QSAR analysis of polyamine transport inhibitors in L1210 cells

Lipophilic lysine-spermine conjugates are potent polyamine transport inhibitors for use in combination with a polyamine biosynthesis inhibitor

Cancer cells can overcome the ability of polyamine biosynthesis inhibitors to completely deplete their internal polyamines by the importation of polyamines from external sources. This paper discusses the development of a group of lipophilic polyamine analogues that potently inhibit the cellular polyamine uptake system and greatly increase the effectiveness of polyamine depletion when used in combination with DFMO, a well-studied polyamine biosynthesis inhibitor. The attachment of a length-optimized C(16) lipophilic substituent to the epsilon-nitrogen atom of an earlier lead compound, D-Lys-Spm (5), has produced an analogue, D-Lys(C(16)acyl)-Spm (11) with several orders of magnitude more potent cell growth inhibition on a variety of cultured cancer cell types including breast (MDA-MB-231), prostate (PC-3), melanoma (A375), and ovarian (SK-OV-3), among others. These results are discussed in the context of a possible membrane-catalyzed interaction with the extracellular polyamine transport apparatus. The resulting novel two-drug combination therapy targeting cellular polyamine metabolism has shown exceptional efficacy against cutaneous squamous cell carcinomas (SCC) in a transgenic ornithine decarboxylase (ODC) mouse model of skin cancer. A majority (88%) of large, aggressive SCCs exhibited complete or nearly complete remission to this combination therapy, whereas responses to each agent alone were poor. The availability of a potent polyamine transport inhibitor allows, for the first time, for a real test of the hypothesis that starving cells of polyamines will lead to objective clinical response.

Prediction of solubility parameters using partial least square regression

The total solubility parameter (delta) values were effectively predicted by using computed molecular descriptors and multivariate partial least squares (PLS) statistics. The molecular descriptors in the derived models included heat of formation, dipole moment, molar refractivity, solvent-accessible surface area (SA), surface-bounded molecular volume (SV), unsaturated index (Ui), and hydrophilic index (Hy). The values of these descriptors were computed by the use of HyperChem 7.5, QSPR Properties module in HyperChem 7.5, and Dragon Web version. The other two descriptors, hydrogen bonding donor (HD), and hydrogen bond-forming ability (HB) were also included in the models. The final reduced model of the whole data set had R(2) of 0.853, Q(2) of 0.813, root mean squared error from the cross-validation of the training set (RMSEcv(tr)) of 2.096 and RMSE of calibration (RMSE(tr)) of 1.857. No outlier was observed from this data set of 51 diverse compounds. Additionally, the predictive power of the developed model was comparable to the well recognized systems of Hansen, van Krevelen and Hoftyzer, and Hoy.

Prediction of the aqueous solubility of benzylamine salts using QSPR model

Models predicting aqueous solubility of benzylamine salts were developed using multivariate partial least squares (PLS) and artificial neural network (ANN). Molecular descriptors, including binding energy (BE) and surface area of salts (SA), were calculated by the use of Hyperchem and ChemPlus QSAR programs for Windows. Other physicochemical properties, such as hydrogen acceptor for oxygen atoms, hydrogen acceptor for nitrogen atoms, hydrogen bond donors, hydrogen bond forming ability, molecular weight (MW), and calculated log partition coefficient (clog P) of p-substituted benzoic acids, were also used as descriptors. In this study, the predictive ability of ANN, especially multilayer perceptron (MLP) architecture networks, was founded to be superior to PLS models. The best ANN model derived, a 6-1-1 architecture, had an overall R(2) of 0.850 and root mean square error (RMSE) for cross-verification and test set of 0.189 and 0.185 log units, respectively. Since all the utilized descriptors are readily obtained from calculation, these derived models offer the advantage of not requiring the experimental determination of some descriptors.

Prediction of aqueous solubility of organic salts of diclofenac using PLS and molecular modeling

Organic salts of diclofenac were predicted by using computed molecular descriptors and multivariate partial least squares (PLS). The molecular descriptors including binding energy and surface area of salts were calculated by the use of Hyperchem and ChemPlus QSAR programs for Windows. Other physicochemical properties such as hydrogen acceptor for oxygen atoms, hydrogen acceptor for nitrogen atoms, hydrogen bond donors, hydrogen bond-forming ability, molecular weight, and log partition coefficient (logP) of bases were also used as descriptors. Good statistical models were derived that permit simple computational prediction of salt solubility of a same parent structure. The final models derived had R2 value = 0.96 and root mean square error for prediction (RMSEP) values ranging from 0.021 to 0.054 (log scale). Preferably all utilized descriptors in the final models can readily obtain from the chemical structure of salt and base. Molecular weight of base is one of the important factors associated with salt solubility. While increased molecular weight of base, surface area of salt and hydrogen bonding ability of base increase solubility, and increased binding energy and logP of base have negative effect on salt solubility.