A high throughput imaging database of toxicological effects of nanomaterials tested on HepaRG cells.

The large amount of existing nanomaterials demands rapid and reliable methods for testing their potential toxicological effect on human health, preferably by means of relevant in vitro techniques in order to reduce testing on animals. Combining high throughput workflows with automated high content imaging techniques allows deriving much more information from cell-based assays than the typical readouts (i.e. one measurement per well) with optical plate-readers. We present here a dataset including data based on a maximum of 14 different read outs (including viable cell count, cell membrane permeability, apoptotic cell death, mitochondrial membrane potential and steatosis) of the human hepatoma HepaRG cell line treated with a large set of nanomaterials, coatings and supernatants at different concentrations. The database, given its size, can be utilized in the development of in silico hazard assessment and prediction tools or can be combined with toxicity results from other in vitro test systems.

Insights into in vitro biokinetics using Virtual Cell Based Assay simulations.

The Virtual Cell Based Assay (VCBA) is an in silico model that simulates the biokinetics of chemicals in in vitro test systems. Simulations by the VCBA can indicate the degree to which the bioavailable concentration varies across chemicals and experimental conditions, thereby providing important contextual information when comparing the results of different in vitro toxicity experiments. The simulated results can also be used to support in vitro to in vivo extrapolation of toxicity data, especially when the VCBA is coupled to a physiologically based kinetic model. The VCBA requires only a limited number of physicochemical properties as input parameters to model the fate of a chemical in the in vitro environment; optionally, in vitro toxicity concentration-response curves can be used to optimise the toxicity and effects model. In this work, we selected 83 chemicals previously tested in vitro and used the in vitro data to optimise the toxicity and effects model, simulating the 3T3 BALB/c cell line in a 96-well microplate with 5% serum supplementation. We then used the optimised parameters to simulate alternative experimental conditions. By incorporating a diverse group of chemicals, the simulations show the impact of different physicochemical properties on chemical fate and how the different partitioning (to protein, lipid and plastic) and kinetic (evaporation and degradation) events are intrinsically connected. The results of VCBA simulations were interpreted in the light of the domain of applicability of the different QSARs incorporated in the model and the underlying assumptions and uncertainties of the VCBA.

Microscopy-based high-throughput assays enable multi-parametric analysis to assess adverse effects of nanomaterials in various cell lines.

Manufactured nanomaterials (MNMs) selected from a library of over 120 different MNMs with varied compositions, sizes, and surface coatings were tested by four different laboratories for toxicity by high-throughput/-content (HT/C) techniques. The selected particles comprise 14 MNMs composed of CeO2, Ag, TiO2, ZnO and SiO2 with different coatings and surface characteristics at varying concentrations. The MNMs were tested in different mammalian cell lines at concentrations between 0.5 and 250 µg/mL to link physical-chemical properties to multiple adverse effects. The cell lines are derived from relevant organs such as liver, lung, colon and the immune system. Endpoints such as viable cell count, cell membrane permeability, apoptotic cell death, mitochondrial membrane potential, lysosomal acidification and steatosis have been studied. Soluble MNMs, Ag and ZnO, were toxic in all cell types. TiO2 and SiO2 MNMs also triggered toxicity in some, but not all, cell types and the cell type-specific effects were influenced by the specific coating and surface modification. CeO2 MNMs were nearly ineffective in our test systems. Differentiated liver cells appear to be most sensitive to MNMs, Whereas most of the investigated MNMs showed no acute toxicity, it became clear that some show adverse effects dependent on the assay and cell line. Hence, it is advised that future nanosafety studies utilise a multi-parametric approach such as HT/C screening to avoid missing signs of toxicity. Furthermore, some of the cell type-specific effects should be followed up in more detail and might also provide an incentive to address potential adverse effects in vivo in the relevant organ.

The virtual cell based assay: Current status and future perspectives.

In order to replace the use of animals in toxicity testing, there is a need to predict in vivo toxic doses from concentrations that cause toxicological effects in relevant in vitro systems. The Virtual Cell Based Assay (VCBA) estimates time-dependent concentration of a test chemical in the cell and cell culture for a given in vitro system. The concentrations in the different compartments of the cell and test system are derived from ordinary differential equations, physicochemical parameters of the test chemical and properties of the cell line. The VCBA has been developed for a range of cell lines including BALB/c 3T3 cells, HepG2, HepaRG, lung A459 cells, and cardiomyocytes. The model can be used to design and refine in vitro experiments and extrapolate in vitro effective concentrations to in vivo doses that can be applied in risk assessment. In this paper, we first discuss potential applications of the VCBA: i) design of in vitro High Throughput Screening (HTS) experiments; ii) hazard identification (based on acute systemic toxicity); and iii) risk assessment. Further extension of the VCBA is discussed in the second part, exploring potential application to i) manufactured nanomaterials, ii) additional cell lines and endpoints, and considering iii) other opportunities.

Practical Aspects of Designing and Conducting Validation Studies Involving Multi-study Trials.

This chapter focuses on practical aspects of conducting prospective in vitro validation studies, and in particular, by laboratories that are members of the European Union Network of Laboratories for the Validation of Alternative Methods (EU-NETVAL) that is coordinated by the EU Reference Laboratory for Alternatives to Animal Testing (EURL ECVAM). Prospective validation studies involving EU-NETVAL, comprising a multi-study trial involving several laboratories or "test facilities", typically consist of two main steps: (1) the design of the validation study by EURL ECVAM and (2) the execution of the multi-study trial by a number of qualified laboratories within EU-NETVAL, coordinated and supported by EURL ECVAM. The approach adopted in the conduct of these validation studies adheres to the principles described in the OECD Guidance Document on the Validation and International Acceptance of new or updated test methods for Hazard Assessment No. 34 (OECD 2005). The context and scope of conducting prospective in vitro validation studies is dealt with in Chap. 4 . Here we focus mainly on the processes followed to carry out a prospective validation of in vitro methods involving different laboratories with the ultimate aim of generating a dataset that can support a decision in relation to the possible development of an international test guideline (e.g. by the OECD) or the establishment of performance standards.

Extending (Q)SARs to incorporate proprietary knowledge for regulatory purposes: A case study using aromatic amine mutagenicity.

Statistical-based and expert rule-based models built using public domain mutagenicity knowledge and data are routinely used for computational (Q)SAR assessments of pharmaceutical impurities in line with the approach recommended in the ICH M7 guideline. Knowledge from proprietary corporate mutagenicity databases could be used to increase the predictive performance for selected chemical classes as well as expand the applicability domain of these (Q)SAR models. This paper outlines a mechanism for sharing knowledge without the release of proprietary data. Primary aromatic amine mutagenicity was selected as a case study because this chemical class is often encountered in pharmaceutical impurity analysis and mutagenicity of aromatic amines is currently difficult to predict. As part of this analysis, a series of aromatic amine substructures were defined and the number of mutagenic and non-mutagenic examples for each chemical substructure calculated across a series of public and proprietary mutagenicity databases. This information was pooled across all sources to identify structural classes that activate or deactivate aromatic amine mutagenicity. This structure activity knowledge, in combination with newly released primary aromatic amine data, was incorporated into Leadscope's expert rule-based and statistical-based (Q)SAR models where increased predictive performance was demonstrated.