Toward a More Realistic QSAR Approach to Predicting Metal Toxicity

Abstract

The subject of this paper is to present molecular descriptors representing the electronegativity of OMO (occupied molecular orbital) and UMO (unoccupied molecular orbital) quantum molecular states that could be used to obtain information on the mechanism of electron transfer between metal compound and biological receptor. The molecular descriptors that were used suggested that the (s,p) and (d N ) metal ions have different mechanisms of interaction with the receptor. This result explains why the correlation activity -descriptor is rather poor or practically does not exist when all metal ions are analyzed together (irrespective of their valence shell). Since this interaction is the last event of the long chain of processes of the metal ion up to the biological target, such molecular descriptors could be used together with other descriptors in QSAR models for prediction of biological activity.

Development of structure–activity relationship for metal oxide nanoparticles

“…33 The hydration enthalpy, which provides a measure of the affinity of the metal ion for water molecules, has been proposed as a relevant descriptor for correlating the toxicity of metal ions. 33,47,48 Moreover, it has been postulated that greater transport across cellular membranes should be expected for metal ions of lower hydration enthalpy (ÀDH hyd ). 49 Indeed, studies have suggested that the formation and stability of hydrated metal ions and their redox potential 47 in biological aqueous media may be key to their impact on biological activity.…”

“…49 Indeed, studies have suggested that the formation and stability of hydrated metal ions and their redox potential 47 in biological aqueous media may be key to their impact on biological activity. 48,49 In order to interpret nano-SAR predictions regarding the likelihood that a NP may be classied as either toxic or nontoxic, one can quantify the probability of NP x as being toxic (i.e., P(T|x)) from the intrinsic probability function used in the classication model. Given that the SVM based nano-SAR model was of the highest performance among the different nano-SARs, it was selected to demonstrate the relevance of establishing a statistically meaningful applicability domain 50 as well as decision boundaries.…”

Evaluating the Impact of Uncertainties in Clearance and Exposure When Prioritizing Chemicals Screened in High-Throughput Assays

“…In a previous study, 10 out of 30 active chemicals identified in the ToxCast AChE inhibition in vitro assay were found to be unable to reach AChE in brain due to limited exposure or their ADME properties. Of the remaining 20 active chemicals, five were excluded from the current study: (1) two pharmaceuticals (SSR241586, SSR150106) with no exposure or ADME information, (2) mercuric chloride, which is a metal salt with ADME properties that are difficult to predict using traditional cheminformatics methods, and (3) gentian violet and 1-benzylquinolinium, which are pan-assay interference compounds capable of exhibiting signs of in vitro activity due to reasons other than binding to the molecular target …”

“…In a previous study, 17 10 out of 30 active chemicals identified in the ToxCast AChE inhibition in vitro assay were found to be unable to reach AChE in brain due to limited exposure or their ADME properties. Of the remaining 20 active chemicals, five were excluded from the current study: (1) two pharmaceuticals (SSR241586, SSR150106) with no exposure or ADME information, (2) mercuric chloride, which is a metal salt with ADME properties that are difficult to predict using traditional cheminformatics methods, 18 and (3) gentian violet and 1-benzylquinolinium, which are pan-assay interference compounds capable of exhibiting signs of in vitro activity due to reasons other than binding to the molecular target. 19 The previous study 17 also identified 22 possible false negatives out of chemicals considered inactive in the same assay, based on their structural similarities to active chemicals.…”