Pharmacophore modeling, virtual screening, and 3D-QSAR studies on a series of non-steroidal aromatase inhibitors

Abstract

Aromatase inhibitors are the most important targets in treatment of estrogen-dependent cancers. In order to search for potent non-steroidal aromatase inhibitors (NSAIs) with lower side effects and overcome cellular resistance, Genetic Algorithm with Linear Assignment of Hypermolecular Alignment of Database was used to derive 3D pharmacophore models. The obtained best pharmacophore model contains one acceptor atom, one donor atom, and two hydrophobes, which was used in effective alignment of dataset. In succession, comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were performed on 84 structurally diverse NSAIs to build 3D-QSAR models based on both pharmacophore and docking alignments. The CoMFA and CoMSIA models based on the pharmacophore alignment show better statistical results (CoMFA: q 2 = 0.634, r ncv 2 = 0.986, r pred 2 = 0.737; CoMSIA: q 2 = 0.668, r ncv 2 = 0.926, r pred 2 = 0.708). This 3D-QSAR approach provides significant insights that can be used to develop novel and potent NSAIs. In addition, the best pharmacophore model was used as a 3D query for virtual screening against NCI2000 database. The hit compounds were further filtered by docking, and their biological activities were predicted by the CoMFA and CoMSIA models, and six structurally diverse compounds with good predicted pIC 50 values were obtained, which are expected to design novel NSAIs with new skeletons.

Towards understanding aromatase inhibitory activity via QSAR modeling

“…Adhikari et al (2017[ 1 ]) performed an extensive study employing a wide range of QSAR models including 2D and 3D QSAR as well as molecular docking to also confirm the importance of the electrostatic property of the nitrogen-containing azole moiety, p -cyanophenyl moiety, p -nitrophenyl, hydro-phobicity as well as the appropriate size and shape of AIs were crucial for aromatase inhibitory activity. Xie et al (2015[ 105 ]) performed both CoMFA and CoMSIA studies and both studies further supported the importance of bulky steric groups as well as the importance of electrostatic properties pertaining to the presence of azole nitrogen atoms.…”

“…Additionally, the increase in popularity of QSAR models for predicting AIs was greatly observed in 2011-2015 (Narayana et al, 2012[ 65 ]; Kishore et al, 2013[ 44 ]; Nantasenamat et al, 2013[ 61 ][ 64 ], 2014[ 63 ]; Dai et al, 2014[ 25 ]; Xie et al, 2014[ 104 ]; Worachartcheewan et al, 2014[ 101 ][ 103 ]; Shoombuatong et al, 2015[ 85 ]; Awasthi et al, 2015[ 7 ]; Xie et al, 2015[ 105 ]; Kumar et al, 2016[ 48 ]) whereby the number of publications increased to thirteen, with an even more dramatic rise in the number of compounds used for calculating descriptors using LOO-CV and external validation (Figure 5 (Fig. 5) ).…”

“…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.…”

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

“…Two techniques are commonly used for the global application of the predictive modeling in toxicology, for example, employing either a large dataset using techniques such as machine learning methods or deriving mechanistically interpretable simple models [14,15]. Both techniques have been exploited to create predictive models for the antagonist activity of azoles with CYP19A1 [16][17][18][19][20]. 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].…”

Design, Synthesis and Biological Evaluation of Aromatase Inhibitors Based on Sulfonates and Sulfonamides of Resveratrol

“…Although their effectiveness is superior to that of Tamoxifen, which is the most used drug in endocrine therapy in ER+ breast cancer, the search for novel classes of AIs is still required because of their side effects, such as bone loss and cardiovascular disease, and of the potential resistance occurrence due to prolonged use. The discovery of potent nonsteroidal aromatase inhibitors (NSAIs) provided with fewer side effects and cell resistance is thus extremely pursued [7,8]. The triazole ring system replacement and the proper functionalization of additional aromatic/cyclic moieties represent suitable strategies for addressing a more selective inhibition towards the aromatase enzyme.…”