Introduction

Quality Control is an integral operational part of the pharma industry where drugs at various stages of production,  raw materials, finished products, and other related states are systematically checked for efficacy and safety to detect and remove deviations and defects.

Quality Control, along with different sections of pharma, works to mitigate the risks related to the product’s adverse effects, recall, and legal complications to improve the product’s reliability, safety, and quality.

Pharmaceutical Quality control generates a considerable amount of data due to its critical nature of measuring and monitoring product quality. Additionally, using electronic devices & instruments for sample collection, analytical instruments to test the sample, and computers for record-keeping has also increased the amount of data in pharma quality control.

The amount of data generated through QC processes is analyzed to make many useful decisions. It helps to detect faults or deviations at various stages of different manufacturing operations. Additionally, process improvement is also performed through the use of data collected at QC.

Traditional Strategy for Quality Control

The traditional QC approach is to use manual and human-based methods to inspect & collect samples. The sample is taken at the end of the manufacturing process and after fixed frequency. The process is also repeated when a defect is detected.

The sample is transferred to the laboratory, where the analyst performs the required test. Depending on the test, it could sometimes take days for the final result; until then, the production process remains on hold. Human error is also possible during the test, which could also hamper the quality of the product. The capacity of the instrument is also limited, making the process more dependent and time-consuming.

This type of intermittent sample collection and measurement is reactive and initiates on a fixed frequency or time interval. Process improvement can be achieved in limited terms and does not focus on  real-time analysis, rather than initiates on a fixed frequency or time interval. It is also not capable of testing the entire manufacturing process continuously because testing the process continuously will disturb the productivity of the pharma organization.

Data-driven strategies for quality control

The opposite of a transitional QC strategy is a data-driven strategy. In this technique, different data points are used to collect all relevant datasets and use these data sets to detect quality issues. Different data techniques effectively collect all the required data and pass collected data to analytics to make informed & early detection and decisions. Quality control can be applied continuously rather than spontaneously and/or after fixed intervals. It increases flexibility for the QC personnel for monitoring and decision-making.

Data data-driven strategy enables personnel to act proactively to predict and prevent faults before they occur. It is made possible by smart sensors that provide real - time value of the process going on. The data collected is then used to improve the process during the production stage by altering different process variables. It results in higher efficiency and quality control in the pharma process.

Components of a Data-Driven Quality Control Strategy

Data-driven strategies are enabled by a series of components that play their corresponding part in implementing their role.

Some core components of data-driven quality control include but are not limited to, the following.

Data Collection

Data collection is used to measure the required parameters from the manufacturing process. The main difference between these data collection and traditional data collection techniques is their ability to measure the parameter without taking out the sample to the lab and waiting for the results. These data collection sensors can collect directly from the process and measure the real - time value.

Another major difference is  that result of sensors are made  available in a real-time manner.

The sensors for data collection are directly embedded into the process machine that continuously measures the value without stopping the process, taking the samples, and waiting for the results. These sensors use compatible communication media to send the collected data to a centralized controller or storage, where appropriate software algorithms are applied for decision-making.

For example, a moisture analyzer is attached to the machine for continuous moisture monitoring of the chemical substance during manufacturing. The values can be used to determine the real-time value and, based on the values, can adjust the manufacturing process if required.  

Predictive analysis modeling

After data collection, the most important thing is to utilize this collected data for the benefit of process, product, and organization. The predictive modeling defines the path through which the data will be used most beneficially for quality control.

Predictive modeling is the statistical approach where models predict the future based on historical data. The prediction made by the model can be used to detect a deviation in the process before it occurs, which can be prevented by adjusting necessary process or product parameters timely.

For quality control in the pharma industry, predictive modeling can be used to assess the current process & product condition, visualizing different parameters as they proceed before various manufacturing stages. The results from predictive quality control can be used to adjust process and product parameters, to prevent deviations and defects in the future.

Predictive analysis modeling is implemented by utilizing the large set of data collected through smart sensors and using appropriate software to develop suitable algorithms.

Data Insights

Data insights are using the information obtained from the data analytics stage to the benefit of the process and organization. It works by cleaning unnecessary data and transforming it into useful information.

Data insights are performed by a software tool that automates the data insight depending on the user specifications and requirements.

For the pharma quality control, data insights include all the quality-related insights.

For example, identifying the root causes of defects and increasing productivity through continuous feedback. It also includes real-time adjustments to immediately correct the process parameters instead of waiting to end the production process.

In addition to the above components, it also includes

● Storage - because data collected from various sensors must be stored, storage is also required and part of the data-driven strategy. For predictive modeling,  cloud storage is preferred due to its ability to handle large amounts of data. However, some pharma manufacturers also use in-house data centers.

● Communication Media - to handle large amounts of data collection and transfer, efficient communication media is required. It must be able to transfer data without delay and data loss.

Enablers of Data-Driven Quality Control

The enablers of data-driven quality control are the same as those being used in digitization techniques in other industries. Let’s briefly discuss those.

Internet of Things - IoT

Internet of Things refers to the physical devices or sensors capable of measuring and exchanging data between other devices, controllers, or centralized storage. These devices are able to measure and communicate in real-time, allowing  AI and ML to make decisions as the process undergoes. For data-driven quality control includes devices that collect process data and send it to centralized storage for analysis and necessary adjustments to protect or enhance the production process.

Data analytic techniques, such as AI and ML for further processing must analyze the data collected through IoT devices.

AI and Machine Learning

AI and Machine Learning - ML are technologies used to analyze large amounts of data,  efficient decision-making, provide real-time insights, and create forecasts & predictions. Although, technologically, they are different from each other,  they are used in combination with automated data analytics and predictions.

AI and ML are used to analyze huge amounts of data collected through process sensors for large production batches. It processes the collected data and improves data integrity for efficient decision-making.  It also enables predictive analysis to predict failure and allows personnel to make necessary adjustments.

Cloud and Edge Computing

Since data is the core ingredient for all  data-driven strategies, there must be some efficient storage that would store these large amounts of data in a secure way and the processes. Cloud Computing and Edge computing are two technologies common in this area

Cloud computing is remote on-demand storage where data is directly poured into. It offers an option for pharma owners to leverage the technology of third-party vendors and prevent them from investing in physical data centers and related costs. The biggest advantage of cloud computing is that it prevents data storage in silos for better utilization.

Edge computing is another storage technique where storage is moved closer to the measuring devices. In edge computing, the processing is performed at the device location, and only data relevant and important data is sent. There are some limitations to edge computing, such as less storage, complacent requirements, and real-time addressing at the device’s locations.

Each storage technology has its pros and cons. It is best to discuss with your service providers considering your requirements and various factors.

Conclusion

Digitization technologies in the pharma industry are rapidly being introduced and are making success in every department. They are supporting pharma manufacturers in achieving their goals of compliance, quality, productivity, reliability and market.

For Quality Control, data driven strategies can make them at par with other departments of the pharma industry. It can help to  enhance their performance and make them reputable for reliable & real - time results.