PMID- 37771186
OWN - NLM
STAT- MEDLINE
DCOM- 20240209
LR  - 20240209
IS  - 1613-6829 (Electronic)
IS  - 1613-6810 (Linking)
VI  - 20
IP  - 6
DP  - 2024 Feb
TI  - Machine Learning Reinforced Genetic Algorithm for Massive Targeted Discovery of 
      Selectively Cytotoxic Inorganic Nanoparticles.
PG  - e2305375
LID - 10.1002/smll.202305375 [doi]
AB  - 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.
CI  - (c) 2023 Wiley-VCH GmbH.
FAU - Jyakhwo, Susan
AU  - Jyakhwo S
AUID- ORCID: 0000-0003-3333-4202
AD  - International Institute "Solution Chemistry of Advanced Materials and 
      Technologies", ITMO University, Saint-Petersburg, 191002, Russian Federation.
FAU - Serov, Nikita
AU  - Serov N
AD  - International Institute "Solution Chemistry of Advanced Materials and 
      Technologies", ITMO University, Saint-Petersburg, 191002, Russian Federation.
FAU - Dmitrenko, Andrei
AU  - Dmitrenko A
AD  - International Institute "Solution Chemistry of Advanced Materials and 
      Technologies", ITMO University, Saint-Petersburg, 191002, Russian Federation.
FAU - Vinogradov, Vladimir V
AU  - Vinogradov VV
AUID- ORCID: 0000-0002-5081-4876
AD  - International Institute "Solution Chemistry of Advanced Materials and 
      Technologies", ITMO University, Saint-Petersburg, 191002, Russian Federation.
LA  - eng
GR  - 21-73-10150/Russian Science Foundation/
PT  - Journal Article
DEP - 20230928
PL  - Germany
TA  - Small
JT  - Small (Weinheim an der Bergstrasse, Germany)
JID - 101235338
RN  - 0 (Antineoplastic Agents)
SB  - IM
MH  - Humans
MH  - *Nanoparticles/chemistry
MH  - *Antineoplastic Agents
MH  - *Liver Neoplasms
MH  - Machine Learning
MH  - Algorithms
OTO - NOTNLM
OT  - genetic algorithm
OT  - machine learning
OT  - nanoparticles
OT  - selective toxicity
OT  - target discovery
EDAT- 2023/09/29 06:44
MHDA- 2024/02/09 06:42
CRDT- 2023/09/29 02:16
PHST- 2023/09/11 00:00 [revised]
PHST- 2023/06/27 00:00 [received]
PHST- 2024/02/09 06:42 [medline]
PHST- 2023/09/29 06:44 [pubmed]
PHST- 2023/09/29 02:16 [entrez]
AID - 10.1002/smll.202305375 [doi]
PST - ppublish
SO  - Small. 2024 Feb;20(6):e2305375. doi: 10.1002/smll.202305375. Epub 2023 Sep 28.