A key stage of exploratory drug development is implementing a proof-of-concept study to demonstrate the safety of a drug. Given the importance of accurate dose-finding for Phase 3 success, methodological improvements to proof-of-concept studies in Phase 2 can translate into greater likelihood of getting a drug to market.
Nov 4, 2014 8:30:00 AM
Oct 28, 2014 9:30:00 AM
Phase 1 oncology trials typically use either rule-based methods or model-based methods to determine the most acceptable level of dose toxicity with which to move forward in Phase 2. This level of toxicity, called the maximum tolerated dose (or the MTD), is the dose which best balances the medical benefits of a higher dose with the risk of toxicity which comes from subjecting a patient to that same dose. Both rule-based methods and model-based methods determine the MTD by relying on small cohorts of patients who test a set of doses against their dose limiting toxicity.
Sep 18, 2014 10:44:00 AM
When designing clinical trials, many trial designers are advised to keep the trial simple. Prima facie, the keep it simple principle seems like sound advice. There are various logistical uncertainties that arise when implementing a clinical trial, and the more simple a trial – so conventional wisdom says – the easier it is to respond to these uncertainties.
According to Zoran Antonijevic, a Senior Director at Cytel Consulting, there is reason to doubt such conventional wisdom. After all, flexibility is hardly a virtue of a traditional trial design. Simple designs may seem to make it easier to monitor data and report results. However, a flexible design can better address remaining uncertainties in product development. These uncertainties are related to treatment effect, dose selection, or a sub-population that would experience the best benefit/risk from the treatment.
Topics: Oncology, Promising Zone, sample size re-estimation, Enrichment, Cytel Consulting, Efficacy, Interim Analyses, forecasting, optimization, Program and Portfolio Optimization, R&D, Adaptive Clinical Trials
Jul 10, 2014 1:53:00 PM
Complexities with identifying suitable test populations in oncology studies contribute significantly to the 60% attrition rate in Phase III trials. Cyrus Mehta, (President of Cytel) has recently authored a paper on ‘Biomarker Driven Population Enrichment for Adaptive Oncology Trials,’ (forthcoming in Statistics in Medicine) which provides an innovative method for using two-stage adaptive designs for population enrichment.
Mehta, et al., are sensitive to the dilemma faced by Phase III trial designers choosing between open and restricted enrollment. Open enrollment allows for a large number of patients, and ensures that all patients who may benefit from a therapy have an opportunity to be involved. By contrast, limiting enrollment is a superior practice for revealing the efficacy of a trial for a targeted population. The proposed method allows for biomarker driven enrichment at interim analysis, meaning that only those subgroups that appear to benefit from therapy need to progress to the second stage of the trial.
Jul 8, 2014 6:00:00 AM
The above graphic is from Cyrus Mehta's slides on 'Adaptive Population Enrichment for Oncology Trials with Time to Event Endpoints.'
Recent advances in precision medicine have meant that therapeutic treatments can now target subsets of a population that are most likely to respond well to treatment. Identification of such subsets largely relies on the presence or absence of particular biomarkers. In order to determine whether or not such biomarkers have predictive diagnostic capabilities, the biomarkers must first be validated as reliable predictive indicators, and thereafter as responding efficaciously to treatment.
Jun 26, 2014 8:44:00 AM
The rise of biomarker based treatments in oncology has meant a reconceptualization of what constitutes a particular disease. According to the American Society for Clinical Oncology, “We can no longer think of cancer as one disease. Even something like lung cancer could be hundreds of different cancers, each defined by specific molecular characteristics requiring different treatment approaches.”  This means that many oncology trials are slowly moving from large-scale studies of generic populations, towards a system where targeted therapies are offered to smaller sets of patients who all possess certain genetic characteristics.
Nina Selaru of Pfizer Oncology, recently gave a talk at a Cytel Seminar in San Diego in which she described a trial for Xalkori, a therapy for non-small cell lung cancer (NSCLC). Pfizer conducted two Phase 3 trials for Xalkori, one for patients who possessed anaplastic lymphoma kinase (ALK-positive patients) and another for other ‘unselected’ patients. The ALK-positive patients were found to respond very well to treatment. Unfortunately, the ALK-positive patients also displayed certain characteristics not present in the other patients: they were younger, non-smokers who displayed signs of adenocarcinoma. There was concern that these characteristics were driving the efficacy of Xalkori.
Jun 10, 2014 7:01:00 AM
The FDA’s Tatiana Prowell (Breast Cancer Scientific Lead in the Office of Hematology & Oncology Products) recently gave an interview to the Nature Review Drug Discovery, in which she discusses the top three pitfalls faced by drug developers in oncology. Issues which Prowell cite include: selection of appropriate dosage, trial designs without sufficient thought given to interim data, and untimely decisions on the use of biomarkers.
According to the article, “some 90% of drugs that enter phase 1 eventually fail.” The prevalence of these pitfalls is noteworthy for oncology drug development, not least becaues of how easy they are to avoid.When coupled with innovative trial design can achieve significant benefits in efficacy and cost-effectiveness. For example, model-based dose-escalation methods can be used to improve the model dose toxicity profile of the drug in question. Cytel Statistician Charles Liu shows how simple it is to use Cytel’s software to select the optimal dose to carry forward.
Jun 5, 2014 9:37:00 AM
In the US, cancer is the most common cause of death after heart disease, accounting for nearly 1 of every 4 deaths . Tackling the immense burden of this disease, researchers are currently investigating an estimated 900 novel cancer agents in over 6,000 clinical trials . Unfortunately, the clinical success rate remains low, and failed trials amount to billions of dollars invested, while providing little direct benefit to patients. Such failures also discourage patients from participating in the testing of novel treatments: Currently, less than 2% of cancer patients enroll in clinical trials .
May 13, 2014 8:31:00 AM
The VALOR trial recently applied a promising zone design to a Phase 3 evaluation of Vosaroxin, a candidate for the treatment of relapsed/refractory acute myeloid leukemia. CMO Dr. Adam Craig reports that there were intial anxieties about financing a trial that required 800 patients. However, a promising interim look raised investors' confidence in the treatment's success, leading to a high-powered trial of 712 patients.
To view a YouTube video of Dr. Adam Craig talking about the Phase 3 VALOR trial, click below.
May 8, 2014 7:54:00 AM
( Editor's note: This post has been refreshed in December 2016)
Model based algorithms for Phase I dose-escalation have been in existence for nearly thirty years. Despite guarantees of increased statistical power and greater accuracy, there remains a clear preference for rule based algorithms amongst clinicians. The explanation for this is as old as the models themselves.
Apr 21, 2014 3:57:00 PM
The core methodological problem that would eventually spur the development of Cytel’s StatXact software was first posed by Harvard’s Marvin Zelen at a computational seminar in the late 1970s. Zelen, a distinguished professor of statistical sciences and head of the Department of Biostatistics at Harvard University, was also serving as the Director of the Dana Farber Cancer Institute.
The analysis of serious adverse events from cytotoxic agents in oncology trials were heavily dependent on an imprecise Cochran rule to measure the signifincance of small sample categorical data. The crude calculation meant that estimations of p-values were wide off the mark. Zelen challenged his students to find ways to expand Fisher’s exact test to r x c contingency tables, and by doing so to seal the promise of more effective development and delivery of urgent cancer treatments.
Cyrus Mehta and Nitin Patel took up Zelen’s challenge, publishing a series of papers on exact significance testing throughout the 1980s. Despite offering novel statistical solutions to persisting problems, the implementation of such solutions clearly required assistance from software. Unfortunately, few venture capitalists were willing to invest in a package of arcane statistical tests that were largely still in development.
Cytel was created with a grant from the National Cancer Institute, with a view to developing software that would make newer exact tests widely available for clinical studies. Its first software package, StatXact, is now used for exact testing in oncology, as well as environmental studies, public health, demography, law, and several areas of medicine and clinical development. The widespread use of exact tests has led to an array of intriguing research questions involving the power of various exact tests. Below we present a favorite finding, on the power of conditional versus unconditional exact tests: