Drug induced kidney injury is one of the leading causes of failure of drug development programs in the clinic. Early prediction of renal toxicity potential of drugs is crucial to the success of drug candidates in the clinic. The dynamic nature of the functioning of the kidney and the presence of drug uptake proteins introduce additional challenges in the prediction of renal injury caused by drugs. Renal injury due to drugs can be caused by a wide variety of mechanisms and can be broadly classified as toxic or obstructive. Several biomarkers are available for in vitro and in vivo detection of renal injury. In vitro static and dynamic (microfluidic) cellular models and preclinical models can provide valuable information regarding the toxicity potential of drugs. Differences in pharmacology and subsequent disconnect in biomarker response, differences in the expression of transporter and enzyme proteins between in vitro to in vivo systems and between preclinical species and humans are some of the limitations of current experimental models. The progress in microfluidic (kidney-on-chip) platforms in combination with the ability of 3-dimensional cell culture can help in addressing some of these issues in the future. Finally, newer in silico and computational techniques like physiologically based pharmacokinetic modeling and machine learning have demonstrated potential in assisting prediction of drug induced kidney injury.
Keywords: Drug-induced kidney injury (DIKI); biomarkers; machine learning; nephrotoxicity; physiologically based pharmacokinetic (PBPK) modeling.