Aromatase is CYP19A1 enzyme which is a member of the cytochrome P450 superfamily. This enzyme is responsible for conversion of androgens into estrogens . There are two reasons for the interest in substances which are able to inhibit the enzyme aromatase. First, because of their use as pharmaceuticals in the treatment of estrogen-sensitive breast cancers. Second, a number of environmental contaminants can act as aromatase inhibitors, thereby disrupting endocrine function in humans and wildlife through suppression of circulating estrogen levels.
The training set of the model consists of 222 chemicals with aromatase inhibition data collected from different literature sources. Data are expressed as log(1/IC50), where IC50 is the test chemical concentration resulting in 50% inhibition of activity. Some aromatase inhibitors in the training set are structural analogs of the man's hormones testosterone and androstenedione. Others, contain heteroatoms in azole ring, fluorenes, flavones and brominated flame retardants . Hence, the model is derived based on steroidal and non-steroidal aromatase inhibitors.
Mechanism-based categorization model for predicting potency of steroidal and non-steroidal inhibitors of the enzyme aromatase has been developed . There are two distinct mechanisms involved in the aromatase inhibition: steroidal and non-steroidal. The most potent steroidal inhibitors are very similar to androstenedione and testosterone (natural substrates). When bound to the catalytic site of CYP19, those inhibitors are metabolized to intermediates which attach irreversibly to the active site thereby blocking subsequent activity of the enzyme . Chemicals acting by non-steroidal mechanisms possess a heteroatom able to coordinate the heme iron of the cytochrome P450, and thus interfere with steroid hydroxylation.
Specific structural boundaries controlling inhibition of aromatase were defined and a software tool was developed that allowed a decision tree (profile) to be built discriminating aromatase inhibitors by mechanism and potency. Structure of the model is presented in Figure 1:
Figure 1. Structure of the aromatase inhibition model
An input chemical follows a profiling path and the structure is examined at each step to decide whether the structural boundaries implemented in the decision tree node are met.
For 222 training set chemicals, sensitivity of the aromatase inhibition model is 87%. Since, the training set of the model is based on substances which are inhibitors of the aromatase and there are no non-inhibitors, specificity of the model cannot be estimated.
Applicability domain of the aromatase inhibition model consists of two sub-domain layers: general parametric requirements and structural features . Two chemical subsets are used for deriving the model domains. The first subset includes the training set chemicals which are correctly predicted by the models, whereas the second subset comprises training set chemicals which are incorrectly predicted by the models.
The correct chemical subset is used for defining the general parametric requirements. Extracted are specific ranges of the molecular weight (MW) and the 1-octanol/water partition coefficient (log KOW):
- Molecular weight MW (in Da) ϵ [183; 959],
- log KOW ϵ [1; 12].
The atom-centered fragments extracted from the correct subset of chemicals are used to define the structural domain. Briefly, the structural domain is assessed based on atom-centered fragments, extracted from correctly and incorrectly predicted (i.e., false positives and false negatives) substances from the model training sets by accounting for the atom type, hybridization and attached H-atoms of the central atom and its first neighbours. If the neighbour is a heteroatom then the diameter of the fragment is increased up to three consecutive heteroatoms or to the first carbon atoms in sp3 hybridization. In order to assess if a new chemical belongs to the structural domain, the system partitions the chemical to atom-centered fragments, which are then matched to the fragments extracted from the correct and incorrect chemical subsets. The new chemical is estimated to belong to the structural domain only when its atom-centered fragments are found in the list of correct fragments.
Predictions of the aromatase inhibition model are reported as a tab delimited text file providing the following information for: chemical identity (where available), observed aromatase inhibition potency (log1/IC50), predicted aromatase inhibition potency (ranges), applicability domain, etc.
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