Anticoagulation Management: Current Landscape and Future Trends
Abstract
:1. Introduction
- Increased Need: The rising prevalence of thrombotic diseases like deep vein thrombosis and pulmonary embolism necessitates wider use of effective anticoagulants.
- Aging Population: As populations age, the risk of blood clots naturally increases, making targeted anticoagulation crucial for this demographic.
- Advancements in Therapy: Improvements in medications and monitoring technologies have enhanced safety, efficacy, and personalized care in anticoagulation.
2. Current Landscape of Anticoagulation Management
2.1. Vitamin K Antagonists
2.2. Injectable Anticoagulants—Heparins
2.3. Direct Oral Anticoagulants
2.4. Challenges in Current Anticoagulation Management
3. Emerging Trends in Anticoagulation Management
3.1. New Oral Anticoagulants in Development
3.2. Personalized Anticoagulation Management
3.3. Point-of-Care Devices
4. Future Direction of Anticoagulation Management
4.1. Development of Anticoagulants with Enhanced Efficacy and Safety Profile
4.2. Artificial Intelligence and Pharmacogenomics in Anticoagulation
- Risk Prediction and Personalized Therapy: AI algorithms can analyze patient data (clinical history, laboratory results, genetic information, imaging studies, etc.) to predict individualized thrombotic and bleeding risks. This personalized approach would help clinicians guide the selection of the most appropriate anticoagulation therapy and dosing regimens tailored to each patient’s risk profile.
- Dose Optimization and Monitoring: AI-powered algorithms can optimize anticoagulant dosing based on real-time patient data, such as drug levels, coagulation parameters, and clinical outcomes. By analyzing ongoing responses, AI systems can adjust dosages to achieve optimal anticoagulation while minimizing side effects. Machine learning models, such as artificial neural networks and reinforcement learning techniques, have been used to develop predictive dosing tools that adjust anticoagulant regimens dynamically, improving efficacy and reducing adverse effects [61].
- Early Detection of Thrombotic and Bleeding Events: AI-based models have the potential to detect subtle changes in patient data indicative of thrombotic or bleeding events before they manifest clinically. By dynamically analyzing coagulation, AI systems can alert healthcare providers to potential complications, enabling early intervention and prevention strategies. For instance, machine learning models have been employed to predict suboptimal anticoagulation control in patients with atrial fibrillation by analyzing clinical data, including patient demographics, comorbidities, and prior anticoagulation responses. Techniques such as Long Short-Term Memory (LSTM) recurrent neural networks and XGBoost have demonstrated the ability to identify patients at risk of inadequate anticoagulation, thereby enabling earlier intervention strategies to optimize therapy and reduce the likelihood of thrombotic or bleeding complications [62].
- Treatment Adherence: AI-powered applications can improve patient adherence to anticoagulation therapy by providing personalized education, reminders, and support tools. This capability of AI is already being actively incorporated into healthcare systems around the globe. For example, a machine learning-based mobile application has been tested on Indian patients with vitamin K antagonists, allowing them to manage their anticoagulation therapy without frequent physician visits. This smartphone-based system, which predicts warfarin doses based on PT-INR values, has demonstrated a high correlation with physician-prescribed doses and may enhance accessibility, particularly in low-resource settings. By reducing the logistical barriers to regular monitoring, AI-driven mobile health (mHealth) tools are helping improve adherence rates among patients on warfarin and other anticoagulants [63]. These applications can also facilitate remote monitoring and communication between patients and healthcare providers, enhancing patient engagement and therapeutic satisfaction.
- Clinical Decision Support Systems: AI-driven decision support systems can assist healthcare providers in making informed decisions by analyzing vast amounts of data and providing real-time recommendations on therapy choices, dosing adjustments, and monitoring strategies. For instance, a deep learning-based CDSS has been created to predict PT/INR values and generate individualized warfarin dosing recommendations, outperforming expert physicians in accuracy. This AI-powered platform integrates patient-specific data and simulates the effects of different dosing regimens, allowing clinicians to make more informed adjustments. When incorporated into hospital-based electronic prescribing systems, such CDSS tools have the potential to reduce errors in warfarin prescription, minimize adverse drug events, and improve the overall safety of anticoagulation therapy [64].
- Quality Improvement and Global Health Management: AI analytics can analyze large datasets to identify trends and opportunities for improvement in anticoagulation management across multiple healthcare systems. By analyzing outcomes, resource utilization, and adherence patterns across healthcare systems, AI systems can inform strategies to optimize care delivery and improve patient outcomes on a broader, perhaps global scale.
- Pharmacogenomic-Guided Dosing Algorithms: As mentioned above, patients with certain polymorphisms of genes responsible for anticoagulant metabolism require lower doses to prevent excessive anticoagulation and bleeding risks [51]. AI-driven models integrating genetic, clinical, and demographic data can be used to optimize anticoagulant dosing. For instance, the EU-PACT trial showed improved time in the therapeutic range with genotype-guided dosing [65]. The pharmacogenomic approach is true for the novel DOACs as well [66]. A study by Ji et al. demonstrated polymorphisms in ABCB1 and CES1 genes are linked to variations in dabigatran absorption and metabolism [67]. AI-driven pharmacogenomic tools may help further refine DOAC selection and dosing, especially in populations with known genetic risk factors [68]. These findings underscore the need for AI-driven dosing to enhance anticoagulation safety and reduce variability.
5. Challenges in Future Anticoagulation Management
5.1. Regulatory Hurdles for New Anticoagulants and Monitoring Technologies
5.2. Cost-Effectiveness Considerations in Novel Therapies
5.3. Data Privacy and Security Concerns with Digital Anticoagulation
6. Conclusions
Author Contributions
Funding
Conflicts of Interest
Abbreviations
aPTT | activated partial thromboplastin time |
ACT | activated clotting time |
CABG | coronary arterial bypass grafting |
CBA | cost-benefit analysis |
CEA | cost-effectiveness analysis |
CYP2C9 | cytochrome P450 family 2 subfamily C member 9 |
CYP4F2 | cytochrome P450 family 4 subfamily F member 2 |
DOAC | direct oral anticoagulant |
DTI | direct thrombin inhibitor |
DVT | deep venous thrombosis |
ECMO | extracorporeal membrane oxygenation |
ECT | ecarin clotting time |
FDA | Food and Drug Administration |
FXa | activated factor Xa |
FXIa | activated factor XI |
GLA | Vitamin K-dependent carboxylation/gamma-carboxy glutamic domain |
INR | international normalized ratio |
IV | intravenously |
LMWH | low molecular weight heparin |
PCI | percutaneous coronary intervention |
PE | pulmonary embolism |
PIVKA | protein induced by vitamin K absence |
PT | prothrombin time |
SC | subcutaneously |
QALY | quality-adjusted life years |
UFH | unfractionated heparin |
VKA | vitamin K antagonist |
VKORC1 | vitamin K epoxide reductase complex 1 |
VTE | venous thromboembolism |
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Anticoagulant | Mechanism of Action | Administration Route | Common Side Effects | Half-Life (h) | Monitoring |
---|---|---|---|---|---|
Warfarin | Inhibits vitamin K epoxide reductase, thereby reducing the synthesis of vitamin K-dependent clotting factors (II, VII, IX, X) | Oral | Bleeding, skin necrosis, hair loss, drug interactions | 20–60 | PT/INR monitoring, typically every 2–4 weeks initially, then less frequent once stable |
Other Vitamin K Antagonists (e.g., Acenocoumarol, Phenprocoumon) | Inhibits vitamin K epoxide reductase, reducing the synthesis of vitamin K-dependent clotting factors | Oral | Bleeding, skin necrosis, drug interactions | 20–60 | |
Heparin | Binds to antithrombin III, enhancing its ability to inactivate thrombin (factor IIa) and Xa, and to a lesser extent VIIa, IXa, XIa | Intravenous | Bleeding, heparin-induced thrombocytopenia (HIT), osteoporosis | 1–2 | aPTT (Activated Partial Thromboplastin Time) monitoring for unfractionated heparin; Anti-Xa assay for LMWH |
Low Molecular Weight Heparin (LMWH) | Similar to unfractionated heparin but with greater inhibition of factor Xa than thrombin | Subcutaneous | Bleeding, HIT, injection site reactions | 4–5 | Anti-Xa assay |
Fondaparinux | Selectively inhibits factor Xa indirectly through binding to antithrombin III. No direct inhibitory effect on thrombin | Subcutaneous | Bleeding, HIT, injection site reactions | 17–21 | |
Danaparoid | Selectively inhibits factor Xa and factor IIa (thrombin) indirectly through binding to antithrombin III | Intravenous | Bleeding, HIT | 23–26 | |
Direct Thrombin Inhibitors (e.g., Dabigatran) | Selectively inhibits thrombin (factor IIa), preventing fibrin formation | Oral | Bleeding, gastrointestinal discomfort, dyspepsia | 12–17 | No routine monitoring required |
Factor Xa Inhibitors (e.g., Rivaroxaban, Apixaban, Edoxaban, Betrixaban) | Selectively inhibits factor Xa, thereby preventing the conversion of prothrombin to thrombin | Oral | Bleeding, gastrointestinal symptoms, hepatotoxicity | 5–27 | |
Intravenous Direct Thrombin Inhibitors (Argatroban, Bivalirudin) | Selectively inhibits thrombin (factor IIa), preventing fibrin formation | Intravenous | Bleeding, thrombocytopenia (bivalirudin), allergic reactions, liver dysfunction (argatroban) | 0.5–1 | aPTT, ecarin clotting time (ECT), activated clotting time (ACT) |
Anticoagulant | Mechanism of Action | Administration Route | Developing Company | Development Stage |
---|---|---|---|---|
Fesomersen | Factor XI antisense oligonucleotide [42] | Subcutaneous | Ionis Pharmaceuticals | Clinical Trials Phase II |
Milvexian | Small-molecule factor XIa inhibitor [43] | Oral | Bristol Myers Squibb | Clinical Trials Phase III |
Asundexian | Small-molecule factor XIa inhibitor [44] | Oral | Bayer | Clinical Trials Phase III |
BMS-962212 | Direct, reversible, selective factor XIa inhibitor [45] | Intravenous | Bristol Myers Squibb | Undisclosed |
ONO-7684 | Small-molecule factor XIa inhibitor [46] | Intravenous | ONO Pharmaceuticals | Undisclosed |
Abelacimab | A monoclonal antibody that binds to factor XI and locks it in zymogen form [47] | Oral | Anthos Therapeutics | Clinical Trials |
Osocimab | A monoclonal antibody that binds adjacent to the active site of factor XIa and prevents it from activating factor IX [48] | Subcutaneous | Aronora, Bayer | Clinical Trials Phase II |
Gruticibart (formerly AB023) | A monoclonal antibody that blocks contact activation of coagulation by inhibiting factor XI activation by factor XIIa but not by thrombin [49] | Subcutaneous | Aronora | Clinical Trials Phase II |
SHR2285 | Small-molecule factor XIa inhibitor [50] | Intravenous | Jiangsu HengRui | Clinical Trials Phase II |
REGN7508 | A monoclonal antibody against the catalytic domain of factor XI | Intravenous, Subcutaneous | Regeneron | Clinical Trials Phase II |
REGN9933 | A monoclonal antibody against the A2 domain of factor XI | Clinical Trials Phase II |
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Kholmukhamedov, A.; Subbotin, D.; Gorin, A.; Ilyassov, R. Anticoagulation Management: Current Landscape and Future Trends. J. Clin. Med. 2025, 14, 1647. https://doi.org/10.3390/jcm14051647
Kholmukhamedov A, Subbotin D, Gorin A, Ilyassov R. Anticoagulation Management: Current Landscape and Future Trends. Journal of Clinical Medicine. 2025; 14(5):1647. https://doi.org/10.3390/jcm14051647
Chicago/Turabian StyleKholmukhamedov, Andaleb, David Subbotin, Anna Gorin, and Ruslan Ilyassov. 2025. "Anticoagulation Management: Current Landscape and Future Trends" Journal of Clinical Medicine 14, no. 5: 1647. https://doi.org/10.3390/jcm14051647
APA StyleKholmukhamedov, A., Subbotin, D., Gorin, A., & Ilyassov, R. (2025). Anticoagulation Management: Current Landscape and Future Trends. Journal of Clinical Medicine, 14(5), 1647. https://doi.org/10.3390/jcm14051647