In silico prediction of drug-induced nephrotoxicity: current progress and pitfalls

Area of Science:

• Pharmacology and Toxicology
• Computational Chemistry
• Biomedical Informatics

Background:

• The kidney is a primary target for drug toxicity, leading to drug-induced nephrotoxicity (DIN).
• Current methods for assessing nephrotoxicity have limitations, necessitating novel approaches.
• In silico methods offer promising potential for predicting DIN.

Purpose of the Study:

• To review the current progress and challenges in in silico prediction of drug-induced nephrotoxicity.
• To discuss the principles and methodologies of computational models used for DIN prediction.

Main Methods:

• A comprehensive literature search was conducted using PubMed and Web of Science.
• The review covered publications from 2013 to February 2023.
• Analysis focused on computational models and their application in DIN prediction.

Main Results:

• Significant advancements have been made in in silico DIN prediction.
• Key challenges include data fidelity, model optimization, and interdisciplinary collaboration.
• Mechanistic comprehension is crucial for improving predictive accuracy.

Conclusions:

• Improving in silico DIN prediction requires integrating diverse data sources and fostering collaboration.
• A holistic approach combining computational, experimental, and clinical methods is essential.
• Enhanced understanding and management of DIN can be achieved through integrated strategies.