12 Steps

The first step of the process is the conceptual design strategy: Which product? Dedicated or flexible line? What capabilities are available at the company? What external capabilities will be needed?

Is the formulation suitable for CM? Critical Material Attributes of ingredients and blends. Flow properties – Segregation tendency – Wetting – Compaction – Electrostatics – Sticking.

Rigorous testing of unit operation performance.

Determine the relationship between operating parameters and equipment performance for the formulation of interest.

Characterize the residence time distribution (RTD) of each unit operation.

Determine local level control requirements.

CM models for process understanding are a regulatory expectation and also needed to enable process optimization and real-time quality control.

Our team has access to a broad library of models for application after completion of Steps 2 and 3.

The unit operation models are integrated into a single dynamic flow sheet model of the complete line to simulate the entire process.

Our team has pioneered the development of integrated modeling tools to design, study, control, and optimize manufacturing processes from the interactions between unit operations to evaluating the performance and design space of the entire process at a global level.

Analyze interaction between unit operations.

Examine system dynamics.

Understand effects of material attributes and process parameters on downstream unit operations.

Confirm unit operation compatibility.

Determine Critical Material Attributes. (CMAs)

Determine Critical Process Parameters. (CPPs)

Identify and characterize perturbations.

Demonstrate achievable quality.

Characterize the residence time distribution (RTD) of the integrated system.

To enable quality assurance of the final product, it is important to develop PAT methods that generate meaningful measurements. This is a regulatory expectation for Continuous Manufacturing.

Our team uses multivariate methodologies to analyze spectroscopic and non-spectroscopic measurements, and implement effective approaches for real-time monitoring and quality assurance: blend composition and homogeneity, blend density, particle size, and product quality (including prediction of dissolution performance).

The next step is to implement and validate PAT performance on the continuous line. PAT chemometric models are developed and tested online, by running materials with known properties, to confirm PAT performance.

In these steps, our team uses manufacturing parameter dynamic information generated by the process equipment to implement effective approaches and controls for real-time monitoring and quality assurance of blend composition and homogeneity, blend density, particle size, and product quality (prediction of dissolution performance).

Once appropriate methods have been developed, the next step is to implement and validate performance on the continuous line. This is an opportunity to identify and address issues that emerge only during extended testing (like material filming on sensor surfaces). PAT chemometric models should also be tested online by running materials with known properties through the process and confirming the ability of the models to accurately predict these properties.

When the continuous process is integrated with a real-time supervisory control system, it is called a “closed-loop process” that has achieved “level 1 control implementation”. This is the preference of regulatory agencies.

A distributed control system acts on a unit operation to modify the outcome of another unit operation, and to ensure efficient operation of the entire system, thereby achieving desired product quality attributes.

Under closed-loop operation, the raw and intermediate Critical Material Attributes (CMAs), the Critical Process Parameters (CPPs) and the final product Critical Quality Attributes (CQAs) are measured in real time. Corrective actions are taken in real time, using feedback and/or feed-forward controllers.

Our team designs efficient control methodologies using integrated flowsheet models of the continuous pharmaceutical manufacturing process to test process performance with the control system engaged. Performance of the control architecture is then verified in the physical line.

With controls implemented in the physical line, detailed studies are conducted to achieve optimum performance.

Our team uses experimental information to perform extensive characterization of system performance in the entire operational space. This minimizes time and cost, finding the best performing process conditions.

Multiple optimization goals (minimum manufacturing cost, maximum productivity, best quality, minimum changeover time) can be pursued individually or simultaneously.