A Novel Lipidomics-Based Approach to Evaluating the Risk of Clinical Hepatotoxicity Potential of Drugs in 3D Human Microtissues
- Laura GoracciLaura GoracciDepartment of Chemistry, Biology, and Biotechnology, University of Perugia, Perugia 06123, ItalyMore by Laura Goracci
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- Aurora Valeri
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- Simone SciabolaSimone SciabolaMedicinal Chemistry, Biogen, 115 Broadway Street, Cambridge, Massachusetts 02139, United StatesMore by Simone Sciabola
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- Michael D. AleoMichael D. AleoDrug Safety R&D, Pfizer Worldwide Research and Development, Groton, Connecticut 06340, United StatesMore by Michael D. Aleo
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- Wolfgang Moritz
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- Jan Lichtenberg
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- Gabriele Cruciani*Gabriele Cruciani*E-mail: [email protected]. Phone: (+39)0755855629.Department of Chemistry, Biology, and Biotechnology, University of Perugia, Perugia 06123, ItalyMore by Gabriele Cruciani
Abstract
The importance of adsorption, distribution, metabolism, excretion, and toxicity (ADMET) analysis is expected to grow substantially due to recent failures in detecting severe toxicity issues of new chemical entities during preclinical/clinical development. Traditionally, safety risk assessment studies for humans have been conducted in animals during advanced preclinical or clinical phase of drug development. However, potential drug toxicity in humans now needs to be detected in the drug discovery process as soon as possible without reliance on animal studies. The “omics”, such as genomics, proteomics, and metabolomics, have recently entered pharmaceutical research in both drug discovery and drug development, but to the best of our knowledge, no applications in high-throughput safety risk assessment have been attempted so far. This paper reports an innovative method to anticipate adverse drug effects in an early discovery phase based on lipid fingerprints using human three-dimensional microtissues. The risk of clinical hepatotoxicity potential was evaluated for a data set of 22 drugs belonging to five different therapeutic chemical classes and with various drug-induced liver injury effect. The treatment of microtissues with repeated doses of each drug allowed collecting lipid fingerprints for five time points (2, 4, 7, 9, and 11 days), and multivariate statistical analysis was applied to search for correlations with the hepatotoxic effect. The method allowed clustering of the drugs based on their hepatotoxic effect, and the observed lipid impairments for a number of drugs was confirmed by literature sources. Compared to traditional screening methods, here multiple interconnected variables (lipids) are measured simultaneously, providing a snapshot of the cellular status from the lipid perspective at a molecular level. Applied here to hepatotoxicity, the proposed workflow can be applied to several tissues, being tridimensional microtissues from various origins.
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