Modeling of Polyelectrolyte Adsorption from Micellar Solutions onto Biomimetic Substrates.

Depositing cationic polyelectrolytes (PEs) from micellar solutions that include surfactants (SU) onto surfaces is a rich, complex, highly relevant, and challenging topic that covers a broad field of practical applications (e.g., from industrial to personal care). The role of the molecular architecture of the constituents of the PEs are often overruled, or at least and either, underestimated in regard to the surface properties. In this work, we aim to evaluate the effect of a model biomimetic surface that shares the key characteristics of the extreme surface of hair and its concomitant chemo- and physisorbed properties onto the deposition of a complex PEs:SU system. To tackle out the effect of the molecular architecture of the PEs, we consider (i) a purely linear and hydrophilic PE (P100) and (ii) a PE with lateral amphiphilic chains (PegPE). Using numerical self-consistent field calculations, we show that the architecture of the constituents interfere with the surface properties in a nonintuitive way such that, depending on the amphiphilicity and hydrophilicity of the PEs and the hydrophobicity of the surface, a re-entrant adsorbing transition can be observed, the lipid coverage of the model hair surface being the unique control parameter. Such a behavior is rationalized by the anticooperative associative properties of the coacervate micelles in solution, which is also controlled by the architecture of the PEs and SU. We now expect that PEs adsorption, as a rule, is governed by the molecular details of the species in solution as well as the surface specificities. We emphasize that molecular realistic modeling is essential to rationalize and optimize the adsorption process of, for example, polymer conditioning agents in water-rinsed cosmetic or textile applications.

Benchmarking the DFT methodology for assessing antioxidant-related properties: quercetin and edaravone as case studies.

The overall objective was to identify an accurate computational electronic method to virtually screen phenolic compounds through their antioxidant and free-radical scavenging activity. The impact of a key parameter of the density functional theory (DFT) approach was studied. Performances of the 21 most commonly used exchange-correlation functionals are thus detailed in the evaluation of the main energetic parameters related to the activities of two prototype antioxidants, namely quercetin and edaravone, is reported. These functionals have been chosen among those belonging to three different families of hybrid functionals, namely global, range separated, and double hybrids. Other computational parameters have also been considered, such as basis set and solvent effects. The selected parameters, namely bond dissociation enthalpy (BDE), ionization potential (IP), and proton dissociation enthalpy (PDE) allow a mechanistic evaluation of the antioxidant activities of free radical scavengers. Our results show that all the selected functionals provide a coherent picture of these properties, predicting the same order of BDEs and PDEs. However, with respect to the reference values, the errors found at CBS-Q3 level significantly vary with the functional. Although it is difficult to evidence a global trend from the reported data, it clearly appears that LC-ωPBE, M05-2X, and M06-2X are the most suitable approaches for the considered properties, giving the lowest cumulative mean absolute errors. These methods are therefore suggested for an accurate and fast evaluation of energetic parameters related to an antioxidant activity via free radical scavenging.

Use of read-across and computer-based predictive analysis for the safety assessment of PEG cocamines.

In the European Union animal testing has been eliminated for cosmetic ingredients while the US Cosmetic Ingredient Review Expert Panel may request data from animal studies. The use of read-across and predictive toxicology provides a path for filling data gaps without additional animal testing. The PEG cocamines are tertiary amines with an alkyl group derived from coconut fatty acids and two PEG chains of varying length. Toxicology data gaps for the PEG cocamines can be addressed by read-across based on structure-activity relationship using the framework described by Wu et al. (2010) for identifying suitable structural analogs. Data for structural analogs supports the conclusion that the PEG cocamines are non-genotoxic and not expected to exhibit systemic or developmental/reproductive toxicity with use in cosmetics. Due to lack of reliable dermal sensitization data for suitable analogs, this endpoint was addressed using predictive software (TIMES SS) as a first step (Laboratory of Mathematical Chemistry). The prediction for PEG cocamines was the same as that for PEGs, which have been concluded to not present a significant concern for dermal sensitization. This evaluation for PEG cocamines demonstrates the utility of read-across and predictive toxicology tools to assess the safety of cosmetic ingredients.

Novel technologies and an overall strategy to allow hazard assessment and risk prediction of chemicals, cosmetics, and drugs with animal-free methods.

Several alternative methods to replace animal experiments have been accepted by legal bodies. An even larger number of tests are under development or already in use for non-regulatory applications or for the generation of information stored in proprietary knowledge bases. The next step for the use of the different in vitro methods is their combination into integrated testing strategies (ITS) to get closer to the overall goal of predictive "in vitro-based risk evaluation processes." We introduce here a conceptual framework as the basis for future ITS and their use for risk evaluation without animal experiments. The framework allows incorporation of both individual tests and already integrated approaches. Illustrative examples for elements to be incorporated are drawn from the session "Innovative technologies" at the 8th World Congress on Alternatives and Animal Use in the Life Sciences, held in Montreal, 2011. For instance, LUHMES cells (conditionally immortalized human neurons) were presented as an example for a 2D cell system. The novel 3D platform developed by InSphero was chosen as an example for the design and use of scaffold-free, organotypic microtissues. The identification of critical pathways of toxicity (PoT) may be facilitated by approaches exemplified by the MatTek 3D model for human epithelial tissues with engineered toxicological reporter functions. The important role of in silico methods and of modeling based on various pre-existing data is demonstrated by Altamira's comprehensive approach to predicting a molecule's potential for skin irritancy. A final example demonstrates how natural variation in human genetics may be overcome using data analytic (pattern recognition) techniques borrowed from computer science and statistics. The overall hazard and risk assessment strategy integrating these different examples has been compiled in a graphical work flow.

Reprint of: Characterising hepatic mitochondrial function as a model for systemic toxicity: a commentary.

A major challenge in toxicology is the development of non-animal methods for the assessment of human health risks that might result from repeated systemic exposure. We present here a perspective that considers the opportunities that computational modelling methods may offer in addressing this challenge. Our approach takes the form of a commentary designed to inform responses to future calls for research in predictive toxicology. It is considered essential that computational model-building activities be at the centre of the initiative, driving an iterative process of development, testing and refinement. It is critical that the models provide mechanistic understanding and quantitative predictions. The aim would be to predict effects in humans; in order to help define a challenging but yet feasible initial goal the focus would be on liver mitochondrial toxicity. This will inevitably present many challenges that naturally lead to a modular approach, in which the overall problem is broken down into smaller, more self-contained sub-problems that will subsequently need to be connected and aligned to develop an overall understanding. The project would investigate multiple modelling approaches in order to encourage links between the various disciplines that hitherto have often operated in isolation. The project should build upon current activities in the wider scientific community, to avoid duplication of effort and to ensure that investment is maximised. Strong leadership will be required to ensure alignment around a set of common goals that would be derived using a problem-statement driven approach. Finally, although the focus here is on toxicology, there is a clear link to the wider challenges in systems medicine and improving human health.

Development of a mechanistic SAR model for the detection of phototoxic chemicals and use in an integrated testing strategy.

Phototoxicity is of increasing concern in dermatology, since modern lifestyle is often associated with exposure to sunlight. The most commonly reported process is via oxidative reactions. Therefore characterizing the "photo-pro-oxidant" potential of a compound early in its industrial development is of utmost interest, especially for compounds likely to undergo sunlight exposure in skin. Today there is a need for filtering compounds to be tested in the 3T3 neutral red uptake in vitro test for phototoxicity since testing requires resources. A computational model aiming at predicting the mechanisms that imply the generation of reactive oxygen species was developed using a diverse set of 56 chemicals having 3T3 NRU data. An historical mechanistic (Q)SAR model developed for polycyclic aromatic hydrocarbons was used to derive the new mechanistic model: descriptors were selected upfront to describe the modeled phenomenon. The historical parabolic relationships between phototoxicity and the energy gap (E(GAP)) between energies of the highest occupied molecular orbital and the lowest unoccupied molecular orbital was confirmed. The model predicts chemicals to be "phototoxic or photodegradable", or "non-phototoxic and non-photodegradable". A four-step testing strategy is proposed to enable the reduction of experimental testing with the in silico model implemented as a first screen.

Development of a multivariate statistical model to predict peroxisome proliferation in the rat, based on urinary 1H-NMR spectral patterns.

A previous report of this work (Ringeissen et al. 2003) described the use of nuclear magnetic resonance (NMR) spectroscopy coupled with multivariate statistical data analysis (MVDA) to identify novel biomarkers of peroxisome proliferation (PP) in Wistar Han rats. Two potential biomarkers of peroxisome proliferation in the rat were described, N-methylnicotinamide (NMN) and N-methyl-4-pyridone-3-carboxamide (4PY). The inference from these results was that the tryptophan-nicotinamide adenine dinucleotide (NAD(+)) pathway was altered in correlation with peroxisome proliferation, a hypothesis subsequently confirmed by TaqMan analysis of the relevant genes encoding two key enzymes in the pathway, aminocarboxymuconate-semialdehyde decarboxylase (EC 4.1.1.45) and quinolinate phosphoribosyltransferase (EC 2.4.2.19). The objective of the present study was to investigate these data further and identify other metabolites in the NMR spectrum correlating equally with PP. MVDA Partial Least Squares (PLS) models were constructed that provided a better prediction of PP in Wistar Han rats than levels of 4PY and NMN alone. The resulting Wistar Han rat predictive models were then used to predict PP in a test group of Sprague Dawley rats following administration of fenofibrate. The models predicted the presence or absence of PP (above on arbitrary threshold of >2-fold mean control) in all Sprague Dawley rats in the test group.

Potential urinary and plasma biomarkers of peroxisome proliferation in the rat: identification of N-methylnicotinamide and N-methyl-4-pyridone-3-carboxamide by 1H nuclear magnetic resonance and high performance liquid chromatography.

This study identified two potential novel biomarkers of peroxisome proliferation in the rat. Three peroxisome proliferator-activated receptor (PPAR) ligands, chosen for their high selectivity towards the PPARalpha, -delta and -gamma subtypes, were given to rats twice daily for 7 days at doses known to cause a pharmacological effect or peroxisome proliferation. Fenofibrate was used as a positive control. Daily treatment with the PPARalpha and -delta agonists produced peroxisome proliferation and liver hypertrophy. 1H nuclear magnetic resonance spectroscopy and multivariate statistical data analysis of urinary spectra from animals given the PPARalpha and -delta agonists identified two new potential biomarkers of peroxisome proliferation--N-methylnicotinamide (NMN) and N-methyl-4-pyridone-3-carboxamide (4PY)--both endproducts of the tryptophan-nicotinamide adenine dinucleotide (NAD+) pathway. After 7 days, excretion of NMN and 4PY increased 24- and three-fold, respectively, following high doses of fenofibrate. The correlation between total NMN excretion over 7 days and the peroxisome count was r=0.87 (r2=0.76). Plasma NMN, measured using a sensitive high performance liquid chromatography method, was increased up to 61-fold after 7 days' treatment with high doses of fenofibrate. Hepatic gene expression of aminocarboxymuconate-semialdehyde decarboxylase (EC 4.1.1.45) was downregulated following treatment with the PPARalpha and -delta agonists. The decrease was up to 11-fold compared with controls in the groups treated with high doses of fenofibrate. This supports the link between increased NMN and 4PY excretion and regulation of the tryptophan-NAD+ pathway in the liver. In conclusion, NMN, and possibly other metabolites in the pathway, are potential non-invasive surrogate biomarkers of peroxisome proliferation in the rat.