Steroidal 5α-Reductase Inhibitors: A Comparative 3D-QSAR Study

Mar 18, 2015 - ... inhibitors: a drug design aspect using molecular docking-based self-organizing molecular field analysis. Sant K. Verma , Suresh Tha...
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Steroidal 5α-Reductase Inhibitors: A Comparative 3D-QSAR Study Review Suresh Thareja* School of Pharmaceutical Sciences, Guru Ghasidas Central University, Bilaspur, Chhattisgarh 495 009, India NADPH.2 DHT stimulates cell growth in the tissue, thereby causing a rapid prostate enlargement in later adulthood. The proposed chemical mechanism for the reduction of T to DHT by 5α-reductase catalysis involves the formation of a binary complex between the enzyme and NADPH, followed by the formation of a ternary complex with the substrate T.3 Steroidal 5α-reductase plays a key role in many conditions and diseases with elevated DHT levels, including benign prostatic hyperplasia, prostate cancer, hirsutism, acne, and male-patterned baldness.4 Steroidal 5α-reductase has two isoforms, namely, type I and II CONTENTS from human and rat prostatic complementary DNA (c-DNA) 1. Introduction A libraries.5,6 Type I is prevalent in hair follicles and subcutaneous 2. Historical Aspect of Published 2D-QSAR Studies B glands of the skin, while type II is prevalent in the prostate, 3. Current Aspect of 3D-QSAR Studies C genital skin, seminal vesicles, and epididymis.7,8 Recently, with 4. 3D-QSAR Methodology Employed for Steroidal the development of genome-wide gene expression profile 5α-Reductase Inhibitors C analyses, a third type of 5α-reductase enzyme (type III) has 4.1. Selection of Data Sets and Inhibitory been identified in hormone-refractory prostate cancer cells Activities C 9,10 (HRPC). 4.2. Selection of Training and Test Sets C Steroidal 5α-reductase isozymes differ in the constitution of 4.3. Molecular Modeling and Alignment D amino acids as well as molecular weight.11 Selective steroidal 5α4.4. 3D-QSAR Models D reductase-I inhibitors could treat acne without significantly 4.5. Statistical Analyses D affecting testosterone metabolism in the prostate, thus allowing 5. Results and Discussion of 3D-QSAR Studies on sexual function to continue as normal.12,13 Steroidal 5αSteroidal 5α-Reductase Inhibitors D reductase-II inhibitors suppresses the DHT concentration by 5.1. Finasteride Analogues (4-Azasteroids) As blocking the enzyme and thus shrinking the size of the prostate Inhibitors of Steroidal 5α-Reductase-II D and ultimately provides relief from the symptoms related to the 5.2. Epristeride Analogues (3-Carboxysteroids) As static mechanical obstruction caused by BPH (Figure 1).14,15 Inhibitors of Steroidal 5α-Reductase-II F Crystal structure knowledge of steroidal 5α-reductase isoforms is 5.3. Pregnane Derivatives As Inhibitors of Human essential for the rational design of novel, potent, selective, and Steroidal 5α-Reductase-II F specific inhibitors. 5.4. 6-Azasteroids As Dual Inhibitors of Both During purification, isolation of the enzyme in pure form is not Isoforms of Steroidal 5α-Reductase F possible due to its unstable nature. Therefore, estimation of 6. Conclusions H crystal structure data of both isoforms of steroidal 5α-reductase is Author Information J still unavailable.16,17 The only information available about the Corresponding Author J steroidal 5α-reductase isozymes is their primary sequence Notes J estimated from c-DNAs.18 In the absence of crystal structure Biography J and binding site information, the design of novel inhibitors using Acknowledgments J a rational designing approach is not possible. Therefore, References J inhibitors were designed by modifying the structure of natural substrates, including the substitution of one carbon atom of the rings of the steroids by a heteroatom such as nitrogen, thereby 1. INTRODUCTION forming azasteroids.19 Alternatively, the 3D structure of 5αBenign prostatic hyperplasia (BPH) is the noncancerous growth reductase can also be generated using homology modeling with of the prostate gland due to overproliferation of the stromal and the available amino acid sequence.20 glandular elements of the prostate, resulting in the obstruction of 1 Several steroidal and nonsteroidal compounds have been proximal urethra, thus causes urinary flow disturbances. It is developed during the last two decades, but steroidal derivatives caused due to the augmented levels of the androgen attracted more attention, as these are highly active and small dihydrotestosterone (DHT), which plays a key role in prostate growth. Steroidal 5α-reductase (EC 1.3.99.5) is a nuclear membrane bound enzyme that converts endogenous testosterReceived: October 14, 2014 one (T) to dihydrotestosterone in the presence of cofactor © XXXX American Chemical Society

A

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Figure 1. Mechanism of steroidal 5α-reductase inhibitors in the treatment of benign prostatic hyperplasia (BPH).

reduce the cost and time of the synthesis of the drug, so as to improve the biological activity of the drug molecule. QSAR provides the guidelines for making structural changes in the compound so that drugs of desired potency with fewer side effects can be obtained.35 Our research group has been actively involved in identifying the structural framework using a self-organizing molecular field analysis−based 3D-QSAR approach for designing new, selective steroidal inhibitors of 5α-reductase for the treatment of BPH and other associated disorders.20,36−41 Taking into consideration the results obtained from the preliminary design studies, a library of novel compounds was designed and synthesized in our laboratory. Inhibitory activities of the designed compounds were in agreement with our predicted results of 3D-QSAR.42 On the basis of the above result, a comparative 3D-QSAR study on various classes of steroidal 5α-reductase inhibitors has been performed to explore the area of steroidal nucleus for substitutions having favorable interaction with the steroidal 5αreductase isoforms. Further, the results of our studies can be applied for the rational design and library screening of novel compounds as selective 5α-reductase inhibitors.

changes in the steroid nucleus may result in significant alteration in biological activity.21,22 Steroidal inhibitors of 5α-reductase having heterosteroid scaffold are available in the market. Finasteride (1), a 4-azasteroide (2), was the first clinically used inhibitor of steroidal 5α-reductase used for the treatment of benign prostatic hyperplasia by Merck and Co.23,24 It is a competitive inhibitor of 5α-reductase type II with 10-fold higher affinity than type I due to the formation of stable complex with enzyme. Dutasteride is another closely related heterosteroid approved by U.S. FDA in 2002 for the symptomatic treatment of BPH, having affinity for both type I and type II.25,26 Epristeride (SK&F 105657) (3), an interesting 3-carboxysteroid (4), entered clinical trials for the treatment of BPH.27 It is a potent inhibitor of steroidal 5α-reductase-II while a weak inhibitor of steroidal 5α-reductase-I. It has been shown to be an uncompetitive inhibitor against both testosterone and NADPH.28 As X-ray crystallographic structure of human steroidal 5αreductase isoforms is not available, there is an immense interest in designing novel inhibitors based on ligand-based drug design approaches that rely upon the binding properties of the existing inhibitors.29,30 Ligand-based drug design methods capitalize on the fact that ligands similar to an active ligand are more likely to be active than random ligands.31 Ligand-based approaches commonly consider two- or three-dimensional chemistry, steric, electrostatic, and interaction points (e.g., pharmacophore points) to assess similarity.32 Ligand similarity approaches (be they 2D or 3D) require only a single active molecule, which may come from the literature, patents, or in-house experimental data. In these cases, activity might be determined only by an inaccurate high-throughput screen.33 Quantitative structure−activity relationship (QSAR) analysis is one of the ligand-based approaches in computational research to correlate biological activity and physicochemical properties of a series of molecules.34 The main objective of QSAR is to develop the best drug model to overcome the difficulty of trial-and-error methods. QSAR is also used to

2. HISTORICAL ASPECT OF PUBLISHED 2D-QSAR STUDIES 2D-QSAR techniques are of particular interest as they eliminate the need for determining 3D structure, putative binding conformation, and molecular alignment. In the literature, a wide variety of 2D-QSAR studies pertaining to steroidal 5αreductase inhibitors have been reported. Hansch and co-workers reported a conventional 2D-QSAR study on several series of steroidal 5α-reductase inhibitors using multiple linear regression analysis. The conclusion of their studies was quite descriptive, and they proposed the structural architecture based on descriptors used in QSAR studies for the development of potent steroidal 5α-reductase inhibitors.43 B

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molecular target binding site. This technique provides critical information on the interaction between the ligand and the putative receptors. In this method, it is possible to predict the biological activity of molecules and represent the relationships between molecular properties and biological activity in the form of 3D maps.60−63 SOMFA has been successfully applied in our laboratory for the design of steroidal 5α-reductase, protein tyrosine phosphatase1B (PTP-1B), dipeptidylpeptidase-IV (DPP-IV), aldose reductase (AR), and cyclooxygenase-II (COX-II) inhibitors.64−67 For the generation of molecular architecture, different pharmacophoric models were developed using 3D-QSAR studies on a wide variety of data sets including 4-azasteroids (Finasteride analogues), 3-carboxysteroids (Epristeride analogues), pregnane derivatives, and 6-azasteroids.20,36−40 By superimposing the pharmacophoric elements of all the molecules, it is possible to identify the structural features that contribute to or detract from receptor-binding affinity.62 Further, steric and electrostatic maps generated from the study will be used for distinguishing the mode of binding of both the isoforms and key structural features of both isoforms of the enzyme.68 A ligand-based method such as SOMFA is widely used not only because it is not very computationally intensive but also because it can lead to the rapid generation of QSARs from which the biological activity of newly designed compounds can be predicted. In contrast, an accurate prediction of activity of untested compounds based on the computation of binding free energies is both complicated and lengthy. The SOMFA maps are a clear indicator for the intuitionistic medicinal chemist for predicting novel molecules with enhanced steroidal 5α-reductase inhibitory activity, along with maintaining selectivity toward steroidal 5α-reductase. Critical interpretation of the SOMFA maps led to the identification of key structural features that could be exploited for improving the potency of the most potent reference compound.69 A combined structural framework for designing newer inhibitors of steroidal 5α-reductase has been developed in the present study using steric and electrostatic maps obtained in the above-mentioned 3D-QSAR studies on a wide variety of data sets.

Further, Hutter and Hartmann reported their QSAR studies on the human steroidal 5α-reductase inhibitors including 6azasteroids and nonsteroidal compounds.44 The results of their study indicated strong similarities between the variables for the prediction of the binding affinity to the type I and IC50 values for the type II isoforms of the steroidal 5α-reductase. Furthermore, the topological indices together with the surface-related descriptors point toward a lower content of aromatic amino acids in the binding site of the type II isoenzyme. Our research laboratory also performed a descriptor-based QSAR study on a series of carboxysteroids as steroidal 5αreductase inhibitors. The results of the QSAR study indicated a characteristic influence of various physicochemical parameters, mainly positive contribution of molar refractivity, total dipole moment, H-bond donor while negative contribution of Balaban index, log P, and lowest unoccupied molecular orbital (LUMO) for designing steroidal 5α-reductase inhibitors.40 These 2D-QSAR models were informative in terms of physicochemical parameters but lack the fundamental premise of a 3D-QSAR model for receptor binding, i.e., a pharmacophore required to facilitate molecular recognition and binding. This necessitates the requirement for the development of a 3D-QSAR model, essential for molecular recognition and target binding.45

3. CURRENT ASPECT OF 3D-QSAR STUDIES Three-dimensional quantitative structure−activity relationship (3D-QSAR) techniques are the most prominent computational means used for rational ligand-based drug design. There are numerous 3D-QSAR algorithms available for analyzing molecules in terms of molecular descriptors that are more meaningful to medicinal chemists.46−50 The primary aim of these techniques is to establish a correlation of biological activities of a series of structurally and biologically characterized compounds with the spatial fingerprints of major field properties of each molecule, such as steric and electrostatic potential. 3D-QSAR techniques are particularly effective in correlating the 3D structures of the molecules and their bioactivities based on statistical techniques. A validated 3D-QSAR model not only helps in better understanding of the structure−activity relationships of any class of molecules but also provides the researcher an insight at the molecular level about the lead molecules for further developments.51−53 Thus, information obtained from 3DQSAR analysis provides important guidelines for the drug design process. Comparative molecular field analysis (CoMFA) has been a new paradigm tool of cheminformatics for three-dimensional ligand-based drug design.48 Self-organizing molecular field analysis (SOMFA) is a new field-based tool for rational drug design developed by Robinson and co-workers.54 It is a gridbased technique where no probe interaction energies are needed as in the case of CoMFA. The similarity of SOMFA to CoMFA makes it an attractive technique for the development of 3DQSAR models.55−57 SOMFA samples the intrinsic molecular properties (electrostatic and steric potential) around a set of ligands and constructs 3D-QSAR models by correlating these 3D fields with the corresponding experimental activities of ligands interacting with a common target receptor.58,59 It is simple and intuitive in concept and avoids the complex statistical tools and variable selection procedures favored by other methods. SOMFA analysis involves the alignment of molecules in a structurally and pharmacologically reasonable manner on the basis of the assumption that each compound acts via a common macro-

4. 3D-QSAR METHODOLOGY EMPLOYED FOR STEROIDAL 5α-REDUCTASE INHIBITORS 4.1. Selection of Data Sets and Inhibitory Activities

A wide variety of data sets bearing 4-azasteroids (Finasteride analogues),70,71 3-carboxysteroids (Epristeride analogues),72 pregnane scaffold,73−79 and 6-azasteroids80−82 as potential inhibitors of steroidal 5α-reductase were employed for the present comparative 3D-QSAR studies. Data sets where the inhibitory activities of these compounds were measured by a single protocol and derived from the same laboratory were selected in each study. Steroidal inhibitors in a data set that does not exhibit a definite spectrum of inhibitory potential against steroidal 5α-reductase were also not considered in the study. To arrange data in a linear and ascending manner, negative logarithms of inhibitory activities were taken against both isoforms of 5α-reductase.83,84 4.2. Selection of Training and Test Sets

To produce a training set including as much information as possible, while still allowing the exclusion of compounds for a validation set, a diverse subset of compounds was assigned to the training set. The ratio of compounds for the training and test set was 3:1. Data sets were divided into test set molecules and C

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Figure 2. Clinically used Finasteride (1) bearing 4-azasteroid (2) scaffold employed for the 3D-QSAR study.

presence of a sterically bulky substituent, while blue points indicated a less bulky substituent results in compounds with better interaction with the target.64,66 Similarly, electrostatic maps also contain red and blue points representing eletrostatically favorable and unfavorable area, respectively. Red points indicated the presence of an electropositive substituent, while blue points indicated an electronegative substituent results in compounds with better interaction with the target. Steric and electrostatic maps are helpful in designing and predicting the biological activity of new compounds prior to their synthesis.20

training set molecules on the assumption that the molecules in the test set are structurally similar to the compounds of the training set, which concluded the study with high representative and predictive ability of the training set toward the test set of compounds.85 4.3. Molecular Modeling and Alignment

Molecular modeling is the most essential step in the development of a reliable 3D-QSAR model. The three-dimensional (3D) structures of all the data set molecules were modeled using Chemdraw Ultra 8.0.86−89 In a search for structures having global minima, energy minimization was carried out using a combination of both molecular mechanics (MM2) and semiempirical techniques (AM1) method available in the MOPAC. Initially, the MM2 technique was used for all the data set structures until a root-mean-square (RMS) gradient value less than 0.001 kcal/mol Å was achieved. The Hamiltonian approximation Austin model 1 (AM1) was used on the minimized structures in order to achieve final structures of the data set having the lowest energy.90,91 Alignment of all the molecules in various data sets is another crucial step in the development of the final 3D-QSAR models. In the 3D-QSAR studies, a common principle involving use of the most potent compound was employed for alignment purposes.92 In some of the studies, clinically used Finasteride was also employed for the alignment of the data set.36−40 All the aligned structures were converted into .cssr file format using VEGA ZZ software.93

4.5. Statistical Analyses

A partial least-squares (PLS) approach, an extension of multiple regression analysis, was used to derive the 3D-QSAR, in which the SOMFA descriptors were used as independent variables and inhibitory activities values were used as dependent variables. Cross-validation with the leave-one-out (LOO) option was carried out to obtain the optimal number of components to be used in the final analysis. In evaluating the performance of the constructed QSAR model, a commonly used approach in the field of QSAR follows the recommendation of Tropsha that a predictive QSAR model should possess cross-validated correlation coefficient rcv2 (Q2) values in the range from 1, suggesting a perfect model, to less than 0, where errors of prediction are greater than the error from assigning each compound mean activity of the model.96 Test set molecules were aligned in the same way as those in the training set, and their activities were predicted by each PLS analysis. Fischer statistics (F-Test) is the ratio between explained and unexplained variances for a given number of degrees of freedom. The larger the value of F, the greater is the probability that the QSAR models will be statistically significant.97−100

4.4. 3D-QSAR Models

The SOMFA program utilizes energy-minimized aligned structures along with a well-defined spectrum of inhibitory activity against steroidal 5α-reductase to develop a reliable 3DQSAR model. Initially, 3D maps with 40 × 40 × 40 Å dimensions originating at (−20, −20, −20) were developed around using all of the training set molecules.94 Both steric and electrostatic properties were used in developing these maps. Steric and electrostatic potentials of all the data set molecules including the test set were generated using these maps.95 The Grid-Visualizer module in the SOMFA program is used to visualize the structural features responsible for variation in the biological activity of all the compounds in the data set. Steric maps generated during the studies contain red and blue points representing steric favorable and unfavorable areas, respectively. Red points indicated the

5. RESULTS AND DISCUSSION OF 3D-QSAR STUDIES ON STEROIDAL 5α-REDUCTASE INHIBITORS 5.1. Finasteride Analogues (4-Azasteroids) As Inhibitors of Steroidal 5α-Reductase-II

3D-QSAR studies on a data set that comprises Finasteride analogues (4-azasteroids) as inhibitors of steroidal 5α-reductaseII taken from the work of Rasmusson et al.70 and Liang et al.71 were reported by our research group (2010).36,37 Selected 4D

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Figure 3. Electrostatic and steric requirement at 4-azasteroids for designing potent steroidal 5α-reductase-II inhibitors.

Figure 4. Clinically used Epristeride (3) bearing a 3-carboxysteroid (4) scaffold employed in the present 3D-QSAR study.

few blue points were observed, indicating unfavorable steric interaction. In the electrostatic potential map, a high density of blue points around “R1” of the steroid skeleton indicated the presence of electronegative groups, whereas some red points in the neighborhood indicated electropositive groups, favorable for optimal activity. A high density of blue points around substituent “R2” indicated the requirement of electronegative groups for interaction with steroidal 5α-reductase-II. A molecular architecture depicted in Figure 3 was designed by visualizing the 3D steric and electrostatic maps. Our study indicated a nonbulky electropositive substituent is favorable at “R1” and a bulky electronegative substituent is favorable at “R2” for compounds having potent steroidal 5α-reductase-II inhibitory activity.

azasteroids have structural similarity with the clinically used Finasteride (Figure 2). Various models were developed using a training set of 23 molecules, and the developed models were further evaluated using a test set of 8 molecules. The validity of the developed models was evaluated using various statistical measures. Statistical analysis of the best model developed using PLS analysis showed good cross-validated correlation coefficient Q2 (0.783), noncross-validated correlation coefficient R2 values (0.806), and high F-test value (87.282), with satisfactory correlation and predictive ability. Analysis of three-dimensional SOMFA steric and electrostatic maps obtained in the 3D-QSAR study helps in correlating the variation of biological activity with the structural change. 3DQSAR steric maps showed a high density of red points around the substituent “R2”, suggesting a favorable steric interaction due to branching of the side chain. Meanwhile, around position “R1”, E

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Figure 5. Electrostatic and steric requirements at 3-carboxysteroids for designing potent steroidal 5α-reductase-II inhibitors.

5.2. Epristeride Analogues (3-Carboxysteroids) As Inhibitors of Steroidal 5α-Reductase-II

derivatives having diversity in structure and potency profile (Figure 6). A 3D-QSAR study was reported on a data set of 30 molecules of pregnane series in our laboratory.73−79 3D-QSAR models were developed using a training set of 23 molecules, and the developed models were further evaluated using a test set of 7 molecules. Selections of test set molecules were made by considering the fact that they possess structural similarities with molecules in the training set. Statistical analysis of the best model developed using PLS analysis showed excellent cross-validated correlation coefficient Q2 (0.881), noncrossvalidated correlation coefficient R2 values (0.893), and high Ftest value (175.527), with good correlation and excellent predictive ability.39 3D-QSAR steric maps showed a cluster of red points around C-3, C-6, and C-17 of the steroid skeleton, indicating the presence of bulky substituents for favorable interaction. Additionally, few blue points were also observed in the vicinity at C-4 and C-16, indicating unfavorable steric interaction due to bulky substitution. Meanwhile in the electrostatic potential map, a cluster of blue points around C-3, C-6, C-16, and C-17 of the steroid skeleton indicated the presence of electronegative substituents, whereas some red points in the neighborhood at C-4 and C-16 indicated electropositive substituents, favorable for interaction with the enzyme. The presence of unsaturation at 6− 7 does not have profound effect, while at 16−17 it results in compounds with excellent inhibitory activity. A molecular architecture depicted in Figure 7 was designed by visualizing the steric and electrostatic maps of pregnane derivatives. A QSAR study indicated that a bulky electronegative substituent at C-6 and C-17 and a bulky electropositive substituent at C-4 and C-16 along with Δ16,17 are favorable for compounds with good interaction with steroidal 5α-reductase-II.

Holt et al.72 reported a series of unsaturated 3-carboxysteroids having favorable electrostatic interaction between the carboxylate and the positively charged oxidized cofactor leading to uncompetitive inhibition of steroidal 5α-reductase-II. Unsaturated 3-carboxysteroids have structural similarity with Epristeride, a clinically used drug (Figure 4). A 3D-QSAR study was performed on the data set of unsaturated 3-carboxysteroids by our research group (2009).38 3D-QSAR models were developed using a training set of 18 molecules, and the developed models were further evaluated using a test set of 5 molecules. Statistical analysis of the best model developed using PLS analysis showed good crossvalidated correlation coefficient Q2 (0.693), noncross-validated correlation coefficient R2 values (0.732), and high F-test value (43.816), with satisfactory correlation and good predictive ability. 3D-QSAR steric maps showed a high density of red points around the substituent “R”, suggesting a favorable steric interaction due to branching of the side chain. Meanwhile, around position C-3 of the steroid skeleton, few blue points were also observed, indicating unfavorable steric interaction. In the electrostatic potential map, a cluster of blue points around C-3 of the steroid skeleton indicated the presence of electronegative groups, whereas some red points in the neighborhood indicated electropositive groups, favorable for maximal activity. A high density of blue points around substituent “R” indicated the requirement of electronegative substituents for optimal inhibitory activity. The presence of unsaturation at 3−4, 5−6, and 11− 12 results in compounds with potent inhibitory activity. A molecular architecture depicted in Figure 5 was designed by visualizing the steric and electrostatic maps of 3-carboxysteroids. The study indicated a nonbulky electronegative substituent is favorable at C-3 and a bulky electronegative substituent along with Δ3−4,5−6,11−12 is favorable for compounds having potent steroidal 5α-reductase-II inhibitory activity.

5.4. 6-Azasteroids As Dual Inhibitors of Both Isoforms of Steroidal 5α-Reductase

Frye et al.80−82 reported the synthesis and biological evaluation of different series of 6-azasteroids (Figure 8) as dual inhibitors of both isoforms of steroidal 5α-reductase-I and -II. A 3D-QSAR study was performed on the selected azasteroidal data set to generate a comparative pharmacophoric model for both isoforms of steroidal 5α-reductase using 6-azasteroids.20 The selected data set possesses a wide spectrum of biological activity against both

5.3. Pregnane Derivatives As Inhibitors of Human Steroidal 5α-Reductase-II

Cabeza et al. reported the synthesis and human steroidal 5αreductase-II inhibitory activities of several series of pregnane F

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Figure 6. Representative structures of different pregnane scaffolds employed in the present 3D-QSAR study.

correlation coefficient R2 (0.662), and F-test value (47.096) against 5α-reductase-I while showing excellent cross-validated correlation coefficient Q2 (0.669), noncross-validated correlation coefficient r2 (0.718), and F-test value (61.099) against 5αreductase-II with satisfactory correlation and good predictive ability. In the electrostatic master maps of steroidal 5α-reductase-I, few blue points were observed around substituent “R1” and “R2” of the steroid nucleus, showing the importance of electronegative substituents, while a high cluster of red points around “R3” indicated the importance of electropositive substituents for

isoforms of the steroidal 5α-reductase, making it an interesting target to develop a comparative 3D-QSAR model. 3D-QSAR models were developed using a training set of 25 molecules, and the developed models were further evaluated using a test set of 14 molecules. Structural similarity between training and test set molecules was the criteria for the selection of a test set molecule. For development of comparative models, the same molecules were used in the training set against both steroidal 5α-reductase-I and -II. Statistical analysis of the best model developed using PLS analysis showed good crossvalidated correlation coefficient Q2 (0.604), noncross-validated G

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Figure 7. Electrostatic and steric requirements at pregnane derivatives for designing potent human steroidal 5α-reductase-II inhibitors.

steroidal 5α-reductase-II also showed the presence of a cluster of blue points around “R1” and “R2” in the steroid skeleton, indicating unfavorable steric interaction for steroidal 5αreductase-II inhibitory activity. A cluster of red points around the substituent “R3” suggested a favorable steric interaction due to bulkiness/branching in the side chain, whereas few blue points in the vicinity indicated unfavorable steric interaction. Replacement of −H at “R1” and “R2” with −CH3 and −Cl results in the compound having improved 5α-reductase-I inhibitory activity while having a slight decrease in steroidal 5α-reductase-II inhibitory activity. Therefore, a complete analysis of the above replacement indicated a decrease in the steroidal 5α-reductase-II while significant improvement of steroidal 5α-reductase-I inhibitory activity was observed. Overall, the proposed combination is acceptable for designing newer steroidal molecules having potent, selective inhibitory activity. Molecular architectures designed by visualizing the steric and electrostatic maps of both isoforms of steroidal 5α-reductase-I and -II are depicted in Figures 9 and 10, respectively.

Figure 8. Representative structure of 6-azasteroid scaffold employed in the present 3D-QSAR study.

designing compounds having favorable interaction with steroidal 5α-reductase-I. Additionally, in the vicinity of red points, few blue points were also observed, showing branching at “R3” having electronegative functionality for the design of selective and potent steroidal 5α-reductase-I inhibitors. Electrostatic maps of steroidal 5α-reductase-II also showed the presence of few blue points around C-3 of the steroid nucleus, confirming the presence of an electronegative substituent, while red points around “R1 and R2” designated the electropositive substituents for favorable interactions with steroidal 5αreductase-II. A cluster of red points around “R3” indicates the presence of electropositive substituent with electropositive branching for potent and selective steroidal 5α-reductase-II inhibitory activity. Steric maps of steroidal 5α-reductase-I also showed some important characteristics. The presence of red points around “R1” and “R2” with a cluster around “R3” of the steroid nucleus indicated favorable steric interaction due to the bulky substituents for designing selective steroidal 5α-reductase-I inhibitors. Further, the presence of few blue points in the branching showed unfavorable steric interaction. Overall, bulky substituents with less branching are favorable at “R3” for potent steroidal 5α-reductase-I inhibitory activity. Steric master maps of

6. CONCLUSIONS Selective inhibition of steroidal 5α-reductase is the mainstay intervention in the treatment of BPH and other skin-related disorders. In the absence of X-ray crystallographic structures of both isoforms of steroidal 5α-reductase, there is an immense interest in designing selective and specific inhibitors based on ligand-based 3D-QSAR studies that rely upon the binding properties of the existing inhibitors. 3D-QSAR studies are widely employed to alter the structural scaffold to develop newer molecules with an improved spectrum of activity. For the generation of molecular structural framework, different pharmacophoric models were developed using 3D-QSAR studies on a wide variety of data sets including 4-azasteroids (Finasteride analogues), 3-carboxysteroids (Epristeride analogues), pregnane derivatives, and 6-azasteroids. Resemblances between the two isoforms of the steroidal 5α-reductase have been observed by comparing their developed pharmacophoric 3D-QSAR models. The combined result of various 3D-QSAR studies indicated that the replacement of an electropositive with an electronegative substituent at C-4 and C-6 along with bulky electropositive substituent with nonbulky branching having electronegative functionality at C-17 will result in the compounds having good H

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Figure 9. Electrostatic and steric requirements at 6-azasteroid scaffold for designing potent steroidal 5α-reductase-I inhibitors.

Figure 10. Electrostatic and steric requirements at 6-azasteroid scaffold for designing potent steroidal 5α-reductase-II inhibitors.

that the contribution of steric and electrostatic potential is almost the same, indicating that these two factors are equally important for designing steroidal molecules with good binding affinities with steroidal 5α-reductase. These combined pharmacophoric features can be further used for identifying and developing nonsteroidal inhibitors of both isoforms. The results of our studies demonstrated that the constructed models were reliable enough to be applied in both rational design and library screening. Comparative 3D-QSAR studies performed on a wide variety of data sets on the steroidal scaffold explored the structural features of steroidal compounds that can be used for structural

dual inhibitory characteristics with improved steroidal 5αreductase-I inhibitory and a slight decrease in steroidal 5αreductase-II inhibitory activity. Further, the presence of Δ5−6,11−12,16−17 results in compounds with potent, selective 5αreductase-II inhibitory activity. A combined structural framework (Figure 11) on the steroid nucleus for designing new, potent, selective inhibitors having favorable interactions with steroidal 5α-reductase-II has been proposed herewith using steric and electrostatic maps obtained from 3D-QSAR studies. SOMFA maps offered enough information for us to understand the binding mode of different steroidal inhibitors. PLS calculations in all the studies predicted I

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Figure 11. Structural framework on the steroid nucleus for designing new inhibitors having favorable interaction with steroidal 5α-reductase-II.

modifications in order to develop selective inhibitors of both isoforms. As research in the area of steroidal 5α-reductase, specifically in the development of human steroidal 5α-reductaseII inhibitors, is still new, results of the present 3D-QSAR studies in combination with reported 2D-QSAR reports provide meaningful information in terms of molecular recognition and target binding for the design of more promising inhibitors in the management of BPH.

Scientific Writer-II in Novartis Healthcare, India. In 2012, he joined Guru Ghasidas Central University, Bilaspur, as a faculty member in Pharmaceutical Chemistry. His areas of interest include computer-aided drug design as well as synthesis and evaluation of novel PTP-1B inhibitors and steroidal 5α-reductase inhibitors. He has published various articles in national and international peer-reviewed journals. He is also the recipient of many awards for his presentations related to his research work at various national and international conferences.

AUTHOR INFORMATION

ACKNOWLEDGMENTS The author gratefully acknowledges Dr. Daniel Robinson (Computational Chemistry Research Group, Oxford University, U.K.) for SOMFA software and all the members of Molecular Modeling research group at UIPS, Panjab University for their constant support, suggestions, and encouragement throughout the work on steroids modeling.

Corresponding Author

*E-mail: [email protected], sureshthareja@hotmail. com. Phone: +91-9617605869 (M); +91-7752-260027 (O). Notes

The authors declare no competing financial interest. Biography

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Dr. Suresh Thareja (born November 11, 1983, in Haryana, India) is currently working as Assistant Professor of Pharmaceutical Chemistry at School of Pharmaceutical Sciences, Guru Ghasidas Central University, Bilaspur, India. He was awarded B. Pharm. degree from MDU University, Rohtak (2004), and M. Pharm. in Pharmaceutical Chemistry from Poona College of Pharmacy, Pune (2007). He received his Ph.D. degree in Pharmaceutical Chemistry from Panjab University, Chandigarh (2011). He is the recipient of GATE and ICMR-Senior Research Fellowship. After completing his Ph.D. (2011), he worked as J

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