Cortisol is a glucocorticoid hormone that modulates many processes in the body such as blood sugar levels, immune system activity, metabolism of proteins, carbohydrates and fats, and bone formation (Cain and Cidlowski, 2017). Hypercortisolism in an unwanted increase in the secretion of cortisol and is the cause of many diseases such as Cushing's syndrome, metabolic disorders, and suppression of the immune system leading to delayed wound healing (Zhu et al., 2016). Cushing's syndrome is a condition that has symptoms like obesity, facial plethora, round face, decreased libido, thin skin, and easy bruising, impaired growth in children, menstrual irregularities, hypertension, hirsutism, depression, glucose intolerance, weakness, osteopenia, and nephrolithiasis in more than 50% of clinically observed patients (Newell-Price et al., 1998; Savage et al., 2001; Faggiano et al., 2003; Pecori Giraldi et al., 2003). A tumor of the pituitary or adrenal gland is the main reason for the over-secretion of cortisol. In most cases, a surgical removal or radiation therapy of the tumor is not applied, and instead the patients are treated with drugs (Tritos et al., 2011). The use of glucocorticoid receptor antagonists for treating this situation often comes with an increased secretion of cortisol, potentially due to the pituitary feedback mechanism (Orth, 1978). An alternative treatment could be the reduction of cortisol formation by inhibiting cytochrome P450 11B1. It catalyzes the final step in the formation of cortisol by hydroxylating 11-deoxycortisol in the zona fasciculate of adrenal cortex (Figure 1) (Sayers, 1950). This mechanism of action is expected not to cause the adverse effects observed for glucocorticoid receptor antagonists (Nieman, 2002).

Aldosterone is a potent mineralocorticoid hormone, which regulates blood pressure by increasing the reabsorption of sodium at the distal convoluted tubule in the kidney. Under normal conditions, aldosterone secretion is controlled by the renin-angiotensin-aldosterone-system (RAAS). In case of insufficient renal flow, excessive aldosterone is released by the activation of the RAAS pathway (Young and Funder, 2000). The increase in aldosterone levels causes an increase in blood volume that elevates blood pressure. An unwanted increase in plasma aldosterone levels results in various pathological conditions like hyperaldosteronism, congestive heart failure, myocardial fibrosis, cardiac hypertrophy, ventricular arrhythmia, and other adverse effects through triggering cardiac fibroblasts (Ramires et al., 1998; Brilla, 2000; Lijnen and Petrov, 2000; Briet and Schiffrin, 2010). CYP11B2 catalyzes the rate-limiting step in the formation of aldosterone from corticosterone in the zona glomerulosa of the adrenal cortex (Sayers, 1950; Lifton et al., 2001). The anti-mineralocorticoid spironolactone is used to treat hypertension and heart failure (Pitt et al., 1999). However, this therapy is accompanied by severe antiandrogenic adverse effects (Soberman and Weber, 2000). An alternative approach for the management of congestive heart failure and hypertension would be the inhibition of CYP11B2, probably leading to fewer adverse effects (Azizi et al., 2013).

Both CYP11B1 and CYP11B2 are mitochondrial enzymes and belong to the cytochrome P450 family. They use NADPH as a cofactor (Guengerich, 2007). After moving to the mitochondrial matrix, the enzymes length is reduced to 479 amino acids, of which 450 (93%) amino acids are identical in both of them (Belkina et al., 2001). The molecular mass of CYP11B1 is 50 kDa and for CYP11B2 is 48.5 kDa (Ogishima et al., 1991). Although their primary sequence is highly similar, they have different functionalities (Belkina et al., 2001).

Several potent inhibitors of CYP11B1 and CYP11B2 have been reported (Figure 2). Some of these compounds were discovered using rational SAR studies and molecular modeling approaches. In 2006, Ulmschneider et al. developed a ligand-based pharmacophore model for CYP11B2 inhibitors by superimposing previously synthesized active and inactive ligands for CYP11B2 from their research group (Ulmschneider et al., 2006). Their pharmacophore consisted of four points: three ring centroids and an aromatic nitrogen. The model had a steric inclusion area that mapped the active compounds and a steric exclusion area that was derived from inactive compounds. They validated their pharmacophore model by designing and synthesizing acenaphthalene-based inhibitors of CYP11B2, followed by in vitro testing. In another study performed by Lucas et al. (2008a), the authors designed and synthesized potential lead compounds for CYP11B2 inhibition with the help of a ligand-based pharmacophore model containing hydrophobic and hydrogen bond acceptor features. After the biological testing, the compounds were docked into a homology model of CYP11B2 (Lucas et al., 2008a). In 2011, the same group refined their previous ligand-based pharmacophore hypothesis based on diverse inhibitors. They added two hydrophobic features to their previous pharmacophore. Their final pharmacophore had four essential features, seven optional features, and five exclusion spheres. The refined pharmacophore of this study was validated by synthesizing and testing predicted inhibitors for CYP11B2 from the tetrahydropyrroloquinolinone scaffold, which led to potent compounds (Lucas et al., 2011). In addition to this, Gobbi et al. designed and synthesized several xanthone-based inhibitors of CYP11B1 and CYP11B2 based on the pharmacophore models by Lucas et al. (Lucas et al., 2011; Gobbi et al., 2013). The rationally designed inhibitors of CYP11B1 and CYP11B2 had a hydrophobic part in addition to the imidazolylmethyl ring, which was assumed to form a complex with the heme iron of CYP11B1 and CYP11B2 enzymes. This complexation is believed to play an important role for the inhibition of CYP11B1 and CYP11B2 enzymes (Gobbi et al., 2013).

All the above mentioned pharmacophore models have been successfully used to optimize already known active compound classes. However, none of them has been used to prospectively screen large, chemically diverse 3D molecular databases and identify novel active scaffolds. Our goal was therefore to create and validate an in silico model for future virtual screening (VS) experiments to find diverse inhibitors of either CYP11B1 or CYP11B2 or both, which could be used as pharmacological tool compounds. For this purpose, ligand-based pharmacophore queries of CYP11B1 and CYP11B2 inhibitors were generated. This method was chosen because of its frequently higher retrieval of active hits compared to docking (Chen et al., 2009) and because ligand-based models can often be better trained to recognize structurally diverse compounds binding to the same target compared to structure-based models (Schuster et al., 2010).

This study was performed to generate and validate novel pharmacophore models for CYP11B1 and CYP11B2 inhibitors (Figures 3, 4). The developed pharmacophore queries were experimentally validated by screening the SPECS database. After removing the 69 PAINS (Baell and Holloway, 2010) compounds from a total of 1,120 virtual hits, 24 were selected for in vitro testing. These hits were biologically evaluated on hamster V79MZh cells expressing human CYP11B1 or CYP11B2 (Denner et al., 1995a; Ehmer et al., 2002). Five out of 24 selected hits inhibited CYP11B1 and/or CYP11B2 (Table 1). The predictive power of both pharmacophore models was analyzed. Eleven out of 24 compounds were selected by model 1, of them compounds 8 and 10 inhibited both CYP11B1 and CYP11B2 in vitro (Table 1). This implies a success rate of 18%. Among the 13 compounds selected by model 2, compounds 9–11 inhibited both CYP11B1 and CYP11B2, and compound 12 showed selective inhibition of CYP11B2. This results in a success rate of 31%. Compound 10 was a consensus hit and inhibited both CYP11B1 and CYP11B2. Thus, an overall success rate of both pharmacophore models was 21%. These findings showed that both models 1 and 2 had adequate prospective, predictive power with success rates quite typical for this virtual screening method. According to a search of the SciFinder database, none of the compounds discovered in this study were reported as CYP11B1 and CYP11B2 inhibitors in literature before. Due to the 93.9% identical amino acid residues in CYP11B1 and CYP11B2 (Kawamoto et al., 1992; Taymans et al., 1998) it is challenging to generate selective pharmacophore models for CYP11B1 and CYP11B2 inhibition. Model 1 found compounds 8 and 10, both are novel dual inhibitors of CYP11B1 and CYP11B2. The IC₅₀ values for compounds 8 and 10 for CYP11B1 inhibition were 3.04 and 0.13 μM, respectively, and for CYP11B2 inhibition were 2.77 and 0.11 μM, respectively (Table 1). Model 2 found compounds 9–12, of them 9 and 10 were dual inhibitors of CYP11B1 and CYP11B2. The IC₅₀ value for compound 9 for CYP11B1 inhibition was 0.21 μM and for CYP11B2 inhibition was 0.08 μM, respectively The IC₅₀ values of compound 11 (CYP11B1 = 2.52 μM, CYP11B2 = 15.58 μM) showed that it had a selectivity factor of 6 for CYP11B1 inhibition over CYP11B2. Compound 12 was a selective inhibitor of CYP11B2 with an IC₅₀ = 1.12 μM, while it was a very weak inhibitor of CYP11B1 with an inhibition of 33% at a concentration of 10 μM.

An X-ray crystal structure of CYP11B1 has not been published yet; however the crystal structure of CYP11B2 was available from the PDB (Berman et al., 2000) (PDB ID = 4FDH) (Strushkevich et al., 2013). The positioning of the novel inhibitors into the binding pocket of CYP11B2, which is similar to the well-known inhibitor fadrozole, rationalizes their biological activities (Figure 7).

A close analysis of the mapping of the active hits and fadrozole into the pharmacophore models was performed. Combined aromatic ring-HBA features (AR-1 and HBA-1) of the respective pharmacophore models (Figure 9) mapped an aromatic nitrogen of all the novel inhibitors 8–12. The angle and position of the aromatic nitrogen toward the heme iron is important for making an inhibition complex at the binding site. In the docking analysis, all active hits formed this interaction in an angle of around 90° (Figure 7).

According to the results obtained in this study, we compared our pharmacophore queries with previously reported pharmacophore models (Ulmschneider et al., 2006; Lucas et al., 2008a, 2011; Gobbi et al., 2013), Previously published studies used molecular modeling as a tool for designing optimized CYP11B1 and CYP11B2 inhibitors (Ulmschneider et al., 2006; Lucas et al., 2008a, 2011; Gobbi et al., 2013). Our pharmacophore queries were based on diverse training compounds (Ulmschneider et al., 2005a; Hille et al., 2011b; Meredith et al., 2013), and had different numbers and locations of pharmacophore features in space. In comparison to the previous models, our pharmacophores additionally include aromatic features (AR-1 and AR-2) (Figure 9, Table 2). The alignment of our pharmacophore models reveals that they have four features in common, including HBA-1, AR-1, AR-2, and H-1 (Figure 8). All the novel inhibitors found in this study were mapped to analyze the importance of different pharmacophore features. The alignment showed that HBA-1, HBA-3 AR-1, AR-2, H-1, H-2 were essential features in mapping the active compounds during virtual screening run (Figure 9, Table 2). All of the newly discovered inhibitors in this study have aromatic nitrogen-containing heterocycles and hydrophobic parts (Figure 9). The heterocyclic nitrogen part has a crucial role in forming an iron-binding interaction with heme of these CYP enzymes and was mapped by the HBA-1 and AR-1 features of the pharmacophores. This type of interaction inhibited the catalytic process of the target enzymes and has been reported earlier (Denner et al., 1995a,b; Hartmann et al., 2003; Bureik et al., 2004; Ulmschneider et al., 2005b; Hoyt et al., 2015).

Both active hits from model 1 did not map the two optional features of the model. This suggests that these features may be deleted from the model without losing active hits. A model with fewer and no optional features is much faster in screening virtual compound libraries. In future studies, a refined model 1 without those optional features can be applied for screening millions of compounds in a still reasonable time.

To compare the ligand-based features of the models to the protein-ligand interactions observed in the available X-ray structures of CYP11B2, the co-crystallized inhibitor fadrozole (4FDH) was aligned to pharmacophore model 1. Fadrozole mapped five features of the model (Figure S1), but also didn't map the two optional features supporting the hypothesis that those are not advantageous. A comparison of structure-based pharmacophore models derived from 4FDH (Strushkevich et al., 2013) and 4ZGX (Martin et al., 2015) co-crystallized structures is given in the supporting information (Figure S2). The general description about the generation of pharmacophore models has been previously outlined (Vuorinen et al., 2014; Akram et al., 2015; Kaserer et al., 2015).

During the validation of our pharmacophore models, three novel dual CYP11B1 and CYP11B2 inhibitors, one novel selective CYP11B1 inhibitor, and one novel selective CYP11B2 inhibitor were discovered. Compound 11 was a selective inhibitor of CYP11B1 that is the principal enzyme for the production of cortisol, which inhibition may be a strategy for the treatment of Cushing's syndrome and delayed wound healing (Nieman, 2002). Compound 12 was a selective CYP11B2 inhibitor, which is the key enzyme for the production of aldosterone, which inhibition is a potential target for the treatment of congestive heart failure, myocardial fibrosis, and hypertension. Compounds 8–10 are potent dual inhibitors of CYP11B1 and CYP11B2, which makes them interesting lead compounds for the development of drugs that could achieve a complete blockade of adrenal corticoid formation. Compounds 8–12 could be further chemically optimized to enhance their biological efficacies and selectivities by bioisosteric replacements or substitution of rings.

Compounds 8–10 and 12 were also tested for inhibition of human steroidogenic enzyme CYP17 (Table 1), because it belongs to the same class and has same inhibition mechanism as other CYP enzymes (Devore and Scott, 2012). None of the novel inhibitors showed inhibition of human CYP17 of more 3% at a concentration of 10 μM. This showed the selectivity of these novel inhibitors over CYP17.

The virtually selected hits 13–31 that showed no or only very weak inhibition during in vitro testing on human CYP11B1 and CYP11B2 might not be able to bind to the target, may have suffered from degradation or did not reach the binding site of the enzyme, and/or could have been pumped out of the cells via cellular efflux pumps (Johnstone et al., 2000). A precise conclusion for their inactivity is difficult to draw (Figure 6).

In the course of this study, ligand-based pharmacophore models for CYP11B1 and CYP11B2 inhibition were developed. For experimental validation of pharmacophore queries, the virtually selected hits were tested in vitro. This process resulted in the identification of new structural features advantageous for CYP11B inhibition (AR-1, AR-2, H-1, H-2, and HBA-3) and five novel CYP11B1 and/or CYP11B2 inhibitors. All of the novel inhibitors contained a heterocyclic nitrogen that is frequently present in CYP inhibitors. This project validated our pharmacophore model for future virtual screening campaigns. Regarding the quality of the pharmacophore models, model 2 gave more active hits than model 1. Both models will be refined further based on the biological testing to enhance their sensitivity and specificity.

DS planned and supervised the study. WW and MA created the pharmacophore models. MA performed the virtual screening and hits selection along with DS. MA conducted biological experiments under supervision of JH and RH. MA, JH, DS, and RH analyzed the data. All authors were involved in the preparation of the manuscript and approved the final version.