A Quantitative Process for Enhancing End of Phase 2 Decisions.

The objectives of the phase 2 stage in a drug development program are to evaluate the safety and tolerability of different doses, select a promising dose range, and look for early signs of activity. At the end of phase 2, a decision to initiate phase 3 studies is made that involves the commitment of considerable resources. This multifactorial decision, generally made by balancing the current condition of a development organization's portfolio, the future cost of development, the competitive landscape, and the expected safety and efficacy benefits of a new therapy, needs to be a good one. In this article, we present a practical quantitative process that has been implemented for drugs entering phase 2 at Amgen Ltd. to ensure a consistent and explicit evidence-based approach is used to contribute to decisions for new drug candidates. Broadly following this process will also help statisticians increase their strategic influence in drug development programs. The process is illustrated using an example from the pancreatic cancer indication. Embedded within the process is a predominantly Bayesian approach to predicting the probability of efficacy success in a future (frequentist) phase 3 program.

A randomized phase 2 study of paclitaxel and carboplatin with or without conatumumab for first-line treatment of advanced non-small-cell lung cancer.

INTRODUCTION: This study evaluated the efficacy, safety, and pharmacokinetics of conatumumab combined with paclitaxel-carboplatin (PC) as first-line treatment for advanced non-small-cell lung cancer (NSCLC). METHODS: Patients (aged >18 years) with previously untreated advanced or recurrent NSCLC were randomized 1:1:1 (stratified by Eastern Cooperative Oncology Group performance status and disease stage) to receive up to six 3-week cycles of PC combined with conatumumab (arm 1, 3 mg/kg; arm 2, 15 mg/kg) or placebo (arm 3) every 3 weeks. The primary endpoint was progression-free survival (PFS). This study is registered with ClinicalTrials.gov (NCT00534027). RESULTS: Between August 8, 2007 and April 9, 2009, 172 patients were randomized (arm 1, n = 57; arm 2, n = 56; arm 3, n = 59). Median PFS was 5.4 months (95% confidence interval [CI] 4.1-6.3) in arm 1 (hazard ratio [HR] 0.84 [95% CI 0.57-1.24]; p = 0.41), 4.8 months (95% CI 3.2-6.5) in arm 2 (HR 0.93 [0.64-1.35]; p = 0.57), and 5.5 months (95% CI 4.3-5.7) in arm 3. There was an interaction between tumor histology and the effect of conatumumab on PFS (squamous HR 0.47 [0.23-0.94]; nonsquamous HR 1.08 [0.74-1.57]; interaction p = 0.039).The most common grade of three or more adverse events were neutropenia, anemia, and thrombocytopenia. There was no evidence of pharmacokinetic interactions between conatumumab and PC. Of 158 patients assessable for FCGR3A polymorphisms, conatumumab treatment was associated with a trend toward longer overall survival (HR 0.72 [0.43-1.23]) among V-allele carriers (V/V or F/V; n = 54) but not among F-allele homozygotes (n = 34; HR 1.37 [0.66-2.86]). CONCLUSION: Although well tolerated, the addition of conatumumab to PC did not improve outcomes in unselected patients with previously untreated advanced NSCLC.

A statistician's perspective on biomarkers in drug development.

Biomarkers play an increasingly important role in many aspects of pharmaceutical discovery and development, including personalized medicine and the assessment of safety data, with heavy reliance being placed on their delivery. Statisticians have a fundamental role to play in ensuring that biomarkers and the data they generate are used appropriately and to address relevant objectives such as the estimation of biological effects or the forecast of outcomes so that claims of predictivity or surrogacy are only made based upon sound scientific arguments. This includes ensuring that studies are designed to answer specific and pertinent questions, that the analyses performed account for all levels and sources of variability and that the conclusions drawn are robust in the presence of multiplicity and confounding factors, especially as many biomarkers are multidimensional or may be an indirect measure of the clinical outcome. In all of these areas, as in any area of drug development, statistical best practice incorporating both scientific rigor and a practical understanding of the situation should be followed. This article is intended as an introduction for statisticians embarking upon biomarker-based work and discusses these issues from a practising statistician's perspective with reference to examples.