The Clinical Research Associate (CRA) Discipline of the Children's Oncology Group (COG) comprises CRAs from approximately 235 institutions throughout North America, Switzerland, Australia, and New Zealand. COG was formed by a merger of the former pediatric cancer trial groups Children's Cancer Group, Pediatric Oncology Group, National Wilms Tumor Study Group, and Intergroup Rhabdomyosarcoma Study Group. This merger afforded the combined CRA Discipline an opportunity to assess job responsibilities in the form of a time and effort survey.  

The purpose of the time and effort survey was to determine what percentage of time the institutional CRA spends on different tasks within an academic institution. This would better identify the number of personnel needed to facilitate COG clinical trial research. A second purpose was to use the results to assist in writing the first COG CRA Discipline grant to secure NCI funding of the CRA Discipline to support discipline education and research endeavors.  

It is well established that patients using therapeutic clinical trials in an academic setting contribute to improvements in clinical outcomes over time.

 This important clinical trial process must be facilitated. 			

The CRAs of the COG transition team, made up of the CRA leadership of four pediatric groups, formed a task force to determine time and effort devoted to clinical trial management and facilitation. The task force first identified appropriate duty categories and outlined these in a document titled "Requirements for a Cooperative Group Clinical Research Office." The COG executive committee vetted and approved this document, which delineates the tasks that must be accomplished for a site to manage clinical trials in COG. It further delineated tasks recommended to be handled by a certified CRA (CCRP). 

Table 2.  Survey task definitions for "Research" category			

To assure all eligible pediatric cancer patients are registered on trials, CRAs at each COG member institution perform several tasks to manage and facilitate clinical trials. To activate a study at a site, the institutional CRA must obtain local scientific and regulatory approval, examine resource utilization, and prepare institutional consent and assent forms, all according to federal and local guidelines and procedures. Once a study is activated, the CRA confirms subject eligibility, coordinates the consenting process, registers the subject, obtains and submits biologic specimens, and initiates data collection. While subjects are on study, the CRA assesses and reports toxicity and response data by multiple reporting periods, provides additional data at relapse, off-study, second malignancy and death, provides ongoing regulatory review including reporting of serious adverse events, and fulfils requirements such as serial biologic specimens, radiology films, and reports. When a subject completes protocol therapy, the CRA is forever responsible for follow-up data, documenting survival, significant events, and late effects. There are currently more than 45,000 patients in follow-up on more than 500 COG therapeutic studies. 

A survey was designed with the help of the Mayo Survey Research Center. The categories and tasks were drawn from the duty category document and divided into five components: administrative/coordination, research, consultation, education, and other. Each category was broken down further into task areas as indicated in Tables 1 through 5. Besides listing specific duty categories, the survey also evaluated demographic data about personnel and institutional function. 			

The CRA transition team undertook a pilot review of the time and effort survey over a two-week period to confirm that all major task categories were covered in the survey document, and to test the feasibility of the categorization. 

Table 3.  Survey task definitions for "Administrative" category			

The sample included small, medium, and large institutions, with size based on average annual subject accrual to therapeutic studies. We determined that different sites would have different numbers and types of individuals facilitating clinical trials, some whose primary responsibility was COG trials and others whose contribution was unfunded by COG (e.g., lab technician assisting in specimen procurement or secretarial support for typing consents).  

The aim was to collect information from all individuals who assisted with COG CRA functions, whether or not they were categorized as CRAs in the COG roster. As the total number of individuals undertaking CRA-type functions was unknown, eight surveys were sent to institutions that had 25 or more therapeutic enrollments in the previous year, and four surveys to those with less than 25 enrollments. A total of 2000 surveys were distributed. The survey copies were sent to the lead CRA at each site with instructions to distribute to anyone who assisted with CRA functions and tasks at the site. 

The 235 institutions were divided into six groups alphabetically, and one group was surveyed each month of the six-month survey period. Individual CRAs were given the opportunity to select any continuous two-week segment in their survey month. The Mayo Survey Center facilitated survey distribution. Survey instructions were presented to the CRA Discipline at a semi-annual COG group meeting before distribution. Procedures also were described in articles in the monthly CRA bulletin, which was distributed to all CRAs. The survey included printed instructions, and three individuals from the original task force were identified as contacts for specific questions about the survey process. 

Table 4.  Survey task definitions for "Education"  category			

The survey was divided into 10 working days and further subdivided into hourly slots from 6 a.m. to 7 p.m. Respondents were asked to indicate different tasks they did within each hour. If there was more than one task, the total hour was divided equally by the number of tasks. We also thought that CRAs might not spend 100% of their time on COG cooperative group studies alone, participating in those sponsored by pharmaceutical companies, other cooperative groups, or investigator-initiated trials. Therefore, respondents were asked to delineate tasks as Cooperative (meaning COG) or non-Cooperative (all others). Demographic information included CRA experience, education, and type of facility in which they worked. Other information included number of subjects assessed and/or enrolled, and number of new protocols submitted to the institutional review board (IRB) or that required regulatory maintenance. 

Two hundred sixty-six surveys were returned. Six had no information for the task category questions and were eliminated from analysis. Two more had unrealistic answers, with an average of 15-19 tasks worked per hour per day, and these were similarly eliminated. Therefore, the data represents 258 respondents performing CRA-type functions. Of these 258 respondents, 77% identified themselves as CRAs. 			

More than 80% of respondents indicated they had college courses, an associate's degree, a bachelor's degree, or a master's degree. At the time of the survey, 66% of CRAs had been working as CRAs for more than three years. This is an important finding because the discipline offers full membership to those with two years' experience, the time at which they are believed to be fully qualified and able to contribute to the discipline. Equally important was that at the time of the survey one-third of respondents (individuals performing CRA functions) had less than two years' experience and thus were not eligible to obtain CCRP certification, a credential important for many CRA functions. 

Figure 1. Clinical trial facilitation.			

				On average, respondents spent 75% of each day on COG trial facilitation and 25% on similar non-COG tasks (Figure 1).

For COG-related activity, overall daily task distribution expressed as a percentage of total time was determined for each individual and then averaged over 258 total CRAs. Adjusted for personal time, the following chart shows average daily CRA task distribution for COG work. Daily time by major categories was distributed as follows: research 64%, administrative 23%, consultation 4%, education 5%, and other 4%. A breakdown of each of these major categories is shown in Figure 2. 

The goal of this survey was to help identify the number of personnel needed to facilitate COG clinical trial research, and to portray duties conducted in the day and life of a CRA. A second goal was to use this information to write the first COG CRA discipline grant to secure funding for discipline education and research projects.  

Figure 2. Average percent daily CRA co-op task distribution.						

From our results we could not provide a mechanism to indicate how many CRAs would be required based on enrollment. We were able to determine that 0.06 subjects per institution per day were enrolled in clinical trials during the survey, or 23 subjects registered per year. We are not certain that funding the number of CRAs based on the number of registrations is appropriate. Many potential clinical trial subjects must be evaluated for each subject that is ultimately enrolled in the trial. For many of the therapeutic registrations,  there are companion COG nontherapeutic studies that are not reflected in these numbers. In addition, many sites open protocols that close before an eligible subject is enrolled. The site must continue all regulatory aspects of studies, including annual renewals and amendment approvals, for as long as there is potential to enroll a subject. We concluded that it would be better to establish a formula based on tasks within each study rather than enrollments in order to better understand time and effort required to facilitate clinical trials. 

It is interesting that the tasks for a certified CRA (CCRP), as recommended by the COG Executive Committee, include almost 100% of the tasks under the categories of Research, Education, and Consultation. These categories incorporate nearly 75% of the average daily tasks of the CRA survey respondents. To obtain certification, an individual must have worked in clinical trials for a minimum of two years. We found in this survey that at least one-third of respondents were not eligible for certification. This information is compelling to study backgrounds of CRAs (certification and education) and to correlate it with site performance. Such a study would assist sites in understanding the type of individual they require to appropriately facilitate and administer clinical trials. 

The CRA Discipline received an excellent to outstanding rating from the NCI, thus receiving full funding for CRA education and research projects. 

Table 5.  Survey task definitions for "Consultation" category			

It is imperative that sufficient staffing of appropriately trained individuals be available at all sites participating in clinical trials to ensure the integrity of research and the safety of participants. 

This study was supported by a grant from the National Cancer Institute, Bethesda, MD, and was made possible through the commitment of the CRA transition team and CRAs at the participating institutions. 			

1.  S.B. Murphy, "The National Impact of Clinical Cooperative Group Trials for Pediatric Cancer," 

, "Swiss Pediatric Oncology Group (SPOG): Childhood NHL in Switzerland: Incidence and Survival of 120 Study and 42 Non-Study Patients," 

3.  M.J. Coppes, M.L. Ritchey, G.J. D'Angio, "The Path to Progress in Medical Science:  A Wilms Tumor Conspectus," 

Hematology/Oncology Clinics of North America

4.  D.G. Tubergen and A. Bleyer, "The Leukemias" in 

, 17th Ed., R. E. Behrman, R. M. Kliegman, H. B. Jenson, eds. (New York, NY, W.B. Saunders Co., 2003). 

 CCRP, is CTSU manager at The Hospital for Sick Children, 555 University Ave., Toronto, ON, M5G1X8, (416) 813-8571, email: 

RN/CCRP, is lead CRA at McMaster University, Hamilton Health Sciences, 1200 Main St. W., Hamilton, ON, L8N 3Z5, (905) 521-2100 x73228, email: 

 is a CCRP at Connecticut Children's Medical Center, 282 Washington St., Hartford, CT 06160, (860) 545-9967, email: 

 PhD, is a CCRP at COG Operations Center, P.O. Box 60012, Arcadia, CA 91066-6012, (626) 241-1529, email: 

Articles

Feature Article

The aim of the study was to collect information from all individuals who assisted with COG CRA functions.

CRA Discipline Time and Effort Study: Children's Oncology Group

Each year, many promising therapeutic agents intended to address some of our most serious unmet medical needs fail in clinical development due to the lack of efficacy or unacceptable toxicity. Inevitably, many of these agents offer significant clinical benefit for some patients. However, they often fail to demonstrate sufficient safety and efficacy across a larger patient population to the degree needed to surmount the high clinical and economic thresholds often established by pharmaceutical companies, regulatory agencies, and the marketplace.  

 Figure 1. Evolutionary Drug Developmentâ¢, a proprietary iterative process to         identify optimal rules.			

There is a growing body of evidence and increasing acceptance that many of these promising compounds can be made commercially and economically viable. By identifying patient subpopulations that are most likely to benefit from and/or least likely to adversely react to these therapeutic agents, researchers could "personalize medicine." This targeted approach holds the promise of more rapidly and safely bringing both old and new medicines to those patients most in need.  

Among the medicines that have become successfully commercialized consequent to identifying a responsive patient subpopulation is trastuzamab (Herceptin), an anticancer monoclonal antibody that targets the HER-2 growth factor receptor on breast cancer cells. It is approved as a single agent for second-line treatment and in combination with paclitaxel for first-line treatment of patients with metastatic breast cancer whose tumors specifically overexpress the HER-2 protein.

 Had this responsive subpopulation not been prospectively identified during the clinical development of trastuzamab, the drug would likely not have been approved in breast cancer, consequently depriving many women of a treatment that has proven to have prolonged many of their lives. 

On the other side of the equation are drugs that are efficacious in many patients but are not tolerated by some. Thalidomide (Thalomid) is the classic example of a medicine that has been commercially resurrected subsequent to its complete withdrawal from the world market (in 1961) for unacceptable toxicities. Initially promoted as a sedative and treatment of such conditions as asthma, hypertension, and migraine, it soon came into disfavor consequent to an associated epidemic of severe and often fatal perinatal malformations, most frequently phocomelia. Capitalizing on its diverse beneficial effects as an immune modulator and the successful identification of those patients for which it is contraindicated for safety reasons, thalidomide is now successfully overcoming its stigma as an unacceptably toxic compound and is showing promising activity in a wide variety of solid malignant tumors, multiple myeloma, graft-versus-host disease, and Crohn's disease.

Clozapine is another example of a medicine that has found a therapeutic niche despite being associated with significant toxicity, in this case life-threatening agranulocytosis. Clozapine was approved in 1989 for second-line symptomatic management of schizophrenia and is now being investigated for use in Parkinson's disease, Huntington's chorea, and depression.

The key to the successes of thalidomide and clozapine has been the ability to prevent their distribution to patients identified to be most at risk for their toxic side effects, namely pregnant women and those with severe granulocytopenia, respectively. In both cases, mandatory controlled distribution programs are in effect, helping to ensure the safe and effective prescribing, dispensing, and use of these two medicines.

There are three key challenges to introducing a personalized medicine approach to clinical practice: identifying the optimal patient subpopulation for receipt of a specific therapeutic agent, based on potential benefit-versus-risk; developing rapid, economical, accurate, and clinically practical methods for determining whether or not a specific individual belongs to such a patient subpopulation; and ensuring distribution of the agent to those for whom the agent will be potentially most effective and nontoxic.  

Although all disciplines of medicine could potentially benefit from the personalized medicine approach, this paper will concentrate on the challenges being faced in clinical oncology—a field that is fostering and benefiting from many emerging technologies and in which molecularly targeted therapies are showing great promise.  

Identifying the optimal cancer patient subpopulation

What can be done to ensure that there is a high likelihood that the patients entered in a clinical trial will respond to and tolerate a particular anticancer agent? Traditionally, this has been addressed by incorporating fairly standard patient inclusion and exclusion entry criteria in Phase II and III clinical trial designs. To some extent, this narrows the patient population down to including those that have at least some chance of responding while excluding those that are likely not to tolerate the treatment. Although this routinely generates an enrolled study population that would be commercially attractive if enough patients responded, the response rate is usually not high enough to convince most drug developers and regulatory agencies that marketing it for that indication is warranted.  			

This is a major cause for the high rate of perceived "drug failures" in oncology drug development and a source of much frustration for both investigators, patients, and the general public. A new clinical trial design paradigm that could potentially transform a drug failure into a drug success would be one that continues to narrow the target population to those patients most likely to benefit from such treatment while also continuing to tolerate it. 

Many novel molecularly targeted therapies are being developed in oncology. Although it is assumed requisite for anti-tumor activity that a compound's cellular molecular target be present on or in the patient's tumor, the degree to which this target needs to be expressed remains unclear. Although overexpression of HER-2 is required for trastuzamab to be clinically beneficial in metastatic breast cancer, the same appears not to be the case for those agents targeting the epidermal growth factor receptor (EGFR), such as gefitinib (Iressa) and erlotinib (Tarceva). For gefitinib in advanced non-small cell lung cancer (NSCLC), the presence of genomic mutations in the EGFR appears to correlate with gefitinib-induced tumor regression.

 However, in a large Phase III placebo-controlled registrational trial of gefitinib, such biological responses were relatively uncommon and did not translate into an overall statistically significant survival benefit for the advanced NSCLC patient population studied.

 In contrast, erlotinib has been shown to produce a significant prolongation of survival in advanced NSCLC.

In similar NSCLC patient populations, why did erlotinib demonstrate prolonged survival while gefitinib did not? The answer is likely hidden within the complexity and heterogeneity of tumor cells and the diverse biologies, demographics, and medical histories of cancer patients—all interacting to determine an individual's ultimate response to a particular anticancer agent.  

To answer such a question for oncology compounds in general, we are testing the hypothesis that by integrating the vast array of existent baseline patient and tumor information that is usually readily retrievable from concluded Phase II and/or Phase III studies of that agent, one can identify particularly sensitive, insensitive, tolerant, and/or intolerant patient subpopulations. Examples of such baseline patient and tumor data currently available include: patient demographics, medical history, response to prior cancer therapy, as well as tumor type, stage, and histology. Baseline tumor and patient genomic and protein biomarker data will become increasingly available in the near future and hold promise to significantly enhance the accuracy of such subpopulation assessments. 

To proceed with such investigations, the key question of what are the most clinically meaningful outcomes for the oncology compound under study first needs to be answered. Phase II trial designs may range from single-arm, uncontrolled studies that depend on comparison of results with historical controls to multi-arm, controlled studies that have a standard-of-care arm for comparison, as with Phase III studies. Depending on the intent of the trial, the stage and natural history of the cancer, and the mechanism of action of the study drug, clinically meaningful efficacy endpoints might include survival (e.g., one-year, overall, etc.), time-to-disease progression, the incidence of stable disease (as defined in the protocol), quality-of-life, and/or changes in cancer-related biomarkers, e.g., serum PSA in prostate cancer.  

The goal is to generate "outcome predictors" (a.k.a. "screening classifiers"), that can be statistically validated in subsequent proof-of-concept Phase II clinical studies. If shown to have adequate positive and negative predictive values, these new "rules" for patient selection can then be incorporated into larger targeted Phase III proof-of-efficacy trials. Because trials designed to incorporate such outcome predictors will require fewer randomized patients than untargeted designs, such an approach could potentially conserve significant amounts of drug development resources.

A major challenge to generating useful outcome predictors in this fashion, both now and in the future, is how to efficiently condense and analyze the ever increasing amount of baseline data obtained from a relatively small number of patients. Such information is derived from diverse technologies like quantitative immunohistochemistry, flow cytometry, polymerase chain reaction, fluorescent in situ hybridization, and gene expression profiling.  			

Whereas traditional statistical analysis, cluster analysis, and neural networks are available to tackle such analyses, their utility will become more limited as the number and diversity of input variables increase. An analysis technique that is particularly capable of handling such voluminous data is a machine learning approach called "genetic programming."

 Borrowing ideas from natural selection and population dynamics, genetic programming is an iterative analytical process that searches for computer programs that are good enough to survive in competition with others in a population of programs. The result is a set of computer programs that solves a particular problem, in this case the prediction of clinical response and/or tolerance to a specific therapy.  

Evolutionary Drug Development™ is a process developed by Genetics Squared that uses a proprietary implementation of genetic programming to describe a set of data, gaining insight into the data and suggesting how a drug can be best used for a particular patient population.

 This iterative process of analysis, research, and refinement affords powerful advantages over other approaches, namely: 1) the ability to combine very large and diverse data types (e.g., genomic, proteomic, medical history, etc.); 2) the ability to find novel combinations of factors; 3) the ability to combine variables in nonlinear ways; and 4) the generation of simple and readable rules that may support known mechanisms of action for specific targeted compounds and/or suggest novel molecular interactions that may warrant further investigation (Figure 1). 

A recent Phase II study conducted by a major pharmaceutical company had failed to meet internal thresholds for success with approximately 25 percent of patients experiencing stable disease (SD), as defined in the protocol and appropriately reflecting the natural history of the cancer under study. Team statisticians at the company had been unable to correlate the occurrence of SD with any of the biomarkers or other clinical variables captured.  			

The pharmaceutical company wanted the data analyzed to see if there was a correlation. They supplied to us approximately 30 demographic and medical history variables, efficacy outcomes, and four types of tumor biomarker values obtained by immunohistochemistry. Employing Evolutionary Drug Development™, a predictive rule was discovered very early in the analysis of baseline information that was greater than 70 percent accurate in predicting whether or not a patient would achieve SD (unpublished results). This rule used a single tumor biomarker that the clinical team had assumed was not important because it was highly expressed in all patients' tumors. Our rule discovered a difference between high and very high expression of this biomarker. While the rule was very good at predicting who would not respond (i.e., specificity), it was poor at predicting positive responses (i.e., sensitivity).  

If the drug were being developed to treat a non-life threatening disease and was known to have safety issues, then this would have been an excellent rule. However, because the compound was tolerable and was being developed for metastatic cancer, this rule would miss too many patients that could benefit from it. Therefore, subsequent data analyses were focused on identifying a new rule that optimized sensitivity versus specificity.  

The new predictive rule combined medical history and demographic variables with two biomarkers to produce a much more compelling rule with similar overall accuracy, but sensitivity over 90 percent. If this rule had been used to screen patients for the clinical trial, it would have missed only two patients and resulted in a 60 percent incidence of SD in this patient subpopulation—a compelling outcome for a compound being developed for a metastatic solid tumor. The pharmaceutical company was excited enough by these results to request reviews of two additional Phase II studies of this compound in other tumor types, as well as a clinical study of another oncology compound. These studies are ongoing.  

Practical implementation of personalized medicine

Challenges inherent in the practical implementation of personalized medicines include:

1) producing and validating reliable pharmacodiagnostic tools that can be used by community physicians to help them formulate treatment strategies for individual patients; and 2) convincing regulatory authorities that this novel method of patient selection will ensure adequate levels of patient safety. Matching patients to the most appropriate and promising therapeutic agents must be rapid, economical, accurate, and clinically practical.  							

Aside from reviewing responses to prior treatment modalities, patients and their tumors are tested against a variety of predictive rules and panels of biomarkers. These rules and biomarker data might be housed in large registries that can be accessed in real-time by the prescribing physician or dispensing pharmacist via the Internet or telephone to maximize distribution of drug to "safe responders."  

The clozapine patient registry managed by Outcome houses predictive rules along with specific patient information that permits ongoing checks to be made for specific patterns of response or toxicity each time a prescription is refilled. Using registries such as this as a model will help to ensure the continued safe and appropriate use of drugs after approval. Such successful integration of predictive rules and biomarkers with information technology permits the reliable point-of-care access that is key to enabling personalized medicine to quickly move from the laboratory to the clinic. 

The era of personalized medicine is here. Many medicines that have had their clinical development discontinued or that have been withdrawn from the marketplace due to lack of perceived benefit or excessive toxicity may be able to be rescued and successfully developed if a sensitive and tolerant patient subpopulation can be identified. By finding new and better defined niches for medicines of all types, many novel compounds will become available to those patients that are most likely to benefit from them, while also permitting the use of quicker and less expensive clinical trials during their development.  			

Evolutionary Drug Development™ is an implementation of genetic programming that is being used at the forefront of this effort, one well positioned for meeting the challenges of making sense of the exponentially increasing amount of available patient and tumor data that needs to be analyzed.  

Finding the right rules and markers is but the first critical step. The next step, moving personalized medicine from the laboratory to the clinic, will require increasingly sophisticated methods to deliver the right information rapidly and reliably to prescribers and pharmacists. To this end, real-time accessible, computational patient registries that are focused on appropriate drug distribution are already in use. These, together with cutting-edge technological approaches like Evolutionary Drug Development™, are spearheading the transition of personalized medicine from bench to bedside and hold much promise for rescuing many valuable compounds along the way.  

1.  Herceptin (Genentech): Full Prescribing Information, revised October 2003. 			

2.  M.E. Franks, G.R. Macpherson, W.D. Figg, "Thalidomide," 

3.  Clozapine (Mylan): Full Prescribing Information, revised January 2003. 

4.  Thalomid (Celgene): Full Prescribing Information, revised February 2004. 

 "Activating Mutations in the Epidermal Growth Factor Receptor Underlying Responsiveness of Non-small-cell Lung Cancer to Gefitinib," 

6.  Doctor Letter Regarding Iressa (gefitinib) ISEL Data (December 17, 2004): A double blind, placebo controlled, parallel group, mulitcentre, randomised, Phase III survival study comparing IRESSA (gefitinib) (250mg tablet) plus best supportive care versus placebo plus best supportive care in patients with advanced NSCLC who have received one or two prior chemotherapy regimens and are refractory or intolerant to their most recent regimen. AstraZeneca Pharmaceuticals LP, 2004.  

7.  Tarceva (Genentech; OSI Pharmaceuticals): Full Prescribing Information, initial November 2004. 

8. R. Simon and A. Maitournam, "Evaluating the Efficiency of Targeted Designs for Randomized Clinical Trials," 

9.  J.R. Koza, Genetic Programming (MIT Press, Cambridge, MA, 1992). 

10. D. MacLean, E.A. Wollesen, W.P. Worzel, "Listening to Data: Tuning a Genetic Programming System," Genetic Programming Theory and Practice II, U.-M. O'Reilly, T. Yu, R. Riolo, W.P. Worzel, eds. (New York, Springer Science+Business Media, Inc., 2005) pp. 245-262. 

is president and CEO of Genetics Squared, 210 South Fifth Avenue, Suite A, Ann Arbor, MI 48104, (734) 929-9475, fax (734) 929-9477, email; 

is president of Outcome, 210 Broadway, Cambridge, MA 02139, (617) 621-1600, fax (617) 621-1620.

Notes from the Field

A new paradigm that could transform a drug failure into a drug success would narrow the target population to those patients most likely to benefit from such treatment.

Rescuing Drugs Through Personalized Medicine

Oncology clinical trials are complex and require a different approach than trials in many other therapeutic areas. Patient recruitment is among the most challenging obstacles—especially in the United States, where oncology trials have notoriously low participation rates among adult patients. While more than 1.2 million Americans will be newly diagnosed with cancer this year, participation in oncology trials remains very low. In fact, according to the American Society of Clinical Oncology (ASCO), only 3 percent of U.S. adults with cancer participate in clinical trials.

 Lack of participation can cause an oncology trial to recruit slowly, often lengthening the trial's timeline by months or even years. 

There are many reasons why the rate of participation in cancer clinical trials is much lower than expected. Both physicians who enroll their patients in clinical trials and those individuals who agree to receive their care in a clinical trial may encounter barriers to participation. All of these obstacles must be addressed if the clinical trials system is to improve. Understanding these obstacles and knowing how to improve the process can help bring effective therapeutics to the people who need them. 

In the United States, patient costs associated with clinical trials are often not reimbursed by third-party payers—Medicare, Medicaid or private insurers—since the cost of caring for a patient in a clinical trial can be much higher than the cost of standard care. Currently in the United States, no consistency exists regarding federal or private reimbursement for participation in clinical trials. 			

Advocacy and other nonprofit organizations have stepped in, pushing for more federal regulation over inclusive reimbursement, labeling it an issue of patient rights. For more than a decade, the National Coalition for Cancer Survivorship (NCCS), other patient advocacy organizations, and the American Society of Clinical Oncology have collaborated in a legislative effort to address the failure by third-party payers to pay for routine patient care costs in trials. After many years of unsuccessful legislative effort, these organizations were able to persuade the Clinton administration to issue an executive memorandum instructing the Medicare program to allow all beneficiaries, both those with cancer and with other diseases, to participate in high-quality clinical trials. With this change and the leadership role of Medicare in health policy, reimbursement has seemingly become less of an obstacle than before.

However, a recent decision by the Centers for Medicare and Medicaid Services (CMS)—referred to as the demonstration project—is threatening the recent clinical trial reimbursement advances. This decision by CMS has resulted in the coverage of oral drugs and biologics only for FDA-approved indications. The demonstration project, mandated by the Medicare Drug Improvement and Modernization Act of 2003 (Section 641), is intended to cover drugs taken orally that are not currently paid for by Medicare or will not be covered until the new prescription-drug benefit takes effect in January 2006. Childhood cancer advocates are concerned that with this decision comes the potential to influence Medicaid and private insurance coverage for drugs in pediatric cancer clinical trials. Ninety percent of eligible childhood cancer patients participate in clinical trials, which typically study the treatment effects of drugs approved for adult use, but which are studied in children off-label.

Recognizing the need to strengthen legislative support, several states, including Arizona, California, and New York, have passed laws for clinical research insurance coverage. According to the American Cancer Society, some states have also worked out voluntary agreements with insurance companies to provide clinical trial coverage.

Although awareness of cancer clinical trials may be growing overall, not everyone appears to be getting the message. Racial and ethnic minorities, women, and the elderly are less likely to enroll in cancer clinical trials than whites, men, and younger patients, according to a study from the Yale University School of Medicine in New Haven, CT. Elderly patients are strikingly underrepresented compared to their younger counterparts.

According to the National Cancer Institute (NCI), 60 percent of breast, lung, colorectal, pancreatic, ovarian cancer, and leukemia cases in the United States occur in a population of 65 and older.

 However, the Yale University School of Medicine Study found that from 75,215 participants in therapeutic nonsurgical NCI Clinical Trial Cooperative Group breast, colorectal, lung, and prostate cancer clinical trials (from 2000 through 2002), only 0.5 percent of patients older than 75 and 1.3 percent of patients age 65 to 74 participated in these trials.  							

During a seven-year period, the FDA found that several factors contributed to this disparity, including difficulty understanding complex protocols and poor compliance. In addition, physicians, patients, and families often believe older patients are less likely to benefit from and tolerate aggressive treatments.

 Cytotoxic drugs, the commonly accepted and tested treatment for most cancers, are very toxic.  

The elderly may also be restricted by the availability of caregivers, financial concerns or by travel constraints. And physicians who believe treatment will not be as successful in an elderly patient might opt for a less aggressive treatment. Yet growing evidence shows there are feasible and successful treatment options for elderly patients. A large Italian study compared chemotherapy with Navelbine to supportive care without chemotherapy in elderly lung cancer patients and determined that survival was prolonged in those who received chemotherapy. 

A second trial conducted in Tennessee showed that Taxotere was able to induce tumor shrinkage in 26 percent of lung cancer patients who were elderly and had medical conditions that would have otherwise precluded treatment. This rate is approximately equal to that seen in other Taxotere trials in younger lung cancer patients.  

Lastly, a large clinical trial recently compared chemotherapy with the combination of Gemzar and Navelbine to either Gemzar or Navelbine alone. Administration of two agents is the prevailing preference among lung cancer patients as a whole, but in this group of patients over age 70, Gemzar or Navelbine as single agents worked as well and had fewer side effects than the doublet.

Nonetheless, the delivery of relative dose-intensity (RDI) is particularly problematic in the elderly since advanced age is associated with a higher incidence of neutropenic complications (a direct result of many chemotherapy regimens). In a 2004 study conducted by the University of Rochester Medical Center of 4,522 Non-Hodgkin's lymphoma patients, 60 percent of older patients received less than 85 percent of the relative dose-intensity (RDI) in any chemotherapy regimen they were given, compared with 44 percent in younger patients. A major cause of this reduction in dosage was the development of patient complications, including neutropenia.

 When you couple the issue of relative dose intensity with new drugs or regimens that are being tested, especially in Phase I/II dose escalation studies, the problem of toxicity among the elderly becomes exacerbated. 

The issue surrounding the toxicity or aggressive nature of therapy of cancer trial drugs or regimens may be changing, however. Many new innovative drugs have been introduced in the last decade that are biologics—including monoclonal antibodies like Rituxan. Many of these drugs are tolerated rather well and present a promising solution to current oncology treatments. More research is needed, however, to study the long-term side effects of these drugs or the side effects that can result when they are in combination with other drugs from oncology treatment regimens. 

Negative perceptions of clinical trial treatments

Many patients are not willing to participate in cancer clinical trials because of negative perceptions that the treatments are "experimental" and therefore inferior to currently accepted treatments. Likewise, if a physician does not believe a trial is better than current treatments, then he or she is not likely to recommend it.  			

For example, the ethical basis of oncology Phase I trials has often been questioned because these trials involve potentially vulnerable patients at the end of their lives. Patients who choose to participate may experience significant risks with limited chance to benefit. Another concern is that those who participate in these trials may have unrealistic expectations about their chances to benefit from the trials, despite having participated in the informed consent process. 

Researchers at the Massachusetts General Hospital Cancer Center, however, have recently challenged these concerns through data compiled of Phase I trial results, reported at ASCO's annual meetings from 1991 through 2002. The study included an analysis of 213 trials, excluding radiation therapy trials, lymphoma, and leukemia trials. In total, this database included about 6,500 enrolled patients. The researchers analyzed how often patients died from drug toxicity, cancer-related deaths or other toxic treatment effects, and whether or not a treatment caused tumors to shrink. The most significant change during the study period from 1991-2002 was the more than  90 percent drop in drug-related deaths. The risk was about 1 percent in the first four years and dropped to .06 percent in the last four years.

The reasons for this significant change can be attributed to the fact that organizations are more actively increasing their attention to patient safety regulations, using less toxic targeted therapies, particularly monoclonal antibodies, and are using more improved supportive care products, such as the new treatments for chemotherapy-induced anemia and neutropenia. 

The gap in referral patterns and site locations

The United States' decentralized health care system contributes to the challenge of recruiting oncology trial participants. Many of the most promising oncology trials in the United States involving novel treatments are recruited first at major cancer centers across the country. These centers have the resources—personnel, state-of-the-art equipment, "thought leaders," and NIH funding—to lead successful trials. The obstacle is that there are only 61 major cancer centers officially recognized by the NCI. Cancer patients across the country and especially in rural areas or non-major metropolitan areas may need to travel long distances in order to participate in promising studies at these centers. This can lead to absence from work, conflicts with family time, and cost of travel that often exceed what patients are willing to manage. For the elderly, the issue of distances becomes one of logistical support and quality of life. 			

The challenge of logistics, however, is gradually being addressed. Sponsors of clinical trials such as pharmaceutical and biotech companies, along with CROs, are partnering with site management organizations (SMOs) that specialize in oncology clinical research to access a network of sites to directly recruit patients. SMOs offer clinical trials through their network of community cancer centers that have experienced clinical investigators. These centers are spread throughout the United States in various cities and towns, not just major metropolitan centers. This network of sites offers an alternative for patients wanting state-of-the-art treatments without the hassle of traveling hundreds of miles to the nearest major comprehensive cancer center. 

Another drawback to the United States' decentralized system is a lack of a comprehensive, easily accessible database of trials being conducted throughout the country. Currently, there are various databases listing clinical trials such as 

. However, these databases are not inclusive of all cancer trials being performed—making it difficult for patients and doctors to do their own research on what trials are available to them. 			

The pharmaceutical industry is addressing this issue on a global scale through an initiative to better disclose clinical trial data and establish a worldwide register of trials relating to new prescription drugs. According to the industry's leading trade group, the Pharmaceutical Research Manufacturers Association, the global clinical trial register will be free and available to the public online so that patients and doctors can access more inclusive information about what trials are available and how to enroll.

 Links to medical journal articles and peer-reviewed research is a part of the proposed registry, adding to the depth of information that will be available from one comprehensive resource. The first clinical trial data expected to be published using the registry is scheduled for July 2005. 

In the United States, the cost of performing cancer trials has become increasingly expensive and time consuming. For drug companies, many have turned to international trials, which can save money and time. Research in different countries can also give better results because it allows for testing a drug or method on a population with diversity of genomes, environments, diet, and—in cancer trials in particular—on patients that are treatment-naïve, having no exposure to previous treatments. The FDA will not release current numbers, but in 2000, 27 percent of all drug applications submitted by pharmaceutical companies to the agency relied upon at least one foreign clinical trial, a number that experts say is significantly higher today.

Progress is being made in the United States to address these unique complexities of conducting oncology trials. According to the Center for Drug Evaluation and Research's 2003 Report to the Nation, five new drugs were approved in the United States for people with chronic myelogenous leukemia (CML), prostate cancer, small cell lung cancer (SCLC), and multiple myeloma in 2003 alone.

And the American Cancer Society states that new treatments resulting from high-quality, peer-reviewed clinical trials offer the best hope for survival and improved quality of life.

 Overcoming issues of reimbursement, the need to recruit older patients and treatment-naïve patients, negative perceptions of clinical trials and logistical complications are important factors that are contributing to the success of these trials.  

1.  2004 ASCO Annual Meeting Proceedings (Post-Meeting Edition), 

2.  E. Stovall, CEO and President of the National Coalition of Cancer Survivorship, Testimony by Before the House Committee on Government Reform, May 13, 2004. 

3.  "CMS Stance on Off-Label Use Concerns Cancer Advocates," 

4.  "Clinical Trials: What You Need to Know," American Cancer Society,  

, "Participation in Cancer Clinical Trials," 

Journal of the American Medical Association

6.  "FDA Study Shows Older People Are Underrepresented in Cancer Clinical Trials," National Cancer Institute, 

7.  L. Talarico, G. Chen, R. Pazdur, "Enrollment of Elderly Patients in Clinical Trials from Cancer Drug Registration: A 7-year experience by the Food and Drug Administration (FDA)," 

8.  C. Dunn and A. D'Orazio, "Clearing the Air: New Lung Cancer Therapies," 

9.  "Risk of Treatment-Related Death Down in Phase I Trials," 

10.  T.G. Roberts, Jr., B.H. Goulart, L. Squitieri, S.C. Stallings, E.F. Halpern, B.A. Chabner, G.S. Gazelle, S.N. Finkelstein, J.W. Clark,  "Trends in the Risks and Benefits to Patients With Cancer Participating in Phase 1 Clinical Trials," 

11.  E. Berton, "Drug Cos to Coordinate Clinical Trial Data Publication," 

13. 2003 Report to the Nation, Center for Drug Evaluation and Research, 

14.  "Clinical Trials Cost No More Than Standard Care," American Cancer Society, 

is vice president of program management for Endpoint Research, Mississauga, Ontario, (416) 626-0299, fax (416) 626-2063, e-mail: 

Oncology clinical trials are complex and require a different approach than trials in many other therapeutic areas. Patient recruitment is among the most challenging obstacles-especially in the United States, where oncology trials have notoriously low participation rates among adult patients. While more than 1.2 million Americans will be newly diagnosed with cancer this year, participation in oncology trials remains very low.

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