ATLA 41, 2013

/ATLA 41, 2013

News & Views

ATLA Staff Writer

Human Tissue Used for Research on Parkinson’s Disease
Non-invasive Imaging Used for the Identification of Biomarker in Parkinson’s Disease
Social Development in Primates
Differences in mRNA and Protein Expression
Human Studies Shed Light on Brain Ageing
Pain Research on Humans
3-D In Vitro Skin Model
Human-derived Culture Media Supplement
Beagle Farm Plan Rejected
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2017-01-09T06:38:10+00:00

An Analysis of the Use of Dogs in Predicting Human Toxicology and Drug Safety

Jarrod Bailey, Michelle Thew and Michael Balls

Dogs remain the main non-rodent species in preclinical drug development. Despite the current dearth of new drug approvals and meagre pipelines, this continues, with little supportive evidence of its value or necessity. To estimate the evidential weight provided by canine data to the probability that a new drug may be toxic to humans, we have calculated Likelihood Ratios (LRs) for an extensive dataset of 2,366 drugs with both animal and human data, including tissue-level effects and Medical Dictionary for Regulatory Activities (MedDRA) Level 1–4 biomedical observations. The resulting LRs show that the absence of toxicity in dogs provides virtually no evidence that adverse drug reactions (ADRs) will also be absent in humans. While the LRs suggest that the presence of toxic effects in dogs can provide considerable evidential weight for a risk of potential ADRs in humans, this is highly inconsistent, varying by over two orders of magnitude for different classes of compounds and their effects. Our results therefore have important implications for the value of the dog in predicting human toxicity, and suggest that alternative methods are urgently required.

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IIVS News & Views

ATLA Staff Writer

IIVS, HSI, HSUS and HTPC Sign a Memorandum of Understanding
Chinese Society of Dermatology Meeting
Training and Mentoring Quality Assurance Personnel and Study Directors
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2017-01-09T06:38:10+00:00 Tags: |

Editorial: Computational Toxicology is Now Inseparable from Experimental Toxicology

Mark T.D. Cronin

The developing challenge is not that we have too few data to model, but that we now need to consider how to curate the data, store them in useful and usable databases, determine those data that may be considered ‘reliable’, and cope with duplicate and contradictory data.
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2017-01-09T06:38:50+00:00 Tags: , |

News & Views

ATLA staff writer

Free Access to Early Issues of ATLA
New Alternatives Research Centre
New Chair in Animal Replacement Science
ARDF Research Grants
Spread of Influenza Studied in Human Volunteers
Human Studies for Depression Treatment
Tumour Extravasation Studied In Vitro
Laboratory Equipment Materials Affect Cell Viability
Mouse Models Poorly Mimic Human Inflammatory Diseases
Chimps Seem to Enjoy Puzzles
EU Ban on Cosmetics Testing
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2017-01-09T06:38:50+00:00 Tags: , |

CAAT News & Views

ATLA staff writer

CAAT to Receive $2-million Sub-grant from the US Defense Threat Reduction Agency
CAAT Presentations at the European Parliament
International Symposium on Endocrine Active Substances (EASs)
CAAT Addresses ‘Clinical Trial and Error: Beauty and the Beast’
Middle School Students Hold Fundraiser for CAAT
Job Opening: Post-doctoral Researcher (Human Toxome Project)
New CAAT Supporters and Advisory Board Members
Recent Publications
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2017-01-09T06:38:50+00:00 Tags: , |

Exemplification of the Implementation of Alternatives to Experimental Testing in Chemical Risk Assessment — Case Studies from the CADASTER Project

Willie J.G.M. Peijnenburg and Igor V. Tetko

In this special issue of ATLA, a compilation of contributions from the second CADASTER Workshop in Munich (7–9 October, 2012) regarding the implementation of alternatives to experimental (in vivo) data in risk and hazard assessment, is provided.
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2017-01-09T06:38:50+00:00 Tags: |

Species Sensitivity Distribution Estimation from Uncertain (QSAR-based) Effects Data

Tom Aldenberg and Emiel Rorije

In environmental risk assessment, Species Sensitivity Distributions (SSDs) can be applied to estimate a PNEC (Predicted No-Effect Concentration) for a chemical substance, when sufficient data on species toxicities are available. The European Chemicals Agency (ECHA) recommendation is 10 biological species. The question addressed in this paper, is whether QSAR-predicted toxicities can be included in SSD based PNEC estimates, and whether any modifications need to be made to account for the uncertainty in the QSAR-model estimates. This problem is addressed from a probabilistic modelling point of view. From classical analysis of variation (ANOVA), we review how the error-in-data SSD problem is similar to separation into between-group and within-group variance. ECHA guidance suggests averaging similar endpoint data for a species, which is consistent with group means, as in ANOVA. This exercise reveals that error-indata reduces the estimation of the between species variation, i.e. the SSD variance, rather than enlarging it. A Bayesian analysis permits the assessment of the uncertainty of the SSD mean and variance parameters for given values of mean species toxicity error. This requires a hierarchical model. Prototyping this model for an artificial five-species data set seems to suggest that the influence of data error is relatively minor. Moreover, when neglecting this data error, a slightly conservative estimate of the SSD results. Hence, we suggest including (model-predicted) data as model point estimates and handling the SSD as usual. The Bayesian simulation of the error-in-data SSD leads to predictive distributions, being an average of posterior spaghetti plot densities or cumulative distributions. We derive new predictive extrapolation constants with several improvements over previous median uncertainty log10HC5 estimates, in that they are easily calculable from spreadsheet Student-t functions and based on a more realistic uniform prior for the SSD standard deviation. Other advantages are that they are single-number extrapolation constants and they are more sensitive to small sample size.
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Definition of the Applicability Domain of the Short Time Exposure (STE) Test for Predicting the Eye Irritation of Chemicals

Kazuhiko Hayashi, Takayuki Abo, Yuko Nukada and Hitoshi Sakaguchi

The Short Time Exposure (STE) test is a simple and easy-to-perform in vitro eye irritation test, that uses the viability of SIRC cells (a rabbit corneal cell line) treated for five minutes as the endpoint. In this study, our goal was to define the applicability domain of the STE test, based on the results obtained with a set of 113 substances. To achieve this goal, chemicals were selected to represent both different chemical classes and different chemical properties, as well as to cover, in a balanced manner, the categories of eye irritation potential according to the Globally Harmonised System (GHS). Accuracy analysis indicated that the rates of false negatives for organic/inorganic salts (75.0%), hydrocarbons (33.3%) and alcohols (23.5%) were high. Many of the false negative results were for solid substances. It is noteworthy that no surfactant resulted in a false negative result in the STE test. Further examination of the physical property data and performance showed a significant improvement in the predictive accuracy, when substances with vapour pressures over 6kPa were excluded from the analyses. Our results indicate that several substances i.e. certain solids such as salts, alcohols, hydrocarbons, and volatile substances with a vapour pressure over 6kPa — do not fall within the applicability domain of the STE test. Overall, we are encouraged by the performance and improved accuracy of the STE test.

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