Visual Numerics’ computational analysis libraries offer robust, accurate, and reliable algorithms for use in science, technical, and business environments. Inference for .NET enables computational analysis libraries to be easily encapsulated in Word documents, along with high level scripting commands, data, and explanatory text. With these capabilities at hand, I can rapidly create tailored solutions aimed at solving specific data analysis problems.
In this example, I’m going to show you how I can use Inference for .NET and Visual Numerics’
IMSL C# Numerical Library to perform an analysis of manufacturing data and document the results.
The Scenario
A pharmaceutical manufacturing facility has been producing single dose tablet drug product for several years. Current measurement systems are based on storing finished material and performing offline quality tests to assure the finished product meets performance specifications.
Our manufacturing team is assigned to investigate the manufacturing process and improve its consistency (sigma capability) by using Quality by Design tools.
What is Quality by Design?
The principles of QbD in pharmaceutical development are fairly straightforward. QbD requires the achievement of two levels of understanding:
- Clinical Understanding, which establishes a link between the attributes of the drug product and safety and efficacy in humans; and
- Process Understanding, which establishes a link between the attributes of the drug product and process parameters, process attributes and material attributes of the active pharmaceutical ingredient (API) and excipients that go into the drug product.
By implementing Quality by Design practices, the pharmaceutical manufacturer can increase drug quality and safety while reducing production costs. In this step of the investigation, we use Quality-by-Design tools from the Statistical Process Control arena to establish the current state of the process based on an analysis of historical manufacturing data.
A Tailored Solution
We’ve been tasked with using process control charts to analyze a key tablet performance metric and determine whether the manufacturing process is in a state of statistical control. We’ll collaborate remotely with our team, using a regulatory compliant workflow enabled by Microsoft Office SharePoint Server, to create a dynamic Inference document.
To start, we open a new Word document and add an Inference Parts Container. The Visual Numerics IMSL C# Numerical Libraries contains a plethora of process control functions that members of our team have used successfully in past projects. So we place this .NET assembly (labeled ImslCL) into the Word document. Then, team members import manufacturing data (labeled ManufacturingData) from an Excel spreadsheet:
The first question we need to answer is, "What is the problem with the Manufacturing Process." To address this we decide to generate a Shewart Control Chart by adding a code block and supporting IronPython code to utilize the class ShewhartControlChart of the embedded .NET library:
At this point, we’ve successfully created a dynamic Inference document that can be executed to create an Inference results document, producing the Shewhart Control Chart for analysis:
This chart shows us that 14 out of 90 batches failed to meet the 60-minute dissolution requirement. Now, we present these findings to our team as an Inference results document, concluding that even though the manufacturing process was not on target for process capability, the spread was small enough (within 3 sigmas) to indicate a process under statistical control.
Other members of our team perform further analysis in our dynamic Inference document using additional functions called from the VNI IMSL C# Numerical Libraries. Upon reviewing their results, our team determines that the manufacturer’s objective in development should be to move the process capability distribution toward the center while maintaining the same spread.