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Exploring the different spin states of central metals in the complex to regulate the anti-tumor activity of cancer cells is of great significance in drug design and clinical use. However, it is a challenge to build a strong coupling between spin states and anti-tumor activities in one system. Herein, we present two complexes {FeII2L2[PdII(CN)4]2}·2H2O (L = Bztpen (1), Bztppn (2); Bztpen = N-benzyl-N,N′,N′-tris(2-pyridylmethyl)ethylenediamine, Bztppn = N-benzyl-N,N′,N′-tris(2-pyridylmethyl)propylenediamine) showing different cytotoxic activities actuated by fine-tuning the structure with different spin states of Fe(II). Magnetic susceptibility measurements and X-ray diffraction revealed that the Fe(II) ion in complexes 1 and 2 remains in the LS and HS state, respectively, at room temperature. Cytotoxicity tests indicate that complex 1 is more biologically effective than complex 2. In complex 2, however, the high-spin Fe(II) played a key role in regulating its in vitro antitumor effects and seems to be associated with ROS-mediated apoptosis. These findings offer a new avenue for developing anti-cancer drugs by designing complexes with different spin states. [ABSTRACT FROM AUTHOR]

Quetiapine is a second-generation atypical antipsychotic drug that has been commonly prescribed for the treatment of schizophrenia, major depressive disorder (depression), and other psychological disorders. Targeted inhibition of hyperpolarization-activated cyclic-nucleotide gated (HCN) channels, which generate Ih, may provide effective resistance against schizophrenia and depression. We investigated if HCN channels could contribute to the therapeutic effect of quetiapine, and its major active metabolite norquetiapine. Two-electrode voltage clamp recordings were used to assess the effects of quetiapine and its active metabolites 7-hydroxyquetiapine and norquetiapine on currents from HCN1 channels expressed in Xenopus laevis oocytes. Norquetiapine, but not quetiapine nor 7-hydroxyquetiapine, has an inhibitory effect on HCN1 channels. Norquetiapine selectively inhibited HCN1 currents by shifting the voltage-dependence of activation to more hyperpolarized potentials in a concentration-dependent manner with an IC50 of 13.9 ± 0.8 µM for HCN1 and slowing channel opening, without changing the kinetics of closing. Inhibition by norquetiapine primarily occurs from in the closed state. Norquetiapine inhibition is not sensitive to the external potassium concentration, and therefore, likely does not block the pore. Norquetiapine inhibition also does not dependent on the cyclic-nucleotide binding domain. Norquetiapine also inhibited HCN4 channels with reduced efficacy than HCN1 and had no effect on HCN2 channels. Therefore, HCN channels are key targets of norquetiapine, the primary active metabolite of quetiapine. These data help to explain the therapeutic mechanisms by which quetiapine aids in the treatment of anxiety, major depressive disorder, bipolar disorder, and schizophrenia, and may represent a novel structure for future drug design of HCN inhibitors. [ABSTRACT FROM AUTHOR]

Since the advent of computational analysis and visualization of chemical compounds, Computer-Aided Drug Design has made significant contributions to drug discovery. Recently, de novo drug design and molecular optimization have garnered considerable attention. Traditional optimization methods often struggle with the discrete nature of molecular space, but evolutionary computations have demonstrated their versatility across various optimization problems, regardless of the nature of the objective functions. This paper introduces a novel evolutionary algorithm, the Swarm Intelligence-Based Method for Single-Objective Molecular Optimization. Several experiments were conducted to showcase the efficiency of the proposed method, which identifies near-optimal solutions in a remarkably short time. The results were then compared with those of other state-of-the-art methods in the field. [ABSTRACT FROM AUTHOR]

α7 nicotinic acetylcholine receptors (nAChRs) are homopentameric ligand-gated ion channels with critical roles in the nervous system. Recent studies have resolved and functionally annotated closed, open, and desensitized states of these receptors, providing insight into ion permeation and lipid binding. However, the process by which α7 nAChRs transition between states remains unclear. To understand gating and lipid modulation, we generated two ensembles of molecular dynamics simulations of apo α7 nAChRs, with or without cholesterol. Using symmetry-adapted Markov state modeling, we developed a five-state gating model. Free energies recapitulated functional behavior, with the closed state dominating in absence of agonist. Open-to-nonconducting transition rates corresponded to experimental open durations. Cholesterol relatively stabilized the desensitized state, and reduced open-desensitized barriers. These results establish plausible asymmetric transition pathways between states, define lipid modulation effects on the α7 nAChR conformational cycle, and provide an ensemble of structural models applicable to rational design of lipidic pharmaceuticals. The α7 nicotinic acetylcholine receptor plays important roles in the human nervous system. Here, authors use molecular simulations to develop a gating model and reveal cholesterol effects, offering insights applicable to drug design. [ABSTRACT FROM AUTHOR]

Macrophages adopt distinct phenotypes in response to environmental cues, with type-2 cytokine interleukin-4 promoting a tissue-repair homeostatic state (M2IL4). Glucocorticoids (GC), widely used anti-inflammatory therapeutics, reportedly impart a similar phenotype (M2GC), but how such disparate pathways may functionally converge is unknown. We show using integrative functional genomics that M2IL4 and M2GC transcriptomes share a striking overlap mirrored by a shift in chromatin landscape in both common and signal-specific gene subsets. This core homeostatic program is enacted by transcriptional effectors KLF4 and the glucocorticoid receptor, whose genome-wide occupancy and actions are integrated in a stimulus-specific manner by the nuclear receptor cofactor GRIP1. Indeed, many of the M2IL4:M2GC-shared transcriptomic changes were GRIP1-dependent. Consistently, GRIP1 loss attenuated phagocytic activity of both populations in vitro and macrophage tissue-repair properties in the murine colitis model in vivo. These findings provide a mechanistic framework for homeostatic macrophage programming by distinct signals, to better inform anti-inflammatory drug design. IL4 and glucocorticoids elicit a homeostatic phenotype in macrophages. Here the authors show that these disparate stimuli yield converging epigenomic and transcriptomic changes, enacted by transcription factors KLF4 and the glucocorticoid receptor, and integrated by their shared coregulator GRIP1. [ABSTRACT FROM AUTHOR]

Proteins are the primary targets of almost all small molecule drugs. However, even the most selectively designed drugs can potentially target several unknown proteins. Identification of potential drug targets can facilitate design of new drugs and repurposing of existing ones. Current state-of-the-art proteomics methodologies enable screening of thousands of proteins against a limited number of drug molecules. Here we report the development of a label-free quantitative proteomics approach that enables proteome-wide screening of small organic molecules in a scalable, reproducible, and rapid manner by streamlining the proteome integral solubility alteration (PISA) assay. We used rat organs ex-vivo to determine organ specific targets of medical drugs and enzyme inhibitors to identify drug targets for common drugs such as Ibuprofen. Finally, global drug profiling revealed overarching trends of how small molecules affect the proteome through either direct or indirect protein interactions. Many drug targets remain unknown for small molecules. Here, the authors develop a label-free Proteome Integral Solubility Alteration (PISA) workflow to automate target identification and analysis. Applied to rat organs, the authors identify previously unknown drug targets for common medication. [ABSTRACT FROM AUTHOR]

Biological variability poses significant challenges in the development of effective therapeutics, particularly when it comes to drug solubility and bioavailability. Poor solubility across varying physiological conditions often leads to reduced absorption and inconsistent therapeutic outcomes. This review examines how nanotechnology, especially through the use of nanomaterials and magnetic nanoparticles, offers innovative solutions to enhance drug solubility and bioavailability. This comprehensive review focuses on recent advancements and approaches in nanotechnology. We highlight both the successes and remaining challenges in this field, emphasizing the role of continued innovation. Future research should prioritize developing universal therapeutic solutions, conducting interdisciplinary research, and leveraging personalized nanomedicine to address biological variability. [ABSTRACT FROM AUTHOR]

Sortase A (SrtA) of Staphylococcus aureus has long been shown to be a relevant molecular target for antibacterial development. Moreover, the designed SrtA inhibitors act via the antivirulence mechanism, potentially causing less evolutional pressure and reduced antimicrobial resistance. However, no marketed drugs or even drug candidates have been reported until recently, despite numerous efforts in the field. SrtA has been shown to be a tough target for rational structure-based drug design (SBDD), which hampers the regular development of small-molecule inhibitors using the available arsenal of drug discovery tools. Recently, several oligopeptides resembling the sorting sequence LPxTG (Leu-Pro-Any-Thr-Gly) of the native substrates of SrtA were reported to be active in the micromolar range. Despite the good experimental design of those works, their molecular modeling parts are still not convincing enough to be used as a basis for a rational modification of peptidic inhibitors. In this work, we propose to use the ensemble docking approach, in which the relevant SrtA conformations are extracted from the molecular dynamics simulation of the LPRDA (Leu-Pro-Arg-Asp-Ala)-SrtA complex, to effectively represent the most significant and diverse target conformations. The developed protocol is shown to describe the known experimental data well and then is applied to a series of new peptidomimetic molecules resembling the active oligopeptide structures reported previously in order to prioritize structures from this work for further synthesis and activity testing. The proposed approach is compared to existing alternatives, and further directions for its development are outlined. [ABSTRACT FROM AUTHOR]

We describe the design, synthesis and structure–activity relationship of a novel series of 1-(4-(7-azaindole)-3,6-dihydropyridin-1-yl)alkyl-3-(1H-indol-3-yl)pyrrolidine-2,5-dione derivatives with combined effects on the serotonin (5-HT1A) and dopamine (D2) receptors and the serotonin (5-HT), noradrenaline (NA), and dopamine (DA) transporters as multi-target directed ligands for the treatment of depression. All of the tested compounds demonstrated good affinity for the serotonin transporter (SERT). Among them, compounds 11 and 4 emerged as the lead candidates because of their promising pharmacological profile based on in vitro studies. Compound 11 displayed a high affinity for the 5-HT1A (Ki = 128.0 nM) and D2 (Ki = 51.0 nM) receptors, and the SERT (Ki = 9.2 nM) and DAT (Ki = 288.0 nM) transporters, whereas compound 4 exhibited the most desirable binding profile to SERT/NET/DAT among the series: Ki = 47.0 nM/167.0 nM/43% inhibition at 1 µM. These results suggest that compounds 4 and 11 represent templates for the future development of multi-target antidepressant drugs. [ABSTRACT FROM AUTHOR]

The search and development of effective sirtuin small molecule inhibitors (SIRTIs) continues to draw great attention due to their wide range of pharmacological applications. Based on SIRTs' involvement in different biological pathways, their ligands were investigated for many diseases, such as cancer, neurodegenerative disorders, diabetes, cardiovascular diseases and autoimmune diseases. The elucidation of a substantial number of SIRT2–ligand complexes is steering the identification of novel and more selective modulators. Among them, SIRT2 in the presence of the SirReal2 analog series was the most studied. On this basis, we recently reported structure-based analyses leading to the discovery of thiazole-based compounds acting as SIRT2 inhibitors (T1, SIRT2 IC50 = 17.3 µM). Herein, ligand-based approaches followed by molecular docking simulations allowed us to evaluate in silico a novel small series of thiazoles (3a–3d and 5a, 5d) as putative SIRT2 inhibitors. Results from the computational studies revealed comparable molecular interaction fields (MIFs) and docking positionings of most of these compounds with respect to reference SIRT2Is. Biochemical and biological assays validated this study and pointed to compound 5a (SIRT2 IC50 = 9.0 µM) as the most interesting SIRT2I that was worthy of further development as an anticancer agent. [ABSTRACT FROM AUTHOR]

AbstractTaste masking of any pediatric oral formulation has always been a hurdle. Cubosomses, a type of lyotropic liquid crystal, have been used in this research to develop an oral formulation of ibuprofen. Ibuprofen cubosomes were prepared with glyceryl monooleate (GMO) and poloxamer 407 by high-pressure homogenization (HPH) technique. The formulation experiments were performed using a 2-level, 3-factor central composite design with drug loading, GMO concentration and HPH pressure as independent variables and vesicle size, entrapment efficiency and %drug content as the dependent variables. The most suitable ibuprofen cubosomes showed 94 nm vesicle size, −17.5 mV zeta potential, >75% drug content and >96% entrapment efficiency. The cubosomes released more than 80% of the drug within 2 hours. In vitro taste masking study showed significantly lower drug release than its bitterness threshold. An in vivo taste masking study in healthy adult volunteers showed significant taste masking compared to the marketed formulation and ibuprofen pure suspension. % of the volunteers observed cubosomes as not bitter. In contrast, 67% of the volunteers reported marketed suspension as very bitter. The developed cubosomes resulted in physical stability for three months of proper storage. The study has successfully achieved considerable taste-masking using cubosomes with GMO and Poloxmaer 407 formulation. This proof of concept can initiate a new field of research in taste-masked oral liquid formulation. [ABSTRACT FROM AUTHOR]

Cryogenic electron microscopy (Cryo-EM) is a widely used technique for visualizing the 3D structures of many drug design targets, including membrane proteins, at atomic resolution. However, the necessary throughput for structure-based drug design (SBDD) is not yet achieved. Currently, data analysis is a major bottleneck due to the rapid advancements in detector technology and image acquisition methods. Here we show "GoToCloud", a cloud-computing-based platform for advanced data analysis and data management in Cryo-EM. With GoToCloud, it is possible to optimize computing resources and reduce costs by selecting the most appropriate parallel processing settings for each processing step. Our benchmark tests on GoToCloud demonstrate that parallel computing settings, including the choice of computational hardware, as well as a required target resolution have significant impacts on the processing time and cost performance. Through this optimization of a cloud computing environment, GoToCloud emerges as a promising platform for the acceleration of Cryo-EM SBDD. The design philosophy, implementation, optimization, and performance evaluation of a cloud-computing platform for data analysis and management in Cryo-EM single particle analysis to achieve a significant reduction in processing time and cost. [ABSTRACT FROM AUTHOR]

Although combination therapy is known for its high efficacy, reduced side effects and drug resistance, toxicity remains a major drawback. Some of the genes are likely to induce hepatotoxicity through ROS-mediated mechanisms when a drug is metabolized alone or in combination in the liver. To address this, we have developed a scientific approach to predict the toxicity of different genotoxin combinations and validate their interactions with various targets. The current study is an extensive study of our previous set of in vivo rat liver microarray data processed using R studio for their functional analysis. About five combinations of genotoxins such as CPT/ETP, CPT/CPL, ETP/CPL, CP/CPT and EES/CP along with their differential gene expression targeting Chemical carcinogenesis—ROS are chosen for this study. We aim to examine the binding affinity of different genotoxin combinations using in silico multiple ligand simultaneous docking (MLSD) and are then bio-evaluated for cytotoxicity in vitro using human hepatocellular carcinoma cell lines (HepG2) with the MTT assay. As a result, dose–response cytotoxicity with its strength of interactions and a significant variance in ROS levels in the treated cells is observed compared to their IC50 values. Out of 5 combinations such as CPT/CPL, ETP/CPL and EES/CP are found not only to be significantly cytotoxic but also induce oxidative stress specifically above their IC50 values with good and moderate binding interactions ensuring their toxicity. On the contrary, the safe combinations are found to be CTP/ETP and CP/CPT possibly with no and tolerable adverse effects standing as preliminary information for researchers in drug design and development. [ABSTRACT FROM AUTHOR]

Two novel ibuprofen cystamine salts (IBU‐CYS 1 and IBU‐CYS 2) are synthesized by coupling the anion of ibuprofen with cystamine dihydrochloride in 1 : 1 and 2 : 1 ratio to improve the solubility and bioavailability of ibuprofen. The salts are characterized by 1H NMR, FT‐IR and UV‐Vis spectroscopy, differential scanning calorimetry (DSC), thermogravimetry (TGA, DTA) and X‐ray diffraction measurements. IBU‐CYS 1 and IBU‐CYS 2 show higher solubility (6.11 and 7.81 mg/mL) compared to ibuprofen (0.04 mg/mL) in water. IBU‐CYS2 was encapsulated into 2‐hydroxyethyl methacrylate: poly (ethylene glycol) acrylate hydrogels for enhanced delivery. The in vitro studies in PBS (pH 7.4) indicate that the salts are effective in relieving inflammatory responses induced by lipopolysaccharide in RAW264.7 macrophage cells (nitrite inhibition percentages of IBU‐CYS 1, IBU‐CYS 2 and ibuprofen: approximately 34.29, 27.03 and 31.50 respectively) while indicating no cytotoxicity. Therefore, these salts may be promising candidates for the development of effective formulations of this drug. [ABSTRACT FROM AUTHOR]

Achieving optimal treatment outcomes for individuals living with schizophrenia remains challenging, despite 70 years of drug development efforts. Many chemically distinct antipsychotics have been developed over the past 7 decades with improved safety and tolerability but with only slight variation in efficacy. All antipsychotics currently approved for the treatment of schizophrenia act as antagonists or partial agonists at the dopamine D 2 receptor. With only a few possible exceptions, antipsychotic drugs have similar and modest efficacy for treating positive symptoms and are relatively ineffective in addressing the negative and cognitive symptoms of the disease. The development of novel treatments focused on targeting muscarinic acetylcholine receptors (mAChRs) has been of interest for more than 25 years following reports that treatment with a dual M 1 /M 4 –preferring mAChR agonist resulted in antipsychotic-like effects and procognitive properties in individuals living with Alzheimer's disease and schizophrenia; more recent clinical trials have confirmed these findings. In addition, advances in our understanding of the receptor binding and activation properties of xanomeline at specific mAChRs have the potential to inform future drug design targeting mAChRs. [ABSTRACT FROM AUTHOR]

Small-molecule degraders of disease-driving proteins offer a clinically proven modality with enhanced therapeutic efficacy and potential to tackle previously undrugged targets. Stable and long-lived degrader-mediated ternary complexes drive fast and profound target degradation; however, the mechanisms by which they affect target ubiquitination remain elusive. Here, we show cryo-EM structures of the VHL Cullin 2 RING E3 ligase with the degrader MZ1 directing target protein Brd4BD2 toward UBE2R1-ubiquitin, and Lys456 at optimal positioning for nucleophilic attack. In vitro ubiquitination and mass spectrometry illuminate a patch of favorably ubiquitinable lysines on one face of Brd4BD2, with cellular degradation and ubiquitinomics confirming the importance of Lys456 and nearby Lys368/Lys445, identifying the "ubiquitination zone." Our results demonstrate the proficiency of MZ1 in positioning the substrate for catalysis, the favorability of Brd4BD2 for ubiquitination by UBE2R1, and the flexibility of CRL2 for capturing suboptimal lysines. We propose a model for ubiquitinability of degrader-recruited targets, providing a mechanistic blueprint for further rational drug design. [ABSTRACT FROM AUTHOR]

ABSTRACT Due to its intricate molecular and structural characteristics, vascular endothelial growth factor receptor 2 (VEGFR‐2) is essential for the development of new blood vessels in various pathological processes and conditions, especially in cancers. VEGFR‐2 inhibitors have demonstrated significant anticancer effects by blocking many signaling pathways linked to tumor growth, metastasis, and angiogenesis. Several small compounds, including the well‐tolerated sunitinib and sorafenib, have been approved as VEGFR‐2 inhibitors. However, the widespread side effects linked to these VEGFR‐2 inhibitors—hypertension, epistaxis, proteinuria, and upper respiratory infection—motivate researchers to search for new VEGFR‐2 inhibitors with better pharmacokinetic profiles. The key molecular interactions required for the interaction of the small molecules with the protein target to produce the desired pharmacological effects are identified using computer‐aided drug design (CADD) methods such as pharmacophore and QSAR modeling, structure‐based virtual screening, molecular docking, molecular dynamics (MD) simulation coupled with MM/PB(GB)SA, and other computational strategies. This review discusses the applications of these methods for VEGFR‐2 inhibitor design. Future VEGFR‐2 inhibitor designs may be influenced by this review, which focuses on the current trends of using multiple screening layers to design better inhibitors. [ABSTRACT FROM AUTHOR]

The Protein Data Bank (PDB) was scrutinized for the presence of noncovalent O ⋅ ⋅ ⋅ Al Triel Bonding (TrB) interactions, involving protein residues (e. g. GLU and GLN), adenosine/guanine diphosphate moieties (ADP and GDP), water molecules and two aluminum fluorides (AlF3 and AlF4−). The results were statistically analyzed, revealing a vast number of O ⋅ ⋅ ⋅ Al contacts in the active sites of phosphoryl transfer enzymes, with a marked directionality towards the Al σ‐/π‐hole. The physical nature of the TrBs studied herein was analyzed using Molecular Electrostatic Potential (MEP) maps, the Quantum Theory of Atoms in Molecules (QTAIM), the Non Covalent Interaction plot (NCIplot) visual index and Natural Bonding Orbital (NBO) studies. As far as our knowledge extends, it is the first time that O ⋅ ⋅ ⋅ Al TrBs are analyzed within a biological context, participating in protein trapping mechanisms related to phosphoryl transfer enzymes. Moreover, since they are involved in the stabilization of aluminum fluorides inside the protein's active site, we believe the results reported herein will be valuable for those scientists working in supramolecular chemistry, catalysis and rational drug design. [ABSTRACT FROM AUTHOR]

Nanocarrier-based delivery of small interfering RNA (siRNA) has been recognized as a promising approach in cancer treatment. To produce siRNA, short nucleotide sequences are generated exogenously. siRNA inhibits target gene expression in a sequence-specific manner and initiates RNA interference (RNAi) in cells. SiRNA is a recently popularized nucleic acid-based medication that shows unique promise in the treatment of cancer. Before clinical siRNA delivery devices are created, there are still a lot of challenges to be solved. This review covers the potential targets for siRNA drug design, elucidates the characteristics and advantages of siRNA drugs and provides a summary of the available clinical siRNA therapies for cancer treatment. Therapeutically complex siRNA chemical alterations and delivery systems are described, and bio-responsive materials for siRNA release have been classified. This study will support continued advancements in clinical applications of siRNA by acting as a resource for disseminating information for more accurate and effective targeted delivery systems. [ABSTRACT FROM AUTHOR]

Motivation Predicting the properties of molecules is a fundamental problem in drug design and discovery, while how to learn effective feature representations lies at the core of modern deep-learning-based prediction methods. Recent progress shows expressive power of graph neural networks (GNNs) in capturing structural information for molecular graphs. However, we find that most molecular graphs exhibit low clustering along with dominating chains. Such topological characteristics can induce feature squashing during message passing and thus impair the expressivity of conventional GNNs. Results Aiming at improving node features' expressiveness, we develop a novel chain-aware graph neural network model, wherein the chain structures are captured by learning the representation of the center node along the shortest paths starting from it, and the redundancy between layers are mitigated via initial residual difference connection (IRDC). Then the molecular graph is represented by attentive pooling of all node representations. Compared to standard graph convolution, our chain-aware learning scheme offers a more straightforward feature interaction between distant nodes, thus it is able to capture the information about long-range dependency. We provide extensive empirical analysis on real-world datasets to show the outperformance of the proposed method. Availability and implementation The MolPath code is publicly available at https://github.com/Assassinswhh/Molpath. [ABSTRACT FROM AUTHOR]

Advancements in computational techniques have revolutionized the field of drug design, offering a powerful arsenal of tools collectively known as in silico methods. This review provides an overview of the diverse computational techniques employed in the in silico assisted drug design process. From molecular docking and molecular dynamics simulations to Quantitative Structure-Activity Relationship (QSAR) models and artificial intelligence-based approaches, these methods play a pivotal role in expediting drug discovery and optimization. The utilization of molecular docking facilitates the prediction of ligand-receptor interactions, aiding in the identification of potential drug candidates. Molecular dynamics simulations contribute by unraveling the dynamic behavior of biomolecular complexes, offering insights into their stability and flexibility. QSAR models, relying on mathematical correlations between molecular descriptors and biological activities, enable the prediction of compound behaviors, guiding the optimization of lead compounds. The integration of machine learning and artificial intelligence further enhances drug design workflows. Deep learning algorithms, such as neural networks, have demonstrated remarkable capabilities in predicting complex biological activities and uncovering hidden patterns within large datasets. High-throughput screening, coupled with in silico methodologies, allows for the rapid exploration of vast chemical spaces, accelerating the identification of promising drug candidates. Despite these advancements, challenges persist, including the accurate representation of biological systems, the validation of computational predictions and the ethical implications of relying solely on in silico methods. This review critically evaluates the current state of computational techniques in silico assisted drug design, highlighting their strengths, limitations and potential future directions. The integration of computational techniques in drug design has significantly reshaped the landscape of pharmaceutical research. As these methods continue to evolve, bridging the gap between computational predictions and experimental validations, the synergy between in silico and in vitro approaches holds immense promise for the rapid and effective development of novel therapeutic agents. [ABSTRACT FROM AUTHOR]

Bacterial surface display in combination with fluorescence‐activated cell sorting is a versatile and robust system and an interesting alternative approach to phage display for the generation of therapeutic affinity proteins. The system enables real‐time monitoring and sorting of cell populations, which presents unique possibilities for drug development. It has been used to develop several affibody molecules currently being evaluated preclinically for the treatment and diagnosis of, for example, cancer and neurodegenerative diseases. Additionally, it can be implemented in other areas of drug design, such as for mapping epitopes and evolving enzyme specificities. [ABSTRACT FROM AUTHOR]

Background/Objectives: Alterations in the actin cytoskeleton correlates to tumor progression and affect critical cellular processes such as adhesion, migration and invasion. Rho-associated coiled-coil-containing protein kinases (ROCK1 and ROCK2), important regulators of the actin cytoskeleton, are frequently overexpressed in various malignancies. The aim of this study was therefore to identify the key structural features of ROCK1/ROCK2 inhibitors using computer-aided drug design (CADD) approaches. In addition, new developed ROCK inhibitors provided a significant framework for the development of multitarget therapeutics—ROCK/HDAC (histone deacetylases) multitarget inhibitors. Methods: 3D-QSAR (Quantitative structure-activity relationship study) and molecular docking study were employed in order to identify key structural features that positively correlate with ROCK inhibition. MDA-MB-231, HCC1937, Panc-1 and Mia PaCa-2 cells were used for evaluation of anticancer properties of synthesized compounds. Results: C-19 showed potent anti-cancer properties, especially enhancement of apoptosis and cell cycle modulation in pancreatic cancer cell lines. In addition, C-19 and C-22 showed potent anti-migratory and anti-invasive effects comparable to the well-known ROCK inhibitor fasudil. Conclusions: In light of the results of this study, we propose a novel multi-target approach focusing on developing dual HDAC/ROCK inhibitors based on the structure of both C-19 and C-22, exploiting the synergistic potential of these two signaling pathways to improve therapeutic efficacy in metastatic tumors. Our results emphasize the potential of multi-target ROCK inhibitors as a basis for future cancer therapies. [ABSTRACT FROM AUTHOR]

Fluorine's high electronegativity, lipophilicity, and metabolic stability make it a valuable element for drug design and development. Consequently, significant synthetic efforts have focused on introducing fluorine and fluorinated motifs into organic molecules, with the synthesis of trifluoromethyl ketones (TFMKs) recently attracting considerable attention. Building on our ongoing research on radical organofluorine chemistry, we present herein the divergent synthesis of trifluoromethyl ketones directly from olefins using readily available chloro‐ and bromotrifluoroacetone as synthetic precursors of the trifluoroacetonyl radical under photoredox catalysis. Mechanistic studies revealed that the divergence is attained through radical polar crossover (RPC) and hydrogen atom transfer (HAT) mechanisms. [ABSTRACT FROM AUTHOR]

Caffeic acid (CA) is a polyphenolic acid compound widely distributed in plant seeds. As natural compounds with high research interest, caffeic acid and its derivatives show good activity in the treatment of tumors and inflammation and have antibacterial properties. In recent years, caffeic acid derivatives have been studied extensively, and these derivatives fall roughly into three categories: (1) caffeic acid ester derivatives, (2) caffeic acid amide derivatives, (3) caffeic acid hybrids. These caffeic acid analogues exert mainly antibacterial and antioxidant activities. Among the caffeic acid analogues summarized in this paper, compounds 1g and CAP10 have good activity against Candida albicans, and their MIC50 is 32 µg/mL and 13 μM, respectively. In a DPPH assay, compounds 3k, 5a, CS2, Phellinsin A and 8j showed strong antioxidant activity, and their IC50 values are 18.6 μM, 67.85 μM, 40.29 μM, 0.29 ± 0.004 mM, 4774.37 ± 137.20 μM, respectively. Overall, compound CAP10 had the best antibacterial activity and compound 3k had the best antioxidant activity. This paper mainly summarizes and discusses some representative caffeic acid analogs, hoping to provide better drug design strategies for the subsequent development of caffeic acid analogs. [ABSTRACT FROM AUTHOR]

Tertiary phosphines have been recognized as powerful organocatalysts for the Michael reaction, which is one of the most important atom-economical methods for carbon-carbon and carbon-heteroatom bond construction. In the presence of tertiary phosphines, a vast array of Michael acceptors and Michael donors can be coupled with each other under neutral and metal-free conditions to produce useful densely functionalized molecules. This review highlights the role of phosphine-catalyzed Michael reaction in cutting-edge areas, such as asymmetric synthesis, natural products synthesis, drug design, and polymer material science. As the central part of catalysis science, the kinetic and mechanistic issues are also discussed when possible. The review is organized by the type of the Michael acceptor and the Michael donor used in the reaction. [ABSTRACT FROM AUTHOR]

Aims: To summarize the results of clinical studies of insulin icodec, an investigational insulin analog designed for once‐weekly administration, in adults with type 1 and type 2 diabetes. Methods: Thirteen published articles describing clinical studies of insulin icodec were identified in PubMed, and data pertinent to key study outcomes were selected for inclusion in this review. Results: In insulin‐naïve and insulin‐treated individuals, icodec demonstrated efficacy in glycaemic control superior or noninferior to that of insulins glargine U100, glargine U300 and degludec. Icodec exhibited a safety profile comparable to marketed insulins, with the exception of hypoglycaemic event rates. Conclusions: As a once‐weekly alternative to daily basal insulin, icodec is expected to improve patient adherence and satisfaction, reducing the required number of injections per year from 365 to 52 and providing a dosing option potentially attractive to a wide range of insulin users. However, clinical data suggest a notable risk of hypoglycaemia with weekly icodec administration, especially in individuals with type 1 diabetes. [ABSTRACT FROM AUTHOR]

This study aimed to construct genome-wide genetic and epigenetic networks (GWGENs) of atopic dermatitis (AD) and healthy controls through systems biology methods based on genome-wide microarray data. Subsequently, the core GWGENs of AD and healthy controls were extracted from their real GWGENs by the principal network projection (PNP) method for Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway annotation. Then, we identified the abnormal signaling pathways by comparing the core signaling pathways of AD and healthy controls to investigate the pathogenesis of AD. Then, IL-1β, GATA3, Akt, and NF-κB were selected as biomarkers for their important roles in the abnormal regulation of downstream genes, leading to cellular dysfunctions in AD patients. Next, a deep neural network (DNN)-based drug–target interaction (DTI) model was pre-trained on DTI databases to predict molecular drugs that interact with these biomarkers. Finally, we screened the candidate molecular drugs based on drug toxicity, sensitivity, and regulatory ability as drug design specifications to select potential molecular drugs for these biomarkers to treat AD, including metformin, allantoin, and U-0126, which have shown potential for therapeutic treatment by regulating abnormal immune responses and restoring the pathogenic signaling pathways of AD. [ABSTRACT FROM AUTHOR]

The Sam (Sterile alpha motif) domain of the lipid phosphatase Ship2 (Ship2-Sam) is engaged by the Sam domain of the receptor tyrosine kinase EphA2 (EphA2-Sam) and, this interaction is principally linked to procancer effects. Peptides able to hinder the formation of the EphA2-Sam/Ship2-Sam complex could possess therapeutic potential. Herein, by employing the FoldX software suite, we set up an in silico approach to improve the peptide targeting of the so-called Mid Loop interface of Ship2-Sam, representing the EphA2-Sam binding site. Starting from a formerly identified peptide antagonist of the EphA2-Sam/Ship2-Sam association, first, the most stabilizing mutations that could be inserted in each peptide position were predicted. Then, they were combined, producing a list of potentially enhanced Ship2-Sam ligands. A few of the in silico generated peptides were experimentally evaluated. Interaction assays with Ship2-Sam were performed using NMR and BLI (BioLayer Interferometry). In vitro assays were conducted as well to check for cytotoxic effects against both cancerous and healthy cells, and also to assess the capacity to regulate EphA2 degradation. This study undoubtedly enlarges our knowledge on how to properly target EphA2-Sam/Ship2-Sam associations with peptide-based tools and provides a promising strategy that can be used to target any protein–protein interaction. [ABSTRACT FROM AUTHOR]

Gprotein-coupled receptors (GPCRs) regulate several physiological and pathological processes and represent the target of approximately 30% of Food and Drug Administration-approved drugs. GPCR-mediated signaling was thought to occur exclusively at the plasma membrane. However, recent studies have unveiled their presence and function at subcellular membrane compartments. There is a growing interest in studying compartmentalized signaling of GPCRs. This requires development of tools to separate GPCR signaling at the plasma membrane from the ones initiated at intracellular compartments. We leveraged the structural and pharmacological information available for β-adrenergic receptors (βARs) and focused on β1AR as exemplary GPCR that functions at subcellular compartments, and rationally designed spatially restricted antagonists. We generated a cell-impermeable βAR antagonist by conjugating a suitable pharmacophore to a sulfonate-containing fluorophore. This cell-impermeable antagonist only inhibited β1AR on the plasma membrane. In contrast, a cell-permeable βAR antagonist containing a nonsulfonated fluorophore efficiently inhibited both the plasma membrane and Golgi pools of β1ARs. Furthermore, the cell-impermeable antagonist selectively inhibited the phosphorylation of PKA downstream effectors near the plasma membrane, which regulate sarcoplasmic reticulum (SR) Ca2+ release in adult cardiomyocytes, while the β1AR Golgi pool remained active. Our tools offer promising avenues for investigating compartmentalized βAR signaling in various contexts, potentially advancing our understanding of βAR-mediated cellular responses in health and disease. They also offer a general strategy to study compartmentalized signaling for other GPCRs in various biological systems. [ABSTRACT FROM AUTHOR]

Nuclear magnetic resonance (NMR) is recognized as the gold standard method in fragment‐based drug design for screening, hit validation, and affinity determination. However, its deployment at a large scale is limited by the cost and expertise needed to implement NMR as a routine drug discovery method. The increase in the adoption of fragment‐based drug design created a need for biophysics to provide high‐quality data for weak ligand‐target interactions that can be implemented at scale. NMR must adapt its position in drug design operations to enter this new era. The recent development of commercially available benchtop NMR spectrometers in combination with hyperpolarization methods represents an opportunity for highly deployable and scalable systems. [ABSTRACT FROM AUTHOR]

The development of all kinds of covalent drugs had a major impact on the improvement of the human health system. Covalent binding to target proteins is achieved by so‐called electrophilic warheads, which are incorporated in the respective drug molecule. In the last decade, specifically acrylamides emerged as attractive warheads in covalent drug design. Herein, a straightforward palladium‐catalyzed hydroaminocarbonylation of acetylene has been developed, allowing a modular and diverse synthesis of bio‐active acrylamides. This general protocol features high atom efficiency, wide functional group compatibility, high chemoselectivity and proceeds additive free under mild reaction conditions. The synthetic utility of this protocol is showcased in the synthesis of ibrutinib, osimertinib, and other bio‐active compound derivatives. [ABSTRACT FROM AUTHOR]

Bridged cyclobutanes and sulfur heterocycles are currently under intense investigation as building blocks for pharmaceutical drug design. Two formal cycloaddition modes involving bicyclobutanes (BCBs) and pyridinium 1,4‐zwitterionic thiolate derivatives were described to rapidly expand the chemical space of sulfur‐containing bridged cyclobutanes. By using Ni(ClO4)2 as the catalyst, an uncommon higher‐order (5+3) cycloaddition of BCBs with quinolinium 1,4‐zwitterionic thiolate was achieved with broad substrate scope under mild reaction conditions. Furthermore, the first Lewis acid‐catalyzed asymmetric polar (5+3) cycloaddition of BCB with pyridazinium 1,4‐zwitterionic thiolate was accomplished. In contrast, pyridinium 1,4‐zwitterionic thiolates undergo an Sc(OTf)3‐catalyzed formal (3+3) reaction with BCBs to generate thia‐norpinene products, which represent the initial instance of synthesizing 2‐thiabicyclo[3.1.1]heptanes (thia‐BCHeps) from BCBs. Moreover, we have successfully used this (3+3) protocol to rapidly prepare thia‐BCHeps‐substituted analogues of the bioactive molecule Pitofenone. Density functional theory (DFT) computations imply that kinetic factors govern the (5+3) cycloaddition reaction between BCB and quinolinium 1,4‐zwitterionic thiolate, whereas the (3+3) reaction involving pyridinium 1,4‐zwitterionic thiolates is under thermodynamic control. [ABSTRACT FROM AUTHOR]

With the increasing pollution of the planet, the search for natural multifunctional alternatives to petroleum-based plastics has assumed to be a great important proposition. Polysaccharides, an inexhaustible natural resource with good biocompatibility as well as mechanical properties, are considered as an ideal alternative to petroleum-based materials. However, blind experimentation and development will inevitably lead to waste of raw materials and contamination of reagents. Therefore, researchers desire a technology which can assist in predicting and screening experimental materials at the higher level. Molecular docking simulations, an emerging computer technology that can effectively predict the structure of interactions between molecules and analyze the optimal conformation, are a common aid for materials and drug design. In this review, we describe the origins and development of molecular docking techniques, mainly performed an overview of various molecular docking software on their applications in the field of different polysaccharide materials. [ABSTRACT FROM AUTHOR]

Twenty 3-acyloxymaltol/ethyl maltol derivatives (7a-j and 8a-j) were synthesized and evaluated in vitro for their anti-oomycete activity against Phytophthora capsici, respectively. Among all of twenty derivatives, more than half of the compounds 7f, 7h, 8a-h and 8j had anti-oomycete activity higher than the positive control zoxamide (EC50 = 22.23 mg/L), and the EC50 values of 18.66, 20.32, 12.80, 16.18, 10.59, 14.98, 16.80, 10.36, 15.32, 12.64, and 13.59 mg/L, respectively. Especially, compounds 8c and 8f exhibited the best anti-oomycete activity against P. capsici with EC50 values of 10.59 and 10.36 mg/L, respectively. Overall, hydroxyl group of maltol/ethyl maltol is important active modification site. [ABSTRACT FROM AUTHOR]

Peptides are native binders involved in numerous physiological life procedures, such as cellular signaling, and serve as ready-made regulators of biochemical processes. Meanwhile, small molecules compose many drugs owing to their outstanding advantages of physiochemical properties and synthetic convenience. A novel field of research is converting peptides into small molecules, providing a convenient portable solution for drug design or peptidomic research. Endowing properties of peptides onto small molecules can evolutionarily combine the advantages of both moieties and improve the biological druggability of molecules. Herein, we present eight representative recent cases in this conversion and elaborate on the transformation process of each case. We discuss the innovative technological methods and research approaches involved, and analyze the applicability conditions of the approaches and methods in each case, guiding further modifications of peptides to small molecules. Finally, based on the aforementioned cases, we summarize a general procedure for peptide-to-small molecule modifications, listing the technological methods available for each transformation step and providing our insights on the applicable scenarios for these methods. This review aims to present the progress of peptide-to-small molecule modifications and propose our thoughts and perspectives for future research in this field. This review outlines the transformation of peptides into small molecules, highlighting classical and innovative methods contributing in four stages, providing insights and perspectives for future research in novel drug design. [Display omitted] [ABSTRACT FROM AUTHOR]

Quantum computing is a rapidly emerging and promising field with the potential to transform various research domains including drug design, network technologies, and sustainable energy solutions. Due to the inherent complexity and divergence from classical computing, several major quantum computing libraries have been developed to implement quantum algorithms, namely IBM Qiskit, Amazon Braket, Cirq, PyQuil, and PennyLane. These libraries enable quantum simulations on classical computers and execution on corresponding quantum hardware, such as Qiskit programs on IBM quantum computers. Despite the variations among these platforms, the core concepts remain the same. One notable challenge is the absence of a Python-based quantum interpreter to connect these five frameworks, a gap that remains to be fully addressed. In response, our work introduces a tool called Qinterpreter, accessible through a user-friendly web interface, the Quantum Science Gateway QubitHub, which operates alongside Jupyter Notebooks. Built using the Python Object-Oriented Programming System, Qinterpreter unifies the five well-known quantum libraries into a single framework. Designed as an educational tool for students and researchers entering the quantum domain, Qinterpreter enables the straightforward development and execution of quantum circuits across such platforms. This work highlights the quantum programming versatility and accessibility of Qinterpreter and underscores our ultimate goal of pervading Quantum Computing through younger, less specialized, and diverse cultural and national communities. [ABSTRACT FROM AUTHOR]

Quantum chemical (QC) property prediction is crucial for computational materials and drug design, but relies on expensive electronic structure calculations like density functional theory (DFT). Recent deep learning methods accelerate this process using 1D SMILES or 2D graphs as inputs but struggle to achieve high accuracy as most QC properties depend on refined 3D molecular equilibrium conformations. We introduce Uni-Mol+, a deep learning approach that leverages 3D conformations for accurate QC property prediction. Uni-Mol+ first generates a raw 3D conformation using RDKit then iteratively refines it towards DFT equilibrium conformation using neural networks, which is finally used to predict the QC properties. To effectively learn this conformation update process, we introduce a two-track Transformer model backbone and a novel training approach. Our benchmarking results demonstrate that the proposed Uni-Mol+ significantly improves the accuracy of QC property prediction in various datasets. Quantum chemical (QC) property prediction is crucial in computational chemistry. Here, the authors introduce Uni-Mol+, a deep model that uses iterative updates of 3D molecular conformations to improves the accuracy of QC property prediction. [ABSTRACT FROM AUTHOR]

We propose a new machine learning formulation designed specifically for extrapolation. The textbook way to apply machine learning to drug design is to learn a univariate function that when a drug (structure) is input, the function outputs a real number (the activity): f(drug) → activity. However, experience in real-world drug design suggests that this formulation of the drug design problem is not quite correct. Specifically, what one is really interested in is extrapolation: predicting the activity of new drugs with higher activity than any existing ones. Our new formulation for extrapolation is based on learning a bivariate function that predicts the difference in activities of two drugs F(drug1, drug2) → difference in activity, followed by the use of ranking algorithms. This formulation is general and agnostic, suitable for finding samples with target values beyond the target value range of the training set. We applied the formulation to work with support vector machines , random forests , and Gradient Boosting Machines. We compared the formulation with standard regression on thousands of drug design datasets, gene expression datasets and material property datasets. The test set extrapolation metric was the identification of examples with greater values than the training set, and top-performing examples (within the top 10% of the whole dataset). On this metric our pairwise formulation vastly outperformed standard regression. Its proposed variations also showed a consistent outperformance. Its application in the stock selection problem further confirmed the advantage of this pairwise formulation. [ABSTRACT FROM AUTHOR]

Metabolism‐related diseases, including diabetes mellitus, obesity, hyperlipidemia, and nonalcoholic fatty liver disease, are becoming increasingly prevalent, thereby posing significant threats to human health and longevity. Proteins, as the primary mediators of biological activities, undergo various posttranslational modifications (PTMs), including phosphorylation, ubiquitination, acetylation, methylation, and SUMOylation, among others, which substantially diversify their functions. These modifications are crucial in the physiological and pathological processes associated with metabolic disorders. Despite advancements in the field, there remains a deficiency in contemporary summaries addressing how these modifications influence processes of metabolic disease. This review aims to systematically elucidate the mechanisms through which PTM of proteins impact the progression of metabolic diseases, including diabetes, obesity, hyperlipidemia, and nonalcoholic fatty liver disease. Additionally, the limitations of the current body of research are critically assessed. Leveraging PTMs of proteins provides novel insights and therapeutic targets for the prevention and treatment of metabolic disorders. Numerous drugs designed to target these modifications are currently in preclinical or clinical trials. This review also provides a comprehensive summary. By elucidating the intricate interplay between PTMs and metabolic pathways, this study advances understanding of the molecular mechanisms underlying metabolic dysfunction, thereby facilitating the development of more precise and effective disease management strategies. [ABSTRACT FROM AUTHOR]

Protein–protein interactions (PPIs) are fundamental to many critical biological processes and are crucial in mediating essential cellular functions across diverse organisms, including bacteria, parasites, and viruses. A notable example is the interaction between the SARS-CoV-2 spike (S) protein and the human angiotensin-converting enzyme 2 (hACE2), which initiates a series of events leading to viral replication. Interrupting this interaction offers a promising strategy for blocking or significantly reducing infection, highlighting its potential as a target for anti-SARS-CoV-2 therapies. This review focuses on the hACE2 and SARS-CoV-2 spike protein interaction, exemplifying the latest advancements in peptide-based strategies for developing PPI inhibitors. We discuss various approaches for creating peptide-based inhibitors that target this critical interaction, aiming to provide potential treatments for COVID-19. [ABSTRACT FROM AUTHOR]

In this study, the catechin structure was modified with Halogen and Nitrogen base at C-6 and C-8 Positions in Ring A. Pharmacokinetic parameters affirm the drug-likeness property of the designed compounds. Molecular Docking was performed for all the compounds towards the myostatin inhibition target (PDB: 3HH2). Such desirable quality of modified Catechin will create a spark in the novel drug discovery using acting as a bioenhancer. As a result, the present research is aimed to offer an overview of the structural simulation of Cl, F, I, NH2, NO2, and Br at C-6 and C-8 positions in A Ring A of Catechin. This preliminary evidence creates an impact on the novelsemi0synthetic drug discovery for the therapeutic management of sarcopenia. [ABSTRACT FROM AUTHOR]

A drug design strategy called a codrug or mutual prodrug binds two or more pharmacophore with similar or different pharmacological activities elicit synergistic action or help to target the parent drug to specific site/organ/cells respectively and to improve physicochemical, biopharmaceutical and drug delivery properties of therapeutic agents. This review presents various codrug design approach, objectives, properties and therapeutic approach in a methodical manner. Codrugs offer a promising approach in pharmaceutical research, combining multiple active compounds into one molecule to improve drug efficacy and minimize side effects. They can be tailored for specific tissues or organs, making them effective in treating CNS, cardiovascular, cancerous, inflammatory, infectious and comorbid disorders. By enhancing drug delivery through increased lipophilicity, codrugs provide targeted therapy and better outcomes across various medical conditions. Since the 2000s, mutual prodrugs and codrugs have been recognized as potential treatments for a range of illnesses. This approach is commonly used to improve physicochemical, biopharmaceutical and drug delivery properties of therapeutic agents. Additional clinical trial profiles will help expand the scope of codrug therapy. Care must be taken to maximise the positive effects of codrugs while minimising their negative side effects and toxicity. [ABSTRACT FROM AUTHOR]

Background and Objectives: Dovitinib (DVB) is a pan-tyrosine kinase inhibitor (TKI) that can be administered orally. In September 2023, the FDA granted Oncoheroes approval to proceed with an Investigational New Drug (IND) application for dovitinib. This application is intended for the treatment of relapsed or advanced juvenile solid tumors, namely, osteosarcoma. Materials and Methods: The target of the present study was to develop a rapid, green, accurate, and sensitive UHPLC-MS/MS method for measuring DVB levels in human liver microsomes (HLMs). The validations of the HLMs were performed via the established UHPLC-MS/MS approach, as stated in the US FDA reported guidelines for the standards of bioanalytical method validation protocol. The StarDrop in silico software package (version 6.6), which involves the DEREK and WhichP450 in silico modules, was used to check the DVB structure for hazardous alerts and metabolic instability. The DVB and encorafenib (EFB), internal standard, and chromatographic peaks were successfully separated using a reversed phase column (an Eclipse Plus Agilent C8 column) and an isocratic mobile phase. The production of DVB parent ions was accomplished by utilizing the positive ionization mode of an ESI source. The identification and measurement of DVB daughter ions were conducted using the MRM mode. Results: The inter-day accuracy and precision exhibited a spectrum of values in the range of −0.56% to 9.33%, while the intra-day accuracy and precision showcased a range of scores between 0.28% and 7.28%. The DVB calibration curve showed a linear relationship that ranged from 1 to 3000 ng/mL. The usefulness of the currently validated UHPLC-MS/MS method was approved by the lower limit of quantification (LLOQ) of 1 ng/mL. The AGREE findings demonstrate that the UHPLC-MS/MS method had a noteworthy degree of ecological greenness. The in vitro half-life (t1/2) and intrinsic clearance (Clint) of DVB were calculated to be 15.48 min and 52.39 mL/min/kg, respectively, which aligned with the findings from the WhichP450 software (version 6.6). Conclusions: Via the usage of in silico software, it has been observed that making small changes to the structure of the aryl piperazine ring and quinolinone moieties, or replacing these groups in the drug design process, shows potential for enhancing the metabolic safety and stability of newly developed derivatives compared to DVB. [ABSTRACT FROM AUTHOR]

Poly(ADP-ribose)polymerases (PARPs) have emerged as promising targets for the treatment of diseases, particularly in cancers, due to their significant biological functions involved in DNA damage. As a result, researchers worldwide have made substantial advances in this field. However, studies have revealed that PARPs inhibitors lack selectivity due to the conserved domain, limiting their clinical applications and emphasizing the need for selective inhibitors. In this perspective, we summarize the recent advancements in PARPs inhibitors, with a focus on selective inhibitors among PARP family. We discuss the designed strategy, structure-activity relationships, and crystal structure, while explaining the underlying mechanisms of selectivity, hoping to provide insights for the design of next generation of PARPs selective inhibitors. [ABSTRACT FROM AUTHOR]

Cytotoxic payloads for drug conjugates suitable for directed tumor therapy need to be highly potent and require a functional group for conjugation with the homing device (antibody, peptide, or small molecule). Cryptophycins are cyclodepsipeptides that stand out from the realm of natural products due to their extraordinarily high cytotoxicity. However, the installation of a suitable conjugation handle without compromising the toxicity is highly challenging. The unit D, natively 2‐hydroxyisocaproic acid (leucic acid), was envisaged as a promising attachment site based on structural information from X‐ray analysis. A versatile, scalable and efficient synthetic route towards conjugable cryptophycins with modification in unit D was developed and an array of new cryptophycin analogues was synthesized. Several derivatives, especially those containing lipophilic groups with low steric demand such as alkylated amino groups, exhibit low picomolar cytotoxicity often combined with efficacy against multidrug‐resistant tumor cells. The newly established cryptophycin analogues comprise a broad range of relevant functional groups used as conjugation handles, among them amino, hydroxy, carboxy, as well as sulfur‐containing derivatives. X‐ray crystallographic analysis of a tubulin‐bound cryptophycin together with quantitative structure activity relationship manifested rationales for the synthesis of most potent cryptophycin derivatives and further confirmed the suitability of modifications in unit D. [ABSTRACT FROM AUTHOR]

Background: Type-2 Diabetes (T2DM) is a long-term medical disorder characterized by Insulin deficiency and high blood glucose levels. Among other medications to cure T2DM, the review of the literature found that various Pyrimidine derivatives act as an agonist for G-protein-coupled receptor 119 (GPR119) was proposed to control blood glucose levels by enhancing the function of pancreatic Beta-cells and its mechanism of action with fewer adverse effects. In the present research work, In-silico investigations were carried out to investigate the potential of the Pyrimidine analog as an agonist to the protein target GPR119 receptor. We performed exhaustive molecular modeling and protein modeling methodologies such as homology modeling, and molecular docking along with various drug designing tools such as 3D-QSAR and Pharmacophore Mapping to ascertain the design of better GPR119 agonists. Results: Based on in-depth computational studies, we designed new pyrimidine moiety and analyzed them for GPR119 receptor agonist and further explored the ADMET properties. Designed compounds were found to exhibit better-predicted activities as compared to reference compound. Conclusions: The current research on pyrimidine derivatives, using molecular docking, 3D-QSAR and Pharmacophore mapping demonstrated that the obtained computational model has significant properties and the designed molecules and Dataset from this model, produced antidiabetic compound against the target GPR119 i.e., compound 1S, 1Z and 1D with the docking score of − 11.696, − 9.314 and − 8.721, respectively. The pharmacokinetics and drug-likeness studies revealed that these compounds may be the future candidates for the treatment of diabetes acting via the GPR119 agonist mechanism. [ABSTRACT FROM AUTHOR]

Computer-aided drug design and discovery methods have been essential in developing small molecules with therapeutic properties over the last decades. Application of computational resources includes drug target identification, hit discovery, and lead optimization. Accordingly, with tremendous research efforts and the availability of financial support from government agencies across the world, and multinational drug companies, the overall research level in this area will continue to advance. The methodology used in this review paper entailed a thorough examination of research studies on relevant literature on drug design and development using computational resources. Extensive searches using Scopus, International Pharmaceutical Abstracts (OvidSp, WHO Global Health Library, Cochrane, Google Scholar, Web of Science, Science Direct, ProQuest dissertation & theses, Worldwide Political Science Abstracts (CSA), and PubMed was carried out. A standardized template was used to ensure that the selected papers met the inclusion criteria, and relevant to the review. Ultimately, there are robust technologies developed to enhance the drug discovery process. Therefore, this review provides insights into computational resources in Silico and ab initio methods and algorithms, not restricted to drug metabolism predictions for drug design, and the practical applications of artificial intelligence (AI) in drug discovery. Computational tools and methods for drug design and development such as molecular dynamics (MD), molecular docking, quantum mechanics (QM), hybrid quantum mechanics/molecular mechanics (QM/MM), and Density functional theory (DFT) have been reviewed. Accordingly, the emerging technique of synergistically employing these techniques influences the fundamental challenges of conventional medicines for complex diseases. Herein, we discuss ligand-based and structure-based drug discoveries, force field models in MD simulations, docking algorithms, subtractive and additive QM/MM coupling. Nonetheless, as computer-aided drug (CADD) approaches continue to evolve with significant improvements, the focus areas will be on docking and virtual screening, scoring functions, optimization of hits, and assessment of adsorption, distribution, metabolism, excretion, and toxicity (ADMET) properties. With the current success, the present computational resources will aid in the future discovery of novel compounds with high therapeutic performance. The ongoing oncology research efforts will also significantly contribute to UN sustainable development goals – good health and well-being, sustainable innovation and industrialization. [ABSTRACT FROM AUTHOR]

Three‐dimensional printing (3DP) has emerged as a game‐changing technology in the pharmaceutical industry, providing novel formulation development in the pharmaceutical sector as a whole, which improved patients' individualized therapy. The pediatric population is among the key targets for individualized therapy. Children are a diverse group that includes neonates, infants, and toddlers, each with unique physiological characteristics. Treatment adherence has a significant impact on safe and effective pharmacotherapy in the pediatric population. Improvement of therapeutic dosage forms that provide for the special demands of the pediatric population is a significant challenge for the pharmaceutical industry. Scientists have actively explored 3DP, a quick prototype manufacturing method that has emerged in recent years from many occupations due to its benefits of modest operation, excellent reproducibility, and vast adaptability. This review illuminates the most widely used 3DP technology and its application in the development of pediatric‐friendly drug formulations. This 3DP technology allows optimization of pediatric dosage regimens and cases that require individualized treatment, such as geriatrics, renal impairment, liver impairment, critically ill, pregnancy populations, and drugs with nonlinear pharmacokinetics. The fast evolution of 3DP expertise, in addition to the introduction of pharmaceutical inks, has enormous promise for patient dosage form customization. [ABSTRACT FROM AUTHOR]

Metals in medicine is a distinct and mature field of investigation. Its progress in recent times cannot be denied, as it provides opportunities to advance our knowledge of the properties, speciation, reactivity and biological effects of metals in a medicinal context. The development of novel Pt(II) compounds to combat cancer continues to make valuable contributions but it has not yet achieved a complete cure. The chemistry of this field is basic for drug design improvements and our analysis of the chemical procedures is a practical tool for achieving effective Pt(II) anticancer drugs. We present chemical approaches in a manner that can be used to strategically plot new synthetic routes choosing right pathways. Clarifying the chemical challenge will help the scientific community to be aware of the ease and/or difficulty of the procedure before and after further studies, such as speciation, reactivity and biological action which are also very arduous and costly. The work provides information to tackle many challenges in chemistry, combining the knowledge on the Pt(II) reagent preparation together with the reactivity of the biological units used in the Pt(II) drug design. We discuss and include the description of the chemical reactions, the importance of multiple steps and the right order of such reactions to achieve the final drugs, analyzing the coordination principles as well as the organic and organometallic basis. This thorough study of the routes helps to detect the simpler or more complicated reactivity and will serve to improve the synthesis performance with possible post-modifications. [ABSTRACT FROM AUTHOR]