A Computationally-Based Hazard Identification Algorithm That Incorporates Ligand Flexibility. 1. Identification of Potential Androgen Receptor Ligands

Abstract

To advance techniques for screening large data sets of diverse structures for toxicologically active compounds, an algorithm was developed that is not dependent upon a predetermined and specified toxicophore or an alignment of conformers to a lead compound. Instead, the approach provides the means to identify and quantify specific global and local stereoelectronic characteristics associated with active compounds through a comparison of energeticallyreasonable conformer distributions for specific descriptors. To illustrate the algorithm, the stereoelectronic requirements associated with the binding affinity of 28 steroidal and non-steroidal ligands to the androgen receptor were defined. Common ranges of interatomic distances, atomic charges, and atom polarizabilities of oxygen atoms for conformers of the ligands with the highest affinity for the androgen receptor (most active) did not overlap with those identified for conformers with the lowest binding affinity (least active). Using a set of stereoelectronic parameters that provided a maximal measure of pairwise similarity among the conformers of the most active ligands, a model was developed to screen compounds for binding affinity. The model was capable of discriminating inactive ligands, as defined by a specified binding affinity threshold. This modeling technique could be a useful initial component in an integrated approach of employing computational and toxicological techniques in hazard identifications for large databases.

Androgen receptor binding affinity: a QSAR evaluation

“…The analysis was focused on the distance between nucleophilic sites, the oxygen atoms of 3-carbonyl and a 17-OH group. Results from previous SAR investigations have indicated that the 3-carbonyl atom in the A-ring and a 17-OH group in the D-ring, along with a steroid hydrophobic backbone, significantly contribute to the high affinity binding of steroids to the AR [30]. In this respect the probabilistic conformer distributions of most active chemicals was analysed.…”

“…Based on thermodynamic and kinetic considerations [30], it has been shown that at macromolecular binding sites conformational states can be populated that are substantially different from those of isolated, lowest-energy or crystal-phase states. Conformers of an individual chemical that have free energy within a range of approximately 20 kcal mol À1 from the lowest energy structure (usually accepted as a threshold) often exhibited significant variation in potentially relevant electronic descriptors.…”

Mechanism-based common reactivity pattern (COREPA) modelling of aryl hydrocarbon receptor binding affinity

“…The COREPA is a probabilistic classification scheme identifying criteria which will classify an unknown object into predefined classes using a training set composed of objects from multiple classes [45,46]. The COREPA formalism uses a Bayesian probabilistic method to identify common structural characteristics among chemicals that elicit similar biological activity.…”

Recent Trends and Developments in the EU in the Environmental Control and Management of Chemicals

“…Thus, an expert system (OASIS) was employed to screen the chemicals from the HPV list for phototoxicity and endocrine disruption ability. Of all HPV organics, 3.3% were found to meet the electronic and bioaccumulation requirements for phototoxicity (Mekenyan et al, 1994), whereas about 1.5% were found to be similar to the electronic pattern enhancing binding affinity to androgen receptors (Mekenyan et al, 1997). E Dangerous to the aquatic environment E Acute toxicity E Dermal irritation/corrosion E Eye irritation/corrosion E Sensitization E Reproductive toxicity E Germ-cell mutagenicity E Carcinogenicity E Organ toxicity after repeated exposure E End points not currently covered by existing classification systems (e.g., neurotoxicity, immunotoxicity, terrestrial toxicity)…”