Dave Winkler

Modelling and predicting the biological effects of nanomaterials.

The commercial applications of nanoparticles are growing rapidly, but we know relatively little about how nanoparticles interact with biological systems. Their value – but also their risk – is related to their nanophase properties being markedly different to those of the same material in bulk. Experiments to determine how nanoparticles are taken up, distributed, modified, and elicit any adverse effects are essential. However, cost and time considerations mean that predictive models would also be extremely valuable, particularly assisting regulators to minimize health and environmental risks. We used novel sparse machine learning methods that employ Bayesian neural networks to model three nanoparticle data sets using both linear and nonlinear machine learning methods. The first data comprised iron oxide nanoparticles decorated with 108 different molecules tested against five cell lines, HUVEC, pancreatic cancer, and three macrophage or macrophage-like lines. The second data set comprised 52 nanoparticles with various core compositions, coatings, and surface attachments. The nanoparticles were characterized using four descriptors (size, relaxivities, and zeta potential), and their biological effects on four cells lines assessed using four biological assays per cell line and four concentrations per assay. The third data set involved the biological responses to gold nanoparticles functionalized by 80 different small molecules. Nonspecific binding and binding to AChE were the biological endpoints modelled. The biological effects of nanoparticles were modelled using molecular descriptors for the molecules that decorated the nanoparticle surface. Models with good statistical quality were constructed for most biological endpoints. These proof-of-concept models show that modelling biological effects of nanomaterials is possible using modern modelling methods.

Definitive confirmation for through-space bond dominance in the outermost π-orbitals of Norbornadiene

Abstract High-resolution electron momentum spectroscopy (EMS) has been used to determine the character of the two outermost π-orbitals of norbornadiene. Definitive evidence, from comparisons of measured and calculated momentum distributions, for the dominance of the through-space interaction is presented. This through-space bond dominance is consistent with previous hypotheses based on molecular orbital considerations [Acc. Chem. Res. 4 (1971) 1; J. Am. Chem. Soc. 92 (1970) 706; J. Am. Chem. Soc. 112 (1990) 1710].

Core molecular orbital contribution to N2O isomerization as studied using theoretical electron momentum spectroscopy.

Core molecular orbital contribution to the electronic structure of N2O isomers has been studied using quantum mechanical density functional theory combined with a plane wave impulse approximation method. Momentum distributions of wave functions for inner shell molecular orbitals of the linear NNO, cyclic and linear NON isomers of N2O are calculated through the (e, 2e) differential cross sections in momentum space. This is possible because this momentum distribution is directly proportional to the modulus squared of the momentum space wave function for the molecular orbital in question. While the momentum distributions of the NNO and cyclic N2O isomers demonstrate strong atomic orbital characteristics in their core space, the outer core molecular orbitals of the linear NON isomer exhibit configuration interactions between them and the valence molecular orbitals. It is suggested that the frozen core approximation breaks down in the prediction of the electronic structure of such an isomer. Core molecular orbital contributions to the electronic structure can alter the order of total energies of the isomers and lead to incorrect conclusions of the stability among the isomers. As a result, full electron calculations should be employed in the study of N2O isomerization.

Materiomics: A Toolkit for Developing New Biomaterials

Learning Objectives • To understand the what materiomics is and why it is required • To become familiar with the various approaches used to design materiomic experiments • To learn what a polymer microarray is, what it is used for and how it is produced • To appreciate the complexity of material-biological interactions • To become familiar with computational modelling methods as applied to biomaterials • To gain an insight into how materiomics has and will continue to benefit tissue engineering Scope of the chapter The materials that are employed in regenerative medicine often react unfavourably with in vivo (induce clotting, promote bacterial infection). There is a need to develop new materials that provide the required cell response, but how is this best achieved considering the huge number of polymeric materials that could be synthesised? This chapter is a description of how materials discovery should most effectively be carried out in the developing paradigm of materiomics. We define and describe the components of this approach and methodology with the aim of providing a starting point for new users to effectively ‘dock’ into the existing research.

Chapter 4 – Computational Approaches

While experimental assessment of the biological effects of nanomaterials is essential to properly assign risk to these materials, computational methods provide considerable promise in supplementing experimental approaches. Indeed, although the biological effects of nanomaterials will be more difficult to model than those of small molecules, drugs and chemicals, recent reports have shown that quantitative structure–activity relationships and quantum chemical methods can provide very useful mechanistic and predictive information for nanomaterials. In the present chapter, we explain why in silico computational methods are an essential addition to the nanomaterial research toolkit and why nanomaterials may be more difficult to model than single molecules, and provide examples of recent successful models of the biological effects of nanomaterials.