When thinking about the challenges of oncology drug development, I recall the advice of the 6th century BCE military strategist, Sun Tzu:
If you know the enemy and know yourself, you need not fear the result of a hundred battles. If you know yourself, but not the enemy, for every victory gained you will also suffer a defeat. If you know neither the enemy nor yourself, you will succumb in every battle.
As a scientist at Certara Consulting Services, I work with clients to help them develop safer and more effective medicines to use as “weapons” in the “war” against human disease. In this blog post, I’ll discuss how we used population pharmacokinetic (PK) modeling of an investigational drug, motesanib, to assess sources of variability in cancer patients.
Motesanib: an emerging weapon in the fight against cancer
Tumor angiogenesis is the process by which cancerous tumors develop new blood vessels from existing vasculature. This process is critical for a tumor to be able to grow and metastasize. VEGF (vascular endothelial growth factor) promotes angiogenesis by binding and activating receptor tyrosine kinases on endothelial cells. Thus, drugs that can inhibit tumor angiogenesis may help reduce tumor burden or limit disease progression.
Motesanib is a small molecule antagonist of VEGF receptors. It is also inhibits other receptors (PDGFR and c-kit) that contribute to the pathobiology of several types of solid tumors. The metabolism of motesanib is primarily mediated by CYP3A4 with minor contributions by CYP2D6 and CYP1A. It is broken down into several oxidative metabolites, including N-oxide (M3), indoline lactam (M4), indolinecarbinolamine (M5), and N-dealkylated metabolite isonicotinic acid (Mx).
Knowing the disease, knowing the drug
We had several goals for this project including:
- Characterize the PK of motesanib in patients with different types of solid tumors
- Assess the effects of demographic and physiological variability on PK
- Generate individual exposure parameters of motesanib for cancer patients.
Since systemic exposure of the M4 metabolite is greater than 10% of the parent drug exposure at steady state, we also evaluated the pharmacokinetics of M4 per the FDA guidance on safety testing of drug metabolites.
Population PK modeling of motesanib
A number of steps were involved in performing the pop PK analysis. The concentration-time data for motesanib and M4 was derived from eight clinical PK studies. For most studies, the patients received oral motesanib at different dose levels and frequencies. PK sampling included both intensive and sparse sampling at various time points during the study. We performed data assembly to generate the data sets for analysis that included drug concentration information as well as demographic and pathophysiological covariates. Next, we conducted exploratory data analysis followed by population PK analysis using nonlinear mixed-effect models with a sequential approach. Then, we evaluated the pop PK models using several methods including diagnostic plots, visual predictive check, and bootstrap analysis. To describe the biotransformation of motesanib into M4, we developed a PK model to describe the buildup of M4. Finally, we simulated the concentration-time profiles of motesanib and M4 after a first dose and steady state to predict the exposure levels of motesanib and M4.
The patients in this study had a variety of advanced solid tumors including non-small cell cancer, breast cancer, and gastrointestinal stromal tumors. Roughly equal numbers of men and women were included in the study. The majority of the patients were white with Asians representing 31.5% of the population.
We found that a 2-compartment model with food effect on absorption parameters fitted the PK data of motesanib well. The covariates, albumin and sex, had significant effects on apparent clearance. In contrast to the parent drug, we found that a 1-compartment model provided the best fit for the M4 PK data. The apparent clearance of M4 was significantly affected by race (Asian vs. non-Asian) and dosing frequency (once vs twice daily). Indeed, Asian patients were found to have a typical apparent clearance of M4 that was 1.4 fold higher than in non-Asian patients. This difference in drug metabolite clearance suggests that further research is needed to optimize dosing for patients of differing ethnic backgrounds.
What does the future hold for motesanib?
While characterization of a drug’s pharmacokinetics is an essential step in its development, drug makers are also required to show efficacy. In February, Takeda announced that its Phase 3 study of motesanib in Asian patients with stage IV non-squamous non-small cell lung cancer (NSCLC) failed to meet the primary endpoint of progression-free survival (PFS). Despite this setback, I hope that further studies will help uncover the clinical situations for which motesanib can aid patients in their fight against cancer. For more information on this project, please read my article “Population pharmacokinetic modeling of motesanib and its active metabolite, M4, in cancer patients” which was recently published in Clinical Pharmacology in Drug Development.
All information presented derive from public source materials.
Ready to learn more about how M&S is shaping drug development?
At Certara Consulting Services, we leverage model based drug development approaches to help make safer and more effective drugs for all patients. Pediatric drug development has been particularly challenging for sponsors due to their unique physiology. My colleague, Dr. JF Marier, recently recorded a short webinar that discussed an approach that helps our clients optimize dosing to balance drug safety and efficacy while minimizing the exposure of children to experimental therapeutics. Please listen to the short recording and let me know what you think in the comments section!