Review of QSAR Models and Software Tools for predicting Biokinetic Properties

In the assessment of industrial chemicals, cosmetic ingredients, and active substances in pesticides and biocides, metabolites and degradates are rarely tested for their toxicologcal effects in mammals. In the interests of animal welfare and cost-effectiveness, alternatives to animal testing are needed in the evaluation of these types of chemicals. In this report we review the current status of various types of in silico estimation methods for Absorption, Distribution, Metabolism and Excretion (ADME) properties, which are often important in discriminating between the toxicological profiles of parent compounds and their metabolites/degradation products. The review was performed in a broad sense, with emphasis on QSARs and rule-based approaches and their applicability to estimation of oral bioavailability, human intestinal absorption, blood-brain barrier penetration, plasma protein binding, metabolism and. This revealed a vast and rapidly growing literature and a range of software tools. While it is difficult to give firm conclusions on the applicability of such tools, it is clear that many have been developed with pharmaceutical applications in mind, and as such may not be applicable to other types of chemicals (this would require further research investigation). On the other hand, a range of predictive methodologies have been explored and found promising, so there is merit in pursuing their applicability in the assessment of other types of chemicals and products. Many of the software tools are not transparent in terms of their predictive algorithms or underlying datasets. However, the literature identifies a set of commonly used descriptors that have been found useful in ADME prediction, so further research and model development activities could be based on such studies.JRC.DG.I.6-Systems toxicolog

QUANTITATIVE STRUCTURE–PHARMACOKINETICS MODELING OF THE UNBOUND CLEARANCE FOR NEUTRAL DRUGS

Objective: Prediction of pharmacokinetic behaviour of new candidate drugs is an important step in drug design. Clearance is a key pharmacokinetic parameter, controlling drug exposure in the body. It depends on numerous factors and is frequently restricted by plasma protein binding. The study is focused on the development of quantitative structure-pharmacokinetic relationship (QSPkR) for the unbound clearance (CLu) of neutral drugs.Methods: The dataset consisted of 117 neutral drugs, divided into training set (n = 94) and external test set (n = 23). Chemical structures were encoded by 113 theoretical descriptors. Genetic algorithm and step-wise multiple linear regression were applied for model development. The model was evaluated by cross-validation in the training set and external test set.Results: Significant, predictive and interpretable QSPkR model was developed with explained variance r2 = 0.617, cross-validated correlation coefficient q2LOO-CV = 0.554, external test set predictive coefficient r2pred = 0.656, and root mean square error in prediction RMSEP = 1.89. The model was able to predict CLu for 56% of the drugs in the external test set within the 2-fold error of experimental values.Conclusion: The model reveals the main molecular features governing CLu of neutral drugs. CLu is favoured by lipophilicity, the presence of fused aromatic rings, ester groups, dihydropyridine moieties and nine-member ring systems, while polarity, molecular size and strong electron withdrawing atoms and groups as substituents in aromatic rings affect negatively C

ADME Profiling in Drug Discovery and a New Path Paved on Silica

The drug discovery and development pipeline have more and more relied on in vitro testing and in silico predictions to reduce investments and optimize lead compounds. A comprehensive set of in vitro assays is available to determine key parameters of absorption, distribution, metabolism, and excretion, for example, lipophilicity, solubility, and plasma stability. Such test systems aid the evaluation of the pharmacological properties of a compound and serve as surrogates before entering in vivo testing and clinical trials. Nowadays, computer-aided techniques are employed not just in the discovery of new lead compounds but embedded as part of the entire drug development process where the ADME profiling and big data analyses add a new layer of complexity to those systems. Herein, we give a short overview of the history of the drug development pipeline presenting state-of-the-art ADME in vitro assays as established in academia and industry. We will further introduce the underlying good practices and give an example of the compound development pipeline. In the next step, recent advances at in silico techniques will be highlighted with special emphasis on how pharmacogenomics and in silico PK profiling can enhance drug monitoring and individualization of drug therapy

Computational Estimation of Biliary Excretion of Compounds and the Role of Transporters

Biliary excretion is one of the main elimination pathways for drugs and/or their metabolites. Therefore, an insight into the structural profile of cholephilic compounds through accurate modelling of the biliary excretion is important for the estimation of clinical pharmacokinetics in early stages of drug discovery. The aim of this project was to develop Quantitative Structure-Activity Relationships(QSAR) as computational tools for the estimation of biliary excretion. In addition, the structural requirements for biliary excretion were investigated in relation to the structural requirements for binding to uptake and efflux transporter proteins that are involved in hepatobiliary elimination. The study used three datasets; 1. percentage of dose excreted intact into bile in rat for 217 compounds, 2. P-gp inhibition constants for 219 compound, 3. percentage inhibition of OATP transporters, OATP1B1, OATP1B3 and OATP2B1. Statistical techniques were stepwise regression analysis, Classification and Regression Trees (C&RT), Chi-square Automatic Interaction Detector (CHAID), Boosted trees (BT), Random Forest (RF) and Multivariate Adaptive Regression Splines (MARS) models. The study resulted in QSARs for the prediction of biliary excretion, P-gp binding constants and percentage inhibition of OATPs, along with QSARs incorporating predicted P-gp and OATP inhibition values for the prediction of biliary excretion. Simple regression tree models were of similar accuracy to the boosted trees model in the estimation of the percentage of bile excretion of compounds. Molecular descriptors selected by these models indicated a higher biliary excretion for relatively hydrophilic compounds especially if they have acid/base dissociation, and a large molecular size above 348 Da. The major role of OATPs in biliary excretion was indicated using interactive decision tree models with OATP1B1 binding being the most successful predictor of biliary excretion amongst the three OATP subfamilies. In contrast, predicted P-gp binding parameters were not successful in the prediction of biliary excretion. This may be due to problems in extrapolating the in vitro P-gp binding data to the in vivo situation, or due to the difference in the chemical spaces of the P-gp and biliary excretion datasets which may lead to the compounds in biliary excretion dataset to fall outside the applicability domain of the P-gp models

Machine learning approach in pharmacokinetics and toxicity prediction

Ph.DDOCTOR OF PHILOSOPH

Synthesis, antimalarial evaluation, Ç-hematin inhibition, and in silico and in vitro ADMET profiling of 4-aminoquinoline-hydroxypyridinone hybrids

Includes abstract.With the aim of designing appropriate hybrid molecules as a strategy to fight drug resistant malaria parasites, 4-aminoquinoline-3,4-hydroxypyridinone hybrids were designed and synthesized. Their hypothesized mode of action was studied with respect to inhibition of hemozoin formation

Construcción QSAR de redes complejas de compuestos de interés en Química Farmacéutica, Microbiología y Parasitología

El diseño para la búsqueda y desarrollo de fármacos eficaces para el tratamiento de estas enfermedades, que supriman la eliminación o la degeneración celular respectivamente, es una de las líneas de investigación más importantes dentro de la química farmacéutica. En esto entra el diseño de fármacos; el diseño de fármacos está dedicado al desarrollo de modelos matemáticos para predecir propiedades de interés para una gran variedad de sistemas químicos incluyendo moléculas de bajo peso molecular, polímeros, biopolímeros, sistemas heterogéneos, formulaciones farmacéuticas, conglomerados de moléculas e iones, materiales, nano-estructuras y otros. Este tipo de predicciones no pretenden sustituir las técnicas experimentales sino complementar las mismas ayudando a obtener nuevas moléculas activas con mayor probabilidad de éxito, con la ventaja que ello supone en términos de ahorro de tiempo, recursos materiales, y muy importante: el refinamiento y reducción en el uso de animales de laboratorio. Esta metodología se basa en el uso de cálculos por ordenador y en las nuevas tecnologías de la informática. Las cuales pueden ser usadas: Para moléculas pequeñas: a) Estudios de relación cuantitativa estructura molecular-actividad farmacológica (QSAR) y de estructura molecular propiedades toxicológicas y eco-toxicológicas incluyendo mutagenicidad e carcinogénesis (QSTR). b) Predicción de propiedades químicas y fisicoquímicas de moléculas. Estudios de relación estructura molecular y propiedades de absorción, distribución, metabolismo y eliminación (ADME). c) Predicción de mecanismos de acción biológica de moléculas y evaluación in sílico de alta eficacia para grandes bases de datos (virtual HTS). Para macromoléculas: a) Estudios de interacción fármaco-receptor (neuronas). b) Bioinformática aplicada a estudios de relación secuencia-función y propiedades estructurales de ácidos nucleicos y proteínas. c) Búsqueda de nuevas dianas terapéuticas y “sitio activo” a partir de datos de Genómica, Proteómica. d) Búsqueda de biomarcadores para diagnóstico de enfermedades o como indicadores de contaminaciones. e) Predicción de propiedades fisicoquímicas de polímeros sintéticos, biopolímeros, materiales y nano-estructuras. f) Predicción, diseño, y optimización de enzimas mutadas para procesos biotecnológicos

Development of in silico models for the prediction of toxicity incorporating ADME information

Drug discovery is a process that requires a significant investment in both time and resources. Although recent developments have reduced the number of drugs failing at the later stages of development due to poor pharmacokinetic and/or toxicokinetic profiles, late stage attrition of drug candidates remains a problem. Additionally, there is a need to reduce animal testing for toxicological risk assessment for ethical and financial reasons. In silico methods offer an alternative that can address these challenges. A variety of computational approaches have been developed in the last two decades, these must be evaluated to ensure confidence in their use. The research presented in this thesis has assessed a range of existing tools for the prediction of toxicity and absorption, distribution, metabolism and elimination (ADME) parameters with an emphasis on absorption and xenobiotic metabolism. These two ADME properties largely determine bioavailability of a drug and, in turn, also influence toxicity. In vitro (Caco-2 cells and the parallel artificial membrane permeation assay) and in silico approaches, such as various druglikeness filters, can be used to estimate human intestinal absorption; a comparison between different methods was performed to identify relative strengths and weaknesses of the approaches. In terms of xenobiotic metabolism it is not only important to predict metabolites correctly, but it is also crucial to identify those compounds that can be biotransformed into species that can covalently bind to biomolecules. Structural alerts are routinely used to screen for such potential reactive metabolites. The balance between sensitivity and specificity of such reactive metabolite alerts has been discussed in the context of correctly predicting reactive metabolites of pharmaceuticals (using data available from DrugBank). Off-target toxicity, exemplified by human Ether-à-go-go-Related Gene (hERG) channel inhibition, was also explored. A number of novel structural alerts for hERG toxicity were developed based on groups of structurally similar compounds. Finally, the importance of predicting potential ecotoxicological effects of drugs was also considered. The utility of zebrafish embryos to distinguish between baseline and excess toxicity was investigated. In evaluating this selection of existing tools, improvements to the methods have been proposed where possible