Exploring benzcyclo derivatives as potent aromatase inhibitors using ligand-based modeling studies

Synthesis and Anticancer Activity of Thiadiazole Containing Thiourea, Benzothiazole and Imidazo[2,1-b][1,3,4]thiadiazole Scaffolds

BACKGROUND

A great array of nitrogen-containing heterocyclic rings were being extensively explored for their functional versatility in the field of medicine, especially in anticancer research. 1,3,4- thiadiazole is one of such heterocyclic rings with promising anticancer activity against several cancer cell lines, inhibiting diverse biological targets.

INTRODUCTION

The 1,3,4-thiadiazole, when equipped with other heterocyclic scaffolds, has displayed enhanced anticancer properties. The thiourea, benzothiazole, imidazo[2,1,b][1,3,4]-thiadiazoles are such potential scaffolds with promising anticancer activity.

METHODS

A new series of 5-substituted-1,3,4-thiadiazoles linked with phenyl thiourea, benzothiazole and 2,6-disubstituted imidazo[2,1-b][1,3,4]thiadiazole derivatives were synthesized and tested for invitro anticancer activity on various cancer cell lines.

RESULTS

The National Cancer Institute's preliminary anticancer screening results showed compounds 4b and 5b having potent antileukemic activity. Compound 4b selectively showed 32 percent lethality on Human Leukemia-60 cell line. The docking studies of the derivatives on aromatase enzyme (Protein Data Bank: 3S7S) have shown reversible interactions at the active site with good docking scores comparable to Letrozole and Exemestane. Furthermore, the selected derivatives were tested for anticancer activity on HeLa cell line based on the molecular docking studies.

CONCLUSION

Compounds 4b and 5b showed effective inhibition equivalent to Letrozole. These preliminary biological screening studies have given positive anticancer activity for these new classes of derivatives. An additional research study like the mechanism of action of the anticancer activity of this new class of compounds is necessary. These groundwork studies illuminate a future pathway for research of this class of compounds enabling the discovery of potent antitumor agents.

Towards understanding aromatase inhibitory activity via QSAR modeling

Aromatase is a rate-limiting enzyme for estrogen biosynthesis that is overproduced in breast cancer tissue. To block the growth of breast tumors, aromatase inhibitors (AIs) are employed to bind and inhibit aromatase in order to lower the amount of estrogen produced in the body. Although a number of synthetic aromatase inhibitors have been released for clinical use in the treatment of hormone-receptor positive breast cancer, these inhibitors may lead to undesirable side effects (e.g. increased rash, diarrhea and vomiting; effects on the bone, brain and heart) and therefore, the search for novel AIs continues. Over the past decades, there has been an intense effort in employing medicinal chemistry and quantitative structure-activity relationship (QSAR) to shed light on the mechanistic basis of aromatase inhibition. To the best of our knowledge, this article constitutes the first comprehensive review of all QSAR studies of both steroidal and non-steroidal AIs that have been published in the field. Herein, we summarize the experimental setup of these studies as well as summarizing the key features that are pertinent for robust aromatase inhibition.

Ligand- and Structure-Based Drug Design of Non-Steroidal Aromatase Inhibitors (NSAIs) in Breast Cancer

Aromatase is a multienzyme complex overexpressed in breast cancer and responsible for estrogen production. It is the potential target for designing anti-breast cancer drugs. Ligand and Structure-Based Drug Designing approaches (LBDD and SBDD) are involved in development of active and more specific Nonsteroidal Aromatase Inhibitors (NSAIs). Different LBDD and SBDD approaches are presented here to understand their utility in designing novel NSAIs. It is observed that molecules should possess a five or six membered heterocyclic nitrogen containing ring to coordinate with heme portion of aromatase for inhibition. Moreover, one or two hydrogen bond acceptor features, hydrophobicity, and steric factors may play crucial roles for anti-aromatase activity. Electrostatic, van der Waals, and p-p interactions are other important factors that determine binding affinity of inhibitors. HQSAR, LDA-QSAR, GQSAR, CoMFA, and CoMSIA approaches, pharmacophore mapping followed by virtual screening, docking, and dynamic simulation may be effective approaches for designing new potent anti-aromatase molecules.

Ligand- and Structure-Based Drug Design of Non-Steroidal Aromatase Inhibitors (NSAIs) in Breast Cancer

Aromatase is a multienzyme complex overexpressed in breast cancer and responsible for estrogen production. It is the potential target for designing anti-breast cancer drugs. Ligand and Structure-Based Drug Designing approaches (LBDD and SBDD) are involved in development of active and more specific Nonsteroidal Aromatase Inhibitors (NSAIs). Different LBDD and SBDD approaches are presented here to understand their utility in designing novel NSAIs. It is observed that molecules should possess a five or six membered heterocyclic nitrogen containing ring to coordinate with heme portion of aromatase for inhibition. Moreover, one or two hydrogen bond acceptor features, hydrophobicity, and steric factors may play crucial roles for anti-aromatase activity. Electrostatic, van der Waals, and p-p interactions are other important factors that determine binding affinity of inhibitors. HQSAR, LDA-QSAR, GQSAR, CoMFA, and CoMSIA approaches, pharmacophore mapping followed by virtual screening, docking, and dynamic simulation may be effective approaches for designing new potent anti-aromatase molecules.

Poly(DL-lactide-co-glycolic acid) nanoparticle design and payload prediction: a molecular descriptor based study

Polymer nanoparticles are veritable tools for pharmacokinetic and therapeutic modifications of bioactive compounds. Nanoparticle technology development and scaling up are however often constrained due to poor payload and improper particle dissolution. This work was aimed to develop descriptor based computational models as prior art tools for optimal payload in polymeric nanoparticles. Loading optimization experiments were carried out both in vitro and in-silico. Molecular descriptors generated in three different platforms DRAGON, molecular operating environment (MOE) and VolSurf+ were used. Multiple linear regression analysis (MLR) provided computation models which were further validated based on goodness of fit statistics and correlation coefficients (DRAGON, R(2)=0.889, Q(2)=0.657, R(2)(pred)=0.616; MOE, R(2)=0.826, Q(2)=0.572, R(2)(pred)=0.601; and VolSurf+, R(2)=0.818, Q(2)=0.573, R(2)(pred)=0.653). Pharmacophore space modeling studies were carried out in order to understand the fundamental molecular interactions necessary for drug loading in poly(DL-lactide-co-glycolic acid). The space modeling study (R(2)=0.882, Q(2)=0.662, R(2)(pred)=0.725, Δ(cost)=108.931) indicated that hydrogen bond acceptors and ring aromatic features are of primary significance for nanoparticle drug loading. Results of in vitro experiments have also confirmed the fact as a viable prognosis in case of nanoparticle payload. Polymeric nanoparticles payload prediction can therefore be a useful tool for wider benefits at the preformulation stages itself.