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The trials and tribulations of rare opportunities
Those hoping to follow where Humira, Soliris and Fabrazyme led should be prepared for clinical development to be complex
Rare disease R&D is becoming common, but drug firms hoping to follow where Humira, Soliris and Fabrazyme led should be prepared for clinical development to be long, complex and challenging, say experts.
Pharmaceutical industry interest in developing treatments for rare diseases is increasing, for understandable reasons.
Measures introduced to encourage the development of rare disease drugs are attractive. Depending on the market, an ‘orphan’ designation entitles developers to waived fees, tax breaks for trials and enhanced IP protection.
Rare disease drugs can also generate high revenues, said Garth Ringheim, Therapeutic Area Director, Clarivate Analytics.
“Revenue for Fabrazyme for treating Fabry disease, which affects 5,000 people worldwide, was $891m. Revenue projection in the year 2025 is $1.259bn,” he continued.
Ringheim cited Celgene’s beta thalassemia candidate Sotatercept, which is expected to achieve sales of $458m by 2023, as a further example.
Such products’ potential is enhanced if they achieve orphan designation for a range of conditions. Humira, the world’s best-selling drug, has multiple orphan designations. Likewise Soliris (eculizomab) – which generated revenue of $3.56bn last year – is an orphan drug for five diseases.
A rare challenge
In addition to their high revenue potential, rare disease drugs share another thing in common: the studies on which their approval is based were more complex than those involving common illnesses.
Preclinical trials of rare disease drugs are challenging, said Jeffrey Keefer, MD VP, Medical Strategy Head of Pediatric and Rare Disease at IQVIA.
“Preclinical studies for rare disease therapies are similar to therapies of the same modality of more common diseases. However, for many rare diseases, the ability to develop effective therapies has been hampered by a lack of understanding of the causative factors of the disease and the ability to develop disease models.”
Fortunately for developers, new genomic techniques are starting to make things easier, Keefer continued.
“Recent scientific advances, including genomics and elucidation of disease mechanisms, have led to significantly greater understanding of the underlying cause of many rare diseases. In turn, this has allowed the generation of models for a variety of rare diseases, particularly monogenic diseases where tools such as CRISPR/ Cas9 have enabled the creation of cell- and animal-based models.”
Such models are particularly important for rare disease treatments where greater emphasis is placed on testing mechanism of action and therapeutic impact.
And development looks set to continue. Keefer said: “There is still a wide array of rare diseases with complex disease mechanisms which we do not understand and do not have good models of, but based on recent trends we are hopeful that significant advances will be made in the near future.”
Fewer patients
At the clinical stage there are also additional challenges when a rare disease is involved. One obvious complication is that there are fewer potential trial participants, said Keefer.
“The lack of patients with a given rare disease makes it difficult to conduct a clinical study that these geographically diverse patients will easily be able to participate in.
“In addition, patients will often struggle to find a physician who can recognise and diagnose their condition. The average diagnosis time for a rare disease patient is typically somewhere between 2-5 years. The reality is patients may not get access to trials because they are simply not being diagnosed.”
Jack Brownrigg, Medical Affairs, Rare Disease, Pfizer UK, told us clinical trials with fewer potential participants required more nuanced trial designs.
“These small, geographically diverse patient populations can make finding patients to participate in rare disease clinical trials more challenging and may require running studies in multiple centres, often in multiple countries to achieve enrolment requirements. Patient eligibility criteria can also impact patient enrolment.
“When studying potential disease-modifying therapies, there is often a desire from study sponsors to enroll patients in the early stages of the disease, where medicines may have their greatest benefit. Unfortunately, patients with rare diseases often experience a delay in diagnosis, meaning the number of eligible patients could be further restricted.”
Patient groups
So how do developers find rare disease sufferers who are willing to take part in studies? One approach is to interact with support groups, said Keefer.
“Ideally, the sponsor would connect with patients at the earliest stages of clinical drug development. The most successful programmes are those where patients have had an input on endpoint, protocol and study design throughout the clinical development process.
“This allows the patient community to be actively engaged with the research and the programme, and results in a protocol that patients are willing and able to participate in.”
Patient-centricity is also core to Pfizer’s rare disease recruitment strategy, said Brownrigg.
“At Pfizer, we employ innovative clinical trial designs and participant recruitment strategies to help overcome the challenge of small, geographically dispersed patient populations.”
The key is taking patient needs into account. Pfizer’s approach is to ask clinical teams to gain patient perspective to inform trial design. Brownrigg continued: “To get this insight we consult with national patient groups to better understand the patient journey and share relevant resources.
“By partnering with local advocacy groups, we are also able to broadly drive awareness and education on clinical trials and how they can help to provide new treatment options for patients in need.”
Data mining
In clinical trials focused on common diseases, recruiters often use diagnostic data to find people. In rare disease studies, similar approaches are possible, although the process is more complex, Keefer said.
“In some cases, healthcare data including diagnostic, claims or procedure data can be used to find patients but often in rare diseases, the ability to use data is limited because of a lack of specific diagnostic codes and a lack of standard medications used by patients.
“In other instances where data is available for a large number of patient journeys with the rare disease of interest, predictive analytics can be used to find undiagnosed patients and connect them with studies and other treatment options.”
Endpoints
The US FDA outlined the challenges involved in trialling rare disease drugs in a guidance document in February.
According to the agency, developers are often unsure of the amount of data required to prove efficacy, which is why an orphan designation allows for increased discussion about issues surrounding trial size.
And drug developers should take every opportunity to understand how best to illustrate efficacy, according to Keefer.
“For any disease, the drug must show efficacy, and the number of patients needed will depend upon the size of the effect. For a rare disease which is fatal and where there is no existing treatment, a drug that shows efficacy is likely to be a strong candidate for an early licence,” he said.
For such products, regulators have been willing to grant approval based on limited data. Strimvelis, a gene therapy for severe combined immunodeficiency due to adenosine deaminase deficiency, was licensed based on data from 12 patients in a single arm trial and a further five providing supporting evidence.
Natural history
The FDA also stressed the need to gather as much information as possible about a rare disease’s natural history – how it manifests and progresses – when planning a clinical trial.
The rationale is sound, according to Brownrigg. “For a rare disease with a well-understood natural history or when there is a large potential treatment effect, it may be appropriate to conduct a trial with an innovative design that can increase the power of a study within the constraints of a small study population.”
Examples of innovative designs include single- arm uncontrolled studies, cross-over studies where participants serve as their own controls, or adaptive trials where the design can be modified based on data obtained.
Brownrigg added: “Sponsors may also select outcome measures that use a continuous variable, a surrogate marker or composite endpoints which can all reduce sample size requirements.”
Innovative trial design is also the focus of regulators’ efforts to make rare disease studies more straightforward on the other side of the Atlantic.
The European Union has funded three research projects – called Asterix, IDeAl and InSPiRe – which are investigating different approaches for new statistical methodology for design and analysis of small population clinical trials.
Manufacturing success
In some respects, making supplies for a rare disease study may be more straightforward than one for a common disease, according to Keefer, who said the smaller batch sizes involved provide a few advantages.
“The manufacturer could develop the final product as early as possible, preferably prior to the initiation of the clinical programme,” he said, adding that scale-up and comparability studies can also be eliminated.
In other respects, making supplies for a rare disease study can be more difficult, continued Brownrigg.
“Challenges in the manufacture and distribution of investigational rare disease medicines are unique to the specific therapy, but may be compounded by the large number of sites often required in rare disease trials.”
Manufacturing and distributing gene therapies is challenging. Brownrigg said that this is likely to be an issue that impacts a disproportionate number of rare disease trials.
“Given that 80% of 7,000 or so rare diseases have a genetic component, gene therapies have the potential to provide meaningful improvements in the lives of patients with rare diseases. The complexity of manufacture and distribution are high for gene therapies, and production is difficult to scale.
Brownrigg added: “Generating sufficient vector quantities is perhaps the biggest obstacle, and the pharmaceutical industry is making substantial investment in this area to address this challenge.
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