Systems Pharmacology

 Age-related cognitive decline is the gradual decrease in memory, reduced attention, difficulty with multitasking, reduced ability to recall information, and decreased processing speed with age. These changes are considered normal, and the rate and extent of cognitive decline can be significantly impacted by genetics and environmental factors, such as lifestyle behavior, diet, and physical activity. Mild cognitive impairment (MCI) is an intermediate phase between normal aging and Alzheimer’s disease (AD). MCI is a risk factor for Alzheimer’s disease, but not all individuals with MCI develop AD. There exists a significant challenge in diagnosing MCI and early Alzheimer’s disease as they involve very subtle differences in cognitive ability. Thus, several studies are focused on identifying biomarkers that could be utilized for early diagnosis. The decline in cognition with age is influenced by several molecular processes that include protein aggregation, synaptic and mitochondrial dysfunction, epigenetic changes, and oxidative damage. The interplay between these mechanisms can be tracked using genetic, neurophysiological, neuroimaging, biochemical, and neuroinflammatory biomarkers. Despite the existence of several molecular biomarkers, detecting cognitive decline before the clinical symptoms appear remains challenging. There is an unmet need for reliable and non-invasive biomarkers that can precisely monitor the impact of lifestyle on normal aging, which would be able to identify individuals at risk of cognitive decline. In this review, we have summarized the different classes of biomarkers that have been associated with age-related cognitive decline and highlighted the need for integrating different approaches for accurate diagnosis of early cognitive decline.

While modern pharmacokinetic-pharmacodynamic models, such as physiologically-based models incorporating dynamic organ function, have advanced our understanding of temporal changes in drug disposition, they fundamentally lack a spatial dimension to explain drug-induced alterations in tissue architecture. The Morphogenetic Pharmacodynamic Model (MPM) addresses this critical gap by proposing that the primary therapeutic effect of many drugs is morphological: the guided reorganization of diseased tissue patterns through modulation of morphogenetic fields. Using a Turing-type reactiondiffusion system of partial differential equations, MPM models drugs as bifurcation parameters that destabilize pathological attractors (e.g., fibrotic lattices or tumor spheroids) and drive tissues toward healthy, functional states. Key innovations include the Pattern Transition Concentration (PTC) as a bifurcation threshold and the Pattern Stability Index (PSI) as a patient-specific metric of attractor robustness. Case studies demonstrate how MPM explains previously enigmatic therapeutic effects, such as metformin's restoration of pancreatic islet architecture and anti-fibrotic agents' reversal of scarring patterns. The MPM provides a predictive, quantitative framework to transition drug discovery from targeting individual pathways to reprogramming tissue geometry, with profound implications for regenerative medicine and personalized therapeutics. This paradigm shift positions morphological reorganization as the fundamental mechanism underlying therapeutic efficacy in chronic diseases characterized by disrupted tissue architecture.

Traditional pharmacokinetic-pharmacodynamic (PK/PD) models rely on static parameters derived from population averages, limiting their ability to predict individual patient responses and adverse drug reactions. This paper introduces the Hepatic Response Unified Model (HRUM), a novel dynamical systems approach that incorporates real-time metabolic capacity fluctuations into PK/PD modeling. HRUM's core innovation lies in its Dynamic Metabolic Feedback Loop, where hepatic function is modeled as a state variable that responds dynamically to drug concentration and toxicity. The model consists of three coupled ordinary differential equations describing drug concentration dynamics, pharmacodynamic effects, and instantaneous metabolic capacity. Mathematical analysis reveals critical stability conditions that determine whether a patient will maintain therapeutic drug levels or experience metabolic collapse leading to drug-induced liver injury (DILI). The model introduces two key clinical metrics: the Metabolic Resilience Index (MRI) for assessing dosing sensitivity and the Efficacy-Toxicity Gradient (ETG) for optimizing therapeutic strategies. HRUM demonstrates particular utility for drugs with narrow therapeutic windows, including chemotherapeutics, anticoagulants, and certain antibiotics. By enabling patientspecific parameter estimation through biomarkers, HRUM represents a significant advancement toward personalized medicine in clinical pharmacology. The model's predictive capabilities address the critical question of why standard dosing regimens work for most patients while causing severe adverse reactions in others, providing a mathematical framework for understanding and preventing drug-induced hepatotoxicity.

Drug development in pediatric rare diseases is complicated by practical and ethical constraints on clinical trial design, stemming from small, highly heterogeneous, and vulnerable patient populations. Virtual patients (VPs) created with machine-learning (ML), mechanistically driven computational approaches, or hybrids thereof, have the potential to expedite and maximize the impact of trials. We discuss the potential of VPs to transform the efficiency and impact of clinical trials in pediatric rare diseases, based on adult and pediatric examples.

The use of Disease progression models (DPMs) in Drug Development has been widely adopted across therapeutic areas as a method for integrating previously obtained disease knowledge to elucidate the impact of novel therapeutics or vaccines on disease course, thus quantifying the potential clinical benefit at different stages of drug development programs. This paper provides a brief overview of DPMs and the evolution in data types, analytic methods, and applications that have occurred in their use by Quantitive Clinical Pharmacologists. It also provides examples of how these models have informed decisions and clinical trial design across several therapeutic areas and at various stages of development. It briefly describes potential new applications of DPMs utilizing emerging data sources, and utilizing new analytic techniques, and discuss new challenges faced such as requiring description of multiple endpoints, rapid model development, application of machine learning-based analytics, and use of high dimensional and real-world data. Considerations for the continued evolution future of DPMs to serve as community-maintained expert systems are also provided.

The analysis of information on the spatial structure of molecules and the physical fields of their interactions with biological targets is extremely important for solving various problems in drug discovery. This mini-review article surveys the main features of the continuous molecular fields approach and its use for analyzing structure-activity relationships in 3D space, building 3D quantitative structure-activity models and conducting similarity based virtual screening. Particular attention is paid to the consideration of the concept of molecular co-fields and their use for the interpretation of 3D structure-activity models. The principles of molecular design based on the overlapping and the similarity of molecular fields with corresponding co-fields are formulated.

Drug discovery attrition rates, particularly at advanced clinical trial stages, are high because of unexpected adverse drug reactions (ADR) elicited by novel drug candidates. Predicting undesirable ADRs produced by the modulation of certain protein targets would contribute to developing safer drugs, thereby reducing economic losses associated with high attrition rates. As opposed to the more traditional drug-centric approach, we propose a targetcentric approach to predict associations between protein targets and ADRs. The implementation of the predictor is based on a machine learning classifier that integrates a set of eight independent network-based features. These include a network diffusion-based score, identification of protein modules based on network clustering algorithms, functional similarity among proteins, network distance to proteins that are part of safety panels used in preclinical drug development, set of network descriptors in the form of degree and betweenness centrality measurements, and conservation. This diverse set of descriptors were used to generate predictors based on different machine learning classifiers ranging from specific models for individual ADR to higher levels of abstraction as per MEDDRA hierarchy such as system organ class. The results obtained from the different machine-learning classifiers, namely, support vector machine, random forest, and neural network were further analyzed as a meta-predictor exploiting three different voting systems, namely, jury vote, consensus vote, and red flag, obtaining different models for each of the ADRs in analysis. The level of accuracy of the predictors justifies the identification of problematic protein targets both at the level of individual ADR as well as a set of related ADRs grouped in common system organ classes. As an example, the prediction of ventricular tachycardia achieved an accuracy and precision of 0.83 and 0.90, respectively, and a Matthew correlation coefficient of 0.70. We believe that this approach is a good complement to the existing methodologies devised to foresee potential liabilities in preclinical drug discovery. The method is available through the DocTOR utility at GitHub ().

The level of attrition on drug discovery, particularly at advanced stages, is very high due to unexpected adverse drug reactions (ADRs) caused by drug candidates, and thus, being able to predict undesirable responses when modulating certain protein targets would contribute to the development of safer drugs and have important economic implications. On the one hand, there are a number of databases that compile information of drug-target interactions. On the other hand, there are a number of public resources that compile information on drugs and ADR. It is therefore possible to link target and ADRs using drug entities as connecting elements. Here, we present T-ARDIS (Target-Adverse Reaction Database Integrated Search) database, a resource that provides comprehensive information on proteins and associated ADRs. By combining the information from drug-protein and drug-ADR databases, we statistically identify significant associations between proteins and ADRs. Besides describing the relationship between proteins and ADRs, T-ARDIS provides detailed description about proteins along with the drug and adverse reaction information. Currently T-ARDIS contains over 3000 ADR and 248 targets for a total of more 17 000 pairwise interactions. Each entry can be retrieved through multiple search terms including target Uniprot ID, gene name, adverse effect and drug name. Ultimately, the T-ARDIS database has been created in response to the increasing interest in identifying early in the drug development pipeline potentially problematic protein targets whose modulation could result in ADRs.

FLAP (5-Lipoxygenase Activating Protein) inhibitors, offering targeted intervention in leukotriene biosynthesis and holding therapeutic promise for inflammatory diseases like asthma, are hindered by current inhibitors' off-target effects, limited efficacy, safety concerns, potential drug interactions, and accessibility issues. Given these challenges, computational methods, particularly Quantitative Structure Activity Relationships (QSAR) modeling, are vital for developing novel FLAP inhibitors. This study specifically investigates the QSAR of FLAP inhibitors using Kernel Partial Least Squares (KPLS) modeling. Leveraging a dataset of FLAP inhibitors, we employ KPLS within the Schrödinger Canvas environment to correlate molecular descriptors with biological activity. Out of the eight models developed, the "atom pairs" fingerprint yielded a statistically significant 2D QSAR model with outstanding regression coefficient values (R 2 =0.9624). The model demonstrated high predictive ability for external test set data (R 2 pred = 0.7105), underscoring its robustness and reliability in accurately predicting biological activity based on molecular structure. Additionally, we tried to visualize the relative contributions of individual atoms within FLAP inhibitors, providing insights into their favorable and unfavorable characteristics. Through the analysis of atomic contributions, we identify key structural motifs crucial for predicting FLAP inhibitor activity. Our findings not only advance our understanding of FLAP inhibitor SAR but also demonstrate the utility of KPLS modeling and atomic contribution analysis in drug discovery efforts. Furthermore, this study contributes to the development of anti-inflammatory therapeutics by elucidating the structural determinants of FLAP inhibitor activity, with potential applications in the treatment of inflammatory disorders.

This review performs an exhaustive evaluation of present epilepsy treatment alternatives which include drug medications toget her with surgery and additional supportive methods. Before focusing on modern therapeutic approaches, the manuscript first addres ses epilepsy types and prevalence as well as how medical treatment has evolved for this condition. The article describes antiseizure medic ations with detailed assessments based on both generational groups and operational principles. The review analyzes drugs which influence voltage-gated ion channels as well as GABAergic pharmaceuticals and medication that manage synaptic release processes and glutamate recepto rs. The essay explores both the outcome success of temporal lobe resection and investigates the brain's cognitive modifications after the procedure. The evaluation examines vagus nerve stimulation along with the ketogenic diet as alternative options in conjunction with planned research on herbal medicine treatments. Additionally, the paper explores recent uses of artificial intelligence in the diagnosis and clinical management of epilepsy. New medical findings from different epilepsy treatments merge into an extensive compilation of modern epilepsy therapy methods that outlines possible future research directions.

The RM values of a series of xanthine and adenosine derivatives were measured using silicone reversed-phase thin-layer chromatographic (TLC) and C1 s reversedphase high-performance TLC systems. The two series of data were well correlated. Both were compared with experimental log P and calculated CLOGP values. For xanthine derivatives a good linear relationship was shown between the RM values from the two chromatographic systems and the log P or CLOGP data. For adenosine derivatives the CLOGP values had to be corrected in order to tit the data to the same equation. The TLC data proved to be reliable parameters for describing the lipophilic properties of the test compounds. OF XANTHINES ANLI ADENUSINES. 1.

Crohn's disease (CD) is a complex inflammatory bowel disease whose pathogenesis appears to involve several immunologic defects causing functional impairment of the gut. Its complexity and the reported loss of effectiveness over time of standard of care together with the increase in its worldwide incidence require the application of techniques aiming to find new therapeutic strategies. Currently, systems pharmacology modeling has been gaining importance as it integrates the available knowledge of the system into a single computational model. In this work, the following workflow for robust application of systems pharmacology modeling was followed: 1) scope definition; 2) species selection and circulating plasma levels based on a search in the literature; 3) representation of model topology and parametrization of the interactions, after literature data extraction and curation, and the implementation of ordinary differential equations in SimBiology (MATLAB version R2018b); and 4) model curation and evaluation by visual comparison of simulated interleukin (IL) concentrations with the reported levels in plasma, and sensitivity analysis performed to confirm model robustness and identify the most influential parameters. Finally, 5) exposure to two dose levels of recombinant human IL10 was evaluated by simulation and comparison with reported clinical study results. In summary, we present a quantitative systems pharmacology model for the main ILs involved in CD developed using a standardized methodology and supported by a comprehensive repository summarizing the most relevant literature in the field. However, it has to be taken into account that external validation is still pending as available clinical data were primarily used for model training. Crohn's disease (CD) is a complex heterogeneous inflammatory bowel disorder. Systems pharmacology modeling offers a great opportunity for integration of the available knowledge on the disease using a computational framework. As a result of this work, a comprehensive repository along with a quantitative systems pharmacology model for the main interleukins involved in CD is provided. This model is useful for the in silico evaluation of biomarkers and potential therapeutic targets and can be adapted to address research gaps regarding CD.

Crohn's Disease (CD) is a complex inflammatory bowel disease whose pathogenesis appears to involve several immunological defects causing functional impairment of the gut. Its complexity and the reported loss of effectiveness over time of standard of care together with the increase in its worldwide incidence require the application of techniques aiming to find new therapeutic strategies. Currently, systems pharmacology (SP) modelling has been gaining importance as it integrates the available knowledge of the system into a single computational model. In this work, the following workflow for robust application of SP modelling was followed: (i) scope definition, (ii) species selection and circulating plasma levels based on a search in the literature, (iii) representation of model topology and parametrization of the interactions, after literature data extraction and curation, and the implementation of ordinary differential equations in SimBiology® ( ) and (iv) model curation and evaluation by visual comparison of simulated interleukins (ILs) concentrations with the reported levels in plasma, and sensitivity analysis performed to confirm model robustness and identify the most influential parameters. Finally, (v) exposure to two dose levels of recombinant human IL10 was evaluated by simulation and comparison with reported clinical study results. In summary, we present a QSP model for the main ILs involved in CD developed using a standardized methodology and supported by a comprehensive repository summarizing the most relevant literature in the field. However, it has to be taken into account that external validation is still pending as available clinical data was primarily used for model training.

Lung cancer is one of the leading cancers and causes of cancer-related deaths worldwide. Due to its high prevalence and mortality rate, its clinical management remains a significant challenge. Previously, the in vitro anticancer activity of Aspiletrein A, a steroid and a saponin from Aspidistra letreae, against non-small cell lung cancer (NSCLC) cells was reported. However, the anticancer molecular mechanism of other Aspiletreins from A. letreae remains unknown. Using in silico network pharmacology approaches, the targets of Aspiletreins were predicted using the Swiss Target Prediction database. In addition, key mediators in NSCLC were obtained from the Genetic databases. The compound-target interacting networks were constructed using the STRING database and Cytoscape, uncovering potential targets, including STAT3, VEGFA, HSP90AA1, FGF2, and IL2. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analysis demonstrated that several pathways were highly relevant to canc...

Among adverse drug reactions, drug-induced liver injury presents particular challenges because of its complexity, and the underlying mechanisms are still not completely characterized. Our knowledge of the topic is limited and based on the assumption that a drug acts on one molecular target. We have leveraged drug polypharmacology, i.e., the ability of a drug to bind multiple targets and thus perturb several biological processes, to develop a systems pharmacology platform that integrates all drug-target interactions. Our analysis sheds light on the molecular mechanisms of drugs involved in drug-induced liver injury and provides new hypotheses to study this phenomenon.

Parkinson's complaint is a progressive, neurodegenerative complaint of growing that affects both motor and cognitive function. The etiology of Parkinson's disease is substantially unknown, but it probably involves both inheritable and terrain factor. In contrast ,the network -level pathology motor symptoms of Parkinson's The most prescribed medication to treat is (Levodopa+Carbidopa) are given in combined form giving effective pharmacological action and also includes majority of adverse drug reaction like dyskinesia, sedation, constipation etc.

There has always been a particular difficulty with in-depth research on the mechanisms of food nutrition and bioactivity. The main function of food is to meet the nutritional needs of the human body, rather than to exert a therapeutic effect. Its relatively modest biological activity makes it difficult to study from the perspective of general pharmacological models. With the popularity of functional foods and the concept of dietary therapy, and the development of information and multi-omics technology in food research, research into these mechanisms is moving towards a more microscopic future. Network pharmacology has accumulated nearly 20 years of research experience in traditional Chinese medicine (TCM), and there has been no shortage of work from this perspective on the medicinal functions of food. Given the similarity between the concept of 'multi-component-multi-target' properties of food and TCM, we think that network pharmacology is applicable to the study of the complex mechanisms of food. Here we review the development of network pharmacology, summarize its application to 'medicine and food homology', and propose a methodology based on food characteristics for the first time, demonstrating its feasibility for food research.

Recent growth in annual new therapeutic entity (NTE) approvals by the U.S. Food and Drug Administration (FDA) suggests a positive trend in current research and development (R&D) output. Prior to this, the cost of each NTE was considered to be rising exponentially, with compound failure occurring mainly in clinical phases. Quantitative systems pharmacology (QSP) modelling, as an additional tool in the drug discovery arsenal, aims to further reduce NTE costs and improve drug development success. Through in silico mathematical modelling, QSP can simulate drug activity as perturbations in biological systems and thus understand the fundamental interactions which drive disease pathology, compound pharmacology and patient response. Here we review QSP, pharmacometrics and systems biology models with respect to the diseases covered as well as their clinical relevance and applications. Overall, the majority of modelling focus was aligned with the priority of drug-discovery and clinical trials. However, a few clinically important disease categories, such as Immune System Diseases and Respiratory Tract Diseases, were poorly covered by computational models. This suggests a possible disconnect between clinical and modelling agendas. As a standard element of the drug discovery pipeline the uptake of QSP might help to increase the efficiency of drug development across all therapeutic indications.

The lack of standardization in the way that quantitative and systems pharmacology (QSP) models are developed, tested, and documented hinders their reproducibility, reusability, and expansion or reduction to alternative contexts. This in turn undermines the potential impact of QSP in academic, industrial, and regulatory frameworks. This article presents a minimum set of recommendations from the UK Quantitative and Systems Pharmacology Network (UK QSP Network) to guide QSP practitioners seeking to maximize their impact, and stakeholders considering the use of QSP models in their environment.

ABSTRACTAdvances in systems biology in conjunction with the expansion in knowledge of drug effects and diseases present an unprecedented opportunity to extend traditional pharmacokinetic and pharmacodynamic modeling/analysis to conduct systems pharmacology modeling. Many drugs that cause liver injury and myopathies have been studied extensively. Mitochondrion‐centric systems pharmacology modeling is important since drug toxicity across a large number of pharmacological classes converges to mitochondrial injury and death. Approaches to systems pharmacology modeling of drug effects need to consider drug exposure, organelle and cellular phenotypes across all key cell types of human organs, organ‐specific clinical biomarkers/phenotypes, gene–drug interaction and immune responses. Systems modeling approaches, that leverage the knowledge base constructed from curating a selected list of drugs across a wide range of pharmacological classes, will provide a critically needed blueprint for ma...

This study examined the underlying mechanisms of SJD's anti-inflammatory and analgesic effects on acute GA flares. Methods: This study used pharmacology network and molecular docking methods. The active ingredients of ShuiJingDan (SJD) were obtained from the Traditional Chinese Medicine Systems Pharmacology Analysis Platform (TCMSP), and the relevant targets of GA were obtained from the Online Mendelian Inheritance in Man (OMIM) database and Therapeutic Target Database (TTD). The core drug group-target-disease Venn diagram was formed by crossing the active ingredients of SJD and the relevant targets. Gene Ontology (GO) analysis was conducted for functional annotation, DAVID was used for Kyoto Encyclopedia of Genes, and Genomes pathway enrichment analysis, and R was used to find the core targets. The accuracy of SJD network pharmacology analysis in GA treatment was verified by molecular docking simulations. Finally, a rat GA model was used to further verify the anti-inflammatory mechanism of SJD in the treatment of GA. Results: SJD mainly acted on target genes including IL1B, PTGS2, CXCL8, EGF, and JUN, as well as signal pathways including NF-κB, Toll-like receptor (TLR), IL-17, and MAPK. The rat experiments showed that SJD could significantly relieve ankle swelling, reduce the local skin temperature, and increased the paw withdrawal threshold. SJD could also reduce synovial inflammation, reduced the concentrations of interleukin-1β (IL-1β), IL-8, and COX-2 in the synovial fluid, and suppressed the expression of IL1B, CXCL8, and PTGS2 mRNA in the synovial tissue. Conclusion: SJD has a good anti-inflammatory effect to treat GA attacks, by acting on target genes such as IL-1β, PTGS2, and CXCL8.

Chemoinformatics is a modern computational tool that works by bringing together both information technology and chemistry to solve problems related to drug discovery. These methods can also be used in chemical and allied industries in various other forms. Chemoinformatics gives us an opportunity to transform the data obtained via linking the two fields into knowledge which can then be extended to make proper and better decisions in areas such as drug discovery, understanding chemical interaction, standardization of drug manufacturing protocols etc. The primary application of chemoinformatics is in the storage, indexing and search of information relating to compounds. The efficient search of such stored information includes topics that are dealt with in computer science as data mining, information retrieval, information extraction and machine learning. Chemoinformatics can help us to carry out virtual experiments, which provides insights as to how our body might actually respond to t...

Technology in bioanalysis,-omics, and computation have evolved over the past half century to allow for comprehensive assessments of the molecular to whole body pharmacology of diverse corticosteroids. Such studies have advanced pharmacokinetic and pharmacodynamic (PK/PD) concepts and models that often generalize across various classes of drugs. These models encompass the "pillars" of pharmacology,

Technology in bioanalysis,-omics, and computation have evolved over the past half century to allow for comprehensive assessments of the molecular to whole body pharmacology of diverse corticosteroids. Such studies have advanced pharmacokinetic and pharmacodynamic (PK/PD) concepts and models that often generalize across various classes of drugs. These models encompass the "pillars" of pharmacology,

Immunomodulation constitutes a crucial part of individual organisms’ defense systems. Moreover, the utilization of plant-based natural products as herbal medicine for immunomodulation has garnered significant interest. Herein, we examined the immunomodulatory potentials of active phytocompounds extracted from Withania somnifera and Aloe barbadensis by employing ADMET screening, network pharmacology, and molecular docking techniques. This study follows the paradigm in drug discovery, which has shifted from a “one-target, one-drug” mode to a “network-target, multiple-component-therapeutics” mode. Phyto compounds sourced from W. somnifera and A. barbadensis were mined from online databases, including Dr. Duke’s Phytochemical Ethnobotanical Database. After screening these active compounds, their potential targets were predicted through in silico ADMET property prediction models. Network pharmacology was utilized to establish a “compound-protein/gene-disease” network and reveal the regul...

The study of central nervous system (CNS) pharmacology is limited by a lack of drug effect biomarkers. Pharmacometabolomics is a promising new tool to identify multiple molecular responses upon drug treatment. However, the pharmacodynamics is typically not evaluated in metabolomics studies, although being important properties of biomarkers. In this study we integrated pharmacometabolomics with pharmacokinetic/pharmacodynamic (PKPD) modeling to identify and quantify the multiple endogenous metabolite dose-response relations for the dopamine D2 antagonist remoxipride. Remoxipride (vehicle, 0.7 or 3.5 mg/kg) was administered to rats. Endogenous metabolites were analyzed in plasma using a biogenic amine platform and PKPD models were derived for each single metabolite. These models were clustered on basis of proximity between their PKPD parameter estimates, and PKPD models were subsequently fitted for the individual clusters. Finally, the metabolites were evaluated for being significantly affected by remoxipride. In total 44 metabolites were detected in plasma, many of them showing a dose dependent decrease from baseline. We identified 6 different clusters with different time and dose dependent responses and 18 metabolites were revealed as potential biomarker. The glycine, serine and threonine pathway was associated with remoxipride pharmacology, as well as the brain uptake of the dopamine and serotonin precursors. This is the first time that pharmacometabolomics and PKPD modeling were integrated. The resulting PKPD cluster model described diverse pharmacometabolomics responses and provided a further understanding of remoxipride pharmacodynamics. Future research should focus on the simultaneous pharmacometabolomics analysis in brain and plasma to increase the interpretability of these responses.

Thrombus formation in flowing blood is a complex time-and space-dependent process of cell adhesion and fibrin gel formation controlled by huge intricate networks of biochemical reactions. This combination of complex biochemistry, non-Newtonian hydrodynamics, and transport processes makes thrombosis difficult to understand. That is why numerous attempts to use mathematical modeling for this purpose were undertaken during the last decade. In particular, recent years witnessed something of a transition from the "systems biology" to the "systems pharmacology/systems medicine" stage: computational modeling is being increasingly applied to practical problems such as drug development, investigation of particular events underlying disease, analysis of the mechanism(s) of drug's action, determining an optimal dosing protocols, etc. Here we review recent advances and challenges in our understanding of thrombus formation.

BACKGROUND: The protease-antiprotease hypothesis proposes that inflammatory cells and oxidative stress in chronic obstructive pulmonary disease (COPD) produce increased levels of proteolytic enzymes (neutrophil elastase, matrix metalloproteinases [MMP]) which contribute to destruction of parenchyma resulting in progressive decline in forced expiratory volume in one second. Doxycycline, a tetracycline analogue, possesses antiinflammatory properties and inhibits MMP enzymes. OBJECTIVES: To assess the effect of 4 weeks doxycycline in a dose of 100 mg once a day in patients of moderate to severe COPD with stable symptoms. METHODS: In an interventional, randomized, observer-masked, parallel study design, the effect of doxycycline (100 mg once a day for 4 weeks) was assessed in patients of COPD having stable symptoms after a run-in period of 4 weeks. The study participants in reference group did not receive doxycycline. The parameters were pulmonary functions, systemic inflammation marker C-reactive protein (CRP), and medical research council (MRC) dyspnea scale. Use of systemic corticosteroids or antimicrobial agents was not allowed during the study period. RESULTS: A total of 61 patients completed the study (31 patients in doxycycline group and 30 patients in reference group). At 4 weeks, the pulmonary functions significantly improved in doxycycline group and the mean reduction in baseline serum CRP was significantly greater in doxycycline group as compared with reference group. There was no significant improvement in MRC dyspnea scale in both groups at 4 weeks. CONCLUSION: The anti-inflammatory and MMP-inhibiting property of doxycycline might have contributed to the improvement of parameters in this study.

The global prevalence of obesity is a pressing health issue, increasing the medical burden
and posing significant health risks to humans. The side effects and complications associated with
conventional medication and surgery have spurred the search for anti-obesity drugs from plant
resources. Previous studies have suggested that Artemisiae argyi Folium (Aiye) water extracts could
inhibit pancreatic lipase activities, control body weight increase, and improve the plasma lipids
profile. However, the exact components and mechanisms were not precisely understood. Therefore,
this research aims to identify the chemical profile of Aiye and provide a comprehensive prediction
of its anti-obesity mechanisms. The water extract of Aiye was subjected to LC-MS analysis, which
identified 30 phenolics. The anti-obesity mechanisms of these phenolics were then predicted, em-
ploying network pharmacology and molecular docking. Among the 30 phenolics, 21 passed the
drug-likeness screening and exhibited 486 anti-obesity targets. The enrichment analysis revealed that
these phenolics may combat obesity through PI3K-Akt signaling and MAPK, prolactin, and cAMP
signaling pathways. Eight phenolics and seven central targets were selected for molecular docking,
and 45 out of 56 docking had a binding affinity of less than − 5 kcal/mol. This research has indicated
the potential therapy targets and signaling pathways of Aiye in combating obesity.

Nuclear receptor pharmacology has, to a certain extent, led the way, compared with other receptor systems, in the appreciation that ligands may exert very diverse pharmacology, based on their individual chemical structure and the allosteric changes induced in the receptor/accessory protein complex. This can lead to very selective pharmacological effects, which may not necessarily be predicted from the experience with other agonists/partial agonists/antagonists. If this is the case, then drug discovery may be back to drug-specific pharmacology (where each drug may have an original profile), rather than specificdrug pharmacology (where agents specific for a receptor have a distinct profile). As functional selectivity is indeed a crucial mechanism to be considered when going through the drug discovery development process, then initial screens using reconstituted systems may not show the appropriate pharmacology, simply because the required stoichiometry of corepressors and coactivators may not be present to select the best compounds; therefore, multiple effector systems are necessary to screen for differential activation, and, even then, screening with in vivo pathophysiological models may ultimately be required for the selection process-a massive but necessary task for pharmacologists. Thus, the characterization of nuclear receptors and their associated proteins and the ligands that interact with them will remain a challenge to pharmacologists.

The cytotoxicity of 19 N‐aryl‐substituted hydroxamic acids has been tested in vitro towards human breast cancer MCF–7 cell lines by MTT assay. The IC50 values were found to be in the range from 61.94 to 337.54 μM. A total of 18 out of 19 molecules had higher inhibitory activities than hydroxyurea against MCF–7 cell. Five compounds with IC50 values in micromolar range were 3‐ to 5‐folds more potent than hydroxyurea (IC50=307.15 μM). By partial least squares (PLS) regression, 2D‐QSAR model reported herein provide interesting insight in understanding hydrophobic, electronic, and structural requirements of antitumor activity among these set of the compounds. The cross‐validated Q2cum values for optimal PLS model of hydroxamic acids is above 0.638 (remarkably higher 0.50), indicating good predictive abilities for log 1/IC50 values of hydroxamic acids (HAs). The k‐nearest neighbor molecular field analysis (kNN‐MFA) approach was used to generate three‐dimensional quantitative structure‐act...

Asparagus consumption is associated with the production of malodorous urine. Interindividual variability was previously characterized by an American Society for Clinical Pharmacology and Therapeutics crowdsourced study. To further characterize urinary odor kinetics, we conducted a study with consenting participants from Takeda Pharmaceutical International Company. The participants were randomized to consume a specified number of asparagus spears and asked to record urine odor. A kinetic-pharmacodynamic model characterized the data from both the newly conducted Takeda study (N = 42) and the previously analyzed American Society for Clinical Pharmacology and Therapeutics studies (total N = 139). The updated model included the identification of an absorption process with a half-life of 25 minutes. We estimated the elimination halflife of the asparagus effect on malodorous urine to be 7.2 hours, which was 44% longer in our study. We built on previous experience using an improved R-Shiny app for conducting the crowdsourcing experiment, further demonstrating the utility of this population kinetics approach in organizational and educational settings.

Asparagus consumption is associated with the production of malodorous urine. Interindividual variability was previously characterized by an American Society for Clinical Pharmacology and Therapeutics crowdsourced study. To further characterize urinary odor kinetics, we conducted a study with consenting participants from Takeda Pharmaceutical International Company. The participants were randomized to consume a specified number of asparagus spears and asked to record urine odor. A kinetic‐pharmacodynamic model characterized the data from both the newly conducted Takeda study (N = 42) and the previously analyzed American Society for Clinical Pharmacology and Therapeutics studies (total N = 139). The updated model included the identification of an absorption process with a half‐life of 25 minutes. We estimated the elimination half‐life of the asparagus effect on malodorous urine to be 7.2 hours, which was 44% longer in our study. We built on previous experience using an improved R‐Shiny...

In systems biology and pharmacology, large-scale kinetic models are used to study the dynamic response of a system to a specific input or stimulus. While in many applications, a deeper understanding of the input-response behaviour is highly desirable, it is often hindered by the large number of molecular species and the complexity of the interactions. An approach that identifies key molecular species for a given input-response relationship and characterises dynamic properties of states is therefore highly desirable. We introduce the concept of index analysis; it is based on different time-and state-dependent quantities (indices) to identify important dynamic characteristics of molecular species. All indices are defined for a specific pair of input and response variables as well as for a specific magnitude of the input. In application to a

A growing understanding of complex processes in biology has led to large-scale mechanistic models of pharmacologically relevant processes. These models are increasingly used to study the response of the system to a given input or stimulus, e.g., after drug administration. Understanding the input-response relationship, however, is often a challenging task due to the complexity of the interactions between its constituents as well as the size of the models. An approach that quantifies the importance of the different constituents for a given input-output relationship and allows to reduce the dynamics to its essential features is therefore highly desirable. In this article, we present a novel state-and time-dependent quantity called the input-response index that quantifies the importance of state variables for a given input-response relationship at a particular time. It is based on the concept of time-bounded controllability and observability, and defined with respect to a reference dynamics. In application to the brown snake venom-fibrinogen (Fg) network, the input-response indices give insight into the coordinated action of specific coagulation factors and about those factors that contribute only little to the response. We demonstrate how the indices can be used to reduce large-scale models in a two-step procedure: (i) elimination of states whose dynamics have only minor impact on the input-response relationship, and (ii) proper lumping of the remaining (lower order) model. In application to the brown snake venom-fibrinogen network, this resulted in a reduction from 62 to 8 state variables in the first step, and a further reduction to 5 state variables in the second step. We further illustrate that the sequence, in which a recursive algorithm eliminates and/or lumps state variables, has an impact on the final reduced model. The input-response indices are particularly suited to determine an informed sequence, since they are based on the dynamics of the original system. In summary, the novel measure of importance provides a powerful tool for analysing the complex dynamics of large-scale systems and a means for very efficient model order reduction of nonlinear systems.

Background: Parkinson's disease (PD) is a highly debilitating disease characterized by tremors, bradykinesia and rigidity. It leads to lowered selfesteem and psychological consequences which affect quality of life. The aim of this study is to study the drug utilization pattern and assess the quality of life in patients of Parkinson's Disease. Methods: 40 patients of PD at least 1 month duration and 20 age-based controls were analyzed for quality of life using Parkinson's Disease Questionnaire-39 (PDQ-39). Drug prescriptions were analyzed. Results: Mean number of anti-Parkinson drugs prescribed is 2.65±1.21. Of 106 anti-Parkinson drugs prescribed, 45% were levodopa and carbidopa combinations, followed by dopamine agonists (18%), anticholinergic drugs (15%), amantadine (12%), MAO inhibitors (5%) and COMT inhibitors (5%). There were significant problems in speech, performance of daily chores and daytime somnolence (p<0.0001). Depression, isolation, cognitive decline and memory loss were noteworthy in the patients as compared to controls (p<0.05). 25% patients felt embarrassed due to their disease; 59% felt affected by others' opinion, 60% felt difficulty in communicating with others (p<0.05). Almost 2/3 rd patients needed help in personal care as compared to the control group (p<0.0001). Conclusions: Quality of life of parkinsonian patients is severely affected in spite of them receiving a large number of drugs. This may be both due to disease progression as well as medication. Levodopa-carbidopa combination is the most prescribed medication. Use of levodopa and carbidopa combination must be evaluated properly. Newer guidelines and interventions are the need of the hour which may provide a better outcome on the quality of life of parkinsonian patients.

Covering: 2011 to 2014It is widely accepted that drug discovery often requires a systems-level polypharmacology approach to tackle problems such as lack of efficacy and emerging resistance of single-targeted compounds. Network pharmacology approaches are increasingly being developed and applied to find new therapeutic opportunities and to re-purpose approved drugs. However, these recent advances have been relatively slow to be translated into the field of natural products. Here, we argue that a network pharmacology approach would enable an effective mapping of the yet unexplored target space of natural products, hence providing a systematic means to extend the druggable space of proteins implicated in various complex diseases. We give an overview of the key network pharmacology concepts and recent experimental-computational approaches that have been successfully applied to natural product research, including unbiased elucidation of mechanisms of action as well as systematic predicti...

A one-pot multi-components synthesis of a series of novel pyrimido(1,2-a)benzimidazoles (4a- j) was achieved from acetoacetamide, aromatic aldehydes and 2-aminobenzimidazole with high yield and purity. The structures of the newly synthesized pyrimido(1,2-a)benzimidazoles (4a-j) were supported by FTIR, PMR and mass spectral data. All the newly synthesized compounds were tested for antimicrobial activity.

Chronic obstructive pulmonary disease (COPD) is a widely prevalent respiratory ailment that can be prevented. It is characterized by the chronic restriction of airflow caused by lung abnormalities resulting from exposure to toxic chemicals or particles. COPD is a respiratory disorder characterized by a gradual and incapacitating progression, impacting a significant number of individuals on a global scale. COPD is distinguished by the presence of chronic bronchitis and emphysema, resulting in considerable morbidity and mortality. The etiology of COPD is multifaceted, encompassing genetic, environmental, and physiological variables. In spite of the existence of global health objectives, the incidence and mortality rates of COPD persistently escalate, exhibiting disparities influenced by factors such as gender, geographical location, and age. The increasing prevalence of COPD, therefore, necessitates a pressing requirement for enhancing treatment approaches and patient outcomes.

The concept of molecular similarity has been commonly used in rational drug design, where structurally similar molecules are examined in molecular databases to retrieve functionally similar molecules. The most used conventional similarity methods used two-dimensional (2D) fingerprints to evaluate the similarity of molecules towards a target query. However, these descriptors include redundant and irrelevant features that might impact the performance of similarity searching methods. Thus, this study proposed a new approach for identifying the important features of molecules in chemical datasets based on the representation of the molecular features using Autoencoder (AE), with the aim of removing irrelevant and redundant features. The proposed approach experimented using the MDL Data Drug Report standard dataset (MDDR). Based on experimental findings, the proposed approach performed better than several existing benchmark similarity methods such as Tanimoto Similarity Method (TAN), Adap...

Virtual screening (VS) is a computational practice applied in drug discovery research. VS is popularly applied in a computer-based search for new lead molecules based on molecular similarity searching. In chemical databases similarity searching is used to identify molecules that have similarities to a user-defined reference structure and is evaluated by quantitative measures of intermolecular structural similarity. Among existing approaches, 2D fingerprints are widely used. The similarity of a reference structure and a database structure is measured by the computation of association coefficients. In most classical similarity approaches, it is assumed that the molecular features in both biological and non-biologically-related activity carry the same weight. However, based on the chemical structure, it has been found that some distinguishable features are more important than others. Hence, this difference should be taken consideration by placing more weight on each important fragment....