteins expressed by the Hippocampus region of the brain of Alzheimer Disease (AD) patients. The model predicted correctly 49312 out of 53783 negative perturbations (Specificity = 91.7%) and 16197 out of 21245 positive perturbations (Sensitivity = 76.2%) in training series. The model also predicted correctly 49312/53783 (91.7%) and 16197/21245 (76.2%) negative or positive perturbations in external validation series. We applied our model in theoretical-experimental studies of organic synthesis, pharmacological assay, and prediction of unmeasured results for a series of compounds similar to Rasagiline (compound of reference) with potential neuroprotection effect.

Alzheimer’s disease (AD) is a neurodegenerative disorder that is described by multiple factors linked with the progression of the disease. The currently approved drugs in the market are not capable of curing AD; instead, they merely provide symptomatic relief. Development of multi-target directed ligands (MTDLs) is an emerging strategy for improving the quality of the treatment against complex diseases like AD. Polypharmacology is a branch of pharmaceutical sciences that deals with the MTDL development. In this mini-review, we have summarized and discussed different strategies that are reported in the literature to design MTDLs for AD. Further, we have discussed the role of different in silico techniques and online resources in computer-aided drug discovery (CADD), for designing or identifying MTDLs against AD.

The major drug discovery efforts in oncology have been concentrated on the development of selective molecules that are supposed to act specifically on one anticancer mechanism by modulating a single or several closely related drug targets. However, a bird's eye view on data from multiple available bioassays implies that most approved anticancer agents do, in fact, target many more proteins with different functions. Here we will review and systematize currently available information on the targets of several anticancer drugs along with revision of their potential mechanisms of action. Polypharmacology of the current antineoplastic agents suggests that drug clinical efficacy in oncology can be achieved only via modulation of multiple cellular mechanisms.

Human topoisomerase IB is an important target in cancer therapy and drugs selectively stabilizing the topoisomerase IB-DNA covalent complex are in clinical use for several cancer types. Tyrosyl- DNA phosphodiesterase 1 is involved in the DNA repair resolving the topoisomerase IB-DNA covalent complex that is extremely dangerous for the survival of the cells since it produces an irreversible DNA damage. Given the close biological relationship between these two enzymes, the development of synergistic inhibitors, called dual-inhibitors, is an important challenge in cancer therapy and computer-aided drug design may help in the identification of the best compounds. In this review, an overview of the compounds inhibiting one of the two enzymes or acting as dual inhibitors is provided. Moreover, the general procedures of the virtual screening approach, providing a description of two widely used opensource programs, namely AutoDock4 and AutoDock Vina, are described. Finally, an application of the two programs on a selected number of dual inhibitors for tyrosyl-DNA phosphodiesterase 1 and topoisomerase IB and their performance is briefly discussed.

Multi-target drugs against particular multiple targets get better protection, resistance profiles and curative influence by cooperative rules of a key beneficial target with resistance behavior and compensatory elements. Computational techniques can assist us in the efforts to design novel drugs (ligands) with a preferred bioactivity outline and alternative bioactive molecules at an early stage. A number of in silico methods have been explored extensively in order to facilitate the investigation of individual target agents and to propose a selective drug. A different, progressively more significant field which is used to predict the bioactivity of chemical compounds is the data mining method. Some of the previously mentioned methods have been investigated for multi-target drug design (MTDD) to find drug leads interact simultaneously with multiple targets. Several cheminformatics methods and structure-based approaches try to extract information from units working cooperatively in a biomolecular system to fulfill their task. To dominate the difficulties of the experimental specification of ligand-target structures, rational methods, namely molecular docking, SAR and QSAR are vital substitutes to obtain knowledge for each structure in atomic insight. These procedures are logically successful for the prediction of binding affinity and have shown promising potential in facilitating MTDD. Here, we review some of the important features of the multi-target therapeutics discoveries using the computational approach, highlighting the SAR, QSAR, docking and pharmacophore methods to discover interactions between drug-target that could be leveraged for curative benefits. A summary of each, followed by examples of its applications in drug design has been provided. Computational efficiency of each method has been represented according to its main strengths and limitations.

Estrogens play a crucial role in the growth, development, and homeostasis of various target tissues, their biological effects being mediated by the estrogen receptor (ER). In order to get a better understanding of the structural features of the modulators associated with the binding to ER, this paper provides an overview of the Quantitative Structure–Activity (QSAR) studies performed so far for estimating or predicting the activity of different ligands towards its two known subtypes (ERα and ERβ). Recent progresses in the application of these modeling studies are additionally pointed out. Finally, ongoing challenges that may lead to new and exciting directions for QSAR modeling studies in this field are discussed.

The most basic principle of drug action is found in the lock and key model, where the highest possible affinity for a target that also avoids side effects is desired. For many years this was understood as being “one drug, for one target, for one disease”, however researchers began to observe that certain diseases are best treated with multi-target drugs. In recent years, studies have sought out polypharmacological compounds acting on multiple targets against complex (multifactorial) diseases, such as cancer, neurodegenerative disease, and certain infections. One of the computational tools used in research for multifunctional drugs is Molecular Docking. Through this methodology of Computer-Aided Drug Design, we observe complexes formed between ligands and interesting targets (often many), for a particular disease. This review reports on docking studies as used in investigations of new multi-target compounds; it also shows the various ways that such studies are used in the search for multifunctional compounds.

In nature, pathogenic parasite species with different susceptibility patterns of antiparasitic drugs abound. In this sense, natural products derived from plants are a potency for drugs with potential antiparasitic activity. Unfortunately, there are many metabolites and studying all of them would be costly in terms of money and resources. To this end, theoretical studies such as QSAR models could be useful. These, for the most part, predict the biological activity of the drugs against a single species of parasite. Consequently, foretell the probability with which a drug is active against many different species with a single QSAR model is an important achievement. This review consists of three parts: the first part is a review of metabolites found in nature that have antiparasitic activity, in particular the antiprotozoal (Leishmania and Trypanosoma); the second part includes a review of theoretical studies looking for a model that predicts the antiprotozoal activity of natural products; the third and final part concerns the study of theoretical models focused on the interaction between drug and receptor, analyzing new metabolites with antiprotozoal activity.