The System Research of the Molecular Mechanism of Quyushengxin Capsule in the Treatment of Osteonecrosis of the Femoral Head

Osteonecrosis of the femoral head (ONFH) is a chronic and irreversible disease that has a risk of eventually developing into a joint collapse and resulting in joint dysfunction. Quyushengxin capsule (QYSXC) is an effective and safe traditional Chinese medicine used in the treatment of ONFH. In this present study, an integrated approach was used to investigate the mechanism of QYSXC in the treatment of ONFH, which contained systems pharmacology, molecular docking, and chip experiment. In the systems pharmacology, target fishing, protein-protein interaction (PPI), Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analysis, and herbs-compounds-targets-pathways (H-C-T-P) network construction were performed to study the mechanism of QYSXC in the treatment of ONFH. The results showed that 15 key compounds, 8 key targets, and 8 key signaling pathways were found for QYSXC in the treatment with ONFH. Then, molecular docking was performed to further explore the interaction between some key compounds and key targets. After that, the chip experiment was performed to verify some target factors, including ICAM-1, IL-6, IL-1α, IL-1β, IL-2, IL-4, IL-10, and TNF-α. The results of this work may provide a theoretical basis for further research on the molecular mechanism of QYSXC in the treatment of ONFH.

Combination of Systems Pharmacology and Experimental Evaluation to Explore the Mechanism of Synergistic Action of Frankincense-Myrrh in the Treatment of Cerebrovascular Diseases

Frankincense-Myrrh is a classic drug pair that promotes blood circulation, and eliminates blood stasis. The combination of the two drugs has a definite clinical effect on the treatment of cerebrovascular diseases (CBVDs), but its mechanism of action and compatibility have not been elucidated. In this study, the bioactive components, core targets, and possible synergistic mechanisms of Frankincense-Myrrh in the treatment of CBVDs are explored through systems pharmacology combined with in vivo and in vitro experiments. Comparing target genes of components in Frankincense and Myrrh with CBVD-related genes, common genes were identified; 15 core target genes of Frankincense-Myrrh for the treatment of CBVDs were then identified using protein-protein interaction (PPI) analysis. It was also predicted through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis that the molecular mechanism of Frankincense-Myrrh action on CBVDs was mainly related to the regulation of neurotrophic factors and inflammatory responses. Frankincense-Myrrh significantly improved neurological function, decreased infarct volume, alleviated histopathological damage, inhibited microglial expression, and promoted the expression of neurons in middle cerebral artery occlusion (MCAO)-induced rats. The results of this study not only provide important theoretical support and experimental basis for the synergistic effect of Frankincense-Myrrh, but also provide new ideas for the prevention and treatment of cerebral ischemic injuries.

Understanding virtual patients efficiently and rigorously by combining machine learning with dynamical modelling

Individual biological organisms are characterized by daunting heterogeneity, which precludes describing or understanding populations of ‘patients’ with a single mathematical model. Recently, the field of quantitative systems pharmacology (QSP) has adopted the notion of virtual patients (VPs) to cope with this challenge. A typical population of VPs represents the behavior of a heterogeneous patient population with a distribution of parameter values over a mathematical model of fixed structure. Though this notion of VPs is a powerful tool to describe patients’ heterogeneity, the analysis and understanding of these VPs present new challenges to systems pharmacologists. Here, using a model of the hypothalamic–pituitary–adrenal axis, we show that an integrated pipeline that combines machine learning (ML) and bifurcation analysis can be used to effectively and efficiently analyse the behaviors observed in populations of VPs. Compared with local sensitivity analyses, ML allows us to capture and analyse the contributions of simultaneous changes of multiple model parameters. Following up with bifurcation analysis, we are able to provide rigorous mechanistic insight regarding the influences of ML-identified parameters on the dynamical system’s behaviors. In this work, we illustrate the utility of this pipeline and suggest that its wider adoption will facilitate the use of VPs in the practice of systems pharmacology.

Exploring the Potential Mechanism of Chuanxiong Rhizoma Treatment for Migraine Based on Systems Pharmacology

Migraine is a disease whose aetiology and mechanism are not yet clear. Chuanxiong Rhizoma (CR) is employed in traditional Chinese medicine (TCM) to treat various disorders. CR is effective for migraine, but its active compounds, drug targets, and exact molecular mechanism remain unclear. In this study, we used the method of systems pharmacology to address the above issues. We first established the drug-compound-target-disease (D-C-T-D) network and protein-protein interaction (PPI) network related to the treatment of migraine with CR and then established gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. The results suggest that the treatment process may be related to the regulation of inflammation and neural activity. The docking results also revealed that PTGS2 and TRPV1 could directly bind to the active compounds that could regulate them. In addition, we found that CR affected 11 targets that were more highly expressed in the liver or heart but were the lowest in the whole brain. It also expounds the description of CR channel tropism in TCM theory from these angles. These findings not only indicate that CR can be developed as a potential effective drug for the treatment of migraine but also demonstrate the application of systems pharmacology in the discovery of herbal-based disease therapies.

Brief Report: Higher Fentanyl Exposures Require Higher Doses of Naloxone for Successful Reversals in a Quantitative Systems Pharmacology Model

Background: Previously, we reported on an opioid receptor quantitative systems pharmacology (QSP) model to evaluate naloxone dosing. Methods: In this study we extended our model to include higher systemic levels of fentanyl (up to 100 ng/ml) and the newly approved 8mg IN naloxone dose (equivalent to 4 mg)Results : As expected, at the lower peak fentanyl concentrations (25 ng/ml and 50 ng/ml), the simulations predicted that 2 mg, 4 mg, 5 mg, and 10 mg IM doses of naloxone displaced fentanyl and reached below the 50% receptor occupancy within 10 minutes. However, at the concentration of 75 ng/ml, the simulation predicted that the 2 mg dose of naloxone failed to reach below the 50% occupancy within 10 minutes. Interestingly, at the highest peak concentration of fentanyl studied (100 ng/ml), the model predicted that the 4 mg of naloxone IM (equivalent to 8 mg IN) failed to reach below the threshold of 50 % occupancy within 10 minutes or even within 15 minutes (Data not shown). In contrast, the model predicted successful reversals when 5 and 10 mg IM doses were utilized. Conclusion:These results support the notion that acutely administered higher doses of naloxone are needed for rapid and adequate clinical reversal, particularly when higher systemic exposure of the potent synthetic opioids occur.

Computational Models for Diagnosing and Treating Endometriosis

Endometriosis is a common but poorly understood disease. Symptoms can begin early in adolescence, with menarche, and can be debilitating. Despite this, people often suffer several years before being correctly diagnosed and adequately treated. Endometriosis involves the inappropriate growth of endometrial-like tissue (including epithelial cells, stromal fibroblasts, vascular cells, and immune cells) outside of the uterus. Computational models can aid in understanding the mechanisms by which immune, hormone, and vascular disruptions manifest in endometriosis and complicate treatment. In this review, we illustrate how three computational modeling approaches (regression, pharmacokinetics/pharmacodynamics, and quantitative systems pharmacology) have been used to improve the diagnosis and treatment of endometriosis. As we explore these approaches and their differing detail of biological mechanisms, we consider how each approach can answer different questions about endometriosis. We summarize the mathematics involved, and we use published examples of each approach to compare how researchers: (1) shape the scope of each model, (2) incorporate experimental and clinical data, and (3) generate clinically useful predictions and insight. Lastly, we discuss the benefits and limitations of each modeling approach and how we can combine these approaches to further understand, diagnose, and treat endometriosis.

Structural systems pharmacology: A framework for integrating metabolic network and structure-based virtual screening for drug discovery against bacteria

Advances in genome-scale metabolic models (GEMs) and computational drug discovery have caused the identification of drug targets at the system-level and inhibitors to combat bacterial infection and drug resistance. Here we report a structural systems pharmacology framework that integrates the GEM and structure-based virtual screening (SBVS) method to identify drugs effective for Escherichia coli infection. The most complete genome-scale metabolic reconstruction integrated with protein structures (GEM-PRO) of E. coli, iML1515_GP, and FDA-approved drugs have been used. FBA was performed to predict drug targets in silico. The 195 essential genes were predicted in the rich medium. The subsystems in which a significant number of these genes are involved are cofactor, lipopolysaccharide (LPS) biosynthesis that are necessary for cell growth. Therefore, some proteins encoded by these genes are responsible for the biosynthesis and transport of LPS which is the first line of defense against threats. So, these proteins can be potential drug targets. The enzymes with experimental structure and cognate ligands were selected as final drug targets for performing the SBVS method. Finally, we have suggested those drugs that have good interaction with the selected proteins as drug repositioning cases. Also, the suggested molecules could be promising lead compounds. This framework may be helpful to fill the gap between genomics and drug discovery. Results show this framework suggests novel antibacterials that can be subjected to experimental testing soon and it can be suitable for other pathogens.

A Quantitative Systems Pharmacology Model of the Pathophysiology and Treatment of COVID-19 Predicts Optimal Timing of Pharmacological Interventions

A quantitative systems pharmacology (QSP) model of the pathogenesis and treatment of SARS-CoV-2 infection can streamline and accelerate the development of novel medicines to treat COVID-19. Simulation of clinical trials allows in silico exploration of the uncertainties of clinical trial design and can rapidly inform their protocols. We previously published a preliminary model of the immune response to SARS-CoV-2 infection. To further our understanding of COVID-19 and treatment we significantly updated the model by matching a curated dataset spanning viral load and immune responses in plasma and lung. We identified a population of parameter sets to generate heterogeneity in pathophysiology and treatment and tested this model against published reports from interventional SARS-CoV-2 targeting Ab and anti-viral trials. Upon generation and selection of a virtual population, we match both the placebo and treated responses in viral load in these trials. We extended the model to predict the rate of hospitalization or death within a population. Via comparison of the in silico predictions with clinical data, we hypothesize that the immune response to virus is log-linear over a wide range of viral load. To validate this approach, we show the model matches a published subgroup analysis, sorted by baseline viral load, of patients treated with neutralizing Abs. By simulating intervention at different timepoints post infection, the model predicts efficacy is not sensitive to interventions within five days of symptom onset, but efficacy is dramatically reduced if more than five days pass post-symptom onset prior to treatment.