Improving the Delivery of Molecularly-Targeted Agents to Effectively Treat Melanoma Brain Metastases

University of Minnesota Ph.D. dissertation.March 2015. Major: Pharmaceutics. Advisor: William Elmquist. 1 computer file (PDF); xvii, 248 pages.The FDA approval of molecularly-targeted drugs that specifically targeted aberrant signaling proteins has brought about new hope for the treatment of advanced melanoma. Historically, metastatic melanoma has been an untreatable devastating disease. Two BRAF inhibitors (vemurafenib and dabrafenib), a MEK inhibitor (trametinib), and a combination of dabrafenib and trametinib are currently in use and several other drugs are in clinical development. Melanoma is known to metastasize to distant organs such as the lung, liver and brain. A critical challenge in the successful treatment of metastatic melanoma is the effective treatment of brain metastases. A significant proportion of melanoma patients have brain metastases at autopsy. It is also known that once patients develop clinical signs of CNS disease, they have an abysmally poor survival (less than 6 months). This brings about an important question about the efficacy of current drugs in treating brain metastases. The blood-brain barrier is comprised of a tight network of endothelial cells that are sealed together by tight-junction (TJ) protein complexes. The BBB also expresses several efflux transport proteins that utilize ATP to pump drug molecules against a concentration gradient. Together, the TJ proteins and ATP-dependent efflux transport proteins are known to effectively limit the permeability of several chemotherapeutics across the blood-brain barrier. Of particular interest are two efflux transporters, P-glycoprotein (P-gp) and breast-cancer resistance protein (BCRP) that are known to be highly expressed at the BBB. One of the aims of this thesis project was to understand the factors that potentially limit the efficacy of molecularly-targeted drugs in treating deadly melanoma brain metastases. Through this work, we have shown that several molecularly-targeted agents are substrates for active efflux by P-gp and BCRP. Through a series of carefully planned in vitro experiments and elegant pharmacokinetic studies in mice we conclude that the limited brain distribution of vemurafenib, dabrafenib, trametinib, and GSK2126458 (a Pi3K/mTOR inhibitor) is due to their interaction with P-gp and BCRP. We also investigated potential differences in pharmacokinetics and pharmacodynamics of vemurafenib when administered as pharmacy grade Zelboraf; versus non-pharmacy grade vemurafenib. We observed that formulation differences that affect the solubility of a drug are extremely critical to designing and interpreting meaningful pre-clinical studies. Currently, we are conducting studies in a novel melanoma mouse model in order to understand the efficacy of molecularly- targeted drugs in treating brain metastases (single agent or in-combination). The findings of this thesis provide significant insight into the selection of rational drug combinations and are highly relevant to improving the treatment of melanoma brain metastase

IMI – Oral biopharmaceutics tools project – Evaluation of bottom-up PBPK prediction success part 4: Prediction accuracy and software comparisons with improved data and modelling strategies

Oral drug absorption is a complex process depending on many factors, including the physicochemical properties of the drug, formulation characteristics and their interplay with gastrointestinal physiology and biology. Physiological-based pharmacokinetic (PBPK) models integrate all available information on gastro-intestinal system with drug and formulation data to predict oral drug absorption. The latter together with in vitro-in vivo extrapolation and other preclinical data on drug disposition can be used to predict plasma concentration-time profiles in silico. Despite recent successes of PBPK in many areas of drug development, an improvement in their utility for evaluating oral absorption is much needed. Current status of predictive performance, within the confinement of commonly available in vitro data on drugs and formulations alongside systems information, were tested using 3 PBPK software packages (GI-Sim (ver.4.1), Simcyp® Simulator (ver.15.0.86.0), and GastroPlusTM (ver.9.0.00xx)). This was part of the Innovative Medicines Initiative (IMI) Oral Biopharmaceutics Tools (OrBiTo) project. Fifty eight active pharmaceutical ingredients (APIs) were qualified from the OrBiTo database to be part of the investigation based on a priori set criteria on availability of minimum necessary information to allow modelling exercise. The set entailed over 200 human clinical studies with over 700 study arms. These were simulated using input parameters which had been harmonised by a panel of experts across different software packages prior to conduct of any simulation. Overall prediction performance and software packages comparison were evaluated based on performance indicators (Fold error (FE), Average fold error (AFE) and absolute average fold error (AAFE)) of pharmacokinetic (PK) parameters. On average, PK parameters (Area Under the Concentration-time curve (AUC0-tlast), Maximal concentration (Cmax), half-life (t1/2)) were predicted with AFE values between 1.11 and 1.97. Variability in FEs of these PK parameters was relatively high with AAFE values ranging from 2.08 to 2.74. Around half of the simulations were within the 2-fold error for AUC0-tlast and around 90% of the simulations were within 10-fold error for AUC0-tlast. Oral bioavailability (Foral) predictions, which were limited to 19 APIs having intravenous (i.v.) human data, showed AFE and AAFE of values 1.37 and 1.75 respectively. Across different APIs, AFE of AUC0-tlast predictions were between 0.22 and 22.76 with 70% of the APIs showing an AFE &gt; 1. When compared across different formulations and routes of administration, AUC0-tlast for oral controlled release and i.v. administration were better predicted than that for oral immediate release formulations. Average predictive performance did not clearly differ between software packages but some APIs showed a high level of variability in predictive performance across different software packages. This variability could be related to several factors such as compound specific properties, the quality and availability of information, and errors in scaling from in vitro and preclinical in vivo data to human in vivo behaviour which will be explored further. Results were compared with previous similar exercise when the input data selection was carried by the modeller rather than a panel of experts on each in vitro test. Overall, average predictive performance was increased as reflected in smaller AAFE value of 2.8 as compared to AAFE value of 3.8 in case of previous exercise.QC 20200930</p