As cancer treatment shifts from broad chemotherapy to highly personalized therapies, drug development in oncology is also evolving. There are currently some 71,000 oncology trials listed on ClinicalTrials.gov, with new studies being added every day. Although the trials may involve varied forms of disease or new modalities such as cell and gene therapies, all share 1 thing in common: They are competing with one another to recruit patients.

In the United States, fewer than 5% of adult patients with cancer enroll in clinical trials. This makes every patient extremely valuable.

 Sufficient enrollment is essential for successful completion of randomized controlled trials (RCTs) and for bringing new oncology drugs to patients sooner. However, we are unlikely to be able to recruit enough participants for many of these trials. In the oncology community, we must rethink how we populate trials and gather data for regulatory decisions.

RCTs remain the gold standard for evaluating the safety and efficacy of drugs, but the industry must identify alternative methods of gaining insight into treatment patterns and drug performance. The growth of personalized medicine and the complexity of the oncology treatment landscape offer an opportunity for the industry to expand the use of real-world evidence (RWE). We must consider expanding RCT eligibility to utilize RWE differently. This shouldn't occur for the purposes of trial rescue, when recruitment and data capture have been inadequate. This use of RWE should involve planned studies with methodologies designed to capture data on the day-to-day usefulness of drugs. Examples of how RWE can be used in trials are below.

The use of pragmatic clinical trials, focusing on the correlation between treatments and outcomes in real-world clinical settings, has increased significantly over the past several years. Pragmatic trials offer the ability to test treatment options on realistic patient populations with fewer exclusions for common conditions that could affect outcomes in practice. The resulting data can be used for standalone analysis or as part of the RCT. For example, real-world data sources were used in a recent study to analyze the treatment patterns and duration of palbociclib (PAL; Ibrance), an oral inhibitor of cyclin-dependent kinase 4/6 inhibitor, with endocrine therapy (ET; aromatase inhibitor or fulvestrant) versus ET in men with metastatic breast cancer (mBC). The study used a retrospective analyses of pharmacy, medical claims, and other data and arrived at the conclusion that men with mBC derived clinical benefit from the addition of PAL to ET.

 According to investigators, given the challenges of conducting RCTs involving men with mBC, RWE data appear to be useful for assessing the benefit of novel therapies in this setting. 

Synthetic control cohorts must be embraced to support drug development and regulatory approval of breakthrough medicines designed to improve patient care. According to the FDA, synthetic control arms could help augment new clinical data by allowing biopharma company sponsors to reduce the number of patients assigned to the control arm in randomized trials or to conduct smaller randomized trials. The 2017 approval of avelumab (Bavencio) for Merkel cell carcinoma (MCC) marked the first-ever use of RWE for FDA approval. The approval was based on a phase II trial with only an active agent arm and a real-world retrospective analysis of patients with MCC treated with chemotherapy. The rarity of the disease made it impractical to conduct an RCT.

Wide Spectrum of Potential Uses of RWD/RWE in Clinical Studies (Click to Enlarge)

Formalize a 2-Step Regulatory Approval Process

Under the FDA’s expanded access program, patients with an immediately life-threatening condition or serious disease may gain access to investigational drugs for treatment outside of clinical trials when no alternative therapy options are available. For patients who have exhausted approved options for treatment, participating in the expanded access program may be worth the risk of using an unapproved drug.

Although RWE is an extremely important tool to help address unmet patient needs, it is not a well-established regulatory pathway. The FDA has predicted that greater use of RWE will result in safety and efficacy information becoming available sooner and will help to further inform regulatory decisions.

 This is an opportunity to consider formalizing expanded access through the implementation of a 2-step approval process, similar to that used in Europe.

The European Medicines Agency (EMA) uses conditional marketing authoriza-tions (CMA) to speed up access to medicines for patients with unmet medical needs. Between 2006 and 2016, CMAs were granted for 30 medicines that target such conditions as HIV infection, cancer, severe epilepsy in infants, and multidrug-resistant tuberculosis. Of these CMAs, 11 have been converted into full marketing authorizations, 2 have been withdrawn for commercial reasons, and the remainder remain valid CMAs.

 Companies that receive CMAs are required to conduct further studies to obtain complete data or meet other postauthorization requirements. According to the EMA, it takes an average of 4 years to generate the additional data needed to receive full marketing authorization.

In the United States, biopharma companies could receive conditional approval from the FDA on the basis of early-phase RCT data, such as a completed phase II trial data or initial data from a phase III trial. Once conditional approval is granted, the sponsor would be required to complete more comprehensive trials for final approval of a new drug application (NDA) or supplemental NDA.

RWE can be analyzed and compared with outcomes expected under an RCT by using a pragmatic trial and monitoring patients longitudinally for 12 to 18 months. The FDA has signaled openness to this approach in its recent RWE framework and in the expansion of its RCT DUPLICATE demonstration project, designed to use RWE to attempt to replicate the results of 30 RCTs. The goal of this project is to help inform the FDA’s standards for using RWE in regulatory decision-making and to identify when and where RWE can provide estimates of treatment safety and efficacy.

Clinical trial recruitment is sometimes challenging, and cancer trials can be closed prematurely because of poor accrual. Additionally, significant disparities exist in RCT participation. Clinical trial populations tend to encompass younger, healthier patient populations than are found in the real world, with limited ethnic and gender diversity. Although RCTs remain the gold standard for the assessment of safety and efficacy, clinical trials alone will not be sufficient to accelerate desperately needed advances in cancer care. RWE will continue to play an important role in drug development and testing by providing critical information about how new treatments perform in real clinical settings. As the FDA finalizes its guidelines for how and when industry can use RWE to support regulatory decisions, we must also work in parallel to expand RCT eligibility to include real-world patient populations. This will improve our ability to recruit patients and complete RCTs as well as bring new oncology drugs to market sooner.

According to the National Cancer Institute, standard of care is defined as treatment that experts agree is appropriate, accepted, and widely used. If a study is comparing new treatment options with a standard treatment that has not changed for years, why not use historic RWE as the control? If multiple biopharma companies are developing new drugs for the treatment of renal cell carcinoma, and each is conducting trials with nearly identical control groups, the possibility exists for all to draw data from the same control group. This may present some risk to the biopharma companies but could be a major boost for patients in the form of faster access to promising new therapies.

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As cancer treatment shifts from broad chemotherapy to highly personalized therapies, drug development in oncology is also evolving.

Real-World Evidence Should Be Part of Clinical Trials in Oncology

Healthcare news is filled with stories about technological advances in artificial intelligence (AI) and machine learning. Some of the biggest and most respected technology companies are backing healthcare data initiatives with a heightened focus on oncology.

Because the purpose of AI is to review and assess enormous quantities of information, in oncology, AI’s power and potential lie in its ability to quickly cull relevant data from clinical studies on traditional and emerging therapies. However, machine learning on its own falls short due to the significant complexities of oncology that still require human intellect for successful outcomes. Knowing a patient’s diagnosis, prior treatments, comorbidities, and, importantly, desires (ie, adverse effects vs quality-of-life considerations) is critically important. As these data are often unique to the individual and not recorded in datasets, AI cannot factor them all into its computations. The collection, analysis, and personalization associated with treatment decisions continue to require human intervention by physicians and clinically trained staff.

IBM’s Watson for Oncology has made a significant investment in AI, and although it has been introduced in various healthcare settings, “no published research shows Watson improving patient outcomes,” concluded a recent Wall Street Journal article by Daniela Hernandez and Ted Greenwald.1 They said that the computerized oncology tool often comes up with recommendations for treatment that don’t augment what doctors already know and, further, the complexities and rapid advances in oncology frustrate efforts to keep the AI tool up-to-date. Although Watson has been abandoned by some, other oncology centers contend it is positively influencing a portion of therapeutic decisions at their institutions, and there remains a coterie of scientists and physicians who maintain that Watson for Oncology will improve.

Beyond performing research tasks in the clinical setting, do cognitive tools such as Watson have the potential to deliver personalized treatment recommendations that encompass knowledge of an individual patient’s needs and wishes and the doctor’s assessments? AI is limited in that it is not a replacement for physician judgment and shared decision making. A patient’s previous diagnoses, prior treatments, comorbid conditions, desires, and treatment goals must be weighed along with clinical trial data and real-world experience. The high hurdle is that this information must be incorporated into the delivery of personalized care. The knowledge and care of the physician must be combined with the wants and needs of the patient, along with all that the world knows about a particular cancer type.

However, every decision we make is based on a series of assumptions, and those assumptions must be supported by concrete data. New treatments and clinical data for oncology are accumulating at an ever-faster pace. More than 2000 oncology clinical trials are underway or in the planning stages for new molecular entities and combination therapies.

Most organizations are not equipped to handle this tsunami of data, and technology has the potential to solve a large part of this challenge. AI can distill huge amounts of data into succinct findings. However, many of our healthcare systems don't have the technology they need to share information, something that AI algorithms rely on for fine-tuned treatment guidance.

In my opinion, there will always be 1 extra assumption and 1 additional data point to consider. In addition, there is the prediction that human beings will behave unpredictably. These difficulties create the need for improved synergy and expertgroup review. Critical to this collaboration are scientists who can combine clinical experience with data expertise. Because clinical decisions will be challenged by assumptions and bias, scientists can ensure that we are interpreting data with the highest level of confidence. As new data are generated, scientists, analysts, and clinicians need to sit down and go over it together. AI, such as Watson, is one way to bring these disparate minds and datasets together.

Another example of a technology that helps oncologists synthesize information for clinical decision making is McKesson’s Clear Value Plus, which integrates National Comprehensive Cancer Network clinical practice guidelines for 20 disease states a with multiple electronic health records, including iKnowMed, iKnowMed Generation 2, Epic, and Varian. Since its inception, the system has aided more than 375,000 clinical decisions from US community oncologists.

To date, Watson for Oncology has been employed in many locations outside the United States, including Thailand, India, and China, which have different healthcare systems—each with its own demographics, costs, and access to care. In regions where advanced training may be rare or unobtainable, Watson can provide valuable support for clinical decisions that involve uncommon cancers or cases that require complex decision making.

As we continue to advance personalized medicine, we find that the ability to capture robust, structured, detailed data—including biomarkers, dosing, duration, histology, and claims information (ie, Internation Classification of Diseases, 10th Revision codes)&mdash;in electronic systems is very powerful. This is the natural synergy of technology taking its rightful place in the clinic. Having this structured data available to us is clearly supporting breakthroughs in many types of cancers.

Furthermore, AI will be able to manage these growing volumes of data and find patterns and outcomes that we didn’t know to look for. Improvements and greater familiarity with AI will lead to systems of checks and balances that will create trust and fulfill the promise of computer-assisted precision medicine. However, for each advance in technology we will still need experienced physicians to question the algorithms, analyses, and underlying assumptions. Physician collaboration with data scientists will build the understanding and fine-tune the research.

Sarah Alwardt, PhD, is vice president of data evidence and insight operations for McKesson Life Sciences.

Hernandez D, Greenwald T. IBM has a Watson dilemma. 

. August 11, 2018. wsj.com/articles/ibm-bet-billions-that-watson-could-improve-cancer-treatment-it-hasnt-worked-1533961147. Accessed October 2, 2018.

Freedman DH. A reality check for IBM’s AI ambitions. 

. June 27, 2017. https://www.technologyreview.com/s/607965/a-reality-check-for-ibms-ai-ambitions/?rel=0" . Accessed October 2, 2018.

Schmidt C. M.D. Anderson breaks with IBM Watson, raising questions about artificial intelligence in oncology. 

Improvements and greater familiarity with artificial intelligence will lead to systems of checks and balances that will create trust and fulfill the promise of computer-assisted precision medicine.

Sarah Alwardt, PhD

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