Modeling of diarylalkyl‐imidazole and diarylalkyl‐triazole derivatives as potent aromatase inhibitors for treatment of hormone‐dependent cancer

Abstract

Aromatase is an enzyme that catalyzes the final step in the conversion of androgen to estrogen. It has become an attractive target for the treatment of estrogen responsive breast cancer. The study has been focused on designing aromatase inhibitors (AIs) that can be selected as probable drug candidate for the treatment of breast cancer. In the present study, long chain diarylalkyl-imidazole and -triazole scaffolds have been considered for exploring pharmacophores as potent AIs using QSAR (Quantitative SAR) and pharmacophore mapping studies. The model generated in linear free energy QSAR study (R(2) = 0.905, Q(2)= 0.885, R(2)(pred(ts)) = 0.763) showed the importance of hydrophobicity, size and shape of the molecule, van der Waals surface and hydrogen atom contribution influence the activity. 3D QSAR of comparative molecular field analysis (CoMFA, R(2)= 0.921, Q(2) = 0.741, R(2)(pred(ts))= 0.583) showed that steric and electrostatic features along with hydrophobicity and electronic charge contribution at C(4) (Fig. 1) influence on the inhibitory activity. Comparative molecular similarity analysis (CoMSIA, R(2) = 0.874, Q(2) = 0.716, R(2)(pred(ts)) = 0.591) study adjudged the presence of steric, electrostatic and hydrophobic fields together with hydrogen bond (HB) donor and acceptor play significant role in inhibitory activity to aromatase enzyme. Further pharmacophore mapping study (Q(2) = 0.947, Delta(cost) = 113.171, R(2)(pred(ts)) = 0.857) suggested that presence of HB acceptor, hydrophobicity with aromatic ring, and the importance of steric contribution influence on the activity. The critical distances among the features are also important for the inhibitor activity.

Poly(<small>DL</small>-lactide-<i>co</i>-glycolic acid) Nanoparticle Design and Payload Prediction: A Molecular Descriptor Based Study

“…The final reduced model was cross validated in each case and similar findings were reported elsewhere. 42,60,61) Descriptor inter-correlations were cross checked and the final MLR equations were developed accommodating only the non-correlated descriptors. The best linear QSPR models for drug loading in PLGA in three different descriptor platform were depicted in Table 3.…”

Towards an Understanding of the Mode of Action of Human Aromatase Activity for Azoles through Quantum Chemical Descriptors-Based Regression and Structure Activity Relationship Modeling Analysis

“…Shoombuatong et al, 2018, reviewed this area and concluded that the modeling of nonsteroidal aromatase inhibition requires nitrogen-containing descriptors, polarizability, the energy of highest occupied molecular orbital (HOMO), the energy gap of highest occupied molecular orbital and lowest unoccupied molecular orbital (HOMO-LUMO gap), and descriptors for hydrogen bond acceptors [21]. A comparison of two multilinear regression QSARs for a dataset of diarylalkylimidazoles and diarylalkyltriazoles for AI, by Ghodsi et al, 2016 [22], Nagar et al, 2010 [23], and Ghodsi et al, 2016 [22], found that the Dragon descriptors, namely topological and van der Waals interactions, were significant, whereas Nagar et al, 2010 [23], modeled the binding interactions through molecular operating environment (MOE) based descriptors and found that van der Waals interactions relating, number of hydrogen bonds and bond angle potential energy were significant. These studies suggested that approximately the same information relating to the inhibition of the aromatase CYP19A1 is encoded by different descriptors for van der Waals and electrostatic interactions [22,23].…”

Towards understanding aromatase inhibitory activity via QSAR modeling

“…4)) with the publication of ten articles in the time period (Bak and Polanski, 2007[8]; Nagar et al, 2008[55]; Castellano et al, 2008[16]; Mittal et al, 2009[53]; Gueto et al, 2009[36]; Nagar and Saha, 2010[57][56]; Roy and Roy, 2010[77][78]; Dai et al, 2010[24]). Most of the QSAR models in this time frame were built utilizing physicochemical descriptors as compared to other techniques in the previous years.…”

“…As summarized in Table 4(Tab. 4) (References in Table 4: Nagy et al, 1994[58]; Recanatini, 1996[75]; Oprea and García, 1996[67]; Sulea et al, 1997[92]; Recanatini and Cavalli, 1998[76]; Cavalli et al, 2000[18]; Beger et al, 2001[10]; Gironés and Carbó-Dorca, 2002[34]; Beger and Wilkes, 2002[12]; Polanski and Gieleciak, 2003[71]; Leonetti et al, 2004[50]; Beger et al, 2004[11]; Cavalli et al, 2005[17]; Bak and Polanski, 2007[8]; Castellano et al, 2008[16]; Nagar et al, 2008[55]; Mittal et al, 2009[53]; Gueto et al, 2009[36]; Dai et al, 2010[24]; Roy and Roy, 2010[78]; Roy and Roy, 2010[77]; Nagar and Saha, 2010[57]; Nagar and Saha, 2010[56]; Narayana et al, 2012[65]; Nantasenamat et al, 2013[64]; Nantasenamat et al, 2013[61]; Kishore et al, 2013[44]; Worachartcheewan et al, 2014[103]; Worachartcheewan et al, 2014[101]; Nantasenamat et al, 2014[63]; Dai et al, 2014[25]; Awasthi et al, 2015[7]; Xie et al, 2015[105]; Shoombuatong et al, 2015[85]; Xie et al, 2014[102]; Kumar et al, 2016[48]; Ghodsi and Hemmateenejad, 2016[32]; Song et al, 2016[91]; Prachayasittikul et al, 2017[72]; Adhikari et al, 2017[1]; Lee and Barron, 2018[49]; Pingaew et al, 2018[70]; Barigye et al, 2018[9]), it can be observed that prior to 2010, MLR and PLS models, also known as white-box approaches, were the most popular and yet simple learning algorithms used for QSAR modeling of AIs. …”