CRO/Sponsor

Clinical Trial Supply Chain

Decentralized Clinical Trials

Early Phase Development

Investigative Sites

Protocol Design

Risk-Based Monitoring

Study Start-Up

Trial Design

Clinical Operations & Strategy

Diversity Action Plans

Industry Initiatives

Recruitment

Analytics

Electronic Data Capture

Metrics & Benchmarks

Real World Evidence

Data Management

CRAACO

Enrollment

Integrated Research

Patient Participation

Institutional Review Board & Ethics Committee

Regulatory

Artificial Intelligence/Machine Learning

eClinical

mHealth

Technology

News

Academy

Publications

All Publications

E-Books

Media

Editorial Podcasts

Editorial Videos

Peer Exchange

Practice Academy

Conferences

Conference Coverage

Conference Listing

Columns

All Columns

A Closing Thought

Clinical Trial Insights

View from Brussels

View from Washington

Resources

Advertise

Blogs

Brand Insights

Events

Front & Center

White Papers

Webcasts

Subscribe

Latest solutions, case studies, interviews, practical articles for clinical trials operations, data management, technology, investigative sites and services.

About

Contact Us

Editorial Contacts

Do Not Sell My Personal Information

Privacy Policy

Terms and Conditions

Applied Clinical Trials Online

LinkedIN

Instagram

Facebook

Youtube

Soundcloud

Threads

Spotlight

DE&I

Topic

1rem

Authors

In this episode, we meet with Micah Lieberman, Executive Conference Director for SCOPE Summit (Summit for Clinical Ops Executives) at Cambridge Innovation Institute. We will dive deep into the critical role of collaboration within the clinical research ecosystem. How do we bring together diverse stakeholders—sponsors, CROs, clinical trial tech innovators, suppliers, patients, sites, advocacy organizations, investors, and non-profits—to share best practices in trial design, program planning, innovation, and clinical operations? We’ll explore why it’s vital for thought leaders to step beyond their own organizations and learn from others, exchanging ideas that drive advancements in clinical research. Additionally, we’ll discuss the pivotal role of scientific conferences like SCOPE Summit in fostering these essential connections and collaborations, helping shape the future of clinical trials. Join us as we uncover how collective wisdom and cross-industry partnerships are transforming the landscape of clinical research.

Behind the Buzz: Why Clinical Research Leaders Flock to SCOPE Summit

As the use of artificial intelligence (AI) has greatly accelerated in recent years, AI has been moving to reshape the clinical trial landscape by addressing critical challenges and improving outcomes across the entire process. According to 

AI is streamlining the clinical trial process through recruiting and enrollment, trial design, protocol optimization, data management and analysis, enhanced patient retention and compliance, regulatory compliance, post-trial analysis, and real-world evidence.

The article states that in trial design, machine learning (ML) models simulate outcomes and enable adaptive protocols to enhance efficiency and decision-making. Additionally, AI automates data collection and performs advanced real-time analyses, reducing errors and uncovering actionable insights, while also improving patient retention through personalized engagement and behavioral monitoring.

 US Pharma and Biotech Summit, Tala Fakhouri, associate director, policy analysis, FDA, discussed the FDA’s point of view on ML and AI in drug development.

“We're actually very excited about AI use,” said Fakhouri. “I think we're seeing that it's increasing efficiencies in different parts of the drug development process. If you think about things such as discovery or protein folding, which again, is outside of what we normally look at, it could potentially cut the development time by years. This is all very exciting, because it could translate into faster, safe and effective drugs coming into the market. It can also fill in certain gaps for rare diseases where we can see a lot of potential use for AI to accelerate the development of drugs. In this type of situation, that's what I would say would be the long term.”

Fakhouri added that in the short-term, the primary challenge that stakeholders are working to address is the adoption curve.

“You need to train your staff, you need to bring in the right expertise, and you need to develop the right tools to solve the right problems,” Fakhouri said. “That's going to take some time and that's why I think the short-term uses of AI are going to be mostly low hanging type of fruits where you're increasing operational efficiency, but then that will translate into the development of safe and effective drugs faster.”

On October 31, 2024, the FDA released updated guidelines on the use of AI for drug development. According to the guidance, the regulatory body recognizes the growing role of AI in drug development, spanning the entire drug lifecycle from nonclinical and clinical phases to manufacturing and post marketing.

In order to support industry innovation and maintain safety and efficacy standards, the Center for Drug Evaluation and Research (CDER) released a draft guidance earlier this year addressing AI’s use in regulatory decision-making. According to the updated FDA guidelines, the guidance is influenced by extensive public feedback, CDER’s review of over 500 AI-related submissions since 2016, workshops with stakeholders, and aligns with broader FDA efforts to ensure responsible AI use.

Fakhouri previously stated that the FDA had no intention of placing restrictions on the use of AI and ML as time goes on.

“We get asked if the FDA regulates large language models or if we’re going to ban generative AI use,” she explained. “My response is that we typically don't regulate linear regression. We look at the data and the information that any modeling technique is producing, and we want to make sure that the information is trustworthy. So, I wouldn't say that we would be banning or prohibiting a certain AI or machine learning type of algorithm, what we're actually interested in is how robust how accurate and how credible the information from these models is.”

According to IBM, there are a number of benefits associated with implementing AI in clinical trials and medicine development, including:

IBM states that a trained ML algorithm can help cut down on research time by giving clinicians valuable search results with evidence-based insights about treatments and procedures while the patient is still in the room with them.

 Peer-reviewed studies have suggested that AI-powered decision support tools can help improve error detection and drug management.

 This includes reducing medication errors, customized virtual health assistance, fraud prevention, and supporting more efficient administrative and clinical workflows.

Increasing Physician-Patient Engagement: 

AI can help provide around-the-clock support through chatbots that can answer basic questions and give patients resources when their provider’s office isn’t open.

There are many thought leaders in the industry who believe that AI has the potential improve healthcare in all areas in the future. However, the biopharma industry has moved at a slower rate to implement AI compared to other fields.

At this year’s Young & Partners Pharmaceutical Executive Summit, Najat Khan, chief R&D officer, chief commercial officer, Recursion, offered her thoughts on why utilization has advanced at a slower rate and how companies can properly integrate AI into their everyday workflows.

“A huge part of it is the mindset, but pharma companies, in general, have high margins,” said Khan. “Whenever an industry has high margins, what’s the reason for change? That's the truth of it. Second, there’s a lack of understanding when it comes to using it. I think everybody knows what the pain points are. But then how do you deploy it? Sometimes, it means that your job changes. That's threatening, right? I've seen that firsthand, and this has happened over many generations. There’s two ways to respond. Either you evolve or you get evolved. You don't have to be a coder to actually understand AI. It's just like another science or another technical topic. But it's more important to understand how to use it, what not to do, what to do—given the complexity of the pharma industry, the regulations that are there, the limited understanding of biology today—there's just a fear of both personally being threatened.”

1. How AI is Revolutionizing Clinical Trials. 

September 24, 2024. Accessed November 1, 2024. https://www.linical.com/articles-research/how-ai-is-revolutionizing-clinical-trials

2. Artificial Intelligence for Drug Development. 

October 31, 2024. Accessed November 1, 2024. https://www.fda.gov/about-fda/center-drug-evaluation-and-research-cder/artificial-intelligence-drug-development

3. US Pharma and Biotech Summit 2024: Artificial Intelligence and Machine Learning Through the Eyes of the FDA Part II. 

May 21, 2024. Accessed November 1, 2024. 

https://www.pharmexec.com/view/us-pharma-and-biotech-summit-2024-artificial-intelligence-and-machine-learning-through-the-eyes-of-the-fda-part-ii

4. What is artificial intelligence in medicine? 

https://www.ibm.com/topics/artificial-intelligence-medicine

5. The Impact of Artificial Intelligence on the Creation of Medicines. 

October 24, 2024. Accessed November 1, 2024. https://www.pharmexec.com/view/young-partners-pharmaceutical-executive-summit-2024-impact-artificial-intelligence-creation-medicines

With the increasing use of artificial intelligence and machine learning in medicine development, what role will they play in the clinical trial space moving forward? 

A New Beginning: The Effect of Artificial Intelligence on Clinical Trials, Medicine Creation 

Alzheimer’s disease (AD), as well as other neurodegenerative dementias, such as frontotemporal dementia (FTD), can be characterized by progressively worsening deficits in several cognitive domains, including language. Patients with these conditions may demonstrate different speech patterns, such as difficulty in retrieving words (anomia), reduced information content, sentence comprehension deficits, and a lack of cohesion in discourse. Despite the prevalence of language characteristics as a symptom of these disorders, tools used to identify the extent of cognitive impairment in the study of AD and FTD do not typically monitor speech or rely on subjective ratings. Growing awareness of the opportunities that language evaluation can bring to clinical research is driving interest in speech-based digital biomarkers to evaluate changes in cognition and achieve more sensitive detection of the impact of treatments.

Challenges of detecting change in AD and FTD trials

Investigational drugs for AD and FTD have an extensive history of failing to demonstrate efficacy in Phase II and Phase III clinical trials. In trying to find reasons for these setbacks there is agreement that the limited sensitivity of current clinical trial endpoints may be a factor. Most of the standard primary endpoints used in AD and FTD research are clinical measures of cognition and/or function, such as the Clinical Dementia Rating (CDR) scale. While global rating scales serve a helpful purpose in providing a quantifiable clinical judgement about disease severity, there is an awareness that these tests do not adequately capture slight changes or improvements in a patient’s condition.

Putting questions on sensitivity aside, these standard approaches to testing are also hugely complex, difficult to administer and time intensive. A single test can take up to an hour to complete. Most also need to be administered by a clinician or psychometrist, meaning that patients are unable to complete them remotely. Repeatability may also pose challenges due to the potential for non-memory impaired patients to learn what to expect if these same tests are revisited regularly.

With the pressing need to develop and evaluate disease-modifying treatments for these diseases, there is an appetite from researchers for new tools and more sensitive means of measuring function and cognition in AD and FTD patients. Language represents a huge opportunity. As more sophisticated, automated, and objective ways of analyzing speech come into the frame, there is potential to gain insight into the language function of patients in a way that has historically not been achievable.

The adoption of speech as a biomarker in clinical research has the potential to capture more subtle speech and language changes in patients with cognitive impairment. People with AD and FTD may struggle to recall words, they may resort to basic vocabulary, construct simpler sentences, and speak at a slower rate. While clinicians and investigators may notice subtle language problems in patients at the early stages of these diseases, they do not currently have the tools to objectively characterize these changes and how they worsen or improve over time.

Technology that can facilitate the automated assessment of speech can provide more objective measures to complement clinical rating scales. Winterlight’s approach involves automatically extracting hundreds of acoustic and linguistic properties of natural speech. The fine-grained data can be used to characterize the speech patterns associated with AD, FTD, and other neurodegenerative disorders. The platform uses short-recorded speech samples captured through speech tasks including picture description, paragraph reading, paragraph recall, phonemic fluency, semantic fluency, and object naming.

These advancements are helping to build a more complete picture of the common aspects of speech and language that decline with the progression of certain dementias. In analyzing just one to five minutes of a patient’s speech, hundreds of linguistic cues can be identified that may offer more accurate insight into the severity of their condition. These cues include lexical diversity, syntactic complexity, semantic content, and acoustics. Integrating speech-based technology into the design of AD and FTD clinical trials has the potential to contribute detailed insights into the subtle ways that patients’ symptoms improve or deteriorate as a study progresses.

From the patient’s perspective, testing via these types of platforms may also bring considerable improvements and reduce burden. As with all clinical trials, keeping AD and FTD patients, and their caregivers, engaged with a study is paramount. Typically, assessments are administered by study sites via tablets. A trained rater would walk the participant through a brief speech assessment, taking under five minutes to complete. The assessment would prompt them to speak in response to simple instructions; this may be a request to describe a picture, or simply asking a patient to express how they are feeling that day. The patient’s speech is recorded via the device’s microphone, with recordings uploaded to a cloud-based platform. The speech recordings are analyzed using Winterlight’s speech analysis platform and outcome measures are provided to the trial sponsor as an exploratory endpoint.

Assessments can also be made available to patients through their own tablets or smartphones, as well as devices provisioned by sponsors, for remote collection. Removing the need for patients to visit a site for this type of testing makes it easier for insights to be collected more frequently than may have been possible using traditional methods. Winterlight’s technology is currently being used in several Phase II and III clinical trials for AD and FTD, with many patients reporting that the assessments are easy-to-use and less taxing than other clinical assessments they have experienced. This work is helping to build greater understanding of the speech changes that occur in these patients over time.

The future of speech assessment in clinical trials

There is tremendous potential for neurological language assessments to improve the way that clinical trials are run, and the impact of new treatments measured. As command of speech and language are ecologically and functionally relevant clinical markers of disease progression, objective tools can help generate novel, sensitive endpoints able to be administered remotely and at lower burden to the patient. While speech-based biomarkers are currently being used as exploratory endpoints, there is increased awareness of the enhancements they may bring to the accurate and sensitive measurement of cognitive health in AD and FTD research. As more evidence-based data and validation is gained, it is anticipated that these measures will eventually be adopted as primary and secondary endpoints and may provide more detailed insight into the subtle ways that new treatments are impacting cognitive health.

 is the Director of Clinical Research at 

Future shows great potential for neurological language assessments in clinical trials.

How Speech-Based Digital Biomarkers are Redefining the Measurement of Disease Progression in Neurodegenerative Dementia Trials

AiCure, an AI and advanced data analytics provider, is working with the Defense Health Agency’s (DHA) PTSD Drug Treatment (PTSD-DT) program to deploy its medication adherence platform to guide patients in their compliance to complex treatment regimens, as well as its digital biomarker solution to capture the subtleties of a patient’s response. As PTSD has a wide range of symptoms, 

 aims to develop a precision medicine approach to prescribing the most effective therapy based on a patient’s unique biological and clinical characteristics. Rich Christie, MD, PhD, Chief Medical Officer at AiCure, spoke to our sister publication 

 Can you provide the background on AiCure’s digital biomarker solution as a whole?

: During a clinical trial, a precise and accurate understanding of a patient’s response to treatment is critical to measure the state of their disease and a drug’s impact. Traditional means of measuring a patient’s well-being, including sporadic in-person assessments or a patient’s self-reported outcomes, only tell clinicians how a patient is feeling at that moment in time. The stretches between these check-ins can tell an entirely different story, filled with critical, subtle cues about their condition.

AiCure’s AI-powered digital biomarker solution enables remote detection of subtle changes in a patient’s health status and response to treatment by capturing audio and visual data between clinic visits. Accessed through AiCure’s Patient Connect application, patients use their smartphone’s front-facing camera to complete brief assessments. AiCure’s algorithm then analyzes behavior, such as emotional expressivity, physical movement and speech patterns. By frequently aggregating these sensitive, objective insights, AiCure empowers pharmaceutical companies to improve their understanding of the disease and treatment side effects, elevating the integrity of their trial data, and optimizing patient outcomes.

 How does the technology work in the context of the DHA partnership?

 The DHA PTSD Drug Treatment Program is conducting an adaptive platform trial designed for the efficient and cost-effective testing of multiple PTSD treatments in both active service members and veterans. AiCure's medication adherence platform guides patients in their compliance to complex treatment regimens. The biomarker solution is described above, and the application features integrated built-in reminders and a communication system, allowing sites to contact the participant if they’ve missed a dose or check-in.

: Can you describe the challenges of PTSD treatment?

 PTSD has a wide range of symptoms and has significant variation in how it presents across patients. Eighty percent of PTSD patients have at least one additional comorbidity such as depression, anxiety or substance abuse disorder, further complicating diagnosis and treatment. Despite the societal, financial, and emotional strain that PTSD exerts upon patients, there are only two FDA-approved therapies on the market.

The PTSD-DT Program's adaptive platform trial will evaluate the effect of therapeutics on a patient's PTSD symptoms over 12 weeks. Because medication compliance has historically been a significant methodological problem in PTSD clinical trials, AiCure's adherence platform will offer patients the necessary support to ensure correct dosing. The digital biomarker solution can be used to capture the subtle changes we discussed earlier, and may help investigators identify the right drug for the right patient, or allow clinicians to intervene in a patient's treatment plan in a timely manner if the drug is not likely to be effective.

 What does it involve in terms of patient interactions?

 Each time a patient doses in the trial, they will use the AiCure application. The smartphone-based application leverages computer vision and AI to remotely assess patient dosing behavior, ensuring the right patient ingested the right drug at the right time. This allows sites to monitor adherence and follow up with participants in instances of non-adherence. For the biomarker assessments, participants will be asked to perform simple tasks, such as describing images presented through the AiCure app in a few sentences to the camera of the smartphone, in order to record and analyze their responses.

 How many patients will participate in the PTSD clinical trial?

Rich Christie, MD, PhD, Chief Medical Officer at AiCure discusses the use of the company's technology in a clinical trial for PTSD.


PTSD Study Uses Precision Medicine Tech from AiCure

The FDA and other regulatory bodies have issued multiple guidance documents addressing technology tools that are used in clinical trials. FDA guidance documents include Computerized Systems;

 is also available online. In 2010, the European Medicines Agency (EMA) issued a Reflection Paper on electronic source records in clinical trials,

 and recently, the Medicines and Healthcare Products Regulatory Agency (MHRA)

 issued a document on data integrity. In 2016, the Chinese FDA issued Technical Guidelines on Electronic Data Capture for Clinical Trials.

 issued a document addressing the use of CDISC standards to facilitate the use of electronic source data in clinical trials.
	
	The regulatory push to the paperless clinical trial has occurred despite a pharmaceutical industry that has been risk-averse in adopting modern-day technology tools that could support clinical trials. This risk aversion is in part due to fear by sites and sponsors of receiving a FDA Form 483.

 What if the FDA turns down an application if it discovers that, for example, a patient was born in 1982, when in the study database it is recorded as 1983, even when there is no impact on the study results? The clinical sites are also fearful of losing business as a result of any FDA Form 483 finding, however minor.
	
	The major stakeholders in the clinical trial enterprise include study subjects/patients, sponsors, clinical researchers, regulators and the public at large. Now that the electronic world has entered the clinical trials space, as part of routine pre-approval inspections there will be an assessment of the impact of electronic systems on 1) informed consent; 2) data integrity and accountability; 3) protocol compliance; 4) monitoring of the study by the sponsor, including risk-based monitoring; 5) patient safety by assuring that all safety events are accurately collected and reported, and 6) drug/device accountability.
	
	The following are examples of regulated clinical trial software currently being used:

		Electronic patient reported outcomes (ePRO)

		Electronic clinical outcome assessments (eCOA)

		Dedicated tablets collecting and storing data in real time and transmitting data on a schedule to the study database

		Web-based systems collecting data in real time which transmit the data to the study database only after the source record is securely received and stored in an independent eSource storage location under control of the study site

		Mobile-based medical apps whose functionality could pose a risk to a patient’s safety if the app were to not function as intended


	
	It is strongly recommended that a risk-based approach to software validation be assessed for any medical software/app, even those not being used in clinical trials, as there are moral and ethical issues if any software used for medical decision-making does not act as intended.
	
	For software to be “reliable,” it needs to validated. Therefore, assuring that software is in a validated state is a key factor when regulators look at software products used in clinical trials. Fortunately, in 2002, FDA provided validation guidelines for software supporting regulated products.


	
	The following sections address some of the needed steps to assure that software used in clinical trials will be adequate to support regulatory scrutiny during pre-approval inspections and the review of marketing applications.
	
	


	
	Here are some critical questions to be asked concerning the “intended use” of the software, and depending on the answers, appropriate steps must be put in place.
	
	1. Why am I using the software?
	2. How is it being used?
	3. Will the data be used to support decision-making such as:

	4. Will the data be used to support primary and secondary outcomes?
	5. Will the data be used to support subject’s safety?
	6. How will patient privacy and confidentiality be maintained?
	
	


	
	A software product used in clinical trials must be validated and compliant with the Code of Federal Regulations (CFR) incorporated in Title 21 (Food and Drugs), Chapter I, Subchapter A, Part 11.

 21CFR Part 11, as it is commonly referred to, sets forth the criteria under which the FDA considers electronic records, electronic signatures, and handwritten signatures executed to electronic records to be trustworthy, reliable, and generally equivalent to paper records and handwritten signatures executed on paper.
	
	There is a distinction between validation and compliance. Validation is objective evidence which demonstrates that a given piece of software performs as intended against the user needs and the documented specifications and requirements.

	While it is true that software requirements and resulting features within the software can support compliance with 21 CFR Part 11, it is also true that no software or hardware system can be 21 CFR Part 11 compliant on its own. The software implementation, including processes and procedures for use of the system, are what constitute 21 CFR Part 11 compliance.
	
	And validation of the software goes beyond just the software requirements and resulting features necessary for Part 11 compliance. Validation is necessary for the entire software application and should be a defined part of the Software Life Cycle (SLC).
	
	


	
	There should be very clear SLC management documentation that contains the software development process from cradle to grave. In fact, many SLCs can go beyond the development process, to include ongoing support and even consideration for end-of-life for the software.
	
	In terms of popular development methodology choices, there is the Agile development methodology versus the traditional Waterfall approach. For the former, when it is very clear what the deliverable will be, one makes a detailed plan and then implements the plan. In contrast, the Agile approach accepts that there will be changes during software development and has built in flexibility to change direction at a moment’s notice.
	
	The first validation element is the software concept/requirements, or in other words, what is the problem we are trying to solve and why do we need this software? Usually an executive or the person driving the software writes the concept document to convey the mission and rationale of this new endeavor. Next, the “requirements” phase must clearly document what the software intends to do. For example, the software should allow for study subjects to enter study outcome data over the web using any device and any browser.
	
	Once the requirements are buttoned down, there needs to be clarity on how the requirements will be executed, which comes under the heading of “functional specifications.” For example, if the requirement is that “changes to the data base need to tracked,” then the functional specification could say that there needs to be an audit trail which must display the original value, the new value, why the change was made, who made the change, and what was the reason for the change.
	
	The next phase is left to the programmers and architects who create the very technical “design document.” This document basically is in “geek-speak” and we leave that document to the specialists. Once programming begins, the programmers work closely with the “testers” to test and debug the program until at some point the development team says the software is ready for a formal user acceptance test (UAT). In an Agile methodology, the design document, test plan and test scripts are written concurrently with development, iterating as features are refined. In a Waterfall methodology, the test plan and test scripts are often written with the design document, all of which is completed before development even begins.
	
	Once the “test plan and test scripts” are written and signed off, formal testing can usually begin right away. During testing, the testers document the “errors, bugs and fixes,” and when all that is completed, the software is “released” and signed off by those responsible and accountable. Post-release, “version control and change management” kick in. A plan is also put in place for the retirement of the software when it will no longer be supported or commercially available.
	
	


	
	A quality by design (QbD) methodology, which includes a risk assessment and risk mitigation strategies, is a critical exercise when building software. Using a Boeing 787 analogy, a plane has several electrical systems that control the engine and the coffee machine. We should not assign the same risk to the coffee machine not delivering hot coffee as we do to the engine failing on takeoff. Therefore, in a clinical trial, we propose the following for each risk:

		Assign a value of the risk to the study participant if a software failure happens

		Assign a value of the risk to the study itself if a software failure happens

		Assign a severity score if a software failure happens

		Assign a likelihood of detecting the software failure

		Then create a score and a risk-mitigation strategy for each risk


	
	A well-designed and well-written protocol can be viewed as one approach to an ideal quality manual. Therefore, in addition to standard protocol elements that we are all familiar with, the protocol should identify where and when electronic systems will be used, and then explicitly describe at a minimum, 1) all electronic systems being used; 2) the reason for their use; 3) how the systems will be used; 4) how the software will be controlled; 5) the basic elements of the risk assessment and risk mitigation strategies and; 6) data ownership and access controls.

	The inspectors will not usually do an assessment of the protocol design, as that was already done by the clinical team at the regulatory agencies. What the inspectors will do is attempt to determine whether 1) the protocol was followed; 2) there are SOPs supporting the protocol; 3) the site and sponsor followed what was stated in the protocol and SOPs; 4) the electronic case report form (eCRF) was intelligently designed to support protocol compliance; 5) there were system-generated management reports of site communications and protocol deviations/violations; 6) there was an integrated risk management plan and risk mitigation strategy; 7) there was a substantiated and complete root cause analysis of observed issues/events; and 8) there were follow-up corrective and preventative action plans (CAPAs) and that the follow-up actions were resolved as planned.
	
	From the safety perspective, the inspectors will want assurances that there was accurate reporting of safety events by evaluating 1) how safety data were collected; 2) the process for determining the source of original safety data based on a data source agreements; 3) a sample of source data from disparate sources, including review of source records written on pieces of paper, if relevant, and; 4) subject records maintained at the study site.
	
	

Are electronic source data reliable and fit for purpose?


	
	We are asked many times by those committed to paper records-sponsors or regulators-that if the system is fully electronic, how do we know that the data are real if there are no source records with which to compare the electronic record? Ironically, you can ask the same question about paper records; unless there are major controls of paper, notebooks, etc., nothing prevents a piece of paper from being destroyed and replaced by a new one. Bottom line, as some suspicious data trends are impossible to spot through manual review of records, fraud and dishonesty can usually only be identified through adaptable, validated checks and the use of informatics and multidisciplinary expertise. Clearly then, misconduct is not dependent of the collection medium of the original record (paper vs. electronic). Consequently, in a double-blind, randomized trial, one effect of fraud, if it occurs, could affect the study results by demonstrating that a potentially effective drug is ineffective from a statistical perspective (Type 2 error). This is where study monitoring techniques and fraud detection programs become vital.
	
	Therefore, when using off-the-shelf or proprietary software systems, the following are some of the quality questions that should be considered:

		Can the software provide any assurances that the study subjects are real?

		Are there validated systems to assure user authentication and control?

		Did the sponsor perform installation qualification (IQ), operational qualification (OQ) and performance qualification (PQ)?

		How was access to the original data controlled?

		Were there date and time stamps for all transactions?

		Were there audit trails that can’t be overwritten?

		How was the data transferred to the study database?

		Can patient privacy and confidentiality be maintained?

Pre-approval inspections of an eSource program


	
	Based on our experience and the experience of others, when the regulators/inspectors arrive, they will need to review:

		Your organizational chart as it relates to the clinical trial

		Copies of relevant contracts/agreements

		Documents supporting vendor qualifications

		Documents supporting software validation/testing

		Corrective actions taken, reasons for actions taken, and the results of any actions

	As of this publication, two regulatory marketing applications have been submitted to FDA where direct data entry at the time of the office visit took place in lieu of maintaining paper source records. From the perspective of the pre-approval inspections at the clinical research sites, there were no Form FDA 483 findings related to study monitoring oversight and any of the electronic systems that were used for data collection. The inspections occurred at three of the five major medical centers that were inspected for the first application, and at five of the 20 outpatient clinical research sites that were inspected for the second application.
	
	The following are the redacted excerpts of the FDA inspection of the CRO (Target Health Inc.) for the first program that received marketing approval where the clinical research sites entered the majority of the data directly into the EDC system at the time of the clinic visit, and all documents were maintained in an electronic Trial Master File (eTMF; target document).
	
	

		This FY 2015 CDRH High-Priority Class II Device Application Inspection was conducted.

		This inspection was limited to the areas of compliance program 7348.810, “Sponsors, clinical research organizations and monitors.”

		Data line listings were provided with the background package, for comparison to the site eCRFs.

		An FDA 483 inspectional observation was not issued to management during this inspection.

 The clinical project manager develops a clinical data monitoring plan and safety monitoring plan for each study, using a company-specific template; these plans are approved by the sponsor before any site monitoring takes place.

a) The clinical data monitoring plan for the inspected study was reviewed and obtained.

 This study utilized direct entry of data, by the sites, into the EDC system using the eSource process (Target e*CTR; eClinical Trial Record).

a) This process allows for the original data to be stored in PDF format, in the repository, prior to being transmitted to the EDC database. The PDFs serve as original records.

The time is now for sites and sponsors to put away fears associated with data acquisition and monitoring technologies-and better support the regulatory push in recent years for standardized adoption of paperless trials.

Regulatory Considerations when Designing and Running 21st Century Paperless Clinical Trials

Conducting successful clinical trials in hematological malignancies requires an understanding of a rapidly evolving treatment paradigm that is increasingly nuanced, complex and patient-directed. Just as the underlying differences in biology and prevalence between blood cancers and solid tumors necessitates differences in treating patients, so, too, do they demand differences in clinical trial expertise and conduct. Sponsors developing hematological oncology therapies must capitalize on the principles and infrastructures shared by solid tumor oncology trials while adapting endpoints, study designs and considering patients' experiences to address the particular challenges related to investigating candidate treatments for blood-based cancers. This article examines the nuances of effectively and successfully conducting hematological oncology clinical trials.

	In the United States, someone is diagnosed with a blood cancer every four minutes and every 10 minutes; these malignancies result in a death.

 Despite this incidence, the three most common forms of blood-based cancers-leukemia, lymphoma and myeloma-comprised only an estimated 9% of all new cancers and 9% of all cancer deaths in 2013 in the United States.

 In comparison, the three most common US solid tumor cancers-breast, lung and colon cancer-together accounted for an estimated 34% of new cancer patients and 43% of cancer deaths in 2013.

Conducting successful clinical trials in hematological malignancies requires an understanding of a rapidly evolving treatment paradigm that is increasingly nuanced, complex and patient-directed.

SCOPE

Articles

Latest Updated Articles