Semi-Mechanistic Pharmacokinetic-Pharmacodynamic Model of Camostat Mesylate-Predicted Efficacy against SARS-CoV-2 in COVID-19.

The SARS-CoV-2 coronavirus, which causes COVID-19, uses a viral surface spike protein for host cell entry and the human cell-surface transmembrane serine protease, TMPRSS2, to process the spike protein. Camostat mesylate, an orally available and clinically used serine protease inhibitor, inhibits TMPRSS2, supporting clinical trials to investigate its use in COVID-19. A one-compartment pharmacokinetic (PK)/pharmacodynamic (PD) model for camostat and the active metabolite FOY-251 was developed, incorporating TMPRSS2 reversible covalent inhibition by FOY-251, and empirical equations linking TMPRSS2 inhibition of SARS-CoV-2 cell entry. The model predicts that 95% inhibition of TMPRSS2 is required for 50% inhibition of viral entry efficiency. For camostat 200 mg dosed four times daily, 90% inhibition of TMPRSS2 is predicted to occur but with only about 40% viral entry inhibition. For 3-fold higher camostat dosing, marginal improvement of viral entry rate inhibition, up to 54%, is predicted. Because respiratory tract viral load may be associated with negative outcome, even modestly reducing viral entry and respiratory tract viral load may reduce disease progression. This modeling also supports medicinal chemistry approaches to enhancing PK/PD and potency of the camostat molecule. IMPORTANCE Strategies to repurpose already-approved drugs for the treatment of COVID-19 has been attractive since the beginning of the pandemic. Camostat mesylate, a serine protease inhibitor approved in Japan for the treatment of acute exacerbations of chronic pancreatitis, inhibits TMPRSS1, a host cell surface serine protease essential for SARS-CoV-2 viral entry. In vitro experiments provided data suggesting that camostat might be effective in the treatment of COVID-19. Multiple clinical trials were planned to test the hypothesis that camostat would be beneficial for treating COVID-19 (for example, clinicaltrials.gov, NCT04353284). The present work used a one-compartment pharmacokinetic (PK)/pharmacodynamic (PD) mathematical model for camostat and the active metabolite FOY-251, incorporating TMPRSS2 reversible covalent inhibition by FOY-251, and empirical equations linking TMPRSS2 inhibition of SARS-CoV-2 cell entry. This work is valuable to guide further development of camostat mesylate and possible medicinal chemistry derivatives for the treatment of COVID-19.

Evaluation of Combination Strategies for the A2AR Inhibitor AZD4635 Across Tumor Microenvironment Conditions via a Systems Pharmacology Model.

Background: Adenosine receptor type 2 (A2AR) inhibitor, AZD4635, has been shown to reduce immunosuppressive adenosine effects within the tumor microenvironment (TME) and to enhance the efficacy of checkpoint inhibitors across various syngeneic models. This study aims at investigating anti-tumor activity of AZD4635 alone and in combination with an anti-PD-L1-specific antibody (anti-PD-L1 mAb) across various TME conditions and at identifying, via mathematical quantitative modeling, a therapeutic combination strategy to further improve treatment efficacy. Methods: The model is represented by a set of ordinary differential equations capturing: 1) antigen-dependent T cell migration into the tumor, with subsequent proliferation and differentiation into effector T cells (Teff), leading to tumor cell lysis; 2) downregulation of processes mediated by A2AR or PD-L1, as well as other immunosuppressive mechanisms; 3) A2AR and PD-L1 inhibition by, respectively, AZD4635 and anti-PD-L1 mAb. Tumor size dynamics data from CT26, MC38, and MCA205 syngeneic mice treated with vehicle, anti-PD-L1 mAb, AZD4635, or their combination were used to inform model parameters. Between-animal and between-study variabilities (BAV, BSV) in treatment efficacy were quantified using a non-linear mixed-effects methodology. Results: The model reproduced individual and cohort trends in tumor size dynamics for all considered treatment regimens and experiments. BSV and BAV were explained by variability in T cell-to-immunosuppressive cell (ISC) ratio; BSV was additionally driven by differences in intratumoral adenosine content across the syngeneic models. Model sensitivity analysis and model-based preclinical study simulations revealed therapeutic options enabling a potential increase in AZD4635-driven efficacy; e.g., adoptive cell transfer or treatments affecting adenosine-independent immunosuppressive pathways. Conclusions: The proposed integrative modeling framework quantitatively characterized the mechanistic activity of AZD4635 and its potential added efficacy in therapy combinations, across various immune conditions prevailing in the TME. Such a model may enable further investigations, via simulations, of mechanisms of tumor resistance to treatment and of AZD4635 combination optimization strategies.

Dose dependence of treatment-related adverse events for immune checkpoint inhibitor therapies: a model-based meta-analysis.

Programmed cell death-1 (PD-1) and/or cytotoxic T lymphocyte-associated antigen 4 (CTLA-4) immune checkpoint inhibitor (ICI) treatments are associated with adverse events (AEs), which may be dependent on ICI dose. Applying a model-based meta-analysis to evaluate safety data from published clinical trials from 2005 to 2018, we analyzed the dose/exposure dependence of ICI treatment-related AE (trAE) and immune-mediated AE (imAE) rates. Unlike with PD-1 inhibitor monotherapy, CTLA-4 inhibitor monotherapy exhibited a dose/exposure dependence on most AE types evaluated. Furthermore, combination therapy with PD-1 inhibitor significantly strengthened the dependence of trAE and imAE rates on CTLA-4 inhibitor dose/exposure.

Cycle Network Model of Prostaglandin H Synthase-1.

The kinetic model of Prostaglandin H Synthase-1 (PGHS-1) was developed to investigate its complex network kinetics and non-steroidal anti-inflammatory drugs (NSAIDs) efficacy in different in vitro and in vivo conditions. To correctly describe the complex mechanism of PGHS-1 catalysis, we developed a microscopic approach to modelling of intricate network dynamics of 35 intraenzyme reactions among 24 intermediate states of the enzyme. The developed model quantitatively describes interconnection between cyclooxygenase and peroxidase enzyme activities; substrate (arachidonic acid, AA) and reducing cosubstrate competitive consumption; enzyme self-inactivation; autocatalytic role of AA; enzyme activation threshold; and synthesis of intermediate prostaglandin G2 (PGG2) and final prostaglandin H2 (PGH2) products under wide experimental conditions. In the paper, we provide a detailed description of the enzyme catalytic cycle, model calibration based on a series of in vitro kinetic data, and model validation using experimental data on the regulatory properties of PGHS-1. The validated model of PGHS-1 with a unified set of kinetic parameters is applicable for in silico screening and prediction of the inhibition effects of NSAIDs and their combination on the balance of pro-thrombotic (thromboxane) and anti-thrombotic (prostacyclin) prostaglandin biosynthesis in platelets and endothelial cells expressing PGHS-1.

Role of T Cell-To-Dendritic Cell Chemoattraction in T Cell Priming Initiation in the Lymph Node: An Agent-Based Modeling Study.

The adaptive immune response is initiated in lymph nodes by contact between antigen-bearing dendritic cells (DCs) and antigen-specific T cells. A selected number of naïve T cells that recognize a specific antigen may proliferate into expanded clones, differentiate, and acquire an effector phenotype. Despite growing experimental knowledge, certain mechanistic aspects of T cell behavior in lymph nodes remain poorly understood. Computational modeling approaches may help in addressing such gaps. Here we introduce an agent-based model describing T cell movements and their interactions with DCs, leading to activation and expansion of cognate T cell clones, in a two-dimensional representation of the lymph node paracortex. The primary objective was to test the putative role of T cell chemotaxis toward DCs, and quantitatively assess the impact of chemotaxis with respect to T cell priming efficacy. Firstly, we evaluated whether chemotaxis of naïve T cells toward a nearest DC may accelerate the scanning process, by quantifying, through simulations, the number of unique T cell-DC contact events. We demonstrate that, in the presence of naïve T cell-to-DC chemoattraction, a higher total number of contacts occurs, as compared to a T cell random walk scenario. However, the forming swarm of naïve T cells, as these cells get attracted to the neighborhood of a DC, may then physically restrict access of additional T cells to the DC, leading to an actual decrease in the cumulative number of unique contacts between naïve T cells and DCs. Secondly, we investigated the potential role of chemotaxis in maintaining cognate T cell clone expansion. The time course of cognate T cells number in the system was used as a quantitative characteristic of the expansion. Model-based simulations indicate that inclusion of chemotaxis, which is selective for already activated (but not naïve) antigen-specific T cells, may strongly accelerate the time of immune response occurrence, which subsequently increases the overall amplitude of the T cell clone expansion process.

Quantitative Systems Pharmacology: An Exemplar Model-Building Workflow With Applications in Cardiovascular, Metabolic, and Oncology Drug Development.

Quantitative systems pharmacology (QSP), a mechanistically oriented form of drug and disease modeling, seeks to address a diverse set of problems in the discovery and development of therapies. These problems bring a considerable amount of variability and uncertainty inherent in the nonclinical and clinical data. Likewise, the available modeling techniques and related software tools are manifold. Appropriately, the development, qualification, application, and impact of QSP models have been similarly varied. In this review, we describe the progressive maturation of a QSP modeling workflow: a necessary step for the efficient, reproducible development and qualification of QSP models, which themselves are highly iterative and evolutive. Furthermore, we describe three applications of QSP to impact drug development; one supporting new indications for an approved antidiabetic clinical asset through mechanistic hypothesis generation, one highlighting efficacy and safety differentiation within the sodium-glucose cotransporter-2 inhibitor drug class, and one enabling rational selection of immuno-oncology drug combinations.

Quantitative Mechanistic Modeling in Support of Pharmacological Therapeutics Development in Immuno-Oncology.

Following the approval, in recent years, of the first immune checkpoint inhibitor, there has been an explosion in the development of immuno-modulating pharmacological modalities for the treatment of various cancers. From the discovery phase to late-stage clinical testing and regulatory approval, challenges in the development of immuno-oncology (IO) drugs are multi-fold and complex. In the preclinical setting, the multiplicity of potential drug targets around immune checkpoints, the growing list of immuno-modulatory molecular and cellular forces in the tumor microenvironment-with additional opportunities for IO drug targets, the emergence of exploratory biomarkers, and the unleashed potential of modality combinations all have necessitated the development of quantitative, mechanistically-oriented systems models which incorporate key biology and patho-physiology aspects of immuno-oncology and the pharmacokinetics of IO-modulating agents. In the clinical setting, the qualification of surrogate biomarkers predictive of IO treatment efficacy or outcome, and the corresponding optimization of IO trial design have become major challenges. This mini-review focuses on the evolution and state-of-the-art of quantitative systems models describing the tumor vs. immune system interplay, and their merging with quantitative pharmacology models of IO-modulating agents, as companion tools to support the addressing of these challenges.

Radiation and PD-(L)1 treatment combinations: immune response and dose optimization via a predictive systems model.

BACKGROUND: Numerous oncology combination therapies involving modulators of the cancer immune cycle are being developed, yet quantitative simulation models predictive of outcome are lacking. We here present a model-based analysis of tumor size dynamics and immune markers, which integrates experimental data from multiple studies and provides a validated simulation framework predictive of biomarkers and anti-tumor response rates, for untested dosing sequences and schedules of combined radiation (RT) and anti PD-(L)1 therapies. METHODS: A quantitative systems pharmacology model, which includes key elements of the cancer immunity cycle and the tumor microenvironment, tumor growth, as well as dose-exposure-target modulation features, was developed to reproduce experimental data of CT26 tumor size dynamics upon administration of RT and/or a pharmacological IO treatment such as an anti-PD-L1 agent. Variability in individual tumor size dynamics was taken into account using a mixed-effects model at the level of tumor-infiltrating T cell influx. RESULTS: The model allowed for a detailed quantitative understanding of the synergistic kinetic effects underlying immune cell interactions as linked to tumor size modulation, under these treatments. The model showed that the ability of T cells to infiltrate tumor tissue is a primary determinant of variability in individual tumor size dynamics and tumor response. The model was further used as an in silico evaluation tool to quantitatively predict, prospectively, untested treatment combination schedules and sequences. We demonstrate that anti-PD-L1 administration prior to, or concurrently with RT reveal further synergistic effects, which, according to the model, may materialize due to more favorable dynamics between RT-induced immuno-modulation and reduced immuno-suppression of T cells through anti-PD-L1. CONCLUSIONS: This study provides quantitative mechanistic explanations of the links between RT and anti-tumor immune responses, and describes how optimized combinations and schedules of immunomodulation and radiation may tip the immune balance in favor of the host, sufficiently to lead to tumor shrinkage or rejection.

A Generic Mechanism for Enhanced Cytokine Signaling via Cytokine-Neutralizing Antibodies.

Enhancement or inhibition of cytokine signaling and corresponding immune cells responses are critical factors in various disease treatments. Cytokine signaling may be inhibited by cytokine-neutralizing antibodies (CNAs), which prevents further activation of cytokine receptors. However, CNAs may result in enhanced-instead of inhibitory-cytokine signaling (an "agonistic effect") in various in vitro and in vivo experiments. This may lead to lack of efficacy or adverse events for cytokine-inhibiting based medicines. Alternatively, cytokine-antibody complexes may produce stronger signaling vs. cytokine alone, thereby increasing the efficacy of stimulating cytokine-based drugs, at equal or lower cytokine doses. In this paper, the effect of cytokine signaling enhancement by a CNA was studied in a generic mathematical model of interleukin-4 (IL-4) driven T-cell proliferation. The occurrence of the agonistic effect depends upon the antibody-to-cytokine binding affinity and initial concentrations of antibody and cytokine. Model predictions were in agreement with experimental studies. When the cytokine receptor consists of multiple subunits with substantially differing affinities (e.g., IL-4 case), the choice of the receptor chain to be blocked by the antibody is critical, for the agonistic effect to appear. We propose a generic mechanism for the effect: initially, binding of the CNA to the cytokine reduces free cytokine concentration; yet, cytokine molecules bound within the cytokine-CNA complex-and released later and over time-are "rescued" from earlier clearance via cellular internalization. Hence, although free cytokine-dependent signalling may be less potent initially, it will also be more sustained over time; and given non-linear dynamics, it will lead ultimately to larger cellular effector responses, vs. the same amount of free cytokine in the absence of CNA. We suggest that the proposed mechanism is a generic property of {cytokine, CNA, receptor} triads, both in vitro and in vivo, and can occur in a predictable fashion for a variety of cytokines of the immune system.

Agent Based Modeling of Human Gut Microbiome Interactions and Perturbations.

BACKGROUND: Intestinal microbiota plays an important role in the human health. It is involved in the digestion and protects the host against external pathogens. Examination of the intestinal microbiome interactions is required for understanding of the community influence on host health. Studies of the microbiome can provide insight on methods of improving health, including specific clinical procedures for individual microbial community composition modification and microbiota correction by colonizing with new bacterial species or dietary changes. METHODOLOGY/PRINCIPAL FINDINGS: In this work we report an agent-based model of interactions between two bacterial species and between species and the gut. The model is based on reactions describing bacterial fermentation of polysaccharides to acetate and propionate and fermentation of acetate to butyrate. Antibiotic treatment was chosen as disturbance factor and used to investigate stability of the system. System recovery after antibiotic treatment was analyzed as dependence on quantity of feedback interactions inside the community, therapy duration and amount of antibiotics. Bacterial species are known to mutate and acquire resistance to the antibiotics. The ability to mutate was considered to be a stochastic process, under this suggestion ratio of sensitive to resistant bacteria was calculated during antibiotic therapy and recovery. CONCLUSION/SIGNIFICANCE: The model confirms a hypothesis of feedbacks mechanisms necessity for providing functionality and stability of the system after disturbance. High fraction of bacterial community was shown to mutate during antibiotic treatment, though sensitive strains could become dominating after recovery. The recovery of sensitive strains is explained by fitness cost of the resistance. The model demonstrates not only quantitative dynamics of bacterial species, but also gives an ability to observe the emergent spatial structure and its alteration, depending on various feedback mechanisms. Visual version of the model shows that spatial structure is a key factor, which helps bacteria to survive and to adapt to changed environmental conditions.

Kinetic modelling of NSAID action on COX-1: focus on in vitro/in vivo aspects and drug combinations.

The detailed kinetic model of Prostaglandin H Synthase-1 (COX-1) was developed to in silico test and predict inhibition effects of nonsteroidal anti-inflammatory drugs (NSAIDs) on target. The model takes into account key features of the complex catalytic mechanism of cyclooxygenase-1, converting arachidonic acid to prostaglandin PGH(2), and includes the description of the enzyme interaction with various types of NSAIDs (reversible/irreversible, non-selective and selective to COX-1/COX-2). Two different versions of the model were designed to simulate the inhibition of COX-1 by NSAIDs in two most popular experimental settings - in vitro studies with purified enzyme, and the experiments with platelets. The developed models were applied to calculate the dose-dependence of aspirin and celecoxib action on COX- 1 in vitro and in vivo conditions. The mechanism of the enhancement of aspirin efficiency in platelet as compared to its action on purified COX-1 was elucidated. The dose-dependence of celecoxib simulated with the use of the "in vivo" version of the model predicted potentially strong inhibitory effect of celecoxib on thromboxan production in platelets. Simulation of the combined effect of two NSAIDs, aspirin and celecoxib, on COX-1 allowed us to reveal the mechanism underlying the suppression of aspirin-mediated COX-1 inhibition by celecoxib. We discuss our modelling results in the context of the on-going debates on the potential cardio-vascular risks associated with co-administration of various types of NSAIDs.

Docking of ATP to Ca-ATPase: considering protein domain motions.

Most standard molecular docking algorithms take into account only ligand flexibility, while numerous studies demonstrate that receptor flexibility may be also important. While some efficient methods have been proposed to take into account local flexibility of protein side chains, the influence of large-scale domain motions on the docking results still represents a challenge for computational methods. In this work we compared the results of ATP docking to different models of Ca-ATPase: crystallographic apo- and holo-forms of the enzyme as well as "flexible" target models generated via molecular dynamics (MD) simulations in water. MD simulations were performed for two different apo-forms and one holo-form of Ca2+-ATPase and reveal large-scale domain motions of type "closure", which is consistent with experimental structures. Docking to a set of MD-conformers yielded correct solutions with ATP bound in both domains regardless of the starting Ca2+-ATPase structure. Also, special attention was paid to proper ranking of docking solutions and some particular features of different scoring functions and their applicability for the model of "flexible" receptor. Particularly, the results of docking ATP were ranked by a scoring criterion specially designed to estimate ATP-protein interactions. This criterion includes stacking and hydrophobic interactions characteristic of ATP-protein complexes. The performance of this ligand-specific scoring function was considerably better than that of a standard scoring function used in the docking algorithm.

Complementarity of hydrophobic properties in ATP-protein binding: a new criterion to rank docking solutions.

ATP is an important substrate of numerous biochemical reactions in living cells. Molecular recognition of this ligand by proteins is very important for understanding enzymatic mechanisms. Considerable insight into the problem may be gained via molecular docking simulations. At the same time, standard docking protocols are often insufficient to predict correct conformations for protein-ATP complexes. Thus, in most cases the native-like solutions can be found among the docking poses, but current scoring functions have only limited ability to discriminate them from false positives. To improve the selection of correct docking solutions obtained with the GOLD software, we developed a new ranking criterion specific for ATP-protein binding. The method is based on detailed analysis of the intermolecular interactions in 40 high-resolution 3D structures of ATP-protein complexes (the training set). We found that the most important factors governing this recognition are hydrogen-bonding, stacking between adenine and aromatic protein residues, and hydrophobic contacts between adenine and protein residues. To address the latter, we applied the formalism of 3D molecular hydrophobicity potential. The results obtained were used to construct an ATP-oriented scoring criterion as a linear combination of the terms describing these intermolecular interactions. The criterion was then validated using the test set of 10 additional ATP-protein complexes. As compared with the standard scoring functions, the new ranking criterion significantly improved the selection of correct docking solutions in both sets and allowed considerable enrichment at the top of the list containing docking poses with correct solutions.

Development of the force field parameters for phosphoimidazole and phosphohistidine.

Phosphorylation of histidine-containing proteins is a key step in the mechanism of many phosphate transfer enzymes (kinases, phosphatases) and is the first stage in a wide variety of signal transduction cascades in bacteria, yeast, higher plants, and mammals. Studies of structural and dynamical aspects of such enzymes in the phosphorylated intermediate states are important for understanding the intimate molecular mechanisms of their functioning. Such information may be obtained via molecular dynamics and/or docking simulations, but in this case appropriate force field parameters for phosphohistidine should be explicitly defined. In the present article we describe development of the GROMOS96 force field parameters for phosphoimidazole molecule--a realistic model of the phosphohistidine side chain. The parameterization is based on the results of ab initio quantum chemical calculations with subsequent refinement and testing using molecular mechanics and molecular dynamics simulations. The set of force constants and equilibrium geometry is employed to derive force field for the phosphohistidine moiety. Resulting parameters and topology are incorporated into the molecular modeling package GROMACS and used in molecular dynamics simulations of a phosphohistidine-containing protein in explicit solvent.

The distinct functional properties of the nucleotide-binding domain of ATP7B, the human copper-transporting ATPase: analysis of the Wilson disease mutations E1064A, H1069Q, R1151H, and C1104F.

Copper transport by the P(1)-ATPase ATP7B, or Wilson disease protein (WNDP),1 is essential for human metabolism. Perturbation of WNDP function causes intracellular copper accumulation and severe pathology, known as Wilson disease (WD). Several WD mutations are clustered within the WNDP nucleotide-binding domain (N-domain), where they are predicted to disrupt ATP binding. The mechanism by which the N-domain coordinates ATP is presently unknown, because residues important for nucleotide binding in the better characterized P(2)-ATPases are not conserved within the P(1)-ATPase subfamily. To gain insight into nucleotide binding under normal and disease conditions, we generated the recombinant WNDP N-domain and several WD mutants. Using isothermal titration calorimetry, we demonstrate that the N-domain binds ATP in a Mg(2+)-independent manner with a relatively high affinity of 75 microm, compared with millimolar affinities observed for the P(2)-ATPase N-domains. The WNDP N-domain shows minimal discrimination between ATP, ADP, and AMP, yet discriminates well between ATP and GTP. Similar results were obtained for the N-domain of ATP7A, another P(1)-ATPase. Mutations of the invariant WNDP residues E1064A and H1069Q drastically reduce nucleotide affinities, pointing to the likely role of these residues in nucleotide coordination. In contrast, the R1151H mutant exhibits only a 1.3-fold reduction in affinity for ATP. The C1104F mutation significantly alters protein folding, whereas C1104A does not affect the structure or function of the N-domain. Together, the results directly demonstrate the phenotypic diversity of WD mutations within the N-domain and indicate that the nucleotide-binding properties of the P(1)-ATPases are distinct from those of the P(2)-ATPases.

Molecular modelling of the nucleotide-binding domain of Wilson's disease protein: location of the ATP-binding site, domain dynamics and potential effects of the major disease mutations.

WNDP (Wilson's disease protein) is a copper-transporting ATPase that plays an essential role in human physiology. Mutations in WNDP result in copper accumulation in tissues and cause a severe hepato-neurological disorder known as Wilson's disease. Several mutations were surmised to affect the nucleotide binding and hydrolysis by WNDP; however, how the nucleotides bind to normal and mutated WNDP remains unknown. To aid such studies, we performed the molecular modelling of the spatial structure and dynamics of the ATP-binding domain of WNDP and its interactions with ATP. The three-dimensional models of this domain in two conformations were built using the X-ray structures of the Ca2+-ATPase in the E1 and E2 states. To study the functional aspects of the models, they were subjected to long-term molecular dynamics simulations in an explicit solvent; similar calculations were performed for the ATP-binding domain of Ca2+-ATPase. In both cases, we found large-scale motions that lead to significant changes of distances between several functionally important residues. The ATP docking revealed two possible modes of ATP binding: via adenosine buried in the cleft near residues H1069, R1151 and D1164, and via phosphate moiety 'anchored' by H-bonds with residues in the vicinity of catalytic D1027. Furthermore, interaction of ATP with both sites occurs if they are spatially close to each other. This may be achieved after relative domain motions of the 'closure' type observed in molecular dynamics simulations. The results provide a framework for analysis of disease mutations and for future mutagenesis studies.