Molecular mechanism matters: Benefits of mechanistic computational models for drug development

Mechanistic computational modeling of sFLT1 secretion dynamics

Constitutively secreted by endothelial cells, soluble FLT1 (sFLT1 or sVEGFR1) binds and sequesters extracellular vascular endothelial growth factors (VEGF), thereby reducing VEGF binding to VEGF receptor tyrosine kinases and their downstream signaling. In doing so, sFLT1 plays an important role in vascular development and in the patterning of new blood vessels in angiogenesis. Here, we develop multiple mechanistic models of sFLT1 secretion and identify a minimal mechanistic model that recapitulates key qualitative and quantitative features of temporal experimental datasets of sFLT1 secretion from multiple studies. We show that the experimental data on sFLT1 secretion is best represented by a delay differential equation (DDE) system including a maturation term, reflecting the time required between synthesis and secretion. Using optimization to identify appropriate values for the key mechanistic parameters in the model, we show that two model parameters (extracellular degradation rate constant and maturation time) are very strongly constrained by the experimental data, and that the remaining parameters are related by two strongly constrained constants. Thus, only one degree of freedom remains, and measurements of the intracellular levels of sFLT1 would fix the remaining parameters. Comparison between simulation predictions and additional experimental data of the outcomes of chemical inhibitors and genetic perturbations suggest that intermediate values of the secretion rate constant best match the simulation with experiments, which would completely constrain the model. However, some of the inhibitors tested produce results that cannot be reproduced by the model simulations, suggesting that additional mechanisms not included here are required to explain those inhibitors. Overall, the model reproduces most available experimental data and suggests targets for further quantitative investigation of the sFLT1 system.

Modelling the effects of 4-factor prothrombin complex concentrate for the management of factor Xa-associated bleeding

The management of factor Xa (FXa) inhibitor-associated bleeding remains a clinical challenge. Massive bleeding is often associated with complex coagulopathy and, thus, the sole reversal of FXa inhibitors might not be sufficient to restore hemostasis, requiring instead a multimodal approach. Four-factor prothrombin complex concentrate (4F-PCC) is widely recognized as a viable treatment option for FXa inhibitor-associated bleeding. Here, we applied computational models to explore the effect 4F-PCC has on the coagulation cascade and restoration of thrombin generation in a system that simulates a patient that has received a FXa inhibitor. The coagulation model is largely based on a previously developed model with modifications incorporated from various other published sources. The model was calibrated and validated using data from a phase 3 clinical trial of vitamin K antagonist reversal with 4F-PCC. Using the parameters and initial conditions determined during the calibration and validation process, the prothrombin time (PT) test simulations predicted a PT of 11.4 seconds. The model successfully simulated the effects of rivaroxaban and apixaban on total thrombin concentration and showed that 4F-PCC increased thrombin generation in the presence of rivaroxaban or apixaban.

Semi-mechanistic modeling of resistance development to β-lactam and β-lactamase-inhibitor combinations

The use of β-lactam (BL) and β-lactamase inhibitor (BLI) combinations, such as piperacillin-tazobactam (PIP-TAZ) is an effective strategy to combat infections by extended-spectrum β-lactamase-producing bacteria. However, in Gram-negative bacteria, resistance (both mutational and adaptive) to BL-BLI combination can still develop through multiple mechanisms. These mechanisms may include increased β-lactamase activity, reduced drug influx, and increased drug efflux. Understanding the relative contribution of these mechanisms during resistance development helps identify the most impactful mechanism to target in designing a treatment to counter BL-BLI resistance. This study used semi-mechanistic mathematical modeling in combination with antibiotic sensitivity assays to assess the potential impact of different resistance mechanisms during the development of PIP-TAZ resistance in a Klebsiella pneumoniae isolate expressing CTX-M-15 and SHV-1 β-lactamases. The mathematical models were used to evaluate the potential impact of several cellular changes as a sole mediator of PIP-TAZ resistance. Our semi-mechanistic model identified 2 out of the 13 inspected mechanisms as key resistance mechanisms that may independently support the observed magnitude of PIP-TAZ resistance, namely porin loss and efflux pump up-regulation. Simulation using the resulting models also suggested the possible adjustment of PIP-TAZ dose outside its commonly used 8:1 dosing ratio. The current study demonstrated how theory-based mechanistic models informed by experimental data can be used to support hypothesis generation regarding potential resistance mechanisms, which may guide subsequent experimental studies.

In silico Approaches for Exploring the Pharmacological Activities of Benzimidazole Derivatives: A Comprehensive Review

BACKGROUND

This article reviews computational research on benzimidazole derivatives. Cytotoxicity for all compounds against cancer cell lines was measured and the results revealed that many compounds exhibited high inhibitions. This research examines the varied pharmacological properties like anticancer, antibacterial, antioxidant, anti-inflammatory and anticonvulsant activities of benzimidazole derivatives. The suggested method summarises In silico research for each activity. This review examines benzimidazole derivative structure-activity relationships and pharmacological effects. In silico investigations can anticipate structural alterations and their effects on these derivative's pharmacological characteristics and efficacy through many computational methods. Molecular docking, molecular dynamics simulations and virtual screening help anticipate pharmacological effects and optimize chemical design. These trials will improve lead optimization, target selection, and ADMET property prediction in drug development. In silico benzimidazole derivative studies will be assessed for gaps and future research. Prospective studies might include empirical verification, pharmacodynamic analysis, and computational methodology improvement.

OBJECTIVES

This review discusses benzimidazole derivative In silico research to understand their specific pharmacological effects. This will help scientists design new drugs and guide future research.

METHODS

Latest, authentic and published reports on various benzimidazole derivatives and their activities are being thoroughly studied and analyzed.

RESULT

The overview of benzimidazole derivatives is more comprehensive, highlighting their structural diversity, synthetic strategies, mechanisms of action, and the computational tools used to study them.

CONCLUSION

In silico studies help to understand the structure-activity relationship (SAR) of benzimidazole derivatives. Through meticulous alterations of substituents, ring modifications, and linker groups, this study identified the structural factors influencing the pharmacological activity of benzimidazole derivatives. These findings enable the rational design and optimization of more potent and selective compounds.

Bifunctional robots inducing targeted protein degradation

The gaining importance of Targeted Protein Degradation (TPD) and PROTACs (PROteolysis-TArgeting Chimeras) have drawn the scientific community's attention. PROTACs are considered bifunctional robots owing to their avidity for the protein of interest (POI) and E3-ligase, which induce the ubiquitination of POI. These molecules are based on event-driven pharmacology and are applicable in different conditions such as oncology, antiviral, neurodegenerative disease, acne etc., offering tremendous scope to researchers. In this review, primarily, we attempted to compile the recent works available in the literature on PROTACs for various targeted proteins. We summarized the design and development strategies with a focus on molecular information of protein residues and linker design. Rationalization of the ternary complex formation using Artificial Intelligence including machine & deep learning models and traditionally followed computational tools are also included in this study. Moreover, details describing the optimization of PROTACs chemistry and pharmacokinetic properties are added. Advanced PROTAC designs and targeting complex proteins, is summed up to cover the wide spectrum.

Can the Kuznetsov Model Replicate and Predict Cancer Growth in Humans?

Several mathematical models to predict tumor growth over time have been developed in the last decades. A central aspect of such models is the interaction of tumor cells with immune effector cells. The Kuznetsov model (Kuznetsov et al. in Bull Math Biol 56(2):295–321, 1994) is the most prominent of these models and has been used as a basis for many other related models and theoretical studies. However, none of these models have been validated with large-scale real-world data of human patients treated with cancer immunotherapy. In addition, parameter estimation of these models remains a major bottleneck on the way to model-based and data-driven medical treatment. In this study, we quantitatively fit Kuznetsov’s model to a large dataset of 1472 patients, of which 210 patients have more than six data points, by estimating the model parameters of each patient individually. We also conduct a global practical identifiability analysis for the estimated parameters. We thus demonstrate that several combinations of parameter values could lead to accurate data fitting. This opens the potential for global parameter estimation of the model, in which the values of all or some parameters are fixed for all patients. Furthermore, by omitting the last two or three data points, we show that the model can be extrapolated and predict future tumor dynamics. This paves the way for a more clinically relevant application of mathematical tumor modeling, in which the treatment strategy could be adjusted in advance according to the model’s future predictions.

Development of and insights from systems pharmacology models of antibody-drug conjugates

Antibody-drug conjugates (ADCs) have gained traction in the oncology space in the past few decades, with significant progress being made in recent years. Although the use of pharmacometric modeling is well-established in the drug development process, there is an increasing need for a better quantitative biological understanding of the pharmacokinetic and pharmacodynamic relationships of these complex molecules. Quantitative systems pharmacology (QSP) approaches can assist in this endeavor; recent computational QSP models incorporate ADC-specific mechanisms and use data-driven simulations to predict experimental outcomes. Various modeling approaches and platforms have been developed at the in vitro, in vivo, and clinical scales, and can be further integrated to facilitate preclinical to clinical translation. These new tools can help researchers better understand the nature and mechanisms of these targeted therapies to help achieve a more favorable therapeutic window. This review delves into the world of systems pharmacology modeling of ADCs, discussing various modeling efforts in the field thus far.

Recent Advances Regarding the Molecular Mechanisms of Triterpenic Acids: A Review (Part I)

Triterpenic acids are phytocompounds with a widespread range of biological activities that have been the subject of numerous in vitro and in vivo studies. However, their underlying mechanisms of action in various pathologies are not completely elucidated. The current review aims to summarize the most recent literature, published in the last five years, regarding the mechanism of action of three triterpenic acids (asiatic acid, oleanolic acid, and ursolic acid), corelated with different biological activities such as anticancer, anti-inflammatory, antidiabetic, cardioprotective, neuroprotective, hepatoprotective, and antimicrobial. All three discussed compounds share several mechanisms of action, such as the targeted modulation of the PI3K/AKT, Nrf2, NF-kB, EMT, and JAK/STAT3 signaling pathways, while other mechanisms that proved to only be specific for a part of the triterpenic acids discussed, such as the modulation of Notch, Hippo, and MALAT1/miR-206/PTGS1 signaling pathway, were highlighted as well. This paper stands as the first part in our literature study on the topic, which will be followed by a second part focusing on other triterpenic acids of therapeutic value.

Characteristics and Research Techniques Associated with the Journal Impact Factor and Other Key Metrics in Pharmacology Journals

In the present age, there is intense pressure on researchers to publish their research in ‘high-impact factor’ journals. It would be interesting to understand the trend of research publications in the field of pharmacology by exploring the characteristics of research articles, including research techniques, in relation to the journal’s key bibliometrics, particularly journal impact factor (JIF), the seemingly most mentioned metric. This study aimed to determine the characteristics and research techniques in relation to research articles in pharmacology journals with higher or lower JIF values. A cross-sectional study was conducted on primary research journals under the ‘Pharmacology and Pharmacy’ category. Analysis of 768 original research articles across 32 journals (with an average JIF of 2.565 ± 0.887) demonstrated that research studies involving molecular techniques, in vivo experiments on animals, and bioinformatics and computational modeling were significantly associated with a higher JIF value of the journal in which such contributions were published. Our analysis suggests that research studies involving such techniques/approaches are more likely to be published in higher-ranked pharmacology journals.

Impact of sex and pathophysiology on optimal drug choice in hypertensive rats: quantitative insights for precision medicine

Less than half of all hypertensive patients receiving treatment are successful in normalizing their blood pressure. Despite the complexity and heterogeneity of hypertension, the current antihypertensive guidelines are not tailored to the individual patient. As a step toward individualized treatment, we develop a quantitative systems pharmacology model of blood pressure regulation in the spontaneously hypertensive rat (SHR) and generate sex-specific virtual populations of SHRs to account for the heterogeneity between the sexes and within the pathophysiology of hypertension. We then used the mechanistic model integrated with machine learning tools to study how variability in these mechanisms leads to differential responses in rodents to the four primary classes of antihypertensive drugs. We found that both the sex and the pathophysiological profile of the individual play a major role in the response to hypertensive treatments. These results provide insight into potential areas to apply precision medicine in human primary hypertension.

A Review on Applications of Computational Methods in Drug Screening and Design

Drug development is one of the most significant processes in the pharmaceutical industry. Various computational methods have dramatically reduced the time and cost of drug discovery. In this review, we firstly discussed roles of multiscale biomolecular simulations in identifying drug binding sites on the target macromolecule and elucidating drug action mechanisms. Then, virtual screening methods (e.g., molecular docking, pharmacophore modeling, and QSAR) as well as structure- and ligand-based classical/de novo drug design were introduced and discussed. Last, we explored the development of machine learning methods and their applications in aforementioned computational methods to speed up the drug discovery process. Also, several application examples of combining various methods was discussed. A combination of different methods to jointly solve the tough problem at different scales and dimensions will be an inevitable trend in drug screening and design.

Discovery of Novel Multi-target Inhibitor of angiotensin type 1 receptor and neprilysin inhibitors from Traditional Chinese Medicine

Angiotensin II type-1 receptor–neprilysin inhibitor (ARNi) is consisted of Angiotensin II type-1 receptor (AT1) antagonist and neprilysin (NEP) inhibitor, which could simultaneously increase the vasodilators of the natriuretic peptides and antagonize vasoconstrictors of Ang II. ARNi has been proved a superior effect and lower risks of death on chronic heart failure (CHF) and hypertension. In this paper, ARNi from Traditional Chinese Medicines (TCM) was discovered based on target combination of AT1 and NEP by virtual screening, biological assay and molecular dynamics (MD) simulations. Two customized strategies of combinatorial virtual screening were implemented to discover AT1 antagonist and NEP inhibitor based on pharmacophore modeling and docking computation respectively. Gyrophoric acid (PubChem CID: 135728) from Parmelia saxatilis was selected as AT1 antagonist and assayed with IC₅₀ of 29.76 μM by calcium influx assay. And 3,5,3′-triiodothyronine (PubChem CID: 861) from Bos taurus domesticus was screened as NEP inhibitor and has a dose dependent inhibitory activity by biochemistry fluorescence assay. Combined with MD simulations, these compounds can generate interaction with the target, key interactive residues of ARG167, TRP84, and VAL108 in AT1, and HIS711 in NEP were also identified respectively. This study designs the combinatorial strategy to discover novel frames of ARNi from TCM, and gyrophoric acid and 3,5,3′-triiodothyronine could provide the clues and revelations of drug design and therapeutic method of CHF and hypertension for TCM clinical applications.

Mechanistic Models of Cellular Signaling, Cytokine Crosstalk, and Cell-Cell Communication in Immunology

The cells of the immune system respond to a great variety of different signals that frequently reach them simultaneously. Computational models of signaling pathways and cellular behavior can help us explore the biochemical mechanisms at play during such responses, in particular when those models aim at incorporating molecular details of intracellular reaction networks. Such detailed models can encompass hypotheses about the interactions among molecular binding domains and how these interactions are modulated by, for instance, post-translational modifications, or steric constraints in multi-molecular complexes. In this way, the models become formal representations of mechanistic immunological hypotheses that can be tested through quantitative simulations. Due to the large number of parameters (molecular abundances, association-, dissociation-, and enzymatic transformation rates) the goal of simulating the models can, however, in many cases no longer be the fitting of particular parameter values. Rather, the simulations perform sweeps through parameter space to test whether a model can account for certain experimentally observed features when allowing the parameter values to vary within experimentally determined or physiologically reasonable ranges. We illustrate how this approach can be used to explore possible mechanisms of immunological pathway crosstalk. Probing the input-output behavior of mechanistic pathway models through systematic simulated variations of receptor stimuli will soon allow us to derive cell population behavior from single-cell models, thereby bridging a scale gap that currently still is frequently addressed through heuristic phenomenological multi-scale models.

Considerations for an In Vitro, Cell-Based Testing Platform for Detection of Drug-Induced Inotropic Effects in Early Drug Development. Part 2: Designing and Fabricating Microsystems for Assaying Cardiac Contractility With Physiological Relevance Using Human iPSC-Cardiomyocytes

Contractility of the myocardium engines the pumping function of the heart and is enabled by the collective contractile activity of its muscle cells: cardiomyocytes. The effects of drugs on the contractility of human cardiomyocytes in vitro can provide mechanistic insight that can support the prediction of clinical cardiac drug effects early in drug development. Cardiomyocytes differentiated from human-induced pluripotent stem cells have high potential for overcoming the current limitations of contractility assays because they attach easily to extracellular materials and last long in culture, while having human- and patient-specific properties. Under these conditions, contractility measurements can be non-destructive and minimally invasive, which allow assaying sub-chronic effects of drugs. For this purpose, the function of cardiomyocytes in vitro must reflect physiological settings, which is not observed in cultured cardiomyocytes derived from induced pluripotent stem cells because of the fetal-like properties of their contractile machinery. Primary cardiomyocytes or tissues of human origin fully represent physiological cellular properties, but are not easily available, do not last long in culture, and do not attach easily to force sensors or mechanical actuators. Microengineered cellular systems with a more mature contractile function have been developed in the last 5 years to overcome this limitation of stem cell-derived cardiomyocytes, while simultaneously measuring contractile endpoints with integrated force sensors/actuators and image-based techniques. Known effects of engineered microenvironments on the maturity of cardiomyocyte contractility have also been discovered in the development of these systems. Based on these discoveries, we review here design criteria of microengineered platforms of cardiomyocytes derived from pluripotent stem cells for measuring contractility with higher physiological relevance. These criteria involve the use of electromechanical, chemical and morphological cues, co-culture of different cell types, and three-dimensional cellular microenvironments. We further discuss the use and the current challenges for developing and improving these novel technologies for predicting clinical effects of drugs based on contractility measurements with cardiomyocytes differentiated from induced pluripotent stem cells. Future research should establish contexts of use in drug development for novel contractility assays with stem cell-derived cardiomyocytes.

Tumor and endothelial cells collaborate via transcellular receptor complexes

Many cellular signaling pathways are initiated by cell-surface ligand-sensing complexes that incorporate not just one but multiple receptors. Most studies focus on receptors coexpressed on a single cell (cis interactions), but complexes containing receptors on adjacent cells (trans interactions) are also possible. Recent work by Morin et al published in this journal provides critical evidence for such trans interactions between Neuropilin-1 (NRP1) expressed on human tumor cells and vascular endothelial growth factor receptor 2 (VEGFR2) expressed on adjacent endothelial cells, with the ligand VEGFA binding and bridging the two receptors. They show that the formation of these complexes is correlated with reduced tumor proliferation and increased patient survival. They also observe trans NRP1-VEGFA-VEGFR2 repressing angiogenesis and cis NRP1-VEGFA-VEGFR2 increasing angiogenesis in selected cancers. The distinct molecular signature of each tumor and each patient will determine which type of complexes dominate and will influence prognosis and treatment. Copyright © 2018 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

Development of a Novel 3D Tumor-tissue Invasion Model for High-throughput, High-content Phenotypic Drug Screening

While much progress has been made in the war on cancer, highly invasive cancers such as pancreatic cancer remain difficult to treat and anti-cancer clinical trial success rates remain low. One shortcoming of the drug development process that underlies these problems is the lack of predictive, pathophysiologically relevant preclinical models of invasive tumor phenotypes. While present-day 3D spheroid invasion models more accurately recreate tumor invasion than traditional 2D models, their shortcomings include poor reproducibility and inability to interface with automated, high-throughput systems. To address this gap, a novel 3D tumor-tissue invasion model which supports rapid, reproducible setup and user-definition of tumor and surrounding tissue compartments was developed. High-cell density tumor compartments were created using a custom-designed fabrication system and standardized oligomeric type I collagen to define and modulate ECM physical properties. Pancreatic cancer cell lines used within this model showed expected differential invasive phenotypes. Low-passage, patient-derived pancreatic cancer cells and cancer-associated fibroblasts were used to increase model pathophysiologic relevance, yielding fibroblast-mediated tumor invasion and matrix alignment. Additionally, a proof-of-concept multiplex drug screening assay was applied to highlight this model's ability to interface with automated imaging systems and showcase its potential as a predictive tool for high-throughput, high-content drug screening.

The Spectrum of Mechanism-Oriented Models and Methods for Explanations of Biological Phenomena

Developing and improving mechanism-oriented computational models to better explain biological phenomena is a dynamic and expanding frontier. As the complexity of targeted phenomena has increased, so too has the diversity in methods and terminologies, often at the expense of clarity, which can make reproduction challenging, even problematic. To encourage improved semantic and methodological clarity, we describe the spectrum of Mechanism-oriented Models being used to develop explanations of biological phenomena. We cluster explanations of phenomena into three broad groups. We then expand them into seven workflow-related model types having distinguishable features. We name each type and illustrate with examples drawn from the literature. These model types may contribute to the foundation of an ontology of mechanism-based biomedical simulation research. We show that the different model types manifest and exert their scientific usefulness by enhancing and extending different forms and degrees of explanation. The process starts with knowledge about the phenomenon and continues with explanatory and mathematical descriptions. Those descriptions are transformed into software and used to perform experimental explorations by running and examining simulation output. The credibility of inferences is thus linked to having easy access to the scientific and technical provenance from each workflow stage.

A computational analysis of pro-angiogenic therapies for peripheral artery disease

Inducing therapeutic angiogenesis to effectively form hierarchical, non-leaky networks of perfused vessels in tissue engineering applications and ischemic disease remains an unmet challenge, despite extensive research and multiple clinical trials. Here, we use a previously-developed, multi-scale, computational systems pharmacology model of human peripheral artery disease to screen a diverse array of promising pro-angiogenic strategies, including gene therapy, biomaterials, and antibodies. Our previously-validated model explicitly accounts for VEGF immobilization, Neuropilin-1 binding, and weak activation of VEGF receptor 2 (VEGFR2) by the "VEGFxxxb" isoforms. First, we examine biomaterial-based delivery of VEGF engineered for increased affinity to the extracellular matrix. We show that these constructs maintain VEGF close to physiological levels and extend the duration of VEGFR2 activation. We demonstrate the importance of sub-saturating VEGF dosing to prevent angioma formation. Second, we examine the potential of ligand- or receptor-based gene therapy to normalize VEGF receptor signaling. Third, we explore the potential for antibody-based pro-angiogenic therapy. Our model supports recent observations that improvement in perfusion following treatment with anti-VEGF165b in mice is mediated by VEGF-receptor 1, not VEGFR2. Surprisingly, the model predicts that the approved anti-VEGF cancer drug, bevacizumab, may actually improve signaling of both VEGFR1 and VEGFR2 via a novel 'antibody swapping' effect that we demonstrate here. Altogether, this model provides insight into the mechanisms of action of several classes of pro-angiogenic strategies within the context of the complex molecular and physiological processes occurring in vivo. We identify molecular signaling similarities between promising approaches and key differences between promising and ineffective strategies.

Systems Pharmacology of VEGF165b in Peripheral Artery Disease

We built a whole‐body computational model to study the role of the poorly understood vascular endothelial growth factor (VEGF)_165b splice isoform in peripheral artery disease (PAD). This model was built and validated using published and new experimental data from cells, mice, and humans, and explicitly accounts for known properties of VEGF_165b: lack of extracellular matrix (ECM)‐binding and weak phosphorylation of vascular endothelial growth factor receptor‐2 (VEGFR2) in vitro. The resulting model captures all known information about VEGF_165b distribution and signaling in human PAD, and provides novel, nonintuitive insight into VEGF_165b mechanism of action in vivo. Although VEGF_165a and VEGF_165b compete for VEGFR2 in vitro, simulations show that these isoforms do not compete for VEGFR2 at much lower physiological concentrations. Instead, reduced VEGF_165a may drive impaired VEGFR2 signaling. The model predicts that VEGF_165b does compete for binding to VEGFR1, supporting a VEGFR1‐mediated response to anti‐VEGF_165b. The model predicts a key role for VEGF_165b in PAD, but in a different way than previously hypothesized.

Lupinifolin from Albizia myriophylla wood: A study on its antibacterial mechanisms against cariogenic Streptococcus mutans

OBJECTIVE

To determine the anti-Streptococcus mutans mechanisms of action of lupinifolin from Albizia myriophylla Benth. (Fabaceae) wood and provide scientific evidence to support the traditional use of the plant against dental caries.

METHODS

The minimum inhibitory concentration (MIC) was evaluated using the broth micro-dilution method. The effects of lupinifolin on bactericidal activity, bacterial cell walls, and membranes were investigated by time-kill, lysis, and leakage assays, respectively. Electron microscopy was utilized to observe any cell morphological changes caused by the compound. Localization of lupinifolin in S. mutans was detected using the thin layer chromatography technique.

RESULTS

The MIC range of lupinifolin against S. mutans (n=6) was 2-4 μg/ml. This compound displayed bactericidal effects on S. mutans ATCC 25175 by 90-99.9% killing at 4MIC-16MIC after 8-24 hours. Lupinifolin-treated cells demonstrated no lysis. However, significant cytoplasmic leakage through the bacterial membrane was observed after treatment with lupinifolin at 4MIC-16MIC. As revealed by ultrastructural analysis, lupinifolin produced some changes in bacterial cell walls and membranes. Moreover, the compound was observed in the cytoplasmic fraction of the lupinifolin-treated cells. These results suggest that lupinifolin can enter the cell of bacteria but does not accumulate in the cell envelope and subsequently disrupts the integrity of the cytoplasmic membrane, leading to cell death.

CONCLUSION

The scientific evidence from this study offers valuable insights into the potential role of lupinifolin in pharmaceutical and antibiotic applications and supports the therapeutic effects of A. myriophylla, which has traditionally been used as an alternative treatment for dental caries.

A computational analysis of in vivo VEGFR activation by multiple co-expressed ligands

The splice isoforms of vascular endothelial growth A (VEGF) each have different affinities for the extracellular matrix (ECM) and the coreceptor NRP1, which leads to distinct vascular phenotypes in model systems expressing only a single VEGF isoform. ECM-immobilized VEGF can bind to and activate VEGF receptor 2 (VEGFR2) directly, with a different pattern of site-specific phosphorylation than diffusible VEGF. To date, the way in which ECM binding alters the distribution of isoforms of VEGF and of the related placental growth factor (PlGF) in the body and resulting angiogenic signaling is not well-understood. Here, we extend our previous validated cell-level computational model of VEGFR2 ligation, intracellular trafficking, and site-specific phosphorylation, which captured differences in signaling by soluble and immobilized VEGF, to a multi-scale whole-body framework. This computational systems pharmacology model captures the ability of the ECM to regulate isoform-specific growth factor distribution distinctly for VEGF and PlGF, and to buffer free VEGF and PlGF levels in tissue. We show that binding of immobilized growth factor to VEGF receptors, both on endothelial cells and soluble VEGFR1, is likely important to signaling in vivo. Additionally, our model predicts that VEGF isoform-specific properties lead to distinct profiles of VEGFR1 and VEGFR2 binding and VEGFR2 site-specific phosphorylation in vivo, mediated by Neuropilin-1. These predicted signaling changes mirror those observed in murine systems expressing single VEGF isoforms. Simulations predict that, contrary to the 'ligand-shifting hypothesis,' VEGF and PlGF do not compete for receptor binding at physiological concentrations, though PlGF is predicted to slightly increase VEGFR2 phosphorylation when over-expressed by 10-fold. These results are critical to design of appropriate therapeutic strategies to control VEGF availability and signaling in regenerative medicine applications.

Inhibition of VEGFR2 Activation and Its Downstream Signaling to ERK1/2 and Calcium by Thrombospondin-1 (TSP1): In silico Investigation

VEGF signaling through VEGFR2 is a central regulator of the angiogenic response. Inhibition of VEGF signaling by the stress-induced matricellular protein TSP1 plays a role in modulating the angiogenic response to VEGF in both health and disease. TSP1 binding to CD47 inhibits VEGFR2 activation. The full implications of this inhibitory interaction are unknown. We developed a detailed rule-based computational model to inquire if TSP1-CD47 signaling through VEGF had downstream effects upon ERK1/2 and calcium. Our Simulations suggest that enhanced degradation of VEGFR2 initiated by the binding of TSP1 to CD47 is sufficient to explain the inhibition of VEGFR2 phosphorylation, calcium elevation, and ERK1/2 activation downstream of VEGF. A complementary mechanism involving the recruitment of phosphatases to the VEGFR2 complex with consequent increase in the rate of receptor dephosphorylation may augment the inhibition of the VEGF signal. The model was then utilized to simulate the effect of inhibiting external TSP1 or the depletion of CD47 as potential therapeutic strategies in restoring VEGF signaling. Results suggest that depleting CD47 is a more efficient strategy in inhibiting the effects of TSP1/CD47 on VEGF signaling. Our results highlight the utility of in silico investigations in elucidating and clarifying molecular mechanisms at the intersection of TSP1 and VEGF biology and in differentiating between competing pro-angiogenic therapeutic strategies relevant to peripheral arterial disease (PAD) and wound healing.

Multiscale Modeling in the Clinic: Drug Design and Development

A wide range of length and time scales are relevant to pharmacology, especially in drug development, drug design and drug delivery. Therefore, multiscale computational modeling and simulation methods and paradigms that advance the linkage of phenomena occurring at these multiple scales have become increasingly important. Multiscale approaches present in silico opportunities to advance laboratory research to bedside clinical applications in pharmaceuticals research. This is achievable through the capability of modeling to reveal phenomena occurring across multiple spatial and temporal scales, which are not otherwise readily accessible to experimentation. The resultant models, when validated, are capable of making testable predictions to guide drug design and delivery. In this review we describe the goals, methods, and opportunities of multiscale modeling in drug design and development. We demonstrate the impact of multiple scales of modeling in this field. We indicate the common mathematical and computational techniques employed for multiscale modeling approaches used in pharmacometric and systems pharmacology models in drug development and present several examples illustrating the current state-of-the-art models for (1) excitable systems and applications in cardiac disease; (2) stem cell driven complex biosystems; (3) nanoparticle delivery, with applications to angiogenesis and cancer therapy; (4) host-pathogen interactions and their use in metabolic disorders, inflammation and sepsis; and (5) computer-aided design of nanomedical systems. We conclude with a focus on barriers to successful clinical translation of drug development, drug design and drug delivery multiscale models.