Side effect prediction based on drug-induced gene expression profiles and random forest with iterative feature selection.
Arzu CakirMelisa TuncerHilal Taymaz-NikerelÖzlem UlucanPublished in: The pharmacogenomics journal (2021)
One in every ten drug candidates fail in clinical trials mainly due to efficacy and safety related issues, despite in-depth preclinical testing. Even some of the approved drugs such as chemotherapeutics are notorious for their side effects that are burdensome on patients. In order to pave the way for new therapeutics with more tolerable side effects, the mechanisms underlying side effects need to be fully elucidated. In this work, we addressed the common side effects of chemotherapeutics, namely alopecia, diarrhea and edema. A strategy based on Random Forest algorithm unveiled an expression signature involving 40 genes that predicted these side effects with an accuracy of 89%. We further characterized the resulting signature and its association with the side effects using functional enrichment analysis and protein-protein interaction networks. This work contributes to the ongoing efforts in drug development for early identification of side effects to use the resources more effectively.
Keyphrases
- drug induced
- liver injury
- protein protein
- gene expression
- clinical trial
- end stage renal disease
- small molecule
- climate change
- machine learning
- ejection fraction
- newly diagnosed
- chronic kidney disease
- deep learning
- dna methylation
- poor prognosis
- peritoneal dialysis
- bioinformatics analysis
- genome wide
- patient reported outcomes
- randomized controlled trial
- optical coherence tomography
- emergency department
- magnetic resonance imaging
- prognostic factors
- image quality
- patient reported
- open label
- binding protein
- electronic health record
- mesenchymal stem cells
- stem cells
- transcription factor