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June 21, 2016

Imagine a robot sitting inside a patients body monitoring essential parameters and dispensing the required drugs something like releasing insulin as soon as it senses rise in blood sugar level. This is not a sci-fi movie. The scenario could be real very soon. As per a 2015 report by Israels, the pharma giant Pfizer, in cooperation with the DNA robot laboratory, is developing innovative disease management techniques, based on DNA molecule behavior and robotic techniques.

Given the ability to function even in potentially hazardous settings, and perform tasks at a rate and pace much beyond human capacity, robots are making inroads not just in factories and homes, but also in the pharma domain. The percentage of robotic automation however, varies across situations. Eliminating redundancy incomplex tasks, dispensing cell culture media into flasks, filling multi-well plates for washing, applying reagents in an immunoassay, and sequencing DNA fragments, are some typical robot-assisted pharma functions. Thanks to these applications, design automation is fast becoming an applied area in pharma too. Companies such as Zymark, Johnson & Johnson, and Boehringer Ingelheim GmbH are collaborating to create robotic automation systems. The Allegro system, which automates throughput screening and plate preparation, is a widely known outcome of such collaboration. Lets also look at a few other interesting examples.

Spanning the drug development lifecycle, and running on wireless scanners, the FactoryTalk Pharma Suite from Rockwell Automation, includes applications for managing recipes, guiding operators, automating tasks, integrating automation layers, and sending notifications. With FactoryTalk, a major pharma company optimized work-in-progress inventory, achieved first time right output, and saved up to $ 1.5 million in labor costs.

Designed for high-speed packaging lines, TaskMate is another robotic loading- unloading system from ESS Technologies. A FANUC robot, and modifiable ESS designed end-of-arm-tooling (EOAT), combined with programmable logic control, work in unison to load and unload products, to and from the packaging line. TaskMate also collects production data, and transmits it to Quality Assurance (QA) teams for analysis.

Apart from the assembly line, robots are also being deployed to address social issues such as enhancing generic health and quality of life. Cody, a robotic nurse developed by the Georgia Institute of Technology, is gentle enough to bathe elderly patients. Another robot named HERB, fetches household objects like cups, and can even clean the kitchen. Paro, is a therapeutic robot, which looks like a baby seal and provides a calming feel to patients with dementia and Alzheimers. As you read this post, scientists are experimenting with chips using micro electromechanical system techniques and complex algorithms, to enable and drive new robotic applications in pharma.

But what if robots malfunction? Can we put critical procedures and even human lives at stake? For instance, a California based manufacturer, Intuitive, developed the da Vinci Surgical System, comprising tools to assist surgeons perform complex surgeries. The multi-armed robot also gained FDA approval in 2000. Its ability to drive precise, minimally invasive surgeries through small incisions, helped prevent opening up of patients abdomen. Yes, this was great ability for a robot to possess. But perfect, zero error performance, required extensive training for the robot a tough ask, by all means.

While most pharma practitioners are in awe of robotic applications, believing the enough, documented proof of superiority of robotic systems over traditional techniques, some doctors, with a different opinion, cite instances of robotic malfunctions that resulted in serious (and even fatal) surgical injuries and law suits.

The FDA classifies medical devices into three categories: Class I, Class II, and Class III based on the risks associated with the devices. Class I are the low risk devices (such as catheters and endoscope cleaning brushes) subjected to the least regulatory controls. Having quality system controls in place generally suffices for Class I devices. Medical robots are classified as Class II (moderate risk) devices. On the other hand, Class III devices, such as coronary replacement valves, carry the highest risk quotient, and in addition to general controls, also require pre-market approval or PMA.

In a pharma robotic discussion, home care medical devices cannot be ignored. Used outside professional healthcare facilities and environments, these products and equipment are specifically targeted to patients with disabilities. Robots that assist healthcare logistics tasks, also fall under this category. The category also includes robots that support softer human-robot interaction (HRI) tasks, to improve general medical condition of patients. HRI also has uses in social care and overall improvement generic health and quality of life.

Cody, HERB and Paro are such robots, and FDAs draft guidance document lays down standards and approval norms for manufacturers of such devices. Given this scenario, what should quality assurance (QA) do?

IT and QA have critical roles to play. GxP compliance and risk management are top areas to address. From a quality perspective, Artificial Intelligence (AI) is treated as a medical device. Hence, regulations associated with medical devices come into play. These include 21 CFR part 820 (Quality System Regulation for medical devices sold in the United States), ISO 13485 (Requirements for a comprehensive quality management system for the design and manufacture of medical devices, and IEC 62304 (Medical Device software software life cycle processes). To ensure compliance and approvals, QA clearly, has a lot of work to do. Specifically, in addition to manufacturing norms, pharma QA units must also ensure compliance with the regulations standards like ISO 13485 and ISO 14971 on one side and IEC 62304, IEC 62304, ISO 60601-1 and IEC 62366 on the other as well as aim at reducing the risk of failure.

I would like to end with a message for our QA & Testing colleagues, specially the usability practitioners, who until now have been focusing on Human Computer Interaction (HCI). It is time to also study interactions between humans and robots. Human-robot interaction (HRI) is another, emerging multi-disciplinary field for QA professionals to keep a watch on.

Radha Ramesh is an ex TCSer and was the life sciences principal consultant with TCS Assurance Services Unit for North America specializing in all aspects of software testing, quality assurance and compliance from infrastructure compliance, through the life cycle of Life Sciences, IT as well as business process support. She brings over 28 years experience in the IT industry in systems development, analysis, quality accreditation, audits, testing and validation. Her past experiences include project implementation and management as well as new project development. In the last 17 years, Radha had focused on test automation, SOX ITGC controls and Computer Systems Validation for regulated industries in the US, Europe and Asia. Prior to joining TCS, Radha held management positions in pharmaceutical companies and software consulting companies.


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