Focus of research
Forschungslinie 2: Pharmacometric data analysis
Paclitaxel is a component of many regimens used in treating solid tumours. Lung, breast, and ovarian cancer are among the most common cancers globally and are treated with this drug. Non-small-cell lung cancer (NSCLC), a specific variant of lung cancer treated with paclitaxel, accounts for approximately 85% of the overall lung cancer cases. To treat NSCLC, paclitaxel is combined with a platinum-based drug (carboplatin or cisplatin), nowadays.
Significant variability in pharmacokinetics (PK) of paclitaxel has been reported and relations between PK and treatment outcome documented. Neutropenia is the major dose-limiting toxicity of paclitaxel, and dose modification based on the degree of neutropenia has been suggested. Beside neutropenia, other commonly reported toxicities of clinical relevance include neurological and gastro-intestinal toxicities; these also form a basis of dose adaptation during treatment
Striking a balance between efficacy and tolerable toxicity burden is a major challenge of chemotherapy; especially for patients with advanced disease stages of cancers with poor prognosis. To achieve this, it is important to understand the incidences and predictors of treatment-related toxicity, as well as the predictors of tumour response. Upon this, it is possible to undertake treatment modifications based on these predictors (i.e. patient- and treatment-related factors) to achieve the desired treatment outcomes.
My PhD project will involve developing and applying mathematical pharmacometric models using data from a previous dose-modification clinical study. The goals of the analysis will be
to explore which treatment-related characteristics (i.e. chemotherapy regimen and paclitaxel pharmacokinetics), and patient characteristics (i.e. age, sex, disease state, liver and renal function) best predict neurologic and haematological toxicities, and tumour response. Simulations, applying the developed models, will be performed to assess different dosing scenarios and divergent patient characteristics (i.e. age, sex, disease state, liver and renal function) to provide a broader understanding of the relationship between treatment and outcomes of treatment. The results should help towards identifying a dosing strategy that reduces the incidence and/or severity of clinically relevant toxicities, and retains efficacy of treatment.