Predictive Structure-Based Toxicology Approaches To Assess the Androgenic Potential of Chemicals

We present a practical and easy-to-run <i>in silico</i> workflow exploiting a structure-based strategy making use of docking simulations to derive highly predictive classification models of the androgenic potential of chemicals. Models were trained on a high-quality chemical collection comprising 1689 curated compounds made available within the CoMPARA consortium from the US Environmental Protection Agency and were integrated with a two-step applicability domain whose implementation had the effect of improving both the confidence in prediction and statistics by reducing the number of false negatives. Among the nine androgen receptor X-ray solved structures, the crystal 2PNU (entry code from the Protein Data Bank) was associated with the best performing structure-based classification model. Three validation sets comprising each 2590 compounds extracted by the DUD-E collection were used to challenge model performance and the effectiveness of Applicability Domain implementation. Next, the 2PNU model was applied to screen and prioritize two collections of chemicals. The first is a small pool of 12 representative androgenic compounds that were accurately classified based on outstanding rationale at the molecular level. The second is a large external blind set of 55450 chemicals with potential for human exposure. We show how the use of molecular docking provides highly interpretable models and can represent a real-life option as an alternative nontesting method for predictive toxicology

Predictive Structure-Based Toxicology Approaches To Assess the Androgenic Potential of Chemicals

We present a practical and easy-to-run <i>in silico</i> workflow exploiting a structure-based strategy making use of docking simulations to derive highly predictive classification models of the androgenic potential of chemicals. Models were trained on a high-quality chemical collection comprising 1689 curated compounds made available within the CoMPARA consortium from the US Environmental Protection Agency and were integrated with a two-step applicability domain whose implementation had the effect of improving both the confidence in prediction and statistics by reducing the number of false negatives. Among the nine androgen receptor X-ray solved structures, the crystal 2PNU (entry code from the Protein Data Bank) was associated with the best performing structure-based classification model. Three validation sets comprising each 2590 compounds extracted by the DUD-E collection were used to challenge model performance and the effectiveness of Applicability Domain implementation. Next, the 2PNU model was applied to screen and prioritize two collections of chemicals. The first is a small pool of 12 representative androgenic compounds that were accurately classified based on outstanding rationale at the molecular level. The second is a large external blind set of 55450 chemicals with potential for human exposure. We show how the use of molecular docking provides highly interpretable models and can represent a real-life option as an alternative nontesting method for predictive toxicology

Thermodynamic integration for the case BAR to BCL.

<p>The plot shows the dV/dλ curves assembled from 13 lambda points. Transformations in water are represented as well as those in the protein for the three simulation steps.</p

The physical property profiles of our academic collection (in black) are compared with property profiles found in DrugBank (in white).

<p>The y-axis represents the percentage of compounds, the x-axis represents the molecular weights (MW), logP, hydrogen bond donor (HBD) and hydrogen bond acceptors (HBA) profiles.</p

Calculated ΔΔG values of the forward and backward transitions of <b>BAR-BCL</b> and <b>BAR-BAM</b>.

<p>Experimental ΔΔG values are also reported for comparison.</p

Biological data of alloxan-based compounds 1–9.

<p>We report the pIC₅₀ or % of inhibition at 100 µM.</p

Thermodynamic cycle. Events A and B represent the binding of two different ligands to a protein, events C and D indicate the conversion from one ligand to the other in the bound and hydrated states, respectively.

<p>The free energy differences between the processes A and C can be obtained calculating the free energy differences between B and D.</p

GOLD fitness values for top-scored docking solutions.

<p>GOLD fitness values for top-scored docking solutions.</p

Keto-enol tautomerism referred to the chemical equilibrium between the keto and enol form established by the alloxan-like structure as described on the left-hand and right-hand side, respectively.

<p>Keto-enol tautomerism referred to the chemical equilibrium between the keto and enol form established by the alloxan-like structure as described on the left-hand and right-hand side, respectively.</p

Synopsis of the synthesis of compounds 3–9 in <b>Figure 1</b>.

<p>Reagents and conditions: (i) Na₂CO₃, (PPh₃)₂PdCl₂, water/dioxane, microwave heating; (ii) alloxan monohydrate, acetic acid, reflux.</p