Gains from no real PAINS: Where 'Fair Trial Strategy' stands in the development of multi-target ligands

In situ chemical reprogramming of astrocytes into neurons: A new hope for the treatment of central neurodegenerative diseases?

Central neurodegenerative disorders (e.g. Alzheimer's disease (AD) and Parkinson's disease (PD)) are tightly associated with extensive neuron loss. Current therapeutic interventions merely mitigate the symptoms of these diseases, falling short of addressing the fundamental issue of neuron loss. Cell reprogramming, involving the transition of a cell from one gene expression profile to another, has made significant strides in the conversion between diverse somatic cell types. This advancement has been facilitated by gene editing techniques or the synergistic application of small molecules, enabling the conversion of glial cells into functional neurons. Despite this progress, the potential for in situ reprogramming of astrocytes in treating neurodegenerative disorders faces challenges such as immune rejection and genotoxicity. A novel avenue emerges through chemical reprogramming of astrocytes utilizing small molecules, circumventing genotoxic effects and unlocking substantial clinical utility. Recent studies have successfully demonstrated the in situ conversion of astrocytes into neurons using small molecules. Nonetheless, these findings have sparked debates, encompassing queries regarding the origin of newborn neurons, pivotal molecular targets, and alterations in metabolic pathways. This review succinctly delineates the background of astrocytes reprogramming, meticulously surveys the principal classes of small molecule combinations employed thus far, and examines the complex signaling pathways they activate. Finally, this article delves into the potential vistas awaiting exploration in the realm of astrocytes chemical reprogramming, heralding a promising future for advancing our understanding and treatment of neurodegenerative diseases.

How do in vitro digestion and cell metabolism affect the biological activity and phenolic profile of grape juice and wine

Cabernet Sauvignon grape juice and wine underwent in vitro digestion, resulting in a reduction of most phenolic compounds (10%-100% decline), notably impacting anthocyanins (82%-100% decline) due to pH variations. However, specific phenolics, including p-hydroxybenzoic, protocatechuic, vanillic, p-coumaric, gallic and syringic acids, and coumarin esculetin, increased in concentration (10%-120%). Grape juice and wine samples showed comparable polyphenolic profile during all phases of digestion. Antioxidant activity persisted, and inhibition of angiotensin-I converting enzyme was improved after the digestion process, likely because of increased concentrations of listed phenolic acids and esculetin. Digested grape juice displayed comparable or superior bioactivity to red wine, indicating it as a promising source of accessible grape polyphenols for a broader audience. Nevertheless, Caco-2 cell model metabolization experiments revealed that only 3 of 42 analyzed compounds passed to the basolateral compartment, emphasizing the significant impact of digestion on polyphenol bioactivity, suggesting potential yet unmeasurable and overlooked implications for human health.

Boosting the Accuracy and Chemical Space Coverage of the Detection of Small Colloidal Aggregating Molecules Using the BAD Molecule Filter

The ability to conduct effective high throughput screening (HTS) campaigns in drug discovery is often hampered by the detection of false positives in these assays due to small colloidally aggregating molecules (SCAMs). SCAMs can produce artifactual hits in HTS by nonspecific inhibition of the protein target. In this work, we present a new computational prediction tool for detecting SCAMs based on their 2D chemical structure. The tool, called the boosted aggregation detection (BAD) molecule filter, employs decision tree ensemble methods, namely, the CatBoost classifier and the light gradient-boosting machine, to significantly improve the detection of SCAMs. In developing the filter, we explore models trained on individual data sets, a consensus approach using these models, and, third, a merged data set approach, each tailored for specific drug discovery needs. The individual data set method emerged as most effective, achieving 93% sensitivity and 90% specificity, outperforming existing state-of-the-art models by 20 and 5%, respectively. The consensus models offer broader chemical space coverage, exceeding 90% for all testing sets. This feature is an important aspect particularly for early stage medicinal chemistry projects, and provides information on applicability domain. Meanwhile, the merged data set models demonstrated robust performance, with a notable sensitivity of 79% in the comprehensive 10-fold cross-validation test set. A SHAP analysis of model features indicates the importance of hydrophobicity and molecular complexity as primary factors influencing the aggregation propensity. The BAD molecule filter is readily accessible for the public usage on https://molmodlab-aau.com/Tools.html. This filter provides a new, more robust tool for aggregate prediction in the early stages of drug discovery to optimize hit rates and reduce associated testing and validation overheads.

Deep simulated annealing for the discovery of novel dental anesthetics with local anesthesia and anti-inflammatory properties

Multifunctional therapeutics have emerged as a solution to the constraints imposed by drugs with singular or insufficient therapeutic effects. The primary challenge is to integrate diverse pharmacophores within a single-molecule framework. To address this, we introduced DeepSA, a novel edit-based generative framework that utilizes deep simulated annealing for the modification of articaine, a well-known local anesthetic. DeepSA integrates deep neural networks into metaheuristics, effectively constraining molecular space during compound generation. This framework employs a sophisticated objective function that accounts for scaffold preservation, anti-inflammatory properties, and covalent constraints. Through a sequence of local editing to navigate the molecular space, DeepSA successfully identified AT-17, a derivative exhibiting potent analgesic properties and significant anti-inflammatory activity in various animal models. Mechanistic insights into AT-17 revealed its dual mode of action: selective inhibition of NaV1.7 and 1.8 channels, contributing to its prolonged local anesthetic effects, and suppression of inflammatory mediators via modulation of the NLRP3 inflammasome pathway. These findings not only highlight the efficacy of AT-17 as a multifunctional drug candidate but also highlight the potential of DeepSA in facilitating AI-enhanced drug discovery, particularly within stringent chemical constraints.

Design and Diversity Analysis of Chemical Libraries in Drug Discovery

Chemical libraries and compound data sets are among the main inputs to start the drug discovery process at universities, research institutes, and the pharmaceutical industry. The approach used in the design of compound libraries, the chemical information they possess, and the representation of structures, play a fundamental role in the development of studies: chemoinformatics, food informatics, in silico pharmacokinetics, computational toxicology, bioinformatics, and molecular modeling to generate computational hits that will continue the optimization process of drug candidates. The prospects for growth in drug discovery and development processes in chemical, biotechnological, and pharmaceutical companies began a few years ago by integrating computational tools with artificial intelligence methodologies. It is anticipated that it will increase the number of drugs approved by regulatory agencies shortly.

rel-2-[4-Chloro-2-[(5R,6R,7S)-6-[5-(4-methoxyphenyl)-3-(2-naphthyl)-3,4-dihydropyrazole-2-carbonyl]-5-methyl-2-oxo-3,5,6,7-tetrahydrothiopyrano[2,3-d]thiazol-7-yl]phenoxy]acetic Acid

The hetero-Diels–Alder reaction is the main synthetic tool for obtaining pharmacological agents with a thiopyrano[2,3-d]thiazole motif. In the present work, an efficient method for the synthesis of pyrazoline-containing thiopyrano[2,3-d]thiazole is described. The pyrazoline-bearing dienophile was proposed and used as effective building block for the synthesis of the title compound. The structure of the synthesized rel-2-[4-chloro-2-[(5R,6R,7S)-6-[5-(4-methoxyphenyl)-3-(2-naphthyl)-3,4-dihydropyrazole-2-carbonyl]-5-methyl-2-oxo-3,5,6,7-tetrahydrothiopyrano[2,3-d]thiazol-7-yl]phenoxy]acetic acid (3) was confirmed by 1H, 13C, 2D NMR, and LC-MS spectra. Anticancer activity in “60 lines screening” (NCI DTP protocol) was studied in vitro for the title compound.

Redox active or thiol reactive? Optimization of rapid screens to identify less evident nuisance compounds

Compounds that exhibit assay interference or undesirable mechanisms of bioactivity are routinely encountered in assays at various stages of drug discovery. We observed that assays for the investigation of thiol-reactive and redox-active compounds have not been collected in a comprehensive review. Here, we review these assays and subject them to experimental optimization to improve their reliability. We demonstrate the usefulness of our assay cascade by assaying a library of bioactive compounds, chemical probes, and a set of approved drugs. These high-throughput assays should complement the array of wet-lab and in silico assays during the initial stages of hit discovery campaigns to pursue only hit compounds with tractable mechanisms of action. Teaser: We provide an overview of assays to detect redox active and thiol reactive compounds and the robust protocols for identification of nuisance compounds during early stages of drug discovery programs.

Fascinating Transformation of SAM-Competitive Protein Methyltransferase Inhibitors from Nucleoside Analogues to Non-Nucleoside Analogues

The abnormal expression of protein methyltransferase (PMT) has been linked with many diseases such as diabetes, neurological disorders, and cancer. S-Adenyl-l-methionine (SAM) is a universal methyl donor and gets converted to S-adenyl-l-homocysteine (SAH), an endogenous competitive inhibitor of SAM. Initially developed SAM/SAH mimetic nucleoside analogues were pan methyltransferase inhibitors. The gradual understanding achieved through ligand-receptor interaction paved the way for various rational approaches of drug design leading to potent and selective nucleoside inhibitors. The present perspective is based on the systematic evolution of selective SAM-competitive heterocyclic non-nucleoside inhibitors from nucleoside inhibitors. This fascinating transition has resolved several issues inherent to nucleoside analogues such as poor pharmacokinetics leading to poor in vivo efficacy. The perspective has brought together various concepts and strategies of drug design that contributed to this rational transition. We firmly believe that the strategies described herein will serve as a template for the future development of drugs in general.

Discovery of Highly Potent Fusion Inhibitors with Potential Pan-Coronavirus Activity That Effectively Inhibit Major COVID-19 Variants of Concern (VOCs) in Pseudovirus-Based Assays

We report the discovery of several highly potent small molecules with low-nM potency against severe acute respiratory syndrome coronavirus (SARS-CoV; lowest half-maximal inhibitory concentration (IC₅₀: 13 nM), SARS-CoV-2 (IC₅₀: 23 nM), and Middle East respiratory syndrome coronavirus (MERS-CoV; IC₅₀: 76 nM) in pseudovirus-based assays with excellent selectivity index (SI) values (>5000), demonstrating potential pan-coronavirus inhibitory activities. Some compounds showed 100% inhibition against the cytopathic effects (CPE; IC₁₀₀) of an authentic SARS-CoV-2 (US_WA-1/2020) variant at 1.25 µM. The most active inhibitors also potently inhibited variants of concern (VOCs), including the UK (B.1.1.7) and South African (B.1.351) variants and the Delta variant (B.1.617.2) originally identified in India in pseudovirus-based assay. Surface plasmon resonance (SPR) analysis with one potent inhibitor confirmed that it binds to the prefusion SARS-CoV-2 spike protein trimer. These small-molecule inhibitors prevented virus-mediated cell-cell fusion. The absorption, distribution, metabolism, and excretion (ADME) data for one of the most active inhibitors, NBCoV1, demonstrated drug-like properties. An in vivo pharmacokinetics (PK) study of NBCoV1 in rats demonstrated an excellent half-life (t_1/2) of 11.3 h, a mean resident time (MRT) of 14.2 h, and oral bioavailability. We expect these lead inhibitors to facilitate the further development of preclinical and clinical candidates.

Indoles and 1-(3-(benzyloxy)benzyl)piperazines: Reversible and selective monoamine oxidase B inhibitors identified by screening an in-house compound library

The therapeutic indications for monoamine oxidases A and B (MAO-A and MAO-B) inhibitors that have emerged from biological studies on animal and cellular models of neurological and oncological diseases have focused drug discovery projects upon identifying reversible MAO inhibitors. Screening of our in-house academic compound library identified two hit compounds that inhibit MAO-B with IC₅₀ values in micromolar range. Two series of indole (23 analogues) and 3-(benzyloxy)benzyl)piperazine (16 analogues) MAO-B inhibitors were derived from hits, and screened for their structure-activity relationships. Both series yielded low micromolar selective inhibitors of human MAO-B, namely indole 2 (IC₅₀ = 12.63 ± 1.21 µM) and piperazine 39 (IC₅₀ = 19.25 ± 4.89 µM), which is comparable to selective MAO-B inhibitor isatin (IC₅₀ = 6.10 ± 2.81 µM), yet less potent in comparison to safinamide (IC₅₀ = 0.029 ± 0.002 µM). Selective MAO-B inhibitors 2, 14, 38 and 39 exhibited favourable permeation of the blood-brain barrier and low cytotoxicity in the human neuroblastoma cell line SH-SY5Y.