Navigating AI Implementation in GxP Environment


Artificial intelligence (AI) has become a major focus for many companies, particularly those in the Life Science industry. With its ability to analyze vast amounts of data quickly, accurately and perform tasks in an automatic way, AI has the potential to accelerate the development of new drugs and improve patient outcomes. As a result, there is a growing interest in applying AI to drug discovery, clinical trials, and other aspects of the industry.

However, for AI to be adopted, it needs to meet the criteria of GxP regulations.

But, before we get into the life cycle and implementation process, we need to understand what AI and Machine learning (ML) are, and why they are so interconnected.


Artificial Intelligence (AI) refers to the ability of machines to mimic human intelligence.

This includes the ability to perceive, reason, learn, solve problems, and make decisions. It creates machines that can perform tasks that require human intelligence.

On the other hand, Machine Learning (ML) is a subfield of AI with a focus on creating algorithms that can learn from data. Machine Learning enables machines or computers to learn by themselves based on examples rather than being programmed by humans to perform specific tasks.

With a more comprehensive understanding of AI and ML, we can dig into the GxP implementation process.

Life Cycle

The AI validation (including ML) approach consists of 3 different phases:

Concept, Project Development, and Operation.

1. Concept

The business requirement or opportunity is identified, established, and agreed upon during the Concept Phase. The specific problem to be solved is determined, and the initial data is selected and prepared. A prototype needs to be created to evaluate and select the appropriate algorithms and parameters.

Key points:

  • Business Opportunity and Definition/Data Dependencies
  • Data Acquisition and Selection
  • Data Types, Preprocessing, and Classification
  • Data Transformation
  • Prototyping

2. Project Development

During the Project Phase, the selected technologies and technical architecture are specified in accordance with a predefined plan. Risk management and supporting activities such as project-based configuration and change management, begin. The ML sub-system’s project phase activities start with model design/selection, engineering, model training, testing, assessment, and parameter tweaking.

A Data management process must be implemented, and the data set must be categorized, validated, and used for model training.
During this phase, Agile methodology can be applied to support the ML sub-system’s activities.

Key points:

  • Model Requirements and Specifications
  • Model Design and Selection
  • Model and Data Engineering
  • Model Training
  • Evaluation and Model Testing
  • Model Integration and Deployment
  • Verification, Acceptance, and Release

3. Operation

During the operation phase, the performance must be monitored and assessed while real-time data becomes available.  Continuous improvement, including configuration, training, and testing is essential and requires effective change and configuration management.

Key points:

  • Monitoring and Continuous Evaluation
  • Performance/Trending

Supporting processes

For a successful AI deployment, the following processes must be defined and implemented over the full life cycle:

  • Risk Management

Risk management supports most of the processes and controls. It is crucial as a direct indicator of the potential harm that might occur in certain circumstances. The goal of machine learning is to increase efficiency while minimizing risks.

  • Data Governance

AI is data-driven, and if you have high-quality data, your AI data-based performance will be the same. Therefore, it is critical to implement a Data governance process to guarantee that the essential data is accurate, trustworthy, relevant to the AI model’s purpose, consistent, impartial, secure, and traceable. The ALCOA+ principles are an excellent example.

  • Change Management

AI can introduce new risks, such as biases, errors, and ethical concerns. Change management can help organizations mitigate these risks by ensuring that AI technologies are implemented and used responsibly and correctly.


For the safety and reliability of manufactured goods, it is critical that the validation process of AI systems in the GxP environment should be thorough and consistent.
It is essential to remember that validation should be performed by specialists in the relevant subject and for the specific purpose intended.

Life Science companies should also ensure that all regulations and legal requirements are met, in addition to providing additional training and support for AI engineers. Only by carefully validating, monitoring, and updating AI systems in the GxP environment; we can ensure the accuracy, safety, and reliability of the products being produced.

At GxP-CC, we offer expert services to support you in leveraging the power of AI technology, while ensuring compliance with GxP regulations. Contact us to learn how we can assist you in navigating this complex landscape and achieving the highest standards of quality, and patient safety.

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