Cathepsin C inhibitors: property optimization and identification of a clinical candidate.

A lead generation and optimization program delivered the highly selective and potent CatC inhibitor 10 as an in vivo tool compound and potential development candidate. Structural studies were undertaken to generate SAR understanding.

Strategies to improve in vivo toxicology outcomes for basic candidate drug molecules.

A valid PLS-DA model to predict attrition in pre-clinical toxicology for basic oral candidate drugs was built. A combination of aromatic/aliphatic balance, flatness, charge distribution and size descriptors helped predict the successful progression of compounds through a wide range of toxicity testing. Eighty percent of an independent test set of marketed post-2000 basic drugs could be successfully classified using the model, indicating useful forward predictivity. The themes within this work provide additional guidance for medicinal design chemists and complement other literature property guidelines.

Synthesis and biological evaluation of N-alkylated 8-oxybenz[c]azepine derivatives as selective PPARδ agonists.

We describe the discovery of small molecule benzazepine derivatives as agonists of human peroxisome proliferator-activated receptor δ (PPARδ) that displayed excellent selectivity over the PPARα and PPARγ subtypes. Compound 8 displayed good PK in the rat and efficacy in upregulation of pyruvate dehydrogenase kinase, isozyme 4 (PDK4) mRNA in human primary myotubes, a biomarker for increased fatty acid oxidation.

A rapid computational filter for predicting the rate of human renal clearance.

In silico models that predict the rate of human renal clearance for a diverse set of drugs, that exhibit both active secretion and net re-absorption, have been produced using three statistical approaches. Partial Least Squares (PLS) and Random Forests (RF) have been used to produce continuous models whereas Classification And Regression Trees (CART) has only been used for a classification model. The best models generated from either PLS or RF produce significant models that can predict acids/zwitterions, bases and neutrals with approximate average fold errors of 3, 3 and 4, respectively, for an independent test set that covers oral drug-like property space. These models contain additional information on top of any influence arising from plasma protein binding on the rate of renal clearance. Classification And Regression Trees (CART) has been used to generate a classification tree leading to a simple set of Renal Clearance Rules (RCR) that can be applied to man. The rules are influenced by lipophilicity and ion class and can correctly predict 60% of an independent test set. These percentages increase to 71% and 79% for drugs with renal clearances of < 0.1 ml/min/kg and > 1 ml/min/kg, respectively. As far as the authors are aware these are the first set of models to appear in the literature that predict the rate of human renal clearance and can be used to manipulate molecular properties leading to new drugs that are less likely to fail due to renal clearance.

Generation of in-silico cytochrome P450 1A2, 2C9, 2C19, 2D6, and 3A4 inhibition QSAR models.

In-silico models were generated to predict the extent of inhibition of cytochrome P450 isoenzymes using a set of relatively interpretable descriptors in conjunction with partial least squares (PLS) and regression trees (RT). The former was chosen due to the conservative nature of the resultant models built and the latter to more effectively account for any non-linearity between dependent and independent variables. All models are statistically significant and agree with the known SAR and they could be used as a guide to P450 liability through a classification based on the continuous pIC50 prediction given by the model. A compound is classified as having either a high or low P450 liability if the predicted pIC(50) is at least one root mean square error (RMSE) from the high/low pIC(50) cut-off of 5. If predicted within an RMSE of the cut-off we cannot be confident a compound will be experimentally low or high so an indeterminate classification is given. Hybrid models using bulk descriptors and fragmental descriptors do significantly better in modeling CYP450 inhibition, than bulk property QSAR descriptors alone.

Quantitative structure activity relationships in drug metabolism.

This review of 61 references delineates contemporary computation quantitative structure activity relationship (QSAR) approaches that have been used to elucidate the molecular features that influence the binding and metabolism of a compound by the major phase 1 and phase 2 metabolising enzymes; Cytochrome P450 (CYP) and UDP-glucuronosyltransferase (UGT), respectively. Contemporary studies are applying 2D and 3D QSAR, pharmacophore approaches and nonlinear techniques (for example: recursive partitioning, neural networks and support vector machines) to model drug metabolism. Furthermore, this review highlights some of the challenges and opportunities for future research; the need to develop 'global' models for CYP and UGT metabolism and to extend QSAR for other important metabolising enzymes.

A rapid computational filter for cytochrome P450 1A2 inhibition potential of compound libraries.

QSAR models for a diverse set of compounds for cytochrome P450 1A2 inhibition have been produced using 4 statistical approaches; partial least squares (PLS), multiple linear regression (MLR), classification and regression trees (CART), and bayesian neural networks (BNN). The models complement one another and have identified the following descriptors as important features for CYP1A2 inhibition; lipophilicity, aromaticity, charge, and the HOMO/LUMO energies. Furthermore all models are global and have been used to predict a diverse independent set of compounds. For the first time in the field of QSAR, the kappa index of agreement has comprehensively been used to assess the overall accuracy of the model's predictive power. The models are statistically significant and can be used as a rapid computational filter for cytochrome P450 1A2 inhibition potential of compound libraries.

Flavin radicals, conformational sampling and robust design principles in interprotein electron transfer: the trimethylamine dehydrogenase-electron-transferring flavoprotein complex.

TMADH (trimethylamine dehydrogenase) is a complex iron-sulphur flavoprotein that forms a soluble electron-transfer complex with ETF (electron-transferring flavoprotein). The mechanism of electron transfer between TMADH and ETF has been studied using stopped-flow kinetic and mutagenesis methods, and more recently by X-ray crystallography. Potentiometric methods have also been used to identify key residues involved in the stabilization of the flavin radical semiquinone species in ETF. These studies have demonstrated a key role for 'conformational sampling' in the electron-transfer complex, facilitated by two-site contact of ETF with TMADH. Exploration of three-dimensional space in the complex allows the FAD of ETF to find conformations compatible with enhanced electronic coupling with the 4Fe-4S centre of TMADH. This mechanism of electron transfer provides for a more robust and accessible design principle for interprotein electron transfer compared with simpler models that invoke the collision of redox partners followed by electron transfer. The structure of the TMADH-ETF complex confirms the role of key residues in electron transfer and molecular assembly, originally suggested from detailed kinetic studies in wild-type and mutant complexes, and from molecular modelling.