Improving Digital Quality in the Pharmaceutical Industry

Technology is helping to de-mystify the digital landscape and drive quality throughout the product life cycle.

By Jan Paul Zonnenberg, Pharmaceutical and Life Sciences Consulting Partner, PwC

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Digital in general and Artificial Intelligence (AI) specifically is one of the transformational technologies in the next decade. It has already transformed many industries and functions. Bitcoin and driverless cars are often touted as the most advanced forms of digital in practice already. However, there are many other examples in place already such as Siri, Google Translate, the iRobot Roomba vacuum cleaner, Surveillance, etc. All these examples use AI in various forms. Within the healthcare industry, AI is now in use to augment the physicians in diagnosing cancer and reading x-rays & medical images. Certainly, these are but the first of many breakthroughs.

In our travels, it is clear that there is no common vision for the role of digital and AI to improve quality within the pharmaceutical industry. Indeed, there is probably a lack of understanding on what it means and what the potential opportunities are. This article will describe a vision on how digital could transform quality within the pharmaceutical industries and describe various use cases. First, we need to define quality, digital and AI.

Quality: Today, many within the industry think of the terms quality and compliance as interchangeable. At PwC, we, however, believe that quality is much more than compliance and indeed more focused on product quality and should be defined from a patient perspective (see figure 1). One definition is “The availability of robust medicines and reliable devices manufactured in a highly predictable and compliant manner that improve the quality-of-life for the patient.” Based on this definition, it is clear that quality is driven by the patient, as well as the design of the product & manufacturing processes, manufacturing operations, and the supply chain functions. From this perspective, the quality organization is an enabler of quality, but development, engineering, technical operations, manufacturing, etc. have a much larger and direct impact on quality. This discussion will focus on the broad definition of quality, not just quality as practiced today by the quality organization.


Digital: Figure 2 below highlights a few of the many components of digital and are certain to evolve over time. It includes hardware (drones, robotics, 3D printers, wearables, mobile devices, smart sensors, Internet of Things, location detection), software (Cloud computing, Big Data analytics, blockchain, Artificial Intelligence (AI), Robotic Process Analytics (RPA), visual recognition, virtual reality, augmented reality).

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Artificial Intelligence: AI is a subset of digital, but it is important enough to call out separately. AI is a very general term and by itself not very useful.  There are many distinct components of AI that are useful and implementable that can impact quality.  Key components of AI are:  voice recognition, computer vision systems, Natural Language Processing (NLP), machine learning, deep learning, predictive analytics, robotic process automation (RPA), and report writing. 

Quality begins in the development phase through designing, formulating, and/or testing with the purpose of developing robust medicines and reliable devices and associated manufacturing processes.  After a technology transfer, manufacturing has the challenge of assuring the availability of highly predictable products. The quality teams ensure the products are manufactured in a compliant manner.  Ultimately, during commercialization, the company must ensure the products are improving the quality-of-life for the patients. The blue initialized words link directly to the definition of quality above and demonstrate that quality is truly a cross-functional effort. Use cases are described below for each of the four phases of the lifecycle.  

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Development:  During the Development Phase, AI can be used in 2 use cases related to improving quality.

1) Product / Process Development: AI can be used to predict optimal product designs (formulations, specifications, manufacturing processes, test methods, etc.) through leveraging concepts such as Knowledge Management (KM) and Quality by Design (QbD).  AI can be used to extract knowledge from all previous development efforts as well as manufacturing data.  Although KM and QbD have been promoted over many years, technology is now at a point where this is feasible.  For example, PwC’s BodylogicalTM is a peer reviewed computational model of a human body that is able to simulate the outcome of health interventions on individuals. It was built after years of extensive research and coding of how the body works and is now able to forecast results for an individual patient. Pharmaceutical companies can use BodylogicalTM to model how the body reacts to therapies, thereby creating possibilities for faster, better targeted and less expensive clinical trials, accelerating time to market and improving health outcomes.

2) Clinical Data Management Quality:  While there are many use cases for AI in the clinic, this use case is specific to the data Quality aspects.  AI can be used to monitor the completeness, accuracy, and timeliness of the data as well as identify trends across data points to highlight potential issues with data integrity.  Ultimately, it can correlate the clinical outcomes back to the manufacturing and QMS related data.  As an example, it will be possible to monitor clinical outcomes of insulin usage through real time diagnostics and correlate the outcomes directly back to the individual lot of insulin used.  This is the ultimate in transforming quality from a compliance focus to a patient-outcome focused activity. 

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