A new approach to predict the biological activity of molecules based on similarity of their interaction fields and the logP and logD values: application to auxins

The activity of a biological compound is dependent both on specific binding to a target receptor and its ADME (Absorption, Distribution, Metabolism, Excretion) properties. A challenge to predict biological activity is to consider both contributions simultaneously in deriving quantitative models. We present a novel approach to derive QSAR models combining similarity analysis of molecular interaction fields (MIFs) with prediction of logP and/or logD. This new classification method is applied to a set of about 100 compounds related to the auxin plant hormone. The classification based on similarity of their interaction fields is more successful for the indole than the phenoxy compounds. The classification of the phenoxy compounds is however improved by taking into account the influence of the logP and/or the logD values on biological activity. With the new combined method, the majority (8 out of 10) of the previously misclassified derivatives of phenoxy acetic acid are classified in accord with their bioassays. The recently determined crystal structure of the auxin-binding protein 1 (ABP1) enabled validation of our approach. The results of docking a few auxin related compounds with different biological activity to ABP1 correlate well with the classification based on similarity of MIFs only. Biological activity is, however, better predicted by a combined similarity of MIFs + logP/logD approach.     

Surface-integral QSPR models: local energy properties

Surface-integral models based on AM1 semiempirical molecular orbital calculations are presented for the free energies of solvation in water, n-octanol, and chloroform and for the enthalpy of solvation in water. A parametrized function of four local properties calculated at the isodensity surface (the molecular electrostatic potential, local ionization energy, electron affinity, and polarizability) is integrated over the triangulated surface area to obtain the target quantity. The resulting models give results only slightly less accurate than those reported for parametrized generalized Born/polar surface area models despite relying only on gas-phase calculations. The water and octanol free-energy models were validated by calculating the water-octanol partition coefficient for a test set of organic compounds with moderate success. The models lead to a local solvation energy, which can be projected onto the molecular isodensity surface and provides insight into "hot" areas for solvation in water or the other solvents.     

Prediction of cyclohexane-water distribution coefficients with COSMO-RS on the SAMPL5 data set

The Conductor-Like-Screening-Model for Real Solvents (COSMO-RS) method has been used for the blind prediction of cyclohexane-water distribution coefficients logD within the SAMPL challenge. The partition coefficient logP of the neutral species was calculated first and then corrected for dissociation or protonation, as appropriate for acidic or basic solutes, to obtain the cyclohexane-water logD. Using the latest version of the COSMOtherm implementation, this approach in combination with a rigorous conformational sampling yielded a predictive accuracy of 2.11 log units (RMSD) for the 53 compounds of the blind prediction dataset. By that it was the most accurate of all contest submissions and it also achieved the best rank order. The RMSD mainly arises from a group of outliers in the negative logD range, which at least partly may arise from dimerization or other experimental problems coming up for very polar molecules in very non-polar solvents.     

Novel methods for the prediction of logP,pK(a), and logD

Novel methods for predicting logP, pK(a), and logD values have been developed using data sets (592 molecules for logP and 1029 for pK(a)) containing a wide range of molecular structures. An equation with three molecular properties (polarizability and partial atomic charges on nitrogen and oxygen) correlates highly with logP (r2 = 0.89). The pK(a)s are estimated for both acids and bases using a novel tree structured fingerprint describing the ionizing centers. The new models have been compared with existing models and also experimental measurements on test sets of common organic compounds and pharmaceutical molecules.     

Successful screening of large encoded combinatorial libraries leading to the discovery of novel p38 MAP kinase inhibitors

Screening of more than 2 million compounds comprising 41 distinct encoded combinatorial libraries revealed a novel structural class of p38 mitogen-activated protein (MAP) kinase inhibitors. The methodology used for screening large encoded combinatorial libraries combined with the statistical interpretation of screening results is described. A strong preference for a particular triaminotriazine aniline amide was discovered based on biological activity observed in the screening campaign. Additional screening of a focused follow-up combinatorial library yielded data expanding the unique combinatorial SAR and emphasizing an extraordinary preference for this particular building block and structural class. The preference is further highlighted when the p38 inhibitor data set is compared to data obtained for a panel of other kinases.     

High-throughput one-bead-one-compound approach to peptide-encoded combinatorial libraries: MALDI-MS analysis of single TentaGel beads

The identification of pharmacologically promising compounds (lead compounds) from combinatorial libraries is frequently limited by the throughput of the analytical technique employed. Fourier transform mass spectrometry (FTMS) offers high sensitivity, mass accuracy (m/Deltam > 500 000), and sequencing capabilities. A rapid and efficient method for high-throughput analysis of single beads from peptide-encoded combinatorial libraries with matrix-assisted laser desorption/ionization (MALDI) mass spectrometry is presented. Encoding peptides on single beads are identified and structurally characterized by MALDI time-of-flight (TOF) and ultrahigh-resolution MALDI Fourier transform ion cyclotron resonance (FT-ICR) mass spectrometry. A strategy of on-probe sample preparation is developed to minimize handling of the beads.     

Use of catalyst pharmacophore models for screening of large combinatorial libraries

Using a data set comprised of literature compounds and structure-activity data for cyclin dependent kinase 2, several pharmacophore hypotheses were generated using Catalyst and evaluated using several criteria. The two best were used in retrospective searches of 10 three-dimensional databases containing over 1,000,000 proprietary compounds. The results were then analyzed for the efficiency with which the hypotheses performed in the areas of compound prioritization, library prioritization, and library design. First as a test of their compound prioritization capabilities, the pharmacophore models were used to search combinatorial libraries that were known to contain CDK active compounds to see if the pharmacophore models could selectively choose the active compounds over the inactive compounds. Second as a test of their utility in library design again the pharmacophore models were used to search the active combinatorial libraries to see if the key synthons were over represented in the hits from the pharmacophore searches. Finally as a test of their ability to prioritize combinatorial libraries, several inactive libraries were searched in addition to the active libraries in order to see if the active libraries produced significantly more hits than the inactive libraries. For this study the pharmacophore models showed potential in all three areas. For compound prioritization, one of the models selected active compounds at a rate nearly 11 times that of random compound selection though in other cases models missed the active compounds entirely. For library design, most of the key fragments were over represented in the hits from at least one of the searches though again some key fragments were missed. Finally, for library prioritization, the two active libraries both produced a significant number of hits with both pharmacophore models, whereas none of the eight inactive libraries produced a significant number of hits for both models.     

meso-Tetra(pentafluorophenyl)porphyrin as an efficient platform for combinatorial synthesis and the selection of new photodynamic therapeutics using a cancer cell line

The four para fluoro groups on 5,10,15,20-tetrakis-(2,3,4,5,6-pentafluorophenyl)-porphyrin (TPPF20) are known to react with a variety of nucleophiles, but the reaction conditions for this substitution reaction depend on the nature of the nucleophiles, e.g. primary amines versus thiols. Glycosylated derivatives of this core porphyrin have been shown to be effective photodynamic agents in the induction of necrosis or apoptosis in several cancer cell lines. The present report demonstrates that TPPF20 can be used as a core platform to efficiently generate a variety of solution-phase combinatorial libraries. The focused combinatorial libraries have substituents that are chosen from a set of motifs known to bind biopolymers such as DNA, be taken up by cancer cells, or to render the compounds amphipathic. Incubation of a breast cancer cell line with these solution-phase libraries, followed by cell lyses and extraction, affords a selection assay. Matrix-assisted laser desorption ionization (MALDI) mass spectrometry of the extracts allows identification of the molecules taken up by the cells. Cell binding assays of the winning compounds synthesized directly indicate that both glycosylation and amphipathicity are key properties since neither tetraglycosylated porphyrins nor those with four polar groups are selected to the same extent. In addition, photodynamic efficacy was evaluated.     

Constructing optimum blood brain barrier QSAR models using a combination of 4D-molecular similarity measures and cluster analysis

A new method, using a combination of 4D-molecular similarity measures and cluster analysis to construct optimum QSAR models, is applied to a data set of 150 chemically diverse compounds to build optimum blood-brain barrier (BBB) penetration models. The complete data set is divided into subsets based on 4D-molecular similarity measures using cluster analysis. The compounds in each cluster subset are further divided into a training set and a test set. Predictive QASAR models are constructed for each cluster subset using the corresponding training sets. These QSAR models best predict test set compounds which are assigned to the same cluster subset, based on the 4D-molecular similarity measures, from which the models are derived. The results suggest that the specific properties governing blood-brain barrier permeability may vary across chemically diverse compounds. Partitioning compounds into chemically similar classes is essential to constructing predictive blood-brain barrier penetration models embedding the corresponding key physiochemical properties of a given chemical class.