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The interpretation of ligand–target interactions at atomistic resolution is central to most efforts in computational drug discovery and optimization. However, the highly dynamic nature of protein targets, as well as possible induced fit effects, makes difficult to sample many interactions effectively with docking studies or even with large-scale molecular dynamics (MD) simulations. We propose a novel application of Self-Organizing Maps (SOMs) to address the sampling and dynamic mapping tasks, particularly in cases involving ligand flexibility and induced fit. The SOM approach offers a data-driven strategy to create a map of the interaction process and pathways based on unbiased MD. Furthermore, we show how the preliminary SOM mapping is complementary to kinetic analysis, with the employment of both network-based approaches and Markov state models. We demonstrate the method by comprehensively mapping a large dataset of 640 μs of unbiased trajectories sampling the recognition process between the phosphorylated YEEI peptide and its high-specificity target lck-SH2. The integration of SOM into unbiased simulation protocols significantly advances our understanding of the ligand binding mechanism. This approach serves as a potent tool for mapping intricate ligand–target interactions with unprecedented detail, thereby enhancing the characterization of kinetic properties crucial to drug design. [ABSTRACT FROM AUTHOR]

DNA-encoded library technology grants access to nearly infinite opportunities to explore the chemical structure space for drug discovery. Successful navigation depends on the design and synthesis of libraries with appropriate physicochemical properties (PCPs) and structural diversity while aligning with practical considerations. To this end, we analyze combinatorial library design constraints including the number of chemistry cycles, bond construction strategies, and building block (BB) class selection in pursuit of ideal library designs. We compare two-cycle library designs (amino acid + carboxylic acid, primary amine + carboxylic acid) in the context of PCPs and chemical space coverage, given different BB selection strategies and constraints. We find that broad availability of amines and acids is essential for enabling the widest exploration of chemical space. Surprisingly, cost is not a driving factor, and virtually, the same chemical space can be explored with budget BBs.

QS-21 is a potent vaccine adjuvant and remains the only saponin-based adjuvant that has been clinically approved for use in humans1,2. However, owing to the complex structure of QS-21, its availability is limited. Today, the supply depends on laborious extraction from the Chilean soapbark tree or on low-yielding total chemical synthesis3,4. Here we demonstrate the complete biosynthesis of QS-21 and its precursors, as well as structural derivatives, in engineered yeast strains. The successful biosynthesis in yeast requires fine-tuning of the host's native pathway fluxes, as well as the functional and balanced expression of 38 heterologous enzymes. The required biosynthetic pathway spans seven enzyme families-a terpene synthase, P450s, nucleotide sugar synthases, glycosyltransferases, a coenzyme A ligase, acyl transferases and polyketide synthases-from six organisms, and mimics in yeast the subcellular compartmentalization of plants from the endoplasmic reticulum membrane to the cytosol. Finally, by taking advantage of the promiscuity of certain pathway enzymes, we produced structural analogues of QS-21 using this biosynthetic platform. This microbial production scheme will allow for the future establishment of a structure-activity relationship, and will thus enable the rational design of potent vaccine adjuvants.

The COVID-19 pandemic underscored the promise of monoclonal antibody-based prophylactic and therapeutic drugs1-3 and revealed how quickly viral escape can curtail effective options4,5. When the SARS-CoV-2 Omicron variant emerged in 2021, many antibody drug products lost potency, including Evusheld and its constituent, cilgavimab4-6. Cilgavimab, like its progenitor COV2-2130, is a class 3 antibody that is compatible with other antibodies in combination4 and is challenging to replace with existing approaches. Rapidly modifying such high-value antibodies to restore efficacy against emerging variants is a compelling mitigation strategy. We sought to redesign and renew the efficacy of COV2-2130 against Omicron BA.1 and BA.1.1 strains while maintaining efficacy against the dominant Delta variant. Here we show that our computationally redesigned antibody, 2130-1-0114-112, achieves this objective, simultaneously increases neutralization potency against Delta and subsequent variants of concern, and provides protection in vivo against the strains tested: WA1/2020, BA.1.1 and BA.5. Deep mutational scanning of tens of thousands of pseudovirus variants reveals that 2130-1-0114-112 improves broad potency without increasing escape liabilities. Our results suggest that computational approaches can optimize an antibody to target multiple escape variants, while simultaneously enriching potency. Our computational approach does not require experimental iterations or pre-existing binding data, thus enabling rapid response strategies to address escape variants or lessen escape vulnerabilities.

Here, an in vitro characterization of a family of prazole derivatives that covalently bind to the C73 site on Tsg101 and assay their ability to inhibit viral particle production is presented. Structurally, increased steric bulk on the 4-pyridyl of the prazole expands the prazole site on the UEV domain toward the β-hairpin in the Ub-binding site and is coupled to increased inhibition of virus-like particle production in HIV-1. Increased bulk also increased toxicity, which is alleviated by increasing flexibility. Further, the formation of a novel secondary Tsg101 adduct for several of the tested compounds and the commercial drug lansoprazole. The secondary adduct involved the loss of the 4-pyridyl substituent to form an irreversible species, with implications for increasing the half-life of the active species or its specificity toward Tsg101 UEV. It is also determined that sulfide derivatives display effective viral inhibition, presumably through cellular sulfoxidation, allowing for delayed conversion within the cellular environment, and identify SARS-COV-2 as a target of prazole inhibition. These results open multiple avenues for the design of prazole derivatives for antiviral applications.

Polypharmacology drugs-compounds that inhibit multiple proteins-have many applications but are difficult to design. To address this challenge we have developed POLYGON, an approach to polypharmacology based on generative reinforcement learning. POLYGON embeds chemical space and iteratively samples it to generate new molecular structures; these are rewarded by the predicted ability to inhibit each of two protein targets and by drug-likeness and ease-of-synthesis. In binding data for >100,000 compounds, POLYGON correctly recognizes polypharmacology interactions with 82.5% accuracy. We subsequently generate de-novo compounds targeting ten pairs of proteins with documented co-dependency. Docking analysis indicates that top structures bind their two targets with low free energies and similar 3D orientations to canonical single-protein inhibitors. We synthesize 32 compounds targeting MEK1 and mTOR, with most yielding >50% reduction in each protein activity and in cell viability when dosed at 1-10 μM. These results support the potential of generative modeling for polypharmacology.

Diastereoselective (3+3) cycloaddition between bicyclobutanes and pyridinium ylides forms azabicyclo[3.1.1]heptanes via pyridine dearomatization. These reactions proceed under ambient conditions with no need for photochemistry or catalysis, and tolerate a wide range of functional gorups. The resulting multicyclic ring systems have diverse synthetic handles for further transformations, making them potentially valuable for the design of Csp³-rich drug candidates. These include semi-reduction of the dihydropyridine, and diastereoselective photochemical skeletal rearrangement to give a tetrasubstituted cyclobutane. [ABSTRACT FROM AUTHOR]

Monocyclic 5‐membered heterocycles including imidazoles, thiazoles, oxazoles, and their related compounds have gained significant attention in medicinal chemistry because of their potent anticancerous activity. These small heterocyclic molecules possess versatile properties, including biological activity, absorption, distribution, metabolism, excretion, and chemical diversity that give them immense potential as anticancer agents. It is also a fact that inherent characteristic of azoles to combine with many biological molecules through hydrogen bond, stacking, and hydrophobic interaction makes them effective against almost all cancer types. In the present paper the author discusses the way which is connected with chemical structure of monocyclic azoles and their anticancer activity namely the ability of these compounds to intercalate with DNA, to inhibit some enzymes and to interfere cellular signaling pathways. Interestingly, several azole derivatives have been seen to be effective in preclinical efficacy studies as well as in clinical trials and are considered to be potent in overcoming the problem of resistance and side effects of the common anticancer agents. As the synthetic chemistry progresses, the structural system of the azoles has diversified and development in the pharmacology has become more specific. This has helped in enhancing the formation of new molecules in the azole class with improved selectivity and efficacy. Furthermore, the comprehensive review explains how computational chemistry and structure‐activity relationship (SAR) approaches are applied to the design of future‐generation azole compounds. In light of these facts, this article is designed to give a broad overview of the current state of monocyclic azole‐based anticancer agents in an attempt to further assert its therapeutic promise and spur further attempts at infusing the said agents into the cancer therapeutics fray. The discoveries made in this study may allow the development the radical different therapeutic approaches, which could lead to improved and targeted treatment of cancer. [ABSTRACT FROM AUTHOR]

Target-first drug discovery relies heavily on protein structure information, which severely limits its application. In recent years, fragment-based drug Design (FBDD) has been identified as an alternative solution, where screening of smaller molecules for lower affinity allowed the use of focused libraries with a higher hit rate. It is shown that coupling an sp2-rich heteroaromatic group with a monofunctional sp3-rich core gives fragments (186 examples) with advantageous physical-chemical properties, covering a chemical space often neglected in traditional libraries. [ABSTRACT FROM AUTHOR]

Variants in the RAS family (HRAS, NRAS and KRAS) are among the most common mutations found in cancer. About 19% patients with cancer harbor RAS mutations, which are typically associated with poor clinical outcomes. Over the past four decades, KRAS has long been considered an undruggable target due to the absence of suitable small-molecule binding sites within its mutant isoforms. However, recent advancements in drug design have made RAS-targeting therapies viable, particularly with the approval of direct KRASG12C inhibitors, such as sotorasib and adagrasib, for treating non-small cell lung cancer (NSCLC) with KRASG12C mutations. Other KRAS-mutant inhibitors targeting KRASG12D are currently being developed for use in the clinic, particularly for treating highly refractory malignancies like pancreatic cancer. Herein, we provide an overview of RAS signaling, further detailing the roles of the RAS signaling pathway in carcinogenesis. This includes a summary of RAS mutations in human cancers and an emphasis on therapeutic approaches, as well as de novo, acquired, and adaptive resistance in various malignancies. [ABSTRACT FROM AUTHOR]

This study delves into the development and optimization of photocatalysts, namely ZnO NPs/Zeolite and TiO2 NPs/Zeolite, for the degradation of two beta-blocker drugs, including Atenolol (AT) and Metoprolol (ME). Structural and morphological analyses of the catalysts were conducted, and optimal conditions for drug degradation were determined using a Box-Behnken design. The results underscored the significant influence of pH, catalyst amount, drug concentration, and H2O2 concentration on the degradation process using ZnO NPs/Zeolite and TiO2 NPs/Zeolite as the catalysts. The optimal values of drug concentration, pH, catalyst amount, and H2O2 concentration, were determined to be 32 and 33 mg L−1, 4.2 and 4.6, 428 and 386 mg, and 2.6 and 2.5 mM utilizing ZnO NPs/Zeolite and TiO2 NPs/Zeolite as the catalyst, respectively. Following optimization, the kinetics of the photodegradation process were investigated, revealing promising rates and half-life times for both drugs. The pseudo-first-order rate constants for Atenolol and Metoprolol degradation were 0.064 ± 0.007 min−1 and 0.065 ± 0.004 min−1 with ZnO NPs/Zeolite and 0.071 ± 0.007 min−1 and 0.071 ± 0.006 min−1 with TiO2 NPs/Zeolite, respectively. Furthermore, ZnO NPs/Zeolite and TiO2 NPs/Zeolite demonstrated reusability up to 5 and 6 times, respectively, without significant activity loss. The comparative analysis highlighted the superior performance of TiO2 NPs/Zeolite over ZnO NPs/Zeolite, attributed to lower consumption, shorter degradation time, improved reusability, and compatibility with milder acidic conditions. Overall, the research showcases the potential of ZnO NPs/Zeolite and TiO2 NPs/Zeolite as an effective and sustainable solution for removing Metoprolol and Atenolol contaminants. [ABSTRACT FROM AUTHOR]

Summary Epilepsy is a brain disorder characterized by recurrent seizures, which are brief episodes of abnormal electrical activity in the brain and involuntary movement that can lead to physical injury and loss of consciousness. Seizures are canonically accompanied by increased inflammatory cytokine production that promotes neuroinflammation, brain pathology, and seizure propagation. Understanding the source of pro‐inflammatory cytokines which promote seizure pathogenesis could be a gateway to precision epilepsy drug design. This review discusses the inflammasome in epilepsy including its role in seizure propagation and negative impacts on brain health. The inflammasome is a multiprotein complex that coordinates IL‐1β and IL‐18 production in response to tissue damage, cellular stress, and infection. Clinical evidence for inflammasome signaling in epileptogenesis is reviewed followed by a discussion of emerging strategies to modulate inflammasome activity in epilepsy. [ABSTRACT FROM AUTHOR]

Predicting protein-small molecule binding sites, the initial step in structure-guided drug design, remains challenging for proteins lacking experimentally derived ligand-bound structures. Here, we propose CLAPE-SMB, which integrates a pre-trained protein language model with contrastive learning to provide high accuracy predictions of small molecule binding sites that can accommodate proteins without a published crystal structure. We trained and tested CLAPE-SMB on the SJC dataset, a non-redundant dataset based on sc-PDB, JOINED, and COACH420, and achieved an MCC of 0.529. We also compiled the UniProtSMB dataset, which merges sites from similar proteins based on raw data from UniProtKB database, and achieved an MCC of 0.699 on the test set. In addition, CLAPE-SMB achieved an MCC of 0.815 on our intrinsically disordered protein (IDP) dataset that contains 336 non-redundant sequences. Case studies of DAPK1, RebH, and Nep1 support the potential of this binding site prediction tool to aid in drug design. The code and datasets are freely available at https://github.com/JueWangTHU/CLAPE-SMB. Scientific contribution: CLAPE-SMB combines a pre-trained protein language model with contrastive learning to accurately predict protein-small molecule binding sites, especially for proteins without experimental structures, such as IDPs. Trained across various datasets, this model shows strong adaptability, making it a valuable tool for advancing drug design and understanding protein-small molecule interactions. [ABSTRACT FROM AUTHOR]

During drug discovery, it is crucial to exclude compounds with toxic effects. The human ether-à-go-go-related gene (hERG) channel is essential for maintaining cardiac repolarization and is a critical target in drug safety evaluation due to its role in drug-induced arrhythmias. Inhibition of the hERG channel can lead to severe cardiac issues, including Torsades de Pointes tachycardia. Understanding hERG inhibition mechanisms is essential to avoid these toxicities. Several structural studies have elucidated the interactions between inhibitors and hERG. However, orientation and resolution issues have so far limited detailed insights. Here, we used digitonin to analyze the apo state of hERG, which resolved orientation issues and improved the resolution. We determined the structure of hERG bound to astemizole, showing a clear map in the pore pathway. Using this strategy, we also analyzed the binding modes of E-4031 and pimozide. These insights into inhibitor interactions with hERG may aid safer drug design and enhance cardiac safety. [Display omitted] • Binding modes of hERG inhibitors were revealed with higher resolution cryo-EM data • Preferred particle orientation issues of cryo-EM data were resolved by using digitonin • The binding modes of hERG to astemizole, E-4031, and pimozide were determined • This study provides insights into hERG-inhibitor interactions for safer drug design Miyashita et al. overcame preferred orientation issue and they obtained improved cryo-EM data, which enabled them to elucidate the binding mechanisms of the hERG inhibitors astemizole, E-4031, and pimozide. These structural data are of interest to guide safer drug design and avoid drug-induced arrhythmias. [ABSTRACT FROM AUTHOR]

Artificial intelligence-assisted drug design is revolutionizing the pharmaceutical industry. Effective molecular features are crucial for accurate machine learning predictions, and advanced mathematics plays a key role in designing these features. Persistent homology theory, which equips topological invariants with persistence, provides valuable insights into molecular structures. The standard homology theory is based on a differential rule for the boundary operator that satisfies d2 = 0. Our recent work has extended this rule by employing Mayer homology with generalized differentials that satisfy dN = 0 for N≥ 2, leading to the development of persistent Mayer homology (PMH) theory and richer topological information across various scales. In this study, we utilize PMH to create a novel multiscale topological vectorization for molecular representation, offering valuable tools for descriptive and predictive analyses in molecular data and machine learning prediction. Specifically, benchmark tests on established protein-ligand datasets, including PDBbind-v2007, PDBbind-v2013, and PDBbind-v2016, demonstrate the superior performance of our Mayer homology models in predicting protein-ligand binding affinities. [ABSTRACT FROM AUTHOR]

Cyclin-dependent kinase 9 (CDK9) and cytochrome P450 3A4 (CYP3A4) have emerged as promising targets in the development of anticancer drugs, presenting a consistent challenge in the quest for potent inhibitors. CDK9 inhibitors can selectively target fast-growing cancer cells by disrupting transcription elongation, which in turn hinders the production of proteins essential for cell cycle progression and survivaŚ. Understanding how CYP3A4 metabolizes specific chemotherapy drugs allows for personalized treatment plans, optimizing drug dosages according to a patient's metabolic profile. Since many cancer patients undergo combination therapies, and CYP3A4 is vital in drug metabolism, its inhibition or induction by one drug can alter the plasma levels of others, potentially leading to treatment failure or increased toxicity. Therefore, managing CYP3A4 activity is critical for effective cancer treatment. Employing a range of computational methodologies, this study systematically investigated the binding mechanisms of pyrimidine derivatives against CDK9 and CYP3A4. The field-based model demonstrated high R 2 values (0.99), with Q2 (0.66), demonstrating its ability to predict in silico inhibitory activity against the target of this study. The screening process followed in this work led to the discovery of powerful new inhibitor compounds. Of the 15 new compounds designed, three have a high affinity with the target (ranging from −8 to −9 kcal/mol kcal/mol) and were singled out through docking filtration for more detailed investigation. As well as, a reference compound with a substantial pIC50 value of 8.4, serving as the foundation for the development of the new compounds, was included for comparative analysis. To elucidate the essential features of CDK9 and CYP3A4 inhibitor design, a comparative analysis was conducted between 3D-QSAR-generated contours and molecular docking conformations of ligands. Molecular dynamics simulations were carried out for a duration of 100 ns on selected docked complexes, specifically those involving novel compounds with CDK9 and CYP3A4 enzymes. Additionally, the binding free energy for these complexes was assessed using the MM/PBSA method, which evaluates the free energy landscape of protein-ligand interactions. The results of MM/PBSA highlighted the strength of the new compounds in enhancing interactions with the target protein, which favors the results of molecular docking and MD simulation. These insights contribute to a deeper understanding of the mechanisms underlying CDK9 and CYP3A4 inhibition, offering potential avenues for the development of innovative and effective CDK9 inhibitors. [ABSTRACT FROM AUTHOR]

Simple Summary: In this study, we applied translational informatics for intelligent medicine of acute myeloid leukemia, a type of cancer characterized by disease relapses even after seemingly successful treatments. Treatment failure is associated, at least in part, with the fact that targeting individual proteins often promotes rewiring of relevant networks and re-organization of interactions of, among others, non-targeted proteins to eventually evade single-target therapies. To develop efficient therapies, these dynamics should be taken into account and target whole network modules instead of singleton genes in order to prevent the establishment of compensating signaling circuits. Therefore, we integrated network-based methods, structural pharmacology, and molecular modeling to establish two complementary multitargeting strategies, one in the form of repurposable drug combinations and the other as a de novo synthesized triple-targeting agent. Of note, our study exploits, for the first time, a greedy algorithm to identify optimal combinations of drugs and therapeutic protein targets. Background/Objectives: Acute myeloid leukemia (AML) is characterized by therapeutic failure and long-term risk for disease relapses. As several therapeutic targets participate in networks, they can rewire to eventually evade single-target drugs. Hence, multi-targeting approaches are considered on the expectation that interference with many different components could synergistically hinder activation of alternative pathways and demolish the network one-off, leading to complete disease remission. Methods: Herein, we established a network-based, computer-aided approach for the rational design of drug combinations and de novo agents that interact with many AML network components simultaneously. Results: A reconstructed AML network guided the selection of suitable protein hubs and corresponding multi-targeting strategies. For proteins responsive to existing drugs, a greedy algorithm identified the minimum amount of compounds targeting the maximum number of hubs. We predicted permissible combinations of amiodarone, artenimol, fostamatinib, ponatinib, procaine, and vismodegib that interfere with 3–8 hubs, and we elucidated the pharmacological mode of action of procaine on DNMT3A. For proteins that do not respond to any approved drugs, namely cyclins A1, D2, and E1, we used structure-based de novo drug design to generate a novel triple-targeting compound of the chemical formula C15H15NO5, with favorable pharmacological and drug-like properties. Conclusions: Overall, by integrating network and structural pharmacology with molecular modeling, we determined two complementary strategies with the potential to annihilate the AML network, one in the form of repurposable drug combinations and the other as a de novo synthesized triple-targeting agent. These target–drug interactions could be prioritized for preclinical and clinical testing toward precision medicine for AML. [ABSTRACT FROM AUTHOR]

Among the new complexes, metal N-heterocyclic carbene (NHC) complexes have recently gained remarkable attention as they are entirely appropriate prerequisites for effective drug design and quick optimization. Furthermore, N-heterocyclic carbenes (NHCs) like phosphines contain strong σ-donating properties, which can bind to metals and create stable complexes. This article reports a general theoretical discussion on the structures and nature of C(carbene/alkenyl) → M, P → M and C≡C bonds. Also, the influence of changing L and Rʹ groups in some adducts of [RʹC≡C → ML], (M=Cu (I), Ag (I), Au (I); R'=C10H7, C9NH12SO2; L=NHC (R), P (R)3; and R=F, Cl, Br, H, CH3, SiH3, Ph) has been studied. In this context, DFT calculations by PBE-D3/def2-TZVP level of theory have been used. The nature of C(carbene/alkenyl) → M bonds in [RʹC≡C → MNHCR] and also P → M and C(alkenyl) → M bonds in [RʹC≡C → MPR3] complexes was surveyed. This was done using natural bond orbital (NBO), atoms in molecules (AIM), energy decomposition analysis (EDA), and energy decomposition analysis natural orbital for chemical valence (EDA-NOCV). The data have shown that σ donation from C(alkenyl) to M atom in [RʹC≡C → MPR3] complexes was greater than corresponding [RʹC≡C → MNHCR] complexes. Also, the C(alkenyl) → M bonds in corresponding complexes were predominantly electrostatic. In addition, the C≡C bond has been also investigated by applying AIM, EDA, and ETS-NOCV analysis. The outcomes indicate that the highest percentage of interaction energy for C≡C bond is related to covalent interaction. [ABSTRACT FROM AUTHOR]

This case study demonstrates how knowledge of degradation products together with predictions can establish a lean stability strategy using the accelerated predictive stability (APS) principles. Applying all available data for AZD4831, (R)-1-(2-(1-aminoethyl)-4-chlorobenzyl)-2-thioxo-2,3-dihydro-1H-pyrrolo[3,2-d]pyrimidin-4(5H)-one, a reliable predictive model was developed despite minor differences in technical batch tablet compositions. Early forced degradation studies were performed to map potential degradation pathways. The insights from these studies guided the design of an APS study, which in turn inform on a suitable clinical stability program, initial specification and shelf-life. The use of APS predictions of degradants as well as total impurities highlighted at an early stage, when designing the clinical stability program, the opportunity to identify which degradation product that would be shelf-life limiting. Hence, it was possible to guide the development stability activities and set an initial shelf-life of a tablet formulation. The presented study displays the importance of combining several sources of information in drug development, e.g., potential degradation pathways, accelerated stability, stability program design, metabolite data, and specification limits. [Display omitted] • The importance of integrating results and information from all stability activities to inform the next steps in a drug development process is emphasized. • Predictions from the accelerated stability testing (APS) model were successfully applied and matched against the real-time stability data of the final drug product composition. • Confidence gained by shelf-life predictions allowed for adjustment of the clinical stability protocol, enabling the reduction of testing points and climates. • The APS model provided crucial information on the shelf-life limiting attributes, thereby informing limits in the drug product initial clinical specification. • The shelf-life limiting degradation product could be considered qualified, justified by available metabolite data, thus underscoring the importance of qualification of impurities. [ABSTRACT FROM AUTHOR]

Liver cancer is one of the aggressive primary tumors as evident by high rate of incidence and mortality. Conventional treatments (e.g. chemotherapy) suffer from various drawbacks including wide drug distribution, low localized drug concentration, and severe off-site toxicity. Therefore, they cannot satisfy the mounting need for safe and efficient cancer therapeutics, and alternative novel strategies are needed. Nano-based drug delivery systems (NDDSs) are among these novel approaches that can improve the overall therapeutic outcomes. NDDSs are designed to encapsulate drug molecules and target them specifically to liver cancer. Thus, NDDSs can selectively deliver therapeutic agents to the tumor cells and avoid distribution to off-target sites which should improve the safety profile of the active agents. Nonetheless, NDDSs should be well designed, in terms of the preparing materials, nanocarriers structure, and the targeting strategy, in order to accomplish these objectives. This review discusses the latest advances of NDDSs for cancer therapy with emphasis on the aforementioned essential design components. The review also entails the challenges associated with the clinical translation of NDDSs, and the future perspectives towards next-generation NDDSs. [Display omitted] • Liver cancer is one of the aggressive primary tumors as evident by its high rate of incidence and mortality. • Nano-based drug delivery systems have shown several advantages over the conventional treatment modalities for liver cancer. • Different materials had been studied as a nanocarrier for cancer management. • Nanocarriers can be classified into two main categories (organic and inorganic) according to the nature of the material used for construction. • Targeting therapeutic molecules to the liver studied to deliver chemotherapy for the treatment of liver cancer. • Various mechanisms that nano-based drug delivery systems rely on to selectively target and accumulate in liver cancer which include Passive Targeting, Stimuli-Responsive Systems, and Active Targeting. [ABSTRACT FROM AUTHOR]

Alzheimer's Disease(AD) is the most common type of dementia. It is a progressive disease beginning with mild memory loss and possibly leading to loss of the ability to carry on a conversation and respond to the environment. This study investigates the relationship between the chemical structure of potential AD drugs and their therapeutic efficacy using Multi-Criteria Decision-Making (MCDM) techniques including The approach for Order Preference by Similarity to Ideal Solution (TOPSIS) and Simple Additive Weighting (SAW) method. A comprehensive dataset comprising molecular descriptors and corresponding pharmacological properties, i.e., melting point, boiling point, molecular weight and density of AD drugs was compiled from diverse sources. Topological indices were calculated to capture the structural characteristics of these compounds. Application of TOPSIS and SAW through Entropy method helps obtain optimal drugs for curing AD. Quantitative Structure Property Relationships (QSPR) analysis has been done between properties and topological indices of AD's drug structures. Results revealed significant relations between specific topological indices and drug efficacy, providing insights into the structural features crucial for AD treatment efficacy. This approach offers a promising avenue for rational drug design and optimization in the quest for novel AD therapeutics. [ABSTRACT FROM AUTHOR]

Intervertebral disc degeneration (IDD) is one of the most frequent causes of low back pain. No treatment is currently available to delay the progression of IDD. Conservative treatment or surgical interventions is only used to target the symptoms of IDD rather than treat the underlying cause. Currently, numerous potential therapeutic strategies are available, including molecular therapy, gene therapy, and cell therapy. However, the hostile environment of degenerated discs is a major problem that has hindered the clinical applicability of such approaches. In this regard, the design of drugs using alternative delivery systems (macro-, micro-, and nano-sized particles) may resolve this problem. These can protect and deliver biomolecules along with helping to improve the therapeutic effect of drugs via concentrating, protecting, and prolonging their presence in the degenerated disc. This review summarizes the research progress of diagnosis and the current options for treating IDD. [ABSTRACT FROM AUTHOR]

Computational biology involves applying computer science and informatics techniques in biology to understand complex biological data. It allows us to collect, connect, and analyze biological data at a large scale and build predictive models. In the twenty first century, computational resources along with Artificial Intelligence (AI) have been widely used in various fields of biological sciences such as biochemistry, structural biology, immunology, microbiology, and genomics to handle massive data for decision-making, including in applications such as drug design and vaccine development, one of the major areas of focus for human and animal welfare. The knowledge of available computational resources and AI-enabled tools in vaccine design and development can improve our ability to conduct cutting-edge research. Therefore, this review article aims to summarize important computational resources and AI-based tools. Further, the article discusses the various applications and limitations of AI tools in vaccine development. PLAIN LANGUAGE SUMMARY: The application of vaccines is one of the most promising treatments for numerous infectious diseases. However, the design and development of effective vaccines involve huge investments and resources, and only a handful of candidates successfully reach the market. Only relying on traditional methods is both time-consuming and expensive. Various computational tools and software have been developed to accelerate the vaccine design and development. Further, AI-enabled computational tools have revolutionized the field of vaccine design and development by creating predictive models and data-driven decision-making processes. Therefore, information and awareness of these AI-enabled computational resources will immensely facilitate the development of vaccines against emerging pathogens. In this review, we have meticulously summarized the available computational tools for each step of in-silico vaccine design and development, delving into the transformative applications of AI and ML in this domain, which would help to choose appropriate tools for each step during vaccine development, and also highlighting the limitations of these tools to facilitate the selection of appropriate tools for each step of vaccine design. [ABSTRACT FROM AUTHOR]

PLK4(polo-like kinase 4), an enzyme regulating centrioles, has been identified as a highly promising target for cancer treatment, making the development of PLK4 inhibitors crucial for advancing cancer therapy. In this study, 26 PLK4 inhibitors based on the imidazo[3,4- d ]pyrimidine scaffold, exhibiting anti-tumor activity, were selected. Reliable 3D/2D-QSAR models, Topomer CoMFA (q 2 =0.618, r 2 =0.965), and HQSAR (q 2 =0.711, r 2 =0.862), were established, demonstrating excellent internal and external predictive abilities and reliable applicability domains. Using the 3D/2D-QSAR models, nine PLK4 inhibitors with high theoretical activity were designed, and subsequent molecular docking and molecular dynamics studies revealed strong interactions between the new compounds and the receptor protein, particularly hydrophobic interactions. This research provides a robust screening model for the development of PLK4 inhibitors, offering valuable insights into the design of potential solutions to clinical challenges associated with PLK4 inhibitors. • Establishment of Reliable QSAR Models: Established excellent QSAR models to provide an in-depth elucidation of the structure-activity relationship of PLK4 inhibitors. • Molecular Docking and Molecular Dynamic Simulation: The study used molecular docking and molecular dynamics simulations to explore the microscopic binding mechanisms and stability between the newly designed inhibitors and the PLK4 receptor protein. • Design of Novel Inhibitors: Utilizing the QSAR models' predictive power, nine novel PLK4 inhibitors with high theoretical activity were designed, paving the way for new anti-cancer agents. • Contribution to Cancer Treatment: The design of these new PLK4-targeted inhibitors plays a significant role in cancer therapy, providing a targeted approach to treatment that may reduce off-target effects and the likelihood of drug resistance, underscoring the importance of computational technology in rational anti-cancer drug design. [ABSTRACT FROM AUTHOR]

Inflammation and immune dysregulation have been linked to the pathogenesis and progression of Parkinson's disease (PD), and represent an attractive target for therapeutic intervention, given the potential for repurposing of existing anti-inflammatory and immunomodulatory agents. Despite the fact that initial studies of drugs with secondary anti-inflammatory effects did not yield positive results, agents specifically targeting immune and inflammatory pathways may hold more promise. This article will briefly review the evidence base for targeting the immune system and neuroinflammation in PD, and discuss in detail the recently completed and currently active trials of primary anti-inflammatory/immunomodulatory drugs in PD. Plain Language Summary: Parkinson's disease is caused by a loss of dopamine-producing nerve cells in the brain. Recent research has suggested that activation of the immune system, leading to inflammation in the brain and body, can contribute to this loss. Current medications that are used to treat Parkinson's disease only help with the symptoms, and do not slow down the damage to nerve cells in the brain. New treatments, aiming to reduce inflammation and thereby slow disease progression, are under investigation in a number of clinical trials which are reviewed in this article. These treatments include medications that have been used in other diseases, as well as new drugs designed to target inflammation and immune activation in PD. Some of these early studies have had encouraging results but further larger trials are needed to determine whether medications targeting inflammation will have benefit for people with PD. [ABSTRACT FROM AUTHOR]

Histone deacetylase 6 (HDAC6) was a potential target for Alzheimer's disease (AD). In this study, a series of novel oxyevodiamine-based HDAC6 inhibitors with a variety of linker moieties were designed, synthesized and evaluated. Compound 12 with a benzyl linker was identified as a high potent and selective HDAC6 inhibitor. It inhibited HDAC6 with an IC50 value of 6.2 nM and was more than 200 fold selectivity over HDAC1. It also had lower cytotoxicity and higher anti-H2O2 activity in vitro comparing with other derivatives. Compound 12 might be a good lead as novel HDAC6 inhibitor for the treatment of AD. [ABSTRACT FROM AUTHOR]

RNA plays important roles in regulating both health and disease biology in all kingdoms of life. Notably, RNA can form intricate three‐dimensional structures, and their biological functions are dependent on these structures. Targeting the structured regions of RNA with small molecules has gained increasing attention over the past decade, because it provides both chemical probes to study fundamental biology processes and lead medicines for diseases with unmet medical needs. Recent advances in RNA structure prediction and determination and RNA biology have accelerated the rational design and development of RNA‐targeted small molecules to modulate disease pathology. However, challenges remain in advancing RNA‐targeted small molecules towards clinical applications. This review summarizes strategies to study RNA structures, to identify small molecules recognizing these structures, and to augment the functionality of RNA‐binding small molecules. We focus on recent advances in developing RNA‐targeted small molecules as potential therapeutics in a variety of diseases, encompassing different modes of actions and targeting strategies. Furthermore, we present the current gaps between early‐stage discovery of RNA‐binding small molecules and their clinical applications, as well as a roadmap to overcome these challenges in the near future. [ABSTRACT FROM AUTHOR]

Background: Single-arm clinical trials (SAT) are common in drug and biologic submissions for rare or life-threatening conditions, especially when no therapeutic options exist. External control arms (ECAs) improve interpretation of SATs but pose methodological and regulatory challenges. Objective: Through narrative reviews and expert input, we developed a framework for considerations that might influence regulatory use and likelihood of regulatory acceptance of an SAT, identifying non-oncology first indication approvals as an area of interest. We systematically analyzed FDA and EMA approvals using SATs as pivotal evidence. The framework guided outcome abstraction on regulatory responses. Methods: We examined all non-oncology FDA and EMA drug and biologic approvals for first indications from 2019 to 2022 to identify those with SAT as pivotal safety or efficacy evidence. We abstracted outcomes, key study design features, regulator responses to SAT and (where applicable) ECA design, and product label content. Results: Among 20 SAT-based FDA approvals and 17 SAT-based EMA approvals, most common indications were progressive rare diseases with high unmet need/limited therapeutic options and a natural history without spontaneous improvement. Of the types of comparators, most were natural history cohorts (45% FDA; 47% EMA) and baseline controls (40% FDA; 47% EMA). Common critiques were of non-contemporaneous ECAs, subjective endpoints, and baseline covariate imbalance between arms. Conclusion: Based on recent FDA and EMA approvals, the likelihood of regulatory success for SATs with ECAs depends on many design, analytic, and data quality considerations. Our framework is useful in early drug development when considering SAT strategies for evidence generation. [ABSTRACT FROM AUTHOR]

Despite regulatory changes designed to stimulate investment in therapies for rare diseases, many of these conditions lack government-approved treatments. Advanced regenerative medicines, which are therapies and clinical interventions aimed at healing or replacing damaged or defective human cells, tissues, and organs, offer great promise for addressing many rare diseases. A major challenge facing advanced regenerative medicines for rare diseases is securing financial support to assist in bringing a therapy to market. This paper describes the factors cited by pharmaceutical industry players globally for sponsoring the development of advanced regenerative medicines for rare diseases. The paper examines the motivations of 53 sponsors that meet the latter criteria. The motivations behind investments were broadly similar amongst sponsors and map closely onto regulatory requirements for clinical development and marketing authorization of advanced therapeutic products, including the presence of accelerated or attenuated pathways for regulatory approval, use for indications with high unmet medical needs, and/or that have advantages over existing therapies, and robust preclinical data. Other factors include availability of investment incentives and opportunities for off-label use in the post-approval stages. [ABSTRACT FROM AUTHOR]

Lipinski's Rule of Five and Ghose filter are empirical guidelines for evaluating the drug-likeness of a compound, suggesting that orally active drugs typically fall within specific ranges for molecular descriptors such as hydrogen bond donors and acceptors, weight, and lipophilicity. We revisit these practices and offer a more analytical perspective using the Dominance-based Rough Set Approach (DRSA). By analyzing representative samples of drug and non-drug molecules and focusing on the same molecular descriptors, we derived decision rules capable of distinguishing between these two classes systematically and reproducibly. This way, we reduced human bias and enabled efficient knowledge extraction from available data. The performance of the DRSA model was rigorously validated against traditional rules and available machine learning (ML) approaches, showing a significant improvement over empirical rules while achieving comparable predictive accuracy to more complex ML methods. Our rules remain simple and interpretable while being characterized by high sensitivity and specificity. [ABSTRACT FROM AUTHOR]

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In recent decades, nanotechnology has significantly advanced drug delivery systems, particularly in targeting subcellular organelles, thus opening new avenues for disease treatment. Mitochondria, critical for cellular energy and health, when dysfunctional, contribute to cancer, neurodegenerative diseases, and metabolic disorders. This has propelled the development of nanomedicines aimed at precise mitochondrial targeting to modulate their function, marking a research hotspot. This review delves into the recent advancements in mitochondrial-targeted nanotherapeutics, with a comprehensive focus on targeting strategies, nanocarrier designs, and their therapeutic applications. It emphasizes nanotechnology’s role in enhancing drug delivery by overcoming biological barriers and optimizing drug design for specific mitochondrial targeting. Strategies exploiting mitochondrial membrane potential differences and specific targeting ligands improve the delivery and mitochondrial accumulation of nanomedicines. The use of diverse nanocarriers, including liposomes, polymer nanoparticles, and inorganic nanoparticles, tailored for effective mitochondrial targeting, shows promise in anti-tumor and neurodegenerative treatments. The review addresses the challenges and future directions in mitochondrial targeting nanotherapy, highlighting the need for precision, reduced toxicity, and clinical validation. Mitochondrial targeting nanotherapy stands at the forefront of therapeutic strategies, offering innovative treatment perspectives. Ongoing innovation and research are crucial for developing more precise and effective treatment modalities. [ABSTRACT FROM AUTHOR]

Lecithin-cholesterol acyltransferase (LCAT) serves as a pivotal enzyme in preserving cholesterol homeostasis via reverse cholesterol transport, a process closely associated with the onset of atherosclerosis. Impaired LCAT function can lead to severe LCAT deficiency disorders for which no pharmacological treatment exists. LCAT-based therapies, such as small molecule positive allosteric modulators (PAMs), against LCAT deficiencies and atherosclerosis hold promise, although their efficacy against atherosclerosis remains challenging. Herein we utilized a quantitative in silico metric to predict the activity of novel PAMs and tested their potencies with in vitro enzymatic assays. As predicted, sodium-glucose cotransporter 2 (SGLT2) inhibitors (gliflozins), sucrose and flavonoids activate LCAT. This has intriguing implications for the mechanism of action of gliflozins, which are commonly used in the treatment of type 2 diabetes, and for the endogenous activation of LCAT. Our results underscore the potential of molecular dynamics simulations in rational drug design. [ABSTRACT FROM AUTHOR]

Accurate prediction of binding free energy is crucial for the rational design of drug candidates and understanding protein-ligand interactions. To address this, we have developed four protocols that combine QM/MM calculations and the mining minima (M2) method, tested on 9 targets and 203 ligands. Our protocols carry out free energy processing with or without conformational search on the selected conformers obtained from M2 calculations, where their force field atomic charge parameters are substituted with those obtained from a QM/MM calculation. The method achieved a high Pearson's correlation coefficient (0.81) with experimental binding free energies across diverse targets, demonstrating its generality. Using a differential evolution algorithm with a universal scaling factor of 0.2, we achieved a low mean absolute error of 0.60 kcal mol-1. This performance surpasses many existing methods and is comparable to popular relative binding free energy techniques but at significantly lower computational cost. Binding free energy calculations are crucial in computational drug discovery, however, the current alchemical free energy perturbation (FEP) requires large computational capabilities to achieve high accuracy. Here, the authors develop an alternative method by combining QM/MM and the mining minima method to predict free energies at lower computational cost and with comparable accuracy to FEP-based methods. [ABSTRACT FROM AUTHOR]

Accurate prediction of protein‐ligand binding affinities is an essential challenge in structure‐based drug design. Despite recent advances in data‐driven methods for affinity prediction, their accuracy is still limited, partially because they only take advantage of static crystal structures while the actual binding affinities are generally determined by the thermodynamic ensembles between proteins and ligands. One effective way to approximate such a thermodynamic ensemble is to use molecular dynamics (MD) simulation. Here, an MD dataset containing 3,218 different protein‐ligand complexes is curated, and Dynaformer, a graph‐based deep learning model is further developed to predict the binding affinities by learning the geometric characteristics of the protein‐ligand interactions from the MD trajectories. In silico experiments demonstrated that the model exhibits state‐of‐the‐art scoring and ranking power on the CASF‐2016 benchmark dataset, outperforming the methods hitherto reported. Moreover, in a virtual screening on heat shock protein 90 (HSP90) using Dynaformer, 20 candidates are identified and their binding affinities are further experimentally validated. Dynaformer displayed promising results in virtual drug screening, revealing 12 hit compounds (two are in the submicromolar range), including several novel scaffolds. Overall, these results demonstrated that the approach offer a promising avenue for accelerating the early drug discovery process. [ABSTRACT FROM AUTHOR]

The molecular representation model is a neural network that converts molecular representations (SMILES, Graph) into feature vectors, and is an essential module applied across a wide range of artificial intelligence‐driven drug discovery scenarios. However, current molecular representation models rarely consider the three‐dimensional conformational space of molecules, losing sight of the dynamic nature of small molecules as well as the essence of molecular conformational space that covers the heterogeneity of molecule properties, such as the multi‐target mechanism of action, recognition of different biomolecules, dynamics in cytoplasm and membrane. In this study, a new model named GeminiMol is proposed to incorporate conformational space profiles into molecular representation learning, which extracts the feature of capturing the complicated interplay between the molecular structure and the conformational space. Although GeminiMol is pre‐trained on a relatively small‐scale molecular dataset (39290 molecules), it shows balanced and superior performance not only on 67 molecular properties predictions but also on 73 cellular activity predictions and 171 zero‐shot tasks (including virtual screening and target identification). By capturing the molecular conformational space profile, the strategy paves the way for rapid exploration of chemical space and facilitates changing paradigms for drug design. [ABSTRACT FROM AUTHOR]

The use of metal compounds such as cisplatin and its derivatives as therapeutic and diagnostic agents against various diseases is well established. Although metallodrugs have been very successful clinically, low therapeutic selectivity and the potential toxicity of the metal compounds mandate the need for a continuous search for more efficient and specific treatments. Hence, the use of computer‐aided drug design (CADD), molecular modelling and theoretical studies in designing, selecting and applying potential drug candidates have become ever more necessary. Among the many computational and theoretical techniques, molecular docking and density functional theory play significant roles in predicting drug activity. Recent methodologies, within the past 5 years, that rely on these techniques to advance the adoption of potential metallodrugs are highlighted and thoroughly discussed. This is because advancements in computing power have led to the wide use of CADD approaches for drug discovery, development and analysis to shorten the time and reduce the costs associated with drug discovery. [ABSTRACT FROM AUTHOR]

Neurotrophin receptors of the Trk family are involved in the regulation of brain development and neuroplasticity, and therefore can serve as targets for anti-cancer and stroke-recovery drugs, antidepressants, and many others. The structures of Trk protein domains in various states upon activation need to be elucidated to allow rational drug design. However, little is known about the conformations of the transmembrane and juxtamembrane domains of Trk receptors. In the present study, we employ NMR spectroscopy to solve the structure of the TrkB dimeric transmembrane domain in the lipid environment. We verify the structure using mutagenesis and confirm that the conformation corresponds to the active state of the receptor. Subsequent study of TrkB interaction with the antidepressant drug fluoxetine, and the antipsychotic drug chlorpromazine, provides a clear self-consistent model, describing the mechanism by which fluoxetine activates the receptor by binding to its transmembrane domain. Neurotrophin receptor TrkB regulates neuronal growth and neuroplasticity. Here, the authors present the NMR structure of the intramembrane region of TrkB activated by antidepressant drugs, yielding insights into receptor function. [ABSTRACT FROM AUTHOR]

Generative drug design facilitates the creation of compounds effective against pathogenic target proteins. This opens up the potential to discover novel compounds within the vast chemical space and fosters the development of innovative therapeutic strategies. However, the practicality of generated molecules is often limited, as many designs focus on a narrow set of drug-related properties, failing to improve the success rate of subsequent drug discovery process. To overcome these challenges, we develop TamGen, a method that employs a GPT-like chemical language model and enables target-aware molecule generation and compound refinement. We demonstrate that the compounds generated by TamGen have improved molecular quality and viability. Additionally, we have integrated TamGen into a drug discovery pipeline and identified 14 compounds showing compelling inhibitory activity against the Tuberculosis ClpP protease, with the most effective compound exhibiting a half maximal inhibitory concentration (IC50) of 1.9 μM. Our findings underscore the practical potential and real-world applicability of generative drug design approaches, paving the way for future advancements in the field. Generative AI holds promise for creating novel compounds. Here, authors introduce TamGen, a GPT-like model designed to generate molecules tailored to specific target proteins. TamGen identified 14 potent compounds against the Tuberculosis ClpP protease, showing its potential for drug discovery. [ABSTRACT FROM AUTHOR]

Mastering of analytical methods for accurate quantitative determinations of enantiomeric excess is a crucial aspect in asymmetric catalysis, chiral synthesis, and pharmaceutical applications. In this context, the phenomenon of Self‐Induced Diastereomeric Anisochronism (SIDA) can be exploited in NMR spectroscopy for accurate determinations of enantiomeric composition, without using a chiral auxiliary that could interfere with the spectroscopic investigation. This phenomenon can be particularly useful for improving the quantitative analysis of mixtures with low enantiomeric excesses, where direct integration of signals can be tricky. Here, we describe a novel analysis protocol to correctly determine the enantiomeric composition of scalemic mixtures and investigate the thermodynamic and stereochemical features at the basis of SIDA. Dipeptide derivatives were chosen as substrates for this study, given their central role in drug design. By integrating the experiments with a conformational stochastic search that includes entropic contributions, we provide valuable information on the dimerization thermodynamics, the nature of non‐covalent interactions leading to self‐association, and the differences in the chemical environment responsible for the anisochronism, highlighting the importance of different stereochemical arrangement and tight association for the distinction between homochiral and heterochiral adducts. An important role played by the counterion was pointed out by computational studies. [ABSTRACT FROM AUTHOR]

AbstractPolymer-drug interactions play a pivotal role in the design and optimization of drug delivery systems. Thermodynamic principles, such as enthalpy, entropy and free energy, govern these interactions and significantly influence the stability, efficacy and release profiles of the drug-polymer complexes. In this review we explore the role of thermodynamics in drug-polymer binding, focusing on non-covalent interactions, including van der Waals forces, hydrogen bonding, ionic interactions and hydrophobic effects. These interactions affect the drug encapsulation efficiency, release kinetics and the overall stability of the delivery system. Understanding the balance between the various thermodynamic forces helps in the rational design of advanced drug delivery platforms, such as nanoparticles, micelles and hydrogels, which rely on optimized drug-polymer affinity. Our review further delves into the experimental techniques and modeling approaches used to characterize these interactions, such as isothermal titration calorimetry (ITC), molecular dynamics (MD) simulations and surface plasmon resonance (SPR). By examining these thermodynamic foundations, we believe this article provides insights into developing more efficient and stable drug delivery systems that ensure targeted release, enhanced bioavailability and reduced side effects. [ABSTRACT FROM AUTHOR]

Antibacterial bone cements (ABCs), such as antibiotic-loaded bone cements (ALBCs), have been widely utilized in clinical treatments. Currently, bone cements loaded with vancomycin, gentamicin, tobramycin, or clindamycin are approved by the US Food and Drug Administration. However, traditional ALBCs exhibit drawbacks like burst release and bacterial resistance. Therefore, there is a demand for the development of antibacterial bone cements containing novel agents to address these defects. In this review, we provide an overview and prospect of the new antibacterial agents that can be used or have the potential to be applied in bone cement, including metallic antibacterial agents, pH-switchable antibacterial agents, cationic polymers, N-halamines, non-leaching acrylic monomers, antimicrobial peptides and enzymes. Additionally, we have conducted a preliminary assessment of the feasibility of bone cement containing N-halamine, which has demonstrated good antibacterial activities. The conclusion of this review is that the research and utilization of bone cement containing novel antibacterial agents contribute to addressing the limitations of ALBCs. Therefore, it is necessary to continue expanding the research and use of bone cement incorporating novel antibacterial agents. This review offers a novel perspectives for designing ABCs and treating bone infections. [ABSTRACT FROM AUTHOR]

The computational construction of small organic molecules (de novo design), directly in a protein binding site, is an effective means for generating novel ligands tailored to fit the pocket environment. In this work, we present two new methods, which aim to improve de novo design outcomes using (1) biasing algorithms to prioritize selection and/or acceptance of fragments and torsions during growth, and (2) parallel‐based clustering and pruning algorithms to remove duplicate molecules as candidate fragment are added. Large‐scale testing encompassing thousands of simulations were employed to interrogate the methods in terms of multiple metrics which include numbers of duplicate molecules generated, pairwise‐similarity, focused library reconstruction rates, fragment and torsion frequencies, fragment and torsion rank scores, interaction energy and drug‐likeness scores, and 3D pose comparisons. The biasing algorithms, particularly those that include fragment and torsion components simultaneously, led to molecules that more closely mimicked the distributions of fragments and torsions found in drug‐like libraries. The new parallel‐based clustering and pruning algorithms, compared with the existing serial approach, also led to larger ensembles comprised of topologically unique molecules with much greater efficiency by removing redundant growth paths. [ABSTRACT FROM AUTHOR]

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]