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Int. J. Mol. Sci. 2012, 13(5), 6009-6025; doi:10.3390/ijms13056009

Quantitative Structure-Activity Relationship Studies on Indenoisoquinoline Topoisomerase I Inhibitors as Anticancer Agents in Human Renal Cell Carcinoma Cell Line SN12C
Yi Zhi 1, Jin Yang 2, Shengchao Tian 1, Fang Yuan 1, Yang Liu 1,, Yi Zhang 2, Pinghua Sun 3, Bo Song 1,* and Zhiwen Chen 1,*
Urology Center, Southwest Hospital, Third Military Medical University, Chongqing 400038, China
Department of Cell Biology, Third Military Medical University, Chongqing 400038, China
Guangdong Province Key Laboratory of Pharmacodynamic Constituents of Traditional Chinese Medicine and New Drugs Research, College of Pharmacy, Jinan University, Guangzhou 510632, China
Authors to whom correspondence should be addressed; Tel.: +86-23-68754686 (B.S.); +86-23-68765817 (Z.C.); Fax: +86-23-65460268 (Z.C.).
Current address: Department of Urology, the 452nd Hospital of People’s Liberation Army, Chengdu 610021, China.
Received: 9 March 2012; in revised form: 4 May 2012 / Accepted: 11 May 2012 / Published: 18 May 2012


: Topoisomerase I is important for DNA replication and cell division, making it an attractive drug target for anticancer therapy. A series of indenoisoquinolines displaying potent Top1 inhibitory activity in human renal cell carcinoma cell line SN12C were selected to establish 3D-QSAR models using CoMFA and CoMSIA methods. Internal and external cross-validation techniques were investigated, as well as some measures taken, including region focusing, bootstrapping and the “leave-group-out” cross-validation method. The satisfactory CoMFA model predicted a q2 value of 0.659 and an r2 value of 0.949, indicating that electrostatic and steric properties play a significant role in potency. The best CoMSIA model, based on a combination of steric, electrostatic and H-bond acceptor descriptors, predicted a q2 value of 0.523 and an r2 value of 0.902. The models were graphically interpreted by contour plots which provided insight into the structural requirements for increasing the activity of a compound, providing a solid basis for future rational design of more active anticancer agents.
CoMFA; CoMSIA; QSAR; indenoisoquinoline; Top1 inhibitors

1. Introduction

Kidney cancer is among the 10 most frequently occurring cancers in western communities. Globally, about 270,000 cases of kidney cancer are diagnosed yearly and 116,000 people die from the disease. Renal cell carcinoma (RCC) accounts for approximately 90% of all kidney cancers and its incidence is on the rise [1,2]. Localized RCC is curable with surgery but a third of patients are diagnosed with metastatic RCC, which is difficult to treat and is generally resistant to conventional radiotherapy, chemotherapy and endocrine therapy. The median survival for patients with metastatic RCC is 10–12 months [3]. Despite a minority of patients with metastatic disease benefiting from cytokine immunotherapy, a need still exists for developing more effective novel anti-renal cell carcinoma agents.

Human topoisomerase type I (Top1) is a member of the topoisomerase family of enzymes that resolve the topological problems associated with DNA supercoiling during various essential cellular processes [46]. It forms a covalent link with the 3′-end of the cut DNA strand in the Top1-DNA cleavage complex at its catalytic tyrosine 723 residue, relieving torsional strain in DNA via reversible single-strand nicks [7,8]. Top1 is important for the successful replication, transcription and recombination of DNA, as well as chromatin remodeling, making it an attractive drug target for anticancer therapy. Camptothecin, isolated and identified in 1966, was the first Top1 inhibitor [9]. Camptothecin derivatives irinotecan and topotecan approved by the Food and Drug Administration (FDA) validate Top1 as a therapeutic target for anticancer drug development [10]. In practice, these Top1 inhibitors exert a promising anticancer effect in the treatment of renal cell carcinoma. For instance, clinically relevant concentrations of topotecan-induced apoptosis in RCC cell lines work more effectively than 5-FU [11]. In addition, combination therapy using topotecan and survivin-specific siRNA could show a synergistic effect and offer an attractive approach for the treatment of advanced renal cancer [12,13]. In clinical practice, the use of a novel combination of irinotecan, cisplatin and mitomycin (IPM chemotherapy) produce symptomatic relief for a majority of patients with renal cancer following failure of cytokine immunotherapy [14]. However, these camptothecin derivatives are not ideal drug molecules, suffering from pharmacokinetic problems, inherent instability due to lactone ring opening and rapid reversibility of the cleavage complexes after drug removal [15,16]. There is a present need for the development of noncamptothecin Top1 inhibitors as anticancer agents. Recently, a number of analogs of the indenoisoquinolines have been reported as novel anticancer agents [1719]. The indenoisoquinoline Top1 inhibitors were examined for antiproliferative activity against different cancer cell lines. The results indicate that these novel noncamptothecin Top1 inhibitors could be potential agents for the treatment of a variety of cancers, including renal cancer. Among these derivatives, two indenoisoquinolines have been selected currently for clinical development by the NCI: NSC 725776 and NSC 724998 [20]. Both exert antiproliferative activity in submicromolar concentrations in cultured human cancer cell lines.

The three-dimensional quantitative structure-activity relationship (3D-QSAR) techniques, including comparative molecular field analysis (CoMFA) [21] and comparative similarity indices analysis (CoMSIA) [22] are useful methods of ligand-based drug design used to correlate physicochemical descriptors from a set of related compounds to their known molecular activity or molecular property values [23]. These computational techniques incorporate 3D information for the ligands and have been proved particularly helpful in the design of novel and more potent inhibitors. The application of QSAR methodology to the indenoisoquinoline derivatives hasn’t been reported. The satisfactory QSAR models on 48 indenoisoquinoline topoisomerase I inhibitors for their anti-renal cell carcinoma activities [18,19] provide a solid basis for future rational design of more active agents.

2. Results and Discussion

2.1. CoMFA Analysis

The compound 20, one of the most active molecules, was selected as the template and the isoquinoline ring as the common structure for alignment (Figure 1). The CoMFA model provided a cross-validation q2 value of 0.602 with 5 components, an r2 value of 0.925 and an F-test value of 66.709 (Table 1). Region focusing resulted in the better CoMFA model which showed a significant increase from 0.602 to 0.659 for the internal validity, 0.632 to 0.680 for group cross-validation, 0.790 to 0.826 for test set activity predictions, and from 0.925 to 0.949 for the non-validated r2 (Table 1). Figure 2 shows CoMFA fields for molecule 20 before and after region focusing. The activity values predicted for the test set are in good agreement with the experimental values (Figure 3) and the rpred2 value of 0.826 further confirms the reliability and accuracy of the model. The electrostatic and steric field contributions to the final model were 58.7% and 41.3%, respectively.

2.2. CoMSIA Analysis

Twelve CoMSIA models were generated using combinations of 2, 3, 4, and all 5 descriptors as shown in Table 2. Model 5, based on steric, electrostatic and H-bond acceptor fields, was found to be the most accurate, yielding a q2 value of 0.523 and an r2 value of 0.902. The Group cross q2 value of 0.524, bootstrapped value of 0.906 ± 0.023 and test set r2 value of 0.704 further approve the best CoMSIA model 5. The predicted values are closely consistent with the experimental data (Figure 4). The steric field explains 13.4% of the variance, the electrostatic field for 47.9% and the H-bond acceptor field for 38.7% of the variance.

2.3. CoMFA Contour Maps

The results of 3D-QSAR models are presented in the contour coefficient maps as shown in Figure 5. Its steric interaction is denoted by green and yellow contours. Both a large green contour and a large yellow contour were located near the end of the side chain linking to the nitrogen atom of the isoquinoline ring of target compounds, indicating that steric fields did not play an important role in this region. This may be the reason why compounds 20 and 28 with almost the same chains showed the most and lowest activities, respectively. Similarly, compounds 1, 24, 28, 31 and 32 showed lower activity while compounds 3, 6, 17, 19 and 29 are more potent. Two large green and two small red contours around the 3-position of the isoquinoline ring suggest that bulky and electron-withdrawing substituents are required in this region to increase activity. This is possibly the reason why compound 39 with the substitution of nitro group showed 24.5 times more potency than its corresponding mother compound 40, likewise 41 is 67.6 times greater than 42. A small red contour located near carbonyl group at position-11 of compound 20 indicates that electron-withdrawing groups are preferred in this region. This is why the compounds 4347, whose carbonyl group at position-11 was replaced by other electron-donating groups, are less potent. A small red contour near the methoxyl substituted at position-9 of compound 20 can be interpreted that groups with an electron-withdrawing factor are desired to increase the activity, and that is why compound 20 with the methoxyl group at position-9 is almost 7000 times more potent than its mother compound 24, also compounds 36 and 9 are far more potent than corresponding 35 and 33, respectively. A large yellow contour around position-1 signifies that the hydrogen atom must not be substituted.

2.4. CoMSIA Contour Maps

The best CoMSIA model contour maps of the most active analog are shown in Figure 6. Its steric and electrostatic contour plots (Figure 6a,b) correlate well with the CoMFA contour maps described above. Hydrogen-bond acceptor contour maps are shown in Figure 6c. Hydrogen bond acceptor-favored regions are represented by magenta contours and unfavorable regions by cyan contours. One large magenta polyhedron is visible around the 3-position of the isoquinoline ring of compound 20, indicating that hydrogen-bond acceptor groups such as nitro, methoxyl group are very important for compound activity. Large cyan polyhedra around 2,4-positions of the isoquinoline ring and around the end of the side chain adjacent to the nitrogen atom of the isoquinoline ring can be interpreted as disfavoring hydrogen-bond acceptor groups in these regions.

2.5. Design of New Inhibitors

Based on the structure–activity relationship obtained by present 3D-QSAR models, a series of new inhibitors was designed and predicted (Table 3). With the most active molecule 20 in the training set used as the parent compound, some hydrogen-bond donors such as amino, hydroxyl and thiol were introduced at 3′ or 4′-position of the heterocycle appended to the lactam side chain, and some bulky and electron-withdrawing groups, such as nitro and cyan, introduced at the 3-position. Most (pGI50 > 8.5) greatly enhanced inhibitory activity in comparison to 20 (pGI50 = 8.145). In particular, compound 20-7 showed the strongest activity with its predicted pGI50 (9.029). Other compounds also exhibited good predicted activity as well as compound 20.

3. Experimental Section

3.1. Data Set

Forty-eight compounds investigated in the present study were taken from the published works of Morrell A. and co-workers [18,19]. The structures of the molecules and their biological data obtained by Morrell A. et al. are given in Tables 4,5. For convenience, we have converted the cytotoxicity GI50 values of topoisomerase inhibitors in renal carcinoma cell line SN12C to their negative logarithm (pGI50) values, which have a span of 4.0 log units from 4.00 to 8.00, providing a broad and homogeneous data set for 3D-QSAR study (see Table 5) [24,25]. Seven compounds were randomly selected as the test set, based on the structural and active diversities with the remaining 41 compounds as the training set.

3.2. Molecular Alignment

Compared to probe atom type, lattice shifting step size and overall orientation of the aligned compounds, a good alignment is the most important element for CoMFA and CoMSIA analysis [26], and the alignment rules will directly determine the quality and the predictive ability of the model. The alignment was often performed in accordance with some rules, such as substructure overlap, pharmacophore overlap and docking [27] as soon as the active conformation was obtained by energy minimization using Powell method and Tripos standard force field [28]. Here, the isoquinoline ring with structural rigidity was selected as the common substructure to overlap and to align all of the molecules and the most active compound 20 was used as the alignment template. Alignment of all compounds was shown in Figure 1. It can be seen that all the compounds studied have similar active conformations.

3.3. Partial Least Squares (PLS) Analysis

To linearly correlate the 3D-QSAR fields to biological activity values, PLS analysis [29] was performed. It was firstly carried out by the leave-one-out (LOO) and leave-group-out (10 groups) cross-validation methods to determine cross-validated r2 (q2) values and the optimal number of components on the basis of the lowest standard error of prediction (SEP) and avoiding over-fitting the models. A higher component was accepted and used only when the q2 differences between two components were larger than 5%. Non-cross-validation was then performed to establish the final 3D-QSAR model with the values of conventional correlation coefficient (r2), standard errors of estimate (SEE), and F ratio between the variances of calculated and observed activities given.

The q2 has been a good indicator of the accuracy of actual predictions. In general, q2 values can be separated into three categories: q2 > 0.6 means a fairly good model, q2 = 0.4–0.6 means a questionable model, and q2 < 0.4 a poor model [30]. q2 is calculated as follows:

q 2 = 1 - ( Y o b s - Y p r e ) 2 ( Y o b s - Y m e a n ) 2

where, Yobs = experimental activity, Ypre = predicted activity, Ymean = mean activity.

To further assess the robustness of the derived models, bootstrapping analysis (10 runs) was also utilized to calculate confidence intervals for the r2 and SEE [29,31]. The equation for SEE is given below.

S E E = P R E S S n - c - 1

Where n means number of compounds, c means number of components, and PRESS (predicted sum of squares) means ∑ (Yobs-Ypre)2.

3.4. Predictive Correlation Coefficient

q2 is a useful but not sufficient criterion for model validation, so an external test set (rpred2) [32] was claimed for the estimation of predictive ability. Equation of predictive values rpred2 is as follows:

r p r e d 2 = 1 - ( P R E S S / S D )

Therein, SD means the sum of squared differences between the measured activities of the test set and the average measured activity of the training set.

3.5. CoMFA Studies

Three-dimensional grid spacing was set at 2 Å in the x, y, and z directions and automatically generated to be a 3D cubic lattice that extended at least 4 Å beyond the van der Waals volume of all aligned molecules in all directions. Lennard-Jones potential and Coulomb potential were employed to calculate steric and electrostatic energies of each molecule using the Tripos force field [28], and the sp3-hybrized carbon atom with a +1 charge taken as the probe atom to determine the magnitude of the field values. The regression analysis was carried out using the partial least squares (PLS) method [29]. All energies that exceeded the cutoff value of 30 kcal/mol were replaced with 30 kcal/mol for the reduction of domination by large steric and electrostatic energies [33]. The column filtering was set to 2.0 kcal/mol and those lattice points whose energy variation was below this threshold were automatically omitted, consequently the signal-to-noise ratio was improved. The final model was developed with the optimum number of components to yield a non-cross-validated r2 value. Despite being unable to describe all of the binding forces, CoMFA is still a useful tool for QSAR analysis at the 3D level.

One method of 3D-QSAR optimization is known as region focusing [34], which may enhance or attenuate the contribution of the lattice points in a further analysis of a given CoMFA or CoMSIA region. Generally, region focusing can maximize the q2 value by rotating the extracted principal components, and give a new model with increased predictive power (q2), enhanced resolution, tighter grid spacing, and greater stability at a higher number of components.

3.6. CoMSIA Studies

CoMSIA is an extension of CoMFA on the same assumption that changes in binding affinities of ligands are related to changes in molecular properties represented by fields. Besides steric and electrostatic fields, three other different fields (hydrophobic, hydrogen bond donor, and hydrogen bond acceptor) are calculated in CoMSIA [35]. Moreover, a Gaussian function was introduced to determine the distance between the probe atom and the molecule atoms, and similarity indices inside and outside different molecular surfaces can be calculated at all grid points in CoMSIA. The equation used to calculate the similarity indices is as follows:

A F , K ( j ) q = i W p r o b e , k W i k e - α r i q 2

Where, A is the similarity index at grid point q, summed over all atoms i of the molecule j under investigation. Wprobe, k is the probe atom with radius 1 Å, charge +1, hydrophobicity +1, hydrogen bond donating +1 and hydrogen bond accepting +1. Wik is the actual value of the physicochemical property k of atom i. riq is the mutual distance between the probe atom at grid point q and atom i of the test molecule. α is the attenuation factor whose optimal value is normally between 0.2 and 0.4, with a default value of 0.3 [36,37].

4. Conclusions

In conclusion, our present studies have established predictive CoMFA and CoMSIA models that are quite reliable to efficiently guide further modification in the molecules for obtaining better drugs. Both of them provided good statistical results in terms of q2 and r2 values, suggesting the significant correlations of molecular structures with its biological activities. Compared with CoMSIA, CoMFA provided a slightly better statistical model. The final CoMFA model has high internal validity (q2 above 0.5) and high predictive ability (test set r2 above 0.7). The 3D-QSAR results also revealed some important sites, where steric, electrostatic and hydrogen-bond acceptor modifications should significantly affect the bioactivities of these compounds. Thus, the results of the quantitative structure activity relationships (QSAR) studies give insight into how to design new inhibitors, and it can be expected that these novel derivatives could be more active anticancer agents in the treatment of renal cell carcinoma as well.


This work was supported by NSFC grant 30972979 (to Z.C.).


  1. Jemal, A.; Siegel, R.; Xu, J.; Ward, E. Cancer statistics, 2010. CA Cancer J. Clin 2010, 60, 277–300. [Google Scholar]
  2. Ljungberg, B.; Campbell, S.C.; Choi, H.Y.; Jacqmin, D.; Lee, J.E.; Weikert, S.; Kiemeney, L.A. The epidemiology of renal cell carcinoma. Eur. Urol 2011, 60, 615–621. [Google Scholar]
  3. Motzer, R.J.; Mazumdar, M.; Bacik, J.; Berg, W.; Amsterdam, A.; Ferrara, J. Survival and prognostic stratification of 670 patients with advanced renal cell carcinoma. J. Clin. Oncol 1999, 17, 2530–2540. [Google Scholar]
  4. Pommier, Y. Topoisomerase I inhibitors: Camptothecins and beyond. Nat. Rev. Cancer 2006, 6, 789–802. [Google Scholar]
  5. Pommier, Y.; Pourquier, P.; Fan, Y.; Strumberg, D. Mechanism of action of eukaryotic DNA topoisomerase I and drugs targeted to the enzyme. Biochim. Biophys. Acta 1998, 1400, 83–105. [Google Scholar]
  6. Wang, J.C. Cellular roles of DNA topoisomerases: A molecular perspective. Nat. Rev. Mol. Cell Biol 2002, 3, 430–440. [Google Scholar]
  7. Koster, D.A.; Croquette, V.; Dekker, C.; Shuman, S.; Dekker, N.H. Friction and torque govern the relaxation of DNA supercoils by eukaryotic topoisomerase IB. Nature 2005, 434, 671–674. [Google Scholar]
  8. Pommier, Y. DNA topoisomerase I inhibitors: Chemistry, biology, and interfacial inhibition. Chem. Rev 2009, 109, 2894–2902. [Google Scholar]
  9. Wall, M.E.; Wani, M.C.; Cook, C.E.; Palmer, K.H.; Mcphail, A.T.; Sim, G.A. Plant antitumor agents. I. The isolation and structure of camptothecin, a novel alkaloidal leukemia and tumor inhibitor from camptotheca acuminata1,2. J. Am. Chem. Soc 1966, 88, 3888–3890. [Google Scholar]
  10. Takimoto, C.H.; Wright, J.; Arbuck, S.G. Clinical applications of the camptothecins. Biochim. Biophys. Acta 1998, 1400, 107–119. [Google Scholar]
  11. Ramp, U.; Mahotka, C.; Kalinski, T.; Ebel, E.; Gabbert, H.E.; Gerharz, C.D. Topotecan (Hycamtin) responsiveness in human renal carcinoma cell lines of the clear cell and papillary types. Anticancer Res 2001, 21, 3509–3517. [Google Scholar]
  12. Sato, A.; Ito, K.; Asano, T.; Sumitomo, M.; Hayakawa, M. Synergistic effect of survivin-specific small interfering RNA and topotecan in renal cancer cells: Topotecan enhances liposome-mediated transfection by increasing cellular uptake. Int. J. Oncol 2007, 30, 695–700. [Google Scholar]
  13. Sato, A.; Ito, K.; Asano, T.; Sumitomo, M.; Hayakawa, M. Topotecan and small interfering RNA suppress survivin expression synergistically in Caki-1 renal cancer cells: Direct suppression of survivin and enhancement of transfection efficiency by topotecan. Int. J. Oncol 2008, 32, 171–176. [Google Scholar]
  14. Shamash, J.; Steele, J.P.; Wilson, P.; Nystrom, M.; Ansell, W.; Oliver, R.T. IPM chemotherapy in cytokine refractory renal cell cancer. Br. J. Cancer 2003, 88, 1516–1521. [Google Scholar]
  15. Kiselev, E.; DeGuire, S.; Morrell, A.; Agama, K.; Dexheimer, T.S.; Pommier, Y.; Cushman, M. 7-azaindenoisoquinolines as topoisomerase I inhibitors and potential anticancer agents. J. Med. Chem 2011, 54, 6106–6116. [Google Scholar]
  16. Kiselev, E.; Dexheimer, T.S.; Pommier, Y.; Cushman, M. Design, synthesis, and evaluation of dibenzo [c, h][1, 6] naphthyridines as topoisomerase I inhibitors and potential anticancer agents. J. Med. Chem 2010, 53, 8716–8726. [Google Scholar]
  17. Fox, B.M.; Xiao, X.; Antony, S.; Kohlhagen, G.; Pommier, Y.; Staker, B.L.; Stewart, L.; Cushman, M. Design, synthesis, and biological evaluation of cytotoxic 11-alkenylindenoisoquinoline topoisomerase I inhibitors and indenoisoquinoline-camptothecin hybrids. J. Med. Chem 2003, 46, 3275–3282. [Google Scholar]
  18. Nagarajan, M.; Morrell, A.; Ioanoviciu, A.; Antony, S.; Kohlhagen, G.; Agama, K.; Hollingshead, M.; Pommier, Y.; Cushman, M. Synthesis and evaluation of indenoisoquinoline topoisomerase I inhibitors substituted with nitrogen heterocycles. J. Med. Chem 2006, 49, 6283–6289. [Google Scholar]
  19. Morrell, A.; Placzek, M.; Parmley, S.; Antony, S.; Dexheimer, T.S.; Pommier, Y.; Cushman, M. Nitrated indenoisoquinolines as topoisomerase I inhibitors: A systematic study and optimization. J. Med. Chem 2007, 50, 4419–4430. [Google Scholar]
  20. Antony, S.; Agama, K.K.; Miao, Z.H.; Takagi, K.; Wright, M.H.; Robles, A.I.; Varticovski, L.; Nagarajan, M.; Morrell, A.; Cushman, M.; Pommier, Y. Novel indenoisoquinolines NSC 725776 and NSC 724998 produce persistent topoisomerase I cleavage complexes and overcome multidrug resistance. Cancer Res 2007, 67, 10397–10405. [Google Scholar]
  21. Cramer, R.D.; Patterson, D.E.; Bunce, J.D. Comparative molecular field analysis (CoMFA). 1. Effect of shape on binding of steroids to carrier proteins. J. Am. Chem. Soc 1988, 110, 5959–5967. [Google Scholar]
  22. Klebe, G.; Abraham, U.; Mietzner, T. Molecular similarity indices in a comparative analysis (CoMSIA) of drug molecules to correlate and predict their biological activity. J. Med. Chem 1994, 37, 4130–4146. [Google Scholar]
  23. Hansch, C.; Smith, N.; Engle, R.; Wood, H. Quantitative structure-activity relationships of antineoplastic drugs: Nitrosoureas and triazenoimidazoles. Cancer Chemother. Rep 1972, 56, 443–456. [Google Scholar]
  24. Qian, C.; Zheng, J.; Xiao, G.; Guo, J.; Yang, Z.; Huang, L.; Chao, W.; Rao, L.; Sun, P. 3D-QSAR Studies on Thiazolidin-4-one S1P(1) Receptor Agonists by CoMFA and CoMSIA. Int. J. Mol. Sci 2011, 12, 6502–6516. [Google Scholar]
  25. Cramer, R.D., III; Patterson, D.E.; Bunce, J.D. Recent advances in comparative molecular field analysis (CoMFA). Prog. Clin. Biol. Res 1989, 291, 161–165. [Google Scholar]
  26. Cho, S.J.; Tropsha, A. Cross-validated R2-guided region selection for comparative molecular field analysis: A simple method to achieve consistent results. J. Med. Chem 1995, 38, 1060–1066. [Google Scholar]
  27. Cramer, R.D., III; Clark, M.; Simeroth, P.; Patterson, D.E. Recent developments in comparative molecular field analysis (CoMFA). Pharmacochem. Libr 1991, 16, 239–242. [Google Scholar]
  28. Clark, M.C.; Cramer, R.D., III; van Opdenbosch, N. Validation of the general purpose tripos 5.2 force field. Comput. Chem 1989, 10, 982–1012. [Google Scholar]
  29. Bush, B.L.; Nachbar, R.B., Jr. Sample-distance partial least squares: PLS optimized for many variables, with application to CoMFA. J. Comput. Aided Mol. Des 1993, 7, 587–619. [Google Scholar]
  30. Clark, M.; Cramer, R.D., III. The probability of chance correlation using partial least squares (PLS). Quant. Struct. Act. Relat 1993, 12, 137–145. [Google Scholar]
  31. Clark, M.; Cramer, R.D., III; Jones, D.M.; Patterson, D.E.; Simeroth, P.E. Comparative molecular field analysis (CoMFA). 2. Toward its use with 3D-structural databases. Tetrahedron Comput. Methodol 1990, 3, 47–59. [Google Scholar]
  32. Golbraikh, A.; Tropsha, A. Beware of q2! J. Mol. Graph. Model 2002, 20, 269–276. [Google Scholar]
  33. Srivastava, V.; Gupta, S.P.; Siddiqi, M.I.; Mishra, B.N. 3D-QSAR studies on quinazoline antifolate thymidylate synthase inhibitors by CoMFA and CoMSIA models. Eur. J. Med. Chem 2010, 45, 1560–1571. [Google Scholar]
  34. Lindgren, F.; Geladi, P.; Rännar, S.; Wold, S. Interactive variable selection (IVS) for PLS. Part 1: Theory and algorithms. J. Chemometrics 1994, 8, 349–363. [Google Scholar]
  35. Klebe, G.; Abraham, U. Comparative molecular similarity index analysis (CoMSIA) to study hydrogen-bonding properties and to score combinatorial libraries. J. Comput. Aided Mol. Des 1999, 13, 1–10. [Google Scholar]
  36. Zheng, J.; Xiao, G.; Guo, J.; Zheng, Y.; Gao, H.; Zhao, S.; Zhang, K.; Sun, P. Exploring QSARs for 5-lipoxygenase (5-LO) inhibitory activity of 2-substituted 5-hydroxyindole-3-carboxylates by CoMFA and CoMSIA. Chem. Biol. Drug Des 2011, 78, 314–321. [Google Scholar]
  37. Bohm, M.; St Rzebecher, J.; Klebe, G. Three-dimensional quantitative structure-activity relationship analyses using comparative molecular field analysis and comparative molecular similarity indices analysis to elucidate selectivity differences of inhibitors binding to trypsin, thrombin, and factor Xa. J. Med. Chem 1999, 42, 458–477. [Google Scholar]
Figure 1. Molecular alignment of indenoisoquinoline derivatives.
Figure 1. Molecular alignment of indenoisoquinoline derivatives.
Ijms 13 06009f1 1024
Figure 2. Region focusing. The CoMFA field calculations are shown for compound 20 before (Upper) and after (Lower) region focusing. Steric fields (Left): Green fields indicate steric bulk favored, yellow fields indicate steric bulk disfavored. Electrostatic fields (Right): Blue fields indicate electropositive groups favored, red fields indicate electronegative groups favored.
Figure 2. Region focusing. The CoMFA field calculations are shown for compound 20 before (Upper) and after (Lower) region focusing. Steric fields (Left): Green fields indicate steric bulk favored, yellow fields indicate steric bulk disfavored. Electrostatic fields (Right): Blue fields indicate electropositive groups favored, red fields indicate electronegative groups favored.
Ijms 13 06009f2 1024
Figure 3. Graph of experimental versus predicted pGI50 of the training set and the test set using the CoMFA model.
Figure 3. Graph of experimental versus predicted pGI50 of the training set and the test set using the CoMFA model.
Ijms 13 06009f3 1024
Figure 4. Graph of experimental versus predicted pGI50 of the training set and the test set using the best CoMSIA model 5.
Figure 4. Graph of experimental versus predicted pGI50 of the training set and the test set using the best CoMSIA model 5.
Ijms 13 06009f4 1024
Figure 5. CoMFA contour maps of the highest active compound 20 and the lowest active compound 28.
Figure 5. CoMFA contour maps of the highest active compound 20 and the lowest active compound 28.
Ijms 13 06009f5 1024
Figure 6. CoMSIA fields. The CoMSIA fields from model 5 are shown with active compound 20; (a) Steric fields: green indicates steric bulk favored, yellow indicates bulk disfavored; (b) Electrostatic fields: blue indicates electropositive groups favored, red fields indicate electronegative groups favored; (c) H-bond acceptor fields: magenta indicates acceptor favored, cyan disfavored.
Figure 6. CoMSIA fields. The CoMSIA fields from model 5 are shown with active compound 20; (a) Steric fields: green indicates steric bulk favored, yellow indicates bulk disfavored; (b) Electrostatic fields: blue indicates electropositive groups favored, red fields indicate electronegative groups favored; (c) H-bond acceptor fields: magenta indicates acceptor favored, cyan disfavored.
Ijms 13 06009f6a 1024Ijms 13 06009f6b 1024
Table 1. Statistical results of CoMFA and best CoMSIA models.
Table 1. Statistical results of CoMFA and best CoMSIA models.
Statistical resultsCoMFA(before region focusing)CoMFA (after region focusing)CoMSIA (Model 5)
PLS statistics *
LOO cross q2/SEP #0.602/0.8550.659/0.7810.523/0.923
Group cross q2/SEP0.632/0.8220.680/0.7570.524/0.922
Non-validated r2/SEE ¤0.925/0.3670.949/0.3340.902/0.436
r2bootstrap0.918 ± 0.0190.973 ± 0.0200.906 ± 0.023
Sbootstrap0.387 ± 0.1930.367 ± 0.1350.373 ± 0.163
Optimal components555
Field distribution%
H-bond acceptor38.7

*PLS = partial least squares,#LOO= leave-one-out,¤SEE = standard errors of estimate.

Table 2. Results of CoMSIA models using combinations of the 5 field descriptors.
Table 2. Results of CoMSIA models using combinations of the 5 field descriptors.
ModelDescriptorsLOO cross q2/SEPGroup cross q2/SEPBootstrap r2Bootstrapped SEENon-validated r2/SEE
1S and E0.474/0.9700.490/0.9550.865 ± 0.0430.479 ± 0.2620.857/0.507
2D and A0.410/1.0560.360/1.1000.797 ± 0.0660.599 ± 0.3390.750/0.687
3S, E and H0.520/0.9290.523/0.9230.788 ± 0.0440.593 ± 0.1980.767/0.637
4S, E and D0.482/0.9760.477/0.9830.862 ± 0.0340.496 ± 0.2340.826/0.565
5S, E and A0.523/0.9230.524/0.9220.906 ± 0.0230.373 ± 0.1630.902/0.436
6E, D and H0.500/0.9450.468/0.9750.834 ± 0.0550.528 ± 0.3010.833/0.574
7E, A andH 0.511/0.9230.500/0.9330.757 ± 0.0480.622 ± 0.2960.765/0.639
8S, E, D and A0.519/0.9270.535/0.9380.922 ± 0.0190.379 ± 0.1690.827/0.556
9S, E, D and H0.503/0.9420.560/0.8860.834 ± 0.0470.530 ± 0.2740.816/0.574
10S, E, A and H0.521/0.9250.533/0.8920.785 ± 0.0620.596 ± 0.3210.808/0.585
11S, D, A and H0.453/0.9960.484/0.9870.870 ± 0.0210.476 ± 0.1740.833/0.562
12S, E, D, A and H0.502/0.9560.519/0.9400.879 ± 0.0510.437 ± 0.2510.899/0.445
Table 3. Results of CoMSIA models using combinations of the five field descriptors.
Table 3. Results of CoMSIA models using combinations of the five field descriptors.
Ijms 13 06009f7

No.SubstituentsPredicted pGI50
20-4NO2Ijms 13 06009f8OCH3OCH38.5998.557
20-5NO2Ijms 13 06009f9methylenedioxy8.6578.701
20-6CNIjms 13 06009f10OCH3OCH38.8788.770
20-7CNIjms 13 06009f11methylenedioxy9.0298.914
20-8CNIjms 13 06009f12OCH3OCH38.3488.430
20-9NO2Ijms 13 06009f13OCH3OCH38.6798.664
20-10CNIjms 13 06009f14OCH3OCH38.8898.791
20-11CNIjms 13 06009f15OCH3OCH38.3208.341
20-12CNIjms 13 06009f16OCH3OCH38.9038.911
20-13CNIjms 13 06009f17methylenedioxy8.3038.295
20-14NO2Ijms 13 06009f18methylenedioxy8.4208.342
20-15CNIjms 13 06009f19OCH3OCH38.7768.808
Table 4. The molecules of indenoisoquinoline derivatives.
Table 4. The molecules of indenoisoquinoline derivatives.
Compd.Ijms 13 06009f20

1 *OCH3OCH3CH3methylenedioxy
3OCH3OCH3Ijms 13 06009f21Methylenedioxy
4OCH3OCH3Ijms 13 06009f22methylenedioxy
5OCH3OCH3Ijms 13 06009f23methylenedioxy
6OCH3OCH3Ijms 13 06009f24methylenedioxy
7OCH3OCH3Ijms 13 06009f25methylenedioxy
8OCH3OCH3Ijms 13 06009f26methylenedioxy
9OCH3NO2Ijms 13 06009f27methylenedioxy
10OCH3OCH3Ijms 13 06009f28methylenedioxy
11 *OCH3OCH3Ijms 13 06009f29methylenedioxy
12OCH3OCH3Ijms 13 06009f30methylenedioxy
13OCH3OCH3Ijms 13 06009f31methylenedioxy
14 *OCH3OCH3Ijms 13 06009f32methylenedioxy
15OCH3OCH3Ijms 13 06009f33methylenedioxy
16HHIjms 13 06009f34HH
17 *OCH3OCH3Ijms 13 06009f35HH
18HNO2Ijms 13 06009f36HH
27 *HNO2(CH2)3BrHH
29 *HH(CH2)3NH2HOCH3
31HHIjms 13 06009f37HH
33HNO2Ijms 13 06009f38HH
34HHIjms 13 06009f39HOCH3
41HNO2Ijms 13 06009f40HOCH3
42HHIjms 13 06009f41HOCH3
454Br48 *2NH2

*Test set.

Table 5. Inhibitory activity and predicted values of indenoisoquinoline derivatives.
Table 5. Inhibitory activity and predicted values of indenoisoquinoline derivatives.
Comp. no.Experiment (pGI50)CoMFACoMSIA

1 *4.1684.0030.1654.335−0.167
11 *4.9395.067−0.1285.163−0.224
14 *4.0003.7610.2394.476−0.476
17 *8.0008.198−0.1988.403−0.403
27 *4.0003.6950.3054.443−0.443
29 *8.0007.7970.2037.6940.306
48 *6.2376.0400.1976.660−0.423

*Test set.

Int. J. Mol. Sci. EISSN 1422-0067 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
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