Prediction of drug-induced kidney injury in drug discovery
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Published on: March 11, 2016
1Department of Drug Metabolism and Pharmacokinetics, Millennium Pharmaceuticals, a fully owned subsidiary of Takeda Pharmaceuticals, Cambridge, MA, USA.
Abstract:
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.
Insights
Predicting drug-induced kidney injury is vital for drug development success. Advanced models, including kidney-on-chip and computational techniques, show promise in overcoming current limitations for early toxicity detection.
Area of Science:
- Pharmacology
- Toxicology
- Biomarker Discovery
Background:
- Drug-induced kidney injury (DIKI) is a major cause of clinical trial failures.
- Predicting renal toxicity early is critical for drug candidate success.
- Kidney function dynamics and drug transporters complicate DIKI prediction.
Purpose of the Study:
- To review current methods for predicting drug-induced kidney injury.
- To highlight limitations of existing experimental models.
- To discuss emerging technologies for improved DIKI prediction.
Main Methods:
- Review of in vitro and in vivo models for renal toxicity assessment.
- Discussion of limitations including inter-species differences and biomarker response variability.
- Exploration of microfluidic kidney-on-chip platforms and 3D cell cultures.
- Evaluation of in silico and computational approaches like PBPK modeling and machine learning.
Main Results:
- Current experimental models face limitations in predicting human renal toxicity.
- Microfluidic and 3D culture systems offer potential improvements.
- In silico and computational methods show promise for DIKI prediction.
Conclusions:
- Accurate prediction of drug-induced kidney injury remains a significant challenge.
- Advancements in kidney-on-chip and computational toxicology are crucial for future drug development.
- Integrating novel in vitro and in silico approaches can enhance early identification of renal toxicants.
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