Development of cryopelletization and formulation measures to improve stability of Echis carinatus venum protein for use in diagnostic rotational thromboelastometry

Point-of-care detection and differentiation of anticoagulant therapy - development of thromboelastometry-guided decision-making support algorithms

Background

DOAC detection is challenging in emergency situations. Here, we demonstrated recently, that modified thromboelastometric tests can reliably detect and differentiate dabigatran and rivaroxaban. However, whether all DOACs can be detected and differentiated to other coagulopathies is unclear. Therefore, we now tested the hypothesis that a decision tree-based thromboelastometry algorithm enables detection and differentiation of all direct Xa-inhibitors (DXaIs), the direct thrombin inhibitor (DTI) dabigatran, as well as vitamin K antagonists (VKA) and dilutional coagulopathy (DIL) with high accuracy.

Methods

Following ethics committee approval (No 17–525-4), and registration by the German clinical trials database we conducted a prospective observational trial including 50 anticoagulated patients (n = 10 of either DOAC/VKA) and 20 healthy volunteers. Blood was drawn independent of last intake of coagulation inhibitor. Healthy volunteers served as controls and their blood was diluted to simulate a 50% dilution in vitro. Standard (extrinsic coagulation assay, fibrinogen assay, etc.) and modified thromboelastometric tests (ecarin assay and extrinsic coagulation assay with low tissue factor) were performed. Statistical analyzes included a decision tree analyzes, with depiction of accuracy, sensitivity and specificity, as well as receiver-operating-characteristics (ROC) curve analysis including optimal cut-off values (Youden-Index).

Results

First, standard thromboelastometric tests allow a good differentiation between DOACs and VKA, DIL and controls, however they fail to differentiate DXaIs, DTIs and VKAs reliably resulting in an overall accuracy of 78%. Second, adding modified thromboelastometric tests, 9/10 DTI and 28/30 DXaI patients were detected, resulting in an overall accuracy of 94%. Complex decision trees even increased overall accuracy to 98%. ROC curve analyses confirm the decision-tree-based results showing high sensitivity and specificity for detection and differentiation of DTI, DXaIs, VKA, DIL, and controls.

Conclusions

Decision tree-based machine-learning algorithms using standard and modified thromboelastometric tests allow reliable detection of DTI and DXaIs, and differentiation to VKA, DIL and controls.

Trial registration

Clinical trial number: German clinical trials database ID: DRKS00015704.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12959-021-00313-7.

Real-time detection and differentiation of direct oral anticoagulants (rivaroxaban and dabigatran) using modified thromboelastometric reagents

INTRODUCTION

Timely measurement of direct oral anticoagulants (DOACs) is challenging, though clinically important. We tested the hypotheses, that thromboelastometry is able to detect dabigatran and rivaroxaban and discriminates between dabigatran and rivaroxaban as representatives of the two groups of DOACs.

METHODS AND MATERIALS

We conducted a prospective-observational study: In-vitro dose-effect-curves for rivaroxaban and dabigatran were performed (n = 10). Ex-vivo: Patients with indication of DOAC treatment (stroke; dabigatran/rivaroxaban) were included (n = 21). Blood samples were analyzed before first intake, at first estimated peak level and at 24 h after first but before following intake and 3 h after 24 h-intake. Standard and modified thromboelastometric-assays, using low tissue factor concentrations (TFTEM) or ecarin (ECATEM) were used. Receiver-operating-characteristics-curve-analysis (ROC), regression-analysis and two-way-ANOVA were performed.

RESULTS

In-vitro: TFTEM detected dabigatran and rivaroxaban (ROC_AUC: 0.99; sensitivity/specificity: 100%/98%) but could not discriminate. Dabigatran prolongs CT_ECATEM whereas rivaroxaban did not. Clotting Time (CT)-ratio TFTEM/ECATEM discriminated highly sensitive (100%) and specific (100%) between dabigatran and rivaroxaban even at very low concentrations (ROC_AUC:1.0). CT_ECATEM correlated with dabigatran spiked concentrations (r = 0.9985; p < 0.001) and CT_TFTEM (r = 0.9363; p = 0.006) with rivaroxaban. Similarly results could be demonstrated with patient data: We confirmed the performance for the differentiation of CT-ratio TFTEM/ECATEM (sensitivity 100%/specificity 100%) at any time after first intake of either DOAC.

CONCLUSION

The thromboelastometric tests TFTEM and ECATEM detect and differentiate rivaroxaban and dabigatran. Further investigations evaluate the other DOACs and the differentiation to phenprocoumon. However, results need to be confirmed in a larger study, and especially cut off values for differentiation need to be calculated from a larger sample size.

Formulation of Ketoconazole Nanocrystal-Based Cryopellets

Commercial development of nanosuspensions for oral drug delivery generally involves a drying step which aims to generate a stable product that rapidly releases the nanocrystals once rehydrated and can be easily processed into a final dosage form (e.g., filled into hard capsule). Cryopelletisation is a freeze drying technique allowing the production of lyophilised micrometric spheres with good flowability. In the current work, the possibility to formulate redispersible ketoconazole nanocrystal-based cryopellets able to withstand intensive handling was investigated. Cryopellets were generated by first freezing regular droplets of nanosuspension using liquid nitrogen followed by water removal by sublimation in a standard freeze dryer. Low-friable cryopellets (< 1%) were produced by embedding the nanocrystals in a stabilizing hydroxypropyl cellulose SSL grade matrix, thus proving that these structures can withstand intensive handling. A threshold quantity of hydroxypropyl cellulose SSL grade (5/20 hydroxypropyl cellulose SSL grade-to-drug mass ratio) was required in combination with D-α-tocopherol polyethylene glycol 1000 succinate (vitamin E TPGS) to successfully recover the nanocrystals over storage. A further addition of micronised crospovidone has shown a positive effect on the dissolution performance of cryopellets. Altogether, this study demonstrated that the design of cryopellets combining the strengths of freeze-dried powders (porous internal structure, low residual humidity) and pellets (free-flowing units, mechanical resistance during handling) can potentially improve the nanocrystal's redispersibility compared with other drying techniques while facilitating the downstream processing.