No abstract

Understanding how protein-protein interactions depend on the choice of buffer, salt, ionic strength, and pH is needed to have better control over protein solution behavior. Here, we have characterized the pH and ionic strength dependence of protein-protein interactions in terms of an interaction parameter kD obtained from dynamic light scattering and the osmotic second virial coefficient B22 measured by static light scattering. A simplified protein-protein interaction model based on a Baxter adhesive potential and an electric double layer force is used to separate out the contributions of longer-ranged electrostatic interactions from short-ranged attractive forces. The ionic strength dependence of protein-protein interactions for solutions at pH 6.5 and below can be accurately captured using a Deryaguin-Landau-Verwey-Overbeek (DLVO) potential to describe the double layer forces. In solutions at pH 9, attractive electrostatics occur over the ionic strength range of 5-275 mM. At intermediate pH values (7.25 to 8.5), there is a crossover effect characterized by a nonmonotonic ionic strength dependence of protein-protein interactions, which can be rationalized by the competing effects of long-ranged repulsive double layer forces at low ionic strength and a shorter ranged electrostatic attraction, which dominates above a critical ionic strength. The change of interactions from repulsive to attractive indicates a concomitant change in the angular dependence of protein-protein interaction from isotropic to anisotropic. In the second part of the paper, we show how the Baxter adhesive potential can be used to predict values of kD from fitting to B22 measurements, thus providing a molecular basis for the linear correlation between the two protein-protein interaction parameters.

In light of new legislation (e.g., the REACH program in the European Union), several initiatives have recently emerged to increase acceptance of (quantitative) structure-activity relationships [(Q)SARs] to reduce reliance on animal (in vivo) testing. Among the principles for assessing the validity of (Q)SARs is the need for a defined domain of applicability, i.e., identification of the range of compounds for which the (Q)SAR can confidently be applied for purposes of toxicity prediction. Here, we attempt to develop a "natural" classification into applicability domains based on considering how a compound and the target organism between them "decide" on the nature and extent of the toxic effect. With particular emphasis on reactive toxicity, we present rules, based on organic reaction mechanistic principles, for classifying reactive toxicants into their appropriate mechanistic applicability domains.

The goal of eliminating animal testing in the predictive identification of chemicals with the intrinsic ability to cause skin sensitization is an important target, the attainment of which has recently been brought into even sharper relief by the EU Cosmetics Directive and the requirements of the REACH legislation. Development of alternative methods requires that the chemicals used to evaluate and validate novel approaches comprise not only confirmed skin sensitizers and non-sensitizers but also substances that span the full chemical mechanistic spectrum associated with skin sensitization. To this end, a recently published database of more than 200 chemicals tested in the mouse local lymph node assay (LLNA) has been examined in relation to various chemical reaction mechanistic domains known to be associated with sensitization. It is demonstrated here that the dataset does cover the main reaction mechanistic domains. In addition, it is shown that assignment to a reaction mechanistic domain is a critical first step in a strategic approach to understanding, ultimately on a quantitative basis, how chemical properties influence the potency of skin sensitizing chemicals. This understanding is necessary if reliable non-animal approaches, including (quantitative) structure-activity relationships (Q)SARs, read-across, and experimental chemistry based models, are to be developed.

The freezing points, enthalpies of dilution, volumetric heat capacities, densities, and sound velocities of the homologous series R(CH3)2NO, for R = butyl, hexyl, octyl, and decyl, were measured in water at 25 °C and as a function of temperature in the case of octyl. The osmotic coefficients and the apparent and partial molar relative enthalpies, heat capacities, volumes, compressibilities, and expansibilities were calculated. Isochoric heat capacities and isothermal compressibilities can also be derived from these data. There is a gradual change in the trends of these functions when going from the lower homologue, which behaves like a medium-size alcohol, to the higher one, which is a typical nonionic surfactant. The osmotic coefficients are positive in the premicellar region at the freezing temperature but become negative at higher temperatures. The concentration dependence of the various functions can be accounted for quantitatively with a simple mass-action model. Aggregation numbers and thermodynamic functions of micellization can be derived with this model.

CAS Electrophile (El) Nucleophile (Nu) Parameter Unit Value Error log(Val) 818-61-1 2-Hydroxyethyl acrylate 4-Nitrobenzenethiol t1/2(NBT) min 2.45E-01 144-48-9 2-Iodoacetamide 4-Nitrobenzenethiol t1/2(NBT) min 1.83E-03 2682-20-4 2-Methyl-2H-isothiazolin-3-one 4-Nitrobenzenethiol t1/2(NBT) min 1.60E-03 25567-67-3 3-Chloro-1.2-dinitrobenzene 4-Nitrobenzenethiol t1/2(NBT) min 1.25E-02 2497-21-4 4-Hexen-3-one 4-Nitrobenzenethiol t1/2(NBT) min 2.77E-02 26172-55-4 5-Chloro-2-methyl-4-isothiazolin-3-one 4-Nitrobenzenethiol t1/2(NBT) min 5.83E-05 108-24-7 Acetic anhydride 4-Nitrobenzenethiol t1/2(NBT) min 9.83E-04 107-02-8 Acrolein 4-Nitrobenzenethiol t1/2(NBT) min 8.25E-02 100-39-0 Benzyl bromide 4-Nitrobenzenethiol t1/2(NBT) min 4.67E-05 57-57-8 beta-Propiolactone 4-Nitrobenzenethiol t1/2(NBT) min 1.62E-04 88-11-9 Diethylthiocarbamoyl chloride 4-Nitrobenzenethiol t1/2(NBT) min 1.52E-03 886-38-4 Diphenylcyclopropenone 4-Nitrobenzenethiol t1/2(NBT) min 1.05E-05 140-88-5 Ethyl acrylate 4-Nitrobenzenethiol t1/2(NBT) min 7.70E-01 50-00-0 Formaldehyde 4-Nitrobenzenethiol t1/2(NBT) min 1.25E-03 55965-84-9 Kathon CG 4-Nitrobenzenethiol t1/2(NBT) min 2.17E-04 124-63-0 Methyl sulfonyl chloride 4-Nitrobenzenethiol t1/2(NBT) min 7.67E-04 128-53-0 N-Ethylmaleimide 4-Nitrobenzenethiol t1/2(NBT) min 3.33E-04 Nitrobenzyl bromide 4-Nitrobenzenethiol t1/2(NBT) min 9.83E-06 15646-46-5 Oxazolone 4-Nitrobenzenethiol t1/2(NBT) min 9.00E-06 106-51-4 p-Benzoquinone 4-Nitrobenzenethiol t1/2(NBT) min 7.33E-06 1939-99-7 Phenylmethanesulfonyl chloride 4-Nitrobenzenethiol t1/2(NBT) min 6.00E-03 2892-51-5 Squaric acid 4-Nitrobenzenethiol t1/2(NBT) min 6.12E-02 584-84-9 Toluene 2.4-diisocyanate 4-Nitrobenzenethiol t1/2(NBT) min 4.50E-04 23726-91-2S2 Schwöbel et al.

Research aimed at nonanimal approaches to provide the relevant information needed for the effective assessment of skin sensitization, for both hazard characterization and risk assessment purposes, is currently an area of high activity, stimulated by regulatory initiatives related to chemicals used in consumer products. The ability of a chemical to react covalently with protein or peptide nucleophiles in the skin is recognized as the key determinant in determining sensitization potency, and initiatives to develop peptide reactivity assays to replace animal testing have been undertaken recently. This paper describes a high throughput kinetic profiling (HTKP) approach, developed as an extension of a published standard assay, with the aim of providing a quantitatively robust end point in the form of a kinetic profile from which reactivity to a model peptide can be quantified in the form of second order rate constants. The approach allows solubility issues to be identified and overcome; these are frequently encountered, but can often go undetected, in aqueous reactivity assays with organic compounds of interest in the skin sensitization context. Using rate constants determined by the HTKP approach we have obtained a quantitative mechanistic model for the Michael acceptor reaction mechanistic domain, relating the sensitization potency in the murine local lymph node assay to the rate constant. The observation that the correlation is not improved by incorporation of a hydrophobicity term has implications regarding the nature and location of the skin nucleophile whose reaction leads to sensitization by Michael acceptor electrophiles.

This article presents an overview of electrophilic reaction mechanisms relevant to skin sensitization, with reference to a published skin sensitization test data set for 106 chemicals. Where appropriate to aid the interpretation, additional data on a small number of further compounds are also discussed. It is shown that there is a close correspondence in the way differences and similarities in skin sensitization potency of chemicals relate to differences and similarities in their physical organic chemistry and electrophilic reaction mechanistic chemistry. The 106 chemicals are classified into their reaction mechanistic applicability domains, and reactivity-sensitization trends are analyzed for each domain: the Michael acceptor and pro-Michael acceptor electrophile domain; the SNAr electrophile domain; the SN2 electrophile domain; the Schiff base electrophile domain; the acyl transfer electrophile domain; and the non-electrophilic non-pro-electrophilic domain. The last of these domains should be populated mainly by non-sensitizers. Classification of 87 of the 106 compounds, using these domains, was straightforward. In most of the domains and subdomains where there are sufficient compounds, clear trends can be seen, in conformity with the Relative Alkylation Index (RAI) model, between sensitization potential and reactivity/hydrophobicity. Of the remaining 19 compounds, 7 are alpha-X-methyl-gamma-lactones that on the basis of published organic chemistry studies and guinea pig sensitization data can be classed as pro-Michael acceptors by elimination of HX but that are mostly negative in the LLNA, indicating a difference in bioactivation capabilities between mice and guinea pigs. The other 12 compounds, whose chemistry was not immediately obvious, were found after further analysis and literature research to fit into appropriate mechanistic domains that rationalize their skin sensitizing properties.