Development of resilient governance structures for the safe use of AI in regulated environments.
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GxP-compliant AI validation
Validation of AI and ML systems in accordance with regulatory requirements and risk-based approaches.
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Model transparency & explainability
Ensuring traceable and verifiable AI decisions for critical GMP processes.
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Data integrity & quality control
Safeguarding data quality and integrity along the entire AI lifecycle.
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Continuous Monitoring & Lifecycle Management
AI systems must be continuously monitored and controlled even after commissioning.
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Risk assessment for AI systems
Carrying out regulatory risk analyses for AI applications in GMP-relevant areas.
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Focus on deterministic & controllable AI
For critical GMP processes in particular, the current Annex 22 draft places high demands on the transparency, reproducibility and controllability of AI systems. Only deterministic models are accepted for certain applications.
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AI systems analyze historical quality data and identify recurring causes of errors much faster than manual processes. Large language models also support the creation of regulatory documentation.
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This can significantly reduce the amount of writing and documentation required.
After market launch, KI continuously analyzes spontaneous reports, scientific literature and clinical reports to identify potential side effects at an early stage.
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This allows risks to be identified more quickly and measures to be initiated at an early stage.
AI analyzes large volumes of batch and production data, detects anomalies and supports decision-making during batch release.
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Strict regulatory requirements apply to highly critical processes, particularly in the context of EU GMP and Annex 22.
AI and machine learning significantly accelerate the development of new active ingredients. Modern models analyze scientific data, identify potential target molecules (target identification) and support the development of new molecular structures. At the same time, potential toxicities can be predicted at an early stage of development, thereby reducing risks and development costs.
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Several AI-generated drugs – including for eye and liver diseases – are already undergoing clinical trials.
AI enables more efficient planning and implementation of clinical trials. Data-based analyses allow suitable patients to be identified more quickly, dosages to be optimized and potential risks to be identified at an early stage.
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This allows development times to be shortened, costs to be reduced and studies to be managed more efficiently.
AI algorithms continuously analyze sensor data from production systems and detect potential failures at an early stage – before production interruptions or quality problems occur.
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Virtual images of production processes enable real-time monitoring of complex manufacturing processes. Process deviations can be detected early on and production parameters can be dynamically optimized.
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Find out how AI and machine learning can make your research, development and processes more efficient.
Face the future in this area of the pharmaceutical industry and make AI a success story for your company.
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