Tatsushi Murase | Chemical Process R&D Researcher
Data Analysis Software
For Pharmaceutical Process Engineers and Scientists
Pharmaceutical engineers and scientists are depended upon to derive insights from data, solve complex problems and design better processes. JMP data analysis software from SAS enables you to explore your process and lab data so you can understand sources of process variation, learn more from root-cause investigations, and optimize process and experimental designs, all without having to learn complicated coding.
Have a minute? See JMP in action.
Ono Pharmaceutical
With its simple operation method – ‘put in data and results come out’ – JMP is really easy to use. The effect of the contour line profiles (created based on the models) on quality within the manufacturing operation parameters is reflected visually. So creating highly robust processes with JMP is easy. In fact, learning the various functions of JMP helped me to develop a deeper knowledge of statistics.”
JMP Capabilities Ideal for Pharmaceutical Engineers and Scientists
Design of Experiments
Actively manipulate factors according to a pre-specified design to quickly and easily gain useful, new understanding.
Statistical Process Control
Separate common and special causes to assist your process analysis efforts, including problem investigation, out-of-control conditions and ongoing monitoring for stability.
Stability and Shelf Life Analysis
Assess poolability of batches, establish expiration dating, and easily calculate confidence limits and crossing times – all in adherence to ICH guidelines.
Quality by Design (QbD)
Identify and evaluate all sources of variability with respect to quality parameters of the finished product.
Dose Response
Analyze precision, accuracy, linearity, bias and reproducibility. Fit curves and compare models for a wide range of sigmoidal responses (4p, 5p). Efficiently assess parallelism for relative potency and apply streamlined methods for cut point determination.
Robust Process Optimization
Find the ‘sweet spot’ in the design where performance is minimally sensitive to variation for all critical quality attribute (CQA) goals in your process, following ICH Q11 guidelines.