Purpose The antisense oligonucleotide, LY2275796, blocks expression of eIF-4E, an mRNA translation regulator upregulated in tumors. This Phase I study sought an appropriate LY2275796 dose in patients with advanced tumors. Experimental Design A 3-day loading dose, then weekly maintenance doses, were given to 1–3 patient cohorts, beginning with 100 mg and escalating. Plasma samples were collected to determine LY2275796 concentrations; tumor biopsies, to quantify eIF-4E mRNA/protein. Results Thirty patients with Stage 4 disease received ≥1 LY2275796 dose. A dose-limiting toxicity was observed at 1200 mg, with 1000 mg the maximum-tolerated dose. Across all dose levels, most patients (87%) had only grade 1–2 toxicities. LY2275796 pharmacokinetics supported the dosing regimen. Comparison of pre- and post-dose biopsies showed eIF-4E decreased in most patients. Fifteen patients had progressive disease, and seven patients achieved stable disease (minimum of 6 weeks) as best response, with two patients on therapy >3 months (one with melanoma, one with cystadenocarcinoma of the head/neck). Conclusions LY2275796 was well tolerated up to 1000 mg. Since tumor eIF-4E expression was decreased, but no tumor response observed, LY2275796 should be studied combined with other treatment modalities.

High development costs and low success rates in bringing new medicines to the market demand more efficient and effective approaches. Identified by the FDA as a valuable prognostic tool for fulfilling such a demand, model-based drug development is a mathematical and statistical approach that constructs, validates, and utilizes disease models, drug exposure-response models, and pharmacometric models to facilitate drug development. Quantitative pharmacology is a discipline that learns and confirms the key characteristics of new molecular entities in a quantitative manner, with goal of providing explicit, reproducible, and predictive evidence for optimizing drug development plans and enabling critical decision making. Model-based drug development serves as an integral part of quantitative pharmacology. This work reviews the general concept, basic elements, and evolving role of model-based drug development in quantitative pharmacology. Two case studies are presented to illustrate how the model-based drug development approach can facilitate knowledge management and decision making during drug development. The case studies also highlight the organizational learning that comes through implementation of quantitative pharmacology as a discipline. Finally, the prospects of quantitative pharmacology as an emerging discipline are discussed. Advances in this discipline will require continued collaboration between academia, industry and regulatory agencies.