In silico approaches in carcinogenicity hazard assessment: case study of pregabalin, a nongenotoxic mouse carcinogen.

In silico toxicology protocols are meant to support computationally-based assessments using principles that ensure that results can be generated, recorded, communicated, archived, and then evaluated in a uniform, consistent, and reproducible manner. We investigated the availability of in silico models to predict the carcinogenic potential of pregabalin using the ten key characteristics of carcinogens as a framework for organizing mechanistic studies. Pregabalin is a single-species carcinogen producing only one type of tumor, hemangiosarcomas in mice via a nongenotoxic mechanism. The overall goal of this exercise is to test the ability of in silico models to predict nongenotoxic carcinogenicity with pregabalin as a case study. The established mode of action (MOA) of pregabalin is triggered by tissue hypoxia, leading to oxidative stress (KC5), chronic inflammation (KC6), and increased cell proliferation (KC10) of endothelial cells. Of these KCs, in silico models are available only for selected endpoints in KC5, limiting the usefulness of computational tools in prediction of pregabalin carcinogenicity. KC1 (electrophilicity), KC2 (genotoxicity), and KC8 (receptor-mediated effects), for which predictive in silico models exist, do not play a role in this mode of action. Confidence in the overall assessments is considered to be medium to high for KCs 1, 2, 5, 6, 7 (immune system effects), 8, and 10 (cell proliferation), largely due to the high-quality experimental data. In order to move away from dependence on animal data, development of reliable in silico models for prediction of oxidative stress, chronic inflammation, immunosuppression, and cell proliferation will be critical for the ability to predict nongenotoxic compound carcinogenicity.

In Silico Approaches In Carcinogenicity Hazard Assessment: Current Status and Future Needs.

Historically, identifying carcinogens has relied primarily on tumor studies in rodents, which require enormous resources in both money and time. In silico models have been developed for predicting rodent carcinogens but have not yet found general regulatory acceptance, in part due to the lack of a generally accepted protocol for performing such an assessment as well as limitations in predictive performance and scope. There remains a need for additional, improved in silico carcinogenicity models, especially ones that are more human-relevant, for use in research and regulatory decision-making. As part of an international effort to develop in silico toxicological protocols, a consortium of toxicologists, computational scientists, and regulatory scientists across several industries and governmental agencies evaluated the extent to which in silico models exist for each of the recently defined 10 key characteristics (KCs) of carcinogens. This position paper summarizes the current status of in silico tools for the assessment of each KC and identifies the data gaps that need to be addressed before a comprehensive in silico carcinogenicity protocol can be developed for regulatory use.

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.

CoMPARA: Collaborative Modeling Project for Androgen Receptor Activity.

BACKGROUND: Endocrine disrupting chemicals (EDCs) are xenobiotics that mimic the interaction of natural hormones and alter synthesis, transport, or metabolic pathways. The prospect of EDCs causing adverse health effects in humans and wildlife has led to the development of scientific and regulatory approaches for evaluating bioactivity. This need is being addressed using high-throughput screening (HTS) in vitro approaches and computational modeling. OBJECTIVES: In support of the Endocrine Disruptor Screening Program, the U.S. Environmental Protection Agency (EPA) led two worldwide consortiums to virtually screen chemicals for their potential estrogenic and androgenic activities. Here, we describe the Collaborative Modeling Project for Androgen Receptor Activity (CoMPARA) efforts, which follows the steps of the Collaborative Estrogen Receptor Activity Prediction Project (CERAPP). METHODS: The CoMPARA list of screened chemicals built on CERAPP's list of 32,464 chemicals to include additional chemicals of interest, as well as simulated ToxCast™ metabolites, totaling 55,450 chemical structures. Computational toxicology scientists from 25 international groups contributed 91 predictive models for binding, agonist, and antagonist activity predictions. Models were underpinned by a common training set of 1,746 chemicals compiled from a combined data set of 11 ToxCast™/Tox21 HTS in vitro assays. RESULTS: The resulting models were evaluated using curated literature data extracted from different sources. To overcome the limitations of single-model approaches, CoMPARA predictions were combined into consensus models that provided averaged predictive accuracy of approximately 80% for the evaluation set. DISCUSSION: The strengths and limitations of the consensus predictions were discussed with example chemicals; then, the models were implemented into the free and open-source OPERA application to enable screening of new chemicals with a defined applicability domain and accuracy assessment. This implementation was used to screen the entire EPA DSSTox database of ∼875,000 chemicals, and their predicted AR activities have been made available on the EPA CompTox Chemicals dashboard and National Toxicology Program's Integrated Chemical Environment. https://doi.org/10.1289/EHP5580.

Integrating computational methods to predict mutagenicity of aromatic azo compounds.

Azo dyes have several industrial uses. However, these azo dyes and their degradation products showed mutagenicity, inducing damage in environmental and human systems. Computational methods are proposed as cheap and rapid alternatives to predict the toxicity of azo dyes. A benchmark dataset of Ames data for 354 azo dyes was employed to develop three classification strategies using knowledge-based methods and docking simulations. Results were compared and integrated with three models from the literature, developing a series of consensus strategies. The good results confirm the usefulness of in silico methods as a support for experimental methods to predict the mutagenicity of azo compounds.

A knowledge-based expert rule system for predicting mutagenicity (Ames test) of aromatic amines and azo compounds.

Cancer is one of the main causes of death in Western countries, and a major issue for human health. Prolonged exposure to a number of chemicals was observed to be one of the primary causes of cancer in occupationally exposed persons. Thus, the development of tools for identifying hazardous chemicals and the increase of mechanistic understanding of their toxicity is a major goal for scientific research. We constructed a new knowledge-based expert system accounting the effect of different substituents for the prediction of mutagenicity (Ames test) of aromatic amines, a class of compounds of major concern because of their widespread application in industry. The herein presented model implements a series of user-defined structural rules extracted from a database of 616 primary aromatic amines, with their Ames test outcomes, aimed at identifying mutagenic and non-mutagenic chemicals. The chemical rationale behind such rules is discussed. Besides assessing the model's ability to correctly classify aromatic amines, its predictivity was further evaluated on a second database of 354 azo dyes, another class of chemicals of major concern, whose toxicity has been predicted on the basis of the toxicity of aromatic amines potentially generated from the metabolic reduction of the azo bond. Good performance in classification on both the amine (MCC, Matthews Correlation Coefficient=0.743) and the azo dye (MCC=0.584) datasets confirmed the predictive power of the model, and its suitability for use on a wide range of chemicals. Finally, the model was compared with a series of well-known mutagenicity predicting software. The good performance of our model compared with other mutagenicity models, especially in predicting azo dyes, confirmed the usefulness of this expert system as a reliable support to in vitro mutagenicity assays for screening and prioritization purposes. The model has been fully implemented as a KNIME workflow and is freely available for downstream users.

[The Ormond's disease: an intriguing obstructive nephrouropathy]. / La malattia di Ormond: una intrigante nefrouropatia ostruttiva.

Idiopathic retroperitoneal fibrosis also known as Ormonds disease is a rare disorder characterized by the development of fibrotic tissue in the retroperitoneum involving the abdominal aorta and iliac arteries, ureters and the inferior vena cava. The aberrant tissue may compress ureters leading to obstructive nephrouropathy and renal failure, which are the most common clinical manifestations of this condition. The nephrologist is often consulted to make differential diagnosis for acute renal failure and obstructive uropathy. Ultrasounds may suggest the disease and the diagnosis will be confirmed by computed tomography or magnetic resonance, but biopsy is still the diagnostic gold standard. The aim of therapy is to remove the ureteral obstruction and prevent the progression and recurrence of the disease. After urine drainage by ureteral stents, medical long-term therapy is usually started whereas the open surgery is reserved as a last resort in selected patients. The pathophysiology of Ormond's disease is uncertain. For years the disease was considered reactive to local and /or systemic triggers with primarily involvement of abdominal aorta but at present is classified in the more broad spectrum of IgG4- Related- Disease, clinical pathological entity on autoimmune basis that can affect almost all of the body districts. This last concept has shed light on the understanding of the pathogenesis and opened new therapeutic perspectives with the use of biological agents. In this paper, on the basis of our paradigmatic clinical case of bilateral obstructive nephrouropathy associated with acute renal failure and examining the recent literature, we describe the clinical and therapeutic approach to Ormonds disease.

Hierarchical Rules for Read-Across and In Silico Models of Mutagenicity.

A broad set of rules has been implemented within the ToxRead software for read-across of chemicals for bacterial mutagenicity. These rules were obtained by manually analyzing more than 6000 chemicals and the associated chemical classes. A hierarchy of rules was established to identify those most specifically relating to the target compounds, linked in sequence to the other, more generic ones, which may match with the target compound. Rules related to both mutagenicity and lack of mutagenicity were found. Some of the latter are exceptions to the mutagenicity rules, while others are modulators of activity. These rules can also be used to predict mutagenicity, offering good performance.

[Management of antirheumatic drugs in kidney failure]. / Gestione dei farmaci antireumatici nell'insufficienza renale.

The nephrologist deals with the management of patients with rheumatic disease, both diagnostically and therapeutically. He must determine whether the renal pathology is related to the rheumatologic disease, mostly through the use of the renal biopsy. In the second case, he must know the nephrotoxic potential of the drugs prescribed and adjust their use to the degree of renal impairment. This task is made difficult by the absence of controlled clinical trials regarding their use on patients with renal insufficiency or on chronic dialysis. For this reason, the prescription will have to take into account the pharmacokinetics of the drugs. Kidney failure can affect the metabolism of antirheumatic drugs determining their accumulation, which can lead to increased toxicity, either renal or systemic. On the other hand, dialysis can cause excessive drug removal, leading to sub-therapeutic pharmacological effects and to the need for additional doses. In this brief review, we will consider the nephrotoxic effects of some important drugs used in rheumatology and examined individually, with specific reference to rheumatoid arthritis: methotrexate, leflunamide, hydroxychloroquine, cyclosporine, biological DMARDs. In the past, therapeutic success in rheumatic diseases associated with kidney impairment was severely limited by the well- known nephrotoxicity of drugs such as gold salts, D-penicillamine, NSAIDs, COX-2 inhibitors. Although generally effective, they are contraindicated in case of kidney failure. Biologic therapies have recently opened new therapeutic perspectives. Nevertheless, it is worth stressing how our knowledge of their action is still incomplete and this may result in exposure to immune-mediated renal disease.

Identification of a pepducin acting as S1P3 receptor antagonist.

Sphingosine-1-phosphate (S1P) is a bioactive lipid with key functions in the immune, inflammatory, and cardiovascular systems. S1P exerts its action through the interaction with a family of five known G protein-coupled receptors, named S1P(1-5). Among them, S1P(3) has been implicated in the pathological processes of a number of diseases, including sepsis and cancer. KRX-725 (compound 1) is a pepducin that mimics the effects of S1P by triggering specifically S1P(3). Here, aiming to identify novel S1P(3) antagonists, we carried out an alanine scanning analysis to address the contribution of the side chains of each amino acid residue to the peptide function. Then, deleted peptides from both the C- and N-terminus were prepared in order to determine the minimal sequence for activity and to identify the structural requirements for agonistic and, possibly, antagonistic behaviors. The pharmacological results of the Ala-scan derived compounds (2-10) suggested a high tolerance of the pepducin 1 to amino acid substitutions. Importantly, the deleted peptide 16 has the ability to inhibit, in a dose-dependent manner, both pepducin 1-induced vasorelaxation and fibroblast proliferation. Finally, a computational analysis was performed on the prepared compounds, showing that the supposed antagonists 16 and 17 appeared to be aligned with each other but not with the others. These results suggested a correlation between specific conformations and activities.