The current clinical trial and drug regulatory process have lagged behind advances in scientific research. Regulatory approval is based on the evidence of efficacy and safety gathered from pivotal trials. Most experts agreed that traditional approach to drug development constitute a blunt tool where a more focused experiment could suffice instead of a one-size-fits-all approach which is ineffective and wasteful.
According to studies conducted by New England Journal of medicine, almost nine in ten clinical trials do not meet FDA requirement and thus do not reach the market. Project fails when they do not meet endpoints. Lack of efficacy and complex protocol are the major cause of failure. To reverse this trend, productivity must be directed towards the poor predictive capability of the current experimental model.
Clinic trial plays a major role in drug development and advancement in technologies such as drug positioning, therapeutic target, and drug efficacy prediction are helping researchers and pharmaceutical companies in drug development.
Traditional trials have fixed parameter that is determined in advance and remain constant throughout out the process. One promising approach to modernizing clinical trials and maximizing efficiency is by adaptive trials which allow for certain parameters such as sample size and treatment regimen to be modified or replace on interim results.
The major hurdle is that investment in clinical research is dwindling as government and stakeholders tightened their budgets. As major sponsors revenue stabilize and cost continue to rise, the clinical trial finds itself in a financial squeeze. Pharmaceutical and medical-device companies have been intent on trimming budgets. Lean contract negotiations are the norm these days, no sponsor is walking around with money burning hole in their budget and most are responding to mandate to save cost by requiring CROs to provide unrealistic competitive budgets which are disastrous. With tightened budget, short-sightedness has become the order of the day and as a result, both sponsors and CROs reputation suffered.
Cutting back drastically or unwillingness to adequately fund a project will produce a poor result. This simplistic strategy is unrealistic and unsustainable. It is important to note that focusing solely on short-term strategy will be detrimental in the long-term. If sustainable research climate is created, prosperity will definitely grow.
There has been increasing effort in utilizing technologies to streamline protocol and boost efficacy in clinical research. The use of technology is empowering research professionals by helping them to make a decision based on resulting data. With the aid of technology, organizations are reducing cost and speeding up evaluation process. Technology allows for accrual of data faster and that information can be disseminated in real time. This can lead to a better decision being made on protocol, patient recruitment, and trial sites.
Here is the list of technologies impacting clinical trials today
Risk Based Monitoring (RMB)
Companies use risk based monitoring to target and prioritize resources, identify risks relating to quality, safety of subjects and integrity of clinical trial data. Risk based monitoring (RMB) can incorporate targeted monitoring or triggered monitoring and reduced source document verification (SDV).
Traditionally, source data verification (SDV) were conducted 100% via on-site monitoring, a labor intensive approach. Reduced SDV limits the measure of SDV at the site, study and subject level.
The quest for more key ways to manage clinical trials has distinguished risk based monitoring which attempts to manage resources without compromising on clinical quality.
The variables of protocol compliance, data integrity, and patient safety etc. impact how assets are deployed.
The impact of technology in risk based monitoring is profound and currently in markets are clinical trial management systems (CTMS) and remote data capture (RDC) which can support risk based monitoring system. Some systems still support manual entering and reporting of clinical trial data, for this reason, there is a need to setup more robust systems for flagging and alerting data automatically, these alerts can be developed for notifying those who need to act when an issue arises.
According to Medidata, the cost of onsite monitoring is approximated at 28.7% of study budget and project management at 26.47%. In moving to RMB, monitors can spend their time more judiciously and reduce cost.
Electronic Trial Master File
In biopharma industry, every organization involved in clinical trial maintains a trial master file containing several thousand pages of regulatory documents needed for each clinical trial.
Using a paper-based or hybrid trial master file system to manage thousands of clinical documents, processes and tasks can be overwhelming and can cause errors or misread that can ruin clinical trial and put it at risk for noncompliance.
Organizations usually employed an Enterprise Content Management System (ECM) to manage clinical trial regulatory documents. The ECM based eTMF offers automated methods to index, archive, and report on documents and content.
To eliminate paper from a clinical trial study, electronic signing utilizing digital signatures from verified users is being employed. Globally, most countries including the US and many countries in EU are accepting digital signatures in place of wet signatures thereby eliminating the need for scanned documents.
An electronic trial master file (eTMF) offers a robust platform to document management which allows study team members to gain a quick insight necessary to efficiently manage clinical trials and speedup time to market. Electronic document management processes are being adopted at a steady pace as it becomes vital to business productivity, shortened biopharma product development timelines, and cost cutting.
In 2013, the FDA release its Guidance Document on Electronic Source Data in Clinical Investigations, since then sponsors and study sites have been adopting and employing eSource as a method of recording data in clinical trials.
The definition of electronic source (eSource) is a clear concept — to capture or process source data electronically, this data exclude the source data that was captured on paper and transcribed into an electronic database. In eSource, the source data element itself must be electronic.
The benefit of eSource is apparent and the FDA endorsed it because it will be useful in: facilitating real-time entering of electronic source data during subject visits, removes the need to duplicate data, allows for accuracy and completeness of data by using electronic prompts for inconsistent and missing data and lessens the chance for transcription errors.
The need to modernize and streamline the way data is collected are evolving, as a result, much of the exploratory done thus far by several pharmaceutical companies have been mostly to build internal competency. In moving forward, it is critical that standards and interoperability within different eSource modalities come together to help create systems that provide accurate data in clinical research.
Using Clinical Data Repository (CDR) to drive optimization
The ability to effective manage, report and analyze data is of paramount importance in clinical trials. The major hurdle is that data from clinical trials are frequently entered by clinicians electronically or manually across multiple channels including EDC, LIMS, CDMS and IVRS and other systems each with unique underpinning needs. This approach causes data to end up in different databases making it complex and time-consuming to leverage and synchronize the data.
There is confusion as to what constitute or defines CDR as distinct from a clinical data warehouse (CDW). CDR can be thought of as consolidated storage and transfer of data for clinical trials including security, workflow, and systems for performing daily task all under one umbrella.
Centralization of the storage and management of data results are the purpose of CDR and to provide a steadfast and reliable infrastructure that supports clinical data analysis and management, facilitate standardization and secure transfer of data, allow for analytics and cross-trial analysis and to leverage data from trials across the pipeline.