A critical review of adverse effects to the kidney: mechanisms, data sources, and in silico tools to assist prediction

ABSTRACT

Introduction: The kidney is a major target for toxicity elicited by pharmaceuticals and environmental pollutants. Standard testing which often does not investigate underlying mechanisms has proven not to be an adequate hazard assessment approach. As such, there is an opportunity for the application of computational approaches that utilize multiscale data based on the Adverse Outcome Pathway (AOP) paradigm, coupled with an understanding of the chemistry underpinning the molecular initiating event (MIE) to provide a deep understanding of how structural fragments of molecules relate to specific mechanisms of nephrotoxicity.

Aims covered: The aim of this investigation was to review the current scientific landscape related to computational methods, including mechanistic data, AOPs, publicly available knowledge bases and current in silico models, for the assessment of pharmaceuticals and other chemicals with regard to their potential to elicit nephrotoxicity. A list of over 250 nephrotoxicants enriched with, where possible, mechanistic and AOP-derived understanding was compiled.

Expert opinion: Whilst little mechanistic evidence has been translated into AOPs, this review identified a number of data sources of in vitro, in vivo, and human data that may assist in the development of in silico models which in turn may shed light on the interrelationships between nephrotoxicity mechanisms.

KEYWORDS:

• Kidney
• nephrotoxicity
• in silico
• computational models
• (Q)SAR
• mechanisms

Article highlights

• As a major organ of elimination and therefore subject to high exposure of compounds, the kidney is a significant target for drug and chemical induced toxicity. While identification of nephrotoxicity is possible through in vivo testing, adverse effects are still apparent with up to one third of acute renal failure cases attributed to drugs.

• In silico models associated with nephrotoxicity are very limited and even fewer differentiate between key mechanisms or incorporate mechanistic data.

• Lack of readily available data is considered to be a key limiting factor when it comes to the generation of future computational models in this field.

• An improved understanding of how novel biomarkers relate to the mechanisms of nephrotoxicity and how they are related quantitatively to each other may result in alternative sources of data and could be facilitated by statistical approaches.

• A global review of the quality of currently available kidney toxicity data is needed as well as an assessment of how these data relate to each other (e.g. cellular vs. tissue vs. organ-level effects). Information would be leveraged more readily if databases allowed for searches on both compounds and mechanistic data (including dosing information) enabling discrimination between the various nephrotoxicity endpoints.

• The generation of more AOPs for nephrotoxicity with MIE- and KE-related data being searchable in a central database linked to respective mechanistic information would also assist the development of computational models.

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Declaration of interest

The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.

Reviewer disclosures

Peer reviewers on this manuscript have no relevant financial or other relationships to disclose.

Supplemental material

Supplemental data can be accessed here.

Additional information

Funding

This paper was written as part of J Pletz’s PhD project which is funded by the 2016 Liverpool John Moores University  PhD Scholarship Scheme.