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Challenges and strategies for rare diseases trials in India

Krutikesh Age, Co-founder, DPHS, sheds light on the challenges that Indian pharma companies and research organisations face while conducting clinical trials for rare diseases, and the strategies to deal with these

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Every year, about 7,000 new rare diseases are discovered, affecting over 400 million people worldwide. The number of approved studies still stands at only around 500. It is a milestone for patients with rare diseases to receive a diagnosis; however, the lack of proven treatment options often leaves their prognosis unchanged. Clinical trials for rare diseases generally present unique challenges, with no precedents or a doubtful commercial outlook. Despite these challenges, the pharma industry is making its way through research and development. Many drug companies place a high priority on these patients, but progress is slow.

India currently conducts very few clinical trials for rare diseases. Despite its top 30 ranking, India is underrepresented in clinical research due to its large population of rare disease patients. Compared to prevalent chronic diseases such as diabetes and hypertension, less than three per cent of rare disease clinical trials include investigational sites in India. Clinical trial sponsors now have the opportunity to recruit patients outside of the usual hotspots.

Challenges

Longer time to recruit: Due to the difficulty in diagnosing rare diseases, clinical trials for these patients enroll at a slower rate than for others. Clinical sites enroll an average of 0.7 patients per
month for non-oncology rare diseases, which is three times slower than the average across all non-oncology diseases (approximately two patients/site/month). Due to this, rare disease trials typically enroll for longer periods of time, adding to the cost burden of R&D.

Increasing study costs: The investigational site remains the focal point of traditional clinical research. Clinical trials with rare diseases require either a greater number of clinical sites or fewer participants in order to compensate for the sparse number of eligible patients. As the number of sites increases, each study becomes more complex, burdensome and costly, while smaller sample sizes limit the statistical strength of the results. Due to the high R&D costs associated with rare diseases, drug developers prioritise keeping R&D costs down, assisted by greater regulatory flexibility.

Lack of awareness: Clinical trials are commonly associated with medicine or new developments in medicine, but people are not well acquainted with how trials are conducted or the safeguards involved. There is a lack of knowledge regarding the different types of trials and phases of trials among participants/patients. It is believed that clinical trials are beneficial to the community as well as the advancement of science. A majority of participants/patients, however, are not willing to participate in trials for the fear of adverse effects.

Designing the study and approvals: For establishing efficacy in a research setting, randomised controlled trials are frequently considered the gold standard. In this manner, we minimise selection bias and make sure that known and unknown confounding factors are evenly distributed among the groups. When combined, randomisation and blinding can limit the bias of investigators and participants in outcomes assessment. The classic design of a clinical trial, which is commonly used for common diseases, requires large sample sizes, is time-consuming, and requires a high level of complexity that is less feasible when treating rare diseases. When a disease is well understood and has a homogeneous clinical course and where the expected effect size is high, a small, uncontrolled study may be the most appropriate option. The neuronal ceroid lipofuscinoses exhibit considerable variation, even when they are single-gene disorders; in fact, children affected by the same mutation and similar modifications are often found in the same families.

By addressing concerns regarding clinical variability, the use of controls strengthens trial design. Placebo-controlled trials, which are commonly used in rare diseases, may raise ethical concerns in these conditions.

In rare diseases, small samples are required for clinical trials. Combined with high interindividual variability in clinical course observed in many rare diseases, this reduces the power of a study. The best way to maximise the data from a small, heterogeneous group of subjects is to develop alternative trial designs and statistical techniques. There have been precedents for approval of orphan drugs based on pivotal studies that are not randomised, placebo-controlled, or double-blinded, and their trial sizes are smaller than those for drugs without an orphan designation. Such approaches should maximise knowledge gain from each study, or introduce efficiency in sample size through crossover, n-of-1, and adaptive design approaches. When a compound fails, researchers should ensure that the failure is not due to a poor study design, but rather a genuine lack of biological effects.

Patient retention: It is difficult to recruit patients for any rare disease in a timely and adequate quate manner. Sometimes, disease-modifying agents are sought in patients with early disease or, alternatively, those with advanced disease that is high-risk for intervention. The presence of few patients, however, may not allow a significant narrowing of entry criteria based on disease stage or other factors. In order to recruit participants, investigators need to collaborate on more than one institution or even across borders. Research participation may be impossible for patients suffering from diseases with significant physical impairments. Off-label use can also threaten recruitment and retention in trials involving repurposed drugs.

Recommendation for strategic directions
Pharma intelligence provides sponsors of rare disease clinical research with a series of strategic recommendations based on rare disease benchmarking and patient perspectives. In order
to speed up patient recruitment, these measures include gathering insights from clinical experts, increasing awareness among healthcare professionals, and leveraging advocacy groups. As a result, sponsors will be able to gain a competitive edge in the development of clinical trials for rare diseases.
Connecting with sub centres and getting retrospective data: Due to the fact that rare diseases affect a few people, finding enough patients who fit inclusion and exclusion criteria can be a challenge. Moreover, rare disease patient populations can be diverse in terms of disease subtype, symptoms, stage and previous treatment exposure.

In order to overcome this challenge, a CRO can partner with a group/sub centres that already collect health records/Electronic Health Records (EHRs) to model various recruitment scenarios using real-time data from patients and physicians.
Patient-centric study design: Industry survey indicates that physicians play an important role in rare disease clinical research. Among the most common routes to study participation are physician referrals, while physicians are also good sources of trial awareness and information. Additionally, physicians can play a crucial role in the design process, since they have a much better understanding of the disease and the unmet needs of patients. Study design is influenced by a number of traditional inputs, including patient availability, treatment practices and clinical outcomes, all of which are comparatively lacking for rare diseases.
Data collection process: Technology is required for researchers and clinicians to capture and integrate data from a variety of sources, such as sensor and app data, images, omics, EHR systems and real-world data sources, including critical biomarker measurements. There must be an automated and always-available data management workflow across randomisation, drug supply, coding and safety. It is imperative to expedite treatment of rare diseases since they are often associated with a shortened lifespan. Machine learning algorithms can be used to analyse quality omic data quicker, improve efficacy and safety, and speed up the process of making drugs available to consumers.

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1 Comment
  1. soundos says

    Nice and helpful information shared. . Good Work. keep it up.

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