Quantum mechanical quantitative structure activity relationships to avoid mutagenicity in dental monomers

Abstract

The objective of this study was to identify through quantum mechanical quantitative structure activity relationships (Q-QSARs) chemical structures in dental monomers that influence their mutagenicity. AMPAC, a semiempirical computer program that provides quantum mechanical information for chemical structures, was applied to three series of reference chemicals: a set of methacrylates, a set of aromatic and a set of aliphatic epoxy compounds. QSAR models were developed using this chemical information together with mutagenicity data (Salmonella TA 100, Ames Test). CODESSA, a QSAR program that calculates quantum chemical descriptors from information generated by AMPAC and statistically matches these descriptors with observed biological properties was used. QSARs were developed which had r2 values exceeding 0.90 for each study series. These QSARs were used to accurately predict the mutagenicity of BISGMA. a monomer commonly used in dentistry, and two epoxy monomers with developing use in dentistry, GY-281 and UVR-6105. The Q-QSAR quantum mechanical descriptors correctly predicted the level of mutagenicity for all three compounds. The descriptors in the correlation equation pointed to components of structure that may contribute to mutagenesis. The QSARs also provided 'dose windows' for testing mutagenicity, circumventing the need for extensive dose exploration in the laboratory. The Q-QSAR method promises an approach for biomaterials scientists to predict and avoid mutagenicity from the chemicals used in new biomaterial designs.

A structure-activity relationship study on the mechanisms of methacrylate-induced toxicity using NMR chemical shift of .BETA.-carbon, RP-HPLC log P and semiempirical molecular descriptor

“…Many SARs with quantum chemical descriptors have been reported for aquatic species 13) , and the SAR of estrogen-like bisphenol A analogs for breast cancer MCF-7 was reported previously using quantum chemical descriptors 14) . Furthermore, using AMPAC, a semiempirical computer program, SARs have been used to accurately predict the mutagenicity of bis-GMA, a monomer commonly used in dentistry 15) . Similarly, conformational and quantum analyses of dental adhesive carboxylic acid and carboxylic acid anhydride monomers have been performed using a semiempirical computer program 16) .…”

“…In another study, it was found that chemical hardness played a key role in the cytotoxic activity of methoxyphenolic compounds against human oral tumor cells 19) . However, with regard to the use of computational chemistry for SAR studies of methacrylates 15,17,18) , the reports are comparatively scarce. The aim of the present study, therefore, was to re-investigate the mechanism of methacrylateinduced toxicity based on SAR.…”

An Application of QM‐QSAR to Predict and Rationalize the Refractive Index of a Wide Variety of Simple Organic/Organosilicon Molecules

“…As we have illustrated in other publications [2,3], such a computationally derived model can also provide direction for the modification of existing monomers to improve performance. Since the frequency of the dental curing light is fixed, our approach to develop-ing a QSAR for these materials is based on molecular structure leading to differences in polarizability, and hence refractive index.…”

A topological sub-structural approach to the mutagenic activity in dental monomers. 2. Cycloaliphatic epoxides

“…In this study, we have selected a data set of 15 cycloaliphatic epoxides for the mutagenicity data reported by Yourtee et al [15]. The mutagenic parameters studied here are the slopes of revertants vs. nanomoles of the test chemical in the Salmonella test strain TA100 with the natural logarithm of the slopes (ln(TA100)) used in the QSARs models.…”

“…As can be seen, these models are statistically significant because of their p , 0:05: This confirms that all variables conforming to the models are significant and essentially all of them could be used for predicting the studied property of this set of compounds. Furthermore, all models have the same number of significant variables and in all of them the same training set was used, which was formed by 15 compounds, as it is shown in Table 1 [15]. However, there are remarkable differences related with the explanation of the experimental variance ðR 2 Þ and their standard deviation ðSÞ are also different.…”