Chemical individuality: concept and outlook

Toward Kidney-Specific Causality Assessment Tool

PURPOSE

Current nonspecific causality assessment tools lack the assessment of drug-induced acute kidney injury (DIAKI). We recently published an editorial letter for developing a specific causality assessment tool for DIAKI. The purpose of the present review was to suggest the possible required parameters and outline the path to developing a kidney-specific causality assessment tool (KSCAT).

METHODS

A stepwise approach for developing a KSCAT is important as this will be first version of this new tool. Thus, as a first step, we performed a screening of previously published articles on nonspecific and liver-specific causality assessment tools to define possible parameters. The suggested parameters for KSCAT fall into 3 categories: (1) drug-related; (2) kidney-related; and (3) terminology. A tri-polar method was then created that consists of definitive adverse drug reactions (ADRS), terminology, and without ADRS to suggest that the new KSCAT be efficient, specific, user friendly, and less time-consuming. Finally, a pyramid model is suggested to offer the perspectives of experts in the fields of pharmacovigilance, pharmacoepidemiology, and nephrology, as well as decision makers, while developing a KSCAT.

FINDINGS

Causality assessment tools, either nonspecific or organ-specific, fall into 3 categories: (1) expert judgment; (2) algorithms; and (3) probabilistic methods. None of the current causality assessment tools is sufficient for assessing the causality of kidney-related ADRs and for screening the expanded definition of ADR included in European Union Directive 2010/84/EU.

IMPLICATIONS

The causal relationship between drug(s) and DIAKI may be difficult and may not be assessed appropriately with the use of nonspecific tools or approaches. The aim of this article was to reiterate the need for KSCAT development and to propose the associated steps by stating the main principles: namely, the definition of ADR, suggested parameters to be included in the KSCAT, and integration of technology. Our ultimate desire is to invite experts to develop this new tool using an interdisciplinary approach and to benefit from our review in pursuing the next steps. The development of a KSCAT should start with regular and interdisciplinary consortium meetings of experts; the tool should then be tested for its usability, specificity, and practicality; and, finally, it should be used in real-life pharmacovigilance practices, as well as in research by health authorities, regulators, decision-makers, scientists, and clinicians. A KSCAT would support the provision of reliable and reproducible measures of the relationship likelihood in suspected cases of ADR to overcome uncertainty and provide a standardized approach. (Clin Ther. 2022;44:XXX-XXX) © 2022 Elsevier HS Journals, Inc.

Metabolomics: a challenge for detecting and monitoring inborn errors of metabolism

Timely newborn screening and genetic profiling are crucial in early recognition and treatment of inborn errors of metabolism (IEMs). A proposed nosology of IEMs has inserted 1,015 well-characterized IEMs causing alterations in specific metabolic pathways. With the increasing expansion of metabolomics in clinical biochemistry and laboratory medicine communities, several research groups have focused their interest on the analysis of metabolites and their interconnections in IEMs. Metabolomics has the potential to extend metabolic information, thus allowing to achieve an accurate diagnosis for the individual patient and to discover novel IEMs. Structural and functional information on 247 metabolites associated with 147 IEMs and 202 metabolic pathways involved in various IEMs have been reported in the human metabolome data base (HMDB). For each metabolic gene, a new computational approach can be developed for predicting a set of metabolites, whose concentration is predicted to change after gene knockout in urine, blood and other biological fluids. Both targeted and untargeted mass spectrometry (MS)-based metabolomic approaches have been used to expand the range of disease-associate metabolites. The quantitative targeted approach, in conjunction with chemometrics, can be considered a basic tool for validating known diagnostic biomarkers in various metabolic disorders. The untargeted approach broadens the identification of new biomarkers in known IEMs and allows pathways analysis. Urine is an ideal biological fluid for metabolomics in neonatology; however, the lack of standardization of preanalytical phase may generate potential interferences in metabolomic studies. The integration of genomic and metabolomic data represents the current challenge for improving diagnosis and prognostication of IEMs. The goals consist in identifying both metabolically active loci and genes relevant to a disease phenotype, which means deriving disease-specific biological insights.