Advances in selecting appropriate non-rodent species for regulatory toxicology research: Policy, ethical, and experimental considerations

In vivo animal studies are required by regulatory agencies to investigate drug safety before clinical trials. In this review, we summarize the process of selecting a relevant non-rodent species for preclinical studies. The dog is the primary, default non-rodent used in toxicology studies with multiple scientific advantages, including adequate background data and availability. Rabbit has many regulatory advantages as the first non-rodent for the evaluation of reproductive and developmental as well as local toxicity. Recently, minipigs have increasingly replaced dogs and rabbits in toxicology studies due to ethical and scientific advantages including similarity to humans and breeding habits. When these species are not relevant, nonhuman primates (NHPs) can be used as the available animal models, especially in toxicology studies investigating biotherapeutics. Particularly, based on the phylogenetic relationships, the use of New-World marmosets can be considered before Old-World monkeys, especially cynomolgus with robust historical data. Importantly, the use of NHPs should be justified in terms of scientific benefits considering target affinity, expression pattern, and pharmacological cross-reactivity. Strict standards are required for the use of animals. Therefore, this review is helpful for the selection of appropriate non-rodent in regulatory toxicology studies by providing sufficient regulatory, ethical, and scientific data for each species.

An in vitro and in silico investigation of human pregnane X receptor agonistic activity of poly- and perfluorinated compounds using the heuristic method-best subset and comparative similarity indices analysis

Poly- and perfluorinated compounds (PFCs) may induce potential endocrine-disrupting hormonal effects. However, the molecular mechanism of the toxicology of PFCs remains unclear, and the insufficient information is available on the biological activities of PFCs at present. In this study, the cell-based reporter gene assays were used to determine the agonistic activity of PFCs on the human pregnane X receptor (hPXR). The heuristic method combined with best subset modeling (HM-BSM) based on Dragon descriptors and comparative similarity indices analysis (CoMSIA) were employed to build classical quantitative structure-activity relationship (QSAR) and three-dimensional QSAR models, respectively. The applicability domain (AD) of the classical QSAR model was assessed. Both the HM-BSM and CoMSIA approaches demonstrated good robustness, predictive ability, and mechanistic interpretability. The r² and leave-one-out cross-validation squared correlated coefficient (q²_LOO) values were 0.872 and 0.759 for the HM-BSM, and 0.976 and 0.751 for the CoMSIA model, respectively. The hPXR agonistic activity of the PFCs predicted by the built HM-BSM and CoMSIA agreed well with experimental activity, with root mean square error (RMSE) values of 0.0803 and 0.117, respectively, and external validation squared correlated coefficients (q²_EXT) of 0.972 and 0.932, respectively. The hPXR agonistic activity of PFCs was related to their molecular polarizability, charge and atomic mass. Hydrogen bonding and hydrophobic interactions constituted the primary intermolecular forces between PFCs and the hPXR. The developed models were used to screen the PFCs with high hPXR agonistic activity.

Use of computational modeling approaches in studying the binding interactions of compounds with human estrogen receptors

Estrogens have a whole host of physiological functions in many human organs and systems, including the reproductive, cardiovascular, and central nervous systems. Many naturally-occurring compounds with estrogenic or antiestrogenic activity are present in our environment and food sources. Synthetic estrogens and antiestrogens are also important therapeutic agents. At the molecular level, estrogen receptors (ERs) mediate most of the well-known actions of estrogens. Given recent advances in computational modeling tools, it is now highly practical to use these tools to study the interaction of human ERs with various types of ligands. There are two common categories of modeling techniques: one is the quantitative structure activity relationship (QSAR) analysis, which uses the structural information of the interacting ligands to predict the binding site properties of a macromolecule, and the other one is molecular docking-based computational analysis, which uses the 3-dimensional structural information of both the ligands and the receptor to predict the binding interaction. In this review, we discuss recent results that employed these and other related computational modeling approaches to characterize the binding interaction of various estrogens and antiestrogens with the human ERs. These examples clearly demonstrate that the computational modeling approaches, when used in combination with other experimental methods, are powerful tools that can precisely predict the binding interaction of various estrogenic ligands and their derivatives with the human ERs.

Structural characterization of the binding interactions of various endogenous estrogen metabolites with human estrogen receptor α and β subtypes: a molecular modeling study

In the present study, we used the molecular docking approach to study the binding interactions of various derivatives of 17β-estradiol (E₂) with human estrogen receptor (ER) α and β. First, we determined the suitability of the molecular docking method to correctly predict the binding modes and interactions of two representative agonists (E₂ and diethylstilbesterol) in the ligand binding domain (LBD) of human ERα. We showed that the docked structures of E₂ and diethylstilbesterol in the ERα LBD were almost exactly the same as the known crystal structures of ERα in complex with these two estrogens. Using the same docking approach, we then characterized the binding interactions of 27 structurally similar E₂ derivatives with the LBDs of human ERα and ERβ. While the binding modes of these E₂ derivatives are very similar to that of E₂, there are distinct subtle differences, and these small differences contribute importantly to their differential binding affinities for ERs. In the case of A-ring estrogen derivatives, there is a strong inverse relationship between the length of the hydrogen bonds formed with ERs and their binding affinity. We found that a better correlation between the computed binding energy values and the experimentally determined logRBA values could be achieved for various A-ring derivatives by re-adjusting the relative weights of the van der Waals interaction energy and the Coulomb interaction energy in computing the overall binding energy values.

In silico methods to predict drug toxicity

This review describes in silico methods to characterize the toxicity of pharmaceuticals, including tools which predict toxicity endpoints such as genotoxicity or organ-specific models, tools addressing ADME processes, and methods focusing on protein-ligand docking binding. These in silico tools are rapidly evolving. Nowadays, the interest has shifted from classical studies to support toxicity screening of candidates, toward the use of in silico methods to support the expert. These methods, previously considered useful only to provide a rough, initial estimation, currently have attracted interest as they can assist the expert in investigating toxic potential. They provide the expert with safety perspectives and insights within a weight-of-evidence strategy. This represents a shift of the general philosophy of in silico methodology, and it is likely to further evolve especially exploiting links with system biology.

Docking and CoMSIA studies on steroids and non-steroidal chemicals as androgen receptor ligands

While some synthetic chemicals have been demonstrated to disrupt normal endocrine function by binding to the androgen receptor (AR), the mechanism by which ligands bind to the ligand binding domain (LBD) remained unclear. In this study, docking and comparative molecular similarity index analysis (CoMSIA) were performed to study the AR ligand binding mechanism of steroids and non-steroidal chemicals. The obtained docking conformations and predictive CoMSIA models (r(pred)(2)values as 0.842 and 0.554) indicated the primary interaction site and key residues in the binding process. The major factors influence the binding affinity of steroids and non-steroidal chemicals were electrostatic and hydrophobic interactions, respectively. The results indicated that besides amino-acid residues Gln711, Arg752 and Thr877 which have previously been reported to be important in binding ligands, Leu701 and Leu704 are also important. Residues Val746, Met749 and Phe764 are crucial only for steroids, while Met742 and Met787 are important only for non-steroidal chemicals. This knowledge of key interactions and important amino-acid residues governing ligands to the AR allow better prediction of potency of AR agonists so that their potential to disrupt AR-mediated pathways and to design less potent alternatives.

Substitute of Animals in Drug Research: An Approach Towards Fulfillment of 4R's

The preclinical studies for drug screening involve the use of animals which is very time consuming and expensive and at times leads to suffering of the used organism. Animal right activists around the world are increasingly opposing the use of animals. This has forced the researchers to find ways to not only decrease the time involved in drug screening procedures but also decrease the number of animals used and also increase the humane care of animals. To fulfill this goal a number of new in vitro techniques have been devised which are called 'Alternatives' or 'Substitutes' for use of animals in research involving drugs. These 'Alternatives' are defined as the adjuncts which help to decrease the use as well as the number of animals in biomedical research. Russell and Burch have defined these alternatives by three R's - Reduction, Refinement and Replacement. These alternative strategies include physico-chemical methods and techniques utilizing tissue culture, microbiological system, stem cells, DNA chips, micro fluidics, computer analysis models, epidemiological surveys and plant-tissue based materials. The advantages of these alternatives include the decrease in the number of animals used, ability to obtain the results quickly, reduction in the costs and flexibility to control the variables of the experiment. However these techniques are not glittering gold and have their own shortcomings. The disadvantages include the lack of an appropriate alternative to study the whole animal's metabolic response, inability to study transplant models and idiosyncratic responses and inability to study the body's handling of drugs and its subsequent metabolites. None-the-less these aalternative methods to certain extent help to reduce the number of animals required for research. But such alternatives cannot eliminate the need for animals in research completely. Even though no animal model is a complete set of replica for a process within a human body, the intact animal does provide a better model of the complex interaction of the physiological processes.

Molecular dynamics

Molecular dynamics (MD) simulation holds the promise of revealing the mechanisms of biological processes in their ultimate detail. It is carried out by computing the interaction forces acting on each atom and then propagating the velocities and positions of the atoms by numerical integration of Newton's equations of motion. In this review, we present an overview of how the MD simulation can be conducted to address computational toxicity problems. The study cases will cover a standard MD simulation performed to investigate the overall flexibility of a cytochrome P450 (CYP) enzyme and a set of more advanced MD simulations to examine the barrier to ion conduction in a human α7 nicotinic acetylcholine receptor (nAChR).

Molecular docking studies of disubstituted diaryl diselenides as mammalian δ-aminolevulinic acid dehydratase enzyme inhibitors

δ-Aminolevulinic acid dehydratase (δ-ALAD) is a metalloprotein that catalyzes porphobilinogen formation. This enzyme is sensitive to pro-oxidants and classically used as a biomarker of lead (Pb) intoxication. Diphenyl diselenide [(PhSe)₂] and analogs bis(4-chlorophenyl) diselenide [(pCl₃PhSe)₂], bis(4-methoxyphenyl)diselenide [(pCH₃OPhSe)₂], and bis[3-(trifluoromethy)phenyl] diselenide [(mCF₃PhSe)₂] inhibit mammalian δ-ALAD by oxidizing enzyme cysteinyl residues, which are involved in diselenide-induced toxicity. 2-Cysteinyl residues from δ-ALAD are believed to sequentially interact with (PhSe)₂. Thus this study utilized protein-ligand docking analyses to determine which cysteinyl residues might be involved in the inhibitory effect of (PhSe)₂ and analogs toward δ-ALAD. All diselenides that interact in a similar manner with the active site of δ-ALAD were examined. Docking simulations indicated an important role for π-π interactions involving Phe208 and cation-π interactions involving Lys199 and Arg209 residues with the aromatic ring of (PhSe)₂ and analogs. Based upon these interactions an approximation between Se atoms and -SH of Cys124, with distances ranging between 3.3 Å and 3.5 Å, was obtained. These data support our previous postulations regarding the mechanism underlying δ-ALAD oxidation mediated by (PhSe)₂ and analogs. Based on protein-ligand docking analyses, data indicated that -SH of Cys124 attacks one of the Se atoms of -SH of (PhSe)₂ releasing one PhSeH (selenophenol). Subsequently, the -SH of Cys132 attacks the sulfur atom of Cys124 (from the bond of E-S-Se-Ph indermediate), generating the second PhSe⁻, and the oxidized and inhibited δ-ALAD. In conclusion, AutoDock Vina 1.1.1 was a useful tool to search for diselenides inhibitors of δ-ALAD, and, most importantly, it provided insight into molecular mechanisms involved in enzyme inhibition.

The acceptance of in silico models for REACH: Requirements, barriers, and perspectives

In silico models have prompted considerable interest and debate because of their potential value in predicting the properties of chemical substances for regulatory purposes. The European REACH legislation promotes innovation and encourages the use of alternative methods, but in practice the use of in silico models is still very limited. There are many stakeholders influencing the regulatory trajectory of quantitative structure-activity relationships (QSAR) models, including regulators, industry, model developers and consultants. Here we outline some of the issues and challenges involved in the acceptance of these methods for regulatory purposes.

A structural view of nuclear hormone receptor: endocrine disruptor interactions

Endocrine-disrupting chemicals (EDCs) represent a broad class of exogenous substances that cause adverse effects in the endocrine system by interfering with hormone biosynthesis, metabolism, or action. The molecular mechanisms of EDCs involve different pathways including interactions with nuclear hormone receptors (NHRs) which are primary targets of a large variety of environmental contaminants. Here, based on the crystal structures currently available in the Protein Data Bank, we review recent studies showing the many ways in which EDCs interact with NHRs and impact their signaling pathways. Like the estrogenic chemical diethylstilbestrol, some EDCs mimic the natural hormones through conserved protein-ligand contacts, while others, such as organotins, employ radically different binding mechanisms. Such structure-based knowledge, in addition to providing a better understanding of EDC activities, can be used to predict the endocrine-disrupting potential of environmental pollutants and may have applications in drug discovery.

In Silico Toxicology in Drug Discovery — Concepts Based on Three-dimensional Models

Animal testing is still compulsory worldwide, for the approval of drugs and chemicals produced in large quantities. Computer-assisted ( in silico) technologies are considered to be efficient alternatives to in vivo experiments, and are therefore endorsed by many regulatory agencies, e.g. for use in the European REACH initiative. Advantages of in silico methods include: the possible study of hypothetical compounds; their low cost; and the fact that such virtual experiments are typically based on human data, thus making the question of interspecies transferability obsolete. Since the mid-1990s, computer-based technologies have become an indispensable tool in drug discovery — used primarily to identify small molecules displaying a stereospecific and selective binding to a regulatory macromolecule. Since toxic effects are still responsible for some 20% of the late-stage failures, there is a continuing need for in silico concepts which can be used to estimate a compound's ADMET ( adsorption, distribution, metabolism, elimination, toxicity) properties — in particular, toxicity. The aim of this paper is to provide an insight into computational technologies that allow for the prediction of toxic effects triggered by pharmaceuticals. As most adverse and toxic effects are mediated by unwanted interactions with macromolecules involved in biological regulatory systems, we have focused on methodologies that are based on three-dimensional models of small molecules binding to such entities, and discuss the results at the molecular level.

Concepts and challenges in quantitative pharmacology and model-based drug development

Model-based drug development (MBDD) has been recognized as a concept to improve the efficiency of drug development. The acceptance of MBDD from regulatory agencies, industry, and academia has been growing, yet today's drug development practice is still distinctly distant from MBDD. This manuscript is aimed at clarifying the concept of MBDD and proposing practical approaches for implementing MBDD in the pharmaceutical industry. The following concepts are defined and distinguished: PK-PD modeling, exposure-response modeling, pharmacometrics, quantitative pharmacology, and MBDD. MBDD is viewed as a paradigm and a mindset in which models constitute the instruments and aims of drug development efforts. MBDD covers the whole spectrum of the drug development process instead of being limited to a certain type of modeling technique or application area. The implementation of MBDD requires pharmaceutical companies to foster innovation and make changes at three levels: (1) to establish mindsets that are willing to get acquainted with MBDD, (2) to align processes that are adaptive to the requirements of MBDD, and (3) to create a closely collaborating organization in which all members play a role in MBDD. Pharmaceutical companies that are able to embrace the changes MBDD poses will likely be able to improve their success rate in drug development, and the beneficiaries will ultimately be the patients in need.

Predicting toxicity through computers: a changing world

The computational approaches used to predict toxicity are evolving rapidly, a process hastened on by the emergence of new ways of describing chemical information. Although this trend offers many opportunities, new regulations, such as the European Community's 'Registration, Evaluation, Authorisation and Restriction of Chemicals' (REACH), demand that models be ever more robust.

In this commentary, we outline the numerous factors involved in the evolution of quantitative structure-regulatory activity relationship (QSAR) models. Such models not only require powerful tools, but must also be adapted for their intended application, such as in using suitable input values and having an output that complies with legal requirements. In addition, transparency and model reproducibility are important factors.

As more models become available, it is vital that new theoretical possibilities are embraced, and efforts are combined in order to promote new flexible, modular tools.