Integrating Artificial Intelligence and Nanotechnology for Precision Cancer Medicine

Abstract

Artificial intelligence (AI) and nanotechnology are two fields that are instrumental in realizing the goal of precision medicine-tailoring the best treatment for each cancer patient. Recent conversion between these two fields is enabling better patient data acquisition and improved design of nanomaterials for precision cancer medicine. Diagnostic nanomaterials are used to assemble a patient-specific disease profile, which is then leveraged, through a set of therapeutic nanotechnologies, to improve the treatment outcome. However, high intratumor and interpatient heterogeneities make the rational design of diagnostic and therapeutic platforms, and analysis of their output, extremely difficult. Integration of AI approaches can bridge this gap, using pattern analysis and classification algorithms for improved diagnostic and therapeutic accuracy. Nanomedicine design also benefits from the application of AI, by optimizing material properties according to predicted interactions with the target drug, biological fluids, immune system, vasculature, and cell membranes, all affecting therapeutic efficacy. Here, fundamental concepts in AI are described and the contributions and promise of nanotechnology coupled with AI to the future of precision cancer medicine are reviewed.

Keywords: artificial intelligence; big data; cancer; nanotechnology; precision medicine.

Figure 1

Advancing from single biomarker sensing to multiplex sensing. Diagnostic screening of patient-derived liquid biopsies with single biomarkers sensors demonstrates high sensitivity and specificity, but is limited by inter-patient heterogeneity in biomarker expression and the low number of approved single biomarkers. Integration of AI in the data analysis of multiplex nanosensors that can detect a number of target molecules enables identification of disease-specific biomarker patterns. These patterns can be used for patient screening, overcoming the variability in biomarker expression.

Figure 2

Exploiting AI and nanomedicine for tailoring a patient-specific treatment regime. Initial drug screening with computational methods based on the patient's specific omics profile will provide a list of drugs with therapeutic potential. These drugs can then be tested in situ with nanoparticle-based technologies in order to select the optimal treatment regime. Applying nanotheranostic methods combining the nanomedicine with an imaging agent will allow to tune the treatment protocol by monitoring the drug's pharmacokinetics and release in the target site.

Figure 3

Differences between computational models and machine learning algorithms: prediction of drug encapsulation as an example. Computational models depend on a devised mathematical model for simulation of the physio-chemical process and therefore prior physical, chemical and biological knowledge of the mechanisms is essential. Machine learning algorithms on the other hand, are based on training on large datasets of previous examples and detecting key features and correlations in the data for increasing the prediction accuracy.

Figure 4

Computational methods contribute to various aspects of nanoparticle design. Current machine-learning algorithms and computational models provide tools for predicting the nanoparticles' size and charge, drug encapsulation efficiency, interactions with biological membranes, biological fluids and drug release kinetics.