OBJECTIVES: Identifying drugs or chemicals that represent hazards to human development is a continuous challenge. Of the approximately 60,000 chemicals in commercial use only 5% have been evaluated for developmental toxicity. Identification of inexpensive, rapid, validated techniques to demonstrate chemical hazards for the human embryo or fetus is the objective of this research. STUDY DESIGN: This research explored identification of structure activity predictors associated with human developmental toxicity by means of MULTICASE (multiple computer-automated structure evaluation), an algorithm that evaluates associations between chemicals and their constituent fragments and a biologic response. This algorithm allows identification of chemicals (and specific substructures) that may be human developmental toxicants. Developmental toxicity data were compiled from two sources (the Teratogen Information System and Food and Drug Administration guidelines) and analyzed to identify structural determinants (biophores) associated with human developmental toxicity. RESULTS: This analysis identified 17 biophores associated with human developmental toxicity. Testing the biophores against the learning set demonstrated 99% concordance, 100% sensitivity, and 98% specificity. Cross-validation studies were conducted, in which the original database was randomly separated into five learning and test sets; these demonstrated a mean concordance of 73%, with a mean sensitivity of 63% and a mean specificity of 79%. CONCLUSIONS: The MULTICASE structure-activity model is useful for identifying potential human developmental toxicants, as well as serving as a starting point for mechanistic investigations.
OBJECTIVES: Identifying drugs or chemicals that represent hazards to human development is a continuous challenge. Of the approximately 60,000 chemicals in commercial use only 5% have been evaluated for developmental toxicity. Identification of inexpensive, rapid, validated techniques to demonstrate chemical hazards for the human embryo or fetus is the objective of this research. STUDY DESIGN: This research explored identification of structure activity predictors associated with humandevelopmental toxicity by means of MULTICASE (multiple computer-automated structure evaluation), an algorithm that evaluates associations between chemicals and their constituent fragments and a biologic response. This algorithm allows identification of chemicals (and specific substructures) that may be human developmental toxicants. Developmental toxicity data were compiled from two sources (the Teratogen Information System and Food and Drug Administration guidelines) and analyzed to identify structural determinants (biophores) associated with humandevelopmental toxicity. RESULTS: This analysis identified 17 biophores associated with humandevelopmental toxicity. Testing the biophores against the learning set demonstrated 99% concordance, 100% sensitivity, and 98% specificity. Cross-validation studies were conducted, in which the original database was randomly separated into five learning and test sets; these demonstrated a mean concordance of 73%, with a mean sensitivity of 63% and a mean specificity of 79%. CONCLUSIONS: The MULTICASE structure-activity model is useful for identifying potential human developmental toxicants, as well as serving as a starting point for mechanistic investigations.