Recent development and strategies towards target interactions: Synthesis, characterization and in silico analysis of benzimidazole based thiadiazole as potential anti-Alzheimer agents.

In the current research study, we aim to design and synthesize highly potent hybrid analogs of benzimidazole derived thiadiazole based Schiff base derivatives which can combat the cholinesterase enzymes (acetylcholinesterase and butyrylcholinesterase) accountable for developing Alzheimer's disease. In this context, we have synthesized 15 analogs of benzimidazole based thiadiazole derivatives, which were subsequently confirmed through spectroscopic techniques including 1H NMR, 13C NMR and HREI-MS. Biological investigation of all the analogs revealed their varied acetylcholinesterase inhibitory potency covering a range between 3.20 ± 0.10 µM to 20.50 ± 0.20 µM as well as butyrylcholinesterase inhibitory potential with a range of 4.30 ± 0.50 µM to 20.70 ± 0.50 µM when compared with the standard drug Donepezil having IC50 = 6.70 ± 0.20 µM for AChE and 7.90 ± 0.10 µM for BuChE. The promising inhibition by the analogs was evaluated in SAR analysis, where analog-1 (IC50 = 3.20 ± 0.10 µM for AChE and 4.30 ± 0.50 µM for BuChE), analog-4 (IC50 = 4.30 ± 0.30 µM for AChE and 5.50 ± 0.20 µM for BuChE) and analog-5 (IC50 = 4.10 ± 0.30 µM for AChE and 4.60 ± 0.40 µM for BuChE) were found as the lead candidates. Moreover, molecular docking and ADME analysis were conducted to explore the better binding interactions and drugs likeness respectively.

The multifaceted roles of mass spectrometric analysis in nucleic acids drug discovery and development.

The deceptively simple concepts of mass determination and fragment analysis are the basis for the application of mass spectrometry (MS) to a boundless range of analytes, including fundamental components and polymeric forms of nucleic acids (NAs). This platform affords the intrinsic ability to observe first-hand the effects of NA-active drugs on the chemical structure, composition, and conformation of their targets, which might affect their ability to interact with cognate NAs, proteins, and other biomolecules present in a natural environment. The possibility of interfacing with high-performance separation techniques represents a multiplying factor that extends these capabilities to cover complex sample mixtures obtained from organisms that were exposed to NA-active drugs. This report provides a brief overview of these capabilities in the context of the analysis of the products of NA-drug activity and NA therapeutics. The selected examples offer proof-of-principle of the applicability of this platform to all phases of the journey undertaken by any successful NA drug from laboratory to bedside, and provide the rationale for its rapid expansion outside traditional laboratory settings in support to ever growing manufacturing operations.

Absorption, Distribution, Metabolism, and Excretion of Icenticaftor (QBW251) in Healthy Male Volunteers at Steady State and In Vitro Phenotyping of Major Metabolites.

Icenticaftor (QBW251) is a potentiator of the CFTR protein and is currently in clinical development for the treatment of chronic obstructive pulmonary disease and chronic bronchitis. An absorption, distribution, metabolism, and excretion (ADME) study was performed at steady state to determine the pharmacokinetics, mass balance, and metabolite profiles of icenticaftor in humans. In this open-label study, six healthy men were treated with unlabeled oral icenticaftor (400 mg b.i.d.) for 4 days. A single oral dose of [14C]icenticaftor was administered on Day 5, and unlabeled icenticaftor was administered b.i.d. from the evening of Day 5 to Day 12. Unchanged icenticaftor accounted for 18.5% of plasma radioactivity. Moderate to rapid absorption of icenticaftor was observed (median Tmax: 4 hours), with 93.4% of the dose absorbed. It exhibited moderate distribution (Vz/F: 335 L) and was extensively metabolized, principally through N-glucuronidation, O-glucuronidation, and/or O-demethylation. The metabolites M8 and M9, formed by N-glucuronidation and O-glucuronidation of icenticaftor, respectively, represented the main entities detected in plasma (35.3% and 14.5%, respectively) in addition to unchanged icenticaftor (18.5%). The apparent mean T1/2 of icenticaftor was 15.4 hours in blood and 20.6 hours in plasma. Icenticaftor was eliminated from the body mainly through metabolism followed by renal excretion, and excretion of radioactivity was complete after 9 days. In vitro phenotyping of icenticaftor showed that cytochrome P450 and uridine diphosphate glucuronosyltransferase were responsible for 31% and 69% of the total icenticaftor metabolism in human liver microsomes, respectively. This study provided invaluable insights into the disposition of icenticaftor. Significance Statement The ADME of a single radioactive oral dose of icenticaftor was evaluated at steady state to investigate the nonlinear pharmacokinetics observed previously with icenticaftor. [14C]Icenticaftor demonstrated good systemic availability after oral administration and was extensively metabolized and moderately distributed to peripheral tissues. The most abundant metabolites, M8 and M9, were formed by N-glucuronidation and O-glucuronidation of icenticaftor, respectively. Phenotyping demonstrated that [14C]icenticaftor was metabolized predominantly by UGT1A9 with a remarkably low Km value.

The ADME characteristics of siRNA therapeutics and the opportunity to predict disposition in pregnant women.

Small interfering RNA (siRNA) therapeutics represent an emerging class of pharmacotherapy with the potential to address previously hard-to-treat diseases. Currently approved siRNA therapeutics include LNP-encapsulated siRNA and triGalNAc-conjugated siRNA. These siRNA therapeutics exhibit distinct pharmacokinetic characteristics and unique absorption, distribution, metabolism, and elimination (ADME) properties. As a new drug modality, limited clinical data are available for siRNA therapeutics in specific populations, including pediatrics, geriatrics, individuals with renal or hepatic impairment, and pregnant women, making dosing challenging. In this review, a mechanistic overview of the ADME properties of the five currently approved siRNA therapeutics is presented. A concise overview of the clinical data available for therapeutic siRNAs in special populations, focusing on the potential impact of physiological changes during pregnancy on siRNA disposition is provided. The utility of physiologically based pharmacokinetic (PBPK) modeling as a tool to elucidate the characteristics and disposition of siRNA therapeutics in pregnant women is explored. Additionally, opportunities to integrate known physiological alterations induced by pregnancy into PBPK models that incorporate siRNA ADME mechanisms to predict the effects of pregnancy on siRNA disposition are discussed. Clinical data regarding the use of therapeutic siRNA in special populations remains limited. Data for precise parameterization of maternal-fetal siRNA PBPK models is lacking presently and underscores the need for further research in this area. Addressing this gap in knowledge will not only enhance our understanding of siRNA pharmacokinetics during pregnancy but also advance possible development of siRNA therapeutics to treat pregnancy related conditions. Significance Statement This review proposes a framework on how siRNA disposition can be predicted in pregnancy based on mechanistic ADME information using physiologically based pharmacokinetic (PBPK) modeling. The mechanistic ADME information and available clinical data in special populations of currently FDA approved siRNA therapeutics are summarized. A detailed discussion on how physiological changes during pregnancy may affect siRNA disposition in pregnant women and on the opportunities to project siRNA disposition in pregnant women using PBPK modeling is provided.

A phase 1 study to assess the absolute bioavailability, mass balance, pharmacokinetics, metabolism, and excretion of [14C]-mobocertinib, an oral inhibitor of EGFR exon 20 insertion mutations, in healthy participants.

Mobocertinib (TAK-788) is a first-in-class oral epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor that received accelerated approval for the treatment of patients with non-small cell lung cancer with EGFR exon 20 insertion mutations previously treated with platinum-based chemotherapy. This phase 1, 2-period, study was conducted to assess the absolute bioavailability of mobocertinib (Period 1), as well as mass balance, pharmacokinetics, metabolism, and excretion of [14C]-mobocertinib (Period 2) in healthy adult males. In Period 1, participants received a single oral capsule dose of 160 mg mobocertinib, followed by a 15-minute intravenous infusion of 50 µg (~ 2 µCi) [14C]-mobocertinib administered from 3.75 to 4 h after the capsule dose. In Period 2, a single oral dose of 160 mg (~ 100 µCi) [14C]-mobocertinib was administered as an oral solution. The geometric mean absolute bioavailability of mobocertinib was determined to be 36.7%. After oral administration of [14C]-mobocertinib, mobocertinib and its active metabolites, AP32960 and AP32914, were minor components in plasma, accounting for only 0.275% of total plasma radioactivity as the majority of mobocertinib-related material was covalently bound to plasma proteins. The geometric mean percentage of the administered radioactive dose recovered in the urine and feces was 3.57% and 76.0%, respectively. Only 0.39% of the oral dose of [14C]-mobocertinib was recovered in the urine as mobocertinib; thus, indicating that renal excretion of unchanged drug was a very minor pathway of elimination. In both treatment periods, mobocertinib was generally safe and well-tolerated as all adverse events were Grade 1 in severity. (Trial registration number ClinicalTrials.gov NCT03811834. Registration date January 22, 2019).

Harnessing high potential benzothiazole chalcones against dengue virus NS5 protein: A multi-faceted theoretical study through molecular docking, ADME, and DFT.

Chalcones bearing tetralone, indanone and benzothiazole cores were synthesized successfully using a general Claisen-Schmidt condensation protocol. The prepared compounds were purified and structurally analyzed by 1H, 13C NMR, and FT-IR techniques. A multi-faceted theoretical approach, combining Density Functional Theory (DFT), molecular docking, and ADME predictions, was employed to evaluate their therapeutic potential. DFT calculations at the B3LYP/def2-TZVP level revealed key electronic properties, with TD3 compound demonstrating the highest chemical reactivity. Molecular Electrostatic Potential (MEP) and Reduced Density Gradient (RDG) analyses provided insights into the compounds' non-covalent interactions and charge distributions. Molecular docking studies against the NS5 protein (PDB: 6KR2) showed superior binding affinities for all three compounds compared to the control ligand SAH, with TD3 exhibiting the lowest binding energy (-8.41 kcal/mol) and theoretical inhibition constant (689.31 nM). ADME predictions indicated favorable drug-like properties with concerns regarding aqueous solubility and potential P-glycoprotein interactions. Toxicity evaluations highlighted challenges, particularly in hepatotoxicity and carcinogenicity. The study identified TD3 as a promising lead compound for Dengue Virus NS5 inhibition, while also emphasizing the need for targeted modifications to address toxicity concerns. This research not only contributes to anti-dengue drug discovery efforts but also provides a robust methodological framework for the theoretical evaluation of similar small compounds in future investigations.

Prediction of Human Clearance Using In Silico Models with Reduced Bias.

Predicting human clearance with high accuracy from in silico-derived parameters alone is highly desirable, as it is fast, saves in vitro resources, and is animal-sparing. We derived random forest (RF) models from 1340 compounds with human intravenous pharmacokinetic (PK) data, the largest data set publicly available today. To assess the general applicability of the RF models, we systematically removed structural-therapeutic class analogues and other compounds with structural similarity from the training sets. For a quasi-prospective test set of 343 compounds, we show that RF models devoid of structurally similar compounds in the training set predict human clearance with a geometric mean fold error (GMFE) of 3.3. While the observed GMFE illustrates how difficult it is to generate a useful model that is broadly applicable, we posit that our RF models yield a more realistic assessment of how well human clearance can be predicted prospectively. We deployed the conformal prediction formalism to assess the model applicability and to determine the prediction confidence intervals for each prediction. We observed that clearance can be predicted better for renally cleared compounds than for other clearance mechanisms. We show that applying a classification model for predicting renal clearance identifies a subset of compounds for which clearance can be predicted with higher accuracy, yielding a GMFE of 2.3. In addition, our in silico RF human clearance models compared well to models derived from scaling human hepatocytes or preclinical in vivo data.

Adapting Deep Learning QSPR Models to Specific Drug Discovery Projects.

Medicinal chemistry and drug design efforts can be assisted by machine learning (ML) models that relate the molecular structure to compound properties. Such quantitative structure-property relationship models are generally trained on large data sets that include diverse chemical series (global models). In the pharmaceutical industry, these ML global models are available across discovery projects as an "out-of-the-box" solution to assist in drug design, synthesis prioritization, and experiment selection. However, drug discovery projects typically focus on confined parts of the chemical space (e.g., chemical series), where global models might not be applicable. Local ML models are sometimes generated to focus on specific projects or series. Herein, ML-based global models, local models, and hybrid global-local strategies were benchmarked. Analyses were done for more than 300 drug discovery projects at Novartis and ten absorption, distribution, metabolism, and excretion (ADME) assays. In this work, hybrid global-local strategies based on transfer learning approaches were proposed to leverage both historical ADME data (global) and project-specific data (local) to adapt model predictions. Fine-tuning a pretrained global ML model (used for weights' initialization, WI) was the top-performing method. Average improvements of mean absolute errors across all assays were 16% and 27% compared with global and local models, respectively. Interestingly, when the effect of training set size was analyzed, WI fine-tuning was found to be successful even in low-data scenarios (e.g., ∼10 molecules per project). Taken together, this work highlights the potential of domain adaptation in the field of molecular property predictions to refine existing pretrained models on a new compound data distribution.

Identification and characterization of a novel decalin derivative with anti-Candida activity from Streptomyces chrestomyceticus strain ADP4.

A novel decalin derivative, trans-1-oxo-2,4-diacetylaminodecalin (1) with anti-Candida activity, had been isolated from Streptomyces chrestomyceticus strain ADP4. The structure of the compound was determined from the analysis of spectral data (LCMS/MS, UV, FTIR, 1D- and 2D-NMR). The anti-Candida activity of 1 was specific to Candida albicans and Candida auris. Further, it displayed inhibition of the early-stage biofilm of C. albicans. In-silico analysis of the compound revealed its drug likeness properties without any violations and PAINS alert when investigated for ADME properties. Along with the overall bioavailability, compound 1 did not show any predicted bioaccumulation and mutagenicity in the analysis by TEST software. Non-cytotoxic property was further confirmed by in-vitro assay on the HepG2 cell line.

Therapeutic drug monitoring, liquid biopsies or pharmacogenomics for prediction of human drug metabolism and response.

Pharmacokinetics plays a central role in understanding the significant interindividual differences that exist in drug metabolism and response. Effectively addressing these differences requires a multi-faceted approach that encompasses a variety of tools and methods. In this review, we examine three key strategies to achieve this goal, namely pharmacogenomics, therapeutic drug monitoring (TDM) and liquid biopsy-based monitoring of hepatic ADME gene expression and highlight their advantages and limitations. We note that larger cohort studies are needed to validate the utility of liquid biopsy-based assessment of hepatic ADME gene expression, which includes prediction of drug metabolism in the clinical setting. Modern mass spectrometers have improved traditional TDM methods, offering versatility and sensitivity. In addition, the identification of endogenous or dietary markers for CYP metabolic traits offers simpler and more cost-effective alternatives to determine the phenotype. We believe that future pharmacogenomic applications in clinical practice should prioritize the identification of missing heritable factors, using larger, well-characterized patient studies and controlling for confounding factors such as diet, concomitant medication and physical health. The intricate regulation of ADME gene expression implies that large-scale studies combining long-read next-generation sequencing (NGS) of complete genomes with phenotyping of patients taking different medications are essential to identify these missing heritabilities. The continuous integration of such data into AI-driven analytical systems could provide a comprehensive and useful framework. This could lead to the development of highly effective algorithms to improve genetics-based precision treatment by predicting drug metabolism and response, significantly improving clinical outcomes.

A Combination of Machine Learning and PBPK Modeling Approach for Pharmacokinetics Prediction of Small Molecules in Humans.

PURPOSE: Recently, there has been rapid development in model-informed drug development, which has the potential to reduce animal experiments and accelerate drug discovery. Physiologically based pharmacokinetic (PBPK) and machine learning (ML) models are commonly used in early drug discovery to predict drug properties. However, basic PBPK models require a large number of molecule-specific inputs from in vitro experiments, which hinders the efficiency and accuracy of these models. To address this issue, this paper introduces a new computational platform that combines ML and PBPK models. The platform predicts molecule PK profiles with high accuracy and without the need for experimental data. METHODS: This study developed a whole-body PBPK model and ML models of plasma protein fraction unbound ( f up ), Caco-2 cell permeability, and total plasma clearance to predict the PK of small molecules after intravenous administration. Pharmacokinetic profiles were simulated using a "bottom-up" PBPK modeling approach with ML inputs. Additionally, 40 compounds were used to evaluate the platform's accuracy. RESULTS: Results showed that the ML-PBPK model predicted the area under the concentration-time curve (AUC) with 65.0 % accuracy within a 2-fold range, which was higher than using in vitro inputs with 47.5 % accuracy. CONCLUSION: The ML-PBPK model platform provides high accuracy in prediction and reduces the number of experiments and time required compared to traditional PBPK approaches. The platform successfully predicts human PK parameters without in vitro and in vivo experiments and can potentially guide early drug discovery and development.

AChE inhibitory activity of N-substituted natural galanthamine derivatives.

Galanthamine derivatives are known for their AChE inhibitory activity. Among them, galanthamine has been approved for treatment of Alzheimer's disease. N-Acetylnorgalanthamine (narcisine) and N-(2'-methyl)allylnorgalanthamine (the most potent natural AChE inhibitor of galanthamine type) were synthetized using N-norgalanthamine as a precursor. The NMR data described previously for narcisine were revised by two-dimensional 1H-1H and 1H-13C chemical shift correlation experiments. AChE inhibitory assays showed that N-acetylnorgalanthamine and N-formylnorgalanthamine (with previously unknown activity) are 4- and 43-times, respectively, less potent than galanthamine. In vitro (AChE inhibitory) and in silico (docking, ADME) assays and comparison of N-(2'-methyl)allylnorgalanthamine with galanthamine prove that this molecule is a very promising natural AChE inhibitor (33-times more potent than galanthamine) which further in vivo studies would provide better estimation about its applicability as a drug.

Optimizing and characterizing 4-methyl substituted pyrazol-3-carboxamides leading to the peripheral cannabinoid 1 receptor inverse agonist TM38837.

Several series of diverse pyrazole-3-carboxamides functionalized with 4-methylamides, 4-methylcarboxylic acids and 4-methyltetrazoles were prepared from the corresponding 4-cyanomethylpyrazoles and investigated as Cannabinoid receptor 1 (CB1) antagonists and inverse agonists with the aim of making compounds with less CNS (Central Nervous System) mediated side-effects compared to rimonabant. The compounds were evaluated and optimized with respect to lipophilicity, solubility, CB1 potency, metabolism, distribution to brain and liver, effect on weight loss in diet-induced mice models. A few carboxylic acids and tetrazoles were selected as especially promising with the tetrazole TM38837 subsequently demonstrating impressive efficacy in various animal models of obesity, producing considerable weight loss and improvements on plasma markers of inflammation and glucose homeostasis, at doses apparently producing negligible brain exposure. TM38837 became the first peripherally restricted CB1 antagonist or inverse agonist to enter clinical trials supporting its lack of CNS effects and it is now believed that the non-CNS mediated efficacy is linked to high liver exposure. This opens opportunities to be explored in other indications such as nonalcoholic fatty liver disease (NAFLD) and steatohepatitis (NASH). Note that this is a first-time disclosure of the structure of TM38837 and other structures appearing in literature are not connected with this program.

Screening and Evaluation of Children's Sensitively Toxic Chemicals in New Mosquito Repellent Products Based on a Nationwide Investigation.

New mosquito repellent products (NMRPs) are emerging popular repellents among children. There are increasing reports on children's sensitization reactions caused by NMRPs, while regulations on their productions, sales, or usage are still lacking. One of the reasons could be the missing comprehensive risk assessment. We first conducted a nationwide investigation on children's NMRP usage preferences. Then, we high-throughput screened volatile or semivolatile organic chemicals (VOCs/SVOCs) in five representative NMRPs by the headspace gas chromatography-orbitrap high-resolution mass spectrometry analytical method. After that, toxic compounds were recognized based on the toxicity forecaster (ToxCast) database. A total of 277 VOCs/SVOCs were recognized, and 70 of them were identified as toxic compounds. In a combination of concentrations, toxicities, absorption, distribution, metabolism, and excretion characteristics in the body, 28 chemicals were finally proposed as priority-controlled compounds in NMRPs. Exposure risks of recognized toxic chemicals through NMRPs by inhalation and dermal intake for children across the country were also assessed. Average daily intakes were in the range of 0.20-7.31 mg/kg/day for children in different provinces, and the children in southeastern coastal provinces were found to face higher exposure risks. By controlling the high-priority chemicals, the risks were expected to be reduced by about 46.8% on average. Results of this study are therefore believed to evaluate exposure risks, encourage safe production, and promote reasonable management of NMRPs.

Development of a Generic Physiologically Based Kinetic Model for the Prediction of Internal Exposure to Organophosphate Pesticides.

Since their introduction into agriculture, the toxicity of organophosphate (OP) pesticides has been widely studied in animal models. However, next generation risk assessment (NGRA) intends to maximize the use of novel approach methodologies based on in vitro and in silico methods. Therefore, this study describes the development and evaluation of a generic physiologically based kinetic (PBK) model for acute exposure to OP pesticides in rats and humans using quantitative structure property relationships and data from published in vitro studies. The models were evaluated using in vivo studies from the literature for chlorpyrifos, diazinon, fenitrothion, methyl-parathion, ethyl-parathion, dimethoate, chlorfenvinphos, and profenofos. Evaluation was performed by comparing simulated and in vivo observed time profiles for blood, plasma, or urinary concentrations and other toxicokinetic parameters. Of simulated concentration-time profiles, 87 and 91% were within a 5-fold difference from observed toxicokinetic data from rat and human studies, respectively. Only for dimethyl-organophosphates further refinement of the model is required. It is concluded that the developed generic PBK model provides a new tool to assess species differences in rat and human kinetics of OP pesticides. This approach provides a means to perform NGRA for these compounds and could also be adopted for other classes of compounds.

Multi-target potential of newly designed tacrine-derived cholinesterase inhibitors: Synthesis, computational and pharmacological study.

Simple and scalable synthetic approach was used for the preparation of thirteen novel tacrine derivatives consisting of tacrine and N-aryl-piperidine-4-carboxamide moiety connected by a five-methylene group linker. An anti-Alzheimer disease (AD) potential of newly designed tacrine derivatives was evaluated against two important AD targets, acetylcholinesterase (AChE) and butyrylcholinesterase (BuChE). In vitro pharmacological evaluation showed strong ChE inhibitory activity of all compounds, with IC50 values ranging from 117.5 to 455 nM for AChE and 34 to 324 nM for BuChE. As a representative of the series with the best cytotoxicity / ChE inhibitory activity ratio, expressed as the selectivity index (SI), 2-chlorobenzoyl derivative demonstrated mixed-type inhibition on AChE and BuChE, suggesting binding to both CAS and PAS of the enzymes. It also exhibited antioxidant capacity and neuroprotective potential against amyloid-ß (Aß) toxicity in the culture of neuron-like cells. In-depth computational analysis corroborated well with in vitro ChE inhibition, illuminating that all compounds exhibit significant potential in targeting both enzymes. Molecular dynamics (MD) simulations revealed that 2-chlorobenzoyl derivative, created complexes with AChE and BuChE that demonstrated sufficient stability throughout the observed MD simulation. Computationally predicted ADME properties indicated that these compounds should have good blood-brain barrier (BBB) permeability, an important factor for CNS-targeting drugs. Overall, all tested compounds showed promising pharmacological behavior, highlighting the multi-target potential of 2-chlorobenzoyl derivative which should be further investigated as a new lead in the drug development process.

Novel anti-inflammatory agents featuring phenoxy acetic acid moiety as a pharmacophore for selective COX-2 inhibitors: Synthesis, biological evaluation, histopathological examination and molecular modeling investigation.

Inflammation management presents a critical challenge in modern medicine, with nonsteroidal anti-inflammatory drugs (NSAIDs) being a widely used therapeutic option. However, their efficacy is often accompanied by significant gastrointestinal adverse effects, necessitating the exploration of safer alternatives, particularly through the investigation of cyclooxygenase-2 (COX-2) inhibitors. This study endeavors to address this imperative through the synthesis and evaluation of pyrazoline-phenoxyacetic acid derivatives. Among the synthesized compounds, 6a and 6c emerged as promising candidates, demonstrating potent COX-2 inhibition with IC50 values of 0.03 µM for both and selectivity index = 365.4 and 196.9, respectively. Furthermore, these compounds exhibited efficacy in mitigating formalin-induced edema in male Wistar rats, accompanied by favorable safety profiles upon histological examination of vital organs. Comprehensive safety assessments, including evaluation of creatinine, AST, and ALT enzymatic as well as troponin T and creatine kinase-MB levels, further reinforce the promising attributes of the synthetic candidates. Molecular docking studies endorsed by molecular dynamic simulations corroborate the biological findings, elucidating significant protein-ligand interactions at COX-2 active sites indicative of therapeutic potential.

Green approach to the synthesis of α-aminophosphonate-tetrahydroisoquinoline hybrids and their anti-cholinesterase activity.

A series of 19 novel α-aminophosphonate-tetrahydroisoquinoline hybrids were synthesized through a cross dehydrogenative coupling reaction between N-aryl-tetrahydroisoquinolines and dialkylphosphites, using tert-butyl hydroperoxide as oxidazing agent. This simple procedure provided products with high atom economy and moderate to high yields. In vitro cholinesterase inhibitory activity of these compounds was evaluated. All the synthesized compounds showed good to excellent selective inhibition against butyrylcholinesterase. Compound 3bc was found to be the most active derivative with an IC50 of 9 nM. Molecular modelling studies suggested that the inhibitor is located in the peripheral anionic site (PAS) of the enzyme and interacts with some residue of the catalytic anionic site. Kinetic studies revealed that 3bc acts as a non-competitive inhibitor. Predicted ADME showed good pharmacokinetics and drug-likeness properties for most hybrids. Each newly synthesized compound was characterized by IR, 1H NMR, 13C NMR, 31P NMR spectral studies and also HRMS. The results of this study suggest that α-aminophosphonate-tetrahydroisoquinoline hybrids can be promising lead compounds in the discovery of new and improved drugs for the treatment of Alzheimer's disease and related neurodegenerative disorders.

Novel acyl hydrazide derivatives of polyhydroquinoline as potent anti-diabetic and anti-glycating agents: Synthesis, in vitro α-amylase, α-glucosidase inhibition and anti-glycating activity with molecular docking insights.

In this study, eleven novel acyl hydrazides derivative of polyhydroquinoline were synthesized, characterized and screened for their in vitro anti-diabetic and anti-glycating activities. Seven compounds 2a, 2d, 2i, 2 h, 2j, 2f, and 2 g exhibited notable α-amylase inhibitory activity having IC50 values from 3.51 ± 2.13 to 11.92 ± 2.30 µM. Similarly, six compounds 2d, 2f, 2 h, 2i, 2j, and 2 g displayed potent α-glucosidase inhibitory activity compared to the standard acarbose. Moreover, eight derivatives 2d, 2 g, 2f, 2j, 2a, 2i, 2 g, and 2e showed excellent anti-glycating activity with IC50 values from 6.91 ± 2.66 to 15.80 ± 1.87 µM when compared them with the standard rutin (IC50 = 22.5 ± 0.90 µM). Molecular docking was carried out to predict the binding modes of all the compounds with α-amylase and α-glucosidase. The docking analysis revealed that most of the compounds established strong interactions with α-amylase and α-glucosidase. All compounds fitted well into the binding pockets of α-amylase and α-glucosidase. Among all compounds 2a and 2f were most potent based on docking score -8.2515 and -7.3949 against α-amylase and α-glucosidase respectively. These results hold promise for the development of novel candidates targeted at controlling postprandial glucose levels in individuals with diabetes.

Discovery of novel quinolin-2-one derivatives as potential GSK-3ß inhibitors for treatment of Alzheimer's disease: Pharmacophore-based design, preliminary SAR, in vitro and in vivo biological evaluation.

Recently, glycogen synthase kinase-3ß (GSK-3ß) has been considered as a critical factor implicated in Alzheimer's disease (AD). In a previous work, a 3D pharmacophore model for GSK-3ß inhibitors was created and the results suggested that derivative ZINC67773573, VIII, may provide a promising lead for developing novel GSK-3ß inhibitors for the AD's treatment. Consequently, in this work, novel series of quinolin-2-one derivatives were synthesized and assessed for their GSK-3ß inhibitory properties. In vitro screening identified three compounds: 7c, 7e and 7f as promising GSK-3ß inhibitors. Compounds 7c, 7e and 7f were found to exhibit superior inhibitory effect on GSK-3ß with IC50 value ranges between 4.68 ± 0.59 to 8.27 ± 0.60 nM compared to that of staurosporine (IC50 = 6.12 ± 0.74 nM). Considerably, compounds 7c, 7e and 7f effectively lowered tau hyperphosphorylated aggregates and proving their safety towards the SH-SY5Y and THLE2 normal cell lines. The most promising compound 7c alleviated cognitive impairments in the scopolamine-induced model in mice. Compound 7c's activity profile, while not highly selective, may provide a starting point and valuable insights into the design of multi-target inhibitors. According to the ADME prediction results, compounds 7c, 7e and 7f followed Lipinski's rule of five and could almost permeate through the BBB. Molecular docking simulations showed that these compounds are well accommodated in the ATP binding site interacting by its quinoline-2-one ring through hydrogen bonding with the key amino acids Asp133 and Val135 at the hinge region. The findings of this study suggested that these new compounds may have potential as anti-AD drugs targeting GSK-3ß.