Category Archives: Resource Library

Aldehyde oxidase (AOX) is a molibdo-flavoenzyme that has raised great interest in recent years, since its contribution in xenobiotic metabolism has not always been identified before clinical trials, with consequent negative effects on the fate of new potential drugs. The fundamental role of AOX in metabolizing xenobiotics is also due to the attempt of medicinal chemists to stabilize candidates toward cytochrome P450 activity, which increases the risk for new compounds to be susceptible to AOX nucleophile attack.

Therefore, novel strategies to predict the potential liability of new entities toward the AOX enzyme are urgently needed to increase effectiveness, reduce costs, and prioritize experimental studies. In the present work, we present the most up-to-date computational method to predict liability toward human AOX (hAOX), for applications in drug design and pharmacokinetic optimization. The method was developed using a large data set of homogeneous experimental data, which is also disclosed as Supporting Information. 

FMO enzymes (FMOs) play a key role in the processes of detoxification and/or bioactivation of specific pharmaceuticals and xenobiotics bearing nucleophilic centers. The N-oxide and S-oxide metabolites produced by FMOs are often active metabolites. The FMOs are more active than cytochromes in the brain and work in tandem with CYP3A4 in the liver. FMOs might reduce the risk of phospholipidosis of CAD-like drugs, although some FMOs metabolites seem to be neurotoxic and hepatotoxic. However, in silico methods for FMO metabolism prediction are not yet available.

This paper reports, for the first time, a substrate-specificity and catalytic-activity model for FMO3, the most relevant isoform of the FMOs in humans. The application of this model to a series of compounds with unknown FMO metabolism is also reported. The model has also been very useful to design compounds with optimal clearance and in finding erroneous literature data, particularly cases in which substances have been reported to be FMO3 substrates when, in reality, the experimentally validated in silico model correctly predicts that they are not. 

Drug-induced phospholipidosis indicates an accumulation of phospholipids within lysosomes, which can occur during therapeutic treatment. Whether or not phospholipidosis represents a toxicological phenomenon is still under investigation, and in the last decade the Food and Drug Administration has been raising concerns about the possible consequences of this adverse event. Cationic amphiphilic drugs represent the majority of phospholipidosis inducers, followed by aminoglycoside and macrolide antibiotics. Although the mechanism of phospholipidosis induction is still uncertain, the interaction of drugs with phospholipids in the lysosomal membrane represents a key step. Therefore, the study of the drug/lipid complex formation will

provide valuable insight into the causation of phospholipidosis at the molecular level and to identify the potential phospholipidosis risk associated with drug. In this study, we investigated

the insertion profile of eleven drugs with known phospholipidosis effect into preformed Langmuir monolayers of various lipid compositions, to evaluate for the first time the drug/lipid interaction for phospholipidosis inducers and non-inducers in a dynamic approach. We found

that the addition of dipalmitoylphosphatidylserine (DPPS) to dipalmitoylphosphatidylcholine (DPPC) to form the lipid monolayer allowed a clear identification of the phospholipidosis effect of the selected drugs based on the variation of the surface pressure, not only for cationic amphiphilic drugs but also for the aminoglycoside and the macrolide antiobiotics tested. Compared to a standard PAMPA assay, the new method appears to be more effective for the study of poorly soluble drugs.

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.