Tranexamic Acid Mechanisms and Pharmacokinetics in Traumatic Injury (TAMPITI)

The purpose of this study is to evaluate the effects of TXA on the immune system, its pharmacokinetics, as well as safety and efficacy in severely injured trauma patients.

Trauma is the leading cause of death in persons younger than 40 years. Hemorrhage is the etiology in 30% of these deaths, and remains the leading cause of potentially preventable mortality (66-80%) on the battlefield. Death secondary to hemorrhagic shock occurs from both surgical bleeding and coagulopathy. Due to the knowledge of increased fibrinolysis promoting a hypocoagulable state in severe trauma, trials have been performed to determine if antifibrinolytics such as tranexamic acid (TXA) could reduce morbidity and mortality by reducing death from hemorrhage. TXA is an antifibrinolytic that inhibits both plasminogen activation and plasmin activity, thus preventing clot break-down rather than promoting new clot formation. Despite the extensive use of TXA in many surgical populations and an increasing use in severe trauma patients, TXA does not have an FDA approved indication for patients with traumatic injuries. The effect of TXA on immune function has not been thoroughly examined, especially in patients with severe traumatic injury. The study of the effects of TXA use on endothelial activation and injury is also important due to the inter-relationship between coagulation and endothelial function. Endothelial injury secondary to local hypoperfusion causes acute traumatic coagulopathy with fibrinolysis. Therefore a thorough and comprehensive evaluation of the effects of TXA on immune, coagulation, and endothelial parameters is important to allow for a better understanding of the mechanisms of action of this agent. This is a randomized placebo controlled trial to obtain mechanism of action data, pharmacokinetic information, and efficacy and safety data for the use of TXA in severely injured trauma patients. Participants will be randomized into 1 of 3 treatment arms (1:1:1): TXA 2 gram IV bolus, TXA 4 gram IV bolus, or placebo. The study period is from time of enrollment to hospital discharge or transfer. The study intervention will occur only once upon enrollment in the trial. Participants will receive study drug within two hours from their initial injury. Blood samples will be drawn at multiple time points for immune parameters, Pharmacodynamics, and repository samples. Immune parameter samples will be drawn at at approximately 0, 6, 24 and 72 hours after study drug/placebo administration. Pharmacokinetic and pharmacodynamic samples will be drawn according to two schedules. Even number sampling times, blood will be drawn at the approximate time points: 0, 20 min, 1 hr, 2 hr, 4 hr, 6 hr, 8 hr, and 12 hr. A patient sampled on odd number sampling times will have samples drawn at the approximate time points: 0, 10 min, 40 min, 1.5 hr, 3 hr, 6 hr, 10 hr and 24 hr. Repository samples will be drawn at approximate time points: 0, 1, 6, 24, and 72 hours.

Tranexamic acid is a man-made form of an amino acid (protein) called lysine. Tranexamic acid prevents enzymes in the body from breaking down blood clots.

Change in HLA-DR Expression on Monocytes 72 Hours After Drug or Placebo Administration in Patient Groups (0g TXA (Placebo); 2g TXA; 4g TXA)."

Blood was drawn from patients at baseline (0 h, just before placebo or drug administration) and at 72 hours post placebo or drug administration. Leukocytes in these blood samples were stained with fluroescent antibodies specific for CD45, CD14, and HLA-DR, analyzed by flow cytometry, and the median fluorescen intensity (MFI) of HLA-DR signal was recorded for monocytes (CD45+CD14+). The fold change in HLA-DR expression from prior to placebo/drug administration to 72 h after placebo/drug administration ("0 h : 72 h") was calculated as HLA-DR MFI72hours ÷ HLA-DR CD14 MFI0hours. Non-paramteric one-way ANOVA (Kruskal-Wallis test) was performed between each treatment group at the given time pont, and the p-value reported.

To evaluate the effects of TXA on immune function parameters we will, in a RCT, analyze samples from 150 patients (50 in each study group), at multiple time points. Parameters are: a. Cytokines measured from time 0 to 72 hours.

To evaluate the effects of TXA on immune function parameters we will, in a RCT, analyze samples from 150 patients (50 in each study group), at multiple time points. Parameters are: a. Flow cytometric analyses on leukocytes measured from time 0 to 72 hours.

Pharmacokinetic data was analyzed with NONMEM, using both the first-order and conditional non-Laplacian (with centering) estimation techniques. We considered two- and three-compartment models, parameterized in terms of both compartment volumes and clearances (distribution and elimination). We compared a basic model (in which pharmacokinetic parameters were independent of weight) to a model in which the pharmacokinetic parameters were assumed to be proportional to weight. The optimal model was selected on the basis of the objective function logarithm of the likelihood of the results) using standard criteria (NONMEM guide). Equations from optimal model: CL=109*((WT/70)**0.75) * (SCRint^-0.084) * ((NIRSInt)/96)^ -0.27 ) * ((PLTint)/130)^0.45) V1=1,160*(WT/70) * (TxTot)^0.03) Q=174*((WT/70)**0.75) V2=1080 *(WT/70) "Total Transfusion Volume CL" equals clearance (CL) affected by the covariate of Total Transfusion Volume (TxTot). This value is unitless per NONMEM reporting.

The number of events per group for the incidence of thromboembolic events (DVT, MI, PE, Stroke) in all three study groups.

The incidence of seizures at 24 hours in all three study groups. Number of participants with seizures are reported

Pharmacokinetic data was analyzed with NONMEM, using both the first-order and conditional non-Laplacian (with centering) estimation techniques. We considered two- and three-compartment models, parameterized in terms of both compartment volumes and clearances (distribution and elimination). We compared a basic model (in which pharmacokinetic parameters were independent of weight) to a model in which the pharmacokinetic parameters were assumed to be proportional to weight. The optimal model was selected on the basis of the objective function logarithm of the likelihood of the results) using standard criteria (NONMEM guide). Equations from optimal model: CL=109*((WT/70)**0.75) * (SCRint^-0.084) * ((NIRSInt)/96)^ -0.27 ) * ((PLTint)/130)^0.45) V1=1,160*(WT/70) * (TxTot)^0.03) Q=174*((WT/70)**0.75) V2=1080 *(WT/70) "Platelet Count CL" equals clearance (CL) affected by the covariate of Platelet Count (PLTint). This value is unitless per NONMEM reporting.

Pharmacokinetic data was analyzed with NONMEM, using both the first-order and conditional non-Laplacian (with centering) estimation techniques. We considered two- and three-compartment models, parameterized in terms of both compartment volumes and clearances (distribution and elimination). We compared a basic model (in which pharmacokinetic parameters were independent of weight) to a model in which the pharmacokinetic parameters were assumed to be proportional to weight. The optimal model was selected on the basis of the objective function logarithm of the likelihood of the results) using standard criteria (NONMEM guide). Equations from optimal model: CL=109*((WT/70)**0.75) * (SCRint^-0.084) * ((NIRSInt)/96)^ -0.27 ) * ((PLTint)/130)^0.45) V1=1,160*(WT/70) * (TxTot)^0.03) Q=174*((WT/70)**0.75) V2=1080 *(WT/70) "Near Infrared Spectroscopy CL" equals clearance (CL) affected by the covariate of Near Infrared Spectroscopy (NIRSint). This value is unitless per NONMEM reporting.

Pharmacokinetic data was analyzed with NONMEM, using both the first-order and conditional non-Laplacian (with centering) estimation techniques. We considered two- and three-compartment models, parameterized in terms of both compartment volumes and clearances (distribution and elimination). We compared a basic model (in which pharmacokinetic parameters were independent of weight) to a model in which the pharmacokinetic parameters were assumed to be proportional to weight. The optimal model was selected on the basis of the objective function logarithm of the likelihood of the results) using standard criteria (NONMEM guide). Equations from optimal model: CL=109*((WT/70)**0.75) * (SCRint^-0.084) * ((NIRSInt)/96)^ -0.27 ) * ((PLTint)/130)^0.45) V1=1,160*(WT/70) * (TxTot)^0.03) Q=174*((WT/70)**0.75) V2=1080 *(WT/70) "Creatinine Count CL" equals clearance (CL) affected by the covariate of Creatinine levels (SCRint). This value is unitless per NONMEM reporting.

Pharmacokinetic data was analyzed with NONMEM, using both the first-order and conditional non-Laplacian (with centering) estimation techniques. We considered two- and three-compartment models, parameterized in terms of both compartment volumes and clearances (distribution and elimination). We compared a basic model (in which pharmacokinetic parameters were independent of weight) to a model in which the pharmacokinetic parameters were assumed to be proportional to weight. The optimal model was selected on the basis of the objective function logarithm of the likelihood of the results) using standard criteria (NONMEM guide). Equations from optimal model: CL=109*((WT/70)**0.75) * (SCRint^-0.084) * ((NIRSInt)/96)^ -0.27 ) * ((PLTint)/130)^0.45) V1=1,160*(WT/70) * (TxTot)^0.03) Q=174*((WT/70)**0.75) V2=1080 *(WT/70) "V2" equals Peripheral Volume in L/70kg.

Pharmacokinetic data was analyzed with NONMEM, using both the first-order and conditional non-Laplacian (with centering) estimation techniques. We considered two- and three-compartment models, parameterized in terms of both compartment volumes and clearances (distribution and elimination). We compared a basic model (in which pharmacokinetic parameters were independent of weight) to a model in which the pharmacokinetic parameters were assumed to be proportional to weight. The optimal model was selected on the basis of the objective function logarithm of the likelihood of the results) using standard criteria (NONMEM guide). Equations from optimal model: CL=109*((WT/70)**0.75) * (SCRint^-0.084) * ((NIRSInt)/96)^ -0.27 ) * ((PLTint)/130)^0.45) V1=1,160*(WT/70) * (TxTot)^0.03) Q=174*((WT/70)**0.75) V2=1080 *(WT/70) "Q" equals intercompartmental clearance in L/70kg.

Pharmacokinetic data was analyzed with NONMEM, using both the first-order and conditional non-Laplacian (with centering) estimation techniques. We considered two- and three-compartment models, parameterized in terms of both compartment volumes and clearances (distribution and elimination). We compared a basic model (in which pharmacokinetic parameters were independent of weight) to a model in which the pharmacokinetic parameters were assumed to be proportional to weight. The optimal model was selected on the basis of the objective function logarithm of the likelihood of the results) using standard criteria (NONMEM guide). Equations from optimal model: CL=109*((WT/70)**0.75) * (SCRint^-0.084) * ((NIRSInt)/96)^ -0.27 ) * ((PLTint)/130)^0.45) V1=1,160*(WT/70) * (TxTot)^0.03) Q=174*((WT/70)**0.75) V2=1080 *(WT/70) "V1" equals central volume in L/70kg.

Pharmacokinetic data was analyzed with NONMEM, using both the first-order and conditional non-Laplacian (with centering) estimation techniques. We considered two- and three-compartment models, parameterized in terms of both compartment volumes and clearances (distribution and elimination). We compared a basic model (in which pharmacokinetic parameters were independent of weight) to a model in which the pharmacokinetic parameters were assumed to be proportional to weight. The optimal model was selected on the basis of the objective function logarithm of the likelihood of the results) using standard criteria (NONMEM guide). Equations from optimal model: CL=109*((WT/70)**0.75) * (SCRint^-0.084) * ((NIRSInt)/96)^ -0.27 ) * ((PLTint)/130)^0.45) V1=1,160*(WT/70) * (TxTot)^0.03) Q=174*((WT/70)**0.75) V2=1080 *(WT/70) "CL" equals clearance of TXA in mL/(min*70kg).

Inclusion Criteria: Patients with traumatic injury that are ordered to receive at least 1 blood product and/or Patients admitted to the Emergency Department with a traumatic injury and require immediate transfer to the operating room to control the bleeding Able to receive the study drug within 2 hours from estimated time of injury **Please note that in circumstances where the patient initially met inclusion/exclusion criteria (i.e. received blood products in the ED before a full evaluation of their injuries is complete) but is later found to only have a soft tissue involved injury or does not have a traumatic bleeding source), the Investigator may determine that the patient should not be randomized into the trial and the patient should be considered a screen failure Exclusion Criteria: Patients known to be < 18 years of age Suspected Acute MI or stroke(thromboembolic and/or hemorrhagic) on admission Known inherited coagulation disorders Known history of thromboembolic events (DVT, PE, MI, Stroke) • Please note that past medical history of hemorrhagic stroke is permitted, but not current admission with hemorrhagic stroke Known history of seizures and/or seizure after injury/on admission related to this hospitalization Suspected or known pregnancy Known to be lactating Suspected or known prisoners Futile care Known current state of immunosuppression (i.e. on high dose steroids, chemotherapeutics, etc.) Unknown estimated time of injury 12). Patients wearing an "Opt Out" TAMPITI Study bracelet 13). Known presence of subarachnoid hemorrhage. 14.) Isolated injuries to hands and/or feet (distal) 15.) Administration of antifibrinolytics pre-hospital and/or during this ED admission prior to enrollment

Spinella PC, Thomas KA, Turnbull IR, Fuchs A, Bochicchio K, Schuerer D, Reese S, Coleoglou Centeno AA, Horn CB, Baty J, Shea SM, Meledeo MA, Pusateri AE, Levy JH, Cap AP, Bochicchio GV; TAMPITI Investigators. The Immunologic Effect of Early Intravenous Two and Four Gram Bolus Dosing of Tranexamic Acid Compared to Placebo in Patients With Severe Traumatic Bleeding (TAMPITI): A Randomized, Double-Blind, Placebo-Controlled, Single-Center Trial. Front Immunol. 2020 Sep 8;11:2085. doi: 10.3389/fimmu.2020.02085. eCollection 2020.