Details

Autor(en) / Beteiligte

• Jyakhwo, Susan
• Serov, Nikita
• Dmitrenko, Andrei
• Vinogradov, Vladimir V.

Titel

Machine Learning Reinforced Genetic Algorithm for Massive Targeted Discovery of Selectively Cytotoxic Inorganic Nanoparticles

Ist Teil von

• Small (Weinheim an der Bergstrasse, Germany), 2024-02, Vol.20 (6), p.e2305375-n/a

Ort / Verlag

Germany: Wiley Subscription Services, Inc

Erscheinungsjahr

2024

Quelle

MEDLINE

Beschreibungen/Notizen

• Nanoparticles (NPs) have been employed as drug delivery systems (DDSs) for several decades, primarily as passive carriers, with limited selectivity. However, recent publications have shed light on the emerging phenomenon of NPs exhibiting selective cytotoxicity against cancer cell lines, attributable to distinct metabolic disparities between healthy and pathological cells. This study revisits the concept of NPs selective cytotoxicity, and for the first time proposes a high‐throughput in silico screening approach to massive targeted discovery of selectively cytotoxic inorganic NPs. In the first step, this work trains a gradient boosting regression model to predict viability of NP‐treated cell lines. The model achieves mean cross‐validation (CV) Q2 = 0.80 and root mean square error (RMSE) of 13.6. In the second step, this work develops a machine learning (ML) reinforced genetic algorithm (GA), capable of screening >14 900 candidates/min, to identify the best‐performing selectively cytotoxic NPs. As proof‐of‐concept, DDS candidates for the treatment of liver cancer are screened on HepG2 and hepatocytes cell lines resulting in Ag NPs with selective toxicity score of 42%. This approach opens the door for clinical translation of NPs, expanding their therapeutic application to a wider range of chemical space of NPs and living organisms such as bacteria and fungi. Nanoparticles (NPs) are often used as passive drug carriers, although some are known to exhibit selective cytotoxicity against cancer cells. In this study, a high‐throughput in silico screening approach is developed by merging a gradient boosting regression model with a genetic algorithm (GA) for massive discovery of selectively cytotoxic inorganic NPs with high potential for cancer treatment.