Prediction of the permeability of neutral drugs inferred from their solvation properties

Determination of drug absorption is an important component of the drug discovery and development process in that it plays a key role in the decision to promote drug candidates to clinical trials. We have developed a method that, on the basis of an analysis of the dynamic distribution of water molecules around a compound obtained by molecular dynamics simulations, can compute a parameter-free value that correlates very well with the compound permeability measured using the human colon adenocarcinoma (Caco-2) cell line assay

Synthesis, In Silico Studies, Antiprotozoal and Cytotoxic Activities of Quinoline‐Biphenyl Hybrids

This is the pre-peer reviewed version of the following article: Synthesis, In Silico Studies, Antiprotozoal and Cytotoxic Activities of Quinoline‐Biphenyl Hybrids, which has been published in final form at https://doi.org/10.1002/slct.201903835. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived VersionsThe synthesis, in silico studies, antiprotozoal and cytotoxic activities of eleven quinoline‐biphenyl hybrids are described herein. The structure of the synthesized products was elucidated by a combination of spectrometric analyses. The synthesized compounds were evaluated against Plasmodium falciparum, and amastigotes forms both Leishmania (V) panamensis and Trypanosoma cruzi. Cytotoxicity was evaluated against human U‐937 macrophages. 8‐phenylquinoline (4 a) showed similar activity than meglumine antimoniate and 4‐(quinolin‐8‐yl)phenol (4 b) exhibited an activity similar to that of benznidazole. 8‐(3,4‐dimethoxyphenyl) quinoline (4 k) showed the best activity against P. falciparum. Although these compounds were toxic for mammalian U‐937 cells, however they may still have potential to be considered as candidates for drug development because of their antiparasite activity. Molecular docking was used to determine the in silico inhibition of some of the designed compounds against PfLDH and cruzipain, two important pharmacological targets involved in antiparasitic diseases. All hybrids were docked to the three‐dimensional structures of PfLDH and T. cruzi cruzipain as enzymes using AutoDock Vina. Notably, the docking results showed that the most active compounds 4‐(quinolin‐8‐yl)phenol (4 b, CE50: 11.33 μg/mL for T. cruzi) and 8‐(3,4‐dimethoxyphenyl) quinoline (4 k, CE50: 8.84 μg/mL for P. falciparum) exhibited the highest scoring pose (−7.5 and −7.7 kcal/mol, respectively). This result shows a good correlation between the predicted scores with the experimental data profile, suggesting that these ligands could act as competitive inhibitors of PfLDH or T. cruzi cruzipain enzymes, respectively. Finally, in silico ADME studies of the quinoline hybrids showed that these novel compounds have suitable drug‐like properties, making them potentially promising agents for antiprotozoal therapy

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

Data mining methods for the prediction of intestinal absorption using QSAR

Oral administration is the most common route for administration of drugs. With the growing cost of drug discovery, the development of Quantitative Structure-Activity Relationships (QSAR) as computational methods to predict oral absorption is highly desirable for cost effective reasons. The aim of this research was to develop QSAR models that are highly accurate and interpretable for the prediction of oral absorption. In this investigation the problems addressed were datasets with unbalanced class distributions, feature selection and the effects of solubility and permeability towards oral absorption prediction. Firstly, oral absorption models were obtained by overcoming the problem of unbalanced class distributions in datasets using two techniques, under-sampling of compounds belonging to the majority class and the use of different misclassification costs for different types of misclassifications. Using these methods, models with higher accuracy were produced using regression and linear/non-linear classification techniques. Secondly, the use of several pre-processing feature selection methods in tandem with decision tree classification analysis – including misclassification costs – were found to produce models with better interpretability and higher predictive accuracy. These methods were successful to select the most important molecular descriptors and to overcome the problem of unbalanced classes. Thirdly, the roles of solubility and permeability in oral absorption were also investigated. This involved expansion of oral absorption datasets and collection of in vitro and aqueous solubility data. This work found that the inclusion of predicted and experimental solubility in permeability models can improve model accuracy. However, the impact of solubility on oral absorption prediction was not as influential as expected. Finally, predictive models of permeability and solubility were built to predict a provisional Biopharmaceutic Classification System (BCS) class using two multi-label classification techniques, binary relevance and classifier chain. The classifier chain method was shown to have higher predictive accuracy by using predicted solubility as a molecular descriptor for permeability models, and hence better final provisional BCS prediction. Overall, this research has resulted in predictive and interpretable models that could be useful in a drug discovery context

Prediction of Human Intestinal Absorption by GA Feature Selection and Support Vector Machine Regression

QSAR (Quantitative Structure Activity Relationships) models for the prediction of human intestinal absorption (HIA) were built with molecular descriptors calculated by ADRIANA.Code, Cerius2 and a combination of them. A dataset of 552 compounds covering a wide range of current drugs with experimental HIA values was investigated. A Genetic Algorithm feature selection method was applied to select proper descriptors. A Kohonen's self-organizing Neural Network (KohNN) map was used to split the whole dataset into a training set including 380 compounds and a test set consisting of 172 compounds. First, the six selected descriptors from ADRIANA.Code and the six selected descriptors from Cerius2 were used as the input descriptors for building quantitative models using Partial Least Square (PLS) analysis and Support Vector Machine (SVM) Regression. Then, another two models were built based on nine descriptors selected by a combination of ADRIANA.Code and Cerius2 descriptors using PLS and SVM, respectively. For the three SVM models, correlation coefficients (r) of 0.87, 0.89 and 0.88 were achieved; and standard deviations (s) of 10.98, 9.72 and 9.14 were obtained for the test set

Computational prediction of formulation strategies for beyond-rule-of-5 compounds

AbstractThe physicochemical properties of some contemporary drug candidates are moving towards higher molecular weight, and coincidentally also higher lipophilicity in the quest for biological selectivity and specificity. These physicochemical properties move the compounds towards beyond rule-of-5 (B-r-o-5) chemical space and often result in lower water solubility. For such B-r-o-5 compounds non-traditional delivery strategies (i.e. those other than conventional tablet and capsule formulations) typically are required to achieve adequate exposure after oral administration. In this review, we present the current status of computational tools for prediction of intestinal drug absorption, models for prediction of the most suitable formulation strategies for B-r-o-5 compounds and models to obtain an enhanced understanding of the interplay between drug, formulation and physiological environment. In silico models are able to identify the likely molecular basis for low solubility in physiologically relevant fluids such as gastric and intestinal fluids. With this baseline information, a formulation scientist can, at an early stage, evaluate different orally administered, enabling formulation strategies. Recent computational models have emerged that predict glass-forming ability and crystallisation tendency and therefore the potential utility of amorphous solid dispersion formulations. Further, computational models of loading capacity in lipids, and therefore the potential for formulation as a lipid-based formulation, are now available. Whilst such tools are useful for rapid identification of suitable formulation strategies, they do not reveal drug localisation and molecular interaction patterns between drug and excipients. For the latter, Molecular Dynamics simulations provide an insight into the interplay between drug, formulation and intestinal fluid. These different computational approaches are reviewed. Additionally, we analyse the molecular requirements of different targets, since these can provide an early signal that enabling formulation strategies will be required. Based on the analysis we conclude that computational biopharmaceutical profiling can be used to identify where non-conventional gateways, such as prediction of ‘formulate-ability’ during lead optimisation and early development stages, are important and may ultimately increase the number of orally tractable contemporary targets

Predicting human intestinal absorption in the presence of bile salt with micellar liquid chromatography

Understanding intestinal absorption for pharmaceutical compounds is vital to estimate bioavailability and therefore the in vivo potential of a drug. This study considers the application of micellar liquid chromatography (MLC) to predict passive intestinal absorption with a selection of model compounds. MLC is already known to aid prediction of absorption using simple surfactant systems however, with this study the focus was on the presence of a more complex, bile salt surfactant, as would be encountered in the in vivo environment. As a result, MLC using a specific bile salt has been confirmed as an ideal in vitro system to predict the intestinal permeability for a wide range of drugs, through the development of a quantitative partition-absorption relationship. MLC offers many benefits including environmental, economic, time-saving and ethical advantages compared with the traditional techniques employed to obtain passive intestinal absorption values

In silico pharmacokinetic and toxicological study of Cinnamic Acid analogues: Estudo farmacocinético e toxicológico in silico de análogos do Ácido Cinâmico

Cinnamic acid analogs are natural phenolic compounds that have a wide range of biological and therapeutic activities. The present work aimed to predict, through in silico methodologies, the oral bioavailability and pharmacokinetic and toxicological analyzes for four cinnamic acid analogues (caffeic acid, ferulic acid, p-coumaric acid and synaptic acid). The study revealed that the analogues have good oral bioavailability, favorable pharmacokinetic and toxicological parameters. The Virtual Screening performed to predict oral bioavailability indicated that all analogues do not violate Lipinski's Rule. The in silico ADME study of pharmacokinetic parameters showed that all derivatives have high intestinal absorption, are permeable by Caco-2 cells, do not cross the blood-brain barrier, do not inhibit P-glycoprotein. There will be no inhibition of the cytochrome P450 complex isoenzymes (CYP450). The in silico Toxicological study revealed that the analogues do not have toxicity by the AMES Test, are not carcinogenic and do not present acute oral toxicity