Abstract
A combined approach of validated QSAR modeling and virtual screening was successfully applied to the discovery of novel tylophrine derivatives as anticancer agents. QSAR models have been initially developed for 52 chemically diverse phenanthrine-based tylophrine derivatives (PBTs) with known experimental EC50 using chemical topological descriptors (calculated with the MolConnZ program) and variable selection k nearest neighbor (kNN) method. Several validation protocols have been applied to achieve robust QSAR models. The original dataset was divided into multiple training and test sets, and the models were considered acceptable only if the leave-one-out cross-validated R 2 (q 2) values were greater than 0.5 for the training sets and the correlation coefficient R 2 values were greater than 0.6 for the test sets. Furthermore, the q 2 values for the actual dataset were shown to be significantly higher than those obtained for the same dataset with randomized target properties (Y-randomization test), indicating that models were statistically significant. Ten best models were then employed to mine a commercially available ChemDiv Database (ca. 500 K compounds) resulting in 34 consensus hits with moderate to high predicted activities. Ten structurally diverse hits were experimentally tested and eight were confirmed active with the highest experimental EC50 of 1.8 μM implying an exceptionally high hit rate (80%). The same ten models were further applied to predict EC50 for four new PBTs, and the correlation coefficient (R 2) between the experimental and predicted EC50 for these compounds plus eight active consensus hits was shown to be as high as 0.57. Our studies suggest that the approach combining validated QSAR modeling and virtual screening could be successfully used as a general tool for the discovery of novel biologically active compounds.
Similar content being viewed by others
References
Newman DJ, Cragg GM, Snader KM (2003) J Nat Prod 66:1022
Gellert E, Rudzats R (1964) J Med Chem 15:361
Rao KV, Wilson RA, Cummings B (1971) J Pharm Sci 60:1725
Pettit GR, Goswami A, Cragg GM, Schmidt JM, Zou JC (1984) J Nat Prod 47:913
Suffness M, Cordell GA (1985) The alkaloids, chemistry and pharmacology. Academic Press, New York, pp 3–355
The 60-cell line NCI test data, along with in vivo data can be accessed from the NSC numbers at the following web site. http://dtp.nci.nih.gov/dtpstandard/dwindex/index.jsp. 2006.
Donaldson GR, Atkinson MR, Murray AW (1968) Biochem Biophys Res Commun 31:104
Huang MT, Grollman AP (1972) Mol Pharmacol 8:538
Grant P, Sanchez L, Jimenez A (1974) J Bacteriol 120:1308
Gupta RS, Siminovitch L (1977) Biochemistry 16:3209
Rao KN, Bhattacharya RK, Venkatachalam SR (1997) Chem Biol Interact 106:201
Rao KN, Venkatachalam SR (2000) Toxicol In Vitro 14:53
Ganguly T, Khar A (2002) Phytomedicine 9:288
Gao W, Lam W, Zhong S, Kaczmarek C, Baker DC, Cheng YC (2004) Cancer Res 64:678
Suffness M, Douros JD (1980) Anticancer agents based on natural product models. Academic Press, London, pp 465–487
Staerk D, Lykkeberg AK, Christensen J, Budnik BA, Abe F, Jaroszewski JW (2002) J Nat Prod 65:1299
Wei L, Brossi A, Kendall R, Bastow KF, Morris-Natschke SL, Shi Q, Lee KH (2006) Bioorg Med Chem 14:6560
Hadjipavloulitina D, Hansch C (1994) Chem Rev 94:1483
Hansch C, Muir RM, Fujita T, Maloney PP, Geiger E, Streich M (1963) J Am Chem Soc 85:2817
Klein TE, Huang C, Ferrin TE, Langridge R, Hansch C (1986) Acs Symposium Series 306:147
Kubinyi H (1986) Chemie in Unserer Zeit 20:191
Tropsha A (2006) In: Martin YC (ed) Comprehensive medicinal chemistry II. Elsevier, pp 113–126
Tropsha A (2005) In: Oprea T (ed) Cheminformatics in drug discovery. Wiley-VCH, pp 437–455
Tropsha A, Cho SJ, Zheng W (1999) In: Parrill AL, Reddy MR (eds) Rational drug design: Novel methodology and practical applications. pp 198–211
Zheng WF, Tropsha A (2000) J Chem Inf Comput Sci 40:185
Shen M, Beguin C, Golbraikh A, Stables JP, Kohn H, Tropsha A (2004) J Med Chem 47:2356
Oloff S, Mailman RB, Tropsha A (2005) J Med Chem 48:7322
MolConn Z [4.05] (2002) Quincy, MA, Hall Associates Consulting
Golbraikh A, Tropsha A (2002) J Mol Graph Model 20:269
ChemDiv (2005) http://www.chemdiv.com
Rubinstein LV, Shoemaker RH, Paull KD, Simon RM, Tosini S, Skehan P, Scudiero DA, Monks A, Boyd MR (1990) J Natl Cancer Inst 82:1113
SYBYL (2004) [Version 7.0] Tripos, Inc, St Louis, MO
Kier LB, Hall LH (1976) Molecular connectivity in chemistry and drug research. Academic Press, New York
Benigni R, Giuliani A, Franke R, Gruska A (2000) Chem Rev 100:3697
Oloff S, Zhang S, Sukumar N, Breneman C, Tropsha A (2006) J Chem Inf Model 46:844
Trohalaki S, Gifford E, Pachter R (2000) Comput Chem 24:421
Zhang S, Golbraikh A, Tropsha A (2006) J Med Chem 49:2713
Zhang S, Golbraikh A, Oloff S, Kohn H, Tropsha A (2006) J Chem Inf Model 46:1984
Kubinyi H, Hamprecht FA, Mietzner T (1998) J Med Chem 41:2553
Golbraikh A, Tropsha A (2002) J Comput Aided Mol Des 16:357
Snarey M, Terrett NK, Willett P, Wilton DJ (1997) J Mol Graph Model 15:372
Sharaf MA, Illman DL, Kowalski BR (1986) Chemometrics. John Wiley & Sons, New York, pp 1–332
Metropolis N, Rosenbluth AW, Rosenbluth MN, Teller AH (1953) J Chem Phys 21:1087
Tropsha A, Gramatica P, Gombar VK (2003) QSAR Comb Sci 22:69
Golbraikh A, Shen M, Xiao Z, Xiao YD, Lee KH, Tropsha A (2003) J Comput Aided Mol Des 17:241
Wold S, Eriksson L (1995) In: Waterbeemd Hvd (ed) Chemometrics methods in molecular design. VCH, pp 309–318
Wu PL, Rao KV, Su C-H, Kuoh C-S, Wu T-S (2002) Heterocycles 57:2401
Brown RD, Martin YC (1998) Environ Res 8:23
Acknowledgment
This investigation was supported by grant CA17625 from National Cancer Institute awarded to K. H. Lee and by grants GM066940 and P20-RR20751 awarded to AT. We wish to thank Dr. Susan L. Morris-Natschke for her critical reading of the manuscript and Dr. Lowell Hall for his comments on the interpretation of MolConnZ descriptors. The authors dedicate this paper to Dr. Yvonne C. Martin who has been a source of inspiration and encouragement for many years to the senior author.
Author information
Authors and Affiliations
Corresponding authors
Additional information
Shuxing Zhang and Linyi Wei have contributed equally to this paper.
Rights and permissions
About this article
Cite this article
Zhang, S., Wei, L., Bastow, K. et al. Antitumor Agents 252. Application of validated QSAR models to database mining: discovery of novel tylophorine derivatives as potential anticancer agents. J Comput Aided Mol Des 21, 97–112 (2007). https://doi.org/10.1007/s10822-007-9102-6
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10822-007-9102-6