Modeling Phospholipidosis Induction: Reliability and Warnings

Drug-induced phospholipidosis (PLD) is characterized by accumulation of phospholipids, the inducing drugs and lamellar inclusion bodies in the lysosomes of affected tissues. These side effects must be considered as early as possible during drug discovery, and, in fact, numerous in silico models designed to predict PLD have been published. However, the quality of any in silico model cannot be better than the quality of the experimental data set used to build it. The present paper reports an overview of the difficulties and errors encountered in the generation of databases used for the published PLD models. A new database of 466 compounds was constructed from seven literature sources, containing only publicly available compounds. A comparison of the PLD assignations in selected databases proved useful in revealing some inconsistencies and raised doubts about the previously assigned PLD+ and PLD– classifications for some chemicals. Finally, a Partial Least Squares Discriminant Analysis (PLS-DA) approach was also applied, revealing further anomalies and clearly showing that metabolism as well as data quality must be taken into account when generating accurate methods for predicting the likelihood that a compound will induce PLD. A new curated database of 331 compounds is proposed

Flavin Monooxygenase Metabolism: Why Medicinal Chemists Should Matter

FMO enzymes (FMOs) play a key role in the processes of detoxification and/or bioactivation of specific pharmaceuticals and xenobiotics bearing nucleophilic centers. The <i>N</i>-oxide and <i>S</i>-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

Improved Potency of Indole-Based NorA Efflux Pump Inhibitors: From Serendipity toward Rational Design and Development

The NorA efflux pump is a potential drug target for reversal of resistance to selected antibacterial agents, and recently we described indole-based inhibitor candidates. Herein we report a second class of inhibitors derived from them but with significant differences in shape and size. In particular, compounds <b>13</b> and <b>14</b> are very potent inhibitors in that they demonstrated the lowest IC₅₀ values (2 μM) ever observed among all indole-based compounds we have evaluated

Optimization of Small-Molecule Inhibitors of Influenza Virus Polymerase: From Thiophene-3-Carboxamide to Polyamido Scaffolds

Influenza virus infections represent a serious concern to public health, being characterized by high morbidity and significant mortality. To date, compounds targeting the viral ion-channel M2 or the viral neuraminidase are the drugs available for treatment of influenza, but the emergence of drug-resistant viral mutants renders the search for novel targets and their possible inhibitors a major priority. Recently, we demonstrated that the viral RNA-dependent RNA polymerase (RdRP) complex can be an optimal target of protein–protein disruption by small molecules, with thiophene-3-carboxamide derivatives emerging as promising candidates for the development of new anti-influenza drugs with broad-spectrum activity. Here, we report a further dissection of the thiophene-3-carboxamide structure. By using a GRID molecular interaction field (MIF)-based scaffold-hopping approach, more potent and nontoxic polyamido derivatives were identified, highlighting a new space in the chemical variability of RdRP inhibitors. Finally, a possible pharmacophoric model highlighting the key features required for RdRP inhibition is proposed

From Experiments to a Fast Easy-to-Use Computational Methodology to Predict Human Aldehyde Oxidase Selectivity and Metabolic Reactions

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 (<i>h</i>AOX), 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

Discovery and Structure–Activity Relationships of Novel <i>ss</i>DAF-12 Receptor Modulators

The nuclear receptor ssDAF-12 has been recognized as the key molecular player regulating the life cycle of the nematode parasite Strongyloides stercoralis. ssDAF-12 ligands permit the receptor to function as an on/off switch modulating infection, making it vulnerable to therapeutic intervention. In this study, we report the design and synthesis of a set of novel dafachronic acid derivatives, which were used to outline the first structure–activity relationship targeting the ssDAF-12 receptor and to unveil hidden properties shared by the molecular shape of steroidal ligands that are relevant to the receptor binding and modulation. Moreover, biological results led to the discovery of sulfonamide 3 as a submicromolar ssDAF-12 agonist endowed with a high receptor selectivity, no toxicity, and improved properties, as well as to the identification of unprecedented ssDAF-12 antagonists that can be exploited in the search for novel chemical tools and alternative therapeutic approaches for treating parasitism such as Strongyloidiasis

From Experiments to a Fast Easy-to-Use Computational Methodology to Predict Human Aldehyde Oxidase Selectivity and Metabolic Reactions

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 (<i>h</i>AOX), 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

MARS: A Multipurpose Software for Untargeted LC–MS-Based Metabolomics and Exposomics

Untargeted metabolomics is a growing field, in which recent advances in high-resolution mass spectrometry coupled with liquid chromatography (LC-MS) have facilitated untargeted approaches as a result of improvements in sensitivity, mass accuracy, and resolving power. However, a very large amount of data are generated. Consequently, using computational tools is now mandatory for the in-depth analysis of untargeted metabolomics data. This article describes MetAbolomics ReSearch (MARS), an all-in-one vendor-agnostic graphical user interface-based software applying LC-MS analysis to untargeted metabolomics. All of the analytical steps are described (from instrument data conversion and processing to statistical analysis, annotation/identification, quantification, and preliminary biological interpretation), and tools developed to improve annotation accuracy (e.g., multiple adducts and in-source fragmentation detection, trends across samples, and the MS/MS validator) are highlighted. In addition, MARS allows in-house building of reference databases, to bypass the limits of freely available MS/MS spectra collections. Focusing on the flexibility of the software and its user-friendliness, which are two important features in multipurpose software, MARS could provide new perspectives in untargeted metabolomics data analysis

Discovery and Structure–Activity Relationships of Novel <i>ss</i>DAF-12 Receptor Modulators

The nuclear receptor ssDAF-12 has been recognized as the key molecular player regulating the life cycle of the nematode parasite Strongyloides stercoralis. ssDAF-12 ligands permit the receptor to function as an on/off switch modulating infection, making it vulnerable to therapeutic intervention. In this study, we report the design and synthesis of a set of novel dafachronic acid derivatives, which were used to outline the first structure–activity relationship targeting the ssDAF-12 receptor and to unveil hidden properties shared by the molecular shape of steroidal ligands that are relevant to the receptor binding and modulation. Moreover, biological results led to the discovery of sulfonamide 3 as a submicromolar ssDAF-12 agonist endowed with a high receptor selectivity, no toxicity, and improved properties, as well as to the identification of unprecedented ssDAF-12 antagonists that can be exploited in the search for novel chemical tools and alternative therapeutic approaches for treating parasitism such as Strongyloidiasis