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Andrew J Holder

Publications and source records attributed to Andrew J Holder.

5 recordsLinked to original sources
PubMed2005

An application of the QM-QSAR method to predict and rationalize lipophilicity of simple monomers.

OBJECTIVES: The goal of this study is to develop a model used to predict octanol/water partition coefficients (log P(o/w)) values for a variety of potential dental materials. In this way, a primary consideration for potential toxicity and a rough estimate of solubility in various environments can be obtained. METHOD: The AM1 semiempirical quantum mechanical method (in AMPAC) was used to compute chemical data for all compounds in the study. CODESSA then imported the chemical information from AMPAC and computed a large set of informational descriptors. A quantitative structure activity relationship (QSAR) model was derived correlating experimental results from a training set of molecules with certain of the descriptors computed above. RESULTS: A training set of 92 molecules was used to derive the QSAR model and three descriptors were obtained: the molecular surface area, the total dipole moment of the molecule, and FPSA-3 (fractional atom charge weighted partial positive surface area). Various quality indicators were also computed and all fell within acceptable ranges: R(2)=0.945; adjusted R(2)=0.943; R(cv)(2)=0.940; variance inflation factors (VIF) for the descriptors above are 1.116, 1.044, and 1.162, respectively. SIGNIFICANCE: This QSAR model can be used to accurately and rapidly predict log P(o/w) values for a wide variety of small organic molecules, including potential dental monomers.

Databases, Factual↗
PubMed2005

Rational design of dental materials using computational chemistry.

One of our primary research emphases is the rational design of biomaterials. In this effort we apply a combination of theoretical and experimental approaches, both of which contribute directly to the completion of such a project. Computational chemistry has achieved routine status in modern chemical investigations, and the state-of-the-art is briefly summarized. In such a difficult endeavor as the systematic development of biomaterials, computational chemistry is a natural partner for traditional chemists. Herein, we describe several successful applications of this synergy to the design of dental biomaterials. These include reactivity modeling, sensitization, and density. We also report preliminary progress of polymerization volume change experiments on epoxides that have been specifically designed to provide standardized results for computational analysis.

Combinatorial Chemistry Techniques↗
PubMedDate not supplied

Quantum mechanical structure-activity relationship analyses for skin sensitization.

Allergic contact dermatitis (ACD) results in inflammation of the skin due to sensitization of the immunologic system to a particular substance. The sensitization process is limited by the compound's ability to both permeate and react with proteins in the integumentary system. Currently, only in vivo animal tests such as the local lymph node assay (LLNA) are recognized by regulatory authorities for risk assessment of ACD. A quantitative structure-activity relationship has been developed to predict relative potency, which allows for the prediction of relative sensitization potentials. The experimental values used in this study include EC3 values (the concentration at which the stimulation index equals 3) from LLNA tests. The predictions in this model enable categorization of the compounds into three groups on the basis of risk of sensitization and enable screening of candidate molecules using rapid SAM1 semiempirical calculations prior to animal testing. The model may also be used to reduce the number of animals subjected to testing by providing estimated concentrations required for useful data of risk assessment. The effect of averaging available literature values on predictive ability is also investigated. The model includes halogenated compounds, aromatic compounds, alcohols, aldehydes, and ketones. The computational investigation resulted in a two-descriptor model that is consistent with the assumed mechanism for sensitization.

Hypersensitivity↗
PubMedDate not supplied

Chain melting temperature estimation for phosphatidyl cholines by quantum mechanically derived quantitative structure property relationships.

Geometries for 62 phosphatidylcholines (PC) were optimized using the AM1 semiempirical quantum mechanical method. Results obtained from these calculations were used to calculate 463 descriptors for each molecule. Quantitative Structure Property Relationships (QSPR) were developed from these descriptors to predict chain melting temperatures (Tm) for the 41 PCs in the training set. After screening each QSPR for statistical validity, the Tm values predicted by each statistically valid QSPR were compared to corresponding Tm values extracted from the literature. The most predictive, chemically meaningful QSPR provided Tm values which agreed with literature values to within experimental error. This QSPR was used to predict Tm values for the remaining 21 PCs to provide external validation for the model. These values also agreed with literature values to within experimental error. The descriptor developed by the final QSPR was the second order average information content, a topological information-theoretical descriptor.

Calorimetry, Differential Scanning↗