Global Clinical Research Market to Reach $60 Billion in 2020

Scientist looking through microscopeAccording to a newly released report by Zion Research, the global contract research market is expected to reach $59.42 billion in 2020. Last year’s market valuation was $34 billion. This accounts for a 10% compounded annual growth rate.
The demand for outsourcing clinical development to contract research organizations (CROs) has been rising steadily as a result of high in-house R&D cost as well as the high failure rate of clinical trials.
Another growing trend has been a rise in strategic alliances, joint ventures and acquisitions among vendors in the CRO market with the goal of expanding service offerings and global reach.
The report divides the CRO market into Americas, EMEA (Europe, Middle East and Africa) and APAC (Asia Pacific) regions with the US CRO market dominating half of the world market share in 2014. However, Asian, Latin American and Eastern European countries are popular research destinations as they provide access to large, low-cost patient populations as well as low-cost manufacturing and skilled clinical workforce.

According to the report, last-stage clinical development sector was the largest employer in the CRO market with more than 70% of the 2014 total market share. This includes phase phase II-IV clinical studies and central lab services. This sector is projected to have the fastest growth in the next five years.

Some of the major stakeholders in the CRO market are global contract research organizations such as Quintiles, Covance, Parexel, PRA International, Charles River Laboratories, Accenture and Cognizant.



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Electronic Source Data in Clinical Studies

esourcing1The move to conduct clinical trials from paper to Electronic Data Capture (EDC) has accelerated over the past 10 years in an overall effort to increase data quality, regulatory compliance and to reduce cost. However, paper is still prevalent at clinical sites, as the FDA requires record retention for CRF supporting data that is typically stored in source documents. Two years ago, in an effort to move away from paper inefficiencies, the U.S. Food and Drug Administration (FDA) issued its final guidance on Electronic Source Data in Clinical Investigations. In this guidance, the agency promotes capturing source data in electronic form to assist in ensuring the reliability, quality, integrity and traceability of data from electronic source to electronic regulatory submission.

Recently, a new product category of eSource solutions has entered the market to meet needs that EDC systems cannot fulfill. According to the Food and Drug Administration (FDA) eSource Guidance of 2013: “Electronic source data are data initially recorded in electronic format. They can include information in original records and certified copies of original records of clinical findings, observations, or other activities captured prior to or during a clinical investigation used for reconstructing and evaluating the investigation.” In other words, this is data that is entered directly into a digital format without having to first record it on paper and then transfer it to an electronic data capture solution.

Investigators like the flexibility and versatility of pen and paper, and they perceive computerized systems as a drain on their productivity. The Internet is not always easily accessible from the clinical sites, especially overseas. This is why new eSource solutions are built on tablets that can address these two hurdles. Tablet applications are designed to “look and feel” just like paper, but they offer the efficiency of an electronic document. Unlike case report forms (CRFs), which only capture the data necessary for analysis, eSource documents encompass the much broader goal of providing affirmative documentary evidence related to a subject case history and site audit, and allow for random, ad-hoc comments.

Other benefits of eSource documents include increase in clinical data quality through validation checks and the removal of unnecessary duplication of data, as well as the reduction of monitor site visits by eliminating source document verification (SDV) and enabling remote document review. However, despite the many benefits, esourced documents can still be challenged from a GCP compliance perspective.

One way for e-source solutions to comply with regulations and guidelines is to make the first data recording on paper or keep the source data in the clinical investigator’s control by entering it in a medical record or a medical record system. The FDA doesn’t regulate EMR, therefore it is not subject to 21 CFR Part 11 requirements. Collected data can be entered into eCRFs directly on the condition that it meets all regulations. If the clinical data is transferred to an eCRF from an EMR, then that EMR is considered the source. The FDA has made it clear that clinical trial monitors and auditors should have access to verify the data in the EMR.

Electronically collected data can be kept on or off-site. On-site storage can present many logistics challenges such as data corruption or loss, SOPs, software validation plan, restricted access and many others. Data not store locally should be under the control of the investigator in order for it to be compliant. Thin-client architecture, which delivers e-sourced data straight into the CRO’s remote server, can sometimes also be GCP non-compliant.

The FDA has made substantial efforts in supporting the use of electronic data solutions in the past couple of years. Among the many benefits, eSourcing helps control fraud as it is far more difficult to fabricate electronic records over paper ones.




The Data Integration Challenge for CROs

Contract research organizations (CROs) always strive to enhance project oversight and decrease costs while adding value. As clinical trials are becoming more complex, multiple CRO partners often need to collaborate on a single project, and each CRO has its own portal and varying sets of reports, which means transferring information can be problematic. Even in the cases when the sponsor and CRO use the same CTMS, for example, APIs need to be set up for each trial. Simplifying data exchange is therefore crucial to fulfilling their trial-related duties.

Some find a workaround with CTMS by extracting data in spreadsheets, sending it to the sponsor and then importing it into their system manually. But they can run into problems such as inconsistent data requests or problems in mapping it to the sponsor’s back end data tables. Others use an SQL database or SharePoint for data entry, and skip CTMS altogether. That, however, presents many operational issues of providing consistent data extracts in the right format and manually managing their data.
Some sponsors also work with CROs on implementing CDISC clinical trial data models such as the Study Data Tabulation Model (SDTM) and the Analysis Data Model (ADaM). The CROs convert their studies into a common SDTM structure in support of eCTD and Integrated Summary of Safety analyses for a filing. Sometimes these studies were not done in-house which results in inconsistent eCRFs so the SDTM format benefits both internal data inspectors as well as the FDA. Using the SDTM structure has helped standardize the data structure (Treatment Names, Treatment Codes, Visit Names, Visit Numbers, MedDRA Encoding Version, and coding for Disposition Status). The table below further describes some materials that are helpful for the CRO to implement the SDTM conversion.


Credit: Outsourced Data Integration Project with CDISC SDTM and ADaM Deliverables

To learn more about the new trends and solutions in eClinical systems integration, register for our next Silicon Valley BioTalks event happening on October 8th. Panelists would address questions such as: What are the key principles for a successful outcome? What are the new trends and players in place that are tackling the high cost of integrated solutions? Why are eClinical systems vendors and CROs instrumental in making progress and how can they accelerate this process?

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eClinical Integration: Challenges & Solutions

The use of electronic data collection in clinical trials has been increasing rapidly over the last few years, prompting a rise in the demand for integrated systems. The topic of “systems integration” is widely discussed, but its adoption remains slower than expected. Whether you choose EDC & CTMS, eTMF & Safety, or EMR integration, there is no one-stop-shop solution.

For example, CTMS solutions such as Advanced Clinical Software’s StudyManager have been installed at over 2,000 sites but there are still no defined metadata and communication standards that allow CTMS and EDC solutions to share data. A common issue with EDC-CTMS integration occurs when there are complex investigative site business practices. Most EDC systems only capture clinical trial data through eCRFs that lack CTMS information. Another issue is that some EDCs may lack timeline planning features such as reaching target subject recruitment milestones, for instance. As for eTMF & Safety integration, common issue here is the lack of real-time inspection and ICH/GCP compliance.

With increased regulatory requirements and the trend towards personalized medicine, sponsor companies and CROs need to access more specific solutions to meet their need, making systems integration an increasing necessity for a successful clinical trial. In addition, risk management of the product’s life cycle includes investigators, regulators and patients. This is where systems integration comes in; ensuring data is more accurate and consistent. One way for improving this process is to focus on data analysis, not just warehousing it. Most companies only focus on front-end integration without considering the need to generate reports for regulators later. If data were integrated from the start, it would be easily accessible at any point.

However, this is easier said than done, as implementing systems integration is estimated to cost about $500K and take as much as 3-6 months, which can come up to nearly 10% of the research budget. Such decisions usually come from investors or the company board, which adds extra approval steps for CROs to go through. While the medical and technical staff know the value of data integration, it needs to outweigh some of the drawbacks of the integration process, seen as time-consuming and a costly investment. Collaboration and consolidation among front-end and back-end systems, as well as the emergence of advanced eClinical systems or modules, shows that the value of integrating will grow as users see the efficiency in storing and viewing their data on a single interface.

If you’d like to learn more on the topic, you are most welcome to attend our free Silicon Valley BioTalks event on October 8th in Palo Alto. Panelists would address questions such as: What are the key principle for a successful outcome? What are the new trends and players in place that are tackling the high cost of integrated solutions? Why are eClinical systems vendors and CROs instrumental in making progress and how can they accelerate this process?

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Crowdsourcing Clinical Research & Development (Part 1)

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.

Infographic (2)



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Leveraging Real-Time Reviews: Quality Clinical Trial Data

logoClinovo, ClinOps Toolkit, and Patient Profiles recently hosted an exciting event at the Bay Club in Redwood Shores, the heart of the Silicon Valley biotech hub. Clinical trial and eClinical system experts shared their specific experiences, challenges, and insights on obtaining and managing quality clinical trial data. The panel also discussed new tools and technologies to improve clinical data quality.

The event was organized by Nadia Bracken, Clinical Trial Manager and Founder of the ClinOps Toolkit blog and event series. The discussion was moderated by Peter St Wecker, Senior Manager of Clinical Research at Theravance. The experienced panel included Marc Desgrousilliers (CTO at Clinovo), Barbara Elashoff (CEO at Patient Profiles), Nancy Isaac (Vice President CA, RA, QA at NeoTact), and Vaishali Suraj (Director of Statistics at Auxogyn). Here is a recap of the main lessons learned from the event.


More Clinical Data From More Sources

Marc Desgrousilliers, CTO at Clinovo, pointed out that the life science industry is experiencing a significant growth in the amount of data they’re collecting claiming that “Roche is expecting to double the data they collect every other year”. Clinical data is flowing from more and more sources, including EDC, ePRO, EHR, let alone new devices and smart biosensors. Unfortunately, clinical data is often housed in different databases and current systems are not necessarily talking to each other. Vaishali Suraj explained that Auxogyn works with three different databases. She has to spend a lot of time cleaning up data as well as identifying and solving discrepancies. This is a potential threat to data quality, as it makes clinical data more disparate and less accessible.

Streamline and Organize Clinical Data

Clinical trial professionals have higher expectations of eClinical technology. We expect to visualize collected data in real-time, from anywhere, at any time. “We need to access data at our fingertips” states Vaishali Suraj from Auxogyn. “Data storage and infrastructure is a major challenge for us, as we are working with large data and images”. The challenge for eClinical system vendors is to organize the growing flow of data, and deliver it to clinical study teams in a real-time and an interactive format.

Empower Clinical Trial Professionals

“Our clients want to be empowered to build their own studies” states Marc Desgrousilliers from Clinovo. “If you can use PowerPoint, you should be able to use a simple system to build your own CRFs and create edit-checks, without a huge upfront IT investment”. Cloud technology is the answer: It is scalable, so it can grow or shrink with the data and it is seamlessly accessible from anywhere in real-time. Marc announced: “We’re working on an easy-to-use and pay-as-you-go platform to build and manage clinical studies from the ground up without any programming skills”. 

Look for Patterns to Mitigate Risk

Nancy Isaac from NeoTact explains that as far as safety, we need to be able to look at the data in multiple dimensions and look for patterns. More importantly, “clinical trials are very expensive” states Nancy “but there is only one thing more expensive than good data, and it is bad data.”

Patient Profiles offers an innovative statistical approach and an eClinical system that allows users to visualize patient data in real-time, and automatically identify and flag unusual data. Their web portal allows all team members to visualize clinical data, aggregate graphs and tables, and drill-down in to individual patient profiles. Patient Profiles automatically runs algorithms to mine your clinical data and identify data that is unusual. The rules are inferred from the data without needing to hard code the error checks. This solution goes far beyond the simple programming that identifies an out-of-range lab value. The software summarizes the error rates by patient, site, and variable to identify where to target monitoring throughout the course of a trial. This allows clinical trials professionals to react faster to protect patient’s safety, as well as save both time and costs in data monitoring.


Patient Profiles | patientprofiles.com | info@patientprofiles.com

Clinovo | clinovo.com | contact@clinovo.com

ClinOps Toolkit | clinopstoolkit.com | nadia@clinopstoolkit.com

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Olivier Roth, Marketing Manager at Clinovo

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