sasCommunity.org Planet

Big pharma has only scratched the surface in terms of utilizing the potential for accelerating R&D though crowdsourcing. The Internet and social media have created a new public health ecosystem that spans across state borders. According to Google, the number of smart phone users globally was 1.75 billion in 2014, and is projected to increase to 2 billion by the end of 2015. Consumers are increasingly using smart phone applications and wearable technology to track their personal health, and are now more willing to share their data with researchers (Click here to read more on: How Wearable Technology Will Change Clinical Trials).

Open source clinical software and electronic data capture solutions are further enabling the crowdsourcing trend to grow. Apple recently announced its ResearchKit open source software framework for researchers where developers would be able to design health and fitness apps that can communicate with each other. This would give researchers fast and easy access to  more clinical data than ever before. Other benefits to crowdsourcing clinical research include:

Reducing cost

Crowd sourcing offers an innovative approach to reducing the cost of conducting clinical trials. Labor markets such as MTurk, for example, offer many convenient features for accessing a large pool of workers for recruitment. The cost of designing and running a study using this platform is reportedly modest compared to hiring staff directly and custom designing the trial setup. However, it is important to note that the goal here is not to replace traditional expert-driven clinical trials but rather enhance data quality and quantity without increasing cost.

Increasing participation in clinical trials

Crowdsourcing facilitates education about clinical trials and can thus significantly improve participation rates. In the States, only about 3% of cancer patients decide to enroll in cancer clinical trials. Recent research shows than educating the public can dramatically increase the number of volunteers and accelerate trial accrual, which ultimately gets questions answered quicker.

Improving disease understanding

Crowd sourcing allows access to large cohorts and real-time data. The SERMO social media for doctors platform has 382,000 members- all of whom are verified physicians with current credentials. A lot of community doctors don’t have experience with clinical trial protocols but strive to remain active in contributing to advancing research. Crowdsourcing, therefore, provides them with the perfect outlet where they can choose their level of participation and the time they’d like to dedicate, and still develop research ideas with peers from across the globe. Ultimately, this fosters innovation and generates new drugs and treatments that better meet patients’ needs.

Break down barriers

Language or cultural barriers can also pose problems when a diverse population cohort is needed in a clinical study. If the content were made available on an open platform- doctors, caregivers and patients could collaborate which would provide more culturally sensitive solutions to their problems.

For example, during a 2007 medical conference, a group of Italian researchers reported that lithium (used for bipolar disease) had shown to delay amyotrophic lateral sclerosis (ALS) progression. Even before their paper was published officially, ALS patients had already used Google translate to share the paper abstract in English. Soon enough, patients collaborated on a group Google spreadsheet, tracking their own personal data, and comparing it to that of the researchers in Italy. About 160 patients tested lithium on themselves, which prompted PatientsLikeMe to build more tools for data structuring. Some of the patients even posted their personal experience on YouTube.

Accelerate research and development

Crowdsourcing challenges providing a financial incentive to participants attract a fresh pool of perspectives which can bring along new solutions outside the clinical research community. This format for data collection is also beneficial as the problem solvers don’t have access to each other’s algorithms, which allows for a better assessment.

A great example of this is the DREAM-Phil Bowen ALS Prediction Prize4Life challenge. Containing nearly nine thousand patients, PRO-ACT was launched as an open access platform for researchers in December 2012. In the Prize4Life crowdsourcing challenge, solvers were asked to use three months of individual patient level clinical trial information to predict that patient’s disease progression over the subsequent nine months. The challenge resulted in 37 unique algorithms from which two winning entries were selected.

Ultimately, the winning algorithms outperformed a baseline model as well as ALS clinicians using the same data. The organizers estimated that using both winning algorithms in future trial designs could reduce the required number of patients by at least 20%. The challenge also identified several potential predictors of disease progression (including uric acid, creatinine and surprisingly, blood pressure), shedding light on ALS research and development.

Drawbacks to crowdsourcing

Crowdsourcing does come with certain disadvantages. For example, in the cases where clinical trial sponsors don’t use existent crowdsourcing platforms (such as Amazon’s M Turk), they need to create a website for crowd participation, which increases clinical trial cost. Some experts also criticize crowdsourcing initiatives, such as contests, for being unethical and rewarding a few individuals while the work of the majority is exploited for free. The two main clinical crowdsourcing drawbacks, however, are the quality of data and the unregulated nature of this new trend.

Noisy data

In crowd sourced clinical studies, the sheer volume of gathered data can cause clutter and make it hard to dig through the unstructured and noisy information. Another reported complaint is that crowdsourcing labor market platforms such as Amazon’s MTurk can’t weed out spammers or poorly performing workers. On the employee side, there is no guarantee of payment for their work, which is why the federal government is reluctant to approve such research studies. In addition, MTurk studies’ quality control and evaluation are said to be dependent on domain experts, which deems it unsuitable for complex studies with sophisticated algorithms. This is also not a viable option for clinical studies that contain sensitive information and thus rely on confidentiality.

Regulatory hurdles

Crowdsourcing is a new, uncharted territory, still unregulated by most governments. However, innovation calls for change and the social trend of sharing personal health and disease information has started to change the traditional circumstances around clinical research and development.

However, a recent customer survey on data ownership shows that 57% of consumers would only share their data on the condition of privacy protection. Over 90% said this is very important to them, and a small percentage were open to sharing their data with the mobile device company that collected it.

The lack of industry standards around crowdsourcing also raises concern about the validity of the collected patient health data. However, experts believe that the mainstream adoption of tech health devices would force the industry to address information governance and impose standards in the near future.

Click here to read the full crowdsourcing paper for free

Future of clinical crowdsourcing

New platforms, products and services leveraging the power of the crowd are constantly emerging on the market. Amateur scientists are looking for crowd funding to start their own clinical studies on platforms such as GoFundMe and IndieGogo. The wide adoption of smart technology and social media are prompting new behavioral boundaries where sharing personal health data is acceptable. Google X is working on a clinical trial wristband that would allow scientists to gather health data from thousands of users.

However, crowdsourcing is still very much a buzzword and many argue that it cannot be fully utilized in clinical research and development until the FDA regulates it. Even if this happens in the near future, experts agree that crowd sourced health research is not meant to replace traditional clinical studies but rather complement and extend them. It will be very interesting to see how it evolves over time as more tools and apps become common.

The trend of open collaboration has led to innovation across multiple industries. For decades, big pharma has been known as conservative and slow to change. Today however, there is a growing movement toward open access and crowdsourcing scientific information to accelerate research and development. Patients are no longer just research subjects or data providers, they are problem solvers participating in a global community of stakeholders. Online physician communities are breaking down competitive barriers in the name of patients.

A recent example comes from SERMO- a global social network for medical crowdsourcing, where members helped save a little girl’s life from a deadly e. coli strain that killed her brother two weeks ago. Doctors from around the world worked seamlessly as a team across borders on this case, recommending testing and treatment, and reducing the risk of infection in her community.

Another great example of the power of crowdsourcing comes from Stanford University, where a group of researchers stumbled upon an unexpected discovery- while examining autopsied brain tissue of multiple sclerosis (MS) patients, they noticed high levels of angiotensin enzymes, responsible for hypertension. That is how they came up with the hypothesis that angiotensin inhibitor (Lisinopril) would decrease MS flare-ups, and when they used it on mice with ML-like nerve damage, it reversed their paralysis.In 2012, the FDA approved Lisinopril as the first investigational new drug developed through crowdsourcing.

Experts say this phenomenon has emerged from three main trends that are changing the health research environment: 1) citizen science (non-expert trained individuals conducting science-related activities); 2) crowdsourcing (using web-based technologies to recruit clinical trial participants); and 3) medicine 2.0 (active participation of individuals in their health care particularly using the Internet and social media).

Among the leading health research crowdsourcing platforms are the personal genetics company 23andMe and the health information sharing website PatientsLikeMe. 23andMe has more than a million customers, 80% of which have allowed their DNA samples to be used in research. On a typical week, the company collects more than two million individual survey responses from its online community, contributing to over 230 research studies on the human genome.

PatientsLikeMe is the largest free network where patients with life-changing conditions can track and share their experiences to help each other, as well as researchers, pharmaceutical companies, regulators, providers and non-profits, develop better solutions to their problems. The network has a community of over 220,000 members who share their personal data on more than 2,000 medical conditions.

In addition, patients have also started to organize their own research studies with the help of health social networks and online communities such as Quantified Self, Genomera, and DIYgenomics . Recently, some 200 crowdsourced studies have been added to the PubMed database .

Crowd Data Mining

According to the Oxford Bioinformatics Journal, many recent studies on the adverse effects of drugs are mining crowd data from search logs, Twitter, online patient forums, FDA reports as well as electronic health records (EHRs).

While traditional reporting of drug adverse effects is slow and does not capture all cases, web search patterns can provide early clues to adverse drug interactions. For example, a recent study explored the possibility of two drugs (paroxetine and pravastatin) interacting adversely and causing hyperglycemia. The researchers analyzed 82 million drug queries from 6 million web searches in 2010. Patients opted to share their IP addresses and search activities through a Microsoft add-on browser application. The results confirmed that patients who took both drugs at the same time were more likely to search for hyperglycemia-related terms compared to those who searched for only one of the drugs.

Another study used Google Trends (online tool which gives statistics on search term use) to observe the seasonality of sleep disordered breathing. Using search terms such as “snoring”, “sleep apnea” and “snoring children”, researchers collected queries from a seven-year time span in both the United States and Australia. Using regression analysis, researchers found that peak searches happened in the early spring and early winter season in both countries.

Active Crowdsourcing

As opposed to crowd data mining, active crowdsourcing uses community challenges, labor markets, open forums or games to encourage participation in pharmaceutical clinical trials. Labor market platforms such as the Amazon Mechanical Turk or Crowdflower allow researchers to design and submit crowdsourcing tasks or build entire sophisticated interfaces around a study in order to recruit participants.

A group of scientists recently evaluated the viability of using crowdsourcing for creating large data sets through the public health forums MedHelp and CureTogether. They conducted two different studies- one with 30 professional nurses recruited from oDesk (freelance labor market) and another with 50 crowd workers recruited from MTurk. The results showed that the crowd-labeled data achieved an accuracy of 84% compared to 78% for the expert-labeled data.

Yet another form of active crowd sourcing are scientific games such as “Foldit”- a protein folding game with over half a million registered players, which has dramatically improved protein folding algorithms. Foldit’s goal is to predict the structure of a protein through puzzle-solving scenarios which would aid the creation of designing new proteins to combat disease-related proteins found in HIV/AIDS, cancer or Alzheimer’s, for example.

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Our latest blog post on wearable technology highlighted some of the most popular microelectronics and biosensors which are poised to change the way clinical trials are conducted. However, devices such as the Apple watch and the Fitbit wristband are mostly consumer-oriented at this point.

A few weeks ago, the research team at Google X announced they are currently building a wearable cardiac and activity tracking wristband. The wearable’s health sensor is clinical-grade and specifically designed for clinical trial use.  It is designed to measure anything from light and noise exposure to heart rhythm, skin temperature and pulse, sending real time patient data to researchers. In addition, Google’s Android Wear OS has built-in health biometrics features for wearables as well as the Google Fit open platform that collects biometric information while letting users control data through various apps and devices.

The tech giant clearly knows that using wearables to transform clinical trial development and streamline consumer data collection means big business. A recent PWC report found out that 70% of consumers say they would wear employer-provided wearables in exchange for a break on their insurance premiums. If implemented, employers in turn would help mainstream wearable devices by sponsoring wellness programs. Pharmaceutical and provider networks can also leverage wearables to integrate with their current initiatives encouraging behavior change towards a healthier lifestyle.

When it comes to mainstreaming wearable tech in the pharmaceutical and healthcare industries, however, there is always the issue of data ownership. Industry professionals say that the real benefit of adopting wearables would come from educating healthcare consumers and empowering them to better manage their symptoms. But they are not convinced that the patients’ data should be a part of their formal health record.

According to the American Health Information Management Association (Ahima), the social trend of sharing personal health and disease information online is called “biosociality”. People tend to feel more comfortable sharing personal details on online health-related forums because of the interpersonal context. On the other hand, sharing information with clinical trial researchers means a much more formal setting. And while patients who share their self-tracked data expect anonymity, there is no consensus on privacy and ownership standards in the health data space.

A recent customer survey on data ownership shows that 57% of consumers would only share their data on the condition of privacy protection. Over 90% said this is very important to them, and a small percentage were open to sharing their data with the mobile device company that collected it.

The lack of industry standards around wearables also raises concern about the validity of the collected patient health data. However, experts believe that the mainstream adoption of tech health devices would force the industry to address information governance and impose standards in the near future.

Sign up to ClinCapture Free EDC!

Advances in sensor technology and microelectronics have opened new opportunities in the health and life sciences industries. Wearable sensors allow for continuous bio-monitoring without any manual intervention, thus reducing provider-patient interaction and costs while contributing improvements in the quality of the data.

Wearable electronics, or wearables in short, are light electronics with embedded bio-sensors that can be comfortably worn on clothes or the body. Some of today’s wearable electronics include Fitbit wristbands, Google and SONY glasses, and, most impactful, the Apple Watch. The term ‘wearable electronics’ is quickly becoming part of the consumer’s vocabulary, and the market growth is expected to be impressive: from $22.7 billion in 2015 to $173.3 billion by 2020 (Research & Markets).

However, skeptics are pointing to the lukewarm success of the Fitbit wristband, which one-third of owners stopped wearing after six months. Also, given that current wearables only provide basic monitoring, they don’t significantly contribute to patient treatment, and doctors are not clamoring for patients’ heart rate and caloric output data to be added to their medical records. Finally, turning consumer’s wearable electronics into medical devices requires compliance, validation, and clinical trials, and the FDA has thus far been silent on this topic (although experts privately say that the FDA will not likely stand in the way of innovation).

These arguments were valid until the recently launched Apple Watch and a renewed emphasis on the Research Kit. Just like Apple redefined the MP3 music player, the smart phone and computer tablet industries, they can similarly leverage their immense technology, marketing and distribution power to materially impact healthcare. The Apple Watch has already made wearables a fashionable, must-have item, with 30 million units expected to be sold in the first year (BGR Mobile News). But perhaps the biggest news about the Watch launch is the re-introduction of the Research Kit technology that is expected to transform patient recruitment and data collection. With the Research Kit, iPhone and Apple watch owners will be able to download clinical apps and allow their data to be collected and anonymized. In partnership with IBM, Apple devices will collect unfathomed amounts of clinical data for different populations and diseases and store them in the IBM’s Watson Healthcare cloud.

The Research Kit will potentially corral the difficulty and the cost of recruiting subjects for clinical trials. Current campus flyers and mass mailing approaches are not very productive. According to Kathryn Schmitx, PhD Penn Medicine, an email campaign sending out 60,000 emails led to just a few hundred patients for a breast cancer trial. Similarly, it can cost sponsors thousands of dollars per subject when patients are enrolled by physicians.

Compare these methods to John Wilbank’s, developer of mPower, a Parkinson’s App for the Research Kit. He tweeted, “After six hours, we have 7,406 people enrolled in our Parkinson’s study. Largest one ever before was 1,700 people. #ResearchKit”. Bloomberg reported that Stanford University’s cardiovascular trial attracted 11,000 volunteers in one day after releasing their MyHeart Counts App in the App Store. It would normally take a year to enroll that many subjects. This data is anecdotal, but it seems that the Research Kit has the potential to make a tremendous difference in patient recruiting.

Today’s Apple Watch bio sensors are pretty basic and include a four-ring infrared optical sensor to measure heart rate; an accelerometer to measure balance, sleep pattern, gait, and activity level; a microphone to analyze speech impairment; and a speaker to test hearing. Additionally, certain functionalities provided by the iPhone (which is required to enable many features on the Apple Watch), can collect additional data, such as photographic evidence of health issues and distance traveled using the GPS.

The Apple Watch bio-sensors will soon be extended by a plethora of advanced sensors and other wearable testing instruments to bring data collection to the next level, including skin temperature, ECG, Galvanic Skin Response, and others, such as those contained in the recently launched Metria’s IH1™ biosensor. The automatic collection of additional vital signs and health status data will likely trigger significant changes in the patient-provider relationship with regards to health data.

A few years ago, the FDA approved Proteus, an ingestible biosensor to monitor drug compliance. This kind of biosensor would, among other things, be able to test body fluids to establish, in real time, data that otherwise requires a blood draw and analysis at a lab. This futuristic ‘e-pill’ demonstrates the convergence of drug, medical device, biosensors, and nano technologies. For example, as a preparation for the first mission to Mars (still decades away), NASA has been developing ingestibles with therapeutic capabilities to keep astronauts monitored and healthy. If it sounds like science fiction, it kind of is, but then again, so is receiving a phone call on your watch!

The broad adoption of biosensors by consumers, combined with advances in Nano and Cloud technologies are expected to radically change the way research is conducted by the Life Science industry, improving patient recruiting and monitoring, while lowering development and therapy cost.

Glenn Keet (CEO) & Marc Desgrousilliers (CTO),
www.clinovo.com

Link to sign up to ClinCapture Free EDC

In this paper, we aim to educate around the different types of registry studies, and some of the specifications in its execution. This paper was written by Clinovo’s founder and President Ale Gicqueau, for the 2015 Association of Clinical Research Professionals (ACRP) Global Conference and Exhibition.

Click here to read Conducting Post Market Registries – Rationale and Execution (Part 1)

Ethics, Data Ownership, and Privacy

The principles of ethics, data ownership and privacy are the same for registry studies as they are for clinical trials. IRB approval is required to conduct the study, HIPAA waiver to access patient medical records, a financial agreement with the institution regarding payments, data ownership and publication rights, and assurances of patient privacy.

Recruitment

Recruitment of sites becomes a major issue in studies the breadth of registries. Sites should be paid fair-market value for their time and must see a benefit to their operations if they are to join and actively participate in a registry. This is especially true if the registry study is to include community physicians or high-volume specialty centers, as well as academic centers. Community physicians are more likely to participate if the registry is viewed as a scientific endeavor, is endorsed by leading organizations, led by a respected opinion-leader, provides useful self-assessment data to the physician, or helps meet other physician needs such as maintenance of certification, credentialing, or pay-for-performance programs.

Patient recruitment presents the same challenges as clinical studies. The best success comes from recruitment by the patient’s own physician. It also helps to communicate that registry participation may help improve care for future patients, to provide written materials in language easily understood by the lay public, keep survey forms short and simple, and provide incentives such as newsletters, reports, and modest monetary compensation.

Data collection and quality assurance

Three sets of documents, together, form the system for data collection. The first is the case report forms, and there is no reason to stay with paper today with the new appearance of low-cost cloud-based EDC solutions. The eCRFs are the forms whereby data is gathered in the field, entered into coded database fields, and stored in a clinical database. The second is a data dictionary, which contains a detailed description of each variable used in the registry.

Monitoring

Observational still require to be monitored. A monitoring plan is developed for a registry study the same way than a clinical study. At the minimum, monitors have to verify that: 1) the subject exists, 2) has the disease or condition under study, 3) met inclusion and exclusion criteria, 4) signed an informed consent and HIPAA authorization, 5) received or declined treatment.

Adverse event reporting

For device and device procedure registries, adverse event detection, collection, and reporting is the same as adverse event reporting for any other post-approval setting. The time required to report adverse events starts at the moment the investigator becomes aware of symptoms or events reported by the patient. Investigators must report serious injuries to manufacturers and to FDA within 10 days. Investigators are responsible to report deaths to both the manufacturer and FDA as soon as possible but within 10 days. If an adverse event occurs with a comparator device the investigator must also report the event to the comparator’s manufacturer. Manufacturers have 30 days to report deaths, serious injuries and malfunctions to FDA, and 5 days to report events that require remedial action to prevent an unreasonable risk of substantial harm to the public health.

Analysis and Interpretation

Statistical analysis of registry data is no different than statistical analysis of clinical data. There are a couple of points that are different. First, we have to determine how closely the actual study population represents the target population. Second, there should exist a statistical analysis plan for how the data are to be analyzed and interpreted with regard to accepting or refuting the hypothesis. And third, there should exist a plan for how to handle missing data.

Selecting a clinical database for post market registries

New tools have been emerging that allow companies embarking on trials to do a lot of the work themselves. In the past, life science companies had to pay a CRO, software vendor, or other professional services provider to perform functions such as changing case report forms from paper to electronic. The trend now is to provide free or inexpensive software along with do-it-yourself capabilities that allow even small companies to do much of the work themselves.

 The Database chosen must have the ability to be configured easily, FDA part 11 compliant, and capable of collecting large samples of data. EDC software with the same capabilities as the more expensive systems has come down in price dramatically. In some cases, the software is even free. This has happened for two reasons. The costs for supplying cloud software are far reduced from the traditional licensed delivery model that existed in the past. Today we also have a validated regulatory environment in the cloud, whereas in the past companies had to establish their own data center and validate it at a high cost. All of this is causing a dramatic drop in the initial entry cost (the license and startup cost) for starting a trial.

ClinCapture is one such database, with a scalable, and configurable system that allows you to design the database and configure it without expert knowledge of programming and IT. Additionally, they have an innovative business model where the use of the database in its standard version is absolutely free up to 500 CRFs. This is very helpful as post-market registries have smaller budget than randomized clinical trials.

With this new generation of cloud-based EDC, there is no more reason to stay on paper. It has been proven that EDC cut in half monitoring costs for any clinical study thanks to remote monitoring, fewer site visits, shorter patient recruitment times, reduction (or elimination) of printing costs, faster data entry, and lower data cleaning costs. Time-saving is also not negligible. Single data entry—which replaces the completion of paper CRFs followed by double data entry—remote monitoring, and reduction in the number of queries each save a considerable amount of time. Overall, they have been calculated to reduce the duration of clinical development by up to 30%. As we saw earlier, the report of adverse events is just as critical for post marketing studies. Some specific EDC features have an even bigger impact: faster and automatic notification of adverse events, for example, can help with earlier and better decision-making, potentially saving hundreds or thousands of patients from exposure to unsafe medication.

Conclusion

Developing Registries is fast emerging as an effective means of collecting large amounts of clinical data that can be leveraged for market expansion, reimbursement, publications, and post market surveillance for safety. Choosing the most effective database will depend on the capabilities desired, but many cost effective options such as ClinCapture are available today.

End of Part 2

Ale Gicqueau,
President and Founder at Clinovo
www.clinovo.com

In this paper, we aim to educate around the different types of registry studies, and some of the specifications in its execution. This paper was written by Clinovo’s founder and President Ale Gicqueau, for the 2015 Association of Clinical Research Professionals (ACRP) Global Conference and Exhibition.

Click here to read Conducting Post Market Registries – Rationale and Execution (Part 1)

Ethics, Data Ownership, and Privacy

The principles of ethics, data ownership and privacy are the same for registry studies as they are for clinical trials. IRB approval is required to conduct the study, HIPAA waiver to access patient medical records, a financial agreement with the institution regarding payments, data ownership and publication rights, and assurances of patient privacy.

Recruitment

Recruitment of sites becomes a major issue in studies the breadth of registries. Sites should be paid fair-market value for their time and must see a benefit to their operations if they are to join and actively participate in a registry. This is especially true if the registry study is to include community physicians or high-volume specialty centers, as well as academic centers. Community physicians are more likely to participate if the registry is viewed as a scientific endeavor, is endorsed by leading organizations, led by a respected opinion-leader, provides useful self-assessment data to the physician, or helps meet other physician needs such as maintenance of certification, credentialing, or pay-for-performance programs.

Patient recruitment presents the same challenges as clinical studies. The best success comes from recruitment by the patient’s own physician. It also helps to communicate that registry participation may help improve care for future patients, to provide written materials in language easily understood by the lay public, keep survey forms short and simple, and provide incentives such as newsletters, reports, and modest monetary compensation.

Data collection and quality assurance

Three sets of documents, together, form the system for data collection. The first is the case report forms, and there is no reason to stay with paper today with the new appearance of low-cost cloud-based EDC solutions. The eCRFs are the forms whereby data is gathered in the field, entered into coded database fields, and stored in a clinical database. The second is a data dictionary, which contains a detailed description of each variable used in the registry.

Monitoring

Observational still require to be monitored. A monitoring plan is developed for a registry study the same way than a clinical study. At the minimum, monitors have to verify that: 1) the subject exists, 2) has the disease or condition under study, 3) met inclusion and exclusion criteria, 4) signed an informed consent and HIPAA authorization, 5) received or declined treatment.

Adverse event reporting

For device and device procedure registries, adverse event detection, collection, and reporting is the same as adverse event reporting for any other post-approval setting. The time required to report adverse events starts at the moment the investigator becomes aware of symptoms or events reported by the patient. Investigators must report serious injuries to manufacturers and to FDA within 10 days. Investigators are responsible to report deaths to both the manufacturer and FDA as soon as possible but within 10 days. If an adverse event occurs with a comparator device the investigator must also report the event to the comparator’s manufacturer. Manufacturers have 30 days to report deaths, serious injuries and malfunctions to FDA, and 5 days to report events that require remedial action to prevent an unreasonable risk of substantial harm to the public health.

Analysis and Interpretation

Statistical analysis of registry data is no different than statistical analysis of clinical data. There are a couple of points that are different. First, we have to determine how closely the actual study population represents the target population. Second, there should exist a statistical analysis plan for how the data are to be analyzed and interpreted with regard to accepting or refuting the hypothesis. And third, there should exist a plan for how to handle missing data.

Selecting a clinical database for post market registries

New tools have been emerging that allow companies embarking on trials to do a lot of the work themselves. In the past, life science companies had to pay a CRO, software vendor, or other professional services provider to perform functions such as changing case report forms from paper to electronic. The trend now is to provide free or inexpensive software along with do-it-yourself capabilities that allow even small companies to do much of the work themselves.

 The Database chosen must have the ability to be configured easily, FDA part 11 compliant, and capable of collecting large samples of data. EDC software with the same capabilities as the more expensive systems has come down in price dramatically. In some cases, the software is even free. This has happened for two reasons. The costs for supplying cloud software are far reduced from the traditional licensed delivery model that existed in the past. Today we also have a validated regulatory environment in the cloud, whereas in the past companies had to establish their own data center and validate it at a high cost. All of this is causing a dramatic drop in the initial entry cost (the license and startup cost) for starting a trial.

ClinCapture is one such database, with a scalable, and configurable system that allows you to design the database and configure it without expert knowledge of programming and IT. Additionally, they have an innovative business model where the use of the database in its standard version is absolutely free up to 500 CRFs. This is very helpful as post-market registries have smaller budget than randomized clinical trials.

With this new generation of cloud-based EDC, there is no more reason to stay on paper. It has been proven that EDC cut in half monitoring costs for any clinical study thanks to remote monitoring, fewer site visits, shorter patient recruitment times, reduction (or elimination) of printing costs, faster data entry, and lower data cleaning costs. Time-saving is also not negligible. Single data entry—which replaces the completion of paper CRFs followed by double data entry—remote monitoring, and reduction in the number of queries each save a considerable amount of time. Overall, they have been calculated to reduce the duration of clinical development by up to 30%. As we saw earlier, the report of adverse events is just as critical for post marketing studies. Some specific EDC features have an even bigger impact: faster and automatic notification of adverse events, for example, can help with earlier and better decision-making, potentially saving hundreds or thousands of patients from exposure to unsafe medication.

Conclusion

Developing Registries is fast emerging as an effective means of collecting large amounts of clinical data that can be leveraged for market expansion, reimbursement, publications, and post market surveillance for safety. Choosing the most effective database will depend on the capabilities desired, but many cost effective options such as ClinCapture are available today.

End of Part 2

Ale Gicqueau,
President and Founder at Clinovo
www.clinovo.com

Clinovo’s Chief Technology Officer Marc Desgrousilliers was invited to discuss Cloud technologies on behalf of Clinovo at the 2014 December edition of the San Diego Clinical Research Network (SDCRN). Alongside representatives from life science companies Thermo Fisher Scientific and Neurocrine Biosciences, Marc presented his vision of Cloud Computing and how it affects the Life Sciences and clinical trial industry. Here’s a summary of the key points discussed during the conference.

In which capacity do Cloud technologies improve data capture and quality 

All speakers agreed that digital data is growing exponentially worldwide and that it is deeply impacting Cloud technologies. A significant amount of data is generated each day which has led to the overall amount tripling during the last 3 years. In 2020, the overall amount of global data is expected to reach 40026 Exabytes (Billions of Gigabytes). This is a great opportunity for clinical trials and overall science experiments as they can be run from anywhere in the world using the latest and most advanced technology.
This next figure demonstrates the projected spending amount for Cloud computing in the global healthcare market. This significantly increasing forecast testifies of the Cloud computing trend in the life science industry and the benefits attached to it.

As of right now, an important amount of time and resources goes into the maintenance and management of the software in order to ensure data collection transcription and relevancy.
Taking advantage of the Cloud technology is a great opportunity for the life science industry. It ensures a reduction in the maintenance overhead needed for utilizing technology, and, in addition, reduces manual data collection. This lean management approach ultimately results in less human errors and therefore higher data quality. Processes can now be automated in order for scientists to spend more time in their research projects, advancing discovery in a more streamlined and expedient process. The IT infrastructure to handle the growing amount of data would also be significant for companies whereas Cloud technologies offer a very cost-effective alternative.

Automated technologies with the Cloud

While every stakeholder agrees upon the fact that Cloud technologies improve the overall clinical trial process, its implementation still represents a hurdle. There is always a discrepancy between the marketing vision of the Cloud and what it actually means on a technical level.

Cloud computing allows for on-demand access to a shared pool of resources that can be done with little effort or time. Cloud technology can be summarized by these 5 characteristics:

1. On-demand self-service
2. Network access
3. Service level agreements
4. Resource pooling
5. Elasticity

While more and more EDC solutions adopt Cloud technologies, choosing a Cloud-based EDC solution however requires even more precaution. Indeed, buyers need to make sure the system they’re looking at meets the regulatory compliance. Without this, they will not be able to ensure validation to support their clinical trial. Decision makers need to evaluate the EDC vendor and data safety by asking for proof of validation documentation, support for their hosted application, and data encryption and security measures in place.

Evaluating and choosing EDC systems

Choosing the right EDC vendor remains the final step in the adoption of Cloud for clinical trials. This part is obviously crucial and therefore requires time in order to thoroughly evaluate the best vendor for your clinical trial. Many vendors have described this step from a technical point of view, which clearly represents a major point of interest for the buyers, but vendors often forget the end user’s perspective. Indeed, the GUI and overall user experience is an essential topic that vendors should not put aside.

Another factor in the decision making process is the time and ease of implementation.These points are often underestimated by the buyers but the kick-off training length varies greatly between the vendors. ClinCapture, for example, requires a 2-day training program whereas other EDC systems require a whole week.

Overall, the benefits of Cloud-based EDC systems can be summarized in the following: Increased transparency between the stakeholders and real-time overhead into clinical trial operations and data, further communication and collaboration between investigators, sponsors, and partners. enhanced efficiency and reduced speed to conduct trials and cost-effectiveness. While the perks are clearly identified, choosing a system and implementing it still represents a barrier-to-entry that vendors need to tackle.

Co-authored by

Marc Desgrousilliers – Chief Technology Officer at Clinovo – marc@clinovo.com

Joshua Elvert – Business Development Manager at Clinovo – joshua.elvert@clinovo.com

The folks over at Total Orphan Drugs, a hub for strategy and innovation for the orphan drug and rare disease industry, have put together a selection of what they believe lies in the pipeline for rare disease clinical trials for 2014. Their infographic below provides insight on adaptive clinical trials, electronic patient recorded outcomes (ePRO), placebo controlled trials, crowdsourced clinical research, and patient recruitment.

In particular they state the benefits of Electronic Data Capture (EDC) for clinical trials, such as increased data quality, reduced analysis time, and the fact it allows researchers to analyse data continuously (something crucial for successful adaptive trials).

We’re thrilled that they feature ClinCapture’s recently released video overview, suggesting ClinCapture as an alternative to costly proprietary EDC systems. For those not in the know, ClinCapture is an open source system meaning it has no licence fees and can be used for free. It is particularly suited for small-sized clinical trials such as those for orphan drugs.

For some real-life examples, check out our customer stories to see how ClinCapture could work for you!

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Infographic originally produced and published by Cameron of Total Orphan Drugs

Despite holding a degree in philosophy, I’ve a taste for the scientific and the experimental. With interests in innovation, experimentation and business strategy within the life sciences you will find me writing for Vaccine Nation, Total Orphan Drugs, and Total BioPharma.

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