VARIDT 2.0: structural variability of drug transporter.

The structural variability data of drug transporter (DT) are key for research on precision medicine and rational drug use. However, these valuable data are not sufficiently covered by the available databases. In this study, a major update of VARIDT (a database previously constructed to provide DTs' variability data) was thus described. First, the experimentally resolved structures of all DTs reported in the original VARIDT were discovered from PubMed and Protein Data Bank. Second, the structural variability data of each DT were collected by literature review, which included: (a) mutation-induced spatial variations in folded state, (b) difference among DT structures of human and model organisms, (c) outward/inward-facing DT conformations and (d) xenobiotics-driven alterations in the 3D complexes. Third, for those DTs without experimentally resolved structural variabilities, homology modeling was further applied as well-established protocol to enrich such valuable data. As a result, 145 mutation-induced spatial variations of 42 DTs, 1622 inter-species structures originating from 292 DTs, 118 outward/inward-facing conformations belonging to 59 DTs, and 822 xenobiotics-regulated structures in complex with 57 DTs were updated to VARIDT (https://idrblab.org/varidt/ and http://varidt.idrblab.net/). All in all, the newly collected structural variabilities will be indispensable for explaining drug sensitivity/selectivity, bridging preclinical research with clinical trial, revealing the mechanism underlying drug-drug interaction, and so on.

CYPstrate: A Set of Machine Learning Models for the Accurate Classification of Cytochrome P450 Enzyme Substrates and Non-Substrates.

The interaction of small organic molecules such as drugs, agrochemicals, and cosmetics with cytochrome P450 enzymes (CYPs) can lead to substantial changes in the bioavailability of active substances and hence consequences with respect to pharmacological efficacy and toxicity. Therefore, efficient means of predicting the interactions of small organic molecules with CYPs are of high importance to a host of different industries. In this work, we present a new set of machine learning models for the classification of xenobiotics into substrates and non-substrates of nine human CYP isozymes: CYPs 1A2, 2A6, 2B6, 2C8, 2C9, 2C19, 2D6, 2E1, and 3A4. The models are trained on an extended, high-quality collection of known substrates and non-substrates and have been subjected to thorough validation. Our results show that the models yield competitive performance and are favorable for the detection of CYP substrates. In particular, a new consensus model reached high performance, with Matthews correlation coefficients (MCCs) between 0.45 (CYP2C8) and 0.85 (CYP3A4), although at the cost of coverage. The best models presented in this work are accessible free of charge via the "CYPstrate" module of the New E-Resource for Drug Discovery (NERDD).

Global Substance Registration System: consistent scientific descriptions for substances related to health.

The US Food and Drug Administration (FDA) and the National Center for Advancing Translational Sciences (NCATS) have collaborated to publish rigorous scientific descriptions of substances relevant to regulated products. The FDA has adopted the global ISO 11238 data standard for the identification of substances in medicinal products and has populated a database to organize the agency's regulatory submissions and marketed products data. NCATS has worked with FDA to develop the Global Substance Registration System (GSRS) and produce a non-proprietary version of the database for public benefit. In 2019, more than half of all new drugs in clinical development were proteins, nucleic acid therapeutics, polymer products, structurally diverse natural products or cellular therapies. While multiple databases of small molecule chemical structures are available, this resource is unique in its application of regulatory standards for the identification of medicinal substances and its robust support for other substances in addition to small molecules. This public, manually curated dataset provides unique ingredient identifiers (UNIIs) and detailed descriptions for over 100 000 substances that are particularly relevant to medicine and translational research. The dataset can be accessed and queried at https://gsrs.ncats.nih.gov/app/substances.

Mechanistic understanding of dendritic cell activation in skin sensitization: additional evidences to support potency classification.

Allergic contact dermatitis (ACD) is an important occupational and environmental disease caused by topical exposure to chemical allergens. In the EU, it has been calculated that 4 % of animals are used in toxicity test for the assessment of skin sensitization (Peiser et al., 2012). To come a complete replacement of animals, evaluation of relative skin sensitization potency is necessary. The identification of mechanisms influencing allergen potency requires a better understanding of molecular events that trigger cell activation. Therefore, (i) the effects of selected allergens on surface markers expression and cytokines release in contact allergen-induced cell activation were assessed, and (ii) the role of Protein Kinase C (PKC) beta activation in contact allergen-induced cell activation was investigated. The human pro-myelocytic cell line THP-1 was used as experimental model surrogate of dendritic cells. Cells were exposed to select contact allergens of different potency and cell surface marker expression (CD80, CD86, HLA-DR) was determined by flow cytometry analysis. Cytokines production (IL-6, IL-8, IL-10, IL-12p40, IL-18) was evaluated with specific sandwich ELISA. The effective contribution of PKC beta in chemical allergen-induced cell activation was assessed by Western Blot analysis (PKC beta activation) and using a specific PKC beta inhibitor (PKC beta pseudosubstrate). In addition, to investigate if contact allergens are able to induce indeed dendritic cells (DCs) maturation, THP-1 cells were differentiated to immature DC and then exposed to contact allergen of different potency. Overall, our finding provides insights into the process of sensitization and strength of cell activation associated with allergens of different potency. Results obtained suggest that contact allergens of different potency are able to induce a different degree of activation of dendritic cells maturation involved in the process of ACD.

Improving the Utility of the Tox21 Dataset by Deep Metadata Annotations and Constructing Reusable Benchmarked Chemical Reference Signatures.

The Toxicology in the 21st Century (Tox21) project seeks to develop and test methods for high-throughput examination of the effect certain chemical compounds have on biological systems. Although primary and toxicity assay data were readily available for multiple reporter gene modified cell lines, extensive annotation and curation was required to improve these datasets with respect to how FAIR (Findable, Accessible, Interoperable, and Reusable) they are. In this study, we fully annotated the Tox21 published data with relevant and accepted controlled vocabularies. After removing unreliable data points, we aggregated the results and created three sets of signatures reflecting activity in the reporter gene assays, cytotoxicity, and selective reporter gene activity, respectively. We benchmarked these signatures using the chemical structures of the tested compounds and obtained generally high receiver operating characteristic (ROC) scores, suggesting good quality and utility of these signatures and the underlying data. We analyzed the results to identify promiscuous individual compounds and chemotypes for the three signature categories and interpreted the results to illustrate the utility and re-usability of the datasets. With this study, we aimed to demonstrate the importance of data standards in reporting screening results and high-quality annotations to enable re-use and interpretation of these data. To improve the data with respect to all FAIR criteria, all assay annotations, cleaned and aggregate datasets, and signatures were made available as standardized dataset packages (Aggregated Tox21 bioactivity data, 2019).

Cell size sensing in animal cells coordinates anabolic growth rates and cell cycle progression to maintain cell size uniformity.

Cell size uniformity in healthy tissues suggests that control mechanisms might coordinate cell growth and division. We derived a method to assay whether cellular growth rates depend on cell size, by monitoring how variance in size changes as cells grow. Our data revealed that, twice during the cell cycle, growth rates are selectively increased in small cells and reduced in large cells, ensuring cell size uniformity. This regulation was also observed directly by monitoring nuclear growth in live cells. We also detected cell-size-dependent adjustments of G1 length, which further reduce variability. Combining our assays with chemical/genetic perturbations confirmed that cells employ two strategies, adjusting both cell cycle length and growth rate, to maintain the appropriate size. Additionally, although Rb signaling is not required for these regulatory behaviors, perturbing Cdk4 activity still influences cell size, suggesting that the Cdk4 pathway may play a role in designating the cell's target size.

Classification, Source, and Effect of Environmental Pollutants and Their Biodegradation.

Any foreign chemical substance that is unusually present within an organism or is unexpectedly found in the environment at a higher concentration than the permissible limits can be termed a xenobiotic or a pollutant. Such substances include carcinogens, drugs, food additives, hydrocarbons, dioxins, polychlorinated biphenyls, pesticides or even some natural compounds. Pollutants are known for their higher persistence and pervasiveness, and along with their transformed products, they can remain in and interact with the environment for prolonged periods. In this article, the classification of such substances based on their nature, use, physical state, pathophysiological effects, and sources is discussed. The effects of pollutants on the environment, their biotransformation in terms of bioaccumulation, and the different types of remediation such as in situ and ex situ remediation, are also presented.

Drug Induced Liver Injury: Can Biomarkers Assist RUCAM in Causality Assessment?

Drug induced liver injury (DILI) is a potentially serious adverse reaction in a few susceptible individuals under therapy by various drugs. Health care professionals facing DILI are confronted with a wealth of drug-unrelated liver diseases with high incidence and prevalence rates, which can confound the DILI diagnosis. Searching for alternative causes is a key element of RUCAM (Roussel Uclaf Causality Assessment Method) to assess rigorously causality in suspected DILI cases. Diagnostic biomarkers as blood tests would be a great help to clinicians, regulators, and pharmaceutical industry would be more comfortable if, in addition to RUCAM, causality of DILI can be confirmed. High specificity and sensitivity are required for any diagnostic biomarker. Although some risk factors are available to evaluate liver safety of drugs in patients, no valid diagnostic or prognostic biomarker exists currently for idiosyncratic DILI when a liver injury occurred. Identifying a biomarker in idiosyncratic DILI requires detailed knowledge of cellular and biochemical disturbances leading to apoptosis or cell necrosis and causing leakage of specific products in blood. As idiosyncratic DILI is typically a human disease and hardly reproducible in animals, pathogenetic events and resulting possible biomarkers remain largely undisclosed. Potential new diagnostic biomarkers should be evaluated in patients with DILI and RUCAM-based established causality. In conclusion, causality assessment in cases of suspected idiosyncratic DILI is still best achieved using RUCAM since specific biomarkers as diagnostic blood tests that could enhance RUCAM results are not yet available.

CORAL: Binary classifications (active/inactive) for drug-induced liver injury.

INTRODUCTION: The data on human hepatotoxcity (drug-induced liver injury) is extremely important information from point of view of drug discovery. Experimental clinical data on this endpoint is scarce. Experimental way to extend databases on this endpoint is extremely difficult. Quantitative structure - activity relationships (QSAR) is attractive alternative of the experimental approach. METHODS: Predictive models for human hepatotoxicity (drug-induced liver injury) have been built up by the Monte Carlo method with using of the CORAL software (http://www.insilico.eu/coral). These models are the binary classifications into active class and inactive class. These models are calculated with so-called "semi correlations" described in this work. The Mattews correlation coefficient of these models for external validation sets ranged from 0.52 to 0.62. RESULTS DISCUSSION: The approach has been checked up with a group of random splits into the training and validation sets. These stochastic experiments have shown the stability of results: predictability of the models for various splits. Thus, the attempt to build up the classification QSAR model by means of the Monte Carlo technique, based on representation of the molecular structure via simplified molecular input line entry systems (SMILES) and hydrogen suppressed graph (HSG) using the CORAL software (http://www.insilico.eu/coral) has shown ability of this approach to provide quite good prediction of the examined endpoint (drug-induced liver injury).

Use of read-across to simplify the toxicological assessment of a complex mixture of lysimeter leachate metabolites on the basis of chemical similarity and ADME behavior.

This paper describes the further development of a read-across approach applicable to the toxicological assessment of structurally-related xenobiotic metabolites. The approach, which can be applied in the absence of definitive identification of all the individual metabolites, draws on the use of chemical descriptors and multi-variate statistical analysis to define a composite "chemical space" and to classify and characterize closely-related subgroups within this. In this example, consideration of the descriptors driving grouping, combined with empirical evidence for lack of significant further biotransformation of metabolites, leads to the conclusion that, in the absence of any specific structural alerts, the relative toxicity of metabolites within a single grouping will be determined by their relative systemic exposure as described by their ADME characteristics. The in vivo testing of a smaller number of exemplars, selected to have representative ADME properties for each grouping, is sufficient, therefore, to evaluate the toxicity of the remainder. The approach is exemplified using the metabolites of the herbicide S-metolachlor, detected in the leachate of a soil lysimeter.

Net Reclassification Index and Integrated Discrimination Index Are Not Appropriate for Testing Whether a Biomarker Improves Predictive Performance.

One of the goals of the Critical Path Institute's Predictive Safety Testing Consortium (PSTC) is to promote best practices for evaluating novel markers of drug induced injury. This includes the use of sound statistical methods. For rat studies, these practices have centered around comparing the area under the receiver-operator characteristic curve for each novel injury biomarker to those for the standard markers. In addition, the PSTC has previously used the net reclassification index (NRI) and integrated discrimination index (IDI) to assess the increased certainty provided by each novel injury biomarker when added to the information already provided by the standard markers. Due to their relatively simple interpretations, NRI and IDI have generally been popular measures of predictive performance. However recent literature suggests that significance tests for NRI and IDI can have inflated false positive rates and thus, tests based on these metrics should not be relied upon. Instead, when parametric models are employed to assess the added predictive value of a new marker, following (Pepe, M. S., Kerr, K. F., Longton, G., and Wang, Z. (2013). Testing for improvement in prediction model performance. Stat. Med. 32, 1467-1482), the PSTC recommends that likelihood based methods be used for significance testing.

Discovery of Novel Anti-prion Compounds Using In Silico and In Vitro Approaches.

Prion diseases are associated with the conformational conversion of the physiological form of cellular prion protein (PrP(C)) to the pathogenic form, PrP(Sc). Compounds that inhibit this process by blocking conversion to the PrP(Sc) could provide useful anti-prion therapies. However, no suitable drugs have been identified to date. To identify novel anti-prion compounds, we developed a combined structure- and ligand-based virtual screening system in silico. Virtual screening of a 700,000-compound database, followed by cluster analysis, identified 37 compounds with strong interactions with essential hotspot PrP residues identified in a previous study of PrP(C) interaction with a known anti-prion compound (GN8). These compounds were tested in vitro using a multimer detection system, cell-based assays, and surface plasmon resonance. Some compounds effectively reduced PrP(Sc) levels and one of these compounds also showed a high binding affinity for PrP(C). These results provide a promising starting point for the development of anti-prion compounds.

Human pluripotent stem cell-derived neural constructs for predicting neural toxicity.

Human pluripotent stem cell-based in vitro models that reflect human physiology have the potential to reduce the number of drug failures in clinical trials and offer a cost-effective approach for assessing chemical safety. Here, human embryonic stem (ES) cell-derived neural progenitor cells, endothelial cells, mesenchymal stem cells, and microglia/macrophage precursors were combined on chemically defined polyethylene glycol hydrogels and cultured in serum-free medium to model cellular interactions within the developing brain. The precursors self-assembled into 3D neural constructs with diverse neuronal and glial populations, interconnected vascular networks, and ramified microglia. Replicate constructs were reproducible by RNA sequencing (RNA-Seq) and expressed neurogenesis, vasculature development, and microglia genes. Linear support vector machines were used to construct a predictive model from RNA-Seq data for 240 neural constructs treated with 34 toxic and 26 nontoxic chemicals. The predictive model was evaluated using two standard hold-out testing methods: a nearly unbiased leave-one-out cross-validation for the 60 training compounds and an unbiased blinded trial using a single hold-out set of 10 additional chemicals. The linear support vector produced an estimate for future data of 0.91 in the cross-validation experiment and correctly classified 9 of 10 chemicals in the blinded trial.

Prediction of Drug-Induced Liver Injury in Micropatterned Co-cultures Containing iPSC-Derived Human Hepatocytes.

Primary human hepatocytes (PHHs) are a limited resource for drug screening, their quality for in vitro use can vary considerably across different lots, and a lack of available donor diversity restricts our understanding of how human genetics affect drug-induced liver injury (DILI). Induced pluripotent stem cell-derived human hepatocyte-like cells (iPSC-HHs) could provide a complementary tool to PHHs for high-throughput drug screening, and ultimately enable personalized medicine. Here, we hypothesized that previously developed iPSC-HH-based micropatterned co-cultures (iMPCCs) with murine embryonic fibroblasts could be amenable to long-term drug toxicity assessment. iMPCCs, created in industry-standard 96-well plates, were treated for 6 days with a set of 47 drugs, and multiple functional endpoints (albumin, urea, ATP) were evaluated in dosed cultures against vehicle-only controls to enable binary toxicity decisions. We found that iMPCCs correctly classified 24 of 37 hepatotoxic drugs (65% sensitivity), while all 10 non-toxic drugs tested were classified as such in iMPCCs (100% specificity). On the other hand, conventional confluent cultures of iPSC-HHs failed to detect several liver toxins that were picked up in iMPCCs. Results for DILI detection in iMPCCs were remarkably similar to published data in PHH-MPCCs (65% versus 70% sensitivity) that were dosed with the same drugs. Furthermore, iMPCCs detected the relative hepatotoxicity of structural drug analogs and recapitulated known mechanisms of acetaminophen toxicity in vitro. In conclusion, iMPCCs could provide a robust tool to screen for DILI potential of large compound libraries in early stages of drug development using an abundant supply of commercially available iPSC-HHs.

Internal threshold of toxicological concern values: enabling route-to-route extrapolation.

The TTC concept uses toxicological data from animal testing to derive generic human exposure threshold values (TTC values), below which the risk of adverse effects on human health is considered to be low. It uses distributions of no-observed-adverse-effect levels (NOAELs) for substances. The 5th percentile value is divided by an uncertainty factor (100) to give a TTC value. As the toxicological data underpinning the TTC concept are from tests with oral exposure, the exposure is to be understood as an external oral exposure. For risk assessment of substances with a low absorption (by the oral route, or through skin), the internal exposure is more relevant than the external exposure. European legislation allows that tests might not be necessary for substances with negligible absorption with low internal exposure. The aim of this work is to derive internal TTC values to allow the TTC concept to be applied to situations of low internal exposure. The external NOAEL of each chemical of three databases (Munro, ELINCS, Food Contact Materials) was multiplied by the bioavailability of the individual chemical. Oral bioavailability was predicted using an in silico prediction tool (ACD Percepta). After applying a reduced uncertainty factor of 25, we derived internal TTC values. For Cramer class I, the internal TTC values are 6.9 µg/kg bw/d (90 % confidence interval: 3.8-11.5 mg/kg bw/d); for Cramer class II/III 0.1 µg/kg bw/d (90 % confidence interval: 0.08-0.14 µg/kg bw/d).

Chelation therapy and cardiovascular disease: connecting scientific silos to benefit cardiac patients.

Medical practitioners have treated atherosclerotic disease with chelation therapy for over 50 years. Lack of strong of evidence led conventional practitioners to abandon its use in the 1960s and 1970s. This relegated chelation therapy to complementary and alternative medicine practitioners, who reported good anecdotal results. Concurrently, the epidemiologic evidence linking xenobiotic metals with cardiovascular disease and mortality gradually accumulated, suggesting a plausible role for chelation therapy. On the basis of the continued use of chelation therapy without an evidence base, the National Institutes of Health released a Request for Applications for a definitive trial of chelation therapy. The Trial to Assess Chelation Therapy (TACT) was formulated as a 2 × 2 factorial randomized controlled trial of intravenous EDTA-based chelation vs. placebo and high-dose oral multivitamins and multiminerals vs. oral placebo. The composite primary endpoint was death, reinfarction, stroke, coronary revascularization, or hospitalization for angina. A total of 1708 post-MI patients who were 50 years or older with a creatinine of 2.0 or less were enrolled and received 55,222 infusions of disodium EDTA or placebo with a median follow-up of 55 months. Patients were on evidence-based post-MI medications including statins. EDTA proved to be safe. EDTA chelation therapy reduced cardiovascular events by 18%, with a 5-year number needed to treat (NNT) of 18. Prespecified subgroup analysis revealed a robust benefit in patients with diabetes mellitus with a 41% reduction in the primary endpoint (5-year NNT = 6.5), and a 43% 5-year relative risk reduction in all-cause mortality (5-year NNT = 12). The magnitude of benefit is such that it suggests urgency in replication and implementation, which could, due to the excellent safety record, occur simultaneously.

Development of a panel of high-throughput reporter-gene assays to detect genotoxicity and oxidative stress.

The lack of toxicological information on many of the compounds that humans use or are exposed to, intentionally or unintentionally, poses a big problem in risk assessment. To fill this data gap, more emphasis is given to fast in vitro screening tools that can add toxicologically relevant information regarding the mode(s) of action via which compounds can elicit adverse effects, including genotoxic effects. By use of bioassays that can monitor the activation of specific cellular signalling pathways, many compounds can be screened in a high-throughput manner. We have developed two new specific reporter-gene assays that can monitor the effects of compounds on two pathways of interest: the p53 pathway (p53 CALUX) for genotoxicity and the Nrf2 pathway (Nrf2 CALUX) for oxidative stress. To exclude non-specific effects by compounds influencing the luciferase reporter-gene expression non-specifically, a third assay was developed to monitor changes in luciferase expression by compounds in general (Cytotox CALUX). To facilitate interpretation of the data and to avoid artefacts, all three reporter-gene assays used simple and defined reporter genes and a similar cellular basis, the human U2OS cell line. The three cell lines were validated with a range of reference compounds including genotoxic and non-genotoxic agents. The sensitivity (95%) and specificity (85%) of the p53 CALUX was high, showing that the assay is able to identify various types of genotoxic compound, while avoiding the detection of false positives. The Nrf2 CALUX showed specific responses to oxidants only, enabling the identification of compounds that elicit part of their genotoxicity via oxidative stress. All reporter-gene assays can be used in a high-throughput screening format and can be supplemented with other U2OS-based reporter-gene assays that can profile nuclear receptor activity, and several other signalling pathways.

Use and validation of HT/HC assays to support 21st century toxicity evaluations.

Advances in high throughput and high content (HT/HC) methods such as those used in the fields of toxicogenomics, bioinformatics, and computational toxicology have the potential to improve both the efficiency and effectiveness of toxicity evaluations and risk assessments. However, prior to use, scientific confidence in these methods should be formally established. Traditional validation approaches that define relevance, reliability, sensitivity and specificity may not be readily applicable. HT/HC methods are not exact replacements for in vivo testing, and although run individually, these assays are likely to be used as a group or battery for decision making and use robotics, which may be unique in each laboratory setting. Building on the frameworks developed in the 2010 Institute of Medicine Report on Biomarkers and the OECD 2007 Report on (Q)SAR Validation, we present constructs that can be adapted to address the validation challenges of HT/HC methods. These are flexible, transparent, and require explicit specification of context and purpose of use such that scientific confidence (validation) can be defined to meet different regulatory applications. Using these constructs, we discuss how anchoring the assays and their prediction models to Adverse Outcome Pathways (AOPs) could facilitate the interpretation of results and support scientifically defensible fit-for-purpose applications.

Evaluation of the national toxicology program report on carcinogens.

This study evaluates the National Toxicology Program's Report on Carcinogens program (RoCP) and compares it with the International Agency for Research on Cancer Monographs Program (IMP). We tracked agents classified in the RoCP since 1983 as known human carcinogens (A-List), or as reasonably anticipated to be human carcinogens (B-List). The first A-list included 24 agents, and twenty-four unique agents were added in the following 28years; twenty were listed by IMP as Group 1 (carcinogenic to humans) 7years before their A-list appearance. Group 1 also includes 30 or more agents eligible for, but not on, the A-list. The first B-list included 98 agents, and this increased to 185. Of these, 39 are in Group 2A (probably carcinogenic), and 122 are in Group 2B (possibly carcinogenic). Only 5% of the 204 agents ever on the B-list have been upgraded to the A-list. The RoCP is severely limited because it evaluates few agents and because its B-list does not distinguish between probable and possible human carcinogens. Further, it mislabels likely non-carcinogens as reasonably anticipated to be carcinogens. If the RoCP were terminated there would be no loss or delay of information available to scientific, public health and regulatory communities.

Statistical evaluation of mortality in long-term carcinogenicity bioassays using a Williams-type procedure.

Several doses and a control group can be compared under order restriction using the Williams procedure for normally distributed endpoints assuming variance homogeneity. Comparison of the survival functions represents a secondary endpoint in long-term in vivo bioassays of carcinogenicity. Therefore, a Williams-type procedure for the comparison of survival functions is proposed for the assumption of the Cox proportional hazards model or the general frailty Cox model to allow a joint analysis over sex and strains. Interpretation according to both statistical significance and biological relevance is possible with simultaneous confidence intervals for hazard ratios. Related survival data can be analyzed using the R packages survival, coxme, and multcomp. Together with the R packages MCPAN and nparcomp, Dunnett- or Williams-type procedures are now available for the statistical analysis of the following endpoint types in toxicology: (i) normally distributed, (ii) non-normally distributed, (iii) score (ordered categorical) data, (iv) crude proportions, (v) survival functions, and (vi) time-to-tumor data with and without cause-of-death information.