Systems pharmacology to predict drug safety in drug development

Astragalus polysaccharide alleviates alcoholic-induced hepatic fibrosis by inhibiting polymerase I and transcript release factor and the TLR4/JNK/NF-κB/MyD88 pathway

ETHNOPHARMACOLOGICAL RELEVANCE

Astragali Radix (AR), the root of Astragalus membranaceus (Fisch.) Bge. or Astragalus membranaceus (Fisch.) Bge. var. mongholicus (Bge.) Hsiao, known as Huangqi in traditional Chinese medicine, has been widely used in traditional Chinese medicine prescriptions for acute and chronic liver injury. AR was the most important medicine in a Chinese traditional prescription called Huangqi Decoction (HQD), has been used to treat chronic liver diseases since the 11th century. In particular, its major active ingredient, Astragalus polysaccharide (APS), has demonstrated promising effects on inhibiting hepatic fibrosis. However, to date, the effect of APS against alcohol-induced hepatic fibrosis and its underlying molecular mechanisms remains unknown.

AIMS OF THE STUDY

This study aimed to explore the effect and potential molecular mechanisms of APS against alcohol-induced hepatic fibrosis by using network pharmacology and experimental validation.

MATERIALS AND METHODS

The potential targets and underling mechanism of AR in alcoholic liver fibrosis was first predicted using network pharmacology, followed by experimental validation using SD rat model with alcohol-induced hepatic fibrosis. Further, the predicted candidate signaling pathways and potential target polymerase I and transcript release factor (PTRF) were combined to explore the multifaceted mechanism of APS against alcohol-induced hepatic fibrosis. Finally, overexpression of PTRF was explored to reveal the role of PTRF in the mechanism of APS against alcohol-induced hepatic fibrosis.

RESULT

APS exerted potent anti-hepatic fibrosis effects by downregulating genes involved in the Toll-like receptor 4 (TLR4)/JNK/NF-κB/MyD88 pathway. Notably, APS treatment ameliorated the hepatic damage by inhibiting the overexpression of PTRF and decreasing the co-localisation of TLR4/PTRF. Overexpression of PTRF induced reversal of the protective effects of APS on alcohol-induced hepatic fibrosis.

CONCLUSION

This study indicated that APS may alleviate alcohol-induced hepatic fibrosis by inhibiting the activation of PTRF/TLR4/JNK/NF-κB/MyD88 pathway, which provides a scientific elucidation for the mechanisms of APS on the anti-hepatic fibrosis activity and presents a promising therapeutic approach for treating hepatic fibrosis.

[Personalized patient-oriented approach to the treatment of musculoskeletal pain syndrome using over-the-counter medications. Resolution of the Expert Council (May 28, 2023)]

Diseases of the musculoskeletal system in Russia affect 19.2 million people. Untimely diagnosis and inadequate therapy of pain syndrome negatively affect the daily functioning and quality of life of patients, and create significant socioeconomic problems. The most common variants of musculoskeletal pain (MSP) are osteoarthritis (OA) and low back pain (LBP). OA is seen in 57.6% of individuals over 65 years of age. It should be noted that chronic pain syndrome, rather than anatomical and degenerative changes detected by imaging studies, determines to a greater extent the quality of life of patients with OA and prognosis during the course of the disease. The global burden of disability associated with LBPD increased in all age groups between 1990 and 2019 and was highest in the 50-54 age group.

Network pharmacology-based analysis of the mechanism of Saposhnikovia divaricata for the treatment of type I allergy

CONTEXT

Saposhnikovia divaricata (Turcz.) Schischk (Apiaceae) (SD) has various pharmacological activities, but its effects on type I allergy (TIA) have not been comprehensively studied.

OBJECTIVE

This study evaluates the treatment and molecular mechanisms of SD against TIA.

MATERIALS AND METHODS

The effective components and action targets of SD were screened using TCMSP database, and allergy-related targets of SD were predicted using GeneCards and OMIM database. The obtained target intersections were imported into David database for GO analysis, and used R software to perform KEGG analysis. The RBL-2H3 cells sensitised by DNP-IgE/DNP-BSA were treated with different concentrations of SD (root decoction, 0.5, 1, and 2 mg/mL), prim-O-glucosylcimifugin (POG, 10, 40, and 80 μg/mL) and the positive control drug-ketotifen fumarate (KF, 30 μM) for 12 h, then subjected to cell degranulation and qPCR analysis.

RESULTS

Eighteen active compounds of SD and 38 intersection targets were obtained: TIA-related signal pathways mainly include calcium signal pathway, PI3K-Akt signal pathway and MAPK signal pathway. Taking the β-Hex release rate of the model group as the base, the release rate of SD and POG in high dose groups were 43.79% and 57.01%, respectively, which were significantly lower than model group (p < 0.01), and significantly lower than KF group (63.83%, p < 0.01, p < 0.05). SD and POG could down-regulate the expression of related proteins in the Lyn/Syk, PI3K/AKT and MAPK signalling pathways.

DISCUSSION AND CONCLUSION

Saposhnikovia divaricata could inhibit IgE-induced degranulation of mast cells, providing a scientific basis for further research and clinical applications of SD in TIA treatment.

Marine Natural Products in Clinical Use

Marine natural products are potent and promising sources of drugs among other natural products of plant, animal, and microbial origin. To date, 20 drugs from marine sources are in clinical use. Most approved marine compounds are antineoplastic, but some are also used for chronic neuropathic pain, for heparin overdosage, as haptens and vaccine carriers, and for omega-3 fatty-acid supplementation in the diet. Marine drugs have diverse structural characteristics and mechanisms of action. A considerable increase in the number of marine drugs approved for clinical use has occurred in the past few decades, which may be attributed to increasing research on marine compounds in laboratories across the world. In the present manuscript, we comprehensively studied all marine drugs that have been successfully used in the clinic. Researchers and clinicians are hopeful to discover many more drugs, as a large number of marine natural compounds are being investigated in preclinical and clinical studies.

Advancing drug safety science by integrating molecular knowledge with post-marketing adverse event reports

Promising drug development efforts may frequently fail due to unintended adverse reactions. Several methods have been developed to analyze such data, aiming to improve pharmacovigilance and drug safety. In this work, we provide a brief review of key directions to quantitatively analyzing adverse events and explore the potential of augmenting these methods using additional molecular data descriptors. We argue that molecular expansion of adverse event data may provide a path to improving the insights gained through more traditional pharmacovigilance approaches. Examples include the ability to assess statistical relevance with respect to underlying biomolecular mechanisms, the ability to generate plausible causative hypotheses and/or confirmation where possible, the ability to computationally study potential clinical trial designs and/or results, as well as the further provision of advanced features incorporated in innovative methods, such as machine learning. In summary, molecular data expansion provides an elegant way to extend mechanistic modeling, systems pharmacology, and patient‐centered approaches for the assessment of drug safety. We anticipate that such advances in real‐world data informatics and outcome analytics will help to better inform public health, via the improved ability to prospectively understand and predict various types of drug‐induced molecular perturbations and adverse events.

Systems Biology in Periodontitis

Systems biology is a promising scientific discipline that allows an integrated investigation of host factors, microbial composition, biomarkers, immune response and inflammatory mediators in many conditions such as chronic diseases, cancer, neurological disorders, and periodontitis. This concept utilizes genetic decoding, bioinformatic, flux-balance analysis in a comprehensive approach. The aim of this review is to better understand the current literature on systems biology and identify a clear applicability of it to periodontitis. We will mostly focus on the association between this condition and topics such as genomics, transcriptomics, proteomics, metabolomics, as well as contextualize delivery systems for periodontitis treatment, biomarker detection in oral fluids and associated systemic conditions.

Systematic analysis of the potential off-target activities of osimertinib by computational target fishing

Osimertinib is a third-generation epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor used to treat non-small cell lung cancer. However, its off-targets are obscure, and systematic analysis of off-target activities remains to be performed. Here, we identified the off-targets of osimertinib using PharmMapper and DRAR-CPI and analyzed the intersected targets using the GeneMANIA and DAVID servers. A drug-target-pathway network was constructed to visualize the associations. The results showed that osimertinib is associated with 31 off-targets, 40 Kyoto Encyclopedia of Genes and Genomes pathways, and 9 diseases. Network analysis revealed that the targets were involved in cancer and other physiological processes. In addition to EGFR, molecular docking analysis showed that seven proteins, namely Janus kinase 3, peroxisome proliferator-activated receptor alpha, renin, mitogen-activated protein kinases, lymphocyte-specific protein tyrosine kinase, cell division protein kinase 2 and proto-oncogene tyrosine-protein kinase Src, could also be potential targets of osimertinib. In conclusion, osimertinib is predicted to target multiple proteins and pathways, resulting in the formation of an action network via which it exerts systematic pharmacological effects.

Exploring the Pharmacological Mechanism of Liuwei Dihuang Decoction for Diabetic Retinopathy: A Systematic Biological Strategy-Based Research

OBJECTIVE

To explore the pharmacological mechanism of Liuwei Dihuang decoction (LDD) for diabetic retinopathy (DR).

METHODS

The potential targets of LDD were predicted by PharmMapper. GeneCards and other databases were used to collect DR genes. Cytoscape was used to construct and analyze network DR and LDD's network, and DAVID was used for Gene Ontology (GO) and pathway enrichment analysis. Finally, animal experiments were carried out to verify the results of systematic pharmacology.

RESULTS

Five networks were constructed and analyzed: (1) diabetic retinopathy genes' PPI network; (2) compound-compound target network of LDD; (3) LDD-DR PPI network; (4) compound-known target network of LDD; (5) LDD known target-DR PPI network. Several DR and treatment-related targets, clusters, signaling pathways, and biological processes were found. Animal experiments found that LDD can improve the histopathological changes of the retina. LDD can also increase erythrocyte filtration rate and decrease the platelet adhesion rate (P < 0.05) and decrease MDA and TXB2 (P < 0.05). Compared with the model group, the retinal VEGF and HIF-1α expression in the LDD group decreased significantly (P < 0.05).

CONCLUSION

The therapeutic effect of LDD on DR may be achieved by interfering with the biological processes (such as response to insulin, glucose homeostasis, and regulation of angiogenesis) and signaling pathways (such as insulin, VEGF, HIF-1, and ErbB signaling pathway) related to the development of DR that was found in this research.

Multi-objective optimization methods in novel drug design

Introduction: In multi-objective drug design, optimization gains importance, being upgraded to a discipline that attracts its own research. Current strategies are broadly classified into single - objective optimization (SOO) and multi-objective optimization (MOO).Areas covered: Starting with SOO and the ways used to incorporate multiple criteria into it, the present review focuses on MOO techniques, their comparison, advantages, and restrictions. Pareto analysis and the concept of dominance stand in the core of MOO. The Pareto front, Pareto ranking, and limitations of Pareto-based methods, due to high dimensions and data uncertainty, are outlined. Desirability functions and the weighted sum approaches are described as stand-alone techniques to transform the MOO problem to SOO or in combination with pareto analysis and evolutionary algorithms. Representative applications in different drug research areas are also discussed.Expert opinion: Despite their limitations, the use of combined MOO techniques, as well as being complementary to SOO or in conjunction with artificial intelligence, contributes dramatically to efficient drug design, assisting decisions and increasing success probabilities. For multi-target drug design, optimization is supported by network approaches, while applicability of MOO to other fields like drug technology or biological complexity opens new perspectives in the interrelated fields of medicinal chemistry and molecular biology.

Quantitative Systems Pharmacology Model-Based Predictions of Clinical Endpoints to Optimize Warfarin and Rivaroxaban Anti-Thrombosis Therapy

Background

Tight monitoring of efficacy and safety of anticoagulants such as warfarin is imperative to optimize the benefit-risk ratio of anticoagulants in patients. The standard tests used are measurements of prothrombin time (PT), usually expressed as international normalized ratio (INR), and activated partial thromboplastin time (aPTT).

Objective

To leverage a previously developed quantitative systems pharmacology (QSP) model of the human coagulation network to predict INR and aPTT for warfarin and rivaroxaban, respectively.

Methods

A modeling and simulation approach was used to predict INR and aPTT measurements of patients receiving steady-state anticoagulation therapy. A previously developed QSP model was leveraged for the present analysis. The effect of genetic polymorphisms known to influence dose response of warfarin (CYP2C9, VKORC1) were implemented into the model by modifying warfarin clearance (CYP2C9 *1: 0.2 L/h; *2: 0.14 L/h, *3: 0.04 L/h) and the concentration of available vitamin K (VKORC1 GA: −22% from normal vitamin K concentration; AA: −44% from normal vitamin K concentration). Virtual patient populations were used to assess the ability of the model to accurately predict routine INR and aPTT measurements from patients under long-term anticoagulant therapy.

Results

The introduced model accurately described the observed INR of patients receiving long-term warfarin treatment. The model was able to demonstrate the influence of genetic polymorphisms of CYP2C9 and VKORC1 on the INR measurements. Additionally, the model was successfully used to predict aPTT measurements for patients receiving long-term rivaroxaban therapy.

Conclusion

The QSP model accurately predicted INR and aPTT measurements observed during routine therapeutic drug monitoring. This is an exemplar of how a QSP model can be adapted and used as a model-based precision dosing tool during clinical practice and drug development to predict efficacy and safety of anticoagulants to ultimately help optimize anti-thrombotic therapy.

A chronological pharmacovigilance network analytics approach for predicting adverse drug events

Objectives

This study extends prior research by combining a chronological pharmacovigilance network approach with machine-learning (ML) techniques to predict adverse drug events (ADEs) based on the drugs' similarities in terms of the proteins they target in the human body. The focus of this research, though, is particularly centered on predicting the drug-ADE associations for a set of 8 common and high-risk ADEs.

Materials and methods

large collection of annotated MEDLINE biomedical articles was used to construct a drug-ADE network, and the network was further equipped with information about drugs' target proteins. Several network metrics were extracted and used as predictors in ML algorithms to predict the existence of network edges (ie, associations or relationships).

Results

Gradient boosted trees (GBTs) as an ensemble ML algorithm outperformed other prediction methods in identifying the drug-ADE associations with an overall accuracy of 92.8% on the validation sample. The prediction model was able to predict drug-ADE associations, on average, 3.84 years earlier than they were actually mentioned in the biomedical literature.

Conclusion

While network analysis and ML techniques were used in separation in prior ADE studies, our results showed that they, in combination with each other, can boost the power of one another and predict better. Moreover, our results highlight the superior capability of ensemble-type ML methods in capturing drug-ADE patterns compared to the regular (ie, singular), ML algorithms.

Identified the Synergistic Mechanism of Drynariae Rhizoma for Treating Fracture Based on Network Pharmacology

BACKGROUND

Drynariae Rhizoma (DR) has been widely used in the prevention and treatment of various fractures. However, the specific mechanisms of DR's active ingredients have not been elucidated. The purpose of this study was to explore the synergistic mechanisms of DR for treating fracture.

METHODS

A network pharmacology approach integrating ingredient screening, target exploration, active ingredients-gene target network construction, protein-protein interaction network construction, molecular docking, gene-protein classification, gene ontology (GO) functional analysis, KEGG pathway enrichment analysis, and signaling pathway integration was used.

RESULTS

This approach identified 17 active ingredients of DR, interacting with 144 common gene targets and 143 protein targets of DR and fracture. NCOA1, GSK3B, TTPA, and MAPK1 were identified as important gene targets. Five most important protein targets were also identified, including MAPK1, SRC, HRAS, RXRA, and NCOA1. Molecular docking found that DR has a good binding potential with common protein targets. GO functional analysis indicated that common genes involve multiple processes, parts and functions in biological process, cellular component, and molecular function, including positive regulation of transcription from RNA polymerase II promoter, signal transduction, cytosol, extracellular exosome, cytoplasm, and protein binding. The KEGG pathway enrichment analysis indicated that common gene targets play a role in repairing fractures in multiple signaling pathways, including MAPK, PI3K/AKT, Ras, and VEGF signaling pathways. MAPK and PI3K/AKT signaling pathways were involved in osteoblast formation, Ras signaling pathway was involved in enhancing mesenchymal stromal cell migration, and VEGF signaling pathway was involved in angiogenesis.

CONCLUSION

The study revealed the correlation between DR and fracture and the potential synergistic mechanism of different targets of DR in the treatment of fractures, which provides a reference for the development of new drugs.

Computational Advances in Drug Safety: Systematic and Mapping Review of Knowledge Engineering Based Approaches

Drug Safety (DS) is a domain with significant public health and social impact. Knowledge Engineering (KE) is the Computer Science discipline elaborating on methods and tools for developing “knowledge-intensive” systems, depending on a conceptual “knowledge” schema and some kind of “reasoning” process. The present systematic and mapping review aims to investigate KE-based approaches employed for DS and highlight the introduced added value as well as trends and possible gaps in the domain. Journal articles published between 2006 and 2017 were retrieved from PubMed/MEDLINE and Web of Science® (873 in total) and filtered based on a comprehensive set of inclusion/exclusion criteria. The 80 finally selected articles were reviewed on full-text, while the mapping process relied on a set of concrete criteria (concerning specific KE and DS core activities, special DS topics, employed data sources, reference ontologies/terminologies, and computational methods, etc.). The analysis results are publicly available as online interactive analytics graphs. The review clearly depicted increased use of KE approaches for DS. The collected data illustrate the use of KE for various DS aspects, such as Adverse Drug Event (ADE) information collection, detection, and assessment. Moreover, the quantified analysis of using KE for the respective DS core activities highlighted room for intensifying research on KE for ADE monitoring, prevention and reporting. Finally, the assessed use of the various data sources for DS special topics demonstrated extensive use of dominant data sources for DS surveillance, i.e., Spontaneous Reporting Systems, but also increasing interest in the use of emerging data sources, e.g., observational healthcare databases, biochemical/genetic databases, and social media. Various exemplar applications were identified with promising results, e.g., improvement in Adverse Drug Reaction (ADR) prediction, detection of drug interactions, and novel ADE profiles related with specific mechanisms of action, etc. Nevertheless, since the reviewed studies mostly concerned proof-of-concept implementations, more intense research is required to increase the maturity level that is necessary for KE approaches to reach routine DS practice. In conclusion, we argue that efficiently addressing DS data analytics and management challenges requires the introduction of high-throughput KE-based methods for effective knowledge discovery and management, resulting ultimately, in the establishment of a continuous learning DS system.

A Network Pharmacology Approach to Explore Mechanism of Action of Longzuan Tongbi Formula on Rheumatoid Arthritis

Longzuan Tongbi Formula (LZTB) is an effective proved prescription in Zhuang medicine for treating active rheumatoid arthritis (RA). However, its active ingredients, underlying targets, and pharmacological mechanism are still not clear in treating RA. We have applied network pharmacology to study LZTB and found that 8 herbs in LZTB and 67 compounds in the 8 herbs are involved in the regulation of RA-related genes; we have conducted pathway analysis of overlapping genes and found that 7 herbs participate in the regulations of 24 pathways associated with RA and that 5 herbs in the 7 herbs and 25 compounds in the 5 herbs participate in the regulation of hsa05323 (rheumatoid arthritis). The results indicated that all herbs in LZTB and some compounds in those herbs participate in the treatment of RA; 25 compounds are main active ingredients and hsa05323 (rheumatoid arthritis) is the major pathway in the treatment of RA. We have also found that three pathways (inflammatory mediator regulation of TRP channels, PPAR signaling pathway, and mTOR signaling pathway) might have some effect on the treatment of RA.

The Role of Systems Biologic Approach in Cell Signaling and Drug Development Responses-A Mini Review

The immune system is an integral aspect of the human defense system and is primarily responsible for and involved in the communication between the immune cells. It also plays an important role in the protection of the organism from foreign invaders. Recent studies in the literature have described its role in the process of hematopoiesis, lymphocyte recruitment, T cell subset differentiation and inflammation. However, the specific molecular mechanisms underlying these observations remain elusive, impeding the elaborate manipulation of cytokine sequential delivery in tissue repair. Previously, the discovery of new drugs and systems biology went hand in hand; although Systems biology as a term has only originated in the last century. Various new chemicals were tested on the human body, and studied through observation. Animal models replaced humans for initial trials, but the interactions, response, dose and effect between animals and humans could not be directly correlated. Therefore, there is a need to form disease models outside of human subjects to check the effectiveness and response of the newer natural or synthetic chemicals. These emulate human disease conditions wherein the behavior of the chemicals would be similar in the disease model and humans.

Integrative Pharmacology: Advancing Development of Effective Immunotherapies

With the recent advances in cancer immunotherapy, it is now evident that the antigen-specific activation of the patients' immune responses can be utilized for achieving significant therapeutic benefits. Novel molecules have been developed and promising advances have been achieved in cancer therapy. The recent success of cancer immunotherapy clearly reflects the novelty of the approach and importance of this class of therapeutics. Due to the nature of immunotherapy, i.e., harnessing the patient's immune system, it becomes critical to evaluate the important variables that can guide preclinical development, translational strategies, patient selection, and effective clinical dosing paradigms following single and combination therapies. To further boost the durability and efficacy profiles of IO (immuno-oncology) drugs following single agent therapy, novel combination therapies are being sought. Combination strategies have become critical for enhancing the anti-tumor immunity in broader cancer indications. Comprehensive methods are being developed to quantify the synergistic combination effect profiles at various development phases. Further evaluation of the signaling and pathway components can potentially establish a unique "signature" characteristic for specific combination therapies following modulation of various immunomodulatory pathways. In this article, critical topics related to preclinical, translational, and clinical development of IO agents are discussed.

A network pharmacology approach to determine the synergetic mechanisms of herb couple for treating rheumatic arthritis

Purpose

The purpose of this study was to investigate the therapeutic mechanism(s) of Clematis chinensis Osbeck/Notopterygium incisum K.C. Ting ex H.T (CN).

Methods

A network pharmacology approach integrating prediction of ingredients, target exploration, network construction, module partition and pathway analysis was used.

Results

This approach successfully helped to identify 12 active ingredients of CN, interacting with 13 key targets (Akt1, STAT3, TNFsf13, TP53, EPHB2, IL-10, IL-6, TNF, MAPK8, IL-8, RELA, ROS1 and STAT4). Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis indicated that CN-regulated pathways were mainly classified into signal transduction and immune system.

Conclusion

The present work may help to illustrate the mechanism(s) of action of CN, and it may provide a better understanding of antirheumatic effects.

Exploring the Pharmacological Mechanism of Danzhi Xiaoyao Powder on ER-Positive Breast Cancer by a Network Pharmacology Approach

Background

Breast cancer is the most common malignancy among women worldwide, but the long-term endocrine therapy is frequently associated with adverse side effects. Danzhi Xiaoyao powder (DXP) is a herbal formula that has an effect on breast cancer, especially ER-positive breast cancer. However, the active compounds, potential targets, and pharmacological and molecular mechanism of its action against cancer remain unclear.

Methods

A network pharmacology approach comprising drug-likeness evaluation, oral bioavailability prediction, Caco-2 permeability prediction, multiple compound target prediction, multiple known target collection, breast cancer genes collection, and network analysis has been used in this study.

Results

Four networks are set up—namely, ER-positive breast cancer network, compound-compound target network of DXP, DXP-ER-positive breast cancer network, and compound-known target-ER-positive breast cancer network. Some ER-positive breast cancer and DXP related targets, clusters, biological processes, and pathways, and several potential anticancer compounds are found.

Conclusion

This network analysis successfully predicted, illuminated, and confirmed the molecular synergy of DXP for ER-positive breast cancer, got potential anticancer active compounds, and found the potential ER-positive breast cancer associated targets, cluster, biological processes, and pathways. This work also provides clues to the researcher who explores ethnopharmacological or/and herbal medicine's or even multidrugs' various synergies.

Advances in Drug Discovery and Development in Geriatric Psychiatry

PURPOSE OF REVIEW

This article reviews recent advances in drug discovery and development for geriatric psychiatry. Drug discovery for disorders of the central nervous system is a long and challenging process, with a high attrition rate from the preclinical stages through to marketing a compound. Developing drugs for geriatric neuropsychiatric conditions presents additional challenges, due to the complexity of the symptoms, comorbid diagnoses, and the variability of the population. Despite there being limited success over the past two decades, a number of new approaches have identified potential targets for preclinical development and ultimately clinical testing.

RECENT FINDINGS

Recent approaches have tried to address specific mechanisms that relate to the disease progression. These approaches include combining a number of ligands into to multi-target compounds, or targeting specific types of cells such as protein kinases or myeloid cells. In addition, the increased use of induced pluripotent stem cell cultures has enabled new compounds to be tested on disease-specific tissues, increasing the success rate of the lead compounds going through the preclinical stages. New pharmacological agents designed with advanced screening techniques and the shift towards systems pharmacology is changing the landscape of drug discovery in geriatric psychiatry. There is potential for these new agents to produce targeted effects in the framework of disorders that have long been untreatable.

Therapeutic Potential of Pien Tze Huang on Experimental Autoimmune Encephalomyelitis Rat

Multiple sclerosis (MS) is a chronic inflammatory demyelinating disease of the central nervous system (CNS). There is still lack of commercially viable treatment currently. Pien Tze Huang (PZH), a traditional Chinese medicine, has been proved to have anti-inflammatory, neuroprotective, and immunoregulatory effects. This study investigated the possible therapeutic effects of PZH on experimental autoimmune encephalomyelitis (EAE) rats, a classic animal model of MS. Male Lewis rats were immunized with myelin basic protein (MBP) peptide to establish an EAE model and then treated with three doses of PZH. Clinical symptoms, organ coefficient, histopathological features, levels of proinflammatory cytokines, and chemokines as well as MBP and Olig2 were analyzed. The results indicated that PZH ameliorated the clinical severity of EAE rats. It also remarkably reduced inflammatory cell infiltration in the CNS of EAE rats. Furthermore, the levels of IL-17A, IL-23, CCL3, and CCL5 in serum and the CNS were significantly decreased; the p-P65 and p-STAT3 levels were also downregulated in the CNS, while MBP and Olig2 in the CNS of EAE rats had a distinct improvement after PZH treatment. In addition, PZH has no obvious toxicity at the concentration of 0.486 g/kg/d. This study demonstrated that PZH could be used to treat MS.

Adverse Drug Event Discovery Using Biomedical Literature: A Big Data Neural Network Adventure

BACKGROUND

The study of adverse drug events (ADEs) is a tenured topic in medical literature. In recent years, increasing numbers of scientific articles and health-related social media posts have been generated and shared daily, albeit with very limited use for ADE study and with little known about the content with respect to ADEs.

OBJECTIVE

The aim of this study was to develop a big data analytics strategy that mines the content of scientific articles and health-related Web-based social media to detect and identify ADEs.

METHODS

We analyzed the following two data sources: (1) biomedical articles and (2) health-related social media blog posts. We developed an intelligent and scalable text mining solution on big data infrastructures composed of Apache Spark, natural language processing, and machine learning. This was combined with an Elasticsearch No-SQL distributed database to explore and visualize ADEs.

RESULTS

The accuracy, precision, recall, and area under receiver operating characteristic of the system were 92.7%, 93.6%, 93.0%, and 0.905, respectively, and showed better results in comparison with traditional approaches in the literature. This work not only detected and classified ADE sentences from big data biomedical literature but also scientifically visualized ADE interactions.

CONCLUSIONS

To the best of our knowledge, this work is the first to investigate a big data machine learning strategy for ADE discovery on massive datasets downloaded from PubMed Central and social media. This contribution illustrates possible capacities in big data biomedical text analysis using advanced computational methods with real-time update from new data published on a daily basis.

Pathogenesis of thrombosis: cellular and pharmacogenetic contributions

Our understanding of thrombosis formation has evolved significantly ever since physician Rudolf Virchow proposed his "triad" theory in 1856. Modern science has elucidated the mechanisms of stasis, hypercoagulability, and endothelial dysfunction. Today, we have a firm understanding of the key molecular factors involved in the coagulation cascade and fibrinolytic system, as well as the underlying genetic influences. This knowledge of cellular and genetic contributors has been translated into diverse pharmaceutical interventions. Here, we examine the molecular and cellular mechanisms of thrombosis and its associated pathologies. We also review the current state of pharmacologic interventions, including pro- and anti-thrombotics, direct oral anticoagulants, and anti-platelet therapies. The pharmacogenetic factors that guide clinical decision making and prognosis are described in detail. Finally, we explore new approaches to thrombosis drug discovery, repurposing, and diagnostics. We argue that network biology tools will enable a systems pharmacology revolution in the next generation of interventions, facilitating precision medicine applications and ultimately leading to improved patient outcomes.

Is systems pharmacology ready to impact upon therapy development? A study on the cholesterol biosynthesis pathway

Background and Purpose

An ever‐growing wealth of information on current drugs and their pharmacological effects is available from online databases. As our understanding of systems biology increases, we have the opportunity to predict, model and quantify how drug combinations can be introduced that outperform conventional single‐drug therapies. Here, we explore the feasibility of such systems pharmacology approaches with an analysis of the mevalonate branch of the cholesterol biosynthesis pathway.

Experimental Approach

Using open online resources, we assembled a computational model of the mevalonate pathway and compiled a set of inhibitors directed against targets in this pathway. We used computational optimization to identify combination and dose options that show not only maximal efficacy of inhibition on the cholesterol producing branch but also minimal impact on the geranylation branch, known to mediate the side effects of pharmaceutical treatment.

Key Results

We describe serious impediments to systems pharmacology studies arising from limitations in the data, incomplete coverage and inconsistent reporting. By curating a more complete dataset, we demonstrate the utility of computational optimization for identifying multi‐drug treatments with high efficacy and minimal off‐target effects.

Conclusion and Implications

We suggest solutions that facilitate systems pharmacology studies, based on the introduction of standards for data capture that increase the power of experimental data. We propose a systems pharmacology workflow for the refinement of data and the generation of future therapeutic hypotheses.

Computational approaches to understand the adverse drug effect on potassium, sodium and calcium channels for predicting TdP cardiac arrhythmias

Ion channels play a crucial role in the cardiovascular system. Our understanding of cardiac ion channel function has improved since their first discoveries. The flow of potassium, sodium and calcium ions across cardiomyocytes is vital for regular cardiac rhythm. Blockage of these channels, delays cardiac repolarization or tend to shorten repolarization and may induce arrhythmia. Detection of drug risk by channel blockade is considered essential for drug regulators. Advanced computational models can be used as an early screen for torsadogenic potential in drug candidates. New drug candidates that are determined to not cause blockage are more likely to pass successfully through preclinical trials and not be withdrawn later from the marketplace by manufacturer. Several different approved drugs, however, can cause a distinctive polymorphic ventricular arrhythmia known as torsade de pointes (TdP), which may lead to sudden death. The objective of the present study is to review the mechanisms and computational models used to assess the risk that a drug may TdP.

KEY POINTS

There is strong evidence from multiple studies that blockage of the L-type calcium current reduces risk of TdP. Blockage of sodium channels slows cardiac action potential conduction, however, not all sodium channel blocking antiarrhythmic drugs produce a significant effect, while late sodium channel block reduces TdP. Interestingly, there are some drugs that block the hERG potassium channel and therefore cause QT prolongation, but they are not associated with TdP. Recent studies confirmed the necessity of studying multiple distinctionic ion channels which are responsible for cardiac related diseases or TdP, to obtain an improved clinical TdP risk prediction of compound interactions and also for designing drugs.

Quantitative Assessment of Population Variability in Hepatic Drug Metabolism Using a Perfused Three-Dimensional Human Liver Microphysiological System

In this work, we first describe the population variability in hepatic drug metabolism using cryopreserved hepatocytes from five different donors cultured in a perfused three-dimensional human liver microphysiological system, and then show how the resulting data can be integrated with a modeling and simulation framework to accomplish in vitro–in vivo translation. For each donor, metabolic depletion profiles of six compounds (phenacetin, diclofenac, lidocaine, ibuprofen, propranolol, and prednisolone) were measured, along with metabolite formation, mRNA levels of 90 metabolism-related genes, and markers of functional viability [lactate dehydrogenase (LDH) release, albumin, and urea production]. Drug depletion data were analyzed with mixed-effects modeling. Substantial interdonor variability was observed with respect to gene expression levels, drug metabolism, and other measured hepatocyte functions. Specifically, interdonor variability in intrinsic metabolic clearance ranged from 24.1% for phenacetin to 66.8% for propranolol (expressed as coefficient of variation). Albumin, urea, LDH, and cytochrome P450 mRNA levels were identified as significant predictors of in vitro metabolic clearance. Predicted clearance values from the liver microphysiological system were correlated with the observed in vivo values. A population physiologically based pharmacokinetic model was developed for lidocaine to illustrate the translation of the in vitro output to the observed pharmacokinetic variability in vivo. Stochastic simulations with this model successfully predicted the observed clinical concentration-time profiles and the associated population variability. This is the first study of population variability in drug metabolism in the context of a microphysiological system and has important implications for the use of these systems during the drug development process.