CPT: Pharmacometrics & Systems Pharmacology

A mechanistic PK/PD model of AZD0171 (anti‐LIF) to support Phase II dose selection

AbstractAZD0171 (INN: Falbikitug) is being developed as a humanized monoclonal antibody (mAb), immunoglobulin G subclass 1 (IgG1), which binds specifically to the immunosuppressive human cytokine leukemia inhibitory factor (LIF) and inhibits downstream signaling by blocking recruitment of glycoprotein 130 (gp130) to the LIF receptor (LIFR) subunit (gp190) and the phosphorylation of signal transducer and activator of transcription 3 (STAT3) and is intended to treat adult participants with advanced solid tumors. LIF is a pleiotropic cytokine (and a member of the IL‐6 family of cytokines) involved in many physiological and pathological processes and is highly expressed in a subset of solid tumors, including non‐small cell lung cancer (NSCLC), colon, ovarian, prostate, and pancreatic cancer. The aim of this work was to develop a mechanistic PK/PD model to investigate the effect of AZD0171 on tumor LIF levels, predict the level of downstream signaling complex (LIF:LIFR:gp130) inhibition, and examine the dose–response relationship to support dose selection for a Phase II clinical study. Modeling results show that tumor LIF is inhibited in a dose‐dependent manner with >90% inhibition for 95% of patients at the Phase II clinical dose of 1500 mg Q2W.

Prognostic Value of a Joint K‐PD Model With Tumor Size Dynamics and CA‐125 Kinetics in Recurrent Ovarian Cancer Patients: BOLD Phase II GINECO Study

ABSTRACTIn patients with recurrent advanced ovarian cancer, there is a need for companion tests to guide the development of innovative chemotherapy‐free treatments. The modeled longitudinal CA‐125 ELIMination rate constant K KELIM‐B was a major prognostic factor for progression‐free survival (PFS) and overall survival (OS) in recurrent advanced ovarian cancer patients treated with bevacizumab, olaparib, and durvalumab in the BOLD trial. The objective was to determine if a joint semi‐mechanistic model with tumor size and CA‐125 kinetics would increase KELIM‐B accuracy/prognostic value. The BOLD phase II trial (NCT04015739) investigated the triplet regimen in 74 patients with recurrent platinum‐sensitive/resistant advanced ovarian cancer. Two kinetic‐pharmacodynamic models were developed to fit the data collected during the first 100 treatment days: (1) a CA‐125 longitudinal kinetics model, and (2) a joint model integrating both CA‐125 kinetics and tumor size. The prognostic value of KELIM‐B and KELIM‐joint was assessed using univariate/multivariate analyses (PFS/OS). The modeling of CA‐125 and tumor size dynamics was feasible with adequate quality checks. The prognostic value of the categorical KELIM‐joint, binarized by the median (PFS, HR = 0.29, 95% CI [0.12–0.72]; OS, HR = 0.24, 95% CI [0.08–0.74]), was not clinically different from that of KELIM‐B (PFS, HR = 0.35, 95% CI [0.14–0.84]; OS, HR = 0.34, 95% CI [0.12–0.99]). Interactions between tumor size changes and CA‐125 kinetics could be assessed in the joint model. However, the improvement in prognostic value was not sufficient to justify the higher complexity of the joint model. Assessing early longitudinal CA‐125 kinetics alone remains the best pragmatic strategy for future development.

A New Method to Model and Predict Progression Free Survival Based on Tumor Growth Dynamics

Progression‐free survival (PFS) has been increasingly used as a primary endpoint for early clinical development. The aim of the present work was to develop a model where target lesion dynamics and risk for nontarget progression are jointly modeled for predicting PFS. The model was developed based on a pooled platinum‐resistant ovarian cancer dataset comprising four different treatments and a wide range of dose levels. The target lesion progression was derived from tumor growth dynamics based on the Response Evaluation Criteria in Solid Tumors (RECIST) criteria. The nontarget progression hazard was correlated to the first derivative of target lesion tumor size with respect to time. The PFS time was determined by the first occurring event, target lesion progression, or nontarget progression. The final joint model not only captured target lesion tumor growth dynamics but also predicted PFS well. A similar approach can potentially be used to predict PFS in future oncology studies.

Population pharmacokinetics modeling and exposure‐response analyses of cemiplimab in patients with recurrent or metastatic cervical cancer

AbstractA population pharmacokinetic (PopPK) model was previously developed for cemiplimab in patients with solid tumors, including advanced cutaneous squamous cell carcinoma (CSCC). Here, we update the existing PopPK model and characterize exposure‐response relationships using efficacy and safety data obtained in patients with recurrent or metastatic cervical cancer (R/M CC). To improve model stability and robustness of the existing PopPK model in 1062 patients, the random‐effect error model was revised, and structural covariates were removed from the base model to be tested in the covariate analysis. The updated model was used for external validation of cemiplimab pharmacokinetics (PK) in patients with R/M CC on cemiplimab monotherapy (350 mg every 3 weeks intravenously) from a phase III study (NCT03257267). Exposure‐response relationships for cemiplimab efficacy (overall survival [OS], progression‐free survival [PFS], duration of response [DOR], objective response rate [ORR]), and safety (immune‐related adverse events [irAEs]) were analyzed in 295 patients with R/M CC from the aforementioned study. The updated PopPK model showed improved stability with 94.8% successful bootstrap runs vs. 47.6% in the prior model. Cemiplimab exposure was similar across tumor types, including basal cell carcinoma, CSCC, and non‐small cell lung cancer. External validation showed the updated model adequately described cemiplimab PK in patients with R/M CC. In exposure‐response efficacy analyses, Cox proportional hazard modeling (CPHM) showed no trend between exposure and OS, Kaplan–Meier plots showed no trend between exposure and PFS or DOR, and logistic regression analyses conducted on ORR showed no exposure‐response relationship. In exposure‐response safety analyses, CPHM showed no trend between exposure and irAEs.