Gutenberg Open Science: Selection of safe artemisinin derivatives using a machine learning-based cardiotoxicity platform and in vitro and in vivo validation

Please use this identifier to cite or link to this item: http://doi.org/10.25358/openscience-7212

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DC Field	Value	Language
dc.contributor.author	Kadioglu, Onat	-
dc.contributor.author	Klauck, Sabine M.	-
dc.contributor.author	Fleischer, Edmond	-
dc.contributor.author	Shan, Letian	-
dc.contributor.author	Efferth, Thomas	-
dc.date.accessioned	2022-06-27T08:42:34Z	-
dc.date.available	2022-06-27T08:42:34Z	-
dc.date.issued	2021	-
dc.identifier.uri	https://openscience.ub.uni-mainz.de/handle/20.500.12030/7226	-
dc.description.abstract	The majority of drug candidates fails the approval phase due to unwanted toxicities and side effects. Establishment of an effective toxicity prediction platform is of utmost importance, to increase the efficiency of the drug discovery process. For this purpose, we developed a toxicity prediction platform with machine-learning strategies. Cardiotoxicity prediction was performed by establishing a model with five parameters (arrhythmia, cardiac failure, heart block, hypertension, myocardial infarction) and additional toxicity predictions such as hepatotoxicity, reproductive toxicity, mutagenicity, and tumorigenicity are performed by using Data Warrior and Pro-Tox-II software. As a case study, we selected artemisinin derivatives to evaluate the platform and to provide a list of safe artemisinin derivatives. Artemisinin from Artemisia annua was described first as an anti-malarial compound and later its anticancer properties were discovered. Here, random forest feature selection algorithm was used for the establishment of cardiotoxicity models. High AUC scores above 0.830 were achieved for all five cardiotoxicity indications. Using a chemical library of 374 artemisinin derivatives as a case study, 7 compounds (deoxydihydro-artemisinin, 3-hydroxy-deoxy-dihydroartemisinin, 3-desoxy-dihydroartemisinin, dihydroartemisinin-furano acetate-d3, deoxyartemisinin, artemisinin G, artemisinin B) passed the toxicity filtering process for hepatotoxicity, mutagenicity, tumorigenicity, and reproductive toxicity in addition to cardiotoxicity. Experimental validation with the cardiomyocyte cell line AC16 supported the findings from the in silico cardiotoxicity model predictions. Transcriptomic profiling of AC16 cells upon artemisinin B treatment revealed a similar gene expression profile as that of the control compound, dexrazoxane. In vivo experiments with a Zebrafish model further substantiated the in silico and in vitro data, as only slight cardiotoxicity in picomolar range was observed. In conclusion, our machine-learning approach combined with in vitro and in vivo experimentation represents a suitable method to predict cardiotoxicity of drug candidates.	en_GB
dc.language.iso	eng	de
dc.rights	CC BY	*
dc.rights.uri	https://creativecommons.org/licenses/by/4.0/	*
dc.subject.ddc	570 Biowissenschaften	de_DE
dc.subject.ddc	570 Life sciences	en_GB
dc.title	Selection of safe artemisinin derivatives using a machine learning-based cardiotoxicity platform and in vitro and in vivo validation	en_GB
dc.type	Zeitschriftenaufsatz	de
dc.identifier.doi	http://doi.org/10.25358/openscience-7212	-
jgu.type.dinitype	article	en_GB
jgu.type.version	Published version	de
jgu.type.resource	Text	de
jgu.organisation.department	FB 09 Chemie, Pharmazie u. Geowissensch.	de
jgu.organisation.number	7950	-
jgu.organisation.name	Johannes Gutenberg-Universität Mainz	-
jgu.rights.accessrights	openAccess	-
jgu.journal.title	Archives of toxicology	de
jgu.journal.volume	95	de
jgu.pages.start	2485	de
jgu.pages.end	2495	de
jgu.publisher.year	2021	-
jgu.publisher.name	Springer	de
jgu.publisher.place	Berlin u.a.	de
jgu.publisher.issn	1432-0738	de
jgu.organisation.place	Mainz	-
jgu.subject.ddccode	570	de
jgu.publisher.doi	10.1007/s00204-021-03058-4	de
jgu.organisation.ror	https://ror.org/023b0x485
Appears in collections:	JGU-Publikationen

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