Scientific Context of the Project
The rapid rise of nanotechnology has resulted in a parallel rise in the number of products containing nanomaterials. The unusual properties that nano forms of materials exhibit relative to the bulk has driven intense research interest and relatively rapid adoption by industry. Regulatory agencies are charged with protecting workers, the public, and the environment from any adverse effects of nanomaterials that may also arise because of these novel physical and chemical properties. They need data and models that allow them to flag nanomaterials that may be of concern, while balancing potential stifling of commercial innovation.
Life sciences are progressively becoming more computational, relying on data-driven models to assist or replace experimental testing. Unlike bulk chemicals where we have large datasets to learn from, a major constraint hampering the development of models that can robustly predict nanomaterial properties and activity is the frequent lack of adequate descriptor data upon which to base estimates of model parameters. Computed theoretical descriptors that capture important structural properties of nanomaterials provide diverse sources of chemical properties and a broad coverage of the vast chemical property space describing all possible nanomaterials. Experimentally derived descriptors (e.g. size shape, solubility, agglomeration, etc) are of very limited use in machine learning models of biological effects of nanomaterials, not only because of their time and resource intensiveness, but also because they are not available for designed or otherwise hypothetical materials not yet synthesized.
The core principle of machine learning in predictive toxicity domain is the expectation that structurally similar compounds will have similar biological activities. Molecular descriptors are commonly used in studies of molecular similarity to quantify the degree of structural overlap. However, almost all existing theoretical descriptors currently used are not nano-specific, meaning they are incapable of reliably discriminating between different-size forms of the same chemical substance. Development of novel descriptors that capture the specificity of nanoscale properties and the changes they undergo in different biological environments remains a challenging task, and will be an area of active research for some time.
Innovative Aspects of the Project
The outcomes of the project will help develop innovative and feasible safer-by-design strategies for nanomaterials that are commonly used in biomedical applications and enable inherently safer design of advanced materials through structural manipulations. Thanks to the computational models developed using novel nanodescriptors, it will be possible to predict toxicity of nanomaterials at the design stage.
Research Environment and Infrastructure
The selected candidate will have access to the research infrastructure available at Izmir Institute of Technology and Izmir Biomedicine and Genome Center. When a specific instrument or expertise is needed, all other national laboratories will also be contacted.
Asst. Prof. Ceyda Öksel Karakuş (IZTECH)
Prof. Hasan Sahin (IZTECH)
Assoc. Prof. Gökhan Karakülah (IBG)
İzmir Institute of Technology, Graduate School, Urla/İzmir
İzmir Institute of Technology, Graduate School
PhD in Bioengineering
University of Antwerp, Antwerp, Belgium
Siemens Healthineers (TR) and Istanbul Health Industry Cluster (ISEK)