Quantitative Systems Pharmacology Model-Based Predictions of Clinical Endpoints to Optimize Warfarin and Rivaroxaban Anti-Thrombosis Therapy

Warfarin-A natural anticoagulant: A review of research trends for precision medication

BACKGROUND

Warfarin is a widely prescribed anticoagulant in the clinic. It has a more considerable individual variability, and many factors affect its variability. Mathematical models can quantify the quantitative impact of these factors on individual variability.

PURPOSE

The aim is to comprehensively analyze the advanced warfarin dosing algorithm based on pharmacometrics and machine learning models of personalized warfarin dosage.

METHODS

A bibliometric analysis of the literature retrieved from PubMed and Scopus was performed using VOSviewer. The relevant literature that reported the precise dosage of warfarin calculation was retrieved from the database. The multiple linear regression (MLR) algorithm was excluded because a recent systematic review that mainly reviewed this algorithm has been reported. The following terms of quantitative systems pharmacology, mechanistic model, physiologically based pharmacokinetic model, artificial intelligence, machine learning, pharmacokinetic, pharmacodynamic, pharmacokinetics, pharmacodynamics, and warfarin were added as MeSH Terms or appearing in Title/Abstract into query box of PubMed, then humans and English as filter were added to retrieve the literature.

RESULTS

Bibliometric analysis revealed important co-occuring MeShH and index keywords. Further, the United States, China, and the United Kingdom were among the top countries contributing in this domain. Some studies have established personalized warfarin dosage models using pharmacometrics and machine learning-based algorithms. There were 54 related studies, including 14 pharmacometric models, 31 artificial intelligence models, and 9 model evaluations. Each model has its advantages and disadvantages. The pharmacometric model contains biological or pharmacological mechanisms in structure. The process of pharmacometric model development is very time- and labor-intensive. Machine learning is a purely data-driven approach; its parameters are more mathematical and have less biological interpretation. However, it is faster, more efficient, and less time-consuming. Most published models of machine learning algorithms were established based on cross-sectional data sourced from the database.

CONCLUSION

Future research on personalized warfarin medication should focus on combining the advantages of machine learning and pharmacometrics algorithms to establish a more robust warfarin dosage algorithm. Randomized controlled trials should be performed to evaluate the established algorithm of warfarin dosage. Moreover, a more user-friendly and accessible warfarin precision medicine platform should be developed.

Leveraging QSP Models for MIPD: A Case Study for Warfarin/INR

Warfarin dosing remains challenging due to substantial inter-individual variability, which can lead to unsafe or ineffective therapy with standard dosing. Model-informed precision dosing (MIPD) can help individualize warfarin dosing, requiring the selection of a suitable model. For models developed from clinical data, the dependence on the study design and population raises questions about generalizability. Quantitative system pharmacology (QSP) models promise better extrapolation abilities; however, their complexity and lack of validation on clinical data raise questions about applicability in MIPD. We have previously derived a mechanistic warfarin/international normalized ratio (INR) model from a blood coagulation QSP model. In this article, we evaluated the predictive performance of the warfarin/INR model in the context of MIPD using an external dataset with INR data from patients starting warfarin treatment. We assessed the accuracy and precision of model predictions, benchmarked against an empirically based reference model. Additionally, we evaluated covariate contributions and assessed the predictive performance separately in the more challenging outpatient data. The warfarin/INR model performed comparably to the reference model across various measures despite not being calibrated with warfarin initiation data. Including CYP2C9 and/or VKORC1 genotypes as covariates improved the prediction quality of the warfarin/INR model, even after assimilating 4 days of INR data. The outpatient INR exhibited higher unexplained variability, and predictions slightly exceeded observed values, suggesting that model adjustments might be necessary when transitioning from an inpatient to an outpatient setting. Overall, this research underscores the potential of QSP-derived models for MIPD, offering a complementary approach to empirical model development.

Simulating clinical trials for model-informed precision dosing: using warfarin treatment as a use case

Treatment response variability across patients is a common phenomenon in clinical practice. For many drugs this inter-individual variability does not require much (if any) individualisation of dosing strategies. However, for some drugs, including chemotherapies and some monoclonal antibody treatments, individualisation of dosages are needed to avoid harmful adverse events. Model-informed precision dosing (MIPD) is an emerging approach to guide the individualisation of dosing regimens of otherwise difficult-to-administer drugs. Several MIPD approaches have been suggested to predict dosing strategies, including regression, reinforcement learning (RL) and pharmacokinetic and pharmacodynamic (PKPD) modelling. A unified framework to study the strengths and limitations of these approaches is missing. We develop a framework to simulate clinical MIPD trials, providing a cost and time efficient way to test different MIPD approaches. Central for our framework is a clinical trial model that emulates the complexities in clinical practice that challenge successful treatment individualisation. We demonstrate this framework using warfarin treatment as a use case and investigate three popular MIPD methods: 1. Neural network regression; 2. Deep RL; and 3. PKPD modelling. We find that the PKPD model individualises warfarin dosing regimens with the highest success rate and the highest efficiency: 75.1% of the individuals display INRs inside the therapeutic range at the end of the simulated trial; and the median time in the therapeutic range (TTR) is 74%. In comparison, the regression model and the deep RL model have success rates of 47.0% and 65.8%, and median TTRs of 45% and 68%. We also find that the MIPD models can attain different degrees of individualisation: the Regression model individualises dosing regimens up to variability explained by covariates; the Deep RL model and the PKPD model individualise dosing regimens accounting also for additional variation using monitoring data. However, the Deep RL model focusses on control of the treatment response, while the PKPD model uses the data also to further the individualisation of predictions.

Meta-analysis of haematocrit and activated partial thromboplastin time as risk factors for unplanned interruptions in patients undergoing continuous renal replacement therapy

OBJECTIVE

Although continuous renal replacement therapy (CRRT) is common, unplanned interruptions often limit its usefulness. Unplanned interruption refers to the forced interruption of blood purification treatment, the failure to complete blood purification treatment goals or the failure to meet blood purification schedule times. This study aimed to evaluate the effect of haematocrit and activated partial thromboplastin time (APTT) on the incidence of unplanned interruptions in critical patients with CRRT.

METHODS

A systematic review and a meta-analysis were performed by searching the databases of China National Knowledge Infrastructure, Wanfang, VIP, China Biomedical Literature, Cochrane Library, PubMed, Web of Science and Embase from their inception to 31st March 2022 for all studies with a comparator or independent variable relating to the unplanned interruption of CRRT.

RESULTS

Nine studies involving 1165 participants were included. Haematocrit and APTT were independent risk factors for the unplanned interruption of CRRT. The higher the haematocrit level, the greater the risk of unplanned CRRT interruptions (relative risk ratio [RR] = 1.04, 95% confidence interval [CI]: 1.02, 1.07, Z = 4.27, p < 0.001). The prolongation of APPT reduced the risk of unplanned CRRT interruptions (RR = 0.94, 95% CI: 0.92, 0.96, Z = 6.10, p < 0.001).

CONCLUSION

Haematocrit and APTT are the influencing factors on the incidence of unplanned interruptions in critical patients undergoing CRRT.

Deriving mechanism-based pharmacodynamic models by reducing quantitative systems pharmacology models: An application to warfarin

Quantitative systems pharmacology (QSP) models integrate comprehensive qualitative and quantitative knowledge about pharmacologically relevant processes. We previously proposed a first approach to leverage the knowledge in QSP models to derive simpler, mechanism-based pharmacodynamic (PD) models. Their complexity, however, is typically still too large to be used in the population analysis of clinical data. Here, we extend the approach beyond state reduction to also include the simplification of reaction rates, elimination of reactions, and analytic solutions. We additionally ensure that the reduced model maintains a prespecified approximation quality not only for a reference individual but also for a diverse virtual population. We illustrate the extended approach for the warfarin effect on blood coagulation. Using the model-reduction approach, we derive a novel small-scale warfarin/international normalized ratio model and demonstrate its suitability for biomarker identification. Due to the systematic nature of the approach in comparison with empirical model building, the proposed model-reduction algorithm provides an improved rationale to build PD models also from QSP models in other applications.

Asia-Inclusive Clinical Research and Development Enabled by Translational Science and Quantitative Clinical Pharmacology: Toward a Culture That Challenges the Status Quo

Access lag to innovative therapies in Asian populations continues to present a challenge to global health. Recent progressive changes in the global regulatory landscape, including newer guidelines, are enabling simultaneous global drug development and near-simultaneous global drug registration. The International Conference on Harmonization (ICH) E17 guideline outlines general principles for the design and analysis of multiregional clinical trials (MRCTs). We posit that translational research and quantitative clinical pharmacology tools are core enablers for Asia-inclusive global drug development aligned with ICH E17 principles. Assessment of ethnic sensitivity should be initiated early in the development lifecycle to inform the need for, and extent of, Asian phase I ethno-bridging data. Relevant ethno-bridging data may be generated as standalone Asian phase I trials, as part of Western First-In-Human trials, or under accelerated development settings as a lead-in phase in an MRCT. Quantitative understanding of human clearance mechanisms and pharmacogenetic factors is vital to forecasting ethnic sensitivity in drug exposure using physiologically-based pharmacokinetic models. Stratification factors to control heterogeneity in MRCTs can be identified by reverse translational research incorporating pharmacometric disease models and model-based meta-analyses. Because epidemiological variations can extend to the molecular level, quantitative systems pharmacology models may be useful in forecasting how molecular variation in therapeutic targets or pathway proteins across populations might impact treatment outcomes. Through prospective evaluation of conservation in drug- and disease-related intrinsic and extrinsic factors, a pooled East Asian region can be implemented in Asia-inclusive MRCTs to maximize efficiency in substantiating evidence of benefit-risk for the region at-large with a Totality of Evidence approach.

The effects of rivaroxaban, an oral anticoagulant, on human IVD primary cultures

INTRODUCTION

The present study aimed to investigate the potential effects of rivaroxaban, an oral anticoagulant that inhibits the effects of factor Xa, on intact intervertebral disc tissue cells and the extracellular matrix (ECM).

MATERIAL AND METHODS

Rivaroxaban was applied to primary human cell cultures prepared from tissues of the intervertebral disc. Comparative molecular analyses were performed on non-drug-treated control group samples. Descriptive statistics were presented as the mean ± standard deviation. An analysis of variance test was performed to determine whether there were significant differences in the mean across the groups. When differences across groups were observed, Tukey's honestly significant difference post-hoc test was used for multiple pairwise comparisons. The significance of the obtained data was determined statistically. The α significance value was < 0.05.

RESULTS

The cells in the control group and in the rivaroxaban-treated group were viable, healthy, and proliferated (p < 0.05). However, the expression levels of the chondroadherin gene (CHAD), cartilage oligo matrix protein (COMP), matrix metalloproteinase (MMP)-13, and MMP-19 genes were changed (p < 0.05).

CONCLUSIONS

Although rivaroxaban does not suppress cell proliferation due to morphological, biological, and biochemical changes in the intervertebral disc tissue, it may change the expression of genes that are related to ECM maintenance.

Severe Bleeding Risks of Direct Oral Anticoagulants in the Prevention and Treatment of Venous Thromboembolism: A Network Meta-Analysis of Randomised Controlled Trials

OBJECTIVE

The aim of this study was to determine the severe bleeding safety of direct oral anticoagulants (DOACs) for the prevention and treatment of venous thromboembolism (VTE).

METHODS

PubMed, EMBASE, Web of Science, and the Cochrane Library databases were searched up to 6 January 2021. The incidence of severe bleeding (major, gastrointestinal [GI], intracranial, and fatal) was investigated. Using frequentist network meta-analysis, interventions that were not compared directly could be compared indirectly by the 95% confidence interval (CI), making the search results more intuitive. Based on surface under the cumulative ranking curves (SUCRA), the relative ranking probability of each group was generated.

RESULTS

Thirty-one randomised controlled trials (76 641 patients) were included. For the treatment of VTE, the risk of major bleeding with apixaban was significantly lower than dabigatran (odds ratio [OR] 2.10, 95% CI 1.07 - 4.12) and edoxaban (OR 2.64, 95% CI 1.36 - 5.15). The safety of the drugs was ranked from highest to lowest as follows: major bleeding: apixaban (SUCRA 98.0), rivaroxaban (SUCRA 69.6), dabigatran (SUCRA 50.7), edoxaban (SUCRA 26.5), and vitamin K antagonists (VKAs; SUCRA 5.1); GI bleeding: apixaban (SUCRA 80.7), rivaroxaban (SUCRA 66.8), edoxaban (SUCRA 62.3), VKAs (SUCRA 34.4), and dabigatran (SUCRA 5.8); intracranial bleeding: rivaroxaban (SUCRA 74.4), edoxaban (SUCRA 70.4), dabigatran (SUCRA 58.2), apixaban (SUCRA 44.4), and VKAs (SUCRA 5.6); fatal bleeding: edoxaban (SUCRA 82.7), rivaroxaban (SUCRA 59.2), dabigatran (SUCRA 48.6), apixaban (SUCRA 43.0), and VKAs (SUCRA 16.3). For the prevention of VTE, the risk of major bleeding with apixaban was significantly lower than rivaroxaban (OR 2.14, 95% CI 1.02 - 4.52). Among the four types of bleeding, apixaban had the lowest bleeding risk among DOACs (major bleeding: SUCRA 81.6; GI bleeding: SUCRA 75.4; intracranial bleeding: SUCRA 64.1; fatal bleeding: SUCRA 73.6).

CONCLUSIONS

For the treatment of VTE, in terms of major bleeding and GI bleeding, apixaban had the lowest bleeding risk; in terms of intracranial bleeding, rivaroxaban had the lowest bleeding risk; in terms of fatal bleeding, edoxaban had the lowest bleeding risk. For the prevention of VTE, apixaban had the lowest bleeding risk.

Recent applications of quantitative systems pharmacology and machine learning models across diseases

Quantitative systems pharmacology (QSP) is a quantitative and mechanistic platform describing the phenotypic interaction between drugs, biological networks, and disease conditions to predict optimal therapeutic response. In this meta-analysis study, we review the utility of the QSP platform in drug development and therapeutic strategies based on recent publications (2019-2021). We gathered recent original QSP models and described the diversity of their applications based on therapeutic areas, methodologies, software platforms, and functionalities. The collection and investigation of these publications can assist in providing a repository of recent QSP studies to facilitate the discovery and further reusability of QSP models. Our review shows that the largest number of QSP efforts in recent years is in Immuno-Oncology. We also addressed the benefits of integrative approaches in this field by presenting the applications of Machine Learning methods for drug discovery and QSP models. Based on this meta-analysis, we discuss the advantages and limitations of QSP models and propose fields where the QSP approach constitutes a valuable interface for more investigations to tackle complex diseases and improve drug development.

The Combination of Cell Cultured Technology and In Silico Model to Inform the Drug Development

Human-derived in vitro models can provide high-throughput efficacy and toxicity data without a species gap in drug development. Challenges are still encountered regarding the full utilisation of massive data in clinical settings. The lack of translated methods hinders the reliable prediction of clinical outcomes. Therefore, in this study, in silico models were proposed to tackle these obstacles from in vitro to in vivo translation, and the current major cell culture methods were introduced, such as human-induced pluripotent stem cells (hiPSCs), 3D cells, organoids, and microphysiological systems (MPS). Furthermore, the role and applications of several in silico models were summarised, including the physiologically based pharmacokinetic model (PBPK), pharmacokinetic/pharmacodynamic model (PK/PD), quantitative systems pharmacology model (QSP), and virtual clinical trials. These credible translation cases will provide templates for subsequent in vitro to in vivo translation. We believe that synergising high-quality in vitro data with existing models can better guide drug development and clinical use.