In-silico approach for drug induced liver injury prediction: Recent advances
| Autor: | Neha Saini, Sadhna Sharma, S. R. Bakshi |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
0301 basic medicine
Drug medicine.medical_specialty Cirrhosis media_common.quotation_subject Toxicology Models Biological Risk Assessment Machine Learning 03 medical and health sciences Structure-Activity Relationship Cholestasis medicine Animals Humans Computer Simulation Medical prescription Intensive care medicine media_common Liver injury Hepatitis Molecular Structure business.industry Computational Biology General Medicine Jaundice medicine.disease Clinical trial 030104 developmental biology Liver medicine.symptom Chemical and Drug Induced Liver Injury business Biomarkers |
| Zdroj: | Toxicology letters. 295 |
| ISSN: | 1879-3169 |
| Popis: | Drug induced liver injury (DILI) is the prime cause of liver disfunction which may lead to mild non-specific symptoms to more severe signs like hepatitis, cholestasis, cirrhosis and jaundice. Not only the prescription medications, but the consumption of herbs and health supplements have also been reported to cause these adverse reactions resulting into high mortality rates and post marketing withdrawal of drugs. Due to the continuously increasing DILI incidences in recent years, robust prediction methods with high accuracy, specificity and sensitivity are of priority. Bioinformatics is the emerging field of science that has been used in the past few years to explore the mechanisms of DILI. The major emphasis of this review is the recent advances of in silico tools for the diagnostic and therapeutic interventions of DILI. These tools have been developed and widely used in the past few years for the prediction of pathways induced from both hepatotoxic as well as hepatoprotective Chinese drugs and for the identification of DILI specific biomarkers for prognostic purpose. In addition to this, advanced machine learning models have been developed for the classification of drugs into DILI causing and non-DILI causing. Moreover, development of 3 class models over 2 class offers better understanding of multi-class DILI risks and at the same time providing authentic prediction of toxicity during drug designing before clinical trials. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full Text from ScienceDirect