Michael Christel is Pharmaceutical Executive and Applied Clinical Trials' Managing Editor. He can be reached at mchristel@mmhgroup.com.

Michael Christel

	Behind the momentum of precision medicine programs such as 

, coupled with the overall shift taking place toward ultra-targeted treatments-gene therapy, immunotherapy, biologics-the rare disease space seems to be joining the rarefied air of more common disease segments in biopharma business pursuits.      

	After all, by 2020, the rare and orphan drug market is 

 to grab 20.2% of worldwide prescription drug sales (excluding generics) and total $178 billion in annual revenue. In the US, the FDA granted nearly double the number of orphan drug designations in 2015 compared to 2010, and through September, 

 had been approved this year. The demographics are well documented: rare diseases affect fewer than 200,000 patients, but, in aggregate, 

 worldwide-more than the total for cancer and AIDS combined. Children are especially impacted; about 35% of deaths in the first year of life are reportedly caused by these conditions.

	But despite greater financial and regulatory incentives to develop drugs for rare diseases, plenty of caution remains. The low prevalence of these conditions still results in various challenges in development, pricing, and reimbursement. And with treatments usually requiring large per-patient payments, this area won’t be immune to the pricing scrutiny that continues to swarm the industry.

	Juliet Moritz, executive director of strategic development in rare diseases for Premier Research, a CRO, has watched all these trends unfold with a close eye. 

Involved in clinical research for 28 years, with experience in single-site studies as well as large, multinational trials, Moritz is particularly passionate about informed consent and the role of patient advocacy involvement in clinical research. 

 recently chatted with Moritz, where she discussed the challenges and opportunities unique to this specialty market-particularly related to growing focus areas such as patient engagement.        

 What approaches are getting the most attention right now in rare disease research? 

(Note: this interview took place prior to FDA's controversial 

. But, in general, we’re seeing a lot more focus on genetic conditions (80% of the estimated 7,000 rare diseases involve a 

). Now that the genome has been characterized, there’s a lot of work going on that’s relating a particular disease back to the genetics-the genotype and the phenotype. That brings us closer. Once we understand what the mutation is, we can then try to understand what’s going wrong. Is it a protein being misshapen or is it a protein not being produced at all? We’re finding conditions that are considered one condition but might have 10 or 12 different mutations that result in the same presentation of the disease.

	We’re also seeing enzyme replacement therapy-for patients in which their disease results in the deficit of an enzyme or the creation of an enzyme that can’t cross the membrane. We’re seeing a lot more therapies that can address that-either replace the enzyme or chaperone the protein across.

 What are some promising opportunities in patient recruitment for rare disease trials?

 To me, what’s very exciting is the fact that the regulatory agencies are really now paying a lot of attention to patient engagement. We’re seeing them go back to the pharma and biotech companies and specifically asking them, “what did you do in terms of patient engagement during your clinical development plan? Share that information with us.” We’ve seen some legislative changes as well.

	It’s evolved, from when it used to be just putting a link about a study on a patient group’s website. Now it’s engaging in the protocol development, talking about meaningful endpoints and outcome measures. So that the protocols hopefully, over time, begin to reflect the day-to-day course of the disease and how it affects the patient, and in rare disease, often the family and caregivers.

 Do you think the targeted patient engagement approach has been more effective amid advances in digital information tools?

 I think so. There are companies in our space-niche vendors-that are trying to leverage technologies by creating online communities for these patients. I’ve been in the business 28 years. Twenty-eight years ago, we didn’t have the Internet, we didn’t have Facebook, we didn’t have a place where a parent of a newly diagnosed child could go online and learn what resources are available. Now, if you look at almost every patient advocacy group, they’re talking about research in their disease state. They’re talking about clinical trials, and, in general, why people would want to participate in clinical trials. And depending on the therapeutic area and the target indication, they might even point people to specific trials.

	The technology has been a significant boon. It doesn’t completely eliminate the challenges in patient recruitment. It’s still challenging because in some of these conditions you only have 1,000 or even just 2,000 people in the world that have them. You have to connect them to the places where the research is happening. But now it’s less of a “let me reach out and try to find somebody.” It’s more of a combination-we’re reaching out while they’re also reaching out to try to find opportunities to participate in research.

 Are sponsors and CROs, in turn, more sensitive these days to the unique burden rare disease patients experience?

 That’s a big part of protocol development-reaching out to the advocacy groups and evaluating the protocol and determining, well, this might answer all of the scientific questions, but is it so onerous that we’re not going to be able to keep anybody in through the end?

	As a CRO, we have to think what can we do to mitigate that burden so that we can enhance recruitment, but, more importantly, retention. Once someone’s in the study, we need to make sure we’re getting them through so that their effort turns out to be meaningful. It’s incumbent on us to make sure that the protocol is scientifically rigorous but also operationally executable, and not just thinking about what makes sense for us and the sponsor but for the patients and their families.

 How do you go about including those considerations in the protocol?  

 You build in as much as you can. Sometimes you only have a little bit of leeway depending on what the regulatory agencies want to see out of the protocol. But then you work around that to say, “alright, what can we do to support these patients and their families as they’re participating in this experience?” Is it

 more timely reimbursement of expenses? is it working with the sites to make sure they can schedule these folks first thing in the morning if they’ve got a three-hour train ride, for example.

	There’s no magic bullet, there’s no one answer. That’s the nice thing about having been involved in rare disease research for so long. You build a tool box basically of strategies and things that you could do. Not all of them are appropriate for every study, but having that depth of experience allows you to make more informed decisions on what might work.

 Is the use of real-world data beneficial for trials in rare diseases? Is there enough data out there that could help?

 It’s not as comfortable a transition as in more prevalent disease research, that’s for sure. I think where big data might come into play is implementing strategies to try and identify lags in diagnoses. One of the challenges for a lot of people with rare diseases is that it could take quite a while to get a diagnosis. Are there other ways to characterize a patient’s profile as they’re going through their diagnostic arc? Is there any way we can shorten that up based on symptom and test information in medical records?

	But right now I don’t see a lot of that happening in rare disease research where it translates into impacts in clinical trials. 

 What important caution would you advise companies interested in pursuing rare disease research?

 The regulatory pathways are compressed. You have a lot of what I call the slash studies: Phase I/II, Phase II/III. You have smaller patient populations; every single patient counts-and beyond that, every single data point counts. So the operational challenges are a little different. It’s really important to understand and be able to anticipate that. Because otherwise you can take a small company that’s got very limited resources, and if they go down the wrong path, they may not have an opportunity to reinvestigate that potential therapeutic.

 What about the economic challenges that still exist in developing drugs for rare disease?

: On average, the cost per patient to study a drug in rare disease is often higher, largely because of operational issues. The trials are often spread out and have a lot of assessments-and when you’re bringing a patient to a clinic, you’re often bringing their entire family. It’s difficult to find control groups for some of these ultra-rare populations. So you have to be a little creative. Maybe you’re doing crossover studies.

	It’s not as straightforward as Phase I, healthy volunteers; Phase II; Phase III; okay, we’re going to market. It’s not the same at all. It requires whomever you’re working with to be nimble and know the regulatory environment.

Articles

With pursuits in rare and orphan disease beginning to move more into the drug development mainstream, the opportunities and challenges in patient engagement and trial execution for these conditions are ever-evolving, as one strategy expert in the field discusses.

Rare Disease Research: It Pays to Engage

	The data explosion in clinical research is very real-there is no denying that. Industry attention and focus in this area-as a legitimate force for change-is so high these days that it can be dizzying to keep track of it all (we managed to catch our balance to highlight some of the latest thinking in our 

 on eSource and data integration). Today, a biopharma conference, or an industry webcast, or even a regulatory forum, more times than not probably features a headlining theme on new data technology trends and strategies in drug development. Big data, real-world data, electronic health records, wearables, advanced analytics, remote patient monitoring-you name it.   

	These are indeed exciting times to dream big on how these new data tools and process improvements can potentially revolutionize clinical trials. And by extension-critical to changes now taking place on the healthcare level-help usher in more personalized, so-called 

	But before we get too mesmerized by the spectacle, a prudent and pragmatic lens on things is also warranted. R&D aficionados and those plugged into the day-to-day operations of clinical research will likely agree that perhaps the biggest issue in managing this data convergence in the life sciences is connecting reality with the conceptual promise of these “next-generation” information tools and processes.

	“Like so many industries, the drug development enterprise tends to approach the use of technologies with this tremendous exuberance that is so far removed from the realities,” says Ken Getz, the longtime chairman of CISCRP and director of sponsored research at the Tufts Center for the Study of Drug Development. “This exuberance comes without really thinking through the kind of long and painful journey that’s going to be required by all of the workforce within drug development to change legacy processes and systems. Some of that will require cultural change and philosophical change in how we approach development activities.”

	Given those challenges as well as the gaps in data standards and governance that still exist, crafting a practical vision of sorts for how researchers can capitalize on new opportunities in data use will be crucial-whether involving collection, management, reporting, sharing, etc. As part of a panel discussion at this month’s 

 in Philadelphia (entitled “The Future of Clinical Research Data: 2020 and Beyond”), Getz and others will explore such a vision and the steps and mindset necessary to truly harness data to improve the research process of tomorrow. Here are more excerpts from my recent conversation with Getz on the topic. 

 What do you feel are the most compelling trends at the moment in the area of clinical research data?

 I think the biggest trend is the big data movement, and the use of really large datasets on millions of patients. And even the use of real-world data in clinical research and in pharmacovigilance, for example. There are so many sources of structured and unstructured data-data coming from large electronic health record and electronic medical record platforms, from wearable devices, and mobile devices and 

applications. How do you gather and integrate all that data? What gets me most excited is how do you then use that data? Can we use that data to identify targeted patient subpopulations? Can we use that data to really inform us while the study is underway or at different critical points, so that we have a new understanding of data safety or we identify efficacy or safety patterns in the data? How can that data be constantly used to enrich our insight? Not only insight into treatments and their effectiveness, but from a management standpoint-to manage our clinical research more effectively.

	Then there’s a lot of interest in using this data to predict performance. To select a more effective group of investigative site partners, for example. Or to predict patient recruitment and retention rates more effectively. That whole big data movement, it sounds so cliché, but it continues to make great progress and hold tremendous promise. 

What about mHealth, specifically? Can we measure the impact yet on clinical care?         

A lot of companies are piloting the use of select wearable devices and the use of smartphone and mobile applications. The biggest challenge is that some of these technologies are not validated. So some of the biometric data that they are collecting technically can’t be used in a submission. There are issues with some of the technologies, but there’s a growing number of organizations that are finding ways to integrate with really large datasets of patient records. I think we’re going to see a lot of progress in this area over the next 18 months.

How do you view the topic of data transparency in this whole equation? You hear a lot about initiatives in the industry promoting data sharing-whether things like 

 in the precompetitive space to even the 

). What does all the discussion and activity mean right now for R&D practice?

 Transparency-and I would add integration, which is central to that-are critical both within the enterprise at an operating level and obviously essential to partnering with patients and healthcare providers and payers. On the drug development side, we see that so much of this data and the technologies that are being used are siloed. They’re not integrated-they have really poor transparency. That’s requiring a lot of manpower to manage; it’s highly inefficient. Transparency and integration can play a huge role in breaking down those silos from a drug development operating standpoint.

	With patients and healthcare providers and payers, there are huge ethical issues around use of patient data and making sure that it’s entirely transparent. That’s very essential to building trust with the patient community and with healthcare providers and payers. It’s also essential to ultimately best serving patients’ needs by creating what the FDA has called a learning environment, where clinical practice and clinical research are communicating with each other in real time and in an integrated way so that constant feedback and insight is coming from both domains. Integration and transparency are areas that we really have to adhere to the highest principles and ensure that we’re honoring and we’re meeting the obligation to provide unprecedented levels of transparency and disclosure.            

 Any thoughts on efforts in the U.S. and 

 to promote data transparency and require public disclosure of clinical trial results?  

With a lot of these technologies, the patient isn’t even aware that data is being collected. Many of these applications now collect a

 time and geographical stamp on a person while they’re carrying out their day-to day activity. Some of that information, if patients were fully aware of how it’s being used, many would be concerned-or at least should know and give their consent to having a lot of that information gathered. We have a lot of work to do to improve awareness of the kind of transparency that we’re looking for to build meaningful datasets and ultimately best serve the patient community. 

 Have you heard direct feedback from patient groups expressing these concerns?

 We have. Patients generally are extremely willing to share their data when they understand how it’s going to be used-and how the data has been used has been honestly and comprehensively explained to them. They’re quite eager to share their data and they’re quite trusting when that transparency and disclosure is presented completely openly. I think part of the issue is just building into the process the steps that we always need to take to partner fully and adequately with the patient community.   

In coming up with a future vision for research data, what are some logical steps needed to take these new opportunities from concept to reality in advancing the whole trial process?

The reality is that the use of most technologies is remarkably fragmented, it’s poorly integrated, we use so many disparate systems, and we use them inconsistently. All of these approaches ultimately are hurting productivity. They’re increasing inefficiency, they’re actually increasing our cycle times, and there’s just so much redundant activity. For example, the adoption and use of eSource is relatively low. We see so many professionals that have to do multiple data entry to move and transcribe from one system to the next.

	Often, we throw new technologies and new systems that hold great conceptual promise into this stew of these kinds of operating conditions. As a result, there’s that wide gap between the reality and the conceptual promise. At the DIA forum, it’s our hope that we’ll be able to connect some of these dots. We want to discuss the conceptual promise but also suggest a reasonable and feasible roadmap to get us there.

	The Tufts Center and CenterWatch have gathered a lot of data in this area-just showing how siloed and fragmented and inconsistent is the use of various technology solutions. Those individuals involved with executing activities and using these solutions are often quite frustrated.

 You mention eSource. What challenges remain in the adoption of data standards governing the collection and analysis of clinical trial data electronically?

The Tufts Center, in collaboration with CDISC, did a study about nine months ago and we showed that the adoption of data interchange standards continued to grow steadily. Not only the adoption of some of the most mature and traditional standards, but also the use of some of the newer standards, including those that are tied to the sharing of data between compatible systems. But what we also saw is the lack of integration, the lack of interoperability, are hindering adoption. So you have a lot of professionals who are really committed to embracing the CDISC standards but they face a lot of headwinds in the process. Some of those headwinds we want to continue to explore.

	Some integration will come when it has regulatory winds behind it or when there’s such great necessity that it forces the sort of siloed functions to interact in a more integrated way. We haven’t yet reached that point.

Today’s convergence of health data and technology is fun to watch unfold, but a focused vision on system and process integration-and the framework for continuous enrichment-will be key to future lasting gains for clinical research.

Q&A: Research Data Promise vs. Reality

 yesterday that DNA testing company Ambry Genetics is launching a free online database that unlocks the human genome information of 10,000 of its customers. 

 will initially include data focused on breast and ovarian cancer, but the company said it plans to release thousands of genomes every month (covering all the diseases it tests for), potentially making available data from almost 200,000 individuals annually.

	The purpose of the move, according to Ambry, is to help advance the relatively new field of precision medicine. It’s in line with President Obama’s 

, which includes the development of a voluntary national research cohort of at least a million people. Ambry’s move also reinforces its belief that human genes should not be patented or owned-a 

 that has generated much fervor on both sides for decades and was in the spotlight in recent years.  

 caught up with Ambry founder and CEO Charles Dunlop briefly via email to discuss his company’s decision and the larger implications for the medical research community.

ACT: What was the chief motivation to release this data into the public domain? Is it simply a matter of the access need?    

The decision to release our data is a matter of doing the right thing to advance research and save lives. For too long, vital genomic data have been hoarded by commercial and academic organizations, and that practice has stagnated medical progress. It has caused real human suffering and it needs to stop.

	I am a Stage IV cancer survivor, and at a critical point in my recovery, I realized that genomic data are just too valuable for anyone to hoard and profit from. By sharing the data and collaborating with the research community, we can speed up the discovery of new treatments and cures to save more lives, sooner.

ACT: How would you encourage other organizations, whose business model may involve selling genetic information, to share such data in the same manner? 

Anyone collecting genomic data should free that data. It’s just the right choice because everyone benefits if medical research can progress. I would like to see more data sharing across the industry, and would support efforts to make it easier for researchers to share.

ACT: There are other genetic databases accessible to the public. How is AmbryShare different, in terms of the potential value of the information? 

 AmbryShare is the largest free, disease-specific public database of human genome sequencing data. The data released are anonymized, aggregated data of 10,000 human genomes focused on hereditary breast and ovarian cancer. These are allele frequency data, meaning that they allow us to see the occurrence of gene 

	Based on the sheer volume of the data we have released, we have already identified 10-fold more genes implicated in breast and ovarian cancer than was publicly known, broadening understanding and potentially opening up more drug targets than from any single data release in history.  

	Ambry Genetics is committed to continuing this effort to sequence genomes and release the data for all of its consented and de-identified patient samples, to gain these kinds of genetics insights for other cancers and other disease areas.

ACT: With the possible momentum for more public sharing of genomic data such as yours, are there unique privacy concerns that organizations should be aware of? 

 Patient privacy is very important to us, which this data release is aggregated exome data from consented and de-identified patient samples. We also secured approval from an independent research board to conduct research exome sequencing on these patient samples.

Ambry Genetics' CEO discusses his company's decision to release the genetic information of its customers for free - a rare move, reportedly, for a commercial testing firm.

Company: Genomic Data Too Valuable to Hoard

	When tracking the more recent evolution of clinical trial outsourcing in the biopharmaceutical industry, it has usually been the high-profile partnerships and alliances struck between large drugmakers and contract research organizations (CROs) that have received the most attention and coverage. What has been less readily known are the unique outsourcing needs of the small or so-called emerging biopharma company and how those differ from the larger sponsor organizations. The former is characterized by very strategic outsourcing decision-making, while, for the latter, decisions to outsource are often considered more tactical in nature.

	“It's challenging for a virtual or a startup sponsor to get the kind of attention that it needs,” Keith Wenzel, Senior Director of Perceptive Partner Program (part of CRO Parexel), tells 

. “They often only have one program, and if that program fails, they are all out of business.”

	Such realities have spurred increased collaboration to develop more personalized outsourcing approaches to aid fledgling drug sponsors. At the recent DIA 51st Annual Meeting, Wenzel, along with Solomon Babani, Global VP of Alliance Management for Covance, and R&D executives from two small biopharmas, presented a conceptual partnership model built on the idea of delivering value from alliances between networks of biopharma companies and CROs-involving clinical and technology functions.

	“How can you bring a bunch of small, emerging biopharmas into a network and have them agree on standards and agree on technology?” Wenzel told us in an interview. “It’s turning the whole thing around and having a group of sponsors collaborating together so they could possibly get the attention of larger CROs. The large CRO would know that working with this group that it’s going to be able to work within a standardized manner, because they might have agreed on contracts, on forms, on SOPs, on technology, etc. Therefore, the CRO can say, 'Ok, I understand how I’m going to collaborate with these guys in the same way a large pharma and a large CRO agree on standards.'”

	The small/emerging biopharma segment is generally classified as those companies who spend $300 million or less on R&D annually. According to statistics cited by Wenzel during his DIA session, emerging biopharmas are responsible for about one-third of the global drug development outsourcing market, one estimated to total more than $25 billion. Wenzel, who is part of Parexel’s informatics group, which, through the Perceptive Partner Program, partners with technology companies on behalf of sponsors and CROs, notes that eClinical technology is one of the fastest growth areas in outsourcing today (the global eClinical solutions market is projected to reach almost $5 billion annually in 2018). Clinical data management systems (CDMS) and electronic data capture (EDC) tools make up the largest outsourcing segment in this space, followed by clinical trial management systems (CTMS), according to figures presented by Wenzel. He believes applying the principles of alliance management can help small and emerging sponsors capitalize on the eClinical growth.    

	“When you think about the technology side of this, a small sponsor ramps up for a trial and then they stop, and they ramp up again and then they stop. They might not be able to invest in a CTMS system based off of that ebb and flow,” says Wenzel, who has a vast background in information systems, including working as product director at ClinPhone before Parexel acquired the company in 2008. “But if a group of sponsors committed to a CTMS system and they agreed on how they were going to use it and pay for it when they need it, they wouldn’t have this huge infrastructure investment. For example, maybe the CRO brings the CTMS system to the network, and it’s a standard implementation-any of them can use it; they can turn it on and turn it off depending on the needs of their particular trial.”

	The proposed model discussed at the DIA meeting involves leveraging eClinical technology with CRO services, where members of the network can have combined purchasing power and can collaborate on collective and precompetitive innovation. 

	“You can see the advantage of greater purchasing power because the network would have more power,” says Wenzel. “But we really think it’s the combination of the purchasing power plus the standards that will get you there-and it’s good for both sides, not just for one.”    

	Wenzel believes the model also aligns well with the three pillars widely considered the foundation blocks of a strong outsourcing relationship: people, technology, and therapeutic expertise. He points out that small and emerging sponsors often have unique needs in these core areas, and usually have very defined questions when it comes to executing their R&D programs. Some of those questions include how does the sponsor identify issues early, so adjustments can be made before time and money are wasted; how does the company demonstrate that its drug will be commercially attractive; and what core competencies should exist/be kept in-house. 

	 “A major pharma can make a five-year commitment to a large CRO,” says Wenzel. “But these smaller companies are saying, ‘I want that same level of service, but I don’t quite have the same purchasing power.’ That’s where the network of sponsors comes from. They may not have established SOPs, or they may not have a mature certain set of scientific expertise, so they really want the ability to do almost an a la carte. ‘OK, I need this kind of expertise from you, Mr. CRO. How can you package it for me and treat me just the same as you’re going to treat your big clients?’”

	Transforming these concepts of alliance management from idea to reality-the establishment of a bona fide network of small and emerging biopharmas-naturally brings to mind several challenges. Wenzel says the biggest overarching concern may be clearly delineating when something is collaborative versus competitive, and where to draw the line between what members can share and what they’re not willing to share. Examples of the latter may include specific IP or scientific expertise or certain partnership models with payers.

	“Coming in the door and saying you must use this technology or this is the way we handle alliance management-kind of take it or leave it-that’s not obviously the right solution, regardless of the size of the relationship,” Wenzel tells us. “It’s a two-way street. It’s not just about the CRO being responsible to the sponsor; the sponsor also has to find a way to work with the CRO.”

	According to Wenzel, the key will be finding the “sweet spots” in the relationship. Could a network of sponsors, pursuing the same targets, collaborate and share clinical trial placebo arms, for example? Alternately, could a CRO, for instance, invest in a natural history study in a particular disease area, and then make the data available to a group of sponsors, so they wouldn’t each have to conduct the study individually?

	“It’s the question around where could they share for advantage and where can’t they share because of competitive information?” says Wenzel.

Proposed partnership model explores the value of establishing alliances between CROs and networks of small and emerging biopharma companies.

Author | Michael Christel