Designing QSARs for Parameters of High-Throughput Toxicokinetic Models Using Open-Source Descriptors.

The intrinsic metabolic clearance rate (Clint) and the fraction of the chemical unbound in plasma (fup) serve as important parameters for high-throughput toxicokinetic (TK) models, but experimental data are limited for many chemicals. Open-source quantitative structure-activity relationship (QSAR) models for both parameters were developed to offer reliable in silico predictions for a diverse set of chemicals regulated under the U.S. law, including pharmaceuticals, pesticides, and industrial chemicals. As a case study to demonstrate their utility, model predictions served as inputs to the TK component of a risk-based prioritization approach based on bioactivity/exposure ratios (BERs), in which a BER < 1 indicates that exposures are predicted to exceed a biological activity threshold. When applied to a subset of the Tox21 screening library (6484 chemicals), we found that the proportion of chemicals with BER <1 was similar using either in silico (1133/6484; 17.5%) or in vitro (148/848; 17.5%) parameters. Further, when considering only the chemicals in the Tox21 set with in vitro data, there was a high concordance of chemicals classified with either BER <1 or >1 using either in silico or in vitro parameters (767/848, 90.4%). Thus, the presented QSARs may be suitable for prioritizing the risk posed by many chemicals for which measured in vitro TK data are lacking.

Correction to Designing QSARs for Parameters of High Throughput Toxicokinetic Models Using Open-Source Descriptors.

The intrinsic metabolic clearance rate (Clint) and fraction of chemical unbound in plasma (fup) serve as important parameters for high throughput toxicokinetic models, but experimental data are limited for many chemicals. Open-source quantitative structure-activity relationship (QSAR) models for both parameters were developed to offer reliable in silico predictions for a diverse set of chemicals regulated under U.S. law, including pharmaceuticals, pesticides, and industrial chemicals. As a case study to demonstrate their utility, model predictions served as inputs to the TK component of a risk-based prioritization approach based on Bioactivity: Exposure Ratios (BER), in which a BER < 1 indicates exposures are predicted to exceed a biological activity threshold. When applied to a subset of the Tox21 screening library (6631 chemicals) we found that the proportion of chemicals with BER < 1 was similar using either in silico (1337/6631; 20.16%) or in vitro (151/850; 17.76%) parameters. Further, when considering only the chemicals in the Tox21 set with in vitro data, there was a high concordance of chemicals classified with either BER < 1 or >1 using either in silico or in vitro parameters (776/850, 91.30%). Thus, the presented QSARs may be suitable for prioritizing the risk posed by many chemicals for which measured in vitro TK data are lacking.

The acetaminophen metabolite N-acetyl-p-benzoquinone imine (NAPQI) inhibits glutathione synthetase in vitro; a clue to the mechanism of 5-oxoprolinuric acidosis?

1. Metabolic acidosis due to accumulation of l-5-oxoproline is a rare, poorly understood, disorder associated with acetaminophen treatment in malnourished patients with chronic morbidity. l-5-Oxoprolinuria signals abnormal functioning of the γ-glutamyl cycle, which recycles and synthesises glutathione. Inhibition of glutathione synthetase (GS) by N-acetyl-p-benzoquinone imine (NAPQI) could contribute to 5-oxoprolinuric acidosis in such patients. We investigated the interaction of NAPQI with GS in vitro. 2. Peptide mapping of co-incubated NAPQI and GS using mass spectrometry demonstrated binding of NAPQI with cysteine-422 of GS, which is known to be essential for GS activity. Computational docking shows that NAPQI is properly positioned for covalent bonding with cysteine-422 via Michael addition and hence supports adduct formation. 3. Co-incubation of 0.77 µM of GS with NAPQI (25-400 µM) decreased enzyme activity by 16-89%. Inhibition correlated strongly with the concentration of NAPQI and was irreversible. 4. NAPQI binds covalently to GS causing irreversible enzyme inhibition in vitro. This is an important novel biochemical observation. It is the first indication that NAPQI may inhibit glutathione synthesis, which is pivotal in NAPQI detoxification. Further studies are required to investigate its biological significance and its role in 5-oxoprolinuric acidosis.

Informing the Human Plasma Protein Binding of Environmental Chemicals by Machine Learning in the Pharmaceutical Space: Applicability Domain and Limits of Predictability.

The free fraction of a xenobiotic in plasma (Fub) is an important determinant of chemical adsorption, distribution, metabolism, elimination, and toxicity, yet experimental plasma protein binding data are scarce for environmentally relevant chemicals. The presented work explores the merit of utilizing available pharmaceutical data to predict Fub for environmentally relevant chemicals via machine learning techniques. Quantitative structure-activity relationship (QSAR) models were constructed with k nearest neighbors (kNN), support vector machines (SVM), and random forest (RF) machine learning algorithms from a training set of 1045 pharmaceuticals. The models were then evaluated with independent test sets of pharmaceuticals (200 compounds) and environmentally relevant ToxCast chemicals (406 total, in two groups of 238 and 168 compounds). The selection of a minimal feature set of 10-15 2D molecular descriptors allowed for both informative feature interpretation and practical applicability domain assessment via a bounded box of descriptor ranges and principal component analysis. The diverse pharmaceutical and environmental chemical sets exhibit similarities in terms of chemical space (99-82% overlap), as well as comparable bias and variance in constructed learning curves. All the models exhibit significant predictability with mean absolute errors (MAE) in the range of 0.10-0.18Fub. The models performed best for highly bound chemicals (MAE 0.07-0.12), neutrals (MAE 0.11-0.14), and acids (MAE 0.14-0.17). A consensus model had the highest accuracy across both pharmaceuticals (MAE 0.151-0.155) and environmentally relevant chemicals (MAE 0.110-0.131). The inclusion of the majority of the ToxCast test sets within the AD of the consensus model, coupled with high prediction accuracy for these chemicals, indicates the model provides a QSAR for Fub that is broadly applicable to both pharmaceuticals and environmentally relevant chemicals.

Genetic Mutations in the S-loop of Human Glutathione Synthetase: Links Between Substrate Binding, Active Site Structure and Allostery.

The second step in the biosynthesis of the cellular antioxidant glutathione (GSH) is catalyzed by human glutathione synthetase (hGS), a negatively cooperative homodimer. Patients with mutations in hGS have been reported to exhibit a range of symptoms from hemolytic anemia and metabolic acidosis to neurological disorders and premature death. Several patient mutations occur in the S-loop of hGS, a series of residues near the negatively cooperative γ-GC substrate binding site. Experimental point mutations and molecular dynamic simulations show the S-loop not only binds γ-GC through a salt bridge and multiple hydrogen bonds, but the residues also modulate allosteric communication in hGS. By elucidating the role of S-loop residues in active site structure, substrate binding, and allostery, the atomic level sequence of events that leads to the detrimental effects of hGS mutations in patients are more fully understood.

The role of strong electrostatic interactions at the dimer interface of human glutathione synthetase.

The obligate homodimer human glutathione synthetase (hGS) provides an ideal system for exploring the role of protein-protein interactions in the structural stability, activity and allostery of enzymes. The two active sites of hGS, which are 40 Šapart, display allosteric modulation by the substrate γ-glutamylcysteine (γ-GC) during the synthesis of glutathione, a key cellular antioxidant. The two subunits interact at a relatively small dimer interface dominated by electrostatic interactions between S42, R221, and D24. Alanine scans of these sites result in enzymes with decreased activity, altered γ-GC affinity, and decreased thermal stability. Molecular dynamics simulations indicate these mutations disrupt interchain bonding and impact the tertiary structure of hGS. While the ionic hydrogen bonds and salt bridges between S42, R221, and D24 do not mediate allosteric communication in hGS, these interactions have a dramatic impact on the activity and structural stability of the enzyme.