etters

tm

ciobnni

mf

d

PDo

T

hava

m

fnkt5

lavvMm

8ew

di

d

PEh

S

iT

Abstracts / Toxicology L

o date, such approaches are typically applied to a small set ofaterials often derived from a single study.We have developed a nanoQSAR framework that allows for the

omparison of results obtained from different nanotoxicity stud-es, and we use this approach to address the toxicity of metalxide nanoparticles. To this end, we developed a novel knowledge-ase, ontologically capturing the minimal information needed foranoQSAR studies, a library to computationally characterize theanomaterials, and statistical models to predict various toxicolog-

cal end-points e.g. cell viability and DNA damage.The application of this nanoQSAR framework to the toxicity of

etal oxide nanoparticles shows how results may be extrapolatedrom one experimental study to another.

oi:10.1016/j.toxlet.2012.03.245

07-13eveloping mathematical QSPR models to predict evaporationf chemicals

ao Liu, Matias Rauma, Gunnar Johanson

Karolinska Institutet, Sweden

Introduction: Many of the chemicals which are known to haveigh dermal penetration are also highly volatile. Therefore, thevailable amount for systemic exposure after dermal exposure to aolatile chemical will, due to evaporation, vary with temperaturend wind speed.

The aim of this study was to create quantitative structure per-eability relationship (QSPR) models for predicting evaporation.Materials and methods: Evaporation rate data were obtained

rom Braun and Caplan (1989) for 13 chemicals (acetone, benzene,-heptane, 1-heptanol, hexane, 1-hexanol, methanol, methyl ethyletone, octane, 2-octanol, 2-octanone, 1-pentanol, n-propanol,oluene, water and xylene) at different wind speeds (0.5, 2.5 and.1 m/s) and temperatures (10, 21 and 38 ◦C).

QSPR models were developed using 1664 theoretical molecu-ar descriptors as well as Abraham parameters (H-bonding aciditynd basicity, polarity/polarizability, molar refraction and molarolume) combined with air velocity and temperature. Parameteralues were obtained by fitting the models to the evaporation data.ethanol, n-propanol and hexane were used to test model perfor-ance.Results and conclusion: The models show high predictability as

4.7% and 91.1% of the variation is predicted in the theoretical andmpirical models, respectively. Molecular size and polar interactionere found to be dominant.

In conclusion, the developed QSPR models have high pre-ictability, however, more evaporation data is needed to further

mprove and validate the models.

oi:10.1016/j.toxlet.2012.03.246

07-14xtrapolation of toxic concentrations in in vitro assays by aigh throughput PBPK model

ieto Bosgra, Joost Westerhout, Ad Knaapen, Miriam Verwei

TNO, Netherlands

In the US and EU, thousands of chemicals are in use for whichnsufficient toxicity data are available for hazard/risk assessment.he time, money and experimental animals required if applying

211S (2012) S43–S216 S63

standard risk assessment procedures urgently calls for an alter-native strategy. The National Academy of Sciences initiated analternative approach “Tox21”, involving testing of active concen-trations in high throughput (HT), mostly human material-based,in vitro assays. A major challenge in applying this approachin risk assessment is the extrapolation of the in vitro toxicitydata to in vivo doses. This can be achieved using physiology-based pharmacokinetic (PBPK) models. To be useful in thisdata-poor, HT environment, these PBPK models preferably pre-dict the in vivo ADME processes of chemicals from HT in vitrokinetic measurements (e.g. protein binding, intrinsic clearance) andphysico-chemical properties (e.g. logP, ionization). In this study,such a generic HT-PBPK model has been developed and applied.Important questions addressed are: “How accurate are the HT-PBPKpredictions of kinetics based on minimal chemical-specific input?”and “Can chemicals for which this approach is not sufficiently accu-rate be identified a priori?”

Predictions by the HT-PBPK model are compared with publishedobservations of in vivo kinetics in rats to assess the overall accuracyof the HT-approach. A systematic diagnosis of causes for deviationsreveals the parameters contributing most to these deviations, andwhether these can be attributed to known chemical properties.

Finally, a demonstration is given of the application of the HT-PBPK model to the extrapolation of in vitro developmental toxicityassay results.

doi:10.1016/j.toxlet.2012.03.247

P08: Consumers’ Protection

P08-01Six sigma improvement process in Mansoura UniversityToxicology Unit

Mohamed Salama

Mansoura University, Egypt

Quality improvement in medical care is an area of growingresearch. The six sigma approach is one of the well known tech-niques in improving quality in many fields of business. In ourresearch we tried to improve quality of work in a toxicology unitin emergency hospital using the six sigma approach. A qualityimprovement project was made to decrease the time taken forperforming toxicology screen in patients attending toxicology unit.

Six sigma approach comprises 5 phases which were undertakenin the following study: Phase (1): (Define) which involved identi-fying the project then, Establishing the project; Phase 2: (Measure)which involved using some of the quality tools including: SIPOC,performing a high level flow diagram for the process of screening intoxicology unit, then confirming the mission; Phase (3): (Analyze)through Brain storming, testing theories through a cause effect dia-gram, then collecting the data through data sheets followed byanalyzing the data through a histogram and scatter diagrams todefine the root cause of the delay in toxicology screen time; Phase4 (Improve) through evaluating suggested remedies using a remedyselection matrix where the suggested remedy was designed usinga planning matrix; Phase 5 (Implementation & control): where thesystem was assessed regularly using quality control sheets and fail-ure mode and effect analysis matrix.

After 1 year of applying such project the delay in results wasdecreased to be 30 min instead of 1 h and 15 min.

doi:10.1016/j.toxlet.2012.03.249