A proof-of-concept rat toxicity study highlights the potential utility and challenges of virtual control groups.

The virtual control group (VCG) concept provides a potential opportunity to reduce animal use in drug development by replacing concurrent control groups (CCGs) in nonclinical toxicity studies. This work investigated the feasibility and reliability of using VCGs in place of CCGs. A historical control database (HCD), constructed from Genentech Inc.

Analysis of non-mutagenic substances in the context of drug impurity assessment - Few are potent toxicants.

ICH Q3A/B guidelines provide qualification thresholds for impurities or degradation products in new drug substances and products. However, the guidelines note that certain impurities/degradation products may warrant further safety evaluation for being unusually potent or toxic. The purpose of this study was to confirm that especially toxic non-mutagenic compounds are rare and to identify classes of compounds that could warrant lower qualification thresholds.

Points to consider regarding the use and implementation of virtual controls in nonclinical general toxicology studies.

The replacement of a proportion of concurrent controls by virtual controls in nonclinical safety studies has gained traction over the last few years. This is supported by foundational work, encouraged by regulators, and aligned with societal expectations regarding the use of animals in research. This paper provides an overview of the points to consider for any institution on the verge of implementing this concept, with emphasis given on database creation, risks, and discipline-specific perspectives.

Toward implementing virtual control groups in nonclinical safety studies.

Historical data from control groups in animal toxicity studies are currently mainly used for comparative purposes to assess validity and robustness of study results. Due to the highly controlled environment in which the studies are performed and the homogeneity of the animal collectives it has been proposed to use the historical data to build so-called virtual control groups, which could partly or entirely replace the concurrent control group. This would constitute a substantial contribution to the reduction of animal use in safety studies.

The application of acute oral toxicity computational models in dangerous goods classification.

Acute oral toxicity (AOT) data inform the acute toxicity potential of a compound and guides occupational safety and transportation practices. AOT data enable the categorization of a chemical into the appropriate AOT Globally Harmonized System (GHS) category based on the severity of the hazard. AOT data are also utilized to identify compounds that are Dangerous Goods (DGs) and subsequent transportation guidance for shipping of these hazardous materials.

Principles and Procedures for Assessment of Acute Toxicity Incorporating Methods.

Acute models are being used to support an increasing number of application areas including (1) product research and development, (2) product approval and registration as well as (3) the transport, storage and handling of chemicals. The adoption of such models is being hindered, in part, because of a lack of guidance describing how to perform and document an analysis. To address this issue, a framework for an acute toxicity hazard assessment is proposed.

Toward Quantitative Models in Safety Assessment: A Case Study to Show Impact of Dose-Response Inference on hERG Inhibition Models.

Due to challenges with historical data and the diversity of assay formats, in silico models for safety-related endpoints are often based on discretized data instead of the data on a natural continuous scale. Models for discretized endpoints have limitations in usage and interpretation that can impact compound design. Here, we present a consistent data inference approach, exemplified on two data sets of Ether-à-go-go-Related Gene (hERG) K+ inhibition data, for dose-response and screening experiments that are generally applicable for in vitro assays.

approaches in organ toxicity hazard assessment: Current status and future needs for predicting heart, kidney and lung toxicities.

The kidneys, heart and lungs are vital organ systems evaluated as part of acute or chronic toxicity assessments. New methodologies are being developed to predict these adverse effects based on and approaches. This paper reviews the current state of the art in predicting these organ toxicities.

Evaluating Confidence in Toxicity Assessments Based on Experimental Data and Predictions.

Understanding the reliability and relevance of a toxicological assessment is important for gauging the overall confidence and communicating the degree of uncertainty related to it. The process involved in assessing reliability and relevance is well defined for experimental data. Similar criteria need to be established for predictions, as they become increasingly more important to fill data gaps and need to be reasonably integrated as additional lines of evidence.

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. 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 carcinogenicity models, especially ones that are more human-relevant, for use in research and regulatory decision-making.

approaches in organ toxicity hazard assessment: current status and future needs in predicting liver toxicity.

Hepatotoxicity is one of the most frequently observed adverse effects resulting from exposure to a xenobiotic. For example, in pharmaceutical research and development it is one of the major reasons for drug withdrawals, clinical failures, and discontinuation of drug candidates. The development of faster and cheaper methods to assess hepatotoxicity that are both more sustainable and more informative is critically needed.

Skin sensitization in silico protocol.

The assessment of skin sensitization has evolved over the past few years to include in vitro assessments of key events along the adverse outcome pathway and opportunistically capitalize on the strengths of in silico methods to support a weight of evidence assessment without conducting a test in animals. While in silico methods vary greatly in their purpose and format; there is a need to standardize the underlying principles on which such models are developed and to make transparent the implications for the uncertainty in the overall assessment. In this contribution, the relationship between skin sensitization relevant effects, mechanisms, and endpoints are built into a hazard assessment framework.

Genetic toxicology in silico protocol.

In silico toxicology (IST) approaches to rapidly assess chemical hazard, and usage of such methods is increasing in all applications but especially for regulatory submissions, such as for assessing chemicals under REACH as well as the ICH M7 guideline for drug impurities. There are a number of obstacles to performing an IST assessment, including uncertainty in how such an assessment and associated expert review should be performed or what is fit for purpose, as well as a lack of confidence that the results will be accepted by colleagues, collaborators and regulatory authorities. To address this, a project to develop a series of IST protocols for different hazard endpoints has been initiated and this paper describes the genetic toxicity in silico (GIST) protocol.

Principles and procedures for handling out-of-domain and indeterminate results as part of ICH M7 recommended (Q)SAR analyses.

The International Council for Harmonization (ICH) M7 guideline describes a hazard assessment process for impurities that have the potential to be present in a drug substance or drug product. In the absence of adequate experimental bacterial mutagenicity data, (Q)SAR analysis may be used as a test to predict impurities' DNA reactive (mutagenic) potential. However, in certain situations, (Q)SAR software is unable to generate a positive or negative prediction either because of conflicting information or because the impurity is outside the applicability domain of the model.

Extending (Q)SARs to incorporate proprietary knowledge for regulatory purposes: is aromatic N-oxide a structural alert for predicting DNA-reactive mutagenicity?

(Quantitative) structure-activity relationship or (Q)SAR predictions of DNA-reactive mutagenicity are important to support both the design of new chemicals and the assessment of impurities, degradants, metabolites, extractables and leachables, as well as existing chemicals. Aromatic N-oxides represent a class of compounds that are often considered alerting for mutagenicity yet the scientific rationale of this structural alert is not clear and has been questioned. Because aromatic N-oxide-containing compounds may be encountered as impurities, degradants and metabolites, it is important to accurately predict mutagenicity of this chemical class.

In silico toxicology protocols.

The present publication surveys several applications of in silico (i.e., computational) toxicology approaches across different industries and institutions.

Generating Modeling Data From Repeat-Dose Toxicity Reports.

Over the past decades, pharmaceutical companies have conducted a large number of high-quality in vivo repeat-dose toxicity (RDT) studies for regulatory purposes. As part of the eTOX project, a high number of these studies have been compiled and integrated into a database. This valuable resource can be queried directly, but it can be further exploited to build predictive models.

Generalized workflow for generating highly predictive in silico off-target activity models.

Chemical structure data and corresponding measured bioactivities of compounds are nowadays easily available from public and commercial databases. However, these databases contain heterogeneous data from different laboratories determined under different protocols and, in addition, sometimes even erroneous entries. In this study, we evaluated the use of data from bioactivity databases for the generation of high quality in silico models for off-target mediated toxicity as a decision support in early drug discovery and crop-protection research.

Development of in silico filters to predict activation of the pregnane X receptor (PXR) by structurally diverse drug-like molecules.

The pregnane X receptor (PXR), a member of the nuclear hormone superfamily, regulates the expression of several enzymes and transporters involved in metabolically relevant processes. The significant induction of CYP450 enzymes by PXR, in particular CYP3A4, might significantly alter the metabolism of prescribed drugs. In order to early identify molecules in drug discovery with a potential to activate PXR as antitarget, we developed fast and reliable in silico filters by ligand-based QSAR techniques.

A physiologically based toxicokinetic modelling approach to predict relevant concentrations for in vitro testing.

Our study was performed in the context of an in vitro primary hepatic cell culture as an alternative for the in vivo cancerogenic bioassay. The 29 substances which are to be used in the in vitro primary hepatic cell culture have been tested in 2-year bioassays and a 14-day short term study. The aim of this modelling study was to simulate the concentration--time profile of the compounds when given by the oral route at the doses tested in the previous studies taking into account the percentage of the dose absorbed.

Controlled formation of an axially bonded co-phthalocyanine dimer.

Isolated Co-phthalocyanine (CoPc) molecules were moved on a monolayer of CoPc on Cu(111) using an STM tip. If placed almost on top of another, the CoPc molecule in the second layer locks in place and the STM image at negative bias changes substantially. Density functional theory calculations explain the nature of the bonding mode and the change in STM.