Mechanism-Based Insights into Removing the Mutagenicity of Aromatic Amines by Small Structural Alterations

Tailored small molecule for inflammation treatment: Dual scavenger targeting nitric oxide and reactive oxygen species

Inflammation-related diseases are often marked by elevated levels of nitric oxide (NO) and reactive oxygen species (ROS), which play important roles in the modulation of inflammation. However, the development of organic materials effective in managing NO/ROS levels has remained a challenge. This study introduces a novel organic compound, NmeGA, engineered to scavenge both NO and ROS. NmeGA ingeniously integrates N-methyl-1,2,-phenylenediamine (Nme), a NO scavenger, with gallic acid (GA), a ROS scavenger, through an amide bond, endowing it with enhanced scavenging capabilities over its individual component. This compound exhibits reduced toxicity and increased lipophilicity value, underlining its increased biological applicability and highlighting its potential as an inflammation management tool. Through in vitro studies on lipopolysaccharide (LPS)-stimulated RAW 264.7 cells, NmeGA displayed remarkable scavenging efficiency for NO and ROS, coupled with significant anti-inflammatory effects. In an LPS-induced peritonitis model, administration of NmeGA substantially decreased mortality rates, NO and ROS levels, and inflammatory cytokine concentrations. These findings highlight NmeGA's versatility as a therapeutic agent against various inflammatory diseases.

Use of Structural Alerts for Reactive Metabolites in the Application SpotRM

Reactive metabolite (RM) formation is widely accepted as playing a crucial role in causing idiosyncratic adverse drug reactions (IADRs), where the liver is most affected. An important goal of drug design is to avoid selection of drug candidates giving rise to RMs and therefore risk causing problems later on involving IADRs. The simplest, initial approach is to avoid test structures that have substructures known or strongly suspected to be associated with IADRs. However, as is evident from the many case reports of IADRs, in most cases a clear association with any (bio)chemical mechanism is lacking, which makes it hard to establish any structure-toxicity relationship. Separate studies of RM formation, in vitro and in vivo, have led to likely evidence and to establishing many structural alerts (SAs) that can be used for fast selection/deselection of planned test compounds. As a background to a discussion of the concept, 25 kinase inhibitor drugs with known problems of hepatotoxicity were probed against a set of SAs contained in the application SpotRM. A clear majority of the probed drugs show liabilities as evident by being flagged by more than one of the fairly established types of SAs. At the same time, no clear SAs were found in three drugs, which is discussed in the broader context of usefulness and selection tactics of SAs in drug design.

Quantum chemical package Jaguar: A survey of recent developments and unique features

This paper is dedicated to the quantum chemical package Jaguar, which is commercial software developed and distributed by Schrödinger, Inc. We discuss Jaguar's scientific features that are relevant to chemical research as well as describe those aspects of the program that are pertinent to the user interface, the organization of the computer code, and its maintenance and testing. Among the scientific topics that feature prominently in this paper are the quantum chemical methods grounded in the pseudospectral approach. A number of multistep workflows dependent on Jaguar are covered: prediction of protonation equilibria in aqueous solutions (particularly calculations of tautomeric stability and pKa), reactivity predictions based on automated transition state search, assembly of Boltzmann-averaged spectra such as vibrational and electronic circular dichroism, as well as nuclear magnetic resonance. Discussed also are quantum chemical calculations that are oriented toward materials science applications, in particular, prediction of properties of optoelectronic materials and organic semiconductors, and molecular catalyst design. The topic of treatment of conformations inevitably comes up in real world research projects and is considered as part of all the workflows mentioned above. In addition, we examine the role of machine learning methods in quantum chemical calculations performed by Jaguar, from auxiliary functions that return the approximate calculation runtime in a user interface, to prediction of actual molecular properties. The current work is second in a series of reviews of Jaguar, the first having been published more than ten years ago. Thus, this paper serves as a rare milestone on the path that is being traversed by Jaguar's development in more than thirty years of its existence.

Anti-TNF Thioester Glucocorticoid Antibody-Drug Conjugate Fully Inhibits Inflammation with Minimal Effect on Systemic Corticosterone Levels in a Mouse Arthritis Model

We describe the discovery of a thioester-containing glucocorticoid receptor modulator (GRM) payload and the corresponding antibody-drug conjugate (ADC). Payload 6 was designed for rapid hepatic inactivation to minimize systemic exposure of nonconjugated GRM. Mouse PK indicated that 6 is cleared 10-fold more rapidly than a first-generation GRM payload, resulting in 10-fold lower exposure and 3-fold decrease in Cmax. The anti-mTNF conjugate ADC5 fully inhibited inflammation in mouse contact hypersensitivity with minimal effects on corticosterone, a biomarker for systemic GRM effects, at doses up to and including 100 mg/kg. Concomitant inhibition of P1NP suggests potential delivery to cells involved in the remodeling of bone, which may be a consequence of TNF-targeting or bystander payload effects. Furthermore, ADC5 fully suppressed inflammation in collagen-induced arthritis mouse model after one 10 mg/kg dose for 21 days. The properties of the anti-hTNF conjugate were suitable for liquid formulation and may enable subcutaneous dosing.

Chemistry-intuitive explanation of graph neural networks for molecular property prediction with substructure masking

Graph neural networks (GNNs) have been widely used in molecular property prediction, but explaining their black-box predictions is still a challenge. Most existing explanation methods for GNNs in chemistry focus on attributing model predictions to individual nodes, edges or fragments that are not necessarily derived from a chemically meaningful segmentation of molecules. To address this challenge, we propose a method named substructure mask explanation (SME). SME is based on well-established molecular segmentation methods and provides an interpretation that aligns with the understanding of chemists. We apply SME to elucidate how GNNs learn to predict aqueous solubility, genotoxicity, cardiotoxicity and blood-brain barrier permeation for small molecules. SME provides interpretation that is consistent with the understanding of chemists, alerts them to unreliable performance, and guides them in structural optimization for target properties. Hence, we believe that SME empowers chemists to confidently mine structure-activity relationship (SAR) from reliable GNNs through a transparent inspection on how GNNs pick up useful signals when learning from data.

DARK Classics in Chemical Neuroscience: Methaqualone

Commonly known as "Quaaludes," methaqualone (1) is a sedative-hypnotic medication, with effects resembling barbiturates and other downers, that exerts its effects through modulation of γ-aminobutyric acid type A receptors (GABA_AR). Following the discovery of the sedative and euphoric effects of methaqualone (1), it was quickly adopted by pharmaceutical companies and promoted by clinicians around the world as a "safe" sleeping pill option, and for a period it was available over the counter. The popularity of methaqualone (1) soared worldwide, and many people began to use it recreationally for its sedative-hypnotic-like psychoactive effects. Not long after its introduction, many individuals began to misuse the drug leading to overdoses and drug dependence which brought to light methaqualone's (1) addictive nature. In this review, the background, synthesis, pharmacology, metabolism, and pharmacokinetics of methaqualone (1) will be covered along with its discovery, history, and the derivatives that are currently available around the world through manufacture in clandestine laboratories.

Bioactivation and Reactivity Research Advances - 2021 year in review

This year's review on bioactivation and reactivity began as a part of the annual review on biotransformation and bioactivation led by Cyrus Khojasteh (Khojasteh et al., 2021, 2020, 2019, 2018, 2017; Baillie et al., 2016). Increased contributions from experts in the field led to the development of a stand alone edition for the first time this year focused specifically on bioactivation and reactivity. Our objective for this review is to highlight and share articles which we deem influential and significant regarding the development of covalent inhibitors, mechanisms of reactive metabolite formation, enzyme inactivation, and drug safety. Based on the selected articles, we created two sections: (1) reactivity and enzyme inactivation, and (2) bioactivation mechanisms and safety (Table 1). Several biotransformation experts have contributed to this effort from academic and industry settings.