Ensuring Drug Accountability Compliance with IRT
Drug Accountability of the Investigation Medication begins from the selection of location for its storage. Issues with drug accountability are major and the majority of warning letters from regulators are a result of this. Major delays are faced by sites for non-conformity and it often leads to non-acceptance of trial data. Drug accountability is of utmost importance and it is the clinical investigator who remains responsible for the entire process taking place at the clinical site. If the supplies are controlled substances then more attention shall be needed from the Investigator in terms of its storage and disposition. IRT systems allow in streamlining such processes by keeping a single source of data and hence the process of Drug Accountability tracking becomes easier.
It is the complexity surrounding the paper-based systems that makes them more prone to errors. Drug accountability shall be maintained at sponsor level and site level. The Investigational medication documented as shipped should reconcile with the documentation of used and unused. The Drug Accountability at site begins once an investigational drug reaches the site. Records need to be maintained and staffs need to confirm that the contents that have been shipped match its previous record. The authorized person needs to put his signature with date after thorough verification. Once these have been completed, the investigational drug needs to be stored securely maintaining the standards specified in the protocol. Entire details need to be updated in the drug accountability log and with the commencement of trials; drug dispensing records are to be updated in writing in multiple documents. Site shall maintain all the data of the drugs right from drug dispensing to final disposition. Paper-based drug accountability never ends with the end of study, discrepancies in accountability logs need to be resolved, and its copies are to be returned with the original shipment records. The sponsor should receive the reconciled log along with the returned investigational drugs. Any discrepancy in drug accountability not only violates the regulations but also points the integrity of the study and public health.
Utilizing electronic medium for data collection and tracking helps in mitigating the challenges associated with data accountability. When data associated with a trial gets centralized, then the visibility gets doubly enhanced and a single accountable system improves the process of reporting. This entire method eases regulatory compliance achievement process and accountability audit issues get resolved immediately. As the use of the IRT system increases the pace, end-of-study documents are prepared in no time using the single system as the source.
mIRT has a special feature to track Drug Accountability where the reconciliation of the supplies that are used, lost, damaged and returned shall be captured which enables the system to calculate the compliance of the drug automatically.
With the current advancements, as the world is moving ahead and accepting technology, there is an increased level of visibility in the supply chain for both the regulators and sponsors.
Future LABELING: Data Digitization, Structure and Automation
Labeling process prominence has increased over the years and manufacturers are trying to manage product labeling & artwork at the same time while maintaining end-to-end life cycle changes of product labeling. In the current scenario, labeling can be in various forms, both physical and digital as well. Controlling the labeling content becomes difficult as it comes from diverse sources and all these content changes during the product lifecycle process is a daunting task for labeling teams. Labeling Automation can help ease these challenges.
Artificial Intelligence is involved in various domains like education, retail marketing & healthcare sector, etc. To that, data labeling is a vital aspect in the healthcare domain (Pharmaceutical industry) with its keen specifications predefined by the health regulatory authorities (HRA). Systems need to understand what is shown on the display part such as images, symbols, written text & among many other things. Medical labeling is an imperative or integral stage of data preprocessing in supervised learning (machine learning) process. Historical data with predefined target attributes (values) is used for this process model.
Organizations look forward to collaboration so that all the data is available at the source thus enabling them to keep a check on the labeling content. To ensure this control, e-labeling has already entered the market and with time it is taking up much of the space in the life science industry.
Labeling data service comprises many different tasks. This includes adding electronic markings on image files, text files, categorizing texts, etc. As mentioned above, adding markings on images or text is an important part of data labeling service. Data coding is the key aspect in the Automation process and certain modules or templates to be designed and populate internally. All the labeling functional aspects will be identified, captured and coded in way of business rules (predefined data integrity) to the system. Each data element will give the system a better understanding and execution of the outcome for defined processes. This allows the algorithm to recognize different shapes in various positions and also possible to tag or map the data element. The algorithms can only function properly if there is some sort of human intervention then the system machines can produce human-like results
The digital revolution is inevitable. It is already happening. Soon it will ease the burden of brand attraction and information which is at the moment moving swiftly onto packages. Converters will become the dominant producer of paper-based information. In simple terms, consumers will get product information online by scanning the RF tag or barcode with their Smartphone. This is already happening, so it is safe to conclude that the future is here. Fortunately, things can only get better and easier for the consumer. It can be combined with packaging and can be used by marketers to encourage potential buyers to purchase the product
Some specifications regarding labeling activity are the key thing in the machine learning process. For example, some of the label content may require to represent and executing in a particular native language or coming from a specific region. In other cases, a more detailed description of the individual field is necessary on how and what content to be present. In this process for each assigned task user-based credentials are given as needed by the customer for the required job. Understanding all text would be difficult in the machine learning process. Natural language or Natural language process (NLP) is unlike constructed or formal language and can therefore not easily be parsed by machines.
The next decade will demand bigger changes in process systems and expected greatly reduced materials used in packaging and goals of 80− 90% recovery.
Digitization and automation will enable comprehensive recovery and re-use of packaging materials. So the big question is how ready your teams and your company are for these changes. Take small steps with the phased approach supported by the right technology/tool and start applying to different labeling processes without boiling the ocean or waiting for a big silver bullet to rescue.
Future Clinical Technologies - Automation & Analytics
Current situation has led the world to remain secluded and engage more into remote activities. In such situations, like most other industries, healthcare industry too has been forced to think out of the box. Areas that have been mostly impacted are those where congregation and engagement of multiple people was necessary. One such section in healthcare is that of clinical trials. Data sharing, analysis and accumulation remains to be a major and most significant part while conducting a clinical trial. Now it is for us to find out how technology is driving the change.
To conduct the ongoing clinical trials remotely, monitoring and management of data and document is necessary. There is no fixed method that can be implemented and hence files are generally shared in the form of email, fax or such other cloud-based tools. This leads to compliance risk issues if not kept under proper surveillance. Such file sharing systems are generally used as they are low in cost but they fail to be compliant with the authority regulations. Moreover, fax and mailing services require huge manual work and provide greater scope of error in updating the retrieved data. In areas of clinical trial such minute errors too can have severe impacts.
Automation is of great help in current situation as it allows collaboration and a seamless exchange and upgradation of data. Access to Electronic Medical Record (EMR) tends to be of great help in addition to SaaS based tools. These being cloud based, perform the tasks automatically and hence there is less of manual task and lesser scope for errors. This advanced technology comes with a central repository for the storage of bulk data which gets analysed regularly and in real time, thereby easing out much of the task.
Technological transformations especially in the field of clinical trials have already started. Automation has provided greater control, access to vast data, accountability, lesser compliance risks and scope for continuous communication. This allows in performing remote clinical trials with added visibility and helps more people to come forward and adopt the change.
Data Integrity in Clinical Trial through IRT
Integrity of data is ensured only when initial remains valid throughout the product life cycle. If the accuracy comes under question at any point in time then data integrity would collapse. ALCOA system is the best method of defining and measuring data integrity. It can be broadly expressed as:
Attributable: A single person to be held responsible and accountable from the beginning to the completion of a particular task.
Legible: The data gathered should be clear and simplified so that it is easy to read and conceive and can be preserved for future usages.
Contemporaneous: The data created and the activity conducted need to be running simultaneously i.e., in real-time.
Original: The data should be authentic and its validity to be maintained throughout the product life cycle.
Accurate: Resemblance of the task and data is necessary to prove accuracy.
a. Managing of Blinds
Interactive Response Technology (IRT) helps in systematically maintaining data blind. All the data starting with assignment of treatment and continuing towards ultimate data analysis can be easily viewed in the audit trail timeline. Unblinding of data is generally the outcome when there is no or a poor IRT system in effect. Finally, it is the responsibility of all the stakeholders to pay special attention while the blind is being enforced so that it can be maintained all through.
b. Streamlining Audit Trail
Data of Audit Trail are extremely significant as later inspections related to pharmacokinetic studies; randomization or a double-blind could be benefitted from it. The final data can be reviewed by comparing the source data with the end data. It is enough to reveal whether the subjects received opposite treatment meaning active drugs and not a placebo or they received mixed treatment where they were given both active drug and placebo or the drugs were given under wrong dietary conditions like dosage to be given in fasting were given after feeding and vice versa. IRT system makes it easier to trace all these details through the audit trail so that issues can be handled on time rather than discovering them during an inspection.
It is data integrity that the entire industry is concerned about and IRT systems will ensure it only if it is properly verified. So, for vendors it is of utmost importance to verify the IRT not only for vendor qualification but also during the implementation of protocol change or any such other changes. This will ensure that the data that you retrieve is completely integrated.
Start Small & Smart with Regulatory Automation
Companies who did large implementations (like RIM, EDMS, Change Management, ERP, others) have all noticed their costs and timelines doubled than what is budgeted initially.
These large systems that BioPharma implemented have helped them streamline business functions and track data. However many of these systems still require significant manual processing. PwC's 2017 effectiveness benchmark report found that users spend half their time focused on mundane, repetitive tasks of gathering data from various systems. This led to many of the systems reaching the point of diminishing returns.
On positive side, the speed, scale and cost of automation are evolving better with help of robotic automation, natural language processing, machine learning systems that offer companies new opportunities to improve process performance and realize significant cost savings
For Regulatory processes, some of these technologies can be implemented in short sprints, focused on specific sub-processes, with manageable costs. This approach of "small & smart" automation leads to "fast" implementation of flexible and adaptable technologies that fill the gaps left by your current or legacy enterprise tools or document systems, thereby enabling much higher productivity for labeling teams
Small automation can improve the productivity of individual regulatory processes by 80 to 100 percent and overall labeling functions by 30 percent or more
Small automation does not replace large enterprise systems or your future big automation initiatives. These are easier to implement and much less expensive. They can be applied to individual processes or tasks without having to go through complex cross-functional discussions (negotiations) and coordination in large projects.
Traditionally, companies could not begin to capture gains from large IT implementations until they had standardized processes and databases that were accessible to a broad set of users, which could take years. In contrast, automation doesn't depend on standardization or centralization (unlike your large projects or systems) leading to higher flexibility and adaptability
Applying small automation is anew way of working, and company leaders will need to ensure that both they and their teams have right knowledge to be successful.
"small" automation can be applied to several processes like auto authoring, data with document merging, document publishing, submission readiness checks, translation automation, compliance checks, broad QC checks, and few more areas
Please reach out to us to discuss how we are helping customers achieve these automation goals using our patent-pending REGai which is a cloud based modular focused Regulatory platform that will work with your current systems / technology