Cost-effectiveness of short-term tests for carcinogenicity

Most chemicals to which we are exposed are not properly tested for carcinogenicity. The latest methods of in vitro testing provide a way of screening with sufficient accuracy to remedy this situation.</p

Tons of Toxic Chemicals Above

'2 4BILLION Pounds of Toxic Chemicals Poured into Air," recent headlines read. After two decades of major pollution-control efforts and hundreds of billions of dollars spent on reducing discharges, people are shocked to learn that the quantities of toxic chemicals being dumped into the atmosphere are measured in billions of pounds per year.</p

Screening Toxic Chemicals: How Accurate Must Tests Be?

A decision analysis framework is used to explore the value of screening tests for carcinogenicity; Whether a test lowers the social cost of screening depends on the test's sensitivity, specificity, and c~st and the social cost of misclassification (exonerating carcinogenic chemicals or condemning noncarcinogenic chemicals). The model shows lhat the best screening test need .not he either the most accurate or the least expensive.</p

Scientific and cost-effectiveness criteria in selecting batteries of short-term tests

<p>The scientific and cost-effectiveness criteria introduced in this paper can be applied to published datasets and current and proposed batteries of short-term tests. The reports in the current volume will provide a wealth of additional material for such evaluations, but more systematically obtained information will be necessary to assess both the internal and external validity of these tests. Individual tests and batteries of tests should be standardized, employ positive controls, generate results capable of quantitative analyses that may make dichotomous classification as “positive” and “negative” obsolete, be interpreted in light of mechanisms of action, and be cost-effective on a grand scale. For regulatory purposes our long-term goal should be to replace the whole animal lifetime bioassay with an appropriate and cost-effective set of short-term tests.</p

Predictions of Rodent Carcinogenicity Testing Results: Interpretation in Light of the Lave-Omenn Value-of-Information Model

The recent National Institute of Environmental Health Sciences/National Toxicology Program Carcinogen Prediction Challenge elicited a valuable array of predictions of the carcinogenicity of chemicals tested in the lifetime rodent bioassay. The data warrant additional analyses of the similarities and differences of the predictive methods. We provide here analyses of the sensitivity, specificity, and false-positive/false-negative tendencies of the different sets of predictions. Our value-of-information model provides guidance to testing agencies and regulatory agencies in determining the social value of additional information and setting up the framework for assessing the social consequences of different test strategies and nontest predictive methods. These considerations deserve attention in the second round of the Carcinogen Prediction Challenge.</p

Modeling results for DeepFold and AlphaFold on the 31 CASP13 targets.

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Case study from two proteins for which DeepFold significantly outperformed AlphaFold2.

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Non-parametric analysis for DeepFold and the control methods on the 221 test proteins.

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Impact of the general statistical energy function on DeepFold’s modeling performance.

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Comparison of DeepFold and QUARK modeling results.

A) Evaluation of the modeling accuracy of QUARK and DeepFold guided by different numbers of spatial restraints, where the top n*L distances were selected by sorting the Cβ distances according to their predicted probabilities. B) Analysis of the conformational changes that occured during the QUARK fragment assembly simulations. The figure plots the TM-score of the decoy at REMC cycle i compared to the decoy at the previous cycle i-1. The right hand side shows the final QUARK model in red superposed with the native structure in cyan. C) Analysis of the conformational changes that occured during the DeepFold simulations. The figure plots the TM-score of the decoy at L-BFGS step i compared to the decoy at the previous step i-1, where the right hand side shows the final DeepFold model in red superposed with the native structure in cyan. D) Comparison between the DeepFold model at L-BFGS step 100 (blue) with the model at step 1100 (red) and the experimental structure (cyan). The insets show the areas of the structure that changed the most after the 100th L-BFGS step.</p