In oncology, there is a need to optimize drug treatment for efficient eradication of tumors, minimization of adverse effects (AEs), and prolonging patient survival. Pharmacometric models can be developed to streamline information between drug development phases, describe and quantify response to treatment, and determine dose regimens that balance toxicity and efficacy. In this thesis, data from trastuzumab emtansine (T-DM1) and taxane drug treatment were used to develop pharmacometric models of pharmacokinetics (PK), AEs, anti-tumor response, and survival, supporting drug development. T-DM1 is an antibody-drug conjugate (ADC) for treatment of human epidermal growth factor receptor 2 (HER2)–positive breast cancer. ADCs are a relatively new class of oncologic agents, and contain multiple drug-to-antibody ratio (DAR) moieties in their dose product. The complex distribution of T-DM1 was elucidated through PK models developed using in vitro and in vivo rat and cynomolgus monkey DAR data. Mechanism–based PK/pharmacodynamic (PKPD) models were also developed for T-DM1 that described the AEs thrombocytopenia (TCP) and hepatotoxicity in patients receiving T-DM1. Variable patterns of platelet and transaminase (ALT and AST) response were quantified, including an effect of Asian ethnicity that was related to higher incidences of TCP. Model simulations, comparing dose intensities (DI) and Grade 3/4 incidences between the approved T-DM1 dose (3.6 mg/kg every three weeks) and weekly regimens, determined that 2.4 mg/kg weekly provided the highest DI. Docetaxel and paclitaxel are taxane treatment options for HER2–negative breast cancer. Tumor response data from these treatments were used to develop a mechanism–based model of tumor quiescence and drug–resistance. Subsequently, a parametric survival analysis found that tumor baseline and the model–predicted time to tumor growth (TTG) were predictors of overall survival (OS). This tumor and OS modeling approach can be applied to other anticancer treatments with similar patterns of drug–resistance. Overall, the pharmacometric models developed within this thesis present new modeling approaches and provide understanding on ADC PK and PKPD (TCP and hepatotoxicity), as well as drug–resistance tumor response. These models can inform simulation strategies and clinical study design, and be applied towards dose finding for anticancer drugs in development, especially ADCs.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:uu-292617 |
| Date | January 2016 |
| Creators | Bender, Brendan |
| Publisher | Uppsala universitet, Institutionen för farmaceutisk biovetenskap, Uppsala |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Doctoral thesis, comprehensive summary, info:eu-repo/semantics/doctoralThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
| Relation | Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Pharmacy, 1651-6192 ; 217 |
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