Search

An overview of biomarker development is provided in chapter 2.PET tracer-based biomarkers can be used to monitor different biological or clinical metrics. A clinically important biomarker, especially in lung cancer, is the epidermal growth factor receptor (EGFR) abundance. In this chapter we give an overview of current EGFR-directed PET tracers to visualize the tumors’ EGFR binding capacity, and discuss the challenges and opportunities regarding their clinical application. One of the major challenges is the necessity of highly complex and invasive acquisition protocols for quantitative assessment of tracer uptake. Another important challenge for the current tracer developmental process is the rapidly changing treatment landscape. Development of a TKI-based PET tracer can be relatively long and with the current rate of change in treatment options, developed tracers should be brought quickly into clinical usage before they lose their clinical relevance. This chapter also highlights some opportunities. The total body PET scanner could greatly decrease the level of complexity of protocols. This would decrease the time needed for a tracer to be ready for clinical use, which in turn could improve the clinical relevance of EGFR-directed tracers. In chapter 3 we describe the process of quantification of 18F-afatinib. Ten NSCLC patients underwent dynamic PET scanning using 18F-afatinib. Three pharmacokinetic compartment models were assessed using both plasma-derived input functions and image-derived input functions: a single-tissue model (1T2K), a two-tissue reversible model (2T4K) and a two-tissue irreversible model (2T3K). The preferred model was the two-tissue irreversible model. This is consistent with in vitro data showing irreversible binding of afatinib to the EGF receptor. The relationship between 18F-afatinib tumor uptake, EGFR mutational status and response to treatment using afatinib was investigated in chapter 4. In this chapter we compared tracer uptake of EGFR wild type tumors with both EGFR common and uncommon tumors, hypothesizing that uncommon EGFR mutations behave similarly to common mutations. A significant difference was observed between tracer uptake of wild type tumors versus both common and uncommon mutations. Furthermore, a TBR60-90 value of >6 was shown to be predictive of response to treatment using afatinib. Chapter 5 focuses on the biodistribution and image quality of three generations of EGFR TKI PET tracers: 11C-erlotinib, 18F-afatinib and 11C-osimertinib based. The image quality derived from patients in a recently started clinical trial of the 11C-osimertinib derived tracer was remarkably different from the first two generation of TKI tracers. The tumor tissue showed a low tracer uptake compared to the surrounding lung tissue. To investigate this phenomenon, we (re-)analyzed data from three previously published prospective studies and one ongoing clinical trial. Image quality was also quantified for each tracer by calculating the tumor-to-lung contrast and background noise for each tracer. 11C-erlotinib and 18F-afatinib showed the best image quality based on both tumor-to-lung contrast and noise, whereas 11C-osimertinib showed an inverse tumor-to-lung contrast: lung tissue showed a higher level of tracer uptake when compared to tumor tissue. Differences in physicochemical, pharmacological and pharmacokinetic parameters of the three generation of TKI’s may explain the observed the differences in tumor-to-lung contrast. The results of the comparison that we conducted in chapter 5 lead to chapter 6. In this chapter we developed a physiologically-based pharmacokinetic (PBPK) model that accurately predicted tissue uptake for the three EGFR TKIs (erlotinib, afatinib and osimertinib) using physicochemical and drug specific properties. This model was validated using PET data as obtained and described in the previous chapter. Our model yielded an adequate prediction of tumor-to-lung contrast and whole-body distribution of the three EGFR TKIs.