Matthew D. Wessel

Prediction of human intestinal absorption of drug compounds from molecular structure.

The absorption of a drug compound through the human intestinal cell lining is an important property for potential drug candidates. Measuring this property, however, can be costly and time-consuming. The use of quantitative structure−property relationships (QSPRs) to estimate percent human intestinal absorption (%HIA) is an attractive alternative to experimental measurements. A data set of 86 drug and drug-like compounds with measured values of %HIA taken from the literature was used to develop and test a QSPR model. The compounds were encoded with calculated molecular structure descriptors. A nonlinear computational neural network model was developed by using the genetic algorithm with a neural network fitness evaluator. The calculated %HIA (cHIA) model performs well, with root-mean-square (rms) errors of 9.4%HIA units for the training set, 19.7%HIA units for the cross-validation (CV) set, and 16.0%HIA units for the external prediction set.

Prediction of boiling points and critical temperatures of industrially important organic compounds from molecular structure

Numeric representations of molecular structure are used to predict the normal boiling points and critical temperatures for compounds drawn from the Design Institute for Physical Property Data (DIPPR) database. Multiple linear regression analysis and computational neural networks (i.e., using back-propagation and quasi-Newton training) are employed to develop models which can accurately predict the boiling points of 298 organic compounds. This approach is assessed by comparing its results against results obtained using the Joback group contribution approach. Finally, the same methodology is used to develop two separate critical temperature models, one based on the methods of corresponding states and the second based on structurally derived parameters alone.

PREDICTION OF NORMAL BOILING POINTS FOR A DIVERSE SET OF INDUSTRIALLY IMPORTANT ORGANIC COMPOUNDS FROM MOLECULAR STRUCTURE

Models that accurately predict normal boiling points for organic compounds containing heteroatoms have been developed with regression and computational neural network methods. The structures of the compounds are represented by calculated structural descriptors. Two models are presented-one for a set of 277 compounds containing only 0, S , and halogens, and a second for a set of 104 compounds all containing N. Root-mean-square errors of about 9 K result. The accuracy of prediction of these models is compared to a widely used group contribution method for boiling point estimation.

PREDICTION OF NORMAL BOILING POINTS OF HYDROCARBONS FROM MOLECULAR STRUCTURE

Computer assisted methods are used to investigate the relationship between normal boiling point and molecular structure for a set of hydrocarbons. Multiple linear regression methods are used to develop a six-variable linear model with a low root mean square (rms) error. The six descriptors in the linear model are also used to develop a computational neural network model with a significantly lower rms error. The methodology used in this study is also compared to Jobacks group contribution method to estimate physical properties. The methods used here are found to be superior to Jobacks method. However, when one additional variable encoding the square root of the molecular weight is added to Jobacks groups, an excellent model is developed.

Prediction of reduced ion mobility constants of organic compounds from molecular structure.

Quantitative structure-property relationships (QSPRs) are used to develop mathematical models that accurately predict the reduced ion mobility constants (K(0)) for a set of 168 organic compounds directly from molecular structure. The K(0) values are taken from an unpublished database collected by G. A. Eiceman, Chemistry Department, New Mexico State University. The data were collected using a Graseby Ionics environmental vapour monitor (EVM) gas chromatography/ion mobility spectrometer. Standardized conditions with controlled temperature, pressure, and humidity were used, and 2,4-lutidine was used as an internal standard. K(0) values were measured for all monomer peaks. The best model was found with a feature selection routine which couples the genetic algorithm with multiple linear regression analysis. The set of six descriptors was also analyzed with a fully connected, feed-forward neural network. The model contains six molecular structure descriptors and has a root-mean-square error of about 0.04 K(0) unit. The descriptors in the model lend insight into some of the important molecular features that influence ion mobility. The model can be utilized for prediction of K(0) values of compounds for which there are no empirical K(0) data.

Achieving selectivity between highly homologous tyrosine kinases: a novel selective erbB2 inhibitor

The discovery of small molecule kinase inhibitors for use as drugs is a promising approach for the treatment of cancer and other diseases, but the discovery of highly specific agents is challenging because over 850 kinases are expressed in mammalian cells. Systematic modification of the 4-anilino functionality of a selective quinazoline inhibitor of the epidermal growth factor receptor (EGFR) tyrosine kinase can invert selectivity to favor inhibition of the highly homologous erbB2 tyrosine kinase. The selectivity pattern was demonstrated in assays of recombinant kinases and recapitulated in measures of kinase activity in intact cells. The most potent and selective erbB2 inhibitor of the analog series has anti-proliferative activity against an erbB2-overexpressing cell line that was lacking in the original EGFR-selective compound. Subtle changes to the molecular structure of ATP-competitive small molecule inhibitors of tyrosine kinases can yield dramatic changes in potency and selectivity. These results suggest that the discovery of highly selective small molecule inhibitors of very homologous kinases is achievable.

6-amino-4-(pyrimidin-4-yl)pyridones: novel glycogen synthase kinase-3β inhibitors

The synthesis and structure-activity relationships for a novel series of 6-amino-4-(pyrimidin-4-yl)pyridones derived from a high throughput screening hit are discussed. Optimization of lead matter afforded compounds with good potency, selectivity and central nervous system (CNS) exposure.

Discovery and synthesis of novel 4-aminopyrrolopyrimidine Tie-2 kinase inhibitors for the treatment of solid tumors.

The synthesis and biological evaluation of novel Tie-2 kinase inhibitors are presented. Based on the pyrrolopyrimidine chemotype, several new series are described, including the benzimidazole series by linking a benzimidazole to the C5-position of the 4-amino-pyrrolopyrimidine core and the ketophenyl series synthesized by incorporating a ketophenyl group to the C5-position. Medicinal chemistry efforts led to potent Tie-2 inhibitors. Compound 15, a ketophenyl pyrrolopyrimidine urea analog with improved physicochemical properties, demonstrated favorable in vitro attributes as well as dose responsive and robust oral tumor growth inhibition in animal models.

Abstract A86: Design, synthesis, and SAR of focal adhesion kinase (FAK) inhibitors

Focal adhesion kinase (FAK) is a non‐tyrosine kinase that localizes to focal adhesion plaques. It is activated in response to intergin binding to cellular ligands and when phosphorylated inhibits anoikis allowing for anchorage independent cell growth. Recent studies have shown increased FAK expression and phosphorylation status in many types of invasive and aggressive human tumors strongly suggesting FAK is a possible target for anticancer chemotherapy. Literature, in house HTS and de novo studies identified 2, 4‐diaminopyrimidines as potent FAK inhibitors. Early SAR efforts quickly determined that smaller substituents, particularly CF3, were optimal in the C5 position. Parallel medicinal chemistry strategies were executed for the C2 and C4 positions. These studies suggested that substituted aryl and fused heteroaryl groups at the C2 position in conjunction with substituted phenyl and heterocycles at the C4 position imparted optimum activity and metabolic stability. Inhibitor‐FAK co‐crystal structures were utilized to guide in the SAR strategy around the 2, 4‐diaminopyrimidine template which afforded several lead compounds. The team9s effort culminated in the advancement of PF‐562,271 as a potent and reversible inhibitor of FAK (kinase IC50 of 2 nM and cell IC50 of 5 nM) that is > 100x selective against a long list of non‐target kinases. In summary, detailed SAR studies were executed on the 2, 4‐diaminopyrimidine templates that produced potent inhibitors of FAK with improved ADME properties, and identified a novel and potent series of FAK inhibitors that are selective against most other kinases and have shown activity in clinical trials. This poster will present design, synthesis, challenging chemistry, optimization, and complete inhibitor chemical structures of lead analogs. Citation Information: Mol Cancer Ther 2009;8(12 Suppl):A86.