Exploratory toxicology as an integrated part of drug discovery. Part I: Why and how

ADVERPred-Web Service for Prediction of Adverse Effects of Drugs

Application of structure-activity relationships (SARs) for the prediction of adverse effects of drugs (ADEs) has been reported in many published studies. Training sets for the creation of SAR models are usually based on drug label information which allows for the generation of data sets for many hundreds of drugs. Since many ADEs may not be related to drug consumption, one of the main problems in such studies is the quality of data on drug-ADE pairs obtained from labels. The information on ADEs may be included in three sections of the drug labels: "Boxed warning," "Warnings and Precautions," and "Adverse reactions." The first two sections, especially Boxed warning, usually contain the most frequent and severe ADEs that have either known or probable relationships to drug consumption. Using this information, we have created manually curated data sets for the five most frequent and severe ADEs: myocardial infarction, arrhythmia, cardiac failure, severe hepatotoxicity, and nephrotoxicity, with more than 850 drugs on average for each effect. The corresponding SARs were built with PASS (Prediction of Activity Spectra for Substances) software and had balanced accuracy values of 0.74, 0.7, 0.77, 0.67, and 0.75, respectively. They were implemented in a freely available ADVERPred web service ( http://www.way2drug.com/adverpred/ ), which enables a user to predict five ADEs based on the structural formula of compound. This web service can be applied for estimation of the corresponding ADEs for hits and lead compounds at the early stages of drug discovery.

A Round Trip from Medicinal Chemistry to Predictive Toxicology

Predictive toxicology is a new emerging multifaceted research field aimed at protecting human health and environment from risks posed by chemicals. Such issue is of extreme public relevance and requires a multidisciplinary approach where the experience in medicinal chemistry is of utmost importance. Herein, we will survey some basic recommendations to gather good data and then will review three recent case studies to show how strategies of ligand- and structure-based molecular design, widely applied in medicinal chemistry, can be adapted to meet the more restrictive scientific and regulatory goals of predictive toxicology. In particular, we will report: Docking-based classification models to predict the estrogenic potentials of chemicals. Predicting the bioconcentration factor using biokinetics descriptors. Modeling oral sub-chronic toxicity using a customized k-nearest neighbors (k-NN) approach.

ADMET Evaluation in Drug Discovery. Part 17: Development of Quantitative and Qualitative Prediction Models for Chemical-Induced Respiratory Toxicity

As a dangerous end point, respiratory toxicity can cause serious adverse health effects and even death. Meanwhile, it is a common and traditional issue in occupational and environmental protection. Pharmaceutical and chemical industries have a strong urge to develop precise and convenient computational tools to evaluate the respiratory toxicity of compounds as early as possible. Most of the reported theoretical models were developed based on the respiratory toxicity data sets with one single symptom, such as respiratory sensitization, and therefore these models may not afford reliable predictions for toxic compounds with other respiratory symptoms, such as pneumonia or rhinitis. Here, based on a diverse data set of mouse intraperitoneal respiratory toxicity characterized by multiple symptoms, a number of quantitative and qualitative predictions models with high reliability were developed by machine learning approaches. First, a four-tier dimension reduction strategy was employed to find an optimal set of 20 molecular descriptors for model building. Then, six machine learning approaches were used to develop the prediction models, including relevance vector machine (RVM), support vector machine (SVM), regularized random forest (RRF), extreme gradient boosting (XGBoost), naïve Bayes (NB), and linear discriminant analysis (LDA). Among all of the models, the SVM regression model shows the most accurate quantitative predictions for the test set (q²_ext = 0.707), and the XGBoost classification model achieves the most accurate qualitative predictions for the test set (MCC of 0.644, AUC of 0.893, and global accuracy of 82.62%). The application domains were analyzed, and all of the tested compounds fall within the application domain coverage. We also examined the structural features of the compounds and important fragments with large prediction errors. In conclusion, the SVM regression model and the XGBoost classification model can be employed as accurate prediction tools for respiratory toxicity.

ZeGlobalTox: An Innovative Approach to Address Organ Drug Toxicity Using Zebrafish

Toxicity is one of the major attrition causes during the drug development process. In that line, cardio-, neuro-, and hepatotoxicities are among the main reasons behind the retirement of drugs in clinical phases and post market withdrawal. Zebrafish exploitation in high-throughput drug screening is becoming an important tool to assess the toxicity and efficacy of novel drugs. This animal model has, from early developmental stages, fully functional organs from a physiological point of view. Thus, drug-induced organ-toxicity can be detected in larval stages, allowing a high predictive power on possible human drug-induced liabilities. Hence, zebrafish can bridge the gap between preclinical in vitro safety assays and rodent models in a fast and cost-effective manner. ZeGlobalTox is an innovative assay that sequentially integrates in vivo cardio-, neuro-, and hepatotoxicity assessment in the same animal, thus impacting strongly in the 3Rs principles. It Reduces, by up to a third, the number of animals required to assess toxicity in those organs. It Refines the drug toxicity evaluation through novel physiological parameters. Finally, it might allow the Replacement of classical species, such as rodents and larger mammals, thanks to its high predictivity (Specificity: 89%, Sensitivity: 68% and Accuracy: 78%).

In Silico Identification of Proteins Associated with Drug-induced Liver Injury Based on the Prediction of Drug-target Interactions

Drug-induced liver injury (DILI) is the leading cause of acute liver failure as well as one of the major reasons for drug withdrawal from clinical trials and the market. Elucidation of molecular interactions associated with DILI may help to detect potentially hazardous pharmacological agents at the early stages of drug development. The purpose of our study is to investigate which interactions with specific human protein targets may cause DILI. Prediction of interactions with 1534 human proteins was performed for the dataset with information about 699 drugs, which were divided into three categories of DILI: severe (178 drugs), moderate (310 drugs) and without DILI (211 drugs). Based on the comparison of drug-target interactions predicted for different drugs' categories and interpretation of those results using clustering, Gene Ontology, pathway and gene expression analysis, we identified 61 protein targets associated with DILI. Most of the revealed proteins were linked with hepatocytes' death caused by disruption of vital cellular processes, as well as the emergence of inflammation in the liver. It was found that interaction of a drug with the identified targets is the essential molecular mechanism of the severe DILI for the most of the considered pharmaceuticals. Thus, pharmaceutical agents interacting with many of the identified targets may be considered as candidates for filtering out at the early stages of drug research.

A portable and reconfigurable multi-organ platform for drug development with onboard microfluidic flow control

The drug development pipeline is severely limited by a lack of reliable tools for prediction of human clinical safety and efficacy profiles for compounds at the pre-clinical stage. Here we present the design and implementation of a platform technology comprising multiple human cell-based tissue models in a portable and reconfigurable format that supports individual organ function and crosstalk for periods of up to several weeks. Organ perfusion and crosstalk are enabled by a precision flow control technology based on electromagnetic actuators embedded in an arrayed format on a microfluidic platform. We demonstrate two parallel circuits of connected airway and liver modules on a platform containing 62 electromagnetic microactuators, with precise and controlled flow rates as well as functional biological metrics over a two week time course. Technical advancements enabled by this platform include the use of non-sorptive construction materials, enhanced scalability, portability, flow control, and usability relative to conventional flow control modes (such as capillary action, pressure heads, or pneumatic air lines), and a reconfigurable and modular organ model format with common fluidic port architecture. We demonstrate stable biological function for multiple pairs of airway-liver models for periods of 2 weeks in the platform, with precise control over fluid levels, temperature, flow rate and oxygenation in order to support relevant use cases involving drug toxicity, efficacy testing, and organ-organ interaction.

Metabolic profile and safety of piperlongumine

Piperlongumine (PPL), a natural plant product, has been extensively studied in cancer treatment going up on clinical trials. Since the first report related to its use on cancer research (in 2011) around 80 papers have been published in less than 10 years, but a gap still remaining. There are no metabolism studies of PPL in human organism. For the lack of a better view, here, the CYP450 in vitro oxidation of PPL was described for the first time. In addition, the enzymatic kinetic data, the predicted in vivo parameters, the produced metabolites, the phenotyping study and possible piperlongumine-drug interactions in vivo is presented.

Toxicity testing in the 21st century beyond environmental chemicals

After the publication of the report titled Toxicity Testing in the 21^st Century – A Vision and a Strategy, many initiatives started to foster a major paradigm shift for toxicity testing – from apical endpoints in animal-based tests to mechanistic endpoints through delineation of pathways of toxicity (PoT) in human cell based systems. The US EPA has funded an important project to develop new high throughput technologies based on human cell based in vitro technologies. These methods are currently being incorporated into the chemical risk assessment process. In the pharmaceutical industry, the efficacy and toxicity of new drugs are evaluated during preclinical investigations that include drug metabolism, pharmacokinetics, pharmacodynamics and safety toxicology studies. The results of these studies are analyzed and extrapolated to predict efficacy and potential adverse effects in humans. However, due to the high failure rate of drugs during the clinical phases, a new approach for a more predictive assessment of drugs both in terms of efficacy and adverse effects is getting urgent. The food industry faces the challenge of assessing novel foods and food ingredients for the general population, while using animal safety testing for extrapolation purposes is often of limited relevance. The question is whether the latest paradigm shift proposed by the Tox21c report for chemicals may provide a useful tool to improve the risk assessment approach also for drugs and food ingredients.

Toxicology Strategies for Drug Discovery: Present and Future

Attrition due to nonclinical safety represents a major issue for the productivity of pharmaceutical research and development (R&D) organizations, especially during the compound optimization stages of drug discovery and the early stages of clinical development. Focusing on decreasing nonclinical safety-related attrition is not a new concept, and various approaches have been experimented with over the last two decades. Front-loading testing funnels in Discovery with in vitro toxicity assays designed to rapidly identify unfavorable molecules was the approach adopted by most pharmaceutical R&D organizations a few years ago. However, this approach has also a non-negligible opportunity cost. Hence, significant refinements to the "fail early, fail often" paradigm have been proposed recently to reflect the complexity of accurately categorizing compounds with early data points without taking into account other important contextual aspects, in particular efficacious systemic and tissue exposures. This review provides an overview of toxicology approaches and models that can be used in pharmaceutical Discovery at the series/lead identification and lead optimization stages to guide and inform chemistry efforts, as well as a personal view on how to best use them to meet nonclinical safety-related attrition objectives consistent with a sustainable pharmaceutical R&D model. The scope of this review is limited to small molecules, as large molecules are associated with challenges that are quite different. Finally, a perspective on how several emerging technologies may impact toxicity evaluation is also provided.

Computational toxicology

Predictive toxicology plays a critical role in reducing the failure rate of new drugs in pharmaceutical research and development. Despite recent gains in our understanding of drug-induced toxicity, however, it is urgent that the utility and limitations of our current predictive tools be determined in order to identify gaps in our understanding of mechanistic and chemical toxicology. Using recently published computational regression analyses of in vitro and in vivo toxicology data, it will be demonstrated that significant gaps remain in early safety screening paradigms. More strategic analyses of these data sets will allow for a better understanding of their domain of applicability and help identify those compounds that cause significant in vivo toxicity but which are currently mis-predicted by in silico and in vitro models. These ‘outliers’ and falsely predicted compounds are metaphorical lighthouses that shine light on existing toxicological knowledge gaps, and it is essential that these compounds are investigated if attrition is to be reduced significantly in the future. As such, the modern computational toxicologist is more productively engaged in understanding these gaps and driving investigative toxicology towards addressing them.

Comparative study of curcumin and curcumin formulated in a solid dispersion: Evaluation of their antigenotoxic effects

Curcumin (CMN) is the principal active component derived from the rhizome of Curcuma longa (Curcuma longa L.). It is a liposoluble polyphenolic compound that possesses great therapeutic potential. Its clinical application is, however, limited by the low concentrations detected following oral administration. One key strategy for improving the solubility and bioavailability of poorly water-soluble drugs is solid dispersion, though it is not known whether this technique might influence the pharmacological effects of CMN. Thus, in this study, we aimed to evaluate the antioxidant and antigenotoxic effects of CMN formulated in a solid dispersion (CMN SD) compared to unmodified CMN delivered to Wistar rats. Cisplatin (cDDP) was used as the damage-inducing agent in these evaluations. The comet assay results showed that CMN SD was not able to reduce the formation of cDDP-DNA crosslinks, but it decreased the formation of micronuclei induced by cDDP and attenuated cDDP-induced oxidative stress. Furthermore, at a dose of 50 mg/kg b.w. both CMN SD and unmodified CMN increased the expression of Tp53 mRNA. Our results showed that CMN SD did not alter the antigenotoxic effects observed for unmodified CMN and showed effects similar to those of unmodified CMN for all of the parameters evaluated. In conclusion, CMN SD maintained the protective effects of unmodified CMN with the advantage of being chemically water soluble, with maximization of absorption in the gastrointestinal tract. Thus, the optimization of the physical and chemical properties of CMN SD may increase the potential for the therapeutic use of curcumin.

Development and regulatory application of microRNA biomarkers

MicroRNAs, a class of regulatory small non-coding RNAs, are emerging as promising biomarkers for different health outcomes. Due to their tissue specificity, stability in extracellular space and high conservation between preclinical test species, applications of novel miRNA-based biomarkers for drug safety testing regarding hepatotoxicity and cardiotoxicity are investigated. Furthermore, miRNA expression is altered by environmental exposure such as cigarette smoke or polychlorinated biphenyls. As a consequence, miRNAs potentially influence tumor suppressor genes and oncogenes and may influence carcinogenesis. This has raised the interest in the use of miRNA profiles for health risk assessment. This review summarizes the recent developments in miRNA research with focus on biomarkers for drug safety testing and biomarkers for health outcomes related to environmental exposures.

Docking-based classification models for exploratory toxicology studies on high-quality estrogenic experimental data

BACKGROUND

The ethical and practical limitation of animal testing has recently promoted computational methods for the fast screening of huge collections of chemicals.

RESULTS

The authors derived 24 reliable docking-based classification models able to predict the estrogenic potential of a large collection of chemicals provided by the US Environmental Protection Agency. Model performances were challenged by considering AUC, EF1% (EFmax = 7.1), -LR (at sensitivity = 0.75); +LR (at sensitivity = 0.25) and 37 reference compounds comprised within the training set. Moreover, external predictions were made successfully on ten representative known estrogenic chemicals and on a set consisting of >32,000 chemicals.

CONCLUSION

The authors demonstrate that structure-based methods, widely applied to drug discovery programs, can be fairly adapted to exploratory toxicology studies.

In silico assessment of adverse drug reactions and associated mechanisms

During recent years, various in silico approaches have been developed to estimate chemical and biological drug features, for example chemical fragments, protein targets, pathways, among others, that correlate with adverse drug reactions (ADRs) and explain the associated mechanisms. These features have also been used for the creation of predictive models that enable estimation of ADRs during the early stages of drug development. In this review, we discuss various in silico approaches to predict these features for a certain drug, estimate correlations with ADRs, establish causal relationships between selected features and ADR mechanisms and create corresponding predictive models.