Pharmacokinetic and pharmacodynamic analysis of neutropenia following nab-paclitaxel administration in Japanese patients with metastatic solid cancer

Population pharmacokinetic and exposure-toxicity analyses of nab-paclitaxel after pegylated recombinant human granulocyte colony-stimulating factor administration in patients with metastatic breast cancer

PURPOSE

This study aimed to establish a population pharmacokinetic (PK) model to evaluate the dynamic relationship between the concentrations of total and unbound paclitaxel, and the exposure-response analysis of albumin-bound paclitaxel (nab-paclitaxel) after pegylated recombinant human granulocyte colony-stimulating factor (PEG-G-CSF) administration in patients with metastatic breast cancer.

METHODS

A total of 653 concentrations corresponding to total paclitaxel and 334 concentrations corresponding to unbound paclitaxel were analyzed in 24 subjects who randomized received a single 260 mg/m2 dose of two nab-paclitaxel formulations with a 21-35-day washout period. PEG-G-CSF was administered to all the patients in each cycle to prevent neutropenia. The exposure-response relationships were evaluated using the exposure to total, albumin-coated, and unbound paclitaxel, as well as the reduction in neutrophil count. The exposure data were analyzed using nonlinear mixed-effect modeling. A linear regression model was used to test the statistical significance of the correlation between percentage of reduction in neutrophil count and exposure.

RESULTS

The PK characteristics of total paclitaxel were described using a three-compartment model with first-order elimination, and a mechanism-based model incorporating linear release of nab-paclitaxel and the saturated binding of unbound paclitaxel to plasma components was established. The release ratio of paclitaxel from nab-paclitaxel was estimated to be 4.60% and the maximum unbound fraction (2.76%) was reached at the end of the infusion. The study found that a longer duration of total paclitaxel concentration > 0.19 µmol/L was significantly correlated with a reduction in neutrophil count (r2 = 0.23, P = 0.00062). Specifically, a duration of > 8.6 h was a predictor of a decreased neutrophil count.

CONCLUSION

The decrease in neutrophils induced by nab-paclitaxel was significantly correlated with the duration above a total paclitaxel concentration of 0.19 µmol/L despite the use of PEG-G-CSF.

Translational PK-PD/TD modeling of antitumor effects and peripheral neuropathy in gemcitabine and nab-paclitaxel chemotherapy from xenograft mice to patients for optimal dose and schedule

PURPOSE

Gemcitabine and nab-paclitaxel (GnP) treatment, the standard first-line chemotherapy for unresectable pancreatic cancer, often causes peripheral neuropathy (PN). To develop alternative dosing strategies to avoid severe PN, understanding the relationship between pharmacokinetics (PK) and pharmacodynamics/toxicodynamics (PD/TD) is necessary. We established a PK-PD/TD model of GnP treatment to develop an optimal dose schedule.

METHODS

A mouse xenograft model of human pancreatic cancer was generated to measure drug concentrations in the plasma and tumor, antitumor effects, and PN after GnP treatment. The Simeoni tumor growth inhibition model with tumor concentrations and empirical indirect response models were used for the PD and TD models, respectively. Clinical outcomes were predicted with reported population estimates of PK parameters in cancer patients.

RESULTS

The PK-PD/TD model simultaneously described the observed tumor volume and paw withdrawal frequency in the von Frey test. For the standard GnP regimen, the model predicted clinical overall response (75.1%), which was overestimated compared to that in a recent phase II study (42.1%) but lower than the observed disease control rate (96.5%). Model simulation showed that dose reduction to less than 40% GnP dose was not effective; a change of dose schedule from every week for 3 weeks to every 2 weeks was a more favorable approach than dose reduction to 60% every week.

CONCLUSION

The PK-PD/TD model-based translational approach provides a guide for optimal dose determination to avoid severe PN while maintaining antitumor effects during GnP chemotherapy. Further research is needed to enhance its applicability and potential for combination chemotherapy regimens.

Clinical decision support for chemotherapy-induced neutropenia using a hybrid pharmacodynamic/machine learning model

Consensus guidelines recommend use of granulocyte colony stimulating factor in patients deemed at risk of chemotherapy-induced neutropenia, however, these risk models are limited in the factors they consider and miss some cases of neutropenia. Clinical decision making could be supported using models that better tailor their predictions to the individual patient using the wealth of data available in electronic health records (EHRs). Here, we present a hybrid pharmacokinetic/pharmacodynamic (PKPD)/machine learning (ML) approach that uses predictions and individual Bayesian parameter estimates from a PKPD model to enrich an ML model built on her data. We demonstrate this approach using models developed on a large real-world data set of 9121 patients treated for lymphoma, breast, or thoracic cancer. We also investigate the benefits of augmenting the training data using synthetic data simulated with the PKPD model. We find that PKPD-enrichment of ML models improves prediction of grade 3-4 neutropenia, as measured by higher precision (61%) and recall (39%) compared to PKPD model predictions (47%, 33%) or base ML model predictions (51%, 31%). PKPD augmentation of ML models showed minor improvements in recall (44%) but not precision (56%), and data augmentation required careful tuning to control overfitting its predictions to the PKPD model. PKPD enrichment of ML shows promise for leveraging both the physiology-informed predictions of PKPD and the ability of ML to learn predictor-outcome relationships from large data sets to predict patient response to drugs in a clinical precision dosing context.

A Multicenter, Phase II Trial of Schedule Modification for Nab-Paclitaxel in Combination with Ramucirumab for Patients with Previously Treated Advanced Gastric or Gastroesophageal Junction Cancer: The B-RAX Trial (JACCRO GC-09)

BACKGROUND

This study investigated whether schedule modification of bi-weekly nanoparticle albumin-bound paclitaxel (nab-PTX) plus ramucirumab (RAM) is efficacious against gastric cancer (GC) or gastroesophageal junction cancer (GJC).

PATIENTS AND METHODS

Patients with unresectable GC or GJC who were previously treated with fluoropyrimidine-containing regimens received nab-PTX (100 mg/m²) on days 1, 8, and 15 and RAM (8 mg/kg) on days 1 and 15 of a 28-day cycle. Based on the incidence of severe adverse events (AEs) during the first cycle, patients were modified to bi-weekly therapy from the second cycle. The primary endpoint was progression-free survival (PFS) in the bi-weekly therapy population. Based on the hypothesis that bi-weekly nab-PTX plus RAM would improve PFS from 4.5 to 7.0 months, 40 patients were required for power of 0.8 with a one-sided α of 0.05.

RESULTS

Of the 81 patients enrolled, 47 patients (58%) were assigned to bi-weekly therapy. Patient characteristics were Eastern Cooperative Oncology Group performance status of 1 (19%) and diffuse type (45%). Median PFS was 4.7 months (95% confidence interval [CI] 3.7-5.6 months) and overall response rate was 25% (95% CI 11-39%). Severe AEs of grade 3 or worse were mainly neutropenia (83%) and hypertension (23%). EQ-5D scores were maintained during the treatment. In patients who continued standard-schedule therapy, median PFS was 2.7 months (95% CI 1.8-4.0 months).

CONCLUSIONS

The primary endpoint for PFS was statistically not met, but modification of nab-PTX plus RAM to a bi-weekly schedule might be a feasible treatment option as second-line treatment for advanced GC/GJC patients, especially elderly patients, with severe AEs during the first cycle.