BRADSHAW: a system for automated molecular design.

This paper introduces BRADSHAW (Biological Response Analysis and Design System using an Heterogenous, Automated Workflow), a system for automated molecular design which integrates methods for chemical structure generation, experimental design, active learning and cheminformatics tools. The simple user interface is designed to facilitate access to large scale automated design whilst minimising software development required to introduce new algorithms, a critical requirement in what is a very fast moving field. The system embodies a philosophy of automation, best practice, experimental design and the use of both traditional cheminformatics and modern machine learning algorithms.

Correction to: BRADSHAW: a system for automated molecular design.

The original version of this article unfortunately contained some mistakes in the references.

The Differential Absorption of a Series of P-Glycoprotein Substrates in Isolated Perfused Lungs from Mdr1a/1b Genetic Knockout Mice can be Attributed to Distinct Physico-Chemical Properties: an Insight into Predicting Transporter-Mediated, Pulmonary Specific Disposition.

PURPOSE: To examine if pulmonary P-glycoprotein (P-gp) is functional in an intact lung; impeding the pulmonary absorption and increasing lung retention of P-gp substrates administered into the airways. Using calculated physico-chemical properties alone build a predictive Quantitative Structure-Activity Relationship (QSAR) model distinguishing whether a substrate's pulmonary absorption would be limited by P-gp or not. METHODS: A panel of 18 P-gp substrates were administered into the airways of an isolated perfused mouse lung (IPML) model derived from Mdr1a/Mdr1b knockout mice. Parallel intestinal absorption studies were performed. Substrate physico-chemical profiling was undertaken. Using multivariate analysis a QSAR model was established. RESULTS: A subset of P-gp substrates (10/18) displayed pulmonary kinetics influenced by lung P-gp. These substrates possessed distinct physico-chemical properties to those P-gp substrates unaffected by P-gp (8/18). Differential outcomes were not related to different intrinsic P-gp transporter kinetics. In the lung, in contrast to intestine, a higher degree of non-polar character is required of a P-gp substrate before the net effects of efflux become evident. The QSAR predictive model was applied to 129 substrates including eight marketed inhaled drugs, all these inhaled drugs were predicted to display P-gp dependent pulmonary disposition. CONCLUSIONS: Lung P-gp can affect the pulmonary kinetics of a subset of P-gp substrates. Physico-chemical relationships determining the significance of P-gp to absorption in the lung are different to those operative in the intestine. Our QSAR framework may assist profiling of inhaled drug discovery candidates that are also P-gp substrates. The potential for P-gp mediated pulmonary disposition exists in the clinic.

Development of a Novel Quantitative Structure-Activity Relationship Model to Accurately Predict Pulmonary Absorption and Replace Routine Use of the Isolated Perfused Respiring Rat Lung Model.

PURPOSE: We developed and tested a novel Quantitative Structure-Activity Relationship (QSAR) model to better understand the physicochemical drivers of pulmonary absorption, and to facilitate compound design through improved prediction of absorption. The model was tested using a large array of both existing and newly designed compounds. METHODS: Pulmonary absorption data was generated using the isolated perfused respiring rat lung (IPRLu) model for 82 drug discovery compounds and 17 marketed drugs. This dataset was used to build a novel QSAR model based on calculated physicochemical properties. A further 9 compounds were used to test the model's predictive capability. RESULTS: The QSAR model performed well on the 9 compounds in the "Test set" with a predicted versus observed correlation of R(2) = 0.85, and >65% of compounds correctly categorised. Calculated descriptors associated with permeability and hydrophobicity positively correlated with pulmonary absorption, whereas those associated with charge, ionisation and size negatively correlated. CONCLUSIONS: The novel QSAR model described here can replace routine generation of IPRLu model data for ranking and classifying compounds prior to synthesis. It will also provide scientists working in the field of inhaled drug discovery with a deeper understanding of the physicochemical drivers of pulmonary absorption based on a relevant respiratory compound dataset.

Evaluation of an in silico cardiac safety assay: using ion channel screening data to predict QT interval changes in the rabbit ventricular wedge.

INTRODUCTION: Drugs that prolong the QT interval on the electrocardiogram present a major safety concern for pharmaceutical companies and regulatory agencies. Despite a range of assays performed to assess compound effects on the QT interval, QT prolongation remains a major cause of attrition during compound development. In silico assays could alleviate such problems. In this study we evaluated an in silico method of predicting the results of a rabbit left-ventricular wedge assay. METHODS: Concentration-effect data were acquired from either: the high-throughput IonWorks/FLIPR; the medium-throughput PatchXpress ion channel assays; or QSAR, a statistical IC50 value prediction model, for hERG, fast sodium, L-type calcium and KCNQ1/minK channels. Drug block of channels was incorporated into a mathematical differential equation model of rabbit ventricular myocyte electrophysiology through modification of the maximal conductance of each channel by a factor dependent on the IC50 value, Hill coefficient and concentration of each compound tested. Simulations were performed and agreement with experimental results, based upon input data from the different assays, was evaluated. RESULTS: The assay was found to be 78% accurate, 72% sensitive and 81% specific when predicting QT prolongation (>10%) using PatchXpress assay data (77 compounds). Similar levels of predictivity were demonstrated using IonWorks/FLIPR data (121 compounds) with 78% accuracy, 73% sensitivity and 80% specificity. QT shortening (<-10%) was predicted with 77% accuracy, 33% sensitivity and 90% specificity using PatchXpress data and 71% accuracy, 42% sensitivity and 81% specificity using IonWorks/FLIPR data. Strong quantitative agreement between simulation and experimental results was also evident. DISCUSSION: The in silico action potential assay demonstrates good predictive ability, and is suitable for very high-throughput use in early drug development. Adoption of such an assay into cardiovascular safety assessment, integrating ion channel data from routine screens to infer results of animal-based tests, could provide a cost- and time-effective cardiac safety screen.

Quantitative relationship between rat intestinal absorption and Abraham descriptors.

Literature data on the intestinal absorption of 158 drug and drug-like compounds in rats have been collected, and Abraham descriptors for the set of drugs have been calculated using the method of Platts and Abraham et al. Results show that there is a significant relationship between rat intestinal absorption and the Abraham descriptors. In agreement with the human intestinal absorption model, the dominant descriptors in the rat model are the drug hydrogen bond acidity and basicity. In order to compare the absorption models in humans and rats, the absorption model developed from rats was used to predict the absorption in humans. The rat intestinal absorption model is similar to the human absorption model, and data on rats can effectively be used to predict human intestinal absorption.

Bile salt/lecithin mixed micelles optimized for the solubilization of a poorly soluble steroid molecule using statistical experimental design.

Bile salts and lecithin combine physiologically to form mixed micelles which aid the solubilization and absorption of dietary fats and drug molecules. In this series of experiments, we have shown how experimental design procedures aid the optimization of a formulation incorporating a bile salt, lecithin, and water with fluticasone propionate (FP) as the model poorly soluble drug. The initial inclusion of a categorical variable ruled out the use of classic response surface designs; therefore the experimental design was constructed using a d-optimal selection from a candidate set of all possible experimental combinations. A separate 2-factor central composite design was used to determine the optimum lecithin and bile salt concentrations over an extended range after the categorical variable had been eliminated. It has been demonstrated that an increase in either lecithin or cholic acid concentration produces an increase in solubility of FP, while sodium taurocholate appears to depress the solubility of FP compared with the other two bile salts. The increase in solubility associated with the increase in bile salt and lecithin is further demonstrated by a linear relationship between FP solubility and the total lipid in the formulation. The influence of molar ratio of lecithin to bile salt in the formulation is also significant. The physical properties of the mixed micellar system (solution turbidity and viscosity ranking) were used to further discriminate between formulations. The optimization showed that the dominant effect was the lecithin, which improves the solubilizing characteristics of the formulation with increasing concentration. The effect of salt concentration is less marked though slightly quadratic in nature. The overall increase in solubility demonstrated was from <1 microg/mL in water to 205 microg/mL in the optimized mixed micellar system.

Evaluation of rat intestinal absorption data and correlation with human intestinal absorption.

The absorption of 111 drug and drug-like compounds was evaluated from 111 references based on the ratio of urinary excretion of drugs following oral and intravenous administration to intact rats and biliary excretion of bile duct-cannulated rats. Ninety-eight drug compounds for which both human and rat absorption data were available were selected for correlation analysis between the human and rat absorption. The result shows that the extent of absorption in these two species is similar. For 94% of the drugs the absorption difference between humans and rats is less than 20% and for 98% of drugs the difference is less than 30%. There is only one drug for which human absorption is significantly different from rat absorption. The standard deviation is 11% between human and rat absorption. The linear relationship between human and rat absorption forced through the origin, as determined by least squares regression, is %Absorption (human)=0.997%Absorption (rat) (n=98, SD=11). It is suggested that the absorption in rats could be used as an alternative method to human absorption in pre-clinical oral absorption studies.

Rate-limited steps of human oral absorption and QSAR studies.

PURPOSE: To classify the dissolution and diffusion rate-limited drugs and establish quantitative relationships between absorption and molecular descriptors. METHODS: Absorption consists of kinetic transit processes in which dissolution, diffusion, or perfusion processes can become the rate-limited step. The absorption data of 238 drugs have been classified into either dissolution or diffusion rate-limited based on an equilibrium method developed from solubility, dose, and percentage of absorption. A nonlinear absorption model derived from first-order kinetics has been developed to identify the relationship between percentage of drug absorption and molecular descriptors. RESULTS: Regression analysis was performed between percentage of absorption and molecular descriptors. The descriptors used were ClogP, molecular polar surface area, the number of hydrogen-bonding acceptors and donors, and Abraham descriptors. Good relationships were found between absorption and Abraham descriptors or ClogP. CONCLUSIONS: The absorption models can predict the following three BCS (Biopharmaceutics Classification Scheme) classes of compounds: class I, high solubility and high permeability; class III, high solubility and low permeability; class IV, low solubility and low permeability. The absorption models overpredict the absorption of class II, low solubility and high permeability compounds because dissolution is the rate-limited step of absorption.