Authors
Dr. Frank Deruyck
University College Ghent
Dr. Volker Kraft
JMP
Muralidhara A
JMP
Objective
Use Design of Experiments (DOE) to advance knowledge about the process.
Background
FVM Pharmaceuticals is an international drug manufacturer, specialized in manufacturing finished formulations that cater to the most demanding global needs. FVM delivers contract manufacturing of tablets, capsules, and liquids.
A typical manufacturing process involves milling an active pharmaceutical ingredient (API) into a powder of uniform particle size. The milled material is then blended with other ingredients to bulk up and evenly distribute the API. This blended material is then compressed into tablets, which are finally coated to aid shelf life, taste, and other properties. At the end of the process, various quality parameters including critical to quality (CTQ) metrics are populated, which further drive batch acceptance.
The process starts with a raw material that is a concentrated emulsion containing two organic compounds. The raw material is supplied by two vendors, and the incoming quality is monitored by measuring the concentration of the compounds in milligram per liter (mg/l). Each day, a quality lab operator takes raw input material in two batches from each supplier into the process.
The process of chromatography is a laboratory technique for the separation of a mixture. The company is currently leveraging gas chromatography (GC), a common type of chromatography used in analytical chemistry for separating and analyzing compounds that can be vaporized without decomposition.
The Task
Recently, the quality control team observed a significant variability in the quality of the drug delivered. To address this issue, a cross-functional team was formed to identify the root cause of the problem and solve it. Lawrence, head of the team and a quality engineer, is a firm believer of the data-enabled decision making. He also knows that building a strong quality culture into the process demands application of statistical techniques to discover the actionable insights. He is aware that bringing operational excellence into the manufacturing process is a sequential and multistage process starting from raw material to final inspection. At the same time, Quality by Design (QbD) involves ensuring quality throughout the production process (starting from raw material to finished product) while leaving enough flexibility in the manufacturing system.
Lawrence and his team conducted a detailed measurement system analysis to understand the different components or sources of variation in the process and concluded that there has been a significant difference between the batches. The team came to a consensus that the measurement variation results from either the non-optimized UHPLC method or the process settings. The deviations in the concentration 3 of the standard compounds (both Compound 1 and Compound 2) need to be within a standard specification limit.
From the brainstorming exercise and lab tests, the team learned that concentration levels of compounds are being influenced by temperature, eluent flow rate, %Acetonitrile/ml (all the four gradients), and wavelength. Lawrence and his team have a critical task ahead of them to identify optimal process settings to ensure that the concentration of the compounds remains within specification limits. The goal is to design a robust process that also accommodates the random variations.
With this background, Lawrence and his team decided to implement design of experiments (DOE), a scientific way to find the optimal factor settings with limited resources. The concentration levels of the standard samples of Compound 1 and Compound 2 are the two responses, with the goal of matching a target level. Temperature, eluent flow rate, %Acetonitrile/ml, and wavelength are the critical factors influencing the measured concentration of the compounds.
While these are the main effects, the team also wanted to understand the nonlinear effects (quadratic effects) of the critical factors on both responses. They also took experts’ advice to focus on the quadratic effects of temperature, eluent flow rate, %ACN (all gradients) and wavelength on the responses. From an interaction perspective, the team was advised to restrict the study to only two-way interactions between temperature and eluent flow rate, and all two-way interactions between %ACN and eluent flow rate (except % ACN 6ml).
The description of the responses and factors along with the limits are presented in the PDF.