Factor Xa generation by computational modeling: an additional discriminator to thrombin generation evaluation

Mathematical models of coagulation-are we there yet?

BACKGROUND

Mathematical models of coagulation have been developed to mirror thrombin generation in plasma, with the aim of investigating how variation in coagulation factor levels regulates hemostasis. However, current models vary in the reactions they capture and the reaction rates used, and their validation is restricted by a lack of large coherent datasets, resulting in questioning of their utility.

OBJECTIVES

To address this debate, we systematically assessed current models against a large dataset, using plasma coagulation factor levels from 348 individuals with normal hemostasis to identify the causes of these variations.

METHODS

We compared model predictions with measured thrombin generation, quantifying and comparing the ability of each model to predict thrombin generation, the contributions of the individual reactions, and their dependence on reaction rates.

RESULTS

We found that no current model predicted the hemostatic response across the whole cohort and all produced thrombin generation curves that did not resemble those obtained experimentally. Our analysis has identified the key reactions that lead to differential model predictions, where experimental uncertainty leads to variability in predictions, and we determined reactions that have a high influence on measured thrombin generation, such as the contribution of factor XI.

CONCLUSION

This systematic assessment of models of coagulation, using large dataset inputs, points to ways in which these models can be improved. A model that accurately reflects the effects of the multiple subtle variations in an individual's hemostatic profile could be used for assessing antithrombotics or as a tool for precision medicine.

Systems Biology Approach for Personalized Hemostasis Correction

The correction of blood coagulation impairments of a bleeding or thrombotic nature employs standard protocols where the type of drug, its dose and the administration regime are stated. However, for a group of patients, such an approach may be ineffective, and personalized therapy adjustment is needed. Laboratory hemostasis tests are used to control the efficacy of therapy, which is expensive and time-consuming. Computer simulations may become an inexpensive and fast alternative to real blood tests. In this work, we propose a procedure to numerically define the individual hemostasis profile of a patient and estimate the anticoagulant efficacy of low-molecular-weight heparin (LMWH) based on the computer simulation of global hemostasis assays. We enrolled a group of 12 patients receiving LMWH therapy and performed routine coagulation assays (activated partial thromboplastin time and prothrombin time) and global hemostasis assays (thrombodynamics and thrombodynamics-4d) and measured anti-Xa activity, fibrinogen, prothrombin and antithrombin levels, creatinine clearance, lipid profiles and clinical blood counts. Blood samples were acquired 3, 6 and 12 h after LMWH administration. We developed a personalized pharmacokinetic model of LMWH and coupled it with the mechanism-driven blood coagulation model, which described the spatial dynamics of fibrin and thrombin propagation. We found that LMWH clearance was significantly lower in the group with high total cholesterol levels. We generated an individual patient's hemostasis profile based on the results of routine coagulation assays. We propose a method to simulate the results of global hemostasis assays in the case of an individual response to LMWH therapy, which can potentially help with hemostasis corrections based on the output of global tests.

A System-Wide Investigation and Stratification of the Hemostatic Proteome in Premature Myocardial Infarction

Introduction

Advancing understanding of key factors that determine the magnitude of the hemostatic response may facilitate the identification of individuals at risk of generating an occlusive thrombus as a result of an atherothrombotic event such as an acute Myocardial Infarction (MI). While fibrinogen levels are a recognized risk factor for MI, the association of thrombotic risk with other coagulation proteins is inconsistent. This is likely due to the complex balance of pro- and anticoagulant factors in any individual.

Methods

We compared measured levels of pro- and anticoagulant proteins in plasma from 162 patients who suffered an MI at an early age (MI <50 y) and 186 age- and gender-matched healthy controls with no history of CAD. We then used the measurements from these individuals as inputs for an established mathematical model to investigate how small variations in hemostatic factors affect the overall amplitude of the hemostatic response and to identify differential key drivers of the hemostatic response in male and female patients and controls.

Results

Plasma from the MI patients contained significantly higher levels of Tissue Factor (P = 0.007), the components of the tenase (FIX and FVIII; P < 0.0001 for both) and the prothrombinase complexes (FX; P = 0.003), and lower levels of Tissue Factor Pathway Inhibitor (TFPI; P = 0.033) than controls. The mathematical model, which generates time-dependent predictions describing the depletion, activation, and interaction of the main procoagulant factors and inhibitors, identified different patterns of hemostatic response between MI patients and controls, and additionally, between males and females. Whereas, in males, TF, FVIII, FIX, and the inhibitor TFPI contribute to the differences seen between case and controls, and in females, FII, FVIII, and FIX had the greatest influence on the generation of thrombin. We additionally show that further donor stratification may be possible according to the predicted donor response to anticoagulant therapy.

Conclusions

We suggest that modeling could be of value in enhancing our prediction of risk of premature MI, recurrent risk, and therapeutic efficacy.

Coagulation factors directly cleave SARS-CoV-2 spike and enhance viral entry

Coagulopathy is a significant aspect of morbidity in COVID-19 patients. The clotting cascade is propagated by a series of proteases, including factor Xa and thrombin. While certain host proteases, including TMPRSS2 and furin, are known to be important for cleavage activation of SARS-CoV-2 spike to promote viral entry in the respiratory tract, other proteases may also contribute. Using biochemical and cell-based assays, we demonstrate that factor Xa and thrombin can also directly cleave SARS-CoV-2 spike, enhancing infection at the stage of viral entry. Coagulation factors increased SARS-CoV-2 infection in human lung organoids. A drug-repurposing screen identified a subset of protease inhibitors that promiscuously inhibited spike cleavage by both transmembrane serine proteases and coagulation factors. The mechanism of the protease inhibitors nafamostat and camostat may extend beyond inhibition of TMPRSS2 to coagulation-induced spike cleavage. Anticoagulation is critical in the management of COVID-19, and early intervention could provide collateral benefit by suppressing SARS-CoV-2 viral entry. We propose a model of positive feedback whereby infection-induced hypercoagulation exacerbates SARS-CoV-2 infectivity.

Coagulation factors directly cleave SARS-CoV-2 spike and enhance viral entry

Coagulopathy is recognized as a significant aspect of morbidity in COVID-19 patients. The clotting cascade is propagated by a series of proteases, including factor Xa and thrombin. Other host proteases, including TMPRSS2, are recognized to be important for cleavage activation of SARS-CoV-2 spike to promote viral entry. Using biochemical and cell-based assays, we demonstrate that factor Xa and thrombin can also directly cleave SARS-CoV-2 spike, enhancing viral entry. A drug-repurposing screen identified a subset of protease inhibitors that promiscuously inhibited spike cleavage by both transmembrane serine proteases as well as coagulation factors. The mechanism of the protease inhibitors nafamostat and camostat extend beyond inhibition of TMPRSS2 to coagulation-induced spike cleavage. Anticoagulation is critical in the management of COVID-19, and early intervention could provide collateral benefit by suppressing SARS-CoV-2 viral entry. We propose a model of positive feedback whereby infection-induced hypercoagulation exacerbates SARS-CoV-2 infectivity.

Andexanet versus prothrombin complex concentrates: Differences in reversal of factor Xa inhibitors in in vitro thrombin generation

BACKGROUND

Andexanet alfa (andexanet) is a modified human factor Xa (FXa) approved for anticoagulation reversal in patients with life-threatening bleeding treated with rivaroxaban or apixaban. Four-factor prothrombin complex concentrates (4F-PCCs) are approved for reversal of vitamin K antagonist-induced anticoagulation but not FXa inhibitors. The mechanism and effectiveness of 4F-PCCs for FXa inhibitor reversal are unclear.

OBJECTIVE

To investigate the mechanism and impact of 4F-PCCs on reversal of rivaroxaban and apixaban in vitro compared to andexanet.

METHODS

The effect of 4F-PCCs (or individual factors) on tissue factor-initiated thrombin generation (TF-TG) was evaluated in human plasma, with or without rivaroxaban or apixaban, and compared with andexanet under the same conditions.

RESULTS

In the TF-TG assay, 4F-PCC completely reversed warfarin anticoagulation. Andexanet normalized TF-TG over a wide range of apixaban and rivaroxaban concentrations tested (19-2000 ng/mL). However, 4F-PCC (or individual factors) was unable to normalize endogenous thrombin potential (ETP) or peak thrombin (Peak) in the presence of apixaban or rivaroxaban (75-500 ng/mL). TF-TG was only normalized by 4F-PCC at inhibitor concentrations <75 ng/mL (ETP) or <37.5 ng/mL (Peak). These data can be explained by the estimated thresholds of FXa activity required to support normal TF-TG based on the inhibitor:FXa ratios and levels of uninhibited FXa. The data are consistent with healthy volunteer studies where TF-TG is not normalized until inhibitor levels are substantially decreased.

CONCLUSIONS

Both the theoretical calculations and experimental data demonstrated that 4F-PCCs are only able to normalize TG over a low and narrow range of FXa inhibitor concentrations (<75 ng/mL).

A New Test for the Detection of Direct Oral Anticoagulants (Rivaroxaban and Apixaban) in the Emergency Room Setting

Determining whether a patient has taken a direct oral anticoagulant (DOAC) is critical during the periprocedural and preoperative period in the emergency department. However, the inaccessibility of complete medical records, along with the generally inconsistent sensitivity of conventional coagulation tests to these drugs, complicates clinical decision making and puts patients at risk of uncontrollable bleeding. In this study, we evaluate the utility of inhibitor-II-X (i-II-X), a novel, microfluidics-based diagnostic assay for the detection and identification of Factor Xa inhibitors (FXa-Is) in an acute care setting.

DESIGN

First-in-human, 91-patient, single-center retrospective pilot study.

SETTING

Emergency room.

PATIENTS

Adult patients admitted into the emergency department, which received any clinician-ordered coagulation test requiring a 3.2% buffered sodium citrate blood collection tube.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

Plasma samples from patients admitted to the emergency department were screened for the use of FXa-Is, including apixaban and rivaroxaban, within the past 24 hours using our new i-II-X microfluidic test. i-II-X results were then compared with results from conventional coagulation tests, including prothrombin time (PT) and international normalized ratio (INR), which were ordered by treating clinicians, and an anti-Xa assay for rivaroxaban. The i-II-X test detected DOACs in samples collected from the emergency department with 95.20% sensitivity and 100.00% specificity. Unlike PT and INR, i-II-X reliably identified patients who had prolonged clotting times secondary to the presence of a FXa-I.

CONCLUSIONS

The i-II-X test overcomes the limitations of currently available coagulation tests and could be a useful tool by which to routinely screen patients for DOACs in emergency and critical care settings. Our new diagnostic approach is particularly relevant in clinical situations where medical records may be unavailable, or where precautions need to be taken prior to invasive interventions, such as specific reversal agent administration.

Utilizing Plasma Composition Data to Help Determine Procoagulant Dynamics in Patients with Thermal Injury: A Computational Assessment

INTRODUCTION

The development of methods that generate individualized assessments of the procoagulant potential of burn patients could improve their treatment. Beyond its role as an essential intermediate in the formation of thrombin, factor (F)Xa has systemic effects as an agonist to inflammatory processes. In this study, we use a computational model to study the FXa dynamics underlying tissue factor-initiated thrombin generation in a small cohort of burn patients.

MATERIALS AND METHODS

Plasma samples were collected upon admission (Hour 0) from nine subjects (five non-survivors) with major burn injuries and then at 48 hours. Coagulation factor concentrations (II, V, VII, VIII, IX, X, TFPI, antithrombin (AT), protein C (PC)) were measured and used in a computational model to generate time course profiles for thrombin (IIa), FXa, extrinsic tenase, intrinsic tenase and prothrombinase complexes upon a 5 pM tissue factor stimulus in the presence of 1 nM thrombomodulin. Parameters were extracted from the thrombin and FXa profiles (including max rate (MaxRIIa and MaxRFXa) and peak level (MaxLIIa and MaxLFXa)). Procoagulant potential was also evaluated by determining the concentration of the complexes at select times. Parameter values were compared between survivors and non-survivors in the burn cohort and between the burn cohort and a simulation based on the mean physiological (100%) concentration for all factor levels.

RESULTS

Burn patients differed at Hour 0 (p < 0.05) from 100% mean physiological levels for all coagulation factor levels except FV and FVII. The concentration of FX, FII, TFPI, AT and PC was lower; FIX and FVIII were increased. The composition differences resulted in all nine burn patients at Hour 0 displaying a procoagulant phenotype relative to 100% mean physiological simulation (MaxLIIa (306 ± 90 nM vs. 52 nM), MaxRIIa (2.9 ± 1.1 nM/s vs. 0.3 nM/s), respectively p < 0.001); MaxRFXa and MaxLFXa were also an order of magnitude greater than 100% mean physiological simulation (p < 0.001). When grouped by survival status and compared at the time of admission, non-survivors had lower PC levels (56 ± 18% vs. 82 ± 9%, p < 0.05), and faster MaxRFXa (29 ± 6 pM/s vs. 18 ± 6 pM/s, p < 0.05) than those that survived; similar trends were observed for all other procoagulant parameters. At 48 hours when comparing non-survivors to survivors, TFPI levels were higher (108 ± 18% vs. 59 ± 18%, p < 0.05), and MaxRIIa (1.5 ± 1.4 nM/s vs. 3.6 ± 0.7 nM/s, p < 0.05) and MaxRFXa (13 ± 12 pM/s vs. 35 ± 4 pM/s, p < 0.05) were lower; similar trends were observed with all other procoagulant parameters. Overall, between admission and 48 hours, procoagulant potential, as represented by MaxR and MaxL parameters for thrombin and FXa, in non-survivors decreased while in survivors they increased (p < 0.05). In patients that survived, there was a positive correlation between FX levels and MaxLFXa (r = 0.96) and reversed in mortality (r= -0.91).

CONCLUSIONS

Thrombin and FXa generation are increased in burn patients at admission compared to mean physiological simulations. Over the first 48 hours, burn survivors became more procoagulant while non-survivors became less procoagulant. Differences between survivors and non-survivors appear to be present in the underlying dynamics that contribute to FXa dynamics. Understanding how the individual specific balance of procoagulant and anticoagulant proteins contributes to thrombin and FXa generation could ultimately guide therapy and potentially reduce burn injury-related morbidity and mortality.

In silico thrombin generation: Plasma composition imbalance and mortality in human immunodeficiency virus

Background

Effective HIV treatment with antiretroviral therapy has prolonged survival and shifted causes of death to non-AIDS illnesses such as cardiovascular disease. We have shown that inflammation and HIV viral load associate with pro- and anticoagulant factor imbalances resulting in increased thrombin generation when mathematically modeled. We explore the hypothesis that factor compositional imbalance, corresponding to increased in silico thrombin generation, predicts mortality among HIV+ persons.

Methods

In a nested case-control study of HIV+ individuals on continuous antiretroviral therapy in two large trials, we evaluated cases (any non-violent mortality, n = 114) and matched controls (n = 318). Thrombin generation in response to a tissue-factor initiator for each individual was calculated by a mathematical model incorporating levels of factors (F)II, V, VII, VIII, IX, X, antithrombin, tissue factor pathway inhibitor, and protein C (PC) measured at study entry to the trials. In silico thrombin generation metrics included clot time, maximum rate (MaxR), maximum level (MaxL), and area under the curve (AUC).

Results

Levels of antithrombin and PC decreased, while FV and FVIII were higher in cases vs controls. This resulted in a more procoagulant phenotype with increased MaxR, MaxL, and AUC in cases compared to controls (P < 0.05 for all).

Conclusions

Antithrombin, FV, FVIII, and PC were the major contributors to the increased thrombin generation associated with mortality risk. Our results suggest that mortality in HIV is associated with an increase in in silico thrombin generation via altered balance of pro- and anticoagulant factors, likely due to an inflammatory response signal, and resulting coagulopathy.

Dynamics of Thrombin Generation and Flux from Clots during Whole Human Blood Flow over Collagen/Tissue Factor Surfaces

Coagulation kinetics are well established for purified blood proteases or human plasma clotting isotropically. However, less is known about thrombin generation kinetics and transport within blood clots formed under hemodynamic flow. Using microfluidic perfusion (wall shear rate, 200 s^-1) of corn trypsin inhibitor-treated whole blood over a 250-μm long patch of type I fibrillar collagen/lipidated tissue factor (TF; ∼1 TF molecule/μm²), we measured thrombin released from clots using thrombin-antithrombin immunoassay. The majority (>85%) of generated thrombin was captured by intrathrombus fibrin as thrombin-antithrombin was largely undetectable in the effluent unless Gly-Pro-Arg-Pro (GPRP) was added to block fibrin polymerization. With GPRP present, the flux of thrombin increased to ∼0.5 × 10^-12 nmol/μm²-s over the first 500 s of perfusion and then further increased by ∼2-3-fold over the next 300 s. The increased thrombin flux after 500 s was blocked by anti-FXIa antibody (O1A6), consistent with thrombin-feedback activation of FXI. Over the first 500 s, ∼92,000 molecules of thrombin were generated per surface TF molecule for the 250-μm-long coating. A single layer of platelets (obtained with α_IIbβ₃ antagonism preventing continued platelet deposition) was largely sufficient for thrombin production. Also, the overall thrombin-generating potential of a 1000-μm-long coating became less efficient on a per μm² basis, likely due to distal boundary layer depletion of platelets. Overall, thrombin is robustly generated within clots by the extrinsic pathway followed by late-stage FXIa contributions, with fibrin localizing thrombin via its antithrombin-I activity as a potentially self-limiting hemostatic mechanism.

Random Forests Are Able to Identify Differences in Clotting Dynamics from Kinetic Models of Thrombin Generation

Current methods for distinguishing acute coronary syndromes such as heart attack from stable coronary artery disease, based on the kinetics of thrombin formation, have been limited to evaluating sensitivity of well-established chemical species (e.g., thrombin) using simple quantifiers of their concentration profiles (e.g., maximum level of thrombin concentration, area under the thrombin concentration versus time curve). In order to get an improved classifier, we use a 34-protein factor clotting cascade model and convert the simulation data into a high-dimensional representation (about 19000 features) using a piecewise cubic polynomial fit. Then, we systematically find plausible assays to effectively gauge changes in acute coronary syndrome/coronary artery disease populations by introducing a statistical learning technique called Random Forests. We find that differences associated with acute coronary syndromes emerge in combinations of a handful of features. For instance, concentrations of 3 chemical species, namely, active alpha-thrombin, tissue factor-factor VIIa-factor Xa ternary complex, and intrinsic tenase complex with factor X, at specific time windows, could be used to classify acute coronary syndromes to an accuracy of about 87.2%. Such a combination could be used to efficiently assay the coagulation system.

A Systems Pharmacology Model for Predicting Effects of Factor Xa Inhibitors in Healthy Subjects: Assessment of Pharmacokinetics and Binding Kinetics

Factor Xa (FXa) emerged as a promising target for effective anticoagulation and several FXa inhibitors are now available for the prevention of venous thromboembolism. However, in previously reported pharmacokinetic/pharmacodynamic (PK/PD) models, the complex coagulation processes and detailed information of drug action are usually unclear, which makes it difficult to predict clinical outcome at the drug discovery stage. In this study, a large‐scale systems pharmacology model was developed based on several published models and clinical data. It takes into account all pathways of the coagulation network, and captures drug‐specific features: plasma pharmacokinetics and drug‐target binding kinetics (BKs). We aimed to predict the anticoagulation effects of FXa inhibitors in healthy subjects, and to use this model to compare the effects of compounds with different binding properties. Our model predicts the clotting time and anti‐FXa effects and could thus serve as a predictive tool for the anticoagulant potential of a new compound.

Supporting the use of a coagulometric method for rivaroxaban control: a hypothesis-generating study to define the safety cut-offs

Aims

Although quantitative anti-FXa assays can be used to measure rivaroxaban plasma levels, they are not widely performed or available. We aimed to tentatively determine the cut-off for thromboembolism and bleeding prevention based on the clotting effect of non-rivaroxaban conjugate-activated FX plasma levels in patients with rivaroxaban using a coagulometric method.

Methods and results

Rivaroxaban was added in vitro to normal plasma at a range of 0 to 241 μg/L to cover expected peak and trough levels. Rivaroxaban chromogenic (μg/L) and RVV-confirm as a ratio were determined. Patient plasma samples were assayed with the RVV-confirm reagent. The appropriate rivaroxaban plasma concentration to inhibit clotting mechanisms was based on the remaining FXa in plasma, which was expressed as the ratio of patients/normal, R-C. There is a high correlation between R-C in vitro and spiked normal plasma rivaroxaban concentration (R-Square 0.910, linear equation; 0.971 quadratic equation, p < 0.0001 for both) but not with plasma rivaroxaban chromogenic assays. We propose a cut-off R-C value of 1.65 and 4.5 for safety. Based on the proposed therapeutic range, in 158 assays performed in 58 patients, 6.3 % assays were above the level of bleeding tendency at the peak (R-C 5.39 ± 1.01, median 5.13) and 42 % assays were below the prevention cut-off at the trough (R-C 1.31 ± 0.18, median 1.35).

Conclusions

RVVconfirm® is fast and sensitive to measure the effect of rivaroxaban. Clinical studies are needed to establish whether this cut-off is useful for identifying patients at increased risk of hemorrhage or those who exhibit a low level of anticoagulation.

Modeling thrombin generation: plasma composition based approach

Thrombin has multiple functions in blood coagulation and its regulation is central to maintaining the balance between hemorrhage and thrombosis. Empirical and computational methods that capture thrombin generation can provide advancements to current clinical screening of the hemostatic balance at the level of the individual. In any individual, procoagulant and anticoagulant factor levels together act to generate a unique coagulation phenotype (net balance) that is reflective of the sum of its developmental, environmental, genetic, nutritional and pharmacological influences. Defining such thrombin phenotypes may provide a means to track disease progression pre-crisis. In this review we briefly describe thrombin function, methods for assessing thrombin dynamics as a phenotypic marker, computationally derived thrombin phenotypes versus determined clinical phenotypes, the boundaries of normal range thrombin generation using plasma composition based approaches and the feasibility of these approaches for predicting risk.

The discovery of rivaroxaban: translating preclinical assessments into clinical practice

Direct oral anticoagulants that target a single coagulation factor (such as factor Xa or thrombin) have been developed in recent years in an attempt to address some of the limitations of traditional anticoagulants. Rivaroxaban is an oral, direct factor Xa inhibitor that inhibits free and clot-bound factor Xa and factor Xa in the prothrombinase complex. Preclinical studies demonstrated a potent anticoagulant effect of rivaroxaban in plasma as well as the ability of this agent to prevent and treat venous and arterial thrombosis in animal models. These studies led to an extensive phase I clinical development program that investigated the pharmacological properties of rivaroxaban in humans. In these studies, rivaroxaban was shown to exhibit predictable pharmacokinetics and pharmacodynamics and to have no clinically relevant interactions with many commonly prescribed co-medications. The pharmacodynamic effects of rivaroxaban (for example, inhibition of factor Xa and prolongation of prothrombin time) were closely correlated with rivaroxaban concentrations in plasma. The encouraging findings from preclinical and early clinical studies were expanded upon in large, randomized phase III studies, which demonstrated the clinical efficacy and safety of rivaroxaban in a broad spectrum of patients. This article provides an overview of the discovery and development of rivaroxaban, describing the pharmacodynamic profile established in preclinical studies and the optimal translation to clinical studies in healthy subjects and patient populations.

Models for thrombin generation and risk of disease

Computational models can offer an integrated view of blood clotting dynamics and may ultimately be instructive regarding an individual's risk of bleeding or clotting. Appropriately, developed and validated models could allow clinicians to simulate the outcomes of therapeutics and estimate risk of disease. Computational models that describe the dynamics of thrombin generation have been developed and have been used in combination with empirical studies to understand thrombin dynamics on a mechanistic basis. The translation of an individual's specific coagulation factor composition data using these models into an integrated assessment of hemostatic status may provide a route for advancing the long-term goal of individualized medicine. This review details the integrated approaches to understanding: (i) What is normal thrombin generation in individuals? (ii) What is the effect of normal range plasma composition variation on thrombin generation in pathologic states? (iii) Can disease progression or anticoagulation be followed by understanding the boundaries of normal thrombin generation defined by plasma composition? (iv) What are the controversies and limitations of current computational approaches? Progress in these areas can bring us closer to developing models that can be used to aid in identifying hemostatic risk.