Pharmacologic effects of oseltamivir in immunocompromised adult patients as assessed by population PK/PD analysis and drug-disease modelling for dosing regimen optimization.

AIM: Pharmacologic effects were analysed to determine a dose recommendation for oseltamivir in immunocompromised (IC) adults with influenza. METHODS: Quantitative clinical pharmacology methods were applied to data from 160 adult IC patients (aged 18-78 years) from two studies (NV20234, 150 patients; NV25118, 10 patients) who received oseltamivir 75-200 mg twice daily for up to 10 days. An established population-pharmacokinetic (PK) model with additional effects on oseltamivir and oseltamivir carboxylate (OC) clearance described the PK characteristics of oseltamivir in IC patients versus otherwise healthy (OwH) patients from previous clinical trials. Estimated PK parameters were used to evaluate exposure-response relationships for virologic endpoints (time to cessation of viral shedding, viral load measures and treatment-emergent resistance). A drug-disease model characterized the viral kinetics of influenza accounting for the effect of OC on viral production. RESULTS: Oseltamivir clearance was 32.5% lower (95% confidence interval [CI], 26.1-38.8) and OC clearance was 33.7% lower (95% CI, 23.2-44.1) in IC versus OwH patients. No notable exposure-response relationships were identified for exposures higher than those achieved after conventional dose oseltamivir 75 mg, which appeared to be close to the maximum effect of oseltamivir. Simulations of the drug-disease model predicted that initiating treatment within 48 hours of symptom onset had maximum impact, and a treatment duration of 10 days was favourable over 3-5 days to limit viral rebound. CONCLUSIONS: Our findings support the use of conventional-dose oseltamivir 75 mg twice daily for 10 days in the treatment of IC adult patients with influenza.

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.

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.

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.

Characterization of exposure-response relationships of ipatasertib in patients with metastatic castration-resistant prostate cancer in the IPATential150 study.

PURPOSE: The exposure-response relationships for efficacy and safety of ipatasertib, a selective AKT kinase inhibitor, were characterized using data collected from 1101 patients with metastatic castration-resistant prostate cancer in the IPATential150 study (NCT03072238). METHODS: External validation of a previously developed population pharmacokinetic model was performed using the observed pharmacokinetic data from the IPATential150 study. Exposure metrics of ipatasertib for subjects who received ipatasertib 400 mg once-daily orally in this study were generated as model-predicted area under the concentration-time curve at steady state (AUCSS). The exposure-response relationship with radiographic progression-free survival (rPFS) was evaluated using Cox regression and relationships with safety endpoints were assessed using logistic regression. RESULTS: A statistically significant correlation between ipatasertib AUCSS and improved survival was found in patients with PTEN-loss tumors (hazard ratio [HR]: 0.92 per 1000 ng h/mL AUCSS, 95% confidence interval [CI] 0.87-0.98, p = 0.011). In contrast, an improvement in rPFS was seen in subjects receiving ipatasertib treatment (HR: 0.84, 95% CI 0.71-0.99, p = 0.038) but this effect was not associated with ipatasertib AUCSS in the intention-to-treat population. Incidences of some adverse events (AEs) had statistically significant association with ipatasertib AUCSS (serious AEs, AEs leading to discontinuation, and Grade ≥ 2 hyperglycemia), while others were associated with only ipatasertib treatment (AEs leading to dose reduction, Grade ≥ 3 diarrhea, and Grade ≥ 2 rash). CONCLUSIONS: The exposure-efficacy results indicated that patients receiving ipatasertib may continue benefiting from this treatment at the administered dose, despite some variability in exposures, while the exposure-safety results suggested increased risks of AEs with ipatasertib treatment and/or increased ipatasertib exposures.