A Categorical Structure-Activity Relationship Analysis of the Developmental Toxicity of Antithyroid Drugs

Abstract

The choice of therapeutic strategies for hyperthyroidism during pregnancy is limited. Surgery and radioiodine are typically avoided, leaving propylthiouracil and methimazole in the US. Carbimazole, a metabolic precursor of methimazole, is available in some countries outside of the US. In the US propylthiouracil is recommended because of concern about developmental toxicity from methimazole and carbimazole. Despite this recommendation, the data on developmental toxicity of all three agents are extremely limited and insufficient to support a policy given the broad use of methimazole and carbimazole around the world. In the absence of new human or animal data we describe the development of a new structure-activity relationship (SAR) model for developmental toxicity using the cat-SAR expert system. The SAR model was developed from data for 323 compounds evaluated for human developmental toxicity with 130 categorized as developmental toxicants and 193 as nontoxicants. Model cross-validation yielded a concordance between observed and predicted results between 79% to 81%. Based on this model, propylthiouracil, methimazole, and carbimazole were observed to share some structural features relating to human developmental toxicity. Thus given the need to treat women with Graves's disease during pregnancy, new molecules with minimized risk for developmental toxicity are needed. To help meet this challenge, the cat-SAR method would be a useful in screening new drug candidates for developmental toxicity as well as for investigating their mechanism of action.

Searching for novel ligands for the cannabinoid and related receptors.

“…As in previous cat-SAR models (Cunningham, Carrasquer, & Mattison, 2009;Cunningham et al, 2008;Qamar, Carrasquer, Cunningham, & Cunningham, 2011), …”

“…As in previous cat-SAR models (Cunningham et al, 2009;Cunningham et al, 2008;Qamar et al, 2011), setting of parameters for selecting important fragments (fragment compound counts and fragment activity proportion values) was used, with this experiment applying the same rules to fragment clusters. For these analyses, a rule optimization routine was employed wherein the Number Rule varied between 1 and 9 fragments or fragment clusters and the Proportion Rule varied between 0.50 and 0.95.…”

“…One method to classify compounds back to an active or inactive category is to determine an optimal cutoff point that best separates the probabilistic prediction of active and inactive compounds derived from the LOO validations (Cunningham et al, 2009). Depending on the purpose of the model, the cutoff point can be adjusted wherein a model with the best overall concordance can be selected (i.e., a most predictive model), one with equal sensitivity and specificity (i.e., a balanced model that does not overly predictive active compounds at the cost of wrongly predicting inactive ones and vice versa), or one with high sensitivity.…”

“…The resulting list of fragment clusters can then be used for mechanistic analysis, or to predict the activity of an unknown compound from the final model (Cunningham et al, 2009;Qamar et al, 2011). In order to predict the activity of an unknown compound, the cat-SAR program determines which, if any, clusters from the model's collection of important fragment clusters are present in the unknown or test compound (Cunningham et al, 2009;Qamar et al, 2011).…”

“…In order to predict the activity of an unknown compound, the cat-SAR program determines which, if any, clusters from the model's collection of important fragment clusters are present in the unknown or test compound (Cunningham et al, 2009;Qamar et al, 2011). If none are present, no prediction of activity can be made for the compound (i.e.…”