The Challenge of Predicting Drug Toxicity in silico

Stem Cell Based Preclinical Drug Development and Toxicity Prediction

Stem cell based toxicity prediction plays a very important role in the development of the drug. Unexpected adverse effects of the drugs during clinical trials are a major reason for the termination or withdrawal of drugs. Methods for predicting toxicity employ in vitro as well as in vivo models; however, the major drawback seen in the data derived from these animal models is the lack of extrapolation, owing to interspecies variations. Due to these limitations, researchers have been striving to develop more robust drug screening platforms based on stem cells. The application of stem cells based toxicity testing has opened up robust methods to study the impact of new chemical entities on not only specific cell types, but also organs. Pluripotent stem cells, as well as cells derived from them, can be evaluated for modulation of cell function in response to drugs. Moreover, the combination of state-of-the -art techniques such as tissue engineering and microfluidics to fabricate organ- on-a-chip, has led to assays which are amenable to high throughput screening to understand the adverse and toxic effects of chemicals and drugs. This review summarizes the important aspects of the establishment of the embryonic stem cell test (EST), use of stem cells, pluripotent, induced pluripotent stem cells and organoids for toxicity prediction and drug development.

Disruption of androgen receptor signaling by chlorpyrifos (CPF) and its environmental degradation products: a structural insight

Androgen-disruptors are chemicals that interfere with the biosynthesis, metabolism or function of endogenous androgens affecting normal male reproductive development and health. Several epidemiological studies have indicated a link between exposure to androgen disrupting chemicals with reduced sperm counts and increased infertility. The actions of androgens within target cells are transduced by the androgen receptors (ARs). Chlorpyrifos (CPF), a chlorinated organophosphorus pesticide, is known to cause impairment in both male and female reproductive systems. Recent publications have shown molecular interactions of CPF and its environmental degradation products with human progesterone receptor and human estrogen receptor. Exposure to CPF causes a marked reduction in sperm counts with lowering in serum testosterone level, which suggests possible molecular interaction of CPF with AR. The investigation to reveal the possibility and the extent of binding of CPF and some of its degradation products (chlorpyrifos-oxon [CPYO], desethyl chlorpyrifos [DEC], trichloromethoxypyridine [TMP] and trichloropyridinol [TCP]) with AR using molecular docking simulation are reported. The findings of the present docking, binding energy and molecular dynamics studies reveal that CPF and its degradation products may bind to ARs and act as a potent androgen disruptor.Communicated by Ramaswamy H. Sarma.

Development of cardiotoxicity model using ligand-centric and receptor-centric descriptors

Background:

Bioinformatics and statistical analysis have been employed to develop a classification model to distinguish toxic and non-toxic molecules.

Aims:

The primary objective of this study is to enumerate the cut-off values of various physico-chemical (ligand-centric) and target interaction (receptor-centric) descriptors which forms the basis for classifying cardiotoxic and non-toxic molecules. We also sought correlation of molecular docking, absorption, distribution, metabolism, excretion, and toxicology (ADMET) parameters, Lipinski rules, physico-chemical parameters, etc. of human cardiotoxicity drugs.

Methods:

A training and test set of 91 compounds were applied to linear discriminant analysis (LDA) using 2D and 3D descriptors as discriminating variables representing various molecular modeling parameters to identify which function of descriptor type is responsible for cardiotoxicity. Internal validation was performed using the leave-one-out cross-validation methodology ensuing in good results, assuring the stability of the discriminant function (DF).

Results:

The values of the statistical parameters Fisher Discriminant Analysis (FDA) and Wilk’s λ for the DF showed reliable statistical significance, as long as the success rate in the prediction for both the training and the test set attained more than 93% accuracy, 87.50% sensitivity and 94.74% specificity.

Conclusion:

The predictive model was built using a hybrid approach using organ-specific targets for docking and ADMET properties for the FDA (Food and Drug Administration) approved and withdrawn drugs. Classifiers were developed by linear discriminant analysis and the cut-off was enumerated by receiver operating characteristic curve (ROC) analysis to achieve reliable specificity and sensitivity.

Triclocarban, Triclosan, Bromochlorophene, Chlorophene, and Climbazole Effects on Nuclear Receptors: An in Silico and in Vitro Study

BACKGROUND

Endocrine-disrupting chemicals can interfere with hormonal homeostasis and have adverse effects for both humans and the environment. Their identification is increasingly difficult due to lack of adequate toxicological tests. This difficulty is particularly problematic for cosmetic ingredients, because in vivo testing is now banned completely in the European Union.

OBJECTIVES

The aim was to identify candidate preservatives as endocrine disruptors by in silico methods and to confirm endocrine receptors' activities through nuclear receptors in vitro.

METHODS

We screened preservatives listed in Annex V in the European Union Regulation on cosmetic products to predict their binding to nuclear receptors using the Endocrine Disruptome and VirtualToxLab™ version 5.8 in silico tools. Five candidate preservatives were further evaluated for androgen receptor (AR), estrogen receptor (ER α ), glucocorticoid receptor (GR), and thyroid receptor (TR) agonist and antagonist activities in cell-based luciferase reporter assays in vitro in AR-EcoScreen, hER α -HeLa- 9903 , MDA-kb2, and GH3.TRE-Luc cell lines. Additionally, assays to test for false positives were used (nonspecific luciferase gene induction and luciferase inhibition).

RESULTS

Triclocarban had agonist activity on AR and ER α at 1 μ M and antagonist activity on GR at 5 μ M and TR at 1 μ M . Triclosan showed antagonist effects on AR, ER α , GR at 10 μ M and TR at 5 μ M , and bromochlorophene at 1 μ M (AR and TR) and at 10 μ M (ER α and GR). AR antagonist activity of chlorophene was observed [inhibitory concentration at 50% (IC50) IC 50 = 2.4 μ M ], as for its substantial ER α agonist at > 5 μ M and TR antagonist activity at 10 μ M . Climbazole showed AR antagonist (IC 50 = 13.6 μ M ), ER α agonist at > 10 μ M , and TR antagonist activity at 10 μ M .

DISCUSSION

These data support the concerns of regulatory authorities about the endocrine-disrupting potential of preservatives. These data also define the need to further determine their effects on the endocrine system and the need to reassess the risks they pose to human health and the environment. https://doi.org/10.1289/EHP6596.

Value and limitation of structure-based profilers to characterize developmental and reproductive toxicity potential

The uncertainty regarding the safety of chemicals leaching from food packaging triggers attention. In silico models provide solutions for screening of these chemicals, since many are toxicologically uncharacterized. For hazard assessment, information on developmental and reproductive toxicity (DART) is needed. The possibility to apply in silico toxicology to identify and quantify DART alerts was investigated. Open-source models and profilers were applied to 195 packaging chemicals and analogues. An approach based on DART and estrogen receptor (ER) binding profilers and molecular docking was able to identify all except for one chemical with documented DART properties. Twenty percent of the chemicals in the database known to be negative in experimental studies were classified as positive. The scheme was then applied to 121 untested chemicals. Alerts were identified for sixteen of them, five being packaging substances, the others structural analogues. Read-across was then developed to translate alerts into quantitative toxicological values. They can be used to calculate margins of exposure (MoE), the size of which reflects safety concern. The application of this approach appears valuable for hazard characterization of toxicologically untested packaging migrants. It is an alternative to the use of default uncertainty factor (UF) applied to animal chronic toxicity value to handle absence of DART data in hazard characterization.

Graph Classification of Molecules Using Force Field Atom and Bond Types

Classification of the biological activities of chemical substances is important for developing new medicines efficiently. Various machine learning methods are often employed to screen large libraries of compounds and predict the activities of new substances by training the molecular structure-activity relationships. One such method is graph classification, in which a molecular structure can be represented in terms of a labeled graph with nodes that correspond to atoms and edges that correspond to the bonds between these atoms. In a conventional graph definition, atomic symbols and bond orders are employed as node and edge labels, respectively. In this study, we developed new graph definitions using the assignment of atom and bond types in the force fields of molecular dynamics methods as node and edge labels, respectively. We found that these graph definitions improved the accuracies of activity classifications for chemical substances using graph kernels with support vector machines and deep neural networks. The higher accuracies obtained using our proposed definitions can enhance the development of the materials informatics using graph-based machine learning methods.

The integration of pharmacophore-based 3D QSAR modeling and virtual screening in safety profiling: A case study to identify antagonistic activities against adenosine receptor, A2A, using 1,897 known drugs

Safety pharmacology screening against a wide range of unintended vital targets using in vitro assays is crucial to understand off-target interactions with drug candidates. With the increasing demand for in vitro assays, ligand- and structure-based virtual screening approaches have been evaluated for potential utilization in safety profiling. Although ligand based approaches have been actively applied in retrospective analysis or prospectively within well-defined chemical space during the early discovery stage (i.e., HTS screening and lead optimization), virtual screening is rarely implemented in later stage of drug discovery (i.e., safety). Here we present a case study to evaluate ligand-based 3D QSAR models built based on in vitro antagonistic activity data against adenosine receptor 2A (A2A). The resulting models, obtained from 268 chemically diverse compounds, were used to test a set of 1,897 chemically distinct drugs, simulating the real-world challenge of safety screening when presented with novel chemistry and a limited training set. Due to the unique requirements of safety screening versus discovery screening, the limitations of 3D QSAR methods (i.e., chemotypes, dependence on large training set, and prone to false positives) are less critical than early discovery screen. We demonstrated that 3D QSAR modeling can be effectively applied in safety assessment prior to in vitro assays, even with chemotypes that are drastically different from training compounds. It is also worth noting that our model is able to adequately make the mechanistic distinction between agonists and antagonists, which is important to inform subsequent in vivo studies. Overall, we present an in-depth analysis of the appropriate utilization and interpretation of pharmacophore-based 3D QSAR models for safety screening.

Endocrine Disruption at the Androgen Receptor: Employing Molecular Dynamics and Docking for Improved Virtual Screening and Toxicity Prediction

The androgen receptor (AR) is a key target for the development of drugs targeting hormone-dependent prostate cancer, but has also an important role in endocrine disruption. Reliable prediction of the binding of ligands towards the AR is therefore of great relevance. Molecular docking is a powerful computational method for exploring small-ligand binding to proteins. It can be applied for virtual screening experiments but also for predicting molecular initiating events in toxicology. However, in case of AR, there is no antagonist-bound crystal structure yet available. Our study demonstrates that molecular docking approaches are not able to satisfactorily screen for AR antagonists because of this reason. Therefore, we applied Molecular Dynamics simulations to generate antagonist AR structures and showed that this leads to a vast improvement for the docking of AR antagonists. We benchmarked the ability of these antagonist AR structures discriminate between AR antagonists and decoys using an ensemble docking approach and obtained promising results with good enrichment. However, distinguishing AR antagonists from agonists with high confidence is not possible with the current approach alone.

In Silico Study and Bioprospection of the Antibacterial and Antioxidant Effects of Flavone and Its Hydroxylated Derivatives

Flavonoid compounds are widely used as natural protective species, which can act as anti-inflammatory, antioxidant, anticoagulant, antihypertensive and antitumor agents. This study set out to investigate the probable pharmacological activities, along with the antibacterial and antioxidant effects, of flavone and its hydroxy derivatives: 3-hydroxyflavone, 5-hydroxyflavone and 6-hydroxyflavone. To do so, we investigated their pharmacological characteristics, using in silico tests that indicate likelihood of activity or inactivity, with the PASS online software, and the antimicrobial potential against Gram positive and Gram negative bacteria was also analyzed, including bacteria of clinical importance. We also tested for oxidant and antioxidant potential in these molecules in the presence of reactive oxygen species (ROS) and phenylhydrazine (Ph). The results revealed the following characteristics: pharmacological activities for the flavonoids as agonists of cell membrane integrity and as permeability inhibitors, as antagonists of anaphylatoxin receptors, as inhibitors of both kinase and peroxidase, and as having both antimutagenic capacity and vaso-protective potential. All of the flavonoids exhibited moderate antibacterial activity against Gram positive and Gram negative strains, with the flavones being bactericidal at 200 μg/mL for the strains of P. aeruginosa ATCC 8027, S. aureus ATCC 25619 and E. coli 104; the other flavonoids revealed bacteriostatic action. The substances did not promote erythrocyte oxidation and behaved as sequestrators and antioxidants of hydrogen peroxide (H₂O₂) and phenylhydrazine (Ph). It was concluded that the analyzed compounds have various pharmacological activities in accordance with the predictions of PASS online, as their antibacterial and antioxidant activities were confirmed. The study also helps to consolidate the use of computational chemistry in silico tools to guide new drug search and discovery protocols.

An Introduction to the Basic Concepts in QSAR-Aided Drug Design

The need for the development of new drugs to combat existing and newly identified conditions is unavoidable. One of the important tools used in the advanced drug development pipeline is computer-aided drug design. Traditionally, to find a drug many ligands were synthesized and evaluated for their effectiveness using suitable bioassays and if all other drug-likeness features were met, the candidate(s) would possibly reach the market. Although this approach is still in use in advanced format, computational methods are an indispensable component of modern drug development projects. One of the methods used from very early days of rationalizing the drug design approaches is Quantitative Structure-Activity Relationship (QSAR). This chapter overviews QSAR modeling steps by introducing molecular descriptors, mathematical model development for relating biological activities to molecular structures, and model validation. At the end, several successful cases where QSAR studies were used extensively are presented.

In silico toxicology: computational methods for the prediction of chemical toxicity

Determining the toxicity of chemicals is necessary to identify their harmful effects on humans, animals, plants, or the environment. It is also one of the main steps in drug design. Animal models have been used for a long time for toxicity testing. However, in vivo animal tests are constrained by time, ethical considerations, and financial burden. Therefore, computational methods for estimating the toxicity of chemicals are considered useful. In silico toxicology is one type of toxicity assessment that uses computational methods to analyze, simulate, visualize, or predict the toxicity of chemicals. In silico toxicology aims to complement existing toxicity tests to predict toxicity, prioritize chemicals, guide toxicity tests, and minimize late-stage failures in drugs design. There are various methods for generating models to predict toxicity endpoints. We provide a comprehensive overview, explain, and compare the strengths and weaknesses of the existing modeling methods and algorithms for toxicity prediction with a particular (but not exclusive) emphasis on computational tools that can implement these methods and refer to expert systems that deploy the prediction models. Finally, we briefly review a number of new research directions in in silico toxicology and provide recommendations for designing in silico models. WIREs Comput Mol Sci 2016, 6:147-172. doi: 10.1002/wcms.1240 For further resources related to this article, please visit the WIREs website.

Prediction of placental barrier permeability: a model based on partial least squares variable selection procedure

Assessing the human placental barrier permeability of drugs is very important to guarantee drug safety during pregnancy. Quantitative structure–activity relationship (QSAR) method was used as an effective assessing tool for the placental transfer study of drugs, while in vitro human placental perfusion is the most widely used method. In this study, the partial least squares (PLS) variable selection and modeling procedure was used to pick out optimal descriptors from a pool of 620 descriptors of 65 compounds and to simultaneously develop a QSAR model between the descriptors and the placental barrier permeability expressed by the clearance indices (CI). The model was subjected to internal validation by cross-validation and y-randomization and to external validation by predicting CI values of 19 compounds. It was shown that the model developed is robust and has a good predictive potential (r² = 0.9064, RMSE = 0.09, q² = 0.7323, r_p² = 0.7656, RMSP = 0.14). The mechanistic interpretation of the final model was given by the high variable importance in projection values of descriptors. Using PLS procedure, we can rapidly and effectively select optimal descriptors and thus construct a model with good stability and predictability. This analysis can provide an effective tool for the high-throughput screening of the placental barrier permeability of drugs.

An Introduction to the Basic Concepts in QSAR-Aided Drug Design

The need for the development of new drugs to combat existing and newly identified conditions is unavoidable. One of the important tools used in the advanced drug development pipeline is computer-aided drug design. Traditionally, to find a drug many ligands were synthesized and evaluated for their effectiveness using suitable bioassays and if all other drug-likeness features were met, the candidate(s) would possibly reach the market. Although this approach is still in use in advanced format, computational methods are an indispensable component of modern drug development projects. One of the methods used from very early days of rationalizing the drug design approaches is Quantitative Structure-Activity Relationship (QSAR). This chapter overviews QSAR modeling steps by introducing molecular descriptors, mathematical model development for relating biological activities to molecular structures, and model validation. At the end, several successful cases where QSAR studies were used extensively are presented.

Persistence and dioxin-like toxicity of carbazole and chlorocarbazoles in soil

Halogenated carbazoles have recently been detected in soil and water samples, but their environmental effects and fate are unknown. Eighty-four soil samples obtained from a site with no recorded history of pollution were used to assess the persistence and dioxin-like toxicity of carbazole and chlorocarbazoles in soil under controlled conditions for 15 months. Soil samples were divided into two temperature conditions, 15 and 20 °C, both under fluctuating soil moisture conditions comprising 19 and 44 drying-rewetting cycles, respectively. This was characterized by natural water loss by evaporation and rewetting to -15 kPa. Accelerated solvent extraction (ASE) and cleanup were performed after incubation. Identification and quantification were done using high-resolution gas chromatogram/mass spectrometer (HRGC/MS), while dioxin-like toxicity was determined by ethoxyresorufin-O-deethylase (EROD) induction in H4IIA rat hepatoma cells assay and multidimensional quantitative structure-activity relationships (mQSAR) modelling. Carbazole, 3-chlorocarbazole and 3,6-dichlorocarbazole were detected including trichlorocarbazole not previously reported in soils. Carbazole and 3-chlorocarbazole showed significant dissipation at 15 °C but not at 20 °C incubating conditions indicating that low temperature could be suitable for dissipation of carbazole and chlorocarbazoles. 3,6-Dichlorocarbazole was resistant at both conditions. Trichlorocarbazole however exhibited a tendency to increase in concentration with time. 3-Chlorocarbazole, 3,6-dibromocarbazole and selected soil extracts exhibited EROD activity. Dioxin-like toxicity did not decrease significantly with time, whereas the sum chlorocarbazole toxic equivalence concentrations (∑TEQ) did not contribute significantly to the soil assay dioxin-like toxicity equivalent concentrations (TCDD-EQ). Carbazole and chlorocarbazoles are persistent with the latter also toxic in natural conditions.

Exploring the ligand-protein networks in traditional chinese medicine: current databases, methods and applications

While the concept of "single component-single target" in drug discovery seems to have come to an end, "Multi-component-multi-target" is considered to be another promising way out in this field. The Traditional Chinese Medicine (TCM), which has thousands of years' clinical application among China and other Asian countries, is the pioneer of the "Multi-component-multi-target" and network pharmacology. Hundreds of different components in a TCM prescription can cure the diseases or relieve the patients by modulating the network of potential therapeutic targets. Although there is no doubt of the efficacy, it is difficult to elucidate convincing underlying mechanism of TCM due to its complex composition and unclear pharmacology. Without thorough investigation of its potential targets and side effects, TCM is not able to generate large-scale medicinal benefits, especially in the days when scientific reductionism and quantification are dominant. The use of ligand-protein networks has been gaining significant value in the history of drug discovery while its application in TCM is still in its early stage. This article firstly surveys TCM databases for virtual screening that have been greatly expanded in size and data diversity in recent years. On that basis, different screening methods and strategies for identifying active ingredients and targets of TCM are outlined based on the amount of network information available, both on sides of ligand bioactivity and the protein structures. Furthermore, applications of successful in silico target identification attempts are discussed in details along with experiments in exploring the ligand-protein networks of TCM. Finally, it will be concluded that the prospective application of ligand-protein networks can be used not only to predict protein targets of a small molecule, but also to explore the mode of action of TCM.

A systematic study of chemogenomics of carbohydrates

We explored the potential of carbohydrates in drug discovery by using a network-based multi-target computational approach.

Exploring the ligand-protein networks in traditional chinese medicine: current databases, methods, and applications

The traditional Chinese medicine (TCM), which has thousands of years of clinical application among China and other Asian countries, is the pioneer of the “multicomponent-multitarget” and network pharmacology. Although there is no doubt of the efficacy, it is difficult to elucidate convincing underlying mechanism of TCM due to its complex composition and unclear pharmacology. The use of ligand-protein networks has been gaining significant value in the history of drug discovery while its application in TCM is still in its early stage. This paper firstly surveys TCM databases for virtual screening that have been greatly expanded in size and data diversity in recent years. On that basis, different screening methods and strategies for identifying active ingredients and targets of TCM are outlined based on the amount of network information available, both on sides of ligand bioactivity and the protein structures. Furthermore, applications of successful in silico target identification attempts are discussed in detail along with experiments in exploring the ligand-protein networks of TCM. Finally, it will be concluded that the prospective application of ligand-protein networks can be used not only to predict protein targets of a small molecule, but also to explore the mode of action of TCM.

Computational tools and resources for metabolism-related property predictions. 1. Overview of publicly available (free and commercial) databases and software

Metabolism has been identified as a defining factor in drug development success or failure because of its impact on many aspects of drug pharmacology, including bioavailability, half-life and toxicity. In this article, we provide an outline and descriptions of the resources for metabolism-related property predictions that are currently either freely or commercially available to the public. These resources include databases with data on, and software for prediction of, several end points: metabolite formation, sites of metabolic transformation, binding to metabolizing enzymes and metabolic stability. We attempt to place each tool in historical context and describe, wherever possible, the data it was based on. For predictions of interactions with metabolizing enzymes, we show a typical set of results for a small test set of compounds. Our aim is to give a clear overview of the areas and aspects of metabolism prediction in which the currently available resources are useful and accurate, and the areas in which they are inadequate or missing entirely.

Combined 3D-QSAR, molecular docking and molecular dynamics study on thyroid hormone activity of hydroxylated polybrominated diphenyl ethers to thyroid receptors β

Several recent reports suggested that hydroxylated polybrominated diphenyl ethers (HO-PBDEs) may disturb thyroid hormone homeostasis. To illuminate the structural features for thyroid hormone activity of HO-PBDEs and the binding mode between HO-PBDEs and thyroid hormone receptor (TR), the hormone activity of a series of HO-PBDEs to thyroid receptors β was studied based on the combination of 3D-QSAR, molecular docking, and molecular dynamics (MD) methods. The ligand- and receptor-based 3D-QSAR models were obtained using Comparative Molecular Similarity Index Analysis (CoMSIA) method. The optimum CoMSIA model with region focusing yielded satisfactory statistical results: leave-one-out cross-validation correlation coefficient (q²) was 0.571 and non-cross-validation correlation coefficient (r²) was 0.951. Furthermore, the results of internal validation such as bootstrapping, leave-many-out cross-validation, and progressive scrambling as well as external validation indicated the rationality and good predictive ability of the best model. In addition, molecular docking elucidated the conformations of compounds and key amino acid residues at the docking pocket, MD simulation further determined the binding process and validated the rationality of docking results.

Adaptation of high-throughput screening in drug discovery-toxicological screening tests

High-throughput screening (HTS) is one of the newest techniques used in drug design and may be applied in biological and chemical sciences. This method, due to utilization of robots, detectors and software that regulate the whole process, enables a series of analyses of chemical compounds to be conducted in a short time and the affinity of biological structures which is often related to toxicity to be defined. Since 2008 we have implemented the automation of this technique and as a consequence, the possibility to examine 100,000 compounds per day. The HTS method is more frequently utilized in conjunction with analytical techniques such as NMR or coupled methods e.g., LC-MS/MS. Series of studies enable the establishment of the rate of affinity for targets or the level of toxicity. Moreover, researches are conducted concerning conjugation of nanoparticles with drugs and the determination of the toxicity of such structures. For these purposes there are frequently used cell lines. Due to the miniaturization of all systems, it is possible to examine the compound's toxicity having only 1-3 mg of this compound. Determination of cytotoxicity in this way leads to a significant decrease in the expenditure and to a reduction in the length of the study.

Probing small-molecule binding to cytochrome P450 2D6 and 2C9: An in silico protocol for generating toxicity alerts

Drug metabolism, toxicity, and their interaction profiles are major issues in the drug-discovery and lead-optimization processes. The cytochromes P450 (CYPs) 2D6 and 2C9 are enzymes involved in the oxidative metabolism of a majority of marketed drugs. Therefore, the prediction of the binding affinity towards CYP2D6 and CYP2C9 would be beneficial for identifying cytochrome-mediated adverse effects triggered by drugs or chemicals (e.g., toxic reactions, drug-drug, and food-drug interactions). By identifying the binding mode by using pharmacophore prealignment, automated flexible docking, and by quantifying the binding affinity by multidimensional QSAR (mQSAR), we validated a model family of 56 compounds (46 training, 10 test) and 85 compounds (68 training, 17 test) for CYP2D6 and CYP2C9, respectively. The correlation with the experimental data (cross-validated r²=0.811 for CYP2D6 and 0.687 for CYP2C9) suggests that our approach is suited for predicting the binding affinity of compounds towards CYP2D6 and CYP2C9. The models were challenged by Y-scrambling and by testing an external dataset of binding compounds (15 compounds for CYP2D6 and 40 for CYP2C9). To assess the probability of false-positive predictions, datasets of nonbinders (64 compounds for CYP2D6 and 56 for CYP2C9) were tested by using the same protocol. The two validated mQSAR models were subsequently added to the VirtualToxLab (VTL, http://www.virtualtoxlab.org).

In Silico Augmentation of the Drug Development Pipeline: Examples from the study of Acute Inflammation

The clinical translation of promising basic biomedical findings, whether derived from reductionist studies in academic laboratories or as the product of extensive high-throughput and -content screens in the biotechnology and pharmaceutical industries, has reached a period of stagnation in which ever higher research and development costs are yielding ever fewer new drugs. Systems biology and computational modeling have been touted as potential avenues by which to break through this logjam. However, few mechanistic computational approaches are utilized in a manner that is fully cognizant of the inherent clinical realities in which the drugs developed through this ostensibly rational process will be ultimately used. In this article, we present a Translational Systems Biology approach to inflammation. This approach is based on the use of mechanistic computational modeling centered on inherent clinical applicability, namely that a unified suite of models can be applied to generate in silico clinical trials, individualized computational models as tools for personalized medicine, and rational drug and device design based on disease mechanism.

The brown shrimp (Crangon crangon L.) ecdysteroid receptor complex: cloning, structural modeling of the ligand-binding domain and functional expression in an EcR-deficient Drosophila cell line

cDNAs encoding ecdysteroid receptor (EcR) and retinoid X receptor (RXR) were cloned and sequenced from brown shrimp Crangon crangon (Crustacea: Decapoda), a common faunal species and commercially important in the North-West European coastal waters. A 3D model of the ligand-binding domain (LBD) of EcR was created and docking of ponasterone A (PonA) was simulated in silico. Finally, we report the transfection of expression plasmids for these receptors in the mutant Drosophila L57-3-11 cell line. Through an ecdysteroid responsive reporter assay we clearly prove the functionality of shrimp ecdysteroid receptor in the transfected L57-3-11 cell line. Our results indicate that the Drosophila L57-3-11 cell line and in silico LBD modeling can be used to study the function of crustacean ecdysteroid receptors and be applied to assess endocrine disrupting effects on non-target crustacean species.

Mapping drug physico-chemical features to pathway activity reveals molecular networks linked to toxicity outcome

The identification of predictive biomarkers is at the core of modern toxicology. So far, a number of approaches have been proposed. These rely on statistical inference of toxicity response from either compound features (i.e., QSAR), in vitro cell based assays or molecular profiling of target tissues (i.e., expression profiling). Although these approaches have already shown the potential of predictive toxicology, we still do not have a systematic approach to model the interaction between chemical features, molecular networks and toxicity outcome. Here, we describe a computational strategy designed to address this important need. Its application to a model of renal tubular degeneration has revealed a link between physico-chemical features and signalling components controlling cell communication pathways, which in turn are differentially modulated in response to toxic chemicals. Overall, our findings are consistent with the existence of a general toxicity mechanism operating in synergy with more specific single-target based mode of actions (MOAs) and provide a general framework for the development of an integrative approach to predictive toxicology.

Classification of cytochrome p(450) activities using machine learning methods

The cytochrome P(450) (CYP) system plays an integral part in the metabolism of drugs and other xenobiotics. Knowledge of the structural features required for interaction with any of the different isoforms of the CYP system is therefore immensely valuable in early drug discovery. In this paper, we focus on three major isoforms (CYP 1A2, CYP 2D6, and CYP 3A4) and present a data set of 335 structurally diverse drug compounds classified for their interaction (as substrate, inhibitor, or any interaction) with these isoforms. We also present machine learning models using a variety of commonly used methods (k-nearest neighbors, decision tree induction using the CHAID and CRT algorithms, random forests, artificial neural networks, and support vector machines using the radial basis function (RBF) and homogeneous polynomials as kernel functions). We discuss the physicochemical features relevant for each end point and compare it to similar studies. Many of these models perform exceptionally well, even with 10-fold cross-validation, yielding corrected classification rates of 81.7 to 91.9% for CYP 1A2, 89.2 to 92.9% for CYP 2D6, and 87.4 to 89.9% for CYP3A4. Our models help in understanding the structural requirements for CYP interactions and can serve as sensitive tools in virtual screenings and lead optimization for toxicological profiles in drug discovery.

Physiologically based modelling and prediction of drug interactions

A major challenge for drug development and environmental or occupational health is the prediction of pharmacokinetic and pharmacodynamic interactions between drugs, natural chemicals or environmental contaminants. This article reviews briefly past developments in the area of physiologically based pharmacokinetic (PBPK) modelling of interactions. It also demonstrates a systems biology approach to the question, and the capabilities of new software tools to facilitate that development. Individual Systems Biology Markup Language models of metabolic pathways can now be automatically merged and coupled to a template PBPK pharmacokinetic model, using for example the GNU MCSim software. The global model generated is very efficient and able to simulate the interactions between a theoretically unlimited number of substances. Development time and the number of model parameter increase only linearly with the number of substances considered, even though the number of possible interactions increases exponentially.

Computations of Absolute Solvation Free Energies of Small Molecules Using Explicit and Implicit Solvent Model

Accurate determination of absolute solvation free energy plays a critical role in numerous areas of biomolecular modeling and drug discovery. A quantitative representation of ligand and receptor desolvation, in particular, is an essential component of current docking and scoring methods. Furthermore, the partitioning of a drug between aqueous and nonpolar solvents is one of the important factors considered in pharmacokinetics. In this study, the absolute hydration free energy for a set of 239 neutral ligands spanning diverse chemical functional groups commonly found in drugs and drug-like candidates is calculated using the molecular dynamics free energy perturbation method (FEP/MD) with explicit water molecules, and compared to experimental data as well as its counterparts obtained using implicit solvent models. The hydration free energies are calculated from explicit solvent simulations using a staged FEP procedure permitting a separation of the total free energy into polar and nonpolar contributions. The nonpolar component is further decomposed into attractive (dispersive) and repulsive (cavity) components using the Weeks-Chandler-Anderson (WCA) separation scheme. To increase the computational efficiency, all of the FEP/MD simulations are generated using a mixed explicit/implicit solvent scheme with a relatively small number of explicit TIP3P water molecules, in which the influence of the remaining bulk is incorporated via the spherical solvent boundary potential (SSBP). The performances of two fixed-charge force fields designed for small organic molecules, the General Amber force field (GAFF), and the all-atom CHARMm-MSI, are compared. Because of the crucial role of electrostatics in solvation free energy, the results from various commonly used charge generation models based on the semiempirical (AM1-BCC) and QM calculations [charge fitting using ChelpG and RESP] are compared. In addition, the solvation free energies of the test set are also calculated using Poisson-Boltzmann (PB) and Generalized Born model of solvation (GB), which are two widely used continuum electrostatic implicit solvent models. The protocol for running the absolute solvation free energy calculations used throughout is automated as much as possible, with minimum user intervention, so that it can be used in large-scale analysis and force field optimization.

Inhibition and induction of human cytochrome P450 enzymes: current status

Variability of drug metabolism, especially that of the most important phase I enzymes or cytochrome P450 (CYP) enzymes, is an important complicating factor in many areas of pharmacology and toxicology, in drug development, preclinical toxicity studies, clinical trials, drug therapy, environmental exposures and risk assessment. These frequently enormous consequences in mind, predictive and pre-emptying measures have been a top priority in both pharmacology and toxicology. This means the development of predictive in vitro approaches. The sound prediction is always based on the firm background of basic research on the phenomena of inhibition and induction and their underlying mechanisms; consequently the description of these aspects is the purpose of this review. We cover both inhibition and induction of CYP enzymes, always keeping in mind the basic mechanisms on which to build predictive and preventive in vitro approaches. Just because validation is an essential part of any in vitro-in vivo extrapolation scenario, we cover also necessary in vivo research and findings in order to provide a proper view to justify in vitro approaches and observations.

3D-QSAR and molecular docking studies of selective agonists for the thyroid hormone receptor beta

Three-dimensional quantitative structure-activity relationship (3D-QSAR) models were developed using comparative molecular field analysis (CoMFA) and comparative molecular similarity analysis (CoMSIA) on a series of agonists of thyroid hormone receptor beta (TRbeta), which may lead to safe therapies for non-thyroid disorders while avoiding the cardiac side effects. The reasonable q(2) (cross-validated) values 0.600 and 0.616 and non-cross-validated r(2) values of 0.974 and 0.974 were obtained for CoMFA and CoMSIA models for the training set compounds, respectively. The predictive ability of two models was validated using a test set of 12 molecules which gave predictive correlation coefficients (r(pred)(2)) of 0.688 and 0.674, respectively. The Lamarckian Genetic Algorithm (LGA) of AutoDock 4.0 was employed to explore the binding mode of the compound at the active site of TRbeta. The results not only lead to a better understanding of interactions between these agonists and the thyroid hormone receptor beta but also can provide us some useful information about the influence of structures on the activity which will be very useful for designing some new agonist with desired activity.

Combined therapy for tuberculosis and HIV-1: the challenge for drug discovery

Combining drug therapies for dual infection by Mycobacterium tuberculosis and HIV-1 is made complex by high pill burdens, shared drug toxicities, drug-drug and drug-disease interactions, immune reconstitution inflammatory syndrome, co-morbid diseases and drug resistance in both bacillus and virus. Recently, novel anti-tubercular and anti-retroviral drugs have bolstered the tuberculosis-HIV drug pipelines and may help ameliorate these difficulties. This review article discusses the reasons for current problems of therapy for dual infection. It also identifies promising agents, which may significantly improve co-therapy and thus diminish the great morbidity and mortality of these two pandemics.

QSAR study of selective ligands for the thyroid hormone receptor β

In this paper, an accurate and reliable QSAR model of 87 selective ligands for the thyroid hormone receptor beta 1 (TRbeta1) was developed, based on theoretical molecular descriptors to predict the binding affinity of compounds with receptor. The structural characteristics of compounds were described wholly by a large amount of molecular structural descriptors calculated by DRAGON. Six most relevant structural descriptors to the studied activity were selected as the inputs of QSAR model by a robust optimization algorithm Genetic Algorithm. The built model was fully assessed by various validation methods, including internal and external validation, Y-randomization test, chemical applicability domain, and all the validations indicate that the QSAR model we proposed is robust and satisfactory. Thus, the built QSAR model can be used to fast and accurately predict the binding affinity of compounds (in the defined applicability domain) to TRbeta1. At the same time, the model proposed could also identify and provide some insight into what structural features are related to the biological activity of these compounds and provide some instruction for further designing the new selective ligands for TRbeta1 with high activity.