Predictive ability of a semi-mechanistic model for neutropenia in the development of novel anti-cancer agents: two case studies using diflomotecan and indisulam
Elena Soto1,7, Ron J Keizer2,7, Iñaki F. Trocóniz1, Alwin DR Huitema2,4, Jos H Beijnen2,3,4, Jan HM Schellens3,4, Jantien Wanders 5, Josep María Cendrós6, Rosendo Obach6, Concepción Peraire6, Lena E Friberg7, Mats O Karlsson7
Affiliations: 1, Department of Pharmacy and Pharmaceutical Technology, School of Pharmacy, University of Navarra, Pamplona, Spain; 2, Department of Pharmacy & Pharmacology, Slotervaart Hospital / The Netherlands Cancer Institute, Amsterdam, The Netherlands; 3, Department of Pharmaceutical Sciences, Division of Biomedical Analysis, Section of Drug Toxicology, Utrecht University, Utrecht, The Netherlands; 4, Division of Clinical Pharmacology, Antoni van Leeuwenhoek Hospital / the Netherlands Cancer Institute, Amsterdam, the Netherlands; 5, Eisai Limited, Hatfield, Hertfordshire, United Kingdom; 6, Ipsen Pharma S.A., San Feliu de Llobregat, Barcelona, Spain; 7, Department of Pharmaceutical Biosciences, Uppsala University, Sweden
Background: In cancer chemotherapy neutropenia is a common dose-limiting toxicity. The ability to predict the neutropenic effects of cytotoxic agents based on the proposed trial designs and models conditioned on previous studies would be valuable.
Objectives: The aim of this study was to evaluate the ability of a semi-mechanistic pharmacokinetic/pharmacodynamic (PK/PD) model for myelosuppression to predict the neutropenia observed in Phase I clinical studies, based on parameter estimates obtained from prior trials.
Methodology: Pharmacokinetic and neutropenia data from 5 clinical trials for diflomotecan and from 4 clinical trials for indisulam were used. Data were analyzed and simulations were performed using the population approach with NONMEM VI.
Parameter sets were firstly estimated under the following scenarios: (i) data from each trial independently, (ii) pooled data from all clinical trials and (iii) pooled data from trials performed before the tested trial. Then, model performance in each of the scenarios was evaluated by means of predictive (visual and numerical) checks.
Results and conclusions: The semi-mechanistic PK/PD model for neutropenia showed adequate predictive ability for both anti-cancer agents, diflomotecan and indisulam. When the model for each drug was conditioned on data from trials performed prior to a specific study, similar predictions of the drug related-neutropenia profiles and descriptors were obtained for the three estimation scenarios, indicating that neutropenic effects of cytoxic agents can be predicted from early clinical data applying semi-mechanistic PK/PD models.