CTMS Solutions That Help Solve Clinical Trial Sponsor Challenges

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Is Anything Really New in Clinical Trial Management System Platforms?

Robert Webber Vice President, Clinical Trial Management Systems

Today’s clinical trial environment is very much open, involving multiple CROs and software from numerous vendors.  Are the software solutions today keeping up with this increasingly complex environment? What are the challenges faced by today’s model?  And what will tomorrow’s clinical trial management system (CTMS) platform look like?

The State of Today’s CTMS Platform

When we look carefully at today’s CMTS platform, there are five areas that seem to stand out.  Those are clinical trial reporting, the user experience, working with business partners, business agility and managing software costs. 

The state of today’s clinical trial environment poses many substantial challenges for a sponsor.  Some of the biggest challenges that I’ve witnessed include:

1. How can I report on data distributed across all these different clinical trial management systems?
2. How can my clinical operations people deal with the complex user interfaces of numerous clinical applications they have to use?
3. How can I effectively use CROs without losing oversight and control of my studies?
4. How do I implement an IT infrastructure that provides the adaptability to meet my unique and evolving business needs?
5. How do I manage my IT costs, especially if I have to integrate all these different systems for each study?

The challenges faced today by clinical trial sponsors demand a new approach to clinical trial software infrastructure – a broader vision than the conventional clinical trial suite.

The Next Generation of CTMS Offerings

Open Architecture with a Technology Platform.  In this new approach, clinical data is made available in a normalized form to IT organizations and technology partners to customize and develop with the latest software tools – a new level of agility to meet changing business requirements.

Separation of Clinical and Operational Data. Web services alone do not enable open architecture. A next generation platform must provide an operational data model based on a core set of data definitions.

Collaboration.  Collaboration means more than accessing the same data.  It requires the translation of that data into targeted information based on user roles, configuring workflow.

Not Just Integration. Interoperability.  Integrated systems share data.  Interoperable systems allow users to perform similar functions from within their own applications.  The next generation Clinical Trial Management Platform should address how users work within the most prevalent software user environment – Microsoft Office.

Microsoft SharePoint has changed the way clinical trial sponsors operate, providing each of the four CTMS offerings a common denominator from which to co-exist.  BioClinica has found a way to leverage data with SharePoint Lists. This is a new and unique approach that will help clinical trial sponsors find CTMS solutions to the five challenges listed above.

Have you used the SharePoint platform in clinical trials?  What other trial challenges are you seeing currently that this type of platform could assist?  I welcome your thoughts and questions.

Download a copy of my white paper, The Next Generation Clinical Trial Management Platform, here.

Modeling IVR Studies Ship Times

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Modeling IVR Managed Studies that Ship Only on Certain Days of the Week

Jeremy Daniel, Senior Consultant

We often run into cases where an IVR (interactive voice response) system may not generate shipment orders for study kits every day.  For example, commonly it seems they may only ship Monday through Thursday.  In many cases this could be due to cold chain supplies having to be received immediately and being unable to guarantee that someone will be there to receive the shipment on a weekend. Others, however, may be for unrelated policy reasons.  Standard modeling practice says to add a day to the shipping lead-time for every day that is not available for shipping (3 days in the case of the above example).  Related to this concept, there is often some confusion for new users about whether the shipping lead-time for the IVR study refers to calendar days or business days.

One of the nice things about an IVR simulator (for instance, BioClinica Optimizer) is the ability to see the effects of changes in the resupply algorithm on your study.  So, what is the impact on the results of the simulation caused by this simplification of adding days to the shipping time to represent days shipments are not made?  To test this, I took a simple IVR managed study with 2 treatment groups and 600 patients in 14 centers. Sites are supplied with kits for 2-3 patients and each subject has 5 predictable treatments after randomization.  On the baseline scenario, shipments take 6 days to get from depot to site; the comparison uses 3 days with no shipments on Friday through Sunday*.

Using this sample IVR managed study model, I ran a number of monte carlo simulations and found only minor differences.  There were about 5% less mean shipments made by the comparison scenario (417.61 compared to 438.77).  As would be expected, since the original scenario was not fully optimized, there was also a lower mean lost subjects due to stock out (0.22 compared to 0.99).

What have you found in your IVR studies to be the impact of not shipping every day?

* In both versions, we have a maximum of 6 days from depot to site so I used a 6 day short window (triggers shipment if supply will be short in the next 6 days).  The comparison uses a script to stop shipments by setting the floor (minimum stock at site) to 0 on days that shipments are not made and setting the short window demand (patient need for 6 days) to 0 as well.

Top 5 Items That Have Impacted Clinical Trial Research Technology

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Top 5 Items to Impact Clinical Trial Research Technology over the Past 10 Years

Jennifer Price CDISC CDASH Registered Service Provider (RSP)

Internet

From telephone-based dial-up to cable modems to DSL to FIOS to T1 lines, the speed in which we access other computers and applications has changed the world.  The Internet has changed the way we communicate, shop, play games, and access information (remember phone books?).  Perhaps the largest impact to clinical trial research technology (we are now able to share data instantly); the Internet allows us to clean, review and make critical decisions earlier in the process, therefore improving clinical research.

Standards

I never met anyone who thought standards were a bad idea.  When zip codes were introduced in 1963, mail delivery times decreased.  When CDISC CDASH standards were introduced in 2008, build time for CRFs decreased. Standards have streamlined the way we setup databases, move data around, and submit data to the FDA.

Technology Acceptance

The use of the Internet has absolutely changed the field of clinical research, and not only because of the improved connection speeds and connectivity.  The increased adoption rate is simply because the population is more comfortable using computers, and entering data using computers. In the past, sites may have one or two personnel who were ‘computer savvy’ enough to login and enter data, browse websites, update documents, and view study calendars. These days, most personnel at the clinical study sites are comfortable using a computer, and some even insist on a using an electronic system over paper.

Clinical Research Technology Vendor ‘Leaders’

Functional outsourcing of clinical research services and technology makes sense and is here to stay.  As a result, several successful providers have risen to the top of the pack.  These ‘mega-vendors’ have successfully delivered for enough companies that it is now common to outsource data collection and hosting needs to one of these clinical research technology vendors.  I believe the days of hosting systems and software or having large in-house staff is in our rear-view mirror.

Narrowing of Back-end Database Vendors

In the past, there were many databases to choose from.  There was access, foxbase, filemaker, and a few others that are long gone.  Today, it all comes down to two players: Microsoft SQL Server and Oracle. I believe that the heavy use of these two databases have allowed us to speed up standardization of both collection and submission standards.

What do you believe will be the next five impacts to clinical research technology?

Operational Metrics in Clinical Trials, Part 2

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The Use of Consistently Defined Clinical Trial Metrics – Part 2

Jennifer Price CDISC CDASH Registered Service Provider (RSP)

What’s measured improves – Peter Drucker

Metrics defined by the Metrics Champion Consortium (MCC), a group of experts in clinical research, define how to measure the performance of our clinical trials and makes it easy to see where we are, and where we need to be in our study.

Since the metrics are the same for all clinical studies, we are comparing apples to apples.  We can see the number of subjects enrolled, completed, the number of open queries and entered pages.  We can see the data by study, by site, or pool site data from multiple studies.  Since we are using defined metrics, we know that the definition for ‘average time from CRF submission to verification’ is: Sum (CRF Verified Date - CRF Expected Date) / (Number of CRFs).  The definitions remain consistent across different clinical studies and programs.

By setting targeted goals for each of our clinical trial metrics, we can start to generate indicators that tell us if we are within our target or not.

Let’s take our example in “The Use of Consistently Defined Clinical Trial Metrics - Part 1”: the number of days from a query response until the database is updated.  MCC defines this target as 2-3 days, but when using EDC, we may want to shorten that goal to one day.

We can easily create a dashboard to show the data, the metrics, and the performance indicator (green / yellow / red) telling us if we are within our target goal.  Pulling together all of this data in one place is the key to the successful use of clinical trial metrics.

The Only Way to Improve Clinical Trial Metrics Is to See Where We Are Outside Our Targets

This sounds very simple, and it is.  The challenge has always been measuring the same items in the same way for all studies.  The MCC has provided us with the standard clinical trial metrics that can be collected across the studies, individual users can provide their target goals, and all of the data can be shown using tools, such as SharePoint, and shared by all study team members.

Example of dashboard showing performance metrics:

By defining standard clinical trial metrics using operational data that is available for every study, and setting targets to show if we are on track, we can easily tell if our study is moving along smoothly and see which areas need some extra attention.

What types of clinical trial metrics do you think are important to collect for your studies?

Jennifer Price is on the MCC ‘Process Improvement Metrics Development Team.’

Ongoing Development of Imaging Biomarker Standards in Clinical Trials

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Standardization of Imaging Biomarkers in Clinical Trials

Colin G Miller PhD FICR CSci

One of the areas that I am active in is the ongoing development of standards for the qualification of imaging biomarkers.  When the Prescription Drug User Fee Act (PDUFA) was renewed in 2007, the FDA agreed to develop by the end of 2011 a guidance document on “Imaging Standards for Endpoints in Clinical Trials.”  Work on this is ongoing. 

At the recent FDA/RSNA/SNM meeting held on April 13-14, 2010 at the NIH, which I attended, Janet Woodcock M.D., Director of the Center for Drug Evaluation and Research (CDER) gave an update on the FDA’s perspective.  A copy of her presentation can be downloaded here.

The FDA perspective is that “standardization of image acquisition, interpretation, and management of data in multicenter clinical trials is essential for accurate diagnosis and to assess response to therapies.”

One of the issues that the industry faces is establishing qualified imaging biomarkers whose data can be relied upon in an NDA, BLA or NDA submission. 

The process for formally qualifying an imaging biomarker with the FDA is still under development.  What is likely is that sponsors will be required to put together a biomarker concept/information package that can be reviewed by CDER. Comprehensive scientific evidence will be required to support the qualification of an imaging biomarker, so that the conclusions from its usage can be relied upon.  As Dr Woodcock noted, “initiating qualification of an imaging biomarker will be contingent upon adequate standardization of the particular imaging process.”

One of the debates is that if a novel imaging biomarker is qualified by the FDA, to what extent could it be freely used by other companies?  If the initial company has incurred the time and expense of putting together the concept and scientific information package, could subsequent companies free ride? Would this delay development?

What are your thoughts regarding imaging standards? Please send me your comments. I will continue this theme in future blogs with the concept of blinded reads and the Pharmaceutical Imaging Group input to the FDA hearings.

Source:

http://www2.rsna.org/re/TwoTopicImagingWorkshopPresentations/Index%20Files/Woodcock%20CDER%20Perspective.pdf

The Use of Biomarkers and PET in Early Alzheimer’s Disease Diagnosis

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The Use of Imaging Biomarkers and Positron Emission Tomography to Diagnose Early Alzheimer’s Disease

Colin G Miller PhD FICR CSci

Alzheimer’s disease (AD) is the most common form of dementia and is incurable, degenerative and irreversible.  It affects around 4.5 million people in the United States, a number that is expected to exceed 12 million by 2050.  Neuropsychological tests, such as the mini-mental state examination (MMSE), are commonly used to diagnose patients. However, cognitive impairment may be due to another disease, not Alzheimer’s.  There remains an unmet need to be able to differentiate between different forms of dementia.

Imaging biomarkers are providing new diagnostic tools. One of the seminal biomarkers for Alzheimer’s disease is Pittsburgh compound B (PiB).  PiB is a fluorescent analog of thioflavin T; it is used in combination with Positron Emission Tomography (PET) scans to image beta-amyloid plaques in the brains of Alzheimer’s disease patients (more information here). Beta-amyloid has been proven to be a hallmark of Alzheimer’s disease and is accumulated in the brain in the very early course of the disease (more information here).

Recent PiB PET studies Recent PiB PET studies have shown that they can improve the accuracy of dementia diagnosis in early stages of the disease by measuring disease-related amyloid accumulation.

Source:  Society of Nuclear Medicine, 2009 Press Conference on Scientific Paper 251, “PET Neurochemical vs. Clinical Phenotypes in Mild-Early Dementia.” (see PowerPoint document for reference)

Companies are now developing imaging biomarkers for Alzheimer’s disease diagnosis.  Bayer has florbetaben (BAY 94-9172), an 18F radiolabeled tracer that binds to beta-amyloid in phase III clinical trials.  Avid Radiopharmaceuticals is a company purely focused on new imaging biomarkers with AV-45 in Phase III development.

Presuming that florbetaben or AV-45 ends up being approved, it will be interesting to see what price the respective companies charges for these injectable tracers.  Not only do imaging biomarkers have to work, they have to be cost-effective in today’s healthcare environment.

What are your thoughts on using biomarkers and PET in early diagnosis of Alzheimer’s disease?

Sources:

1. http://www.ncbi.nlm.nih.gov/pubmed/14991808
2. http://www.ncbi.nlm.nih.gov/pubmed/16854944
3. http://www.snm.org/index.cfm?PageID=8779&RPID=8729
4. http://clinicaltrials.gov/ct2/show/NCT01020838

Operational Metrics in Clinical Trials, Part 1

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The Use of Consistently Defined Clinical Trial Metrics – Part 1

Jennifer Price CDISC CDASH Registered Service Provider (RSP)

If you can not measure it, you can not improve it.   –Lord Kelvin

The Metrics Champion Consortium (MCC) develops metrics that allow trial sponsors and CROs to share a common set of clinical trial performance metrics across studies.

This not-for-profit group is solving a very complex problem in a very elegant, straightforward way.  They gather industry experts to identify and define a common set of clinical trial metrics that can be used in all studies. See a recent Metrics Champion Consortium press release here.

The idea around using clinical trial metrics sounds so simple, but is not often realized primarily because metrics are not often defined consistently.  For example, Study ABC123 might collect number of pages entered, number of queries answered and total subjects enrolled.  Study XYZ987 might collect a percentage of expected pages that have been completed, queries resolved and total subjects screened.  These are all standard metrics to collect, but are not the SAME metrics across clinical studies.

The MCC has put together common metrics for clinical trials performance, labs, ECGs and Images, and is now working on process improvement metrics.  

Clinical Trial Performance Metrics Example

Below is a subset of the details around one of the clinical trial performance metrics: Receipt of Query Response to Database Update Time.  There are additional details and columns in the official document provided by MCC for their members that are not shown here.

Metric: Receipt of Query Response to Database Update Time

Definition – This is crucial to define exactly how a clinical trial metric is measured so the same data is compared when looking across studies. I like that the Median number of days is used for this particular metric instead of the mean.  The mean could be influenced by a few long outstanding updates, where the median is more representative of how many days it takes to make updates as a result of a query.

Additional analysis on a ‘for cause’ basis – Identify why this metric is important and what process to look at if the metric is out of range.

Reporting Frequency – For some clinical trial metrics such as this one, it is important not to look at the data too often, but to take a ‘big picture’ look at the entire process.

Target – Identify a target range for the particular metric.  This may differ based on your company or processes (particularly EDC or paper data collection).  By identifying a target goal for this metric, it is easy to see if we are on track, or if we are falling outside our target range.

Business driver / benefit statement – It is important to identify the problem that can be solved by collecting this metric.

By using industry standard metrics across all our clinical studies, the clinical trial manager or data manager can identify potential areas for improvement that will benefit the entire study team.

Part 2 will identify how these clinical trial metrics can be used to maximize their impact.

Jennifer Price is on the MCC ‘Process Improvement Metrics Development Team.’

DCE-MRI Biomarkers Technology and Development

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The Development of Dynamic Contrast-Enhanced Magnetic Resonance Imaging Biomarkers

Colin G Miller PhD FICR CSci

Following on from my recent post on Optical Coherence Tomography, another interesting medical imaging technology that I am actively involved with is Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI).  DCE-MRI is an imaging technique that combines the magnetic field and radio frequency imaging of MRI with the injection into a vein of a standard MRI contrast media that makes organs, tumors and blood vessels easier to see.

Essentially DCE-MRI provides the tracking of low molecular weight contrast agents through blood vessels, in particular the vasculature of tumors.  By analyzing the pharmacokinetics of the contrast agent into a specific tumor, it is possible to measure alterations in vascular permeability, blood flow and extracellular volumes.  This is of interest in the development of cancer drugs that inhibit new blood vessel formation (anti-angiogenesis) or disrupt existing blood vessels (vascular disrupting agents) for example. O’Connor et al in the British Journal of Cancer provides a good review of DCE-MRI imaging biomarkers in oncology clinical trials.

Published data shows DCE-MRI biomarkers correlate with a decrease in tumor volume.  Outside of oncology, companies such as Amgen are evaluating the use of DCE-MRI as a biomarker to detect changes in the inflammation of synovial joints associated with rheumatoid arthritis (RA). They hope to be able to better detect changes associated with treatment of their TNF inhibitor, etanercept (Enbrel). View clinical trial information here.

DCE-MRI Imaging Biomarker Validation of Bi-Directional Transfer Coefficient K ^trans

One of the DCE-MRI imaging biomarkers that the Quantitative Imaging Biomarker Alliance (QIBA) sub-committee, which I participate in, is looking to validate is the bi-directional transfer coefficient K ^trans.  We are aiming to make quantitative imaging results in clinical trials comparable in multi-center studies.   

One of the challenges in validation is ensuring that results from different sites in multi-center trials are comparable.  Central Review is one part of the quality control, but standardization of data acquisition is particularly important in DCE-MRI where there are number of user associated parameters related to the time of contrast injection.  There are also issues associated with use of different MRI machines, which the QIBA group is addressing through use of a standard phantom. DCE-MRI imaging biomarkers may have the ability to help companies assess the potential of their products in early phase drug development.  

Further work remains on validating imaging biomarkers and I look forward to sharing insight into this as new developments arise.

Sources:

1. http://info.bioclinica.com/blog/bid/40478/OCT-Novel-Imaging-Technology-Providing-A-Window-into-the-Body
2. http://www.nature.com/bjc/journal/v96/n2/full/6603515a.html
3. http://meeting.ascopubs.org/cgi/content/abstract/25/18_suppl/2029
4. http://clinicaltrials.gov/ct2/show/NCT00361634
5. https://wiki.nci.nih.gov/display/CIP/QIBA+DCE-MRI

Imaging Biomarkers in Oncology Clinical Trials

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Are Imaging Biomarkers Playing an Increasing Role in Oncology Clinical Trials?

Colin G. Miller PhD FICR CSci

Following on in my series of blog posts about imaging biomarkers and the recent BioClinica symposium, I wanted to share some insights provided by Ali Guermazi, MD (a consultant to BioClinica, who was also a co-author on the Cheson Criteria) from Boston University School of Medicine who spoke on “Imaging Biomarkers in Oncology from RECIST to beyond.”

There is no doubt that imaging biomarkers are playing an increasing role in oncology clinical trials.  Imaging endpoints are often based on the morphology, physiology or metabolism and can include examples such as the following:  

• Morphology: tumor diameter, volume, lesion number, tumor burden, infiltration, texture (e.g. solid, necrotic).  
• Physiology: tissue vascularity, microvascular permeability (angiogenic activity). 
• Metabolism: bone scintigraphy, PET/SPECT, MR spectroscopy, biochemical markers.

Professor Guermazi spoke at length about the Response Evaluation Criteria in Solid Tumors (RECIST).  This involves the uni-dimensional measurement of target lesions that are then followed over time.  Quality issues surround the number of lesions, size of lesions and their selection.  Some parameters are also excluded as lesions, for example:

• lesions with poorly visualized margins
• bone lesions
• leptomeningeal disease
• ascites
• pleural/pericardial effusions
• lymphangitis cutis/pulmonis, inflammatory breast disease
• cystic lesions

Importantly, it was emphasized that based on the RECIST 2000 guidelines, “only patients with measurable disease at baseline should be included in protocols where objective tumor response is the primary endpoint.”

Some of the challenges related to RECIST that were mentioned include only non-measurable lesions at baseline, lesions that become “cystic” at follow-up, cavitated lesions, calcified lesions and bone lesions.

From an oncology clinical trial perspective, consistency in imaging technique is vitally important, i.e. the “same method of assessment and same technique to be used to characterize each lesion at baseline and at follow-up.”  Common QA problems in CT or MRI include: inconsistent filming format between visits, CT without contrast, missing anatomical coverage, missing CT or MRI examinations, slice thickness, missing measurement scale, windowing/leveling, missing lung window images on films, poor quality copies.  The main conclusion from the presentation was that centralized QA is very important for study homogeneity and that each type of cancer has its challenges.

It is beyond the scope of a short blog post to discuss all the imaging biomarkers in oncology and RECIST response criteria that Professor Guermazi discussed and the associated measurement criteria for target and non-target lesions.  However, if you are interested in more information, you can register to receive BioClinica’s training CD-Rom on the RECIST criteria that is available upon request here.

Have any questions on the role imaging biomarkers are playing in oncology clinical trials? Ask them here, I am happy to provide further thoughts and insights on this topic.

Imaging Biomarkers in Clinical Trials

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Imaging Biomarkers in Clinical Trials

Colin G. Miller PhD FICR CSci

Imaging biomarkers are currently a “hot topic” in pharmaceutical circles.  This was brought into focus at a recent symposium in Princeton and repeated in Amsterdam on “Current and Innovative Imaging Biomarkers in Neurology and Oncology Drug Development.”  At this symposium, BioClinica brought together several leading experts and key opinion leaders to present on emerging imaging biomarker trends. 

Cornelis van Kuijk (Professor and Chair Department of Radiology, Vrije Universiteit Medical Center, Amsterdam) provided an informative introduction to imaging biomarkers in clinical trials.  He started off his presentation by saying:

“Imaging biomarkers should reflect disease activity and are used to study natural course of disease and to study interventions for treatment such as medication.”

Imaging biomarkers can be used in a clinical trial to assess: disease activity, adverse events and eligibility of patients based on eligibility criteria.

According to Professor Van Kuijk, the requirements for imaging biomarkers are that they should:

1. Reflect disease activity
2. Have excellent reproducibility (rigid quality control)
3. Be sensitive enough to detect (small) changes in disease activity (power of studies)
4. Be validated to be accepted by regulatory agencies (FDA accepted)

Imaging biomarkers have been used in a variety of efficacy studies in different diseases.  Some of the ones he highlighted in his presentation were: 

• Joint space narrowing (JSN) and erosion scores in Rheumatoid arthritis (RA)
• Vertebral morphometry in osteoporosis
• Arthritis scores in degenerative joint diseases
• MS scores in multiple sclerosis
• MTA scores in Alzheimer’s disease
• RECIST criteria (modified) in oncology studies

Van Kuijk’s view is that current imaging biomarkers are based around static features of a disease that can be counted, measured or quantified over time.  Future imaging biomarkers should be metabolic markers and reflect the actual disease activity at the time of imaging.  One of the challenges of this approach will be to deal with the problem of heterogeneity and variability between patients who respond and those that don’t.  The presentation concluded with the thought that the future holds a lot of promise:

“There is a clear expectation that new advanced imaging biomarkers will provide much more insight into a disease as well as its treatment.”

Future blogs will cover other aspects of the symposium which go in depth into certain aspects of the biomarkers presented in the over-view.  Please check back soon as other symposia will be presented during the year covering other topics.