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

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%

Predicting Skin Permeability by means of Computational Approaches : Reliability and Caveats in Pharmaceutical Studies

© 2019 American Chemical Society.The skin is the main barrier between the internal body environment and the external one. The characteristics of this barrier and its properties are able to modify and affect drug delivery and chemical toxicity parameters. Therefore, it is not surprising that permeability of many different compounds has been measured through several in vitro and in vivo techniques. Moreover, many different in silico approaches have been used to identify the correlation between the structure of the permeants and their permeability, to reproduce the skin behavior, and to predict the ability of specific chemicals to permeate this barrier. A significant number of issues, like interlaboratory variability, experimental conditions, data set building rationales, and skin site of origin and hydration, still prevent us from obtaining a definitive predictive skin permeability model. This review wants to show the main advances and the principal approaches in computational methods used to predict this property, to enlighten the main issues that have arisen, and to address the challenges to develop in future research.Peer reviewedFinal Accepted Versio

Quantitative Structure-Property Relationship Modeling & Computer-Aided Molecular Design: Improvements & Applications

The objective of this work was to develop an integrated capability to design molecules with desired properties. An automated robust genetic algorithm (GA) module has been developed to facilitate the rapid design of new molecules. The generated molecules were scored for the relevant thermophysical properties using non-linear quantitative structure-property relationship (QSPR) models. The descriptor reduction and model development for the QSPR models were implemented using evolutionary algorithms (EA) and artificial neural networks (ANNs). QSPR models for octanol-water partition coefficients (Kow), melting points (MP), normal boiling points (NBP), Gibbs energy of formation, universal quasi-chemical (UNIQUAC) model parameters, and infinite-dilution activity coefficients of cyclohexane and benzene in various organic solvents were developed in this work. To validate the current design methodology, new chemical penetration enhancers (CPEs) for transdermal insulin delivery and new solvents for extractive distillation of the cyclohexane + benzene system were designed. In general, the use of non-linear QSPR models developed in this work provided predictions better than or as good as existing literature models. In particular, the current models for NBP, Gibbs energy of formation, UNIQUAC model parameters, and infinite-dilution activity coefficients have lower errors on external test sets than the literature models. The current models for MP and Kow are comparable with the best models in the literature. The GA-based design framework implemented in this work successfully identified new CPEs for transdermal delivery of insulin, with permeability values comparable to the best CPEs in the literature. Also, new solvents for extractive distillation of cyclohexane/benzene with selectivities two to four times that of the existing solvents were identified. These two case studies validate the ability of the current design framework to identify new molecules with desired target properties.Chemical Engineerin

Molecular surface area measures of polarity and hydrogen bonding for QSAR

Modifications were made to the traditional PSA descriptor by decoupling it into its H-bond acidic and basic components. The PSA based descriptors were also scaled according to the known hydrogen bonding characteristics of common functional groups to make them more realistic measures of a molecules hydrogen bonding capacity. Three other surface area descriptors total surface area, total halogen atom surface area and total aromatic carbon surface area were also defined. Various routes to the calculation of these descriptors were explored and it was concluded the best descriptors were those obtained from a single structure generated using the semi empirical-method AMI. It was also shown that descriptors obtained from a vdw surface were more suitable than those obtained from solvent accessible surface area. The scaled PSA descriptors were initially tested against octanol-water, chloroform-water, and cyclohexane-water partition coefficients of 110 organic and drug-like molecules. All of the models produced were seen to be statistically accurate and followed known characteristics of the partition coefficients considered. The scaled PSA descriptors were then applied successfully to a number of important biological processes such as cellular uptake and intestinal absorption models were also produced for important industrial processes such as Fluorophilicity and CMC. The surface area descriptors were also seen to be equally capable of modelling inorganic molecules and excellent models were produced for octanol-water and chloroform-water partitions for a number of platinum containing drugs.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Molecular surface area measures of polarity and hydrogen bonding for QSAR

Modifications were made to the traditional PSA descriptor by decoupling it into its H-bond acidic and basic components. The PSA based descriptors were also scaled according to the known hydrogen bonding characteristics of common functional groups to make them more realistic measures of a molecules hydrogen bonding capacity. Three other surface area descriptors total surface area, total halogen atom surface area and total aromatic carbon surface area were also defined. Various routes to the calculation of these descriptors were explored and it was concluded the best descriptors were those obtained from a single structure generated using the semi empirical-method AMI. It was also shown that descriptors obtained from a vdw surface were more suitable than those obtained from solvent accessible surface area. The scaled PSA descriptors were initially tested against octanol-water, chloroform-water, and cyclohexane-water partition coefficients of 110 organic and drug-like molecules. All of the models produced were seen to be statistically accurate and followed known characteristics of the partition coefficients considered. The scaled PSA descriptors were then applied successfully to a number of important biological processes such as cellular uptake and intestinal absorption models were also produced for important industrial processes such as Fluorophilicity and CMC. The surface area descriptors were also seen to be equally capable of modelling inorganic molecules and excellent models were produced for octanol-water and chloroform-water partitions for a number of platinum containing drugs

Complexity, Emergent Systems and Complex Biological Systems:\ud Complex Systems Theory and Biodynamics. [Edited book by I.C. Baianu, with listed contributors (2011)]

An overview is presented of System dynamics, the study of the behaviour of complex systems, Dynamical system in mathematics Dynamic programming in computer science and control theory, Complex systems biology, Neurodynamics and Psychodynamics.\u