Each year, more patients die from lung cancer than die from any other type of cancer. Non-small cell lung carcinoma (NSCLC) is the most common form of lung cancer, accounting for approximately 85% of cases. Developing reliable therapeutics for NSCLC has proven to be perniciously difficult, with the 5-year survival rate for metastatic NSCLC being less than 5%. Because patients with NSCLC do not typically exhibit obvious symptoms, the disease is often not detected in its early stages. The majority of NSCLC is intrinsically multi-drug resistant. When therapeutics do prove effective NSCLC, drug resistance is rapidly acquired. Patients regularly must be put on experimental therapies because of the high failure-rate of first-line and second-line therapies. And, those experimental therapies themselves often fail. Very few experimental therapies make it past the third phase of clinical trialing. Even when treatments are effective for NSCLC patients, dosages are often suboptimal because of the general intolerability of chemotherapeutics. Chemotherapeutics are commonly administered in combination with one or more targeted therapies to reduce the necessary dosage of each. This is meant to reduce the number of side-effects from chemotherapy the patient must endure.

Mechanistic modeling is an ideal method for making informed decisions in clinical trial design, optimizing dosages, and individualizing therapy. The common framework for supporting mathematical modeling of pharmaceuticals is non-linear mixed effects modeling (NLME). The kinetic properties of a therapeutic – absorption, distribution, metabolism, and elimination – are called pharmacokinetics (PK). The biological dynamics resulting from the biodistribution of a pharmaceutical are called pharmacodynamics (PD). Using NLME modeling, one can simultaneously describe both the pharmacokinetics and pharmacodynamics of a pharmaceutical, even if the patients being modeled have disparate individual characteristics and the sampling schedule is sparse. After initially parameterizing a population model, the model can easily be updated and expanded by either incorporating new data or further informing the model structure with disease biology and therapeutic properties. Parameterized, i.e. fit, models can be used to simulate hypothetical clinical trials, giving an approximate answer without the costly need for clinical experiments. The more mechanistic the model, the more accurate the simulated clinical trials become. As a graduate student, the primary aim of the research I have been involved with was to better characterize the PKPD of various therapies for NSCLC using population modeling. After fitting the models featured in each study, we used simulation to answer questions about optimal dosing regimens. Optimizing a dosing regimen reduces the side-effect burden in the patient, increases efficacy of the therapy, and – if the drug is still in development – reduces the chance that an effective therapy might fail in clinical trials due to an ineffective dosing schedule.

The present body of work is a collection of four papers from my time as a graduate student. They nominally demonstrate both in-depth knowledge of modeling pharmaceuticals as well as summarize related findings in NSCLC research. Concurrently, they were selected to give insight into each stage of population modeling relative to clinical pharmaceutical development. After a hypothesis is proposed, preclinical modeling is used to develop mathematical descriptions of PKPD, and eventually the model is translated into a clinical setting to answer hypothetical questions about therapeutic safety and efficacy.