Predicting Range of Initial Warfarin Dose Based on Pharmacometabolomic and Genetic Inputs

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.

Effects of sodium ferulate for injection on anticoagulation of warfarin in rats in vivo

BACKGROUND

Herb-drug interactions may result in increased adverse drug reactions or diminished drug efficacy, especially for drugs with a narrow therapeutic index such as warfarin. The current study investigates the effects of sodium ferulate for injection (SFI) on anticoagulation of warfarin from aspects of pharmacodynamics and pharmacokinetics in rats and predicts the risk of the combination use.

METHODS

Rats were randomly divided into different groups and administered single- or multiple-dose of warfarin (0.2 mg/kg) with or without SFI of low dose (8.93 mg/kg) or high dose (26.79 mg/kg). Prothrombin time (PT) and activated partial thromboplastin time (APTT) were detected by a blood coagulation analyzer, and international normalized ratio (INR) values were calculated. UPLC-MS/MS was conducted to measure concentrations of warfarin enantiomers and pharmacokinetic parameters were calculated by DAS2.0 software.

RESULTS

The single-dose study demonstrated that SFI alone had no effect on coagulation indices, but significantly decreased PT and INR values of warfarin when the two drugs were co-administered (P < 0.05 or P < 0.01), while APTT values unaffected (P > 0.05). Cmax and AUC of R/S-warfarin decreased but CL increased significantly in presence of SFI (P < 0.01). The multiple-dose study showed that PT, APTT, INR, and concentrations of R/S-warfarin decreased significantly when SFI was co-administered with warfarin (P < 0.01). Warfarin plasma protein binding rate was not significantly changed by SFI (P > 0.05).

CONCLUSIONS

The present study implied that SFI could accelerate warfarin metabolism and weaken its anticoagulation intensity in rats.

Using machine learning to predict the bleeding risk for patients with cardiac valve replacement treated with warfarin in hospitalized

BACKGROUND

Distinguishing warfarin-related bleeding risk at the bedside remains challenging. Studies indicate that warfarin therapy should be suspended when international normalized ratio (INR) ≥ 4.5, or it may sharply increase the risk of bleeding. We aim to develop and validate a model to predict the high bleeding risk in valve replacement patients during hospitalization.

METHOD

Cardiac valve replacement patients from January 2016 to December 2021 across Nanjing First Hospital were collected. Five different machine-learning (ML) models were used to establish the prediction model. High bleeding risk was an INR ≥4.5. The area under the receiver operating characteristic curve (AUC) was used for evaluating the prediction performance of different models. The SHapley Additive exPlanations (SHAP) was used for interpreting the model. We also compared ML with ATRIA score and ORBIT score.

RESULTS

A total of 2376 patients were finally enrolled in this model, 131 (5.5%) of whom experienced the high bleeding risk after anticoagulation therapy of warfarin during hospitalization. The extreme gradient boosting (XGBoost) exhibited the best overall prediction performance (AUC: 0.882, confidence interval [CI] 0.817-0.946, Brier score, 0.158) compared to other prediction models. It also shows superior performance compared with ATRIA score and ORBIT score. The top 5 most influential features in XGBoost model were platelet, thyroid stimulation hormone, body surface area, serum creatinine and white blood cell.

CONCLUSION

A model for predicting high bleeding risk in valve replacement patients who treated with warfarin during hospitalization was successfully developed by using machine learning, which may well assist clinicians to identify patients at high risk of bleeding and allow timely adjust therapeutic strategies in evaluating individual patient.

Electrochemical Sensor for the Direct Determination of Warfarin in Blood

Detecting warfarin levels in the blood is of critical importance in anticoagulant therapy because it is imperative that the concentration of the drug is maintained within a specific range. In this paper, we present a proof-of-concept of a novel sensing device based on ion-selective electrode (ISE) technology for the direct detection of warfarin in blood samples without any sample pretreatment. We used tetradodecylammonium chloride (TDDA) as an ion-exchanger to fabricate an ion-selective membrane. The ISE we developed showed high sensitivity, with a limit of detection (LOD) of 1.25 × 10−7 M and 1.4 × 10−5 M for detecting warfarin in buffer and blood, respectively. The sensor also exhibited promising selectivity in identifying the presence of various ions including chloride and salicylate, the most abundant ions in blood with a calibration slope of 58.8 mV/dec. We envision combining the ISE with a microfluidic system and a simple potentiometer to produce a sensitive, selective, and portable point-of-care testing device for monitoring the level of warfarin in patients’ blood during treatment.