MVDA Software Provides Insights into Your Process Data
Optimize Processes, Reduce Waste and Maintain Compliance
In the pharmaceutical industry and many others, the processes involved with product research and development, manufacturing, and usage, all contribute to a vast reserve of data that may harbor untapped insights. How can you put this data to use to speed your research, improve your processes, and create a stable, reliable product that consistently meets regulatory requirements?
The answer is Multivariate Data Analysis (MVDA). MVDA techniques are increasingly being used for scale and batch-to-batch comparison investigations to support or derive process understanding and, ultimately, to improve the quality, safety and efficacy of drug products.
Whether its historical data that currently resides in your database, time-series or batch process data, tapping into the power of multivariate data analysis will help you gain more effective and actionable insights that lead to huge savings in time, cost and resources.
MVDA transforms your data into visual information for easy interpretation and actionable insights.
What is Multivariate Data Analysis?
Multivariate Data Analysis (MVDA) is a statistical technique used to analyze data generated from more than one source or with multiple variables. MVDA provides a statistically relevant way to organize your data, visualize it and understand the relationships between different data points. The technique helps you understand cause and effect, find outliers and look for deviations. Moreover, MVDA helps you understand how various data points from different sources could be influencing each other.
SIMCA® MVDA Software Helps Optimize Processes
SIMCA® Multivariate Data Analysis software is the benchmark data analytics tool for scientists, engineers, researchers, product developers and others striving to gain information from large quantities of data.
This data analysis software tool:
Enables easy batch interpretation and analysis of large process data sets
Provides a summary of all types of process information, key trends, correlations and patterns all in one convenient data model
Permits faster troubleshooting
Reduces the risk of costly downtime
SIMCA-Q Embedded Multivariate Data Analytics
Find out how you can embed Multivariate Data Analytics into your solution and reduce the risk of error at the same time as speed up your analysis.
An MVDA engine like SIMCA®-Q gives you the power of many different tools like PCA, PLS/OPLS, , and supervised classification OPLS_DA to tackle diverse problems.
EduPack is an educational package for academia with all you need to learn data analytics. With hands-on practical skills to solve problems and explore data, you can become the master of your data.
With Multivariate Data Analytics (MVDA) you can explore complex datasets by visualizing patterns, trends and groups of data. When capturing the systematic parts, you can turn your data into information.
Multivariate vs Univariate Data Analysis
Historically, many pharmaceutical manufacturers took a univariate approach to evaluating and managing their R&D and production processes. But analyzing each parameter individually will not give you the full picture.
In pharmaceutical manufacturing processes, or indeed all manufacturing processes, it is vital to understand the relationship between parameters, since a combination of factors and interactions between variables are generally the cause of events.
This is why Multivariate Data Analysis (MVDA), rather than univariate data analysis, has become the most commonly used method for extracting information from large data sets in the pharmaceutical industry.
MVDA lets you analyze multiple variables simultaneously and understand how various parameters interact and affect each other.
Benefits of Using MVDA
MVDA has a number of valuable attributes for pharmaceutical R&D and manufacturing:
Analyze large amounts of data in a range of formats to understand relationships
Easily visualize cause and effect, relevant vs irrelevant factors
Meet regulatory requirements for reproducibility and transparency of results
Validate your models and analysis, demonstrating predictably and reproducibly
Allow for evolution of the analysis as new data is incorporated
Provide documentation and validation for evolution in your models
Using Principal Component Analysis (PCA) to Visualize Data
The multivariate data analysis techniques used to understand and visualize complex sets of data rely on a statistical method known as Principal Component Analysis (PCA).
PCA is used to present multivariate data as a smaller set of variables (summary indices) in order to observe trends, jumps, clusters and outliers. This overview may uncover the relationships between observations and variables, and among the variables.
Principal component analysis is one of the most popular multivariate statistical techniques. It has been widely used in the areas of pattern recognition and signal processing and is a statistical method under the broad title of factor analysis.
PCA helps you identify correlations between data points and visualize data in representative groups or summary indices.
What is Principal Component Analysis (PCA)?
Principal component analysis (PCA) is a statistical procedure that allows you to summarize the information content in large data tables by means of a smaller set of “summary indices” that can be more easily visualized and analyzed. The underlying data can be measurements describing properties of production samples, chemical compounds or reactions, process time points of a continuous process, batches from a batch process, biological individuals or trials of a DOE-protocol, for example. PCA is a key element of MVDA techniques.
MVDA Supports PAT
In addition to process optimization, MVDA can also be used to mine data from historical databases to help predict the properties of new materials. Process Analytical Technology (PAT) relies on MVDA, for example in monitoring any drift in the properties of excipients. In its most advanced form, MVDA can be used as a powerful tool in PAT to make the most of chemometrics for the release of materials in real time. Overall, MVDA is making major contributions across manufacturing, leading to complete end-to-end understanding.
MVDA Aids Regulatory Compliance
MVDA and software tools built on it, such as SIMCA, help ensure R&D and manufacturing adhere to regulatory requirements. In fact, the transparency and relative ease of validation of MVDA, together with its power in helping communication with colleagues and regulators, has led to the integration of MVDA into the regulatory framework. This means that as long as you use MVDA correctly according to guiding documents, you can be confident your conclusions are valid.
In many fields of life science today, data analysis involves defining the differences between groups of data or interpreting group differences in meaningful ways. For example, finding meaning in omics datasets (such as genomics, proteomics or metabolomics) often requires understanding where to draw the borders of the different class models. Depending on whether you are making comparison between data sets (to understand why classes are different) or trying to group data together (to find outliers), you will use either discriminant analysis or principal component analysis (PCA). MVDA tools are important for both.
Learn More About Multivariate Data Analysis
Past Webinars & Videos
Find out more about MVDA in one of our recorded webinars.
Get familiar with the basics of data analytics. Understand how to use data analytics in your day-to-day business activities. Learn how data analytics can generate value from your data.
EduPack Multivariate Data Analysis
The education package starts with an introduction to MVDA and how the methods can be applied to solve problems and gain information. With EduPack students get the best education in MVDA for generating good results.
MVDA EduPack is designed to give your students hands-on skills to solve problems and learn:
- How to explore data and look deeply into how changing parameters can affect outcomes
- How to visualize trends and clusters using an easy-to-understand graphical interface
- How to analyze, define and identify problems
- How to use sophisticated modeling techniques to create accurate predictions about product quality
- How to interpret and gain in-depth understanding of your data and learn from the data