Hasil untuk "Therapeutics. Pharmacology"

E. Driggers, S. Hale, Jinbo Lee et al.

R. McHugh, S. Whitton, A. Peckham et al.

F. Brozovich, C. Nicholson, Chantal V. Degen et al.

The smooth muscle cell directly drives the contraction of the vascular wall and hence regulates the size of the blood vessel lumen. We review here the current understanding of the molecular mechanisms by which agonists, therapeutics, and diseases regulate contractility of the vascular smooth muscle cell and we place this within the context of whole body function. We also discuss the implications for personalized medicine and highlight specific potential target molecules that may provide opportunities for the future development of new therapeutics to regulate vascular function.

F. Odeh, Hamdi Nsairat, W. Alshaer et al.

Soon after they were first described in 1990, aptamers were largely recognized as a new class of biological ligands that can rival antibodies in various analytical, diagnostic, and therapeutic applications. Aptamers are short single-stranded RNA or DNA oligonucleotides capable of folding into complex 3D structures, enabling them to bind to a large variety of targets ranging from small ions to an entire organism. Their high binding specificity and affinity make them comparable to antibodies, but they are superior regarding a longer shelf life, simple production and chemical modification, in addition to low toxicity and immunogenicity. In the past three decades, aptamers have been used in a plethora of therapeutics and drug delivery systems that involve innovative delivery mechanisms and carrying various types of drug cargos. However, the successful translation of aptamer research from bench to bedside has been challenged by several limitations that slow down the realization of promising aptamer applications as therapeutics at the clinical level. The main limitations include the susceptibility to degradation by nucleases, fast renal clearance, low thermal stability, and the limited functional group diversity. The solution to overcome such limitations lies in the chemistry of aptamers. The current review will focus on the recent arts of aptamer chemistry that have been evolved to refine the pharmacological properties of aptamers. Moreover, this review will analyze the advantages and disadvantages of such chemical modifications and how they impact the pharmacological properties of aptamers. Finally, this review will summarize the conjugation strategies of aptamers to nanocarriers for developing targeted drug delivery systems.

G. Bhardwaj, V. Mulligan, C. Bahl et al.

Naturally occurring, pharmacologically active peptides constrained with covalent crosslinks generally have shapes that have evolved to fit precisely into binding pockets on their targets. Such peptides can have excellent pharmaceutical properties, combining the stability and tissue penetration of small-molecule drugs with the specificity of much larger protein therapeutics. The ability to design constrained peptides with precisely specified tertiary structures would enable the design of shape-complementary inhibitors of arbitrary targets. Here we describe the development of computational methods for accurate de novo design of conformationally restricted peptides, and the use of these methods to design 18–47 residue, disulfide-crosslinked peptides, a subset of which are heterochiral and/or N–C backbone-cyclized. Both genetically encodable and non-canonical peptides are exceptionally stable to thermal and chemical denaturation, and 12 experimentally determined X-ray and NMR structures are nearly identical to the computational design models. The computational design methods and stable scaffolds presented here provide the basis for development of a new generation of peptide-based drugs.

Nazanin Mokari, Bryce Morsky

The evolutionary and ecological dynamics of tumors under immune responses and therapeutic interventions pose major challenges to long-term treatment success. Although treatment may initially achieve short-term disease control, resistant cancer cell subpopulations often arise, leading to relapse with more aggressive and treatment-resistant forms of the disease. Here, we develop and analyze mathematical models describing the interactions among effector cells, chemo-resistant tumor cells, and immuno-resistant tumor cells under distinct immune-evasion strategies. The models incorporate competition and cooperation between resistant and sensitive tumor subpopulations. We identify threshold conditions governing tumor persistence, elimination, and phenotype dominance under varying therapeutic intensities. These findings provide a theoretical framework for designing targeted and combination therapies and offer insights into strategies for mitigating the treatment resistance.

Pooja Singh, Rajesh Sinha, Veena B. Antony

IntroductionFibrosis in the gossamer alveolar capillary membranes of the lung can lead to abnormalities in gas exchange, hypoxemia and death of the individual. These interstitial lung diseases (ILDs) of known or yet undefined etiologies (such as Idiopathic pulmonary fibrosis) highlight the need for predictive, physiologically relevant models for toxicity studies. Three-dimensional (3D) lung organoids derived from animal cells provide an advanced platform that replicates the structural and cellular complexity of lung tissue while reducing whole-animal use.MethodsMouse lung organoids (MiLO) were used to evaluate pulmonary toxicity caused by environmental toxicants and pharmacologic agents. MiLO were generated from perfused, minced mouse lungs that were digested with collagenase, filtered, depleted of red blood cells, and embedded in Matrigel. Organoids were stained for lineage markers to characterize cellular diversity, including SPC, α-SMA, CD31, F4/80 and ECM proteins collagen I and fibronectin. Gene expression in MiLO and native lung tissue was compared for fibrosis- and viability-related markers. A well-characterized mouse model of cadmium induced lung fibrosis was used as an in vivo benchmark to assess α-SMA expression, airway resistance to methacholine, hydroxyproline content, malondialdehyde levels (MDA), and superoxide dismutase (SOD) activity. For drug-induced fibrosis modeling, cell viability assays defined 20% inhibitory concentrations of nitrofurantoin (NF, 5 μM) and amiodarone (AD, 20 μM), which were then used to treat MiLO for assessment of MDA, invasion area on collagen-coated plates, and expression of fibrotic and signaling markers.ResultsMiLOs faithfully recapitulated native lung architecture, extracellular matrix composition, and fibrosis-related gene expression profiles. In vivo cadmium exposure increased α-SMA expression, airway resistance, collagen content, and malondialdehyde (MDA) levels, while reducing superoxide dismutase (SOD) activity. Consistently, Cd- treated MiLOs exhibited increases in COL1A1 deposition, cellular invasion, hydroxyproline content, and oxidative stress. Exposure to nitrofurantoin (NF) or amiodarone (AD) elevated MDA, enhanced invasion, and upregulated fibrogenic and signaling genes including Tgfb1, Col1a1, Acta2, Akt1, Nfkb1, and Mmp9. Environmental toxicant (Cd) and drug (AD or NF) treatments drove the development of hallmark fibrotic features in lung organoids, characterized by increased collagen deposition, oxidative stress, and profibrotic gene activation.ConclusionsThese findings demonstrate that mouse lung organoids effectively recapitulate key molecular and pathological aspects of drug- and toxin-induced pulmonary fibrosis and represent a powerful model for mechanistic investigation and preclinical screening of compounds with potential pro-fibrotic effects.

Kristan H Cleveland, R. Schnellmann

Diabetic kidney disease (DKD) is the leading cause of end-stage renal disease (ESRD) in the United States and many other countries. DKD occurs through a variety of pathogenic processes that are in part driven by hyperglycemia and glomerular hypertension, leading to gradual loss of kidney function and eventually progressing to ESRD. In type 2 diabetes, chronic hyperglycemia and glomerular hyperfiltration leads to glomerular and proximal tubular dysfunction. Simultaneously, mitochondrial dysfunction occurs in the early stages of hyperglycemia and has been identified as a key event in the development of DKD. Clinical management for DKD relies primarily on blood pressure and glycemic control through the use of numerous therapeutics that slow disease progression. Because mitochondrial function is key for renal health over time, therapeutics that improve mitochondrial function could be of value in different renal diseases. Increasing evidence supports the idea that targeting aspects of mitochondrial dysfunction, such as mitochondrial biogenesis and dynamics, restores mitochondrial function and improves renal function in DKD. We will review mitochondrial function in DKD and the effects of current and experimental therapeutics on mitochondrial biogenesis and homeostasis in DKD over time. Significance Statement Diabetic kidney disease (DKD) affects 20% to 40% of patients with diabetes and has limited treatment options. Mitochondrial dysfunction has been identified as a key event in the progression of DKD, and pharmacologically restoring mitochondrial function in the early stages of DKD may be a potential therapeutic strategy in preventing disease progression.

Hossein Sholehrasa, Amirhossein Ghanaatian, Doina Caragea et al.

Pharmacokinetics (PK) plays a critical role in drug development and regulatory decision-making for human and veterinary medicine, directly affecting public health through drug safety and efficacy assessments. However, PK data are often embedded in complex, heterogeneous tables with variable structures and inconsistent terminologies, posing significant challenges for automated PK data retrieval and standardization. AutoPK, a novel two-stage framework for accurate and scalable extraction of PK data from complex scientific tables. In the first stage, AutoPK identifies and extracts PK parameter variants using large language models (LLMs), a hybrid similarity metric, and LLM-based validation. The second stage filters relevant rows, converts the table into a key-value text format, and uses an LLM to reconstruct a standardized table. Evaluated on a real-world dataset of 605 PK tables, including captions and footnotes, AutoPK shows significant improvements in precision and recall over direct LLM baselines. For instance, AutoPK with LLaMA 3.1-70B achieved an F1-score of 0.92 on half-life and 0.91 on clearance parameters, outperforming direct use of LLaMA 3.1-70B by margins of 0.10 and 0.21, respectively. Smaller models such as Gemma 3-27B and Phi 3-12B with AutoPK achieved 2-7 fold F1 gains over their direct use, with Gemma's hallucination rates reduced from 60-95% down to 8-14%. Notably, AutoPK enabled open-source models like Gemma 3-27B to outperform commercial systems such as GPT-4o Mini on several PK parameters. AutoPK enables scalable and high-confidence PK data extraction, making it well-suited for critical applications in veterinary pharmacology, drug safety monitoring, and public health decision-making, while addressing heterogeneous table structures and terminology and demonstrating generalizability across key PK parameters. Code and data: https://github.com/hosseinsholehrasa/AutoPK

Ali Essam Ghareeb, Benjamin Chang, Ludovico Mitchener et al.

Scientific discovery is driven by the iterative process of background research, hypothesis generation, experimentation, and data analysis. Despite recent advancements in applying artificial intelligence to scientific discovery, no system has yet automated all of these stages in a single workflow. Here, we introduce Robin, the first multi-agent system capable of fully automating the key intellectual steps of the scientific process. By integrating literature search agents with data analysis agents, Robin can generate hypotheses, propose experiments, interpret experimental results, and generate updated hypotheses, achieving a semi-autonomous approach to scientific discovery. By applying this system, we were able to identify a novel treatment for dry age-related macular degeneration (dAMD), the major cause of blindness in the developed world. Robin proposed enhancing retinal pigment epithelium phagocytosis as a therapeutic strategy, and identified and validated a promising therapeutic candidate, ripasudil. Ripasudil is a clinically-used rho kinase (ROCK) inhibitor that has never previously been proposed for treating dAMD. To elucidate the mechanism of ripasudil-induced upregulation of phagocytosis, Robin then proposed and analyzed a follow-up RNA-seq experiment, which revealed upregulation of ABCA1, a critical lipid efflux pump and possible novel target. All hypotheses, experimental plans, data analyses, and data figures in the main text of this report were produced by Robin. As the first AI system to autonomously discover and validate a novel therapeutic candidate within an iterative lab-in-the-loop framework, Robin establishes a new paradigm for AI-driven scientific discovery.

Wangdan Liao, Weidong Wang

Background: Small interfering RNA (siRNA) is a promising therapeutic agent due to its ability to silence disease-related genes via RNA interference. While traditional machine learning and early deep learning methods have made progress in predicting siRNA efficacy, there remains significant room for improvement. Advanced deep learning techniques can enhance prediction accuracy, reducing the reliance on extensive wet-lab experiments and accelerating the identification of effective siRNA sequences. This approach also provides deeper insights into the mechanisms of siRNA efficacy, facilitating more targeted and efficient therapeutic strategies. Methods: We introduce DeepSilencer, an innovative deep learning model designed to predict siRNA knockdown efficiency. DeepSilencer utilizes advanced neural network architectures to capture the complex features of siRNA sequences. Our key contributions include a specially designed deep learning model, an innovative online data sampling method, and an improved loss function tailored for siRNA prediction. These enhancements collectively boost the model's prediction accuracy and robustness. Results: Extensive evaluations on multiple test sets demonstrate that DeepSilencer achieves state-of-the-art performance using only siRNA sequences and basic physicochemical properties. Our model surpasses several other methods and shows superior predictive performance, particularly when incorporating thermodynamic parameters. Conclusion: The advancements in data sampling, model design, and loss function significantly enhance the predictive capabilities of DeepSilencer. These improvements underscore its potential to advance RNAi therapeutic design and development, offering a powerful tool for researchers and clinicians.

Swadesh Pal, Roderick Melnik

Alzheimer's disease (AD) is a complex neurodegenerative disorder characterized by the accumulation of amyloid-beta (A$β$) and phosphorylated tau (p-tau) proteins, leading to cognitive decline measured by the Alzheimer's Disease Assessment Scale (ADAS) score. In this study, we develop and analyze a system of ordinary differential equation models to describe the interactions between A$β$, p-tau, and ADAS score, providing a mechanistic understanding of disease progression. To ensure accurate model calibration, we employ Bayesian inference and Physics-Informed Neural Networks (PINNs) for parameter estimation based on Alzheimer's Disease Neuroimaging Initiative data. The data-driven Bayesian approach enables uncertainty quantification, improving confidence in model predictions, while the PINN framework leverages neural networks to capture complex dynamics directly from data. Furthermore, we implement an optimal control strategy to assess the efficacy of an anti-tau therapeutic intervention aimed at reducing p-tau levels and mitigating cognitive decline. Our data-driven solutions indicate that while optimal drug administration effectively decreases p-tau concentration, its impact on cognitive decline, as reflected in the ADAS score, remains limited. These findings suggest that targeting p-tau alone may not be sufficient for significant cognitive improvement, highlighting the need for multi-target therapeutic strategies. The integration of mechanistic modelling, advanced parameter estimation, and control-based therapeutic optimization provides a comprehensive framework for improving treatment strategies for AD.

Yu Liao, Mingchao Wang, Fuli Qin et al.

IntroductionEvidence of the benefits of cordycepin (Cpn) for treating obesity is accumulating, but detailed knowledge of its therapeutic targets and mechanisms remains limited. This study aimed to systematically identify Cpn’s therapeutic targets and pathways in Western diet (WD)-induced obesity using integrated network pharmacology, transcriptomics, and experimental validation.MethodsA Western diet (WD)-induced mice model was used to evaluate the effectiveness of Cpn in ameliorating obesity. A network pharmacology analysis was then employed to identify the potential anti-obesity targets of Cpn. GO functional enrichment and KEGG pathway analysis were performed to elucidate the potential functions of the identified targets, followed by constructing a protein-protein interaction network to screen the core targets. Meanwhile, quantitative transcriptomics was conducted to validate and broaden the network pharmacology findings. Finally, molecular docking and quantitative real-time PCR assay were used for the core target validation.ResultsCpn treatment effectively alleviated obesity-related symptoms in WD-induced mice. The metabolic pathway, insulin signaling pathway, HIF-1 signaling pathway, FoxO signaling pathway, lipid and atherosclerosis pathway, and core targets including CPS1, HRAS, MAPK14, PAH, ALDOB, AKT1, GSK3B, HSP90AA1, BHMT2, EGFR, CASP3, MAT1A, APOM, APOA2, APOC3, and APOA1 are involved in regulating the therapeutic effect of Cpn.ConclusionThis study comprehensively uncovers the potential mechanism of Cpn against obesity based on network pharmacology and quantitative transcriptomics, which provides evidence for revealing the pathogenesis of obesity, suggesting that Cpn is a possible lead compound for anti-obesity treatment.

Yaorui Shi, Sihang Li, Taiyan Zhang et al.

Automated drug discovery offers significant potential for accelerating the development of novel therapeutics by substituting labor-intensive human workflows with machine-driven processes. However, molecules generated by artificial intelligence may unintentionally infringe on existing patents, posing legal and financial risks that impede the full automation of drug discovery pipelines. This paper introduces PatentFinder, a novel multi-agent and tool-enhanced intelligence system that can accurately and comprehensively evaluate small molecules for patent infringement. PatentFinder features five specialized agents that collaboratively analyze patent claims and molecular structures with heuristic and model-based tools, generating interpretable infringement reports. To support systematic evaluation, we curate MolPatent-240, a benchmark dataset tailored for patent infringement assessment algorithms. On this benchmark, PatentFinder outperforms baseline methods that rely solely on large language models or specialized chemical tools, achieving a 13.8% improvement in F1-score and a 12% increase in accuracy. Additionally, PatentFinder autonomously generates detailed and interpretable patent infringement reports, showcasing enhanced accuracy and improved interpretability. The high accuracy and interpretability of PatentFinder make it a valuable and reliable tool for automating patent infringement assessments, offering a practical solution for integrating patent protection analysis into the drug discovery pipeline.

Gregory W. Kyro, Matthew T. Martin, Eric D. Watt et al.

The link between in vitro hERG ion channel inhibition and subsequent in vivo QT interval prolongation, a critical risk factor for the development of arrythmias such as Torsade de Pointes, is so well established that in vitro hERG activity alone is often sufficient to end the development of an otherwise promising drug candidate. It is therefore of tremendous interest to develop advanced methods for identifying hERG-active compounds in the early stages of drug development, as well as for proposing redesigned compounds with reduced hERG liability and preserved on-target potency. In this work, we present CardioGenAI, a machine learning-based framework for re-engineering both developmental and commercially available drugs for reduced hERG activity while preserving their pharmacological activity. The framework incorporates novel state-of-the-art discriminative models for predicting hERG channel activity, as well as activity against the voltage-gated NaV1.5 and CaV1.2 channels due to their potential implications in modulating the arrhythmogenic potential induced by hERG channel blockade. We applied the complete framework to pimozide, an FDA-approved antipsychotic agent that demonstrates high affinity to the hERG channel, and generated 100 refined candidates. Remarkably, among the candidates is fluspirilene, a compound which is of the same class of drugs (diphenylmethanes) as pimozide and therefore has similar pharmacological activity, yet exhibits over 700-fold weaker binding to hERG. We envision that this method can effectively be applied to developmental compounds exhibiting hERG liabilities to provide a means of rescuing drug development programs that have stalled due to hERG-related safety concerns. We have made all of our software open-source to facilitate integration of the CardioGenAI framework for molecular hypothesis generation into drug discovery workflows.

Alex Morehead, Jianlin Cheng

Powerful generative AI models of protein-ligand structure have recently been proposed, but few of these methods support both flexible protein-ligand docking and affinity estimation. Of those that do, none can directly model multiple binding ligands concurrently or have been rigorously benchmarked on pharmacologically relevant drug targets, hindering their widespread adoption in drug discovery efforts. In this work, we propose FlowDock, the first deep geometric generative model based on conditional flow matching that learns to directly map unbound (apo) structures to their bound (holo) counterparts for an arbitrary number of binding ligands. Furthermore, FlowDock provides predicted structural confidence scores and binding affinity values with each of its generated protein-ligand complex structures, enabling fast virtual screening of new (multi-ligand) drug targets. For the well-known PoseBusters Benchmark dataset, FlowDock outperforms single-sequence AlphaFold 3 with a 51% blind docking success rate using unbound (apo) protein input structures and without any information derived from multiple sequence alignments, and for the challenging new DockGen-E dataset, FlowDock outperforms single-sequence AlphaFold 3 and matches single-sequence Chai-1 for binding pocket generalization. Additionally, in the ligand category of the 16th community-wide Critical Assessment of Techniques for Structure Prediction (CASP16), FlowDock ranked among the top-5 methods for pharmacological binding affinity estimation across 140 protein-ligand complexes, demonstrating the efficacy of its learned representations in virtual screening. Source code, data, and pre-trained models are available at https://github.com/BioinfoMachineLearning/FlowDock.

Gustaf J. Wellhagen, Ashraf Yassen, Dirk Garmann et al.

Abstract Item response theory (IRT) models are usually the best way to analyze composite or rating scale data. Standard methods to evaluate covariate or treatment effects in IRT models do not allow to identify item‐specific effects. Finding subgroups of patients who respond differently to certain items could be very important when designing inclusion or exclusion criteria for clinical trials, and aid in understanding different treatment responses in varying disease manifestations. We present a new method to investigate item‐specific effects in IRT models, which is based on inspection of residuals. The method was investigated in a simulation exercise with a model for the Epworth Sleepiness Scale. We also provide a detailed discussion as a guidance on how to build a robust covariate IRT model.

Aliki Peletidi, Vasilis Birlirakis, Michael Petrides

Obukohwo Mega Oyovwi, Benneth Ben-Azu, Edesiri Prince Tesi et al.

Oxidative stress and aberrant inflammatory response have important implications in cyclosporin-induced reproductive functions. Previous studies have shown that agents with antioxidant and anti-inflammatory activities might be beneficial in reversing cyclosporin-induced reproductive impairment. Lutein is a naturally occurring compound with antioxidant and anti-inflammatory properties. However, the effect of lutein against cyclosporin-induced reproductive impairment remains in complete. Hence, we investigated the protective effect of lutein, specifically focusing on the role of nuclear factor erythroid 2 related factor-2 (Nrf2)/heme-oxygenase-1 (HO-1)/connexin-43 (Cx-43) upregulation system against cyclosporine-induced reproductive impairment. Six male Wistar rats were allotted into 5 groups and given daily gavage of cyclosporine (40 mg/kg) and/or lutein (30 mg/kg) for four (4) weeks or in combination, respectively. The testicular antioxidant scaffolds: superoxide dismutase (SOD), catalase (CAT), reduced glutathione (GSH), add to sulfhydryl (T-SH), non-protein sulfhydryl (NP-SH), glutathione reductase (GR), glutathione-S -transferase (GST), glutathione peroxidase (GSH-Px), thiobarbituric acid reactive substances (TBARS), myeloperoxidase (MPO), testicular proinflammatory cytokines, apoptotic related protein, nucleic acids, sialic acid, testicular proton pump ATPase, stress responsive protein, BTB-related protein and total protein levels in the testes were assayed thereafter. Cyclosporin significantly increased NOX-1, TNF-α, IL-1β, MPO, caspase-3 and -9 levels, which were reversed by lutein. Lutein reversed cyclosporin-induced decreases in Nrf2, HO-1, BCL-2, cytochrome C, with corresponding increase in CAT, SOD, GSH, T-SH, NP-SH, GST, GR, GSH-Px, and Cx-43 levels compared to cyclosporin groups. Lutein also abates cyclosporin-induced alterations Na + -K + -ATPase activities. Our findings showed that lutein's protective effect against cyclosporin-induced reproductive impairment might be associated with mechanisms linked to its antioxidant, anti-apoptotic, and anti-inflammatory properties, notably through up-regulation of Nrf2/HO-1/Cx-43 signaling and down-regulation of NOX-1 signaling.

Zaixi Zhang, Zepu Lu, Zhongkai Hao et al.

The design of \emph{de novo} functional proteins that bind specific ligand molecules is paramount in therapeutics and bio-engineering. A critical yet formidable task in this endeavor is the design of the protein pocket, which is the cavity region of the protein where the ligand binds. Current methods are plagued by inefficient generation, inadequate context modeling of the ligand molecule, and the inability to generate side-chain atoms. Here, we present the Full-Atom Iterative Refinement (FAIR) method, designed to address these challenges by facilitating the co-design of protein pocket sequences, specifically residue types, and their corresponding 3D structures. FAIR operates in two steps, proceeding in a coarse-to-fine manner (transitioning from protein backbone to atoms, including side chains) for a full-atom generation. In each iteration, all residue types and structures are simultaneously updated, a process termed full-shot refinement. In the initial stage, the residue types and backbone coordinates are refined using a hierarchical context encoder, complemented by two structure refinement modules that capture both inter-residue and pocket-ligand interactions. The subsequent stage delves deeper, modeling the side-chain atoms of the pockets and updating residue types to ensure sequence-structure congruence. Concurrently, the structure of the binding ligand is refined across iterations to accommodate its inherent flexibility. Comprehensive experiments show that FAIR surpasses existing methods in designing superior pocket sequences and structures, producing average improvement exceeding 10\% in AAR and RMSD metrics. FAIR is available at \url{https://github.com/zaixizhang/FAIR}.