Toxic endpoint

SP Indicator test species Study type Endpoint Toxicity... 

All the values allowing an accurate evaluation of the endpoints (toxicity and clinical response biomarkers, surrogate endpoints, or clinical endpoints). Treatment data, for example, dose and actual times of start and end of infusion. 

Compound 34 (BCZ-1812, RWJ-270201, peramivir) showed selective inhibition of influenza virus sialidases over bacterial and mammalian sialidases (Babu et al. 2000 Bantia et al. 2001 Sidwell and Smee 2002). Successful inhibition of influenza virus infectivity in vitro (Smee et al. 2001) and upon oral administration in vivo [mice (Bantia et al. 2001) and ferrets, reviewed in Sidwell and Smee 2002] led to human clinical trials of orally administered peramivir (Barroso et al. 2005). While orally administrated peramivir successfully completed animal studies and Phase I and Phase II clinical trials, in which the compound was showing neither major side effects nor toxicity (Sidwell and Smee 2002), preliminary results of the Phase III trials (June 2002) demonstrated no statistically significant difference in the primary efficacy endpoint, possibly due to low bioavailability (Barroso et al. 2005). 

Toxic endpoint Assessment metric Measurement metric... 

Fourteen formulations of chemical alternatives were submitted to EPA under confidentiality and they were assessed based on numerous human health and ecotoxicity endpoints in addition to bioaccumulation potential and environmental persistence. They were also screened for potential exposure to workers, users and the aquatic environment. Where data gaps existed, EPA experts used models and chemical analogs to estimate the hazard for a particular endpoint. The literature and test data reviews were published in the final report, Environmentally Preferable Options for Furniture Fire Safety Low Density Furniture Foam . In addition, each hazard endpoint was ranked with a concern level (High, Moderate or Low) based on the criteria used by the EPA s New Chemicals Program to rate the concern level of new chemicals submitted under the Toxic Substance Control Act (TSCA). As seen in Figure 8.2, where the hazard endpoint rankings are bold, the value is based on experimental data. Where the hazard endpoints are presented in italic font, the value is estimated based on models or chemical analogs. In this way, detailed hazard information was summarized and presented in a clear and concise format. 

Both approaches are useful and they are also complementary because it is important to know where a chemical that may be best in its class falls out with respect to hazard. For example, a surfactant that is best in its class will be rapidly biodegradable, but most surfactants have some aquatic toxicity because they are surface active. However, surfactants as a class are typically close to the green end of the hazard spectrum because they tend to have low hazard ratings for most other endpoints. It is also possible to have chemicals that are best in their class but that are still problematic. For example, some dioxin congeners are less toxic than others but one would not presume that a dioxin congener that is best in its class is green . Concurrent use of the best in class approach with the absence of hazard approach is also important because it drives continual advancement within a class toward the ideal green chemistry. Once innovation occurs and a chemical or product is developed that meets the same or better performance criteria with lower hazard, what was once considered best in class shifts. 

The current version of eChemPortal offers the possibility to retrieve information by searching on chemical names or CAS Registry numbers. The second phase will incorporate additional search options to retrieve and compile specific hazard or other effects data (e.g., toxicity endpoints) from the participating databases. 

Just as there is a significant challenge to understand the relationship between expected potency and expected PK parameters in order to define the probability of identifying a low dose compound, there is a similar interplay between potency and safety endpoints to identify nontoxic compounds. Toxicity represents a significant... 

Fig. 10 A schematic of the information collected at hit-triage, and its relationship to relevant endpoints (dose and toxicity)...

Occluded applications Composition relatively invariant in use System size (area) predetermined Specific site prescribed for application Application technique highly reproducible Delivery is sustained Generally operate at unit drug activity, at least operate at steady activity Delivery is zero-order Serum levels related to product efficacy Bioequivalency based on pharmacokinetic (blood level) endpoint Unavoidable local tissue levels consequential only to system toxicity Individual dose interruptable Whole system removed when spent... 

The results from using the Student s f-test for a distributional analysis are presented in Table 4. These results indicate the probability of a given worker in the listed scenario exceeding the NOEL of the toxicity endpoint. The probability of exceeding the LOEL and of thus experiencing a depression of plasma cholinesterase activity is not given (except for chronic exposure scenarios in the "100 ug/kg bw/day" column). Hence, even these probabilities may be considered to be conservative and not fully representative of the probability of a worker actually experiencing a toxic effect. 

Also, the test procedure (protocol) is fundamental because it allows comparing results from different laboratories and from different experimental sets. Moreover, selected test protocol could affect the interpretation of the results, the information content and its application in the safety evaluation process, as stated by Frazer if the biological system is exposed to a test chemical for 24 h and the endpoint assay is immediately conducted, the data produced would be most relevant to the acute toxicity of the test material. If, on the other hand, the system is exposed to material for 24 h and the system is cultured in the absence of the test material for additional 48 h before the endpoint assay is conducted, the data would be more relevant to recovery from toxicity rather than acute toxicity [7]. 

In vitro tools could be used alone or in test batteries. Multiple endpoint batteries increase the power of the evaluation because they provide information of different cellular functions. This information can be useful to investigate the mode of action of toxicity and to provide data regarding the mechanistic nature of the toxicological effects of the chemical [8],... 

Frazier JM (1990) Multiple endpoint measurements to evaluate the intrinsic cellular toxicity of chemicals. J Mol Cell Toxicol 3 349-357... 

The Danish (Q)SAR database is a repository of estimates from over 70 [28] (Q)SAR models for 166,072 chemicals. The (Q)SAR models encompass endpoints for physicochemical properties, fate, ecotoxicity, absorption, metabolism, and toxicity... 

TEST allows for estimates of the value for several toxicity endpoints [29] 96 h Fathead minnow LC50, 48 h Daphnia magna LC50, 48 h Tetrahymena pyriformis IGC50, Oral rat LD50, bioaccumulation factor, developmental toxicity, and Ames mutagenicity. TEST also estimates several physical properties... 

Five endpoints with high relevance for REACH have been addressed [30] within CAESAR bioconcentration factor, skin sensitization, carcinogenicity, mutagenicity, and developmental toxicity... 

Lazar (http //lazar.in silico.de/predict) is a k-nearest-neighbor approach to predict chemical endpoints from a training set based on structural fragments [43]. It derives predictions for query structures from a database with experimentally determined toxicity data [43]. Model provides prediction for four endpoints Acute toxicity to fish (lethality) Fathead Minnow Acute Toxicity (LC50), Carcinogenicity, Mutagenicity, and Repeated dose toxicity. 

QSAR models addressing five endpoints relevant for REACH legislation have been developed by the European funded CAESAR research project [56]. These models are focused on BCF in fish, mutagenesis, carcinogenesis, developmental toxicity, and skin sensitization. The developed models have been implemented into a Java-based applet available through the Internet. 

Lazar [59] derives predictions for four endpoints Fathead Minnow Acute Toxicity (LC50), Carcinogenicity, Mutagenicity, and Repeated dose toxicity. 

ACD/Tox Suite is a collection of software modules that predict probabilities for basic toxicity endpoints. Predictions are made from chemical structure and based upon large validated databases and QSAR models, in combination with expert knowledge of organic chemistry and toxicology. ToxSuite modules for Acute Toxicity, Genotoxicity, Skin Irritation, and Aquatic Toxicity have been used. 

Results from the application of QSARs models for Developmental Toxicity are reported in Table 9. CAESAR bold values indicate that descriptors for these compounds have values outside the descriptor range for the compounds of the training set, suggesting that predictions are not reliable. Focusing on reliable results, obtained predictions are quite controversial underlining the need of more models for this endpoint. 

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