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Diagnostic imaging of smart genetically engineered nanomedicines

Investigators: J. Andrew MacKay, Ph.D.; Peter Conti, M.D., Ph.D.
Innovation: developing a timepoint-independent pharmacokinetic modeling strategy
Clinical significance: improving personalized medicine by more accurately measuring the effects of a particular treatment on an individual patient

Investigators: J. Andrew MacKay, Ph.D.; Peter Conti, M.D., Ph.D.
Innovation: developing a timepoint-independent pharmacokinetic modeling strategy
Clinical significance: improving personalized medicine by more accurately measuring the effects of a particular treatment on an individual patient

Nanomedicine candidates typically lack imaging components, which makes it impossible to assess their distribution throughout the body or predict the
effectiveness of the drugs they carry. For example, some therapeutic carriers peak at their concentration in a tumor within two hours, while others peak later. Longer accumulation within a tumor might mean better response to a drug, but this hypothesis has been impossible to test in a research setting, much less in a clinical one. Without the ability to quantify distribution in the body, it will remain challenging to determine which patients might benefit from a given nanomedicine.

The team developed a math-based model that measures all time points to form a single parameter to more accurately assess how to target an individual
patient’s therapy. When combined with such modeling, a diagnostic scan of the nanomedicine may be able to predict the dose, dose frequency and degree of
response to theranostic nanomedicines. This precision approach could deliver chemotherapy at much higher doses than would be achieved for patients given
unaided drugs.

The project pushes the boundaries of peptide-based drug delivery by developing protein biopharmaceuticals that self-assemble into therapeutic, trackable nanomedicines.