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Machine-Learning-Assisted Rational Design of Si─Rhodamine as Cathepsin-pH-Activated Probe for Accurate Fluorescence

Fei-Fan Xiang¹, Hong Zhang^1,2, Yan-Ling Wu¹

¹Key Laboratory of Green Chemistry and Technology of Ministry of Education, College of Chemistry, Sichuan University, Chengdu, 610064, P. R. China.

Advanced Materials (Deerfield Beach, Fla.)

|May 23, 2024

Summary

Researchers developed a novel fluorescent probe using machine learning to improve cancer imaging. This new probe, SiR─CTS-pH, enhances the visualization of hepatocellular carcinoma, leading to more precise tumor removal and better patient outcomes.

Keywords:

fluorescence navigation fluorescent probe machine learning signal‐to‐background ratio xanthene dyes

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Area of Science:

Biomedical Engineering
Chemical Biology
Molecular Imaging

Background:

High-performance fluorescent probes are essential for accurate fluorescence-guided imaging.
Precise tissue delineation minimizes the removal of healthy tissue during surgical procedures.
Xanthene dyes are a key class of molecules used in developing fluorescent probes.

Purpose of the Study:

To develop a machine-learning-assisted strategy for investigating xanthene dyes.
To construct a quantitative prediction model for synthesizing novel fluorescent molecules with pH responsiveness.
To create and evaluate a novel sequentially activated fluorescent probe for enhanced cancer imaging.

Main Methods:

A machine-learning-assisted strategy was employed to analyze existing xanthene dyes.
A quantitative prediction model was built to guide the synthesis of new fluorescent molecules.
Two novel Si─rhodamine derivatives were synthesized, leading to the cathepsin/pH sequentially activated probe SiR─CTS-pH.

Main Results:

The novel probe SiR─CTS-pH demonstrated a higher signal-to-noise ratio in fluorescence imaging compared to single-analyte probes.
SiR─CTS-pH exhibited strong differentiation capabilities for tumor cells and tissues.
The probe accurately discriminated between complex hepatocellular carcinoma tissues and normal tissues.

Conclusions:

Machine-learning-assisted design enables the rational synthesis of advanced fluorescent molecules.
The developed SiR─CTS-pH probe shows significant potential for clinical applications in cancer diagnosis and surgical guidance.
This approach broadens the development of xanthene dyes and provides advanced tools for researchers in molecular imaging.