Semimechanistic Clearance Models of Oncology Biotherapeutics and Impact of Study Design: Cetuximab as a Case Study

Grisic, Ana-Marija; Khandelwal, Akash; Bertolino, Mauro; Huisinga, Wilhelm; Girard, Pascal; Kloft, Charlotte

doi:10.1002/psp4.12558

Cited by 8 publications

(8 citation statements)

References 21 publications

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“…Using the modified Michaelis-Menten PK/PD model of methotrexate decomposition by CPG2, Monte-Carlo simulation revealed the efficacy of CPG2 at a dose range of 10-80 U/kg. Simulations applying the Michaelis-Menten model have been reported in the oncology field, but to our knowledge, this is the first report covering CPG2 (19,20). This model was considered appropriate because the trend of the blood concentration of methotrexate with CPG2 administration approximated previously reported clinical results (10)(11)(12)(13)(14).…”

Section: Discussionmentioning

confidence: 85%

Pharmacokinetics and Pharmacodynamics of Glucarpidase Rescue Treatment After High-dose Methotrexate Therapy Based on Modeling and Simulation

et al. 2023

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Background: Model-informed approaches are important in drug development, including for dose optimization and the collection of evidence in support of efficacy. Materials and Methods: We developed a modified Michaelis-Menten pharmacokinetics/pharmacodynamics model and used it to conduct simulations of glucarpidase at doses between 10 and 80 U/kg rescue treatment after high-dose methotrexate therapy. We carried out a dose-finding modeling and simulation study before a phase II study of glucarpidase. Monte-Carlo simulations were conducted using the deSolve package of R software (version 4.1.2). The proportion of samples in which the plasma methotrexate concentration was less than 0.1 and 1.0 μmol/l at 70 and 120 h after methotrexate treatment was evaluated for each dosage of glucarpidase. Results: The proportion of samples in which the plasma methotrexate concentration was less than 0.1 μmol/l at 70 h after methotrexate treatment was 71.8% and 89.6% at 20 and 50 U/kg of glucarpidase, respectively. The proportion of samples in which the plasma methotrexate concentration was less than 0.1 μmol/l at 120 h after methotrexate treatment was 46.4% and 59.0% at 20 and 50 U/kg of glucarpidase, respectively. Conclusion: We determined a recommended glucarpidase dose of 50 U/kg to be ethically acceptable. A rebound in the serum concentration of methotrexate may be observed in many patients after the administration of glucarpidase, and longterm monitoring (over 144 h) of the serum methotrexate concentration may be needed after the administration of glucarpidase. Its validity was confirmed in the phase II study and glucarpidase was approved for manufacturing in Japan.

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Section: Discussionmentioning

confidence: 85%

Pharmacokinetics and Pharmacodynamics of Glucarpidase Rescue Treatment After High-dose Methotrexate Therapy Based on Modeling and Simulation

et al. 2023

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“…The time-varying CL of mAbs has been associated with posttreatment effects, with decreased CL observed with improved disease status and response in patients receiving treatment; this may lead to a steeper estimate of the exposure-response relationship when using the classical model with a one-way interaction between PK and PD (18,26). Therefore, a bidirectional PK-TGD model may prevent a biased characterization of the exposure-response relationship by accounting for impact of TS on PK.…”

Section: Discussionmentioning

confidence: 99%

Model-Based Characterization of the Bidirectional Interaction Between Pharmacokinetics and Tumor Growth Dynamics in Patients with Metastatic Merkel Cell Carcinoma Treated with Avelumab

Grisic

Xiong

Tanneau

et al. 2021

Clinical Cancer Research

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Purpose: Empirical time-varying clearance models have been reported for several immune checkpoint inhibitors, including avelumab (anti–programmed death ligand 1). To investigate the exposure-response relationship for avelumab, we explored semimechanistic pharmacokinetic (PK)–tumor growth dynamics (TGD) models. Patients and Methods: Plasma PK data were pooled from three phase I and II trials (JAVELIN Merkel 200, JAVELIN Solid Tumor, and JAVELIN Solid Tumor JPN); tumor size (TS) data were collected from patients with metastatic Merkel cell carcinoma (mMCC) enrolled in JAVELIN Merkel 200. A PK model was developed first, followed by TGD modeling to investigate interactions between avelumab exposure and TGD. A PK-TGD feedback loop was evaluated with simultaneous fitting of the PK and TGD models. Results: In total, 1,835 PK observations and 338 TS observations were collected from 147 patients. In the final PK-TGD model, which included the bidirectional relationship between PK and TGD, avelumab PK was described by a two-compartment model with a positive association between clearance and longitudinal TS, with no additional empirical time-varying clearance identified. TGD was described by first-order tumor growth/shrinkage rates, with the tumor shrinkage rate decreasing exponentially over time; the exponential time-decay constant decreased with increasing drug concentration, representing the treatment effect through tumor shrinkage inhibition. Conclusions: We developed a TGD model that mechanistically captures the prevention of loss of antitumor immunity (i.e., T-cell suppression in the tumor microenvironment) by avelumab, and a bidirectional interaction between PK and TGD in patients with mMCC treated with avelumab, thus mechanistically describing previously reported time variance of avelumab elimination.

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“…First, the base model is estimated using a PMX software (NONMEM in this case), and then a fast screening of covariates is performed using the considered methods. The use case includes data from a recently published study on cetuximab, and described by a two-compartment model with Michaelis-Menten and linear elimination [46] as the base model. Thirty covariates are available in the dataset, including patient-related factors (e.g., age, sex, creatinine clearance), therapy related-factors (e.g., co-medication), and other disease-related measurements (e.g., amphiregulin, interleukin-8).…”

Section: Real Casementioning

confidence: 99%

Fast screening of covariates in population models empowered by machine learning

Sibieude

Khandelwal

Hesthaven

et al. 2021

J Pharmacokinet Pharmacodyn

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One of the objectives of Pharmacometry (PMX) population modeling is the identification of significant and clinically relevant relationships between parameters and covariates. Here, we demonstrate how this complex selection task could benefit from supervised learning algorithms using importance scores. We compare various classical methods with three machine learning (ML) methods applied to NONMEM empirical Bayes estimates: random forest, neural networks (NNs), and support vector regression (SVR). The performance of the ML models is assessed using receiver operating characteristic (ROC) curves. The F1 score, which measures test accuracy, is used to compare ML and PMX approaches. Methods are applied to different scenarios of covariate influence based on simulated pharmacokinetics data. ML achieved similar or better F1 scores than stepwise covariate modeling (SCM) and conditional sampling for stepwise approach based on correlation tests (COSSAC). Correlations between covariates and the number of false covariates does not affect the performance of any method, but effect size has an impact. Methods are not equivalent with respect to computational speed; SCM is 30 and 100-times slower than NN and SVR, respectively. The results are validated in an additional scenario involving 100 covariates. Taken together, the results indicate that ML methods can greatly increase the efficiency of population covariate model building in the case of large datasets or complex models that require long run-times. This can provide fast initial covariate screening, which can be followed by more conventional PMX approaches to assess the clinical relevance of selected covariates and build the final model.

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Semimechanistic Clearance Models of Oncology Biotherapeutics and Impact of Study Design: Cetuximab as a Case Study

Cited by 8 publications

References 21 publications

Pharmacokinetics and Pharmacodynamics of Glucarpidase Rescue Treatment After High-dose Methotrexate Therapy Based on Modeling and Simulation

Pharmacokinetics and Pharmacodynamics of Glucarpidase Rescue Treatment After High-dose Methotrexate Therapy Based on Modeling and Simulation

Model-Based Characterization of the Bidirectional Interaction Between Pharmacokinetics and Tumor Growth Dynamics in Patients with Metastatic Merkel Cell Carcinoma Treated with Avelumab

Fast screening of covariates in population models empowered by machine learning

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