Real world evidence cases to improve the efficiency of clinical trials

Although randomization may always be the gold standard, this real-world use case helps to improve the efficiency of clinical trials and reduce costs Clinical trials often involve a large number of subjects; however, subjects tend to worry that they will eventually be randomly assigned to the placebo group These are two of the most perplexing problems for institutions conducting clinical trials Can such a problem be alleviated by adopting innovative real world use cases? Relevant professionals believe that with the soaring cost of clinical trials and the proliferation of digital data, FDA promised to consider real world data (RWD) in regulatory decisions, and began to try to apply synthetic control arm at the right time The development of pharmaceutical products is on the edge of the new era of evidence generation, which often means that the environment for disruptive innovation has matured The next step requires a certain degree of risk-taking, but such a way of doing things is not what the pharmaceutical industry is good at The synthetic control arm may be a safe and secure way to use real world evidence (RWE) Data were not collected from patients who were enrolled in the control arm or the standard treatment arm The synthetic control arm method uses RWD from health data sources such as daily medical care to generate and compare data with modeling methods; data sources, including electronic medical records; administrative claims Data); data generated from fitness tracker or home medical device; disease registration; and previous clinical trial data In this way, the benefits to the pharmaceutical industry are obvious Reducing the number of patients in the control group, or no longer need to enter the control group, so that the use of synthetic control arm method can improve efficiency, reduce delay, reduce the cost of clinical trials, and accelerate the speed of life-saving drugs on the market Suppose a clinical trial involves 500 participants in the treatment group to prove the effectiveness of a new treatment If the synthetic control arm method is used, only 500 participants need to be recruited; there is no need to recruit 1000 patients (if the synthetic control arm method is not used, usually 500 patients in the treatment arm and 500 patients in the control arm) This hybrid trial design provides the sponsor with the least risk method of introducing RWD elements into clinical trials for drug registration At the same time, through more accurate information, according to this information, we can make a decision whether to continue to promote or stop R & D decisively, so as to reduce the risk of late R & D failure One of the main reasons why patients choose not to participate in clinical trials is that they are worried about being assigned to the placebo-controlled group If the prognosis of an individual is poor, or the effectiveness of the current standard of care is limited, this concern will increase Using a synthetic control arm instead of a standard control arm ensures that all participants receive active treatment, thus eliminating concerns about treatment inclusion The method of synthetic control arm can solve the important problems of clinical trial participants and eliminate the main obstacles of patient recruitment If patients use the disease support social network and use the synthetic control arm in the clinical trial, patients can post the details and progress of treatment through social network, and communicate the side effects that may damage the integrity of the clinical trial, which will help to eliminate the risk of mutual Unblinding between patients Although synthetic control arm may be a new concept for many professionals or patients, it has been successfully used in regulatory decision-making For example, Roche Applied the clinical trial of synthetic control arm to meet the coverage requirements of alecensa (alectinib) in 20 European markets In December 2015, alecensa obtained FDA accelerated approval for the treatment of specific forms of lung cancer and conditional approval from EMA in February 2017 In order to make a pricing decision, EU authorities asked Roche to provide other evidence to prove the effectiveness of alecensa for standard treatment (ceritinib) Roche did not wait for the results of phase 3 clinical trials, but used a comprehensive control group of 67 patients to provide the necessary evidence of relative performance The use of synthetic control arms has brought alecensa 18 months ahead of schedule to cover 20 European countries Another example is Amgen, which uses a synthetic control arm to give blincyto accelerated approval for the treatment of rare leukaemia So far, the application of synthetic control arm is limited In many cases, the charts need to be checked manually to generate the necessary data; however, there is a clear regulatory precedent for the use of synthetic control arms Flatiron health helped create a way to use RWD in clinical research Dr Amy Abernethy, former chief medical officer of flatiron health, served as the executive deputy director and chief information officer of FDA, reflecting FDA's interest and expectation in using RWD It is flatiron that designed the synthetic control arm method for Roche and provided evidence for the EU's underwriting decision on alecensa With Dr Abernethy's expertise and experience, it is expected to generate evidence for regulatory use from real world data (including the use of synthetic control arms) Therefore, the industry should hope that this special hybrid design can achieve regulatory expectations to a certain extent Even though the use of RWD is a strategic focus of FDA, the synthetic control arm is still unable to completely replace the control arm The synthetic control arm requires that the related diseases are predictable (such as idiopathic pulmonary fibrosis), and the treatment standard must be clear and stable; however, not every disease can meet such requirements It is also important to consider that even if information can be obtained from RWD sources, there may be difficulties in data extraction or poor data quality Daily captured healthcare data (e.g electronic medical records) are often isolated, scattered and unstructured, often incomplete and difficult to access New tools and methods are needed to integrate, organize, and structure rwds, generate evidence that can be used for research, and ensure that confounding variables are considered in the analysis In order to extract relevant information from structured and unstructured data, analytical techniques such as natural language processing and machine learning are needed Despite these challenges, in drug development, the synthetic control arm method still has a significant and beneficial effect, which can generate low-risk / high return benefits under suitable circumstances In the phase 2 clinical trials, using the synthetic control arm as the comparator for internal decision-making may be the most concise entry point For the sponsor and drug developers, they are familiar with the synthetic control arm before they intend to use it in key clinical trials Such a way is not only a business minded but also an attractive way For those who are prepared to include the synthetic control arm in key clinical trials, focusing on rare diseases with a small number of patients or treatment areas that are relatively easy to obtain information from RWD sources can help speed up implementation and remove barriers to approval For the pharmaceutical industry that intends to start using RWD, the synthetic control arm is a good method Compared with the traditional randomized controlled trials, the synthetic control arm method is expected to save a lot of time and cost, and solve the patients' concerns about being randomized into the placebo control arm Original title: viewpoint | real world evidence use case: improve clinical trial efficiency and reduce costs, try this method? 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