Personalized medicine is an approach to drug development that seeks to deliver the right drug to the right patient, at the right time. Although a personalized approach to treating patients has existed in one form or another for many years, the mapping of the human genome in 2003 elevated its application to promising new heights. Commonly referred to as 4P medicine, the approach is characterized by the ability to deliver 

solutions for patients' healthcare needs, all while encouraging their 

This move away from a one-size-fits-all mentality towards customized treatment represents a paradigm shift, not only for drug developers, but also for other key stakeholders such as regulators, payers, and patients who are involved in helping shape its direction. Each party is poised to reap benefits, but the promise of a brighter future does not come without its challenges.  

This article will provide a brief overview of the benefits and challenges associated with personalized medicine in the clinical setting, and more specifically to the operational conduct of oncology studies—one therapeutic area that is most likely to benefit.  

It is common knowledge that the present clinical drug development model is broken and in dire need of repairs. There is no shortage of drug candidate discoveries, but unfortunately the vast majority of these fail to prove efficacy in the clinical setting. What's even more troubling is that many compounds in development are failing in later stages (Phase III), which represents as much as 40 percent of the entire clinical drug development budget.

  Only one in 10 compounds are approved for use; in the oncology space, the attrition rate is thought to be much higher. The biggest inefficiencies of the traditional model are largely attributed to the improper selection of drug targets. With thin pipelines and blockbuster drugs coming off patent, pharmaceutical companies are seeing their profits being eroded by low-cost generic competitors. These companies can no longer afford to sustain a drug development model that has become increasingly costly, unpredictable, and inefficient.  

Many believe that pharmacogenetics, a key component to personalized medicine, could provide the solution to the industry's chronic ailment. Pharmacogenetics is used to understand the genetic profile of a specific person's disease. For drug developers, this means being able to pinpoint which patients are likely to respond best to a treatment, thereby increasing the likelihood of demonstrating drug efficacy in clinical testing. It can also help explain adverse events from a genetic and molecular standpoint.  

"If you want to understand a disease, genetics gives you the opportunity to shine a bright light into the darkness of our ignorance so we can find better ways to prevent and treat," says Francis Collins, MD, the former Director of the National Center for Human Genome Research within the NIH, who is now Director of the NIH.

With the prospect of increasing the likelihood of success, while reducing the time and costs associated with clinical trials, it is no surprise that companies are actively exploring these opportunities. According to Price Waterhouse Coopers, in 2007, 20 to 30 percent of Phase II clinical trials incorporated pharmacogenetics; this number is thought to be even higher in oncology.

  Cancer drug development is a likely candidate to be at the forefront of the pharmacogenetic approach, due primarily to the fact that it is a disease of mutant genes. Furthermore, tissue samples are readily available from biopsied tumors and sophisticated tests have subsequently been developed for precision disease diagnosis. There are also inspiring success stories, such as the development of Herceptin; Gleevec; and Oncotype DX™, a diagnostic test that determines the likelihood of breast cancer recurrence in women and the magnitude of benefit from certain types of chemotherapy and hormonal therapy.    

Since approximately 50 percent of the biopharmaceutical industry's early stage drug development pipeline is currently in oncology

 and that the price tag per compound exceeds $2.3 billion for multi-therapeutic drugs,

  hedging a bet on the promise of personalized medicine seems like a reasonable thing to do from an economic perspective (Figure 1). But how will it alter the landscape from a clinical operations perspective? 

Figure 1. Research and development costs nearly doubled in just seven years. 

First and foremost, this new paradigm is likely to dramatically enhance patient recruitment performance. Given the fact that less than 5 percent of adult cancer patients in the United States participate in clinical trials,

  it's no surprise that only 50 percent of sites meet their recruitment targets and up to 30 percent fail to recruit any patients at all.

  The prospect of attracting more patients is an obvious benefit. While the subject of poor patient recruitment cannot be attributed to a single factor, we do know that patients have lost faith in the current drug development model. Under the hospices of personalized medicine, where a patient's disease would be diagnosed with a pharmacogenetic approach, we can expect patients to strongly reconsider their position.  

Patients are going to see the possibility of deriving a direct benefit from study participation, a drastic change from the status quo, and can expect to experience fewer side effects. With this in mind, it is reasonable to conclude that patient interest may be revived, and subsequently, more patients will opt to take part in clinical trials.  

A great example of this trend is a collaborative initiative currently underway between the Susan Love Research Foundation, the Avon Foundation for Women, and the National Cancer Institute's cancer Biomedical Informatics Grid (caBIG) that is leveraging web 2.0 technology to create the first online cohort of one million women to support biomedical research. Members of the Love/Avon Army of Women are invited to join the study cohort and may respond to periodic secure online questionnaires concerning their health history. Authorized researchers may access the database and design study protocols based on the clinical profiles and data of potential research participants.

A survey conducted by KGT Group asked 199 oncologists or hematologists and 100 clinical trial professionals across the United States for their views on personalized medicine trends in cancer treatment and research. Fifty-three percent of respondents thought that recruiting patients to clinical trials with targeted oncology therapies would be less difficult than for large scale randomized trials with more cytotoxic drugs.

Complimenting this movement is the streamlining of patient deployment into trials, as the right patients are directed to trials they are best suited for. Because of the specific genetic characterization of disease, and the use of biomarkers as surrogate endpoints, personalized medicine trials will, for the most part, be shorter in duration and will require fewer patients. When adding all these factors together it is clear that important efficiencies will be attained. For drug developers, this means quicker and easier access to targeted patient populations as well as the prospect of arriving at positive, more meaningful results sooner. From an economic standpoint it is estimated that the adoption of pharmacogenetics in drug development could save drug companies as much as $335 million per drug according to a 2001 report by the Boston Consulting Group.

Clinical investigative teams will have more interesting trials to propose to their patients based on more compelling science. This welcome change will undoubtedly inspire and motivate a group of clinical research professionals all to used to witnessing mass drug development failure. While enjoying their new and improved work environment, these teams will have to simultaneously embrace additional responsibility as they navigate the legal and ethical issues surrounding genetic testing. Consent forms will become increasingly complex and healthcare professionals will have to increase their understanding of the field of genetics in order to successfully manage patient concerns around the meaning of their genetic mutation, how their private information will be handled, and just who will access it. In summary, patient interest in new and improved trials will provide more opportunity for successful recruitment performance, but will be highly contingent on successful patient counseling.  

As select centers become proficient in the conduct of personalized medicine trials, they will develop collaborative networks providing easy access for drug developers. Currently, large university teaching centers in industrialized countries are poised to take the lead. They have the knowledge, state of the art equipment and technology, as well as the clinical care and infrastructure to support sophisticated trials. Working with these centers of excellence is likely to result in a better harmonization of rules in this area. For the western hemisphere, which has been steadily losing clinical development projects to emerging markets, this is positive news particularly when taking into account that the value of the personalized medicine market represented by pharmaceutical, medical device, and diagnostic companies is estimated at $24 billion and is expected to grow by 10 percent annually reaching $42 billion by 2015.

Historically, the size of a clinical trial has been dictated by the phase of clinical research. As a compound moves through the various phases of research, the size of the trial increases. The personalized medicine approach will call for the replacement of a few large-scale trials with many smaller ones. Even in the Phase III setting, it is very possible that the landscape will consist of a conglomerate of smaller trials. Instead of running one trial with 50 patients, sites may very well find themselves conducting five trials of 10 patients each. Regardless of the size of a trial, the burden of certain administrative activities such as contract/budget review and negotiation, IRB submission, protocol and investigator brochure review, study meetings, and the like remain constant. In other words, the new model may have built-in efficiencies, but it will require more time and resources on the part of clinical investigative teams. Drug developers will have to support this shift with a revamped budgeting model that may include among other things, a higher monetary compensation for these administrative tasks. Ultimately, some of the costs saved by shorter development time lines will have to be re-injected to support the clinical development process.  

Industry trends confirm that contract research organizations (CROs) will continue to play a key role in the clinical drug development process. The trend toward a greater number of smaller trials will force many CROs, particularly smaller ones, to adopt a more volume-based business model. Much like clinical investigative teams, their administrative burden will increase significantly. In this new world, CROs will need to attract more small trials to make up for fewer large ones. This in turn will demand greater sales and marketing efforts to generate the same amount of revenue. From an internal operations perspective, this will require more contract review and budget negotiations, more proposal writing, and more accounting for multiple clients and trials.  

On the other side of the equation, it's no secret that drug developers have been tightening their belts and becoming much more price sensitive with regard to service provider budgets in an effort to cut costs. This may be detrimental to the overall efficiency of oncology trials, as tighter study budgets may force CROs to employ cheaper, less experienced workers who are not as well versed in the complexities of clinical trials coming down the pipe. In order for the new model to be sustainable, there will have to be some concessions made by both parties, but how this will eventually play out remains to be seen.  

Drug developers are also bracing themselves for a drastic change in their business models. There is an awful lot riding on the hope that "niche-buster" drugs will command a high price for their efficacy in targeted patient populations and that they will succeed in capturing the lion's share of smaller, more select markets. Biopharmaceutical companies are hoping that the discovery of specific target therapies will lead to other drug development opportunities in which their research and development investments can be maximized.   

The emergence of personalized medicine is having a significant impact on the process of drug discovery and development. The foundation is being laid for a healthcare system in which data will flow freely among all stakeholders, enabling researchers, clinicians, and patients to interact in new and more productive ways.  

Advances in information technology coupled with the increasing popularity of social networking, has given patients access to a wealth of information that was previously only available from their local healthcare providers. Healthcare professionals will have to learn to share the playing field with these direct-to-patient communication channels as they begin to exert even greater influence on patients' opinions and treatment decisions.  

These innovative efforts and initiatives provide a tremendous amount of hope for the future, but there is much speculation about how it will all play out. Will shorter development time lines and the opportunity to market highly targeted "niche-buster" drugs provide enough revenues to offset the increased operational budgets they will require? Only time will tell. One thing is certain: the current situation is not sustainable and that no single industry, company, or agency can fix it alone. It will require a collaborative commitment on behalf of all key stakeholders involved (Figure 2). 

Figure 2. The implementation of personalized medicine requires the combination of several factors. 

 RN, BSc, is the President and co-founder of Scimega Research Inc., 

1. Organisation for  Econonic Co-Operation and Development, report, "Pharmacogenetics: Opportunities and Challenges for Health Innovation", (November 12, 2009).

2. Price Waterhouse Coopers, report, "The New Science of Personalized Medicine: Translating the Promise into Practice," (October 2009).   

4. Wayne A. Rosenkrans, presentation, the Oncology Leadership Forum, Boston, MA, November 8, 2010.  

5. Harris Interactive, "Misconceptions and Low Awareness Slow Recruitment for Cancer Clinical Trials," (October 18, 2000), 

http://www.harrisinteractive.com/news/allnewsbydate.asp?NewsID=213

6. Ken Getz, "Is Investigative Site Feasibility Feasible?" 

7. The Age of Personalized Medicine, Innovation.org, a project of the Pharmaceutical Research and Manufacturers of America, 

http://www.ageofpersonalizedmedicine.org/personalized_medicine/action/cabig.asp

8. Peter Mansell, "Targeted Therapies are a Silver Lining in Oncology Trials," 

http://www.pharmatimes.com/article/09-12-02/Targeted_therapies_are_a_silver_lining_in_oncology_trials.aspx

9.  P. Tollman, P. Guy, J. Altshuler, A. Flanagan, and M. Steiner, "A Revolution in R&D: How Genomics and Genetics are Transforming the Biopharmaceutical Industry," 

http://www.heartland.org/custom/semod_policybot/pdf/10744.pdf

Articles

Feature Article

How will the end of one-size-fits-all medicine effect the clinical trial industry?

Personalized Medicine

Proper history taking is one of the first things that is taught to doctors in medical school. This includes medications that the patient is taking concurrently. With the growing interest in the use of complementary and alternative medicine globally, doctors should be more aware of its effects and interactions with the body and with other medicines as well. 

The US National Center for Complementary and Alternative Medicine (NCCAM) defines complementary and alternative medicine (CAM) as a "group of diverse medical and healthcare systems, practices, and products that are not generally considered part of conventional medicine."

  Complementary medicine is utilized in concert with conventional medicine, whereas alternative medicine is used as a replacement for conventional medicine.  

Traditional medicine, as defined by the World Health Organization (WHO), "is the sum total of knowledge, skills, and practices based on the theories, beliefs, and experiences indigenous to different cultures, that are used to maintain health, as well as to prevent, diagnose, improve, or treat physical and mental illnesses."

  When other populations use the traditional medicine of other cultures, this is now termed as complementary and alternative medicine.  

It is widely known that some of the more popular traditional medicines originated from Asia, such as Indian Ayurveda and Traditional Chinese Medicine (see Traditional Asian Medicine sidebar). Thus, it is commonly presumed that CAM is more widely practiced in this region than in the West. WHO estimates that in some Asian and African countries, 80% of the population actually depends on traditional medicine as their primary healthcare. The exact prevalence rates of CAM use for most of the countries in Asia are unknown as there are very few well conducted population-based studies. 

In more developed countries, WHO estimates that 70% to 80% of the population have used some form of CAM. A national population-based survey conducted in Australia in 2005, revealed that 69% of those surveyed used at least one form of CAM, while 44% visited a CAM practitioner.

  A survey in 2007 in the United States, found that 38% of adults, and 12% of children are using some form of CAM.

  Among cancer patients, CAM use is increasing in popularity, from 34% in 1990

This article examines CAM use among cancer patients in selected countries in Asia, and doctor's attitudes and knowledge about it. However, it is difficult to assess and compare CAM usage and prevalence across studies as there are differences in what constitutes CAM, the study population and methodology of the studies used, and the types of CAM reimbursed by insurance companies. It must be remembered also that what constitutes CAM is often culturally dependent. Ayurveda medicine in India for example, is often regarded as orthodox medicine. Traditional Chinese Medicine has been around for thousands of years, with a theoretical and practical approach to management of disease, albeit different from that of the Western framework.  

In general, CAM use, especially Traditional Chinese Medicine (TCM), is more accepted by Asian governments. In China, TCM has its own medical schools, hospitals, and research institutes, as well as its own department in the Ministry of Public Health.

  Since 1996, TCM use in Taiwan has been reimbursable by the National Health Insurance (NHI). Thus people are free to choose between conventional Western style medicine and Traditional Chinese Medicine,

  although NHI only reimburses TCM in certain forms. In Singapore, the TCM Practitioner Act was passed in November 2000. It requires TCM practitioners to be registered with the TCM Practitioners Board. The Board, aside from registering TCM practitioners, also regulates the professional conduct and ethics of TCM practitioners, while accrediting TCM courses and schools.

  Korea has Oriental medical schools that teach Oriental medicine (as opposed to Western medicine). Students go through a six-year medical program and need to pass a national licensing exam before practicing.

In Asia, CAM use in the general population ranges from 50% to 76%.

  Females consistently are more likely to use it. The significance of age, marital status, educational level, financial capacity, and tumor site differed among countries; could be due to the sample population surveyed and study methodology used.  

 In Japan, the most common therapies used were massage; vitamins; health food, including dietary supplements; acupressure; and kampo (see Traditional Japanese Medicine sidebar).

  This is similar to the Koreans, where the five most common types of CAM therapies used were dietary supplements, Korean Oriental Medicine, non-processed and non-prescribed herbs, animal extracts, and diet-based therapies. In multiracial, multiethnic Singapore, the Chinese were the most frequent users with TCM the most widely used.

  Among the vast array of available traditional Chinese medicine, the majority of patients in Taiwan (86%) use Chinese herbal remedies.

				  Only 11% use acupuncture, and 3% use traumatology manipulative therapies. TCM use among the Chinese is not surprising, as it has been around for more than 2,000 years and is deeply ingrained in their culture. One of the exceptions is Hong Kong, where only 4% prefer to use TCM for their medical problems.

  During British colonial rule, Western medicine dominated the healthcare system, while TCM use was left outside the mainstream. It wasn't until the hand over of the city's sovereignty in 1997 that it was formally recognized. In 1999, the Chinese Medical Council of Hong Kong was established to act as a regulator and ensure the professional standard of TCM practitioners. 

Despite high TCM use, more patients still visited Western medicine clinics than TCM clinics for their illnesses.

  Patients more frequently use CAM for maintenance of health rather than for treatment of illness, which is usually musculoskeletal in nature.  

 It is noteworthy that patients usually do not inform their physician about their CAM use.

  Patients might find it hard to tell their doctor for fear of a negative reaction and disapproval of its use.  

Prevalence and profile of patients using CAM. 

CAM use among cancer patients ranges from 45% to 98%. Among leukemic patients in India, prevalence of CAM use is about 57%.

  A study done at the Ambulatory Treatment Unit of the National Cancer Center Singapore revealed that 56% of patients were currently using CAM, while 44% had used CAM prior to their cancer diagnosis.

  Factors found to be positively associated with CAM use were race (Chinese), education level (secondary level and below), and prior CAM use. Tumor type, gender, and economic status were factors not found to be associated with CAM use.  

A nationwide survey in Japan in 2002 showed the prevalence of CAM use among cancer patients to be 45%, and approximately 26% in non-cancer patients. They were usually female, less than 61 years old, had a higher education, had previously received chemotherapy, were dissatisfied with conventional treatments, and were attending palliative care units (as opposed to cancer centers and related hospitals). This profile is similar to that in Western countries. Patients with lung, breast, or hepatobiliary cancers also used CAM more frequently.

Prevalence of CAM use among cancer patients in South Korea range from 50% to 78%.

  Patient profile revealed them to be younger and more educated.

  Patients who also initiated complementary therapy tend to be female, of younger age (≤60 years old), with a higher household income, with private insurance, and with a diagnosis of stomach or liver cancer.

A study done in Taiwan in 1995 showed the prevalence of CAM use in a cancer clinical trial referral unit to be 64%.

  As TCM use in the general population has generally increased by 29% annually, this figure may be higher in present times

  This is borne out by Yang et coll. who, in May 2008, reported that 98% of cancer patients receiving outpatient chemotherapy also used CAM.

  This figure is very similar to a study among breast cancer patients in Shanghai, China, wherein 97% of patients reported CAM use after diagnosis.

  Females and patients less than 70 years old tended to use CAM more frequently than males. Religious affiliation and educational level were also shown not to be significant factors.

Among leukemic patients in a tertiary care hospital in north India, more than half admitted to using CAM. Males used them more often than females, and across all age groups, although adults aged 36 and above more frequently. Patients from rural communities also used CAM more often, compared to urbanites, who more often used conventional therapy alone. Rural patients also tended to be illiterate, or educated till primary level, compared to urban patients who have tertiary level education.

. Asian cancer patients more often use food supplements, herbs, or medicinal plants/mushrooms instead of physical therapies such as yoga and acupuncture.  

Based on NCCAM categories, the Singaporean study excluded energy therapies, mind-body interventions, and manipulation and body-based methods. The most commonly used CAM were food supplements (59%), TCM (48.5%), special diet such as organic fruit and vegetables (40.5%), and vitamins (39.6%). Among food supplements, bird's nest and essence of chicken were consumed most often. Herbs prescribed by licensed Chinese physicians were used by 53.6% of patients taking TCM. The rest consumed lingzhi (15.4%), and Chinese proprietary medicines (7.5%) in finished dosage forms such as pills and capsules.

A vast majority of those surveyed in Japan relied on CAM products (96%) rather than on non-medical therapies. Of these, more than 60% took certain types of mushrooms, thought to be an interferon inducer or immunomodulator. Only 7% of patients used Chinese herbs. Non-medical therapies such as qigong, moxibustion, and acupressure were used by slightly less than 4% of patients.

In Korea, medicinal mushrooms were also used most frequently, being used by 67% of patients. This was followed by herbs (54%), vegetable diets (51%), and ginseng (46%). However, if we go into specifics, extracts of Korean red ginseng was used most often; followed by Phellinus linteus, a medicinal mushroom; vegetable green juice; and Ulmus davidiana, a medicinal tree. Physical therapy such as moxibustion, heat therapy, and massage were used about 10% of the time.

In an earlier study in Taiwan, Ling chi (lingzhi)—with or without herbs—are used about 60% of the time, followed by any combination of pills plus powdered prescription, pharmaceutical concoctions (bottled in vials), and Ling chi (lingzhi)—with or without herbs—in 25% of patients.

  Patients were only able to describe the physical form of treatment received which accounts for the vagueness of the medicine used. What was noteworthy was that only 10% of patients got their prescription from a qualified Chinese medical practitioner, with 30% of all prescriptions obtained from licensed sellers of Chinese herbs. In a later study, biologically-based therapies were used more than 75% of the time, among which grapeseed and ginseng featured prominently.

  However, there was also a significant proportion who did not know the name of the herbs they were taking.  

In the Shanghai, China study among breast cancer patients, supplements were the most common type of CAM consumed followed by Chinese herbal medicine (CHM) and physical walking. Supplement and CHM users tend to have higher income, with menopausal symptoms, and a history of prior Tamoxifen use. Patients with chemotherapy or radiotherapy and cancer metastasis engaged in more physical activity.

In India, it is not surprising that Ayurveda is used most commonly.

Reason for CAM use and perceived effectiveness

. Asian patients in general tend to use CAM more for augmentation or maintenance of health than for treatment of the cancer itself. 

Singaporean cancer patients' main reason for taking CAM was to boost immunity (53.7%), and for general health (16.7%). On the other hand, the two most common reasons for not taking CAM was fear of interference with chemotherapy (24.4%) and that oncologists were against CAM use (22.2%). Other reasons cited were: lack of advice regarding CAM use from oncologist (7.4%), patients only wanted to take medications prescribed by their oncologists during anticancer treatment (5.7%), and financial difficulties affording CAM (4%). Still, 74% of the patients surveyed felt that CAM was effective for the purpose they were using it, while 23% felt unsure about is effectiveness.

Among Japanese patients, 24% of CAM users felt that it had positive effects, such as tumor shrinkage, tumor growth inhibition, pain relief, lesser side effects from chemotherapy, and generally feeling better. Sixty-nine percent of the patients however were unclear about the beneficial effects of CAM; more than 60% had no adverse effects, while only about 5% felt some form of adverse effect. It should be noted however that among CAM users, the prevalence of patients treated with concomitant conventional therapy is 62%.

The following, in order, are the main reasons why Korean patients use CAM: nutritional support, strengthen the body, boost immunity, increase appetite, and anti-cancer effect. More than half of patients felt no benefit from using CAM. Of those who had positive effects, 13% had positive emotional or psychological comfort, 8% had an increase in physical strength, and 6% just generally felt that CAM was effective. Similar to Japan, about 5% of patients felt adverse effects from using CAM. Thus, majority of patients are still willing to continue with it. 

TCM use is popular in China, and is deeply ingrained in their culture. Patients use TCM for a variety of reasons, one of which is to avoid or decrease the adverse effects of conventional therapy. There is a perception that chemotherapy damages the vital essence—"the root of a person, so that you lose the basic energy to fight the disease." There is a conception also that Western therapy consists of standard products and dosages for all patients, which contrasts with TCM, where the assessment and prescription is tailored to the individual, thus maximizing beneficial effects, reducing adverse events, and neutralizing potential toxicity. TCM is perceived to be safe, cheap, controls symptoms well and has long-term effects in recovery.

Patients hope for a miracle cure with CAM treatment among Indian leukemic patients. Adverse drug reactions with conventional treatment were also a factor. However,  the majority were dissatisfied with CAM, as it was considered expensive, time consuming, and difficult to follow.

 Despite the high use of CAM among cancer patients, patients are generally still unwilling to inform their doctors about CAM use. Doctors on their part are also unaware of its use among their patients.  

The majority of Western-trained oncologists in the region have been asked about CAM therapies by their patients, and the practice is neither to encourage nor discourage its use. It is still the belief that CAM is ineffective against cancer, with the lack of reliable evidence being the main reason. Most also feel that there is drug interaction between anticancer drugs and CAM products.

More than 50% of Singaporean and Korean cancer patients who use CAM informed their oncologist of CAM use.

  This is in contrast to Japan and India where the majority of patients do not even consult their doctor about CAM use.

				  A common reason for not informing their physician was because the physicians themselves never asked them about it. Since CAM is perceived to be effective, patients were also afraid that their doctor might discourage its use.  

. Medical students themselves feel ill equipped to deal with CAM for their future practice.

  There is a fear that patient's trust will be compromised if they are unable to discuss this properly. Most express some knowledge about CAM or TCM, and perceive it to be more efficacious than Western medicine in the management of pain, viral infections, cancer, or chemotherapy-related symptoms such as nausea and vomiting. However, it is generally believed to be lacking in scientific evidence leading to hesitancy in its use. With the extensive selection of CAM available, the majority express limited knowledge for most of these options. Thus, there is interest in learning more so that they are able to talk about this with their patients in the future.  

 A lot of research on CAM focus on symptomatology or as a supportive therapy. Acupuncture is well known to help selected patients with cancer pain. However, oncologists' concerns for lack of scientific evidence is not unfounded as there is a lack of large-scale, randomized clinical trials comparing CAM with conventional chemotherapy in treating cancer.

  It is also a well-known fact that there are herb-drug interactions, one of the most famous being St. John's wort reducing the effectiveness of warfarin. St. John's wort taken with imatinib may reduce imatinib's clinical efficacy. Salicylate herbs such as white willow and wintergreen can also potentially increase methotrexate blood levels and toxicity. However, with the extensive list of herbs and chemotherapy drugs available, more studies need to be done on this matter. In the meantime, it is a good idea to keep in mind when designing clinical trials on conventional therapy to ask patients about the use of CAM, as this may have an inadvertent impact on the drug's adverse event profile, be it positive or negative. 

MD, is Medical Director Oncology Asia-Pacific at i3, 89 Science Park Drive, #03-03 The Rutherford Science Park One, Singapore, e-mail: 

MD, PhD, Head Therapeutic Area Oncology (International) and 

PhD, Medical Research Scientist both at i3, Taunusstrasse 9, D-65189 Wiesbaden, Germany.

*To whom all correspondence should be addressed. 

1. National Center for Complementary and Alternative Medicine (NCAM), 

2. World Health Organization, "Factsheet on Traditional Medicine," 

www.who.int/mediacentre/factsheets/fs134/en/index.html

3. C. C. Xue, A. L. Zhang, V. Lin, C. Da Costa, and D. F. Story, "Complementary and Alternative Medicine Use in Australia: A National Population-Based Survey,"

 Journal of Alternative and Complementary Medicine

4. P. M. Barnes, B. Bloom, and R. Nahin, "Complementary and Alternative Medicine Use among Adults and Children: United States, 2007," 

CDC National Health Statistics Report #12, 

Use of complementary and alternative medicine among adult cancer patients.

CAM Use in Asia-Pacific

Sponsor/CRO Systems and Business Process Integration

 November 13-14, 2013 
              Raleigh, NC 

The National Cancer Institute recently reported concerns that the United States will experience a shortage of medical oncologists, which will put pressure on oncology clinical research and trials. In addition, physicians cite a lack of time as the reason they fail to recruit into these trials. However, only two-thirds of those physicians are involved in clinical trials.

How are CROs positioned to address this shortage as well as get physicians more engaged into enrollment?

 I'm not sure there's a shortage of oncologists, but I do think there is a shortage of oncologists that are able to do clinical trials. And some of the numbers you cite by the NCI are certainly consistent with our experience. 

I think oncologists today, whether you are in an academic center that's directly supported by the NCI or you're in community practice, you're very busy. There's incredible pressure today to generate fees and to see patients. And that just doesn't leave a lot of time to conduct clinical studies. 

I think [CROs] can help because through our breadth of experience with all the clinical trials that we do, we can help sponsors design studies that are more user friendly, that are easier to do, perhaps require a few less procedures, maybe that answers a more important question, that an investigator will want to take the time to be part of. And so I think we can provide some guidance to sponsors. And whether that sponsor is the NCI or a pharma company, to help design trials that are a little bit more pragmatic and have a better chance of gaining participation by investigators. 

Recent reports have stated that oncology has the lowest overall success rates at Phase III. Then drilling down into specific cancer types, the overall success rates range from 2% to 19%, depending on the type of cancer that's involved.

Specifically, in regard to the low success rates at Phase III, what is this a function of? Is it a fact that the drug is just going into more subjects or is there a factor of trial design or something else that we're not understanding yet?

 I think it's a function of both expanding the size of the trials and the design of the trials. We characteristically do Phase II studies in oncology that are rather small, and part of this is because it's very hard to find patients for the trials. You know, for many people, this is their treatment of a very serious disease. It may be their last treatment in many cases. And so finding subjects is difficult. So Phase II trials have tended to be small. 

And, when you have a small study, the result may not be reliable because you have a very large confidence interval because you have a small sample size. And so we'll see success in Phase II, take the drug into Phase III, and now we have a larger sample size and more variability; and all of a sudden that great result we found in Phase II, we don't see in Phase III. 

So it's partly a problem of the size of the studies and how we do studies. We are changing that paradigm more and more; both pharmaceutical sponsors as well as cooperative groups are now doing randomized Phase II studies. They are larger. They do take more time, and they're more expensive. But the value of the data that you get afterward is much greater, and it really gives you a much better picture as to whether or not the drug is worth taking forward into Phase III. 

 I would echo Dr. Kurman's comments noting the criticality of the trial design. 

There are a lot of things that go into clinical trial design, not just from a scientific standpoint but also at the clinic standpoint. So the hematologists/oncologists out in the community, they need to want to enroll these trials. They have to be easy enough for the hematologist/oncologist to enroll. They have to be easy enough for their site support staff, for their study nurses, and their data collection staff to collect and interpret those data and appropriately interpret those data. 

We're out there monitoring those trials on a regular basis, but if it's too difficult for those investigators and their sites to have to enroll these trials, they're just not going to enroll them. And so from that standpoint, the trial can be a failure. 

It also has to have minimal impact on these patients. As Dr. Kurman mentioned, these patients are critically ill. A lot of them are in the end stages of their life and know that they have a terminal disease, and they don't want to spend the last months of their life enrolling or participating in a trial that has them going to a clinic seven days a week or multiple, multiple hours on end. 

So there are a multitude of things; but it really boils down to appropriate clinical trial design and opening them in the right locations. 

How does contract research and trial research outsourcing make cancer trials more successful? 

 Part of it is just helping sponsors design trials that are going to be successful, knowing where the investigators are who have the time or the staff or the interest or whatever it might be to enroll the patients and make the trial successful. 

Mostly it comes down to our breadth of experience. A lot of sponsors who may have three or four cancer drugs in development, and over the course of each of those drug's life cycle, each drug may be involved in maybe four or five or six trials. So each company may do 25 trials over many years. But at any one time, we may be doing between 50 and 100 trials. And so, again, I think the breadth of experience that we bring and all of what we see I think can help sponsors design those studies that will answer important questions but that still can be completed in a reasonable period of time for a reasonable cost. 

 Sponsors, in particular in the oncology community, look to CROs for their expertise and efficiencies in running these trials and enrolling these trials and then collecting the data on the trials and interpreting those data. It's what we do every day, and pharmaceutical sponsors look to the CROs for those processes and that consistency. 

That's what we do every day, and that's why the clinical trials' sponsors come to us. Our processes are predictable. Our outcomes are reproducible. And so that predictability is what the oncology clinical trial sponsors look for, that's what they need, and that's why they come to us because of those efficiencies and success rates that we bring to the community. 

As far as that reproducibility and efficiencies, do you find that it is more difficult in oncology to achieve that, only by virtue of its disease, or can you take similar efficiencies and processes from other therapeutic areas that work fine and just apply it to this complex area?

In the area of hematology and oncology clinical research, we're dealing frequently with very rare diseases. Remember that in a lot of patient populations, if we're doing sinusitis studies or flu studies, these diseases or indications are very widespread and not hard to find those patients. And in a lot of cases there's not a high degree of competition for finding those patients.  

There is a higher degree of complexity that comes into play in the oncology trials, and what we've talked about, we're dealing with real patients with real diseases at the end of their life in a lot of cases. And so all of those things factor into whether or not patients even want to participate in clinical trials. And so it's absolutely critical that we have these structures and these processes and that we invest this time and these resources and that we have these technologies to determine exactly where we need to go to find these patients so that we can bring these trials into the community settings, penetrate the community of patients to find those trials, and make those trials available to those patients so that we can successfully enroll them and provide those key services to our clients and to the oncology community. 

 Additional discussion from this interview can be found at 

Two industry insiders offer scientific and operational insight into oncology clinical trials.

How CROs Help Oncology Sponsors Produce Results

Since the mid-1980s, biomedical science has undergone a major revolution, driven by the advancement of molecular science, ushering in the era of molecularly-driven, "personalized" medicine. This has increased the influence of basic research on the clinical process, best illustrated by the advent of new molecular tools, such as next-generation sequencing, which are pushing the boundaries of data handling. The scientific community now recognizes that genotypes exist not only in clinical settings as key data, but also as contributory factors for the development of treatments. While the current state of technology allows for only limited personalized treatment regimes, it is clear that certain genetic traits lend themselves to the success or failure of drugs within a given population. The need to rapidly move from discovery to development—the essential feature of translational medicine—is driving the technology. 

The pace of change in the clinical trial process has not kept up with that of molecular biology. The widespread acceptance in the 1950s of the controlled clinical trial as a best practice radically changed the face of clinical research and drug development. Since that time, however, there have been no changes of comparable magnitude, and the resulting dissonance between the pace of science and that of the clinical trial process has manifested itself in the "pipeline problem"—a slowdown, instead of an expected acceleration, in the number of innovative therapies being made available to patients. The US Food and Drug Administration (FDA) noted in 2005 that "the applied sciences needed for medical product development have not kept pace with the tremendous advances in the basic sciences."

  New Molecular Entity (NMEs) applications to the FDA's Center for Drug Evaluation and Research have been trending downwards for the past 15 years, from 45 in 1996, to 23 in 2010.

  Meanwhile, research and development spending by pharmaceutical companies more than doubled in the decade following 1996.

Many of the challenges posed by the paradigm of translational research—the ability to take findings in basic science and rapidly translate them into usable therapies—relate directly to those that bioinformatics seeks to solve: the ability to deal with rapidly increasing amount of data; the capacity to process the data; and the ability to share the generated data. The sheer amount of data generated is staggering, as illustrated by Duke University, which is currently running near capacity of its 300 terabytes of storage space at one facility, after encoding 200 whole human genomes and 100 exomes.

  The number of different systems and programs used to produce, collect, and analyze the data further complicates the sharing and analysis across multiple data sets and sources.  

Furthermore, exploration of new models for clinical trials data management is critical; current models do not scale to the rapid increase in information generation and availability, imposing rate-limiting steps on translation of this information into new therapies. The development of new therapies is no longer based on strictly linear, asynchronous sets of experiments and clinical trials results. The National Cancer Institute's (NCI) 1997 report of the Clinical Trials Program Review Group,

  which noted that the complexity of the clinical trials infrastructure had "eroded the ability of the system to generate new ideas to reduce the cancer burden," was an early expression of the need for new models for managing clinical data. More recently, the Institute of Medicine expressed the imperative need for a "learning healthcare system that is designed to generate and apply the best evidence for the collaborative healthcare choices of each patient and provider; to drive the process of discovery as a natural outgrowth of patient care; and to ensure innovation, quality, safety, and value in healthcare."

Nowhere has the difference between the pace of scientific discovery and that of technology development been more keenly felt than in oncology. As noted recently by NCI, "Although many still think of a single disease affecting different parts of the body, research tells us—through new tools and technologies, massive computing power, and new insights from other fields—that cancer is, in fact, a collection of many diseases whose ultimate number, causes, and treatment represent a challenging biomedical puzzle."

 The effect of cytochrome p450 polymorphism on drug metabolism, for example, showed that a group of cancer patients hitherto regarded as relatively homogenous in fact had to be divided into those that could take flutamide, tegafur, cyclophosphamide, paclitaxel, or tamoxifen, depending on the polymorphism, or had to be excluded from treatment by these drugs due to highly toxic side effects.

As personalized medicine becomes more prevalent, and translational medicine becomes the imperative, the need for sophisticated data collection, storage, and analysis will be of vital importance. This was recognized, even prior to the increased availability of next-generation sequencing and the huge amount of data associated with it, in the report of the Clinical Trials Working Group to the National Cancer Advisory Board in 2005.

  The Working Group identified the need for a new clinical trials infrastructure to enhance coordination and communication, scientific quality and prioritization, standardization of tools and procedures, and operational efficiency. In this article the authors will discuss the implications and trends of information and data handling in the modern clinical research setting. 

The increasing number of clinical trials and the rapid pace of technology led to the development of specialized information technology in the late 1980s and early 1990s. Automated analytic and reporting systems were driven by study sponsors and sites—commercial, academic, and government entities—in an effort to manage the growing volume of data and increasingly complex reporting requirements.  

As a result, many large academic medical centers have spent years developing highly customized, tightly integrated in-house boutique systems intended to support local clinical workflow, which can vary significantly between organizations. The tailored nature of the applications led to rapid adoption by the organizations that created them, at the price of becoming increasingly ingrained, sclerotic, and resistant to change as time moved on. These systems have become legacy systems, which are maintained at an extremely high cost, since almost any process change or integration with another system will cause them to "break" and require code modification. The effort needed to maintain the systems leaves few resources for the development of enhancements and new functionality. As a consequence, architectural considerations and refactoring become a low priority, ensuring further "entrenchment" of the legacy systems and their monolithic nature. As standards and open-source capabilities become available, these legacy systems are unable to take advantage of them, since their ability to integrate and interoperate easily is limited. 

The sustainability and scalability of these legacy systems is being challenged by the need to share large volumes of complex data, and the wide availability of affordable technologies such as polymerase chain reaction (PCR) and microarray technologies. A Google search for microarray tools yields over 3.6 million results, and a search for PCR tools yields over nine million.

  As the amount of laboratory-generated data has grown, so has the development of laboratory information management systems (LIMS). The ability to use this data in clinical trials is a sine qua non if researchers and clinicians ever hope to deliver on the promise of a true translational "bench-to-bedside" drug development approach. Clinical trials coordinators increasingly assume that individual patient genetic data is available to evaluate patient eligibility for a trial, and for randomization into an arm of the study. Imaging data is also commonly expected to be readily available for diagnostic and outcomes evaluation, further increasing the complexity of data handling, sharing, and storage. 

The development of these disparate systems presents another well-known problem, that of rekeying data. When systems do not interoperate, manual reentry, which is error-prone and a waste of valuable resource time, is inevitable. Rekeying is even more wasteful if the data was "born digital," meaning that the measurements that generated them were performed by machines and instantly rendered digitally without any human intervention beyond the calibration and operation of the machines. A major advantage over rekeying, but still a labor-intensive and error-prone process, is the development of complex semi-manual ad hoc data synchronization procedures involving interim steps (e.g., export files in comma-delimited or spreadsheet format). In these cases, however, the investigators and their scientific teams end up acting as data integration specialists and software engineers to create these processes, limiting their own time for research and increasing the risk of coding errors, transposition errors, data loss, and data corruption. Data collection, storage, and processing have become an important and increasingly challenging task, even without considering the need for semantic data exchange (i.e., the ability of machines to understand the context and meaning of data being shared between systems) to facilitate collaboration between researchers or organizations using disparate systems. 

The Cancer Biomedical Informatics Grid®  (caBIG®) program was conceived in response to these challenges. In 2004 the National Cancer Institute (NCI) launched caBIG as an information network enabling all constituencies in the cancer community—researchers, physicians, and patients—to share data and knowledge. Prior to caBIG's launch, NCI consulted with many stakeholder groups and visited nearly all of the NCI-designated Cancer Centers (research sites that have been recognized by NCI as meeting specific standards for scientific excellence and for integrating diverse research approaches to the problem of cancer)

  to gather information and clarify the informatics needs of the scientific community. The challenges involved in managing clinical data emerged as a key pain point, along with many others including the increasing requirement to share this data across the basic science, clinical research, and patient care continuum. Additionally, the creation of technology and data sharing standards that would allow for the development of common tools and interfaces was seen as one of the major goals for the new program. Based on this assessment, the mission of caBIG was aimed at developing a collaborative information network that accelerates the discovery of new approaches for the detection; diagnosis; treatment; and prevention of cancer, ultimately improving patient outcomes. Specifically, caBIG's goals are to:  

Connect scientists and practitioners through a shareable and interoperable infrastructure.

Develop standard rules and a common language to more easily share information.

Integrate or adapt tools for collecting, analyzing, integrating, and disseminating information associated with cancer research and care.

caBIG is primarily focused on the interfaces between systems. The idea is to create reusable interfaces through open, published standards, allowing organizations access to new data sources as they become available. Additionally, in response to community-identified need and reflecting a trend throughout software engineering,

  caBIG facilitated the development of standards-based, interoperable "plug and play" modules, intending them as adjuncts to supplement in-house and vendor systems. 

The caBIG framework was developed to be open and available to all. It is intended to be co-created by all the communities likely to use and propagate these tools. Consequently, since its inception, caBIG has followed the following four guiding principles: 

 All caBIG resources, including software, infrastructure, documents, and data, are freely obtainable to all, in order to maximize opportunities for broad sharing and collaboration. 

 caBIG tools and infrastructure are developed through a participatory process open to all. 

. caBIG software and resources are widely distributed, interlinked, and potentially available to everyone, but each organization maintains local control over its own resources and data. 

 All source codes for NCI-funded caBIG modules is freely available to view, download, use, alter, and redistribute without restriction. The caBIG software license

  is non-viral (i.e., derivative works are not subject to the original open source terms, meaning that any of the code can be incorporated into proprietary software). This is consistent with the policy

  of the National Institutes of Health (NIH) that research resources, including software and data, should be broadly disseminated in order to promote further research, development, and applications, and that broad use of these resources will ultimately benefit public health.  

 To align the efforts of caBIG with the structure of translational research, the program was organized around interconnected "workspaces," each representing a particular area of expertise and/or scientific need. These workspaces allowed for a detailed focus on the stakeholders within that domain, as well as the higher-level needs across those realms. The domain workspaces in caBIG comprise the following: integrative cancer research (ICR); in vivo imaging; tissue banks and pathology tools (TBPT); and clinical trials management systems (CTMS). 

Each domain workspace works closely with community stakeholders to document specific needs and allow for organizations to move towards increased data sharing and analysis. Additional "cross-cutting" workspaces were focused on the architecture and standards that would support the necessary interoperability between domains and systems: vocabularies and common data elements (VCDE) and architecture. 

The goal of these workspaces is to develop an open informatics framework of standards, specifications, and vocabularies to support translational research and care. For caBIG, a services-oriented architecture (SOA) was adopted. Benefits of a SOA-based approach include a focus on business capabilities (and not on the technology), increased agility, and technology-independent specifications providing increased vendor participation and cross-vendor interoperability. 

The caBIG clinical trials suite was developed to meet the expressed needs of NCI's cancer research community for the management of oncology clinical trials. The suite is designed as a modular, clinical trials management platform comprised of interoperable components (Figure 1). The following is a list of capabilities, which currently exist as separable, interoperable components of the suite.  

Clinical participant registry (C3PR) enables efficient and streamlined registration of participants into clinical trials.

Patient study calendar (PSC) allows organizations to centrally and consistently manage study participant schedules in clinical trials. 

Adverse event reporting system (caAERS) is used to manage the collection and reporting of adverse event data obtained during clinical trials. 

Lab viewer is used to view laboratory data in transit between clinical source systems (typically in-house clinical chemistry laboratory systems) and clinical trials systems (typically clinical data management systems). 

Clinical connector allows users of third party clinical data management systems (CDMS), both open source and proprietary, to expose caBIG capabilities within the CDMS. All of the CTMS modules are designed to work with a CDMS as the central repository for the clinical data for a trial. The specifications and requirements for all suite modules are free and open-source, so that commercial vendors, academic institutions, and industry alike can utilize and adapt these modules for their use.

Integration hub (iHub) enables tools and applications in the caBIG clinical trials suite and beyond to interface seamlessly with one another. iHub acts as a middleware component, using an enterprise service bus (ESB) to provide integration between preclinical, clinical research, and clinical care data, and ensure reliable transaction control (enabling applications to "talk" with one another and confirm receipt of messages and appropriate responses).

Figure 1. The suite is a modular, clinical trials management platform comprised of interoperable components.  

Key to the ability of the caBIG CTMS modules to interoperate is a single domain analysis model (DAM), (i.e., a shared model of the dynamic and static semantics that collectively defines a shared domain-of-interest). The accepted model for the domain of clinical and preclinical research is the Biomedical Research Integrated Domain Group (BRIDG) model, which has been developed jointly by caBIG, the Clinical Data Interchange Standards Consortium (CDISC), Health Level Seven (HL7), and the FDA. Harmonizing an application's data model with BRIDG not only ensures that its semantics are aligned to those of other applications similarly harmonized, it also enables domain experts to understand, and thus to review for correctness and completeness, the semantics of the application. Furthermore, as BRIDG is itself mapped to HL7's Reference Information Model (RIM), mapping an application to BRIDG ensures that the application's semantics are HL7-compliant.  

Molecular and translational research tools

In keeping with the integrative mission of caBIG, tools have been developed in response to community-expressed needs in molecular and translational research, including:  

caArray is a microarray data management system. 

caIntegrator is a research data mart that allows researchers to set up custom portals to conduct integrative research without programming. These portals bring together heterogeneous clinical, microarray, and medical imaging data to enrich multidisciplinary research. 

geWorkbench (genomics Workbench) is a platform for integrated genomics.

Information network allows constituencies to share data and knowledge.

Supplements-05-01-2011

Personalized Medicine

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How CROs Help Oncology Sponsors Produce Results

Cancer Biomedical Informatics Grid