Population pharmacokinetics and pharmacodynamics of efmarodocokin alfa (IL-22Fc).

Efmarodocokin alfa (IL-22Fc) is a fusion protein of human IL-22 linked to the crystallizable fragment (Fc) of human IgG4. It has been tested in multiple indications including inflammatory bowel disease (IBD). The purposes of the present analyses were to describe the population pharmacokinetics (PK) of efmarodocokin alfa and perform pharmacodynamic (PD) analysis on the longitudinal changes of the PD biomarker REG3A after efmarodocokin alfa treatment as well as identify covariates that affect efmarodocokin alfa PK and REG3A PD. The data used for this analysis included 182 subjects treated with efmarodocokin alfa in two clinical studies. The population PK and PD analyses were conducted sequentially. Efmarodocokin alfa concentration-time data were analyzed using a nonlinear mixed-effects modeling approach, and an indirect response model was adopted to describe the REG3A PD data with efmarodocokin alfa serum concentration linked to the increase in REG3A. The analysis software used were NONMEM and R. A 3-compartment model with linear elimination best described the PK of efmarodocokin alfa. The estimated population-typical value for clearance (CL) was 1.12 L/day, and volume of central compartment was 6.15 L. Efmarodocokin alfa CL increased with higher baseline body weight, C-reactive protein, and CL was 27.6% higher in IBD patients compared to healthy subjects. The indirect response PD model adequately described the longitudinal changes of REG3A after efmarodocokin alfa treatment. A popPK and PD model for efmarodocokin alfa and REG3A was developed and covariates affecting the PK and PD were identified.

Recurrent or progressive pediatric brain tumors: population pharmacokinetics and exposure-response analysis of pomalidomide.

BACKGROUND: Pomalidomide, an immunomodulatory drug, was investigated for pediatric brain tumors. The objectives of this analysis were to characterize the PK of pomalidomide and to examine exposure-response relationship in pediatric patients with recurrent or progressive primary brain tumors. METHODS: Nonlinear mixed effects modeling was employed in developing a population PK model of pomalidomide using a total of 343 concentrations from 70 patients. Logistic regression models were used for exposure-response analyses. RESULTS: The PK of pomalidomide was adequately described with a one compartment model with first-order absorption and elimination. Body surface area (BSA) was identified as a statistically significant covariate of apparent clearance and volume of distribution; however, the impact of BSA on exposure parameters was not deemed clinically relevant. Pomalidomide exposure was not associated with higher probabilities of treatment-emergent adverse events or pomalidomide dose interruptions during Cycle 1. Covariates such as BSA, weight, sex, age, and race had no significant effect on safety endpoints. The PK of pomalidomide in pediatric patients with brain tumors was generally consistent with that in adult patients with multiple myeloma after adjustment for BSA. CONCLUSIONS: This is the first study to characterize PK of pomalidomide in pediatric patients, which supports BSA-based dosing for pediatric patients. IMPACT: This is the first study to characterize PK of pomalidomide in pediatric patients, which supports BSA-based dosing for pediatric patients. There is no significant pomalidomide PK difference between adults and pediatrics. Pomalidomide exposure was not associated with higher probabilities of treatment-emergent adverse event or pomalidomide dose interruptions during Cycle 1.

Population Pharmacokinetics and Safety of Piperacillin-Tazobactam Extended Infusions in Infants and Children.

Piperacillin-tazobactam (TZP) is frequently used to treat severe hospital-acquired infections in children. We performed a single-center, pharmacokinetic (PK) trial of TZP in children ranging in age from 2 months to 6 years from various clinical subpopulations. Children who were on TZP per the standard of care were prospectively included and assigned to receive a dose of 80 mg/kg of body weight every 6 h infused over 2 h (ages 2 to 5 months) or a dose of 90 mg/kg every 8 h infused over 4 h (ages 6 months to 6 years). Separate population PK models were developed for piperacillin and tazobactam using nonlinear mixed-effects modeling. Optimal dosing was judged based on the ability to maintain free piperacillin concentrations above the piperacillin MIC for enterobacteria and Pseudomonas aeruginosa for ≥50% of the dosing interval. Any untoward event occurring during treatment was collected as an adverse event. A total of 79 children contributed 174 PK samples. The median (range) age and weight were 1.7 years (2 months to 6 years) and 11.4 kg (3.8 to 27.6 kg), respectively. A 2-compartment model with first-order elimination best described the piperacillin and tazobactam data. Both final population PK models included weight and concomitant furosemide administration on clearance and weight on the volume of distribution of the central compartment. The optimal dosing regimens in children with normal renal function, based on the piperacillin component, were 75 mg/kg/dose every 4 h infused over 0.5 h in infants ages 2 to ≤6 months and 130 mg/kg/dose every 8 h infused over 4 h in children ages >6 months to 6 years against bacteria with MICs up to 16 mg/liter. A total of 44 children (49%) had ≥1 adverse event, with 3 of these (site infiltrations) considered definitely associated with the extended infusions.

Incorporation of GSTA1 genetic variations into a population pharmacokinetic model for IV busulfan in paediatric hematopoietic stem cell transplantation.

AIMS: The aim of this study is to develop a population pharmacokinetic (PopPK) model for intravenous busulfan in children that incorporates variants of GSTA1, gene coding for the main enzyme in busulfan metabolism. METHODS: Busulfan concentration-time data was collected from 112 children and adolescents (median 5.4 years old, range: 0.1-20) who received intravenous busulfan during the conditioning regimen prior to stem cell transplantation. Weight, sex, baseline disease (malignant vs. non-malignant), age, conditioning regimen and GSTA1 diplotypes were evaluated as covariates of pharmacokinetic parameters by using nonlinear mixed effects analysis. The ability to achieve the target AUC24h (3600-6000 µM min-1 ) was assessed by estimating the first dose based on the present PopPK model and by comparing the results with other available models in children. RESULTS: A one-compartment model with first-order elimination best described the data. Allometric scaling of weight and a factor of busulfan metabolism maturation were included in the base model. GSTA1 diplotypes were found to be a significant covariate of busulfan clearance, which was 7% faster in rapid metabolizers and 12% slower in poor metabolizers, in comparison with normal ones. Busulfan doses calculated using the parameters of the proposed PopPK model were estimated to achieve the target AUC in 85.2% of the cases (95% CI 78.7-91.7%). CONCLUSION: This is the first PopPK for busulfan that successfully incorporated GSTA1 genotype in a paediatric population. Its use may contribute to better prediction of busulfan exposure in children and adolescents since the first dose, by tailoring the dose according to the individual metabolic capacity.

Population pharmacokinetics and Bayesian estimation of tacrolimus exposure in paediatric liver transplant recipients.

AIMS: The objectives of this study were to develop a population pharmacokinetic (PopPK) model for tacrolimus in paediatric liver transplant patients and determine optimal sampling strategies to estimate tacrolimus exposure accurately. METHODS: Twelve hour intensive pharmacokinetic profiles from 30 patients (age 0.4-18.4 years) receiving tacrolimus orally were analysed. The PopPK model explored the following covariates: weight, age, sex, type of transplant, age of liver donor, liver function tests, albumin, haematocrit, drug interactions, drug formulation and time post-transplantation. Optimal sampling strategies were developed and validated with jackknife. RESULTS: A two-compartment model with first-order absorption and elimination and lag time described the data. Weight was included on all pharmacokinetic parameters. Typical apparent clearance and central volume of distribution were 12.1 l h(-1) and 31.3 l, respectively. The PopPK approach led to the development of optimal sampling strategies, which allowed estimation of tacrolimus pharmacokinetics and area under the concentration­time curve (AUC) on the basis of practical sampling schedules (three or four sampling times within 4 h) with clinically acceptable prediction error limit. The mean bias and precision of the Bayesian vs. reference (trapezoidal) AUCs ranged from -2.8 to -1.9% and from 7.4 to 12.5%, respectively. CONCLUSIONS: The PopPK of tacrolimus and empirical Bayesian estimates represent an accurate and convenient method to predict tacrolimus AUC(0-12) in paediatric liver transplant recipients, despite high between-subject variability in pharmacokinetics and patient demographics. The developed optimal sampling strategies will allow the undertaking of prospective trials to define the tacrolimus AUC-based therapeutic window and dosing guidelines in this population.

Conversion from twice- to once-daily tacrolimus in pediatric kidney recipients: a pharmacokinetic and bioequivalence study.

BACKGROUND: The objectives of this study were to investigate pharmacokinetic and pharmacogenetic parameters during the conversion on a 1:1 (mg:mg) basis from a twice-daily (Prograf) to once-daily (Advagraf) tacrolimus formulation in pediatric kidney transplant recipients. METHODS: Twenty-four-hour pharmacokinetic profiles were analyzed before and after conversion in 19 stable renal transplant recipients (age 7-19 years). Tacrolimus pharmacokinetic parameters [area under the concentration-time curve (AUC0-24), minimum whole-blood concentration (Cmin), maximum whole-blood concentration (Cmax), and time to achieve maximum whole-blood concentration (tmax)] were compared between Tac formulations and between CYP3A5 and MDR1 genotypes after dose normalization. RESULTS: Both AUC0-24 and Cmin decreased after conversion (223.3 to 197.5 ng.h/ml and 6.5 to 5.6 ng/ml; p = 0.03 and 0.01, respectively). However, the ratio of the least square means (LSM) for AUC0-24 was 90.8 %, with 90 % CI limits of 85.3 to 96.7 %, falling within bioequivalence limits. The CYP3A5 genotype influences the dose-normalized Cmin with the twice-daily formulation only. CONCLUSIONS: Both tacrolimus formulations are bioequivalent in pediatric renal recipients. However, we observed a decrease in AUC0-24 and Cmin after the conversion, requiring close pharmacokinetic monitoring during the conversion period.

Machine Learning-Based Quantification of Patient Factors Impacting Remission in Patients With Ulcerative Colitis: Insights from Etrolizumab Phase III Clinical Trials.

Etrolizumab, an investigational anti-ß7 integrin monoclonal antibody, has undergone evaluation for safety and efficacy in phase III clinical trials on patients with moderate to severe ulcerative colitis (UC). Etrolizumab was terminated because mixed efficacy results were shown in the induction and maintenance phase in patients with UC. In this post hoc analysis, we characterized the impact of explanatory variables on the probability of remission using XGBoost machine learning (ML) models alongside with the SHapley Additive exPlanations framework for explainability. We used patient-level data encompassing demographics, physiology, disease history, clinical questionnaires, histology, serum biomarkers, and etrolizumab drug exposure to develop ML models aimed at predicting remission. Baseline covariates and early etrolizumab exposure at week 4 in the induction phase were utilized to develop an induction ML model, whereas covariates from the end of the induction phase and early etrolizumab exposure at week 4 in the maintenance phase were used to develop a maintenance ML model. Both the induction and maintenance ML models exhibited good predictive performance, achieving an area under the receiver operating characteristic curve (AUROC) of 0.74 ± 0.03 and 0.75 ± 0.06 (mean ± SD), respectively. Compared with placebo, the highest tertile of etrolizumab exposure contributed to 15.0% (95% confidence interval (CI): 9.7-19.9) and 17.0% (95% CI: 8.1-26.4) increases in remission probability in the induction and maintenance phases, respectively. Additionally, the key covariates that predicted remission were CRP, MAdCAM-1, and stool frequency for the induction phase and white blood cells, fecal calprotectin and age for the maintenance phase. These findings hold significant implications for establishing stratification factors in the design of future clinical trials.

Predicting overall survival from tumor dynamics metrics using parametric statistical and machine learning models: application to patients with RET-altered solid tumors.

In oncology drug development, tumor dynamics modeling is widely applied to predict patients' overall survival (OS) via parametric models. However, the current modeling paradigm, which assumes a disease-specific link between tumor dynamics and survival, has its limitations. This is particularly evident in drug development scenarios where the clinical trial under consideration contains patients with tumor types for which there is little to no prior institutional data. In this work, we propose the use of a pan-indication solid tumor machine learning (ML) approach whereby all three tumor metrics (tumor shrinkage rate, tumor regrowth rate and time to tumor growth) are simultaneously used to predict patients' OS in a tumor type independent manner. We demonstrate the utility of this approach in a clinical trial of cancer patients treated with the tyrosine kinase inhibitor, pralsetinib. We compared the parametric and ML models and the results showed that the proposed ML approach is able to adequately predict patient OS across RET-altered solid tumors, including non-small cell lung cancer, medullary thyroid cancer as well as other solid tumors. While the findings of this study are promising, further research is needed for evaluating the generalizability of the ML model to other solid tumor types.

Understanding CYP3A4 and P-gp mediated drug-drug interactions through PBPK modeling - Case example of pralsetinib.

Pralsetinib, a potent and selective inhibitor of oncogenic RET fusion and RET mutant proteins, is a substrate of the drug metabolizing enzyme CYP3A4 and a substrate of the efflux transporter P-gp based on in vitro data. Therefore, its pharmacokinetics (PKs) may be affected by co-administration of potent CYP3A4 inhibitors and inducers, P-gp inhibitors, and combined CYP3A4 and P-gp inhibitors. With the frequent overlap between CYP3A4 and P-gp substrates/inhibitors, pralsetinib is a challenging and representative example of the need to more quantitatively characterize transporter-enzyme interplay. A physiologically-based PK (PBPK) model for pralsetinib was developed to understand the victim drug-drug interaction (DDI) risk for pralsetinib. The key parameters driving the magnitude of pralsetinib DDIs, the P-gp intrinsic clearance and the fraction metabolized by CYP3A4, were determined from PBPK simulations that best captured observed DDIs from three clinical studies. Sensitivity analyses and scenario simulations were also conducted to ensure these key parameters were determined with sound mechanistic rationale based on current knowledge, including the worst-case scenarios. The verified pralsetinib PBPK model was then applied to predict the effect of other inhibitors and inducers on the PKs of pralsetinib. This work highlights the challenges in understanding DDIs when enzyme-transporter interplay occurs, and demonstrates an important strategy for differentiating enzyme/transporter contributions to enable PBPK predictions for untested scenarios and to inform labeling.

Integrating Dynamic in vitro Systems and Mechanistic Absorption Modeling: Case Study of Pralsetinib.

Dynamic in vitro absorption systems and mechanistic absorption modeling via PBPK have both shown promise in predicting human oral absorption, although these efforts have been largely separate; this work aimed to integrate knowledge from these approaches to investigate the oral absorption of a RET inhibitor, pralsetinib, with BCS Class II properties. Tiny-TIM (TIM B.V., Weteringbrug​, The Netherlands) is a dynamic in vitro model with close simulation of the successive physiological conditions of the human stomach and small intestine. Tiny-TIM runs with pralsetinib were performed at doses of 200 mg and 400 mg under fasting conditions. Mechanistic modeling of absorption was performed in Simcyp V21 (Certara, Manchester, UK). Pralsetinib fasted bioaccessibility in the Tiny-TIM system was 63% at 200 mg and 53% at 400 mg; a 16% reduction at 400 mg was observed under elevated gastric pH. Maximum pralsetinib solubility from the small intestinal compartment in Tiny-TIM directly informed the supersaturation/precipitation model parameters. The PBPK model predicted a similar fraction absorbed at 200 mg and 400 mg, consistent with the dose proportional increases in observed pralsetinib exposure. Integrating dynamic in vitro systems with mechanistic absorption modeling provides a promising approach for understanding and predicting human absorption with challenging low solubility compounds.

Exposure-Response Relationships for Pralsetinib in Patients with RET-Altered Thyroid Cancer or RET Fusion-Positive Nonsmall Cell Lung Cancer.

Pralsetinib is a highly potent oral kinase inhibitor of oncogenic RET (rearranged during transfection) fusions and mutations. Pralsetinib received approval from the United States Food and Drug Administration for the treatment of patients with metastatic RET fusion-positive non-small cell lung cancer (NSCLC), and received accelerated approval for the treatment of patients with RET fusion-positive thyroid cancer. Exposure-response (ER) analyses of efficacy were performed separately in patients with thyroid cancer and in patients with NSCLC, but data for all patients were pooled for the safety analysis. ER models were developed with time-varying exposure; the effect of covariates was also examined. For patients with NSCLC, a higher starting dose was associated with improved progression-free survival (PFS), but this improvement did not correlate with a higher exposure overall. Significant covariates included sex and baseline Eastern Cooperative Oncology Group (ECOG) score. For patients with thyroid cancer, a higher exposure was associated with improved PFS. Significant covariates included prior systemic cancer therapy and ECOG score. For safety, higher exposure was associated with a greater risk of grade ≥3 anemia, pneumonia, and lymphopenia. Patients with an ECOG score of ≥1 had an increased risk of grade ≥3 pneumonia. Non-White patients had a lower risk of grade ≥3 lymphopenia. ER analysis revealed that higher pralsetinib exposure was associated with improved PFS in thyroid cancer, but not in NSCLC. However, a higher starting dose (ie, 400 vs ≤300 mg daily) was correlated with better PFS for all indications. Higher exposure was also associated with an increased risk of grade ≥3 adverse events (AEs); however, the overall incidence of these events was acceptably low (≤20%). This analysis supports the use of a 400 mg starting dose of pralsetinib, allowing for dose reduction in the event of AEs.

Pharmacokinetic and pharmacodynamic modeling to determine the dose of ST-246 to protect against smallpox in humans.

Although smallpox has been eradicated, the United States government considers it a "material threat" and has funded the discovery and development of potential therapeutic compounds. As reported here, the human efficacious dose for one of these compounds, ST-246, was determined using efficacy studies in nonhuman primates (NHPs), together with pharmacokinetic and pharmacodynamic analysis that predicted the appropriate dose and exposure levels to provide therapeutic benefit in humans. The efficacy analysis combined the data from studies conducted at three separate facilities that evaluated treatment following infection with a closely related virus, monkeypox virus (MPXV), in a total of 96 NHPs. The effect of infection on ST-246 pharmacokinetics in NHPs was applied to humans using population pharmacokinetic models. Exposure at the selected human dose of 600 mg is more than 4-fold higher than the lowest efficacious dose in NHPs and is predicted to provide protection to more than 95% of the population.

External validation of a tumor growth inhibition-overall survival model in non-small-cell lung cancer based on atezolizumab studies using alectinib data.

BACKGROUND: A modeling framework was previously developed to simulate overall survival (OS) using tumor growth inhibition (TGI) data from six randomized phase 2/3 atezolizumab monotherapy or combination studies in non-small-cell lung cancer (NSCLC). We aimed to externally validate this framework to simulate OS in patients with treatment-naive advanced anaplastic lymphoma kinase (ALK)-positive NSCLC in the alectinib ALEX study. METHODS: TGI metrics were estimated from a biexponential model using longitudinal tumor size data from a Phase 3 study evaluating alectinib compared with crizotinib in patients with treatment-naive ALK-positive advanced NSCLC. Baseline prognostic factors and TGI metric estimates were used to predict OS. RESULTS: 286 patients were evaluable (at least baseline and one post-baseline tumor size measurements) out of 303 (94%) followed for up to 5 years (cut-off: 29 November 2019). The tumor growth rate estimate and baseline prognostic factors (inflammatory status, tumor burden, Eastern Cooperative Oncology Group performance status, race, line of therapy, and sex) were used to simulate OS in ALEX study. Observed survival distributions for alectinib and crizotinib were within model 95% prediction intervals (PI) for approximately 2 years. Predicted hazard ratio (HR) between alectinib and crizotinib was in agreement with the observed HR (predicted HR 0.612, 95% PI 0.480-0.770 vs. 0.625 observed HR). CONCLUSION: The TGI-OS model based on unselected or PD-L1 selected NSCLC patients included in atezolizumab trials is externally validated to predict treatment effect (HR) in a biomarker-selected (ALK-positive) population included in alectinib ALEX trial suggesting that TGI-OS models may be treatment independent.

Machine Learning for Exposure-Response Analysis: Methodological Considerations and Confirmation of Their Importance via Computational Experimentations.

Exposure-response (E-R) is a key aspect of pharmacometrics analysis that supports drug dose selection. Currently, there is a lack of understanding of the technical considerations necessary for drawing unbiased estimates from data. Due to recent advances in machine learning (ML) explainability methods, ML has garnered significant interest for causal inference. To this end, we used simulated datasets with known E-R "ground truth" to generate a set of good practices for the development of ML models required to avoid introducing biases when performing causal inference. These practices include the use of causal diagrams to enable the careful consideration of model variables by which to obtain desired E-R relationship insights, keeping a strict separation of data for model-training and for inference generation to avoid biases, hyperparameter tuning to improve the reliability of models, and estimating proper confidence intervals around inferences using a bootstrap sampling with replacement strategy. We computationally confirm the benefits of the proposed ML workflow by using a simulated dataset with nonlinear and non-monotonic exposure-response relationships.

Population-Based Pharmacodynamic Modeling of Omalizumab in Pediatric Patients with Moderate to Severe Persistent Inadequately Controlled Allergic Asthma.

Omalizumab is the first approved anti-immunoglobulin E (IgE) agent for the treatment of moderate to severe persistent inadequately controlled allergic asthma in adults and adolescents (≥ 12 years old). In 2016, it was approved in pediatric patients (6-11 years old). The objective of this study was to quantitatively characterize the relationship between serum free IgE and pulmonary function (as measured by forced expiratory volume in 1 s [FEV1]) in pediatrics using a population-based pharmacodynamic model. Data collected during the steroid-stable period (first 24 weeks) of an omalizumab trial with pediatric asthma patients (Study IA05) were used to build the pediatric IgE-FEV1 model. The previously developed population IgE-FEV1 model in adults/adolescents was adapted to characterize the FEV1 and IgE relationship in pediatrics with different magnitude and onset of response. The pediatric IgE-FEV1 model adequately characterized the IgE-FEV1 relationship in pediatrics, particularly at the extremes of the observed body weights (i.e., ≤ 30 kg) and IgE values at screening (i.e., > 700 IU/mL). The estimated sigmoidal free IgE-FEV1 curves were similar in shape and maximum effect, but the estimated free IgE concentration leading to 50% maximum effect (IC50) in pediatric patients (39.4, 95% confidence interval [CI] 24.3-63.9 ng/mL) was higher than estimated in adults (19.8, 95% CI 15.1-24.5 ng/mL). The model further confirmed that the current omalizumab dosing rationale based on the mean target free IgE level of 25 ng/ml was appropriate. The pediatric model can be used to predict population FEV1 response for omalizumab when combined with an omalizumab pharmacokinetic-IgE model.

A disease model predicting placebo response and remission status of patients with ulcerative colitis using modified Mayo score.

The Mayo Clinical Score is used in clinical trials to describe the clinical status of patients with ulcerative colitis (UC). It comprises four subscores: rectal bleeding (RB), stool frequency (SF), physician's global assessment, and endoscopy (ENDO). According to recent US Food and Drug Administration guidelines (Ulcerative colitis: developing drugs for treatment, Guidance Document, https://www.fda.gov/regulatory-information/s. 2022), clinical response and remission should be based on modified Mayo Score (mMS) relying on RB, SF, and ENDO. Typically, ENDO is performed at the beginning and end of each phase, whereas RB and SF are more frequently available. Item response theory (IRT) models allow the shared information to be used for prediction of all subscores at each observation time; therefore, it leverages information from RB and SF to predict ENDO. A UC disease IRT model was developed based on four etrolizumab phase III studies to describe the longitudinal mMS subscores, placebo response, and remission at the end of induction and maintenance. For each subscore, a bounded integer model was developed. The placebo response was characterized by a mono-exponential function acting on all mMS subscores similarly. The final model reliably predicted longitudinal mMS data. In addition, remission was well-predicted by the model, with only 5% overprediction at the end of induction and 3% underprediction at the end of maintenance. External evaluation of the final model using placebo arms from five different studies indicated adequate performance for both longitudinal mMS subscores and remission status. These results suggest utility of the current disease model for informed decision making in UC clinical development, such as assisting future clinical trial designs and evaluations.

Population Pharmacokinetics of Tenecteplase in Patients With Acute Myocardial Infarction and Application to Patients With Acute Ischemic Stroke.

The pharmacokinetics (PK) of tenecteplase in patients with acute ischemic stroke has not been extensively studied. This study aimed to describe PK characteristics of tenecteplase in patients with acute myocardial infarction (AMI) using a population PK approach and to assess applicability of the findings to patients with acute ischemic stroke by means of external validation. A population PK model was developed using nonlinear mixed-effects modeling based on the phase II TIMI 10B study in patients with AMI (785 PK observations from 103 patients). The statistical and clinical impact of selected covariates on PK parameters were evaluated by a stepwise covariate modeling procedure and simulations, respectively. The performance of the final model was evaluated for patients with acute ischemic stroke using summary statistics of tenecteplase concentrations of 75 patients from investigator-initiated study N1811s. Tenecteplase PK was well described by a 2-compartment linear model, incorporating allometric scaling of clearance and volume parameters and weight-normalized creatinine clearance on clearance. Simulations showed that the identified covariates (weight and creatinine clearance) were of limited influence on exposure at the intended dosing regimen for patients with acute ischemic stroke. The model overpredicted mean tenecteplase plasma concentrations from N1811s by 39%, but 72% of the distribution from N1811s was within the 90% prediction interval of the model predictions. The PK characteristics of tenecteplase in patients with AMI were well described by the final model. Simulations from the model indicated that no specific dose recommendations based on covariates are warranted for patients with AMI.

Exposure-response relationships of etrolizumab in patients with moderately-to-severely active ulcerative colitis.

Etrolizumab is an IgG1-humanized monoclonal anti-ß7 integrin antibody. Phase III trials with induction and/or maintenance phases were conducted in patients with moderately-to-severely active ulcerative colitis (UC) who were either previously treated with tumor necrosis factor (TNF) inhibitors (HICKORY) or were TNF inhibitor naïve (HIBISCUS I/II, LAUREL, and GARDENIA). A total of eight exposure-response analyses were conducted for two clinical outcomes (remission and endoscopic improvement) at the end of induction for studies HIBISCUS I/II (combined) and HICKORY and at the end of maintenance for studies HICKORY and LAUREL. Trough concentration at week 4 (Ctrough,wk4 ) of induction was selected as the exposure metric. Exposure-response (ER) modeling was conducted using logistic regression. A full covariate model was used to examine the impact of covariates on clinical outcomes. Linear models with a single intercept for placebo and active treatments adequately described the data for all eight analyses. The etrolizumab exposure-response slope was significant (p < 0.05) for seven of the eight analyses. Baseline Mayo Clinic Score (MCS) was the only statistically significant covariate that impacted induction remission and endoscopic improvement. No statistically significant covariate was identified to impact maintenance outcomes except for baseline fecal calprotectin on endoscopic improvement for LAUREL study. A statistically significant positive ER relationship was identified for most of the clinical outcomes tested, reflecting a better treatment effect in patients with UC with higher etrolizumab Ctrough,wk4 of induction. Baseline MCS was the only other significant covariate impacting induction efficacy. Besides Ctrough,wk4 of induction, no consistent covariate was identified to impact maintenance efficacy.

Population pharmacokinetic analysis of etrolizumab in patients with moderately-to-severely active ulcerative colitis.

Etrolizumab is an IgG1-humanized monoclonal antibody that specifically targets the ß7 subunit of α4ß7 and α4Eß7 integrins, and it has been evaluated for the treatment of moderately-to-severely active ulcerative colitis (UC). Population pharmacokinetic (PK) analysis was performed to characterize etrolizumab PK properties in patients with moderately-to-severely active UC and evaluate covariate impacts on exposure. The population PK model was developed based on etrolizumab serum concentrations from patients with moderately-to-severely active UC enrolled in six studies (one phase I, one phase II, and four phase III) and validated using another phase III clinical trial. Stepwise covariate modeling was used to evaluate the impact of 23 prespecified covariates. Etrolizumab PK was best described by a two-compartment model with first-order absorption, with clearance decreasing over time. Population typical values were 0.260 L/day for clearance (CL) during the first dosing internal, 2.61 L for central volume, 71.2% for bioavailability, and 0.193/day for absorption rate. CL reduced over the study duration, the typical maximum reduction was 26% with an onset half-life of 4.8 weeks. Consequently, the predicted mean terminal half-life was shorter after a single dose (13.0 days) compared to that at steady-state (17.1 days). Baseline body weight and albumin were the most impactful covariates for etrolizumab exposure. Final population PK model well characterized the PK properties of etrolizumab in patients with moderately-to-severely active UC and identified influential covariate effects.

Limited sampling strategies for monitoring tacrolimus in pediatric liver transplant recipients.

OBJECTIVE: To develop and validate limited sampling strategies (LSSs) for tacrolimus in pediatric liver transplant recipients. METHODS: Thirty-six 12-hour pharmacokinetic profiles from 28 pediatric liver transplant recipients (0.4-18.5 years) were collected. Tacrolimus concentrations were measured by immunoassay and area under the curve (AUC0-12) was determined by trapezoidal rule. LSSs consisting of 1, 2, 3, or 4 concentration-time points were developed using multiple regression analysis. Eight promising models (2 per category) were selected based on the following criteria: r2 ≥ 0.90, inclusion of trough concentration (C0), and time points within 4 hours postdose. The predictive performance of these LSSs was evaluated in an independent set of data by measuring the mean prediction error and the root mean squared prediction error. RESULTS: Five models including 2-4 time points predicted AUC0-12 with a ±15% error limit. Bias (mean prediction error) and precision (root mean squared prediction error) of LSS involving C0, C1, and C4 (AUCpredicted = 9.30 + 3.69 × C0 + 2.19 × C1 + 4.69 × C4) were -4.98% and 8.29%, respectively. Among single time point LSSs, the model using C0 had a poor correlation with AUC0-12 (r2 = 0.53), whereas the one with C4 had the highest correlation with tacrolimus exposure (r2 = 0.84). CONCLUSIONS: Trough concentration is a poor predictor of tacrolimus AUC0-12 in pediatric liver transplant recipients. However, LSSs using 2-4 concentration-time points obtained within 4 hours postdose provide a reliable and convenient method to predict tacrolimus exposure in this population. The proposed LSSs represent an important step that will allow the undertaking of prospective trials aiming to better define tacrolimus target AUC in pediatric liver transplant recipients and to determine whether AUC-guided monitoring is superior to C0-based monitoring in terms of efficacy and safety.