Prediction of acute aquatic toxicity in Tetrahymena pyriformis--'Eco-Derek', a knowledge-based system approach.

A 'proof-of-concept' version of a software tool for making transparent predictions of acute aquatic toxicity has been developed. It is primarily limited to semi-quantitative predictions in one species, the ciliated protozoan, Tetrahymena pyriformis. A freely available system, 'Eco-Derek', was derived by adapting a well-established, knowledge-based structure-activity and reasoning platform (Derek for Windows, Lhasa Limited). The Derek reasoning code was modified to express potency rather than confidence. Structure-activity relationship (SAR) development utilised a curated version of a published dataset, supplemented with the CADASTER Challenge datasets. Forty-five structural alerts were produced. The dependence on log P was examined for each alert and entered into the system as qualitative reasoning rules specifying the predicted potency as Very Low, Low, Moderate, High or Very High. Evaluation studies showed: (a) moderate accuracy for the training set but low accuracy for an external test set; (b) non-linearity in the toxicity-log P relationship for chemicals without identified structural alerts; (c) insufficient differentiation of substituent effects in some of the reactivity-based structural alerts resulting in too few chemicals predicted with Very High toxicity; and (d) the need for additional structural alerts covering polar narcosis and less common reactive or metabolically activated chemical functionality.

Computer prediction of possible toxic action from chemical structure: an update on the DEREK system.

Computer-based assessment of potential toxicity has become increasingly popular in recent years. The knowledge-base system DEREK is developed under the guidance of a multinational Collaborative Group of expert toxicologists and provides a qualitative approach to toxicity prediction. Major developments of the DEREK program and knowledge-base have taken place in the last 3 years. Program developments include improvements in both the user interface and data processing. Work on the knowledge-base has concentrated on the areas of genotoxicity and skin sensitisation. DEREK's predictive capabilities for these toxicological end-points has been demonstrated. In addition to the continued expansion of the knowledge-base, a number of enhancements are planned in the DEREK program. In particular, work is in progress to develop further DEREK's ability to report the reasoning behind its predictions.

Structure-activity relationships for skin sensitization potential: development of structural alerts for use in knowledge-based toxicity prediction systems.

The development of qualitative structure-activity relationships for the prediction of skin sensitization potential, based on structural alerts (substructures associated with a toxicological mechanism), and suitable for incorporation as rules into a knowledge-based system is described. The structure dependence of the skin sensitization mechanism may be largely defined in terms of the presence or metabolic/nonmetabolic formation of protein reactive functional groups on the test compound and by the physicochemical requirements of significant skin penetration. The proposed structural alerts were tested on a data set of diverse chemicals. The results showed that the alerts have potential as preliminary indicators of skin sensitization potential for a wide range of low molecular weight chemicals.

Development of an expert system rulebase for identifying contact allergens.

There are currently no in vitro methods for the identification of skin sensitizers (contact allergens). Knowledge relating chemical structure to toxicity can be programmed into expert systems. An historical database containing results of 294 defined single substances tested in the guinea pig maximization test to a single protocol has been used to derive a set of structural alerts for skin sensitization. Where possible, the approach used was to group the substances according to their most likely mechanism of reaction with skin proteins. Where no mechanism could be identified, structural alerts were derived empirically for groups of molecules with similar chemical functionality. This process has currently resulted in the production of 40 structure-activity rules, which have been incorporated into the expert system DEREK. Rulebases of this type have potential for use as a preliminary screen in toxicological hazard identification and may ultimately lead to a reduction in the use of laboratory animals.

Neural network classification of mutagens using structural fragment data.

A neural network was applied to a large, structurally heterogeneous data set of mutagens and non-mutagens to investigate structure-property relationships. Substructural data comprising a total of 1280 fragments were used as inputs. The training of the back-propagation networks was directed by an algorithm which selected an optimal subset of fragments in order to maximize their discriminating power, and a good predictive network. The system comprised three programs: the first used a keyfile of 100 fragments to generate training and test files, the second was the network itself and a procedure for ranking the effectiveness of these fragments and the third randomly replaced the lowest fragments. This cycle was then repeated. After running on a 386/33 PC several networks produced approximately 11% failures in the test set and 6% in the training set. By simplifying the output of the hidden layer it was possible to describe the hidden layer states in terms of clusters of mutagens and non-mutagens. Some of these clusters were structurally homogeneous and contained known mutagenic and non-mutagenic structural classes. This analysis provided a useful means of demonstrating how the network was classifying the data.

Effect of culture media and incubation temperature on growth of selected strains of Francisella tularensis.

The rate and amount of growth of 4 field isolates and reference strain ATCC 6223 of Francisella tularensis were evaluated on isolation media with 2 different agar bases and with different supplements and incubated at 25 C, 35 C, and 42 C. Biochemical reactions on conventional differential media with and without cysteine were evaluated. Two of the field isolates and the reference strain were F. tularensis subspecies tularensis (formerly biovar tularensis or Type A), and 2 isolates were subspecies holarctica (formerly subspecies palaearctica or Type B). Bacto cystine heart blood agar supplemented with 1% hemoglobin, glucose cystine heart blood agar, and brain-heart infusion blood agar supported good growth of all 4 field strains, with the most luxuriant growth occurring on Bacto cystine heart blood agar with hemoglobin. Heart infusion blood agar and trypticase soy blood agar supported growth of the field isolates, although growth was diminished and delayed. Strain 6223 was distinctly fastidious and failed to grow on heart infusion or trypticase soy blood agars. Growth of strain 6223 was best on Bacto cystine heart blood agar with hemoglobin. The agar base did not affect growth unless the supplements became limiting, in which case Bacto agar base generally supported growth better than BiTek agar base. Incubation at 35 C was optimum for all 5 strains. Growth at 42 C was slow, with the greatest decrease in the rate and amount of growth occurring with field isolates of F. tularensis subspecies tularensis. Strain 6223 did not grow at 25 C, and the 4 field isolates grew slowly at the lower temperature.(ABSTRACT TRUNCATED AT 250 WORDS)

The innervation density of serotonergic (5-HT) fibers varies in different subdivisions of the cat lateral geniculate nucleus complex.

The innervation density of serotonin (5-HT)-immunoreactive fibers, identified using an antibody to 5-HT, was found to differ in the 4 subdivisions of the cat lateral geniculate nucleus complex (LGN). The mean density (fiber length per unit area) of anti-5-HT-stained fibers was highest in the ventral LGN (0.062 micron per micron 2), moderate in the medial interlaminar nucleus (MIN) and the parvicellular C laminae of the dorsal LGN (0.039-0.040 per micron 2), and lowest in the A and magnocellular C laminae of the dorsal LGN (0.020 per micron 2). The fiber density in MIN was particularly dense along the medial edge of the nucleus, a region called the geniculate wing. The heaviest serotonin innervation is thus found in geniculate structures receiving input from W-type retinal ganglion cells and lightest in structures receiving X and Y input.