In the field of theragnostic, diagnostic nanoparticles are designed to collect highly patient-selective disease profiles, which is then leveraged by a set of nanotherapeutics to improve the treatment results. Despite their early promise, high inter-patient and intra-tumoral heterogeneities make any rational design and analysis of these theragnostic platforms extremely problematic. Recent advances with the deep learning-based tools may help bridge this gap, using pattern analysis and classification algorithms for better diagnostic precision and therapeutic outcome. Triple-negative breast cancer (TNBC) is a conundrum because of the complex molecular diversity, making its diagnosis and therapy challenging. Currently, there is no FDA-approved targeted therapy for this population of breast cancer. To address these challenges, we designed a high-throughput method to predict the cellular internalization and evaluating nanoparticles (NPs) against different cancer stages using artificial intelligence. Utilizing this approach, we can optimize the nanomaterial properties by quantitatively model the NPs-cellular internalization providing an optimum structure–internalization response against endocytic inhibitors for a given NPs. This methodology could predict the structure–internalization response of the evaluated nanoparticles with Q<sup>2</sup>=0.9. Therefore, it can reduce the effort by minimizing the number of nanoparticles that need to be tested and could be utilized as a screening tool for designing nanotherapeutics. Following this, we have proposed a diagnostic nanomaterial used to assemble a patient-specific cancer profile with the assistant of machine-learning (ML). These nanomaterials comprising eight carbon nanoparticles (CNPs) with multifarious surface chemistries that can differentiate normal breast cells from cancerous cells, then subclassify TNBC cells vs. non-TNBC cells, and within the TNBC group. Here we demonstrate for the first time that a combination of machine learning (ML) algorithm and characteristic cellular uptake responses for individual cancer cell types can be successfully used to classify various cancer cell types. Artificial neural network (AAN) algorithm has also been successfully used in identifying the type of cancer cells from 36 unknown cancer samples with an overall accuracy of &gt;98%, providing potential applications in cancer diagnostics.

Integrating machine-learning and nanomaterial for precision theranostic nanomedicine

Macromolecular Chemistry: The Second Century

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ACS Spring 2020 National Meeting & Expo

The official archive and repository for all ACS Meetings & Expositions abstracts and select posters or presentations.