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

The Use of Computational Methods in the Toxicological Assessment of Chemicals in Food: Current Status and Future Prospects

A wide range of chemicals are intentionally added to, or unintentially found in, food products, often in very small amounts. Depending on the situation, the experimental data needed to complete a dietary risk assessment, which is the scientific basis for protecting human health, may not be available or obtainable, for reasons of cost, time and animal welfare. For example, toxicity data are often lacking for the metabolites and degradation products of pesticide active ingredients. There is therefore an interest in the development and application of efficient and effective non-animal methods for assessing chemical toxicity, including Quantitative Structure-Activity Relationship (QSAR) models and related computational methods. This report gives an overview of how computational methods are currently used in the field of food safety by national regulatory bodies, international advisory organisations and the food industry. On the basis of an international survey, a comprehensive literature review and a detailed QSAR analysis, a range of recommendations are made with the long-term aim of promoting the judicious use of suitable QSAR methods. The current status of QSAR methods is reviewed not only for toxicological endpoints relevant to dietary risk assessment, but also for Absorption, Distribution, Metabolism and Excretion (ADME) properties, which are often important in discriminating between the toxicological profiles of parent compounds and their reaction products. By referring to the concept of the Threshold of Toxicological Concern (TTC), the risk assessment context in which QSAR methods can be expected to be used is also discussed. This Joint Research Centre (JRC) Reference Report provides a summary and update of the findings obtained in a study carried out by the JRC under the terms of a contract awarded by the European Food Safety Authority (EFSA).JRC.DG.I.6-Systems toxicolog

Qualitative prediction of blood–brain barrier permeability on a large and refined dataset

The prediction of blood–brain barrier permeation is vitally important for the optimization of drugs targeting the central nervous system as well as for avoiding side effects of peripheral drugs. Following a previously proposed model on blood–brain barrier penetration, we calculated the cross-sectional area perpendicular to the amphiphilic axis. We obtained a high correlation between calculated and experimental cross-sectional area (r = 0.898, n = 32). Based on these results, we examined a correlation of the calculated cross-sectional area with blood–brain barrier penetration given by logBB values. We combined various literature data sets to form a large-scale logBB dataset with 362 experimental logBB values. Quantitative models were calculated using bootstrap validated multiple linear regression. Qualitative models were built by a bootstrapped random forest algorithm. Both methods found similar descriptors such as polar surface area, pKa, logP, charges and number of positive ionisable groups to be predictive for logBB. In contrast to our initial assumption, we were not able to obtain models with the cross-sectional area chosen as relevant parameter for both approaches. Comparing those two different techniques, qualitative random forest models are better suited for blood-brain barrier permeability prediction, especially when reducing the number of descriptors and using a large dataset. A random forest prediction system (ntrees = 5) based on only four descriptors yields a validated accuracy of 88%

Data quality in the human and environmental health sciences: Using statistical confidence scoring to improve QSAR/QSPR modeling

A greater number of toxicity data are becoming publicly available allowing for in silico modeling. However, questions often arise as how to incorporate data quality and how to deal with contradicting data if more than a single datum point is available for the same compound. In this study, two well-known and studied QSAR/QSPR models for skin permeability and aquatic toxicology have been investigated in the context of statistical data quality. In particular, the potential benefits of the incorporation of the statistical Confidence Scoring (CS) approach within modelling and validation. As a result, robust QSAR/QSPR models for the skin permeability coefficient and the toxicity of nonpolar narcotics to Aliivibrio fischeri assay were created. CSweighted linear regression for training and CS-weighted root mean square error (RMSE) for validation were statistically superior compared to standard linear regression and standard RMSE. Strategies are proposed as to how to interpret data with high and low CS, as well as how to deal with large datasets containing multiple entries

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

Quantitative Structure - Skin permeability Relationships

This paper reviews in silico models currently available for the prediction of skin permeability with the main focus on the quantitative structure-permeability relationship (QSPR) models. A comprehensive analysis of the main achievements in the field in the last decade is provided. In addition, the mechanistic models are discussed and comparative studies that analyse different models are discussed

Computational Prediction and Experimental Validation of ADMET Properties for Potential Therapeutics

The drug development process in the United States is an expensive and lengthy process, usually taking a decade or more to gain approval for a drug candidate. The majority of proposed, early stage therapeutics fail, even though the typical process narrows from hundreds or thousands of small molecules down to one late stage candidate. One reason for failure is due to the drugs poor or unexpected absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties. Researchers attempt to predict ADMET properties as a way to help prioritize compounds for lead development to minimize expense and time. It was the overall goal of this project to further the prediction of two ADMET properties (absorption and distribution) through the development and application of quantitative structure-activity (QSAR) relationship computational models predicting human intestinal absorption (HIA), Caco-2 permeability (in vivo & in vitro measurements of absorption), and protein binding (measurement of distribution). These combined models would then be paired with additional experimental methods to help prioritize compounds for future ligand discovery efforts in our lab group and for our collaborators. Five computational QSAR models for each of these three properties were created using different molecular descriptor types and solvation models in an effort to examine which approach resulted in optimal performance. The model development process and validation stages of these QSAR models is outlined herein, along with analysis and discussion of commonly mispredicted compounds. Performance was similar across all models (independent of the molecular descriptor used and the solvation models applied. Future efforts at model development will depend on the size of the dataset to be analyzed. If the dataset is small, the i3D-Born solvation models will be used because these models better represent physiological conditions and performed slightly better than the other models. However, if the dataset is large, the 2D descriptor models will be used as these models do not require that a time and resource-intensive conformational search be performed and because it performed nearly as well as the i3D-Born solvation models. There were no common structural features consistently found associated with mispredicted structures. As such we are unable, at this time to pinpoint classes of compounds to avoid in future effortsThe experimental methods outlined in this work focused on developing methods to determine protein binding, specifically determining a fast, inexpensive workflow to classify the difference between high and low protein binding small molecules. Two techniques were used to determine protein binding of small molecules to bovine serum albumin (BSA): fluorescence polarization (FP) competition, and Nano Differential Scanning Fluorimetry (NanoDSF). FP assays quantifies the change in polarization of a target fluorophore between its protein bound and free states, an equilibrium that can be impacted by the presence of small molecule competitors. This method can be performed in a quantitative manner, but it also requires more time and more expensive and specialized instrumentation. In contrast, NanoDSF determines the melting temperature of BSA in the presence (higher) or in the absence (lower) small molecules by determining the intrinsic fluorescence of tryptophan and tyrosine residues while applying a temperature gradient. This method is qualitative, at least in our approach, but is very fast and requires much less expensive instrumentation. In our hands both techniques were successful in distinguishing differences between small molecules exhibiting low and high BSA binding. In summary, this project was successful in that we 1) developed computational tools capable of correctly predicting ADMET properties including HIA, Caco-2 permeability, and protein binding and 2) developed experimental workflows to quantitatively and qualitatively separate small molecules into low and high affinity BSA binders. With these in silico models and in vitro methods established, future research in our group and with our collaborators can make use of these tools to help prioritize compounds in ligand/ inhibitor discovery efforts

In silico modelling of permeation enhancement potency in Caco-2 monolayers based on molecular descriptors and random forest

AbstractStructural traits of permeation enhancers are important determinants of their capacity to promote enhanced drug absorption. Therefore, in order to obtain a better understanding of structure–activity relationships for permeation enhancers, a Quantitative Structural Activity Relationship (QSAR) model has been developed.The random forest-QSAR model was based upon Caco-2 data for 41 surfactant-like permeation enhancers from Whitehead et al. (2008) and molecular descriptors calculated from their structure.The QSAR model was validated by two test-sets: (i) an eleven compound experimental set with Caco-2 data and (ii) nine compounds with Caco-2 data from literature. Feature contributions, a recent developed diagnostic tool, was applied to elucidate the contribution of individual molecular descriptors to the predicted potency. Feature contributions provided easy interpretable suggestions of important structural properties for potent permeation enhancers such as segregation of hydrophilic and lipophilic domains. Focusing on surfactant-like properties, it is possible to model the potency of the complex pharmaceutical excipients, permeation enhancers. For the first time, a QSAR model has been developed for permeation enhancement. The model is a valuable in silico approach for both screening of new permeation enhancers and physicochemical optimisation of surfactant enhancer systems