Automatic system dynamics characterization of a pharmaceutical continuous production line.

Continuous Manufacturing (CM) of drug products is a new approach in the pharmaceutical industry. In the presented paper, a GMP continuous wet granulation line for production of solid oral dosage forms was investigated in order to assess the system dynamics of the line and to define the best control and diversion strategy. The following steps were involved in the continuous process: dosing/feeding, blending, twin-screw wet granulation, fluid-bed drying, sieving and tableting. Two drug products with two different drug substances were compared during this study: one drug substance as model drug compound and one formulation of a currently evaluated commercial drug product. Several step tests in API concentration were performed in order to characterize the process flow and assess the process dynamics. API content was monitored in real time by Process Analytical Technologies (PAT) thanks to three Near Infrared (NIR) probes located along the process and measuring the API content after blender, after dryer and in the tablet press feed frame. The process parameter values were changed during production in order to detect the impact on the quality of the final product. An automatic residence time distribution (RTD) computation method has been developed in order automate the RTD calculation on the basis of process data to further define and monitor the system dynamics with the final aim of out of specification material diversion during the continuous production. The RTD has been seen as a process fingerprint: a change in the RTD values implies a change in the process.

Model predictive control in pharmaceutical continuous manufacturing: A review from a user's perspective.

Pharmaceutical continuous manufacturing is considered as an emerging technology by the regulatory agencies, which have defined a framework guided by an effective quality risk management. With the understanding of process dynamics and the appropriate control strategy, pharmaceutical continuous manufacturing is able to tackle the Quality-by-Design paradigm that paves the way to the future smart manufacturing described by Quality-by-Control. The introduction of soft sensors seems to be a helpful tool to reach smart manufacturing. In fact, soft sensors have the ability to keep the quality attributes of the final drug product as close as possible to their references set by regulatory agencies and to mitigate the undesired events by potentially discard out of specification products. Within this review, challenges related to implementing these technologies are discussed. Then, automation control strategies for pharmaceutical continuous manufacturing are presented and discussed: current control tools such as the proportional integral derivative controllers are compared to advanced control techniques like model predictive control, which holds promise to be an advanced automation concept for pharmaceutical continuous manufacturing. Finally, industrial applications of model predictive control in pharmaceutical continuous manufacturing are outlined. Simulations studies as well as real implementation on pharmaceutical plant are gathered from the control of one single operation unit such as the tablet press to the control of a full direct compaction line. Model predictive control is a key to enable the industrial revolution or Industry 4.0.

Deep learning for continuous manufacturing of pharmaceutical solid dosage form.

Continuous Manufacturing (CM) of pharmaceutical drug products is a new approach within the pharmaceutical industry. In the presented paper, a GMP continuous wet granulation line for production of solid dosage forms was investigated. The line was composed of the subsequent continuous unit: operations feeding - twin-screw wet-granulation - fluid-bed drying - sieving and tableting. The formulation of a commercial entity was selected for this study. Several critical process parameters were evaluated in order to probe the process and to characterize the impact on quality attributes. Seven critical process parameters have been selected after a risk analysis: API and excipient mass flows of the two feeders, liquid feed rate and rotation speed of the extruder and rotation speed, temperature and airflow of the dryer. Eight quality attributes were controlled in real time by Process Analytical Technologies (PAT): API content after blender, after dryer, in tablet press feed frame and of tablet, LOD after dryer and PSD after dryer (three PSD parameters: x10 x50 x90). The process parameter values were changed during production in order to detect the impact on the quality of the final product. The deep learning techniques have been used in order to predict the quality attribute (output) with the process parameters (input). The use of deep learning reduces the noise and simplify the data interpretation for a better process understanding. After optimization, three hidden layers neural network were selected with 6 hidden neurons. The activation function ReLU (Rectified Linear Unit) and the ADAM optimizer were used with 2500 epochs (number of learning cycle). API contents, PSD values and LOD values were estimated with an error of calibration lower than 10%. The level of error allow an adequate process monitoring by DNN and we have proven that the main critical process parameters can be identified at a higher levelof process understanding. The synergy between PAT and process data science creates a superior monitoring framework of the continuous manufacturing line and increase the knowledge of this innovative production line and the products that it makes.

Continuous manufacturing process monitoring of pharmaceutical solid dosage form: A case study.

Continuous Manufacturing (CM) of pharmaceutical drug products is a rather new approach within the pharmaceutical industry. In the presented paper, a GMP continuous wet granulation line used for clinical production of solid dosage forms was investigated with a thorough monitoring strategy regarding process performance and robustness. The line was composed of the subsequent continuous unit operations feeding - twin-screw wet-granulation - fluid-bed drying - sieving and tableting; the formulation of a new pharmaceutical entity in development was selected for this study. In detail, a Design of Experiments (DoE) was used to evaluate the impact of the three main factors (amount of water, filling rate, and shear force in twin-screw granulator) on the tablet quality. The process was monitored via in-process control (IPC) tests (e.g. weight, hardness, disintegration, and loss-on-drying), Process Analytical Technologies (PAT), and through the analysis of the process parameters (multivariate process control). The tested formulation was very robust to the large process variation of the DoE: all IPC results were in specification, the PAT probes provided stable results for the content uniformity and no critical variations can be detected in the process parameters. An adequate monitoring strategy was presented and the robustness of the process with one formulation has been demonstrated. In summary, this continuous process in combination with smart formulation development allows the robust production of constant quality tablets. The synergy between PAT, process data science and IPC creates an adequate monitoring framework of the continuous manufacturing line.