Effect-Directed Profiling of Akebia quinata and Clitoria ternatea via High-Performance Thin-Layer Chromatography, Planar Assays and High-Resolution Mass Spectrometry.

Two herbal plants, Akebia quinata D. leaf/fruit and Clitoria ternatea L. flower, well-known in traditional medicine systems, were investigated using a non-target effect-directed profiling. High-performance thin-layer chromatography (HPTLC) was combined with 11 different effect-directed assays, including two multiplex bioassays, for assessing their bioactivity. Individual active zones were heart-cut eluted for separation via an orthogonal high-performance liquid chromatography column to heated electrospray ionization high-resolution mass spectrometry (HPLC-HESI-HRMS) for tentative assignment of molecular formulas according to literature data. The obtained effect-directed profiles provided information on 2,2-diphenyl-1-picrylhydrazyl scavenging, antibacterial (against Bacillus subtilis and Aliivibrio fischeri), enzyme inhibition (tyrosinase, α-amylase, ß-glucuronidase, butyrylcholinesterase, and acetylcholinesterase), endocrine (agonists and antagonists), and genotoxic (SOS-Umu-C) activities. The main bioactive compound zones in A. quinata leaf were tentatively assigned to be syringin, vanilloloside, salidroside, α-hederin, cuneataside E, botulin, and oleanolic acid, while salidroside and quinatic acids were tentatively identified in the fruit. Taraxerol, kaempherol-3-rutinoside, kaempferol-3-glucoside, quercetin-3-rutinoside, and octadecenoic acid were tentatively found in the C. ternatea flower. This straightforward hyphenated technique made it possible to correlate the biological properties of the herbs with possible compounds. The meaningful bioactivity profiles contribute to a better understanding of the effects and to more efficient food control and food safety.

Searching for AChE inhibitors from natural compounds by using machine learning and atomistic simulations.

Acetylcholinesterase (AChE) is one of the most important drug targets for Alzheimer's disease treatment. In this work, a combined approach involving machine-learning (ML) model and atomistic simulations was established to predict the ligand-binding affinity to AChE of the natural compounds from VIETHERB database. The trained ML model was first utilized to rapidly and accurately screen the natural compound database for potential AChE inhibitors. Atomistic simulations including molecular docking and steered-molecular dynamics simulations were then used to confirm the ML outcome. Good agreement between ML and atomistic simulations was observed. Twenty compounds were suggested to be able to inhibit AChE. Especially, four of them including geranylgeranyl diphosphate, 2-phosphoglyceric acid, and 2-carboxy-d-arabinitol 1-phosphate, and farnesyl diphosphate are highly potent inhibitors with sub-nanomolar affinities.

Human carboxylesterase 1A plays a predominant role in the hydrolytic activation of remdesivir in humans.

Remdesivir, an intravenous nucleotide prodrug, has been approved for treating COVID-19 in hospitalized adults and pediatric patients. Upon administration, remdesivir can be readily hydrolyzed to form its active form GS-441524, while the cleavage of the carboxylic ester into GS-704277 is the first step for remdesivir activation. This study aims to assign the key enzymes responsible for remdesivir hydrolysis in humans, as well as to investigate the kinetics of remdesivir hydrolysis in various enzyme sources. The results showed that remdesivir could be hydrolyzed to form GS-704277 in human plasma and the microsomes from human liver (HLMs), lung (HLuMs) and kidney (HKMs), while the hydrolytic rate of remdesivir in HLMs was the fastest. Chemical inhibition and reaction phenotyping assays suggested that human carboxylesterase 1 (hCES1A) played a predominant role in remdesivir hydrolysis, while cathepsin A (CTSA), acetylcholinesterase (AchE) and butyrylcholinesterase (BchE) contributed to a lesser extent. Enzymatic kinetic analyses demonstrated that remdesivir hydrolysis in hCES1A (SHUTCM) and HLMs showed similar kinetic plots and much closed Km values to each other. Meanwhile, GS-704277 formation rates were strongly correlated with the CES1A activities in HLM samples from different individual donors. Further investigation revealed that simvastatin (a therapeutic agent for adjuvant treating COVID-19) strongly inhibited remdesivir hydrolysis in both recombinant hCES1A and HLMs. Collectively, our findings reveal that hCES1A plays a predominant role in remdesivir hydrolysis in humans, which are very helpful for predicting inter-individual variability in response to remdesivir and for guiding the rational use of this anti-COVID-19 agent in clinical settings.

Molecular modeling-guided optimization of acetylcholinesterase reactivators: A proof for reactivation of covalently inhibited targets.

Covalent drugs have been intensively studied in some very important fields such as anti-tumor and anti-virus, including the currently global-spread SARS-CoV-2. However, these drugs may interact with a variety of biological macromolecules and cause serious toxicology, so how to reactivate the inhibited targets seems to be imperative in the near future. Organophosphate was an extreme example, which could form a covalent bound easily with acetylcholinesterase and irreversibly inhibited the enzyme, causing high toxicology. Some nucleophilic oxime reactivators for organophosphate poisoned acetylcholinesterase had been developed, but the reactivation process was still less understanding. Herein, we proposed there should be a pre-reactivated pose during the reactivating process and compounds whose binding pose was easy to transfer to the pre-reactivated pose might be efficient reactivators. Then we refined the previous reactivators based on the molecular dynamic simulation results, the resulting compounds L7R3 and L7R5 were proven as much more efficient reactivators for organophosphate inhibited acetylcholinesterase than currently used oximes. This work might provide some insights for constructing reactivators of covalently inhibited targets by using computational methods.