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BACKGROUND: Heart failure (HF) is a complex cardiovascular syndrome with high mortality. Santalum album L. (SAL) is a traditional Chinese medicine broadly applied for various diseases treatment including HF. However, the potential active compounds and molecular mechanisms of SAL in HF treatment are not well understood.
METHODS: The active compounds and possible mechanisms of action of SAL were analyzed and validated by a systems pharmacology framework and an ISO-induced mouse HF model.
RESULTS: We initially confirmed that SAL alleviates heart damage in ISO-induced HF model. A total of 17 potentially active components in SAL were identified, with Luteolin (Lut) and Syringaldehyde (SYD) in SAL been identified as the most effective combination through probabilistic ensemble aggregation (PEA) analysis. These compounds, individually and in their combination (COMB), showed significant therapeutic effects on HF by targeting multiple pathways involved in anti-oxidation, anti-inflammation, and anti-apoptosis. The active ingredients in SAL effectively suppressed inflammatory mediators and pro-apoptotic proteins while enhancing the expression of anti-apoptotic factors and antioxidant markers. Furthermore, the synergistic effects of SAL on YAP and PI3K-AKT signaling pathways were further elucidated.
CONCLUSIONS: Mechanistically, the anti-HF effect of SAL is responsible for the synergistic effect of anti-inflammation, antioxidation and anti-apoptosis, delineating a multi-targeted therapeutic strategy for HF.

Background: The Juan-Bi decoction (JBD) is a classic traditional Chinese medicines (TCMs) prescription for the treatment of rheumatoid arthritis (RA). However, the active compounds of the JBD in RA treatment remain unclear. Aim: The aim of this study is to screen effective compounds in the JBD for RA treatment using systems pharmacology and experimental approaches. Method: Botanical drugs and compounds in the JBD were acquired from multiple public TCM databases. All compounds were initially screened using absorption, distribution, metabolism, excretion, and toxicity (ADMET) and physicochemical properties, and then a target prediction was performed. RA pathological genes were acquired from the DisGeNet database. Potential active compounds were screened by constructing a compound-target-pathogenic gene (C-T-P) network and calculating the cumulative interaction intensity of the compounds on pathogenic genes. The effectiveness of the compounds was verified using lipopolysaccharide (LPS)-induced RAW.264.7 cells and collagen-induced arthritis (CIA) mouse models. Results: We screened 15 potentially active compounds in the JBD for RA treatment. These compounds primarily act on multiple metabolic pathways, immune pathways, and signaling transduction pathways. Furthermore, in vivo and in vitro experiments showed that bornyl acetate (BAC) alleviated joint damage, and inflammatory cells infiltrated and facilitated a smooth cartilage surface via the suppression of the steroid hormone biosynthesis. Conclusion: We screened potential compounds in the JBD for the treatment of RA using systems pharmacology approaches. In particular, BAC had an anti-rheumatic effect, and future studies are required to elucidate the underlying mechanisms.

UNASSIGNED: Despite the availability of around 30 antiseizure medications, 1/3 of patients with epilepsy fail to become seizure-free upon pharmacological treatment. Available medications provide adequate symptomatic control in two-thirds of patients, but disease-modifying drugs are still scarce. Recently, though, new paradigms have been explored.
UNASSIGNED: Three areas are reviewed in which a high degree of innovation in the search for novel antiseizure and antiepileptogenic medications has been implemented: development of novel screening approaches, search for novel therapeutic targets, and adoption of new drug discovery paradigms aligned with a systems pharmacology perspective.
UNASSIGNED: In the past, worldwide leaders in epilepsy have reiteratively stated that the lack of progress in the field may be explained by the recurrent use of the same molecular targets and screening procedures to identify novel medications. This landscape has changed recently, as reflected by the new Epilepsy Therapy Screening Program and the introduction of many in vitro and in vivo models that could possibly improve our chances of identifying first-in-class medications that may control drug-resistant epilepsy or modify the course of disease. Other milestones include the study of new molecular targets for disease-modifying drugs and exploration of a systems pharmacology perspective to design new drugs.

Over the past 20 years, quantitative proteomics has contributed a wealth of protein expression data, which are currently used for a variety of systems pharmacology applications, as a complement or a surrogate for activity of the corresponding proteins. A symposium at the 25th North American ISSX meeting, in Boston, in September 2023, was held to explore current and emerging applications of quantitative proteomics in translational pharmacology and strategies for improved integration into model-informed drug development based on practical experience of each of the presenters. A summary of the talks and discussions is presented in this perspective alongside future outlooks that were outlined for future meetings. Significance Statement This perspective explores current and emerging applications of quantitative proteomics in translational pharmacology and precision medicine, and outlines outlooks for improved integration into model-informed drug development.

OBJECTIVE: Cotadutide is a dual GLP-1 and glucagon receptor agonist with balanced agonistic activity at each receptor designed to harness the advantages on promoting liver health, weight loss and glycaemic control. We characterised the effects of cotadutide on glucose, insulin, GLP-1, GIP, and glucagon over time in a quantitative manner using our glucose dynamics systems model (4GI systems model), in combination with clinical data from a multiple ascending dose/Phase 2a (MAD/Ph2a) study in overweight and obese subjects with a history of Type 2 diabetes mellitus (NCT02548585).
METHODS: The cotadutide PK-4GI systems model was calibrated to clinical data by re-estimating only food related parameters. In vivo cotadutide efficacy was scaled based on in vitro potency. The model was used to explore the effect of weight loss on insulin sensitivity and predict the relative contribution of the GLP-1 and glucagon receptor agonistic effects on glucose.
RESULTS: Cotadutide MAD/Ph2a clinical endpoints were successfully predicted. The 4GI model captured a positive effect of weight loss on insulin sensitivity and showed that the stimulating effect of glucagon on glucose production counteracts the GLP-1 receptor-mediated decrease in glucose, resulting in a plateau for glucose decrease around a 200-μg cotadutide dose.
CONCLUSIONS: The 4GI quantitative systems pharmacology model was able to predict the clinical effects of cotadutide on glucose, insulin, GLP-1, glucagon and GIP given known in vitro potency. The analyses demonstrated that the quantitative systems pharmacology model, and its successive refinements, will be a valuable tool to support the clinical development of cotadutide and related compounds.

Rationale: Liver cirrhosis is known to affect drug pharmacokinetics, but the functional assessment of the underlying pathophysiological alterations in drug metabolism is difficult. Methods: Cirrhosis in mice was induced by repeated treatment with carbon tetrachloride for 12 months. A cocktail of six drugs was administered, and parent compounds as well as phase I and II metabolites were quantified in blood, bile, and urine in a time-dependent manner. Pharmacokinetics were modeled in relation to the altered expression of metabolizing enzymes. In discrepancy with computational predictions, a strong increase of glucuronides in blood was observed in cirrhotic mice compared to vehicle controls. Results: The deviation between experimental findings and computational simulations observed by analyzing different hypotheses could be explained by increased sinusoidal export and corresponded to increased expression of export carriers (Abcc3 and Abcc4). Formation of phase I metabolites and clearance of the parent compounds were surprisingly robust in cirrhosis, although the phase I enzymes critical for the metabolism of the administered drugs in healthy mice, Cyp1a2 and Cyp2c29, were downregulated in cirrhotic livers. RNA-sequencing revealed the upregulation of numerous other phase I metabolizing enzymes which may compensate for the lost CYP isoenzymes. Comparison of genome-wide data of cirrhotic mouse and human liver tissue revealed similar features of expression changes, including increased sinusoidal export and reduced uptake carriers. Conclusion: Liver cirrhosis leads to increased blood concentrations of glucuronides because of increased export from hepatocytes into the sinusoidal blood. Although individual metabolic pathways are massively altered in cirrhosis, the overall clearance of the parent compounds was relatively robust due to compensatory mechanisms.

The therapeutic effects of traditional Chinese medicine (TCM) involve intricate interactions among multiple components and targets. Currently, computational approaches play a pivotal role in simulating various pharmacological processes of TCM. The application of network analysis in TCM research has provided an effective means to explain the pharmacological mechanisms underlying the actions of herbs or formulas through the lens of biological network analysis. Along with the advances of network analysis, computational science has coalesced around the core chain of TCM research: formula-herb-component-target-phenotype-ZHENG, facilitating the accumulation and organization of the extensive TCM-related data and the establishment of relevant databases. Nonetheless, recent years have witnessed a tendency toward homogeneity in the development and application of these databases. Advancements in computational technologies, including deep learning and foundation model, have propelled the exploration and modeling of intricate systems into a new phase, potentially heralding a new era. This review aims to delves into the progress made in databases related to six key entities: formula, herb, component, target, phenotype, and ZHENG. Systematically discussions on the commonalities and disparities among various database types were presented. In addition, the review raised the issue of research bottleneck in TCM computational pharmacology and envisions the forthcoming directions of computational research within the realm of TCM.

Glioblastoma is the most common and aggressive form of primary brain cancer and the lack of viable treatment options has created an urgency to develop novel treatments. Personalized or predictive medicine is still in its infancy stage at present. This research aimed to discover biomarkers to inform disease progression and to develop personalized prophylactic and therapeutic strategies by combining state-of-the-art technologies such as single-cell RNA sequencing, systems pharmacology, and a polypharmacological approach. As predicted in the pyroptosis-related gene (PRG) transcription factor (TF) microRNA (miRNA) regulatory network, TP53 was the hub gene in the pyroptosis process in glioblastoma (GBM). A LASSO Cox regression model of pyroptosis-related genes was built to accurately and conveniently predict the one-, two-, and three-year overall survival rates of GBM patients. The top-scoring five natural compounds were parthenolide, rutin, baeomycesic acid, luteolin, and kaempferol, which have NFKB inhibition, antioxidant, lipoxygenase inhibition, glucosidase inhibition, and estrogen receptor agonism properties, respectively. In contrast, the analysis of the cell-type-specific differential expression-related targets of natural compounds showed that the top five subtype cells targeted by natural compounds were endothelial cells, microglia/macrophages, oligodendrocytes, dendritic cells, and neutrophil cells. The current approach-using the pharmacogenomic analysis of combined therapies-serves as a model for novel personalized therapeutic strategies for GBM treatment.

Melanoma presents increasing prevalence and poor outcomes. Progression to aggressive stages is characterized by overexpression of the transcription factor E2F1 and activation of downstream prometastatic gene regulatory networks (GRNs). Appropriate therapeutic manipulation of the E2F1-governed GRNs holds the potential to prevent metastasis however, these networks entail complex feedback and feedforward regulatory motifs among various regulatory layers, which make it difficult to identify druggable components. To this end, computational approaches such as mathematical modeling and virtual screening are important tools to unveil the dynamics of these signaling networks and identify critical components that could be further explored as therapeutic targets. Herein, we integrated a well-established E2F1-mediated epithelial-mesenchymal transition (EMT) map with transcriptomics data from E2F1-expressing melanoma cells to reconstruct a core regulatory network underlying aggressive melanoma. Using logic-based in silico perturbation experiments of a core regulatory network, we identified that simultaneous perturbation of Protein kinase B (AKT1) and oncoprotein murine double minute 2 (MDM2) drastically reduces EMT in melanoma. Using the structures of the two protein signatures, virtual screening strategies were performed with the FDA-approved drug library. Furthermore, by combining drug repurposing and computer-aided drug design techniques, followed by molecular dynamics simulation analysis, we identified two potent drugs (Tadalafil and Finasteride) that can efficiently inhibit AKT1 and MDM2 proteins. We propose that these two drugs could be considered for the development of therapeutic strategies for the management of aggressive melanoma.