During 1988–1992, a validation study was carried out in Germany on the capacity of two in vitro tests to replace the Draize eye test for severely eye irritating chemicals, namely, the hen's egg chorio-allantoic membrane (HET-CAM) test and the 3T3 cell neutral red uptake (NRU) cytotoxicity test, which had shown promising results in an earlier test development project. The formal validation study, which was coordinated by Centre for Documentation and Evaluation of Alternative Methods to Animal Experiments (ZEBET) and funded by the German Department of Research and Technology (BMBF), was conducted in two phases: Phase I consisted of a prevalidation study and a blind trial (1988–1990); and Phase II was the database development phase (1991/1992). During prevalidation, the two in vitro tests were established in 13 laboratories, standard protocols were developed, including PC-based software programs for data recording, and 34 chemicals backed by high quality literature data were selected for the ring trial. In the 1-year ring trial, the two in vitro tests were validated with 34 coded chemicals under blind conditions in 13 laboratories, to evaluate the reproducibility of the two tests within and among laboratories. In the blind trial, the 3T3 NRU cytotoxicity test showed a better reproducibility than the HET-CAM test, but compared to the cytotoxicity test, the HET-CAM test permitted a significantly better classification of severely eye irritating chemicals, which are labelled R41 according to EU regulations. Since it was recommended in 1990 by the first Amden validation workshop that a database of around 200 chemicals is required for the assessment of test performance to reach regulatory acceptance at the international level, a 2-year database development was conducted as Phase II, during which 166 coded chemicals were tested in the two in vitro tests, each of them in two laboratories. Test chemicals backed by high-quality Draize eye test data were provided by industry and selected to represent a wide spectrum of chemical classes and eye irritation properties. Independent quality control of in vitro and in vivo data and biostatistical evaluation were performed during an additional BMBF project on biostatistics. In the quality assurance step, which is an essential prerequisite for biostatistics, the number of chemicals was reduced to 143, and these data were entered into an MS-EXCEL database to facilitate determination of in vitro/in vivo correlations. Unexpectedly, the evaluation of the study had to take into account a change of criteria within the EU for classifying severely eye irritating chemicals as R41, since irreversible damage within a 21-day observation period was introduced as a new criterion for R41 chemicals. The results of the 3T3 NRU cytotoxicity test showed an insufficient in vitro/in vivo correlation for classifying R41 chemicals. Classification of HET-CAM data was also insufficient in the Bundesgesundhütsamt (BGA) scoring system, which uses an empirically developed weighted scoring of the three endpoints, namely, haemorrhage, lysis and coagulation. Discriminant analysis of ten endpoints routinely determined in the HET-CAM test and in the 3T3 NRU cytotoxicity test revealed that the detection time of coagulation, the most severe reaction on the CAM, was significantly better suited to identifying severely eye irritating properties than any other endpoint, and better than the BGA score for the HET-CAM test. For water-soluble chemicals (mean time for detection of coagulation [mtc]10), the detection time for coagulation of a 10% solution had the highest discriminant power, and for less water-soluble chemicals (mtc100), the detection time of coagulation of the undiluted chemical was more appropriate. Discriminant analysis of the combination of mtc10 and mtc100 with other endpoints of the two in vitro tests revealed that classification of water-soluble chemicals is significantly improved by combining mtc10 and lgfg50m (logarithm of IC50 value calculated with the Fit-Graph program), the endpoint of the 3T3 NRU cytotoxicity test. Further analysis of data from Phase I and Phase II of the study demonstrated that chemicals characterised by an mtc10 of < 50 seconds can be labelled R41 without any false positive classifications. By using this cut-off point, around 25% of R41 chemicals can be classified without further testing in vitro or in vivo. Classification was further improved when solubility in water and oil was taken into account. The best classification of water-soluble R41 chemicals (> 10%) was obtained when the mtc10 of the HET-CAM test and the lgfg50m of the 3T3 NRU cytotoxicity test were combined. For chemicals soluble in oil (> 10%) and for insoluble chemicals, the mtc100 provided the best classification. The in vitro classification results were confirmed by cross-validation. These promising results allowed a sequential approach to be developed for classifying severely eye irritating chemicals as R41 according to EU regulations by combining the HET-CAM test and the 3T3 NRU cytotoxicity test results. The present study suggests that severely eye irritating chemicals can be classified as R41 with a sufficiently high level of confidence with the two in vitro tests, since the percentage of false positive and false negative results are kept within an acceptably low range. Thus, the combined use of the HET-CAM test and the 3T3 NRU cytotoxicity test meets the requirements for “well-validated” tests, as defined in the escape clause of OECD Guideline 405 for eye irritation testing.

In 1996, the Scientific Committee on Cosmetology of DGXXIV of the European Commission asked the European Centre for the Validation of Alternative Methods to test eight UV filter chemicals from the 1995 edition of Annex VII of Directive 76/768/EEC in a blind trial in the in vitro 3T3 cell neutral red uptake phototoxicity (3T3 NRU PT) test, which had been scientifically validated between 1992 and 1996. Since all the UV filter chemicals on the positive list of EU Directive 76/768/EEC have been shown not to be phototoxic in vivo in humans under use conditions, only negative effects would be expected in the 3T3 NRU PT test. To balance the number of positive and negative chemicals, ten phototoxic and ten non-phototoxic chemicals were tested under blind conditions in four laboratories. Moreover, to assess the optimum concentration range for testing, information was provided on appropriate solvents and on the solubility of the coded chemicals. In this study, the phototoxic potential of test chemicals was evaluated in a prediction model in which either the Photoirritation Factor (PIF) or the Mean Photo Effect (MPE) were determined. The results obtained with both PIF and MPE were highly reproducible in the four laboratories, and the correlation between in vitro and in vivo data was almost perfect. All the phototoxic test chemicals provided a positive result at concentrations of 1μg/ml, while nine of the ten non-phototoxic chemicals gave clear negative results, even at the highest test concentrations. One of the UV filter chemicals gave positive results in three of the four laboratories only at concentrations greater than 100μg/ml; the other laboratory correctly identified all 20 of the test chemicals. An analysis of the impact that exposure concentrations had on the performance of the test revealed that the optimum concentration range in the 3T3 NRU PT test for determining the phototoxic potential of chemicals is between 0.1μg/ml and 10μg/ml, and that false positive results can be obtained at concentrations greater than 100μg/ml. Therefore, the positive results obtained with some of the UV filter chemicals only at concentrations greater than 100μg/ml do not indicate a phototoxic potential in vivo. When this information was taken into account during calculation of the overall predictivity of the 3T3 NRU PT test in the present study, an almost perfect correlation of in vitro versus in vivo results was obtained (between 95% and 100%), when either PIF or MPE were used to predict the phototoxic potential. The management team and participants therefore conclude that the 3T3 NRU PT test is a valid test for correctly assessing the phototoxic potential of UV filter chemicals, if the defined concentration limits are taken into account.

Kinetic modelling of complex metabolic networks - a central goal of computational systems biology - is currently hampered by the lack of reliable rate equations for the majority of the underlying biochemical reactions and membrane transporters. On the basis of biochemically substantiated evidence that metabolic control is exerted by a narrow set of key regulatory enzymes, we propose here a hybrid modelling approach in which only the central regulatory enzymes are described by detailed mechanistic rate equations, and the majority of enzymes are approximated by simplified(non mechanistic) rate equations (e.g. mass action, LinLog, Michaelis-Menten and power law) capturing only a few basic kinetic features and hence containing only a small number of parameters to be experimentally determined. To check the reliability of this approach, we have applied it to two different metabolic networks, the energy and redox metabolism of red blood cells, and the purine metabolism of hepatocytes, using in both cases available comprehensive mechanistic models as reference standards. Identification of the central regulatory enzymes was performed by employing only information on network topology and the metabolic data for a single reference state of the network [Grimbs S, Selbig J, Bulik S, Holzhutter HG & Steuer R (2007) Mol Syst Biol 3, 146, doi:10.1038/msb4100186].Calculations of stationary and temporary states under various physiological challenges demonstrate the good performance of the hybrid models. We propose the hybrid modelling approach as a means to speed up the development of reliable kinetic models for complex metabolic networks.

A software package suitable for personal computers and designed to handle simulation and fitting problems related to the study of biomolecules under pre-steady and steady state conditions is presented, and its overall architecture as well as the implemented algorithms illustrated. The peculiar features of the package are: (i) integrated capability of simulating dynamic models and fitting to them experimental data; (ii) handling of stiff problems; (Hi) free use of algebraic as well as differential equations; (iv) objective comparison of models of different complexity. The above features are discussed through a number of examples taken from the direct experience of the authors in enzyme kinetics and ligand binding.

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