Several Pt(IV) complexes of the general formula [Pt(L)2(L′)2(L″)2] [axial ligands L are Cl−, RCOO−, or OH−; equatorial ligands L′ are two am(m)ine or one diamine; and equatorial ligands L″ are Cl− or glycolato] were rationally designed and synthesized in the attempt to develop a predictive quantitative structure–activity relationship (QSAR) model. Numerous theoretical molecular descriptors were used alongside physicochemical data (i.e., reduction peak potential, Ep, and partition coefficient, log Po/w) to obtain a validated QSAR between in vitro cytotoxicity (half maximal inhibitory concentrations, IC50, on A2780 ovarian and HCT116 colon carcinoma cell lines) and some features of Pt(IV) complexes. In the resulting best models, a lipophilic descriptor (log Po/w or the number of secondary sp3 carbon atoms) plus an electronic descriptor (Ep, the number of oxygen atoms, or the topological polar surface area expressed as the N,O polar contribution) is necessary for modeling, supporting the general finding that the biological behavior of Pt(IV) complexes can be rationalized on the basis of their cellular uptake, the Pt(IV) → Pt(II) reduction, and the structure of the corresponding Pt(II) metabolites. Novel compounds were synthesized on the basis of their predicted cytotoxicity in the preliminary QSAR model, and were experimentally tested. A final QSAR model, based solely on theoretical molecular descriptors to ensure its general applicability, is proposed.
Platinum complexes Anticancer drug Cytotoxicity Quantitative structure–activity relationship analysis
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Financial support for this work was from the Regione Piemonte (CIPE 2006 project-code A 370 and Ricerca Sanitaria Finalizzata 2009) and the ATF Association (Alessandria, Italy). The research was carried out within the framework of the European Cooperation COST D39 (Metallo-Drug Design and Action) and Consorzio Interuniversitario di Ricerca in Chimica dei Metalli nei Sistemi Biologici (CIRCMSB, Bari, Italy).
Hall MD, Dolman RD, Hambley TW (2004) Metal complexes in tumor diagnosis and as anticancer agents. In: Sigel A, Sigel H (eds) Metal ions in biological systems, vol 42. Dekker, New York, pp 298–322Google Scholar
Christian MC, Spriggs D, Tutsc KD, O’Rourke T, VonHoff DD, Jacob JL, Reed E (1991) In: Howell SB (ed) Platinum and other metal coordination compounds in cancer chemotherapy. Plenum Press, New York, pp 453–459Google Scholar
Bramwell VHC, Crowther D, O’Malley S, Swindell R, Johnson R, Cooper EH, Thatcher N, Howell A (1985) Cancer Treat Rep 69:409–416PubMedGoogle Scholar
Pawinski A, Crowther D, Keizer HJ, Voute PA, Somers R, van Glabbeke M, Lentz MA, van Oosterom AT (1999) Eur J Cancer 35:163–164CrossRefPubMedGoogle Scholar
Alley MC, Scudiero DA, Monks A, Hursey ML, Czerwinski MJ, Fine DL, Abbott BJ, Mayo JG, Shoemaker RH, Boyd MR (1988) Cancer Res 48:589–601PubMedGoogle Scholar
Hypercube (2000) HYPERCHEM 7.03 for Windows. Hypercube, GainesvilleGoogle Scholar
Todeschini R, Ballabio D, Consonni V, Mauri A, Pavan M (2008) DRAGON software for the calculation of molecular descriptors, version 5.5 for Windows. Talete, Milan. http://www.talete.mi.it/
Todeschini R, Consonni V, Mauri A, Pavan M (2003) In: Leardi, R (ed) Nature-inspired methods in chemometrics: genetic algorithms and artificial neural networks. Elsevier, Amsterdam, pp 141–167Google Scholar
Todeschini R, Ballabio D, Consonni V, Mauri A, Pavan M (2008) MOBY DIGS-models by descriptors in genetic selection, version 1.0 beta for Windows. Talete, Milan. http://www.talete.mi.it/