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Data Validation

Data Validation

Method validation is a crucial process in analytical chemistry designed to confirm that a given method consistently produces reliable and high-quality results. This process is essential when a method is applied to different sample matrices or when procedural modifications are made, ensuring that the results meet acceptable standards across various applications.
Key parameters for method validation include:

Steady State Concentration

Steady State Concentration

A steady state refers to the level of a drug in the body once it has reached an equilibrium between administration and elimination. It represents the point at which the drug administration rate equals the drug elimination rate, resulting in a relatively constant concentration in the body over time. The dynamic equilibrium is crucial to ensure the drug's effectiveness with minimal risk of toxicity.
Most drugs are administered in repeated doses at fixed intervals or through continuous...

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Steady-State Data Reconciliation Framework for a Direct Continuous Tableting Line.

Mariana Moreno¹, Jianfeng Liu¹, Qinglin Su¹

¹Davidson School of Chemical Engineering, Purdue University, West Lafayette, IN 47906, USA.

Journal of Pharmaceutical Innovation

|February 24, 2023

Summary

This summary is machine-generated.

This study introduces a process model-based framework for reliable pharmaceutical process monitoring. It uses steady-state data reconciliation (SSDR) to accurately estimate process states and detect errors, enhancing real-time decision-making.

Keywords:

Data reconciliation Direct compression Monitoring

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Area of Science:

Chemical Engineering
Pharmaceutical Manufacturing
Process Systems Engineering

Background:

Real-time process monitoring in pharmaceutical manufacturing faces challenges with random and gross measurement errors.
Systematic error handling is crucial for reliable process control and exceptional event management.

Purpose of the Study:

To present a process model-based framework for predicting the most likely state of a process.
To enable real-time process decisions based on reliable state estimates, even with sensor network and measurement uncertainties.

Main Methods:

Employs data reconciliation (DR) and gross error detection (GED) to mitigate measurement errors and sensor malfunctions.
Compares steady-state data reconciliation using a model-based approach (SSDR-M) against a data-driven approach (SSDR-D) utilizing principal component analysis.

Main Results:

Both SSDR-M and SSDR-D yield comparable results for variable estimation and GED in linear or mildly nonlinear systems.
SSDR-M successfully estimates unmeasured variables and adheres to mass balances.

Conclusions:

Steady-state data reconciliation (SSDR) framework reliably estimates the true process state in the presence of gross errors, provided steady state is maintained and redundancy is met.
The SSDR framework, including both SSDR-M and SSDR-D, effectively detects gross errors, leading to more reliable process monitoring.