Evaluation of the efficacy and safety of rivaroxaban using a computer model for blood coagulation

Critical evaluation of kinetic schemes for coagulation

Two well-established numerical representations of the coagulation cascade either initiated by the intrinsic system (Chatterjee et al., PLOS Computational Biology 2010) or the extrinsic system (Butenas et al., Journal of Biological Chemistry, 2004) were compared with thrombin generation assays under realistic pathological conditions. Biochemical modifications such as the omission of reactions not relevant to the case studied, the modification of reactions related to factor XI activation and auto-activation, the adaptation of initial conditions to the thrombin assay system, and the adjustment of some of the model parameters were necessary to align in vitro and in silico data. The modified models are able to reproduce thrombin generation for a range of factor XII, XI, and VIII deficiencies, with the coagulation cascade initiated either extrinsically or intrinsically. The results emphasize that when existing models are extrapolated to experimental parameters for which they have not been calibrated, careful adjustments are required.

A Review of Quantitative Systems Pharmacology Models of the Coagulation Cascade: Opportunities for Improved Usability

Despite the numerous therapeutic options to treat bleeding or thrombosis, a comprehensive quantitative mechanistic understanding of the effects of these and potential novel therapies is lacking. Recently, the quality of quantitative systems pharmacology (QSP) models of the coagulation cascade has improved, simulating the interactions between proteases, cofactors, regulators, fibrin, and therapeutic responses under different clinical scenarios. We aim to review the literature on QSP models to assess the unique capabilities and reusability of these models. We systematically searched the literature and BioModels database reviewing systems biology (SB) and QSP models. The purpose and scope of most of these models are redundant with only two SB models serving as the basis for QSP models. Primarily three QSP models have a comprehensive scope and are systematically linked between SB and more recent QSP models. The biological scope of recent QSP models has expanded to enable simulations of previously unexplainable clotting events and the drug effects for treating bleeding or thrombosis. Overall, the field of coagulation appears to suffer from unclear connections between models and irreproducible code as previously reported. The reusability of future QSP models can improve by adopting model equations from validated QSP models, clearly documenting the purpose and modifications, and sharing reproducible code. The capabilities of future QSP models can improve from more rigorous validation by capturing a broader range of responses to therapies from individual patient measurements and integrating blood flow and platelet dynamics to closely represent in vivo bleeding or thrombosis risk.

Naringenin has an inhibitory effect on rivaroxaban in rats both in vitro and in vivo

Food–drug interactions are reported to have some impacts on the pharmacokinetics and pharmacodynamics of various oral drugs. To better understand the effects of naringenin, one natural product in many fruits, on the pharmacokinetics of rivaroxaban, drug–drug interactions (DDIs) between naringenin and rivaroxaban in vitro were investigated in Sprague–Dawley (SD) rat liver microsomes. For the DDIs in vivo, 12 male SD rats were randomly divided into the experimental group and the control group with six rats in each group. Rats in the experimental group were pre‐treated with naringenin (10 mg/kg/day) for 2 weeks before the administration of rivaroxaban (10 mg/kg) by oral gavage, while the rats in the control group were given rivaroxaban (10 mg/kg) only once. The plasma concentration of rivaroxaban in rats was then measured by UPLC‐MS/MS. In vitro data indicated that naringenin could decrease the metabolic clearance rate of rivaroxaban with the IC₅₀ value of 38.89 μM, and exhibited a mixed inhibition to rivaroxaban (Ki =54.91 μM, aKi =73.33 μM, a = 0.74). In vivo data in rats revealed that as compared with that of the control group, the AUC_(0–t) value of rats in the experimental group was increased from 2406.28 ± 519.69 μg/h/L to 4005.04 ± 1172.76 μg/h/L, the C_max value was increased from 310.23 ± 85.76 μg/L to 508.71 ± 152.48 μg/L, and the V_z/F and CL_z/F were decreased from 23.03 ± 4.81 L/kg to 16.2 ± 8.42 L/kg, 4.26 ± 0.91 L/h/kg to 2.57 ± 0.73 L/h/kg, respectively. These data indicated that naringenin had an inhibitory effect on the pharmacokinetics of rivaroxaban in rats, suggesting that the DDIs between naringenin and rivaroxaban might occur when they were co‐administered in the clinic.

Modeling Thrombin Generation in Plasma under Diffusion and Flow

We investigate the capacity of published numerical models of thrombin generation to reproduce experimentally observed threshold behavior under conditions in which diffusion and/or flow are important. Computational fluid dynamics simulations incorporating species diffusion, fluid flow, and biochemical reactions are compared with published data for thrombin generation in vitro in 1) quiescent plasma exposed to patches of tissue factor and 2) plasma perfused through a capillary coated with tissue factor. Clot time is correctly predicted in individual cases, and some models qualitatively replicate thrombin generation thresholds across a series of tissue factor patch sizes or wall shear rates. Numerical results suggest that there is not a genuine patch size threshold in quiescent plasma-clotting always occurs given enough time-whereas the shear rate threshold observed under flow is a genuine physical limit imposed by flow-mediated washout of active coagulation factors. Despite the encouraging qualitative results obtained with some models, no single model robustly reproduces all experiments, demonstrating that greater understanding of the underlying reaction network, and particularly of surface reactions, is required. In this direction, additional simulations provide evidence that 1) a surface-localized enzyme, speculatively identified as meizothrombin, is significantly active toward the fluorescent thrombin substrate used in the experiments or, less likely, 2) thrombin is irreversibly inhibited at a faster-than-expected rate, possibly explained by a stimulatory effect of plasma heparin on antithrombin. These results highlight the power of simulation to provide novel mechanistic insights that augment experimental studies and build our understanding of complex biophysicochemical processes. Further validation work is critical to unleashing the full potential of coagulation models as tools for drug development and personalized medicine.

Exploring the efficiency of thrombin inhibitors with a quantitative model of the coagulation cascade

A detailed mathematical description of the coagulation cascade is a challenging task due to a huge set of protein-protein interactions. Simplified models do not permit quantitative description of anticoagulants. The detailed mathematical model presented here was constructed with 98 reactions between 70 species. The model was verified using experimental data on thrombin generation. Four thrombin inhibitors, which have different inhibitory mechanisms, were incorporated into the model. All four thrombin inhibitors delayed prothrombin conversion into thrombin, but did not preclude it. At high inhibitor concentration, thrombin-mediated positive feedback loops were strongly inhibited and the proportion of prothrombin, converted with factor Xa only, was considerably increased. The most potent inhibitor of prothrombin conversion was aptamer NU172, which also binds prothrombin and inhibits its conversion.

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.

Review of quantitative systems pharmacological modeling in thrombosis

Hemostasis and thrombosis are often thought as two sides of the same clotting mechanism whereas hemostasis is a natural protective mechanism to prevent bleeding and thrombosis is a blood clot abnormally formulated inside a blood vessel, blocking the normal blood flow. The evidence to date suggests that at least arterial thrombosis results from the same critical pathways of hemostasis. Analysis of these complex processes and pathways using quantitative systems pharmacological model-based approach can facilitate the delineation of the causal pathways that lead to the emergence of thrombosis. In this paper, we provide an overview of the main molecular and physiological mechanisms associated with hemostasis and thrombosis, and review the models and quantitative system pharmacological modeling approaches that are relevant in characterizing the interplay among the multiple factors and pathways of thrombosis. An emphasis is given to computational models for drug development. Future trends are discussed.

Rivaroxaban versus enoxaparin for the prevention of venous thromboembolism after total knee arthroplasty: A meta-analysis

OBJECTIVE

This article analyzed the clinical efficacy and tolerability of rivaroxaban and enoxaparin in patients undergoing total knee arthroplasty (TKA) surgery.

METHODS

Five randomized, controlled clinical trials on rivaroxaban versus enoxaparin in patients who underwent TKA were identified and included in this meta-analysis.

RESULTS

The meta-analysis indicated that rivaroxaban prophylaxis was associated with lower rates of symptomatic venous thromboembolism (VTE) (relative risk[RR]:0.55; 95% confidence interval [CI]: 0.35-0.86; P = .009), symptomatic deep vein thrombosis (DVT) (RR 0.44, 95% CI 0.25-0.80, P = .007), asymptomatic DVT (RR: 0.57; 95% CI: 0.37-0.89; P = .01), distal DVT (RR: 0.62; 95% CI: 0.45-0.85; P = .003) and proximal DVT (RR: 0.42; 95% CI: 0.24-0.75; P = .004). Compared with the enoxaparin group, the incidence of symptomatic pulmonary embolism (PE) (RR: 0.48; 95% CI: 0.19-1.24; P = .13) in the rivaroxaban group was not significantly different. A nonsignificant trend towards all-cause death (RR: 0.38; 95% CI: 0.03-4.92; P = .46) or major bleeding (RR: 1.59; 95% CI: 0.77-3.27; P = .21) risk between rivaroxaban and enoxaparin prophylaxis was found.

CONCLUSION

Compared with the enoxaparin group, the group using rivaroxaban after TKA had a significantly lower rate of symptomatic VTE, symptomatic DVT, asymptomatic DVT, distal DVT, and proximal DVT. Our study shows that rivaroxaban after TKA is more effective than enoxaparin and did not increase major bleeding or all-cause mortality.

Assessment of the effect of direct oral anticoagulants dabigatran, rivaroxaban, and apixaban in healthy male volunteers using a thrombin generation assay

BACKGROUND

There are clinical situations where monitoring direct oral anticoagulants (DOACs) may be useful. The clinical application of thrombin generation assay (TGA) in monitoring the effect of DOACs has not been well established. An ex vivo study was performed to systematically evaluate the anticoagulant effect of dabigatran, rivaroxaban and apixaban on each individual TGA parameter through serial measurements over time to assess suitability of these parameters for monitoring the anticoagulant effect of DOACs.

METHODS

Ten healthy volunteers were given oral dabigatran 150 mg, rivaroxaban 20 mg, or apixaban 10 mg once. TGA parameters lag time, endogenous Thrombin potential (ETP), and thrombin peak height, time to peak, and velocity index were measured at times 0, 2, 4, and 24 hours after intake of DOAC. TGA parameters and DOAC concentrations were correlated.

RESULTS

The lag time was significantly correlated with all DOAC concentrations (r ≥ .81, P < .0001 for all). Thrombin peak height best correlated with direct Factor Xa inhibitor (FXa) concentrations in nonlinear fashion (R² ≥ .87). ETP was weakly correlated with DOAC levels (r ≤ .68). Besides lag time, the other TGA parameters were not significantly altered over time by dabigatran.

CONCLUSION

Lag time was the only sensitive TGA parameter across the different classes of DOACs evaluated. Thrombin peak height was strongly correlated to FXa inhibitor concentrations and potentially a useful parameter to monitor FXa inhibitors at low concentrations. ETP had a weak correlation with achieved DOAC concentrations and is likely less suitable for assessment of DOAC effect as a stand-alone parameter.

Emerging technologies for prediction of drug candidate efficacy in the preclinical pipeline

The pharmaceutical industry is tackling increasingly complex multifactorial diseases, resulting in increases in research & development (R&D) costs and reductions in the success rates for drug candidates during Phase 2 and 3 clinical trials, with a lack of efficacy being the primary reason for drug candidate failure. This implies that the predictive power of current preclinical assays for drug candidate efficacy is suboptimal and, therefore, that alternatives should be developed. Here, I review emerging in vitro, imaging, and in silico technologies and discuss their potential contribution to drug efficacy assessment. Importantly, these technologies are complimentary and can be bundled into the preclinical platform. In particular, patient-on-a-chip recapitulates both human genetics and physiology. The response of a patient-on-a-chip to drug candidate treatment is monitored with light-sheet fluorescent microscopy and fed into the image-analysis pipeline to reconstruct an image-based systems-level model for disease pathophysiology and drug candidate mode of action. Thus, such models could be useful tools for assessing drug candidate efficacy and safety in humans.

Laboratory Assessment of the Anticoagulant Activity of Direct Oral Anticoagulants: A Systematic Review

BACKGROUND

Direct oral anticoagulants (DOACs) are the treatment of choice for most patients with atrial fibrillation and/or noncancer-associated venous thromboembolic disease. Although routine monitoring of these agents is not required, assessment of anticoagulant effect may be desirable in special situations. The objective of this review was to summarize systematically evidence regarding laboratory assessment of the anticoagulant effects of dabigatran, rivaroxaban, apixaban, and edoxaban.

METHODS

PubMed, Embase, and Web of Science were searched for studies reporting relationships between drug levels and coagulation assay results.

RESULTS

We identified 109 eligible studies: 35 for dabigatran, 50 for rivaroxaban, 11 for apixaban, and 13 for edoxaban. The performance of standard anticoagulation tests varied across DOACs and reagents; most assays, showed insufficient correlation to provide a reliable assessment of DOAC effects. Dilute thrombin time (TT) assays demonstrated linear correlation (r2 = 0.67-0.99) across a range of expected concentrations of dabigatran, as did ecarin-based assays. Calibrated anti-Xa assays demonstrated linear correlation (r2 = 0.78-1.00) across a wide range of concentrations for rivaroxaban, apixaban, and edoxaban.

CONCLUSIONS

An ideal test, offering both accuracy and precision for measurement of any DOAC is not widely available. We recommend a dilute TT or ecarin-based assay for assessment of the anticoagulant effect of dabigatran and anti-Xa assays with drug-specific calibrators for direct Xa inhibitors. In the absence of these tests, TT or APTT is recommended over PT/INR for assessment of dabigatran, and PT/INR is recommended over APTT for detection of factor Xa inhibitors. Time since last dose, the presence or absence of drug interactions, and renal and hepatic function should impact clinical estimates of anticoagulant effect in a patient for whom laboratory test results are not available.

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.

Contribution of rivaroxaban to the international normalized ratio when switching to warfarin for anticoagulation as determined by simulation studies

AIM

This study evaluated the influence of rivaroxaban 20 mg once daily on international normalized ratio (INR) during the co-administration period when switching from rivaroxaban to warfarin.

METHODS

We developed a calibrated coagulation model that was qualified with phase I clinical data. Prothrombin time and INR values were simulated by use of phospholipid concentrations that matched Neoplastin Plus® and Innovin® reagents. To simulate the combined effects of rivaroxaban and warfarin on INR during switching, warfarin initiation was simulated by adjusting the magnitude of the warfarin effect to reach the desired target INRs over the course of 21 days. The warfarin effect values (obtained every 6 h) and the desired rivaroxaban plasma concentrations were used. Nomograms were generated from rivaroxaban induced increases in INR.

RESULTS

The simulation had good prediction quality. Rivaroxaban induced increases in the total INR from the warfarin attributed INR were seen, which increased with rivaroxaban plasma concentration. When the warfarin only INR was 2.0-3.0, the INR contribution of rivaroxaban with Neoplastin Plus® was 0.5-1.2, decreasing to 0.3-0.6 with Innovin® at median trough rivaroxaban plasma concentrations (38 μg l(-1) ).

CONCLUSIONS

The data indicate that measuring warfarin induced changes in INR are best performed at trough rivaroxaban concentrations (24 h after rivaroxaban dosing) during the co-administration period when switching from rivaroxaban to warfarin. Furthermore, Innovin® is preferable to Neoplastin Plus® because of its substantially lower sensitivity to rivaroxaban, thereby reducing the influence of rivaroxaban on the measured INR.

Computational investigation of potential dosing schedules for a switch of medication from warfarin to rivaroxaban-an oral, direct Factor Xa inhibitor

The long-lasting anticoagulant effect of vitamin K antagonists can be problematic in cases of adverse drug reactions or when patients are switched to another anticoagulant therapy. The objective of this study was to examine in silico the anticoagulant effect of rivaroxaban, an oral, direct Factor Xa inhibitor, combined with the residual effect of discontinued warfarin. Our simulations were based on the recommended anticoagulant dosing regimen for stroke prevention in patients with atrial fibrillation. The effects of the combination of discontinued warfarin plus rivaroxaban were simulated using an extended version of a previously validated blood coagulation computer model. A strong synergistic effect of the two distinct mechanisms of action was observed in the first 2–3 days after warfarin discontinuation; thereafter, the effect was close to additive. Nomograms for the introduction of rivaroxaban therapy after warfarin discontinuation were derived for Caucasian and Japanese patients using safety and efficacy criteria described previously, together with the coagulation model. The findings of our study provide a mechanistic pharmacologic rationale for dosing schedules during the therapy switch from warfarin to rivaroxaban and support the switching strategies as outlined in the Summary of Product Characteristics and Prescribing Information for rivaroxaban.

Clinical pharmacokinetic and pharmacodynamic profile of rivaroxaban

Rivaroxaban is an oral, direct Factor Xa inhibitor that targets free and clot-bound Factor Xa and Factor Xa in the prothrombinase complex. It is absorbed rapidly, with maximum plasma concentrations being reached 2-4 h after tablet intake. Oral bioavailability is high (80-100 %) for the 10 mg tablet irrespective of food intake and for the 15 mg and 20 mg tablets when taken with food. Variability in the pharmacokinetic parameters is moderate (coefficient of variation 30-40 %). The pharmacokinetic profile of rivaroxaban is consistent in healthy subjects and across a broad range of different patient populations studied. Elimination of rivaroxaban from plasma occurs with a terminal half-life of 5-9 h in healthy young subjects and 11-13 h in elderly subjects. Rivaroxaban produces a pharmacodynamic effect that is closely correlated with its plasma concentration. The pharmacokinetic and pharmacodynamic relationship for inhibition of Factor Xa activity can be described by an E max model, and prothrombin time prolongation by a linear model. Rivaroxaban does not inhibit cytochrome P450 enzymes or known drug transporter systems and, because rivaroxaban has multiple elimination pathways, it has no clinically relevant interactions with most commonly prescribed medications. Rivaroxaban has been approved for clinical use in several thromboembolic disorders.

Kinetic model facilitates analysis of fibrin generation and its modulation by clotting factors: implications for hemostasis-enhancing therapies

Current mechanistic knowledge of protein interactions driving blood coagulation has come largely from experiments with simple synthetic systems, which only partially represent the molecular composition of human blood plasma. Here, we investigate the ability of the suggested molecular mechanisms to account for fibrin generation and degradation kinetics in diverse, physiologically relevant in vitro systems. We represented the protein interaction network responsible for thrombin generation, fibrin formation, and fibrinolysis as a computational kinetic model and benchmarked it against published and newly generated data reflecting diverse experimental conditions. We then applied the model to investigate the ability of fibrinogen and a recently proposed prothrombin complex concentrate composition, PCC-AT (a combination of the clotting factors II, IX, X, and antithrombin), to restore normal thrombin and fibrin generation in diluted plasma. The kinetic model captured essential features of empirically detected effects of prothrombin, fibrinogen, and thrombin-activatable fibrinolysis inhibitor titrations on fibrin formation and degradation kinetics. Moreover, the model qualitatively predicted the impact of tissue factor and tPA/tenecteplase level variations on the fibrin output. In the majority of considered cases, PCC-AT combined with fibrinogen accurately approximated both normal thrombin and fibrin generation in diluted plasma, which could not be accomplished by fibrinogen or PCC-AT acting alone. We conclude that a common network of protein interactions can account for key kinetic features characterizing fibrin accumulation and degradation in human blood plasma under diverse experimental conditions. Combined PCC-AT/fibrinogen supplementation is a promising strategy to reverse the deleterious effects of dilution-induced coagulopathy associated with traumatic bleeding.

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.

Multiscale mechanistic modeling in pharmaceutical research and development

Discontinuation of drug development projects due to lack of efficacy or adverse events is one of the main cost drivers in pharmaceutical research and development (R&D). Investments have to be written-off and contribute to the total costs of a successful drug candidate receiving marketing authorization and allowing return on invest. A vital risk for pharmaceutical innovator companies is late stage clinical failure since costs for individual clinical trials may exceed the one billion Euro threshold. To guide investment decisions and to safeguard maximum medical benefit and safety for patients recruited in clinical trials, it is therefore essential to understand the clinical consequences of all information and data generated. The complexity of the physiological and pathophysiological processes and the sheer amount of information available overcharge the mental capacity of any human being and prevent a prediction of the success in clinical development. A rigorous integration of knowledge, assumption, and experimental data into computational models promises a significant improvement of the rationalization of decision making in pharmaceutical industry. We here give an overview of the current status of modeling and simulation in pharmaceutical R&D and outline the perspectives of more recent developments in mechanistic modeling. Specific modeling approaches for different biological scales ranging from intracellular processes to whole organism physiology are introduced and an example for integrative multiscale modeling of therapeutic efficiency in clinical oncology trials is showcased.

Rivaroxaban delivery and reversal at a venous flow rate

OBJECTIVE

Rivaroxaban is an oral anticoagulant that directly targets both free factor Xa and factor Xa in complex with its protein cofactor, factor Va, in the prothrombinase complex. It is approved in the United States for the prophylaxis of deep vein thrombosis and stroke in patients with atrial fibrillation; however, it also carries a black box warning regarding the risk of thrombosis after discontinuation of treatment. The purpose of this study was to determine the degree to which rivaroxaban, over a range of physiologically relevant free plasma concentrations, inhibits preassembled prothrombinase at a typical venous shear rate (100 s(-1)) and to determine the dynamics of rivaroxaban washout.

METHODS AND RESULTS

Prothrombinase was assembled on phospholipid-coated glass capillaries. Its activity was characterized with respect to the activation of prothrombin (mean plasma concentration, 1.4 μmol/L) in the absence and presence of rivaroxaban (2, 5, and 10 nmol/L). The degree of inactivation of preassembled prothrombinase is sensitive to the solution-phase rivaroxaban concentration; however, prothrombinase unmasking upon removal of rivaroxaban is concentration independent.

CONCLUSIONS

The model system presented suggests that when rivaroxaban plasma concentrations decrease after cessation of therapy, there will be an unmasking of thrombus-associated prothrombinase that may be related to the reported rebound phenomena.