real time release in continuous solid dose manufacturingreal time release in continuous solid dose...

Real Time Release in Continuous Solid Dose Manufacturing:Systematic Characterization of Material Properties, and Optimal Design of Sensing and Control Methods

Data reconciliation in robust fault-tolerant control for continuous direct compaction

Session 5 Safety in the Pharmaceutical Industry and Manufacturing

Content

▪ Introduction▪ Continuous tablet manufacturing▪ Data reconciliation for tablet weight▪ Process control development▪ Risk analysis & assessment▪ Conclusions

▪ Introduction

[1] http://berkeleysciencereview.com/importance-uncertainty/[2] Khaleghi B, Khamis A, Karray FO, Razavi SN. Multisensor data fusion: A review of the state-of-the-art. Information Fusion.

2013; 14: 28-44.[3] https://blogs.salford.ac.uk/careers-employabilty/2017/01/20/day-6-decision-making-made-easier/

SafetyQuality

Control

https://www.truesocialmetrics.com/blog/three-things-everyone-should-learn-about-analytics

min𝑧

𝑧 − 𝑧𝑚𝑇W−1 𝑧 − 𝑧𝑚

subject to 𝑧 ⊆ [𝑦, 𝑥, 𝑢, 𝜃]

𝑓 𝑦, 𝑥, 𝑢, 𝜃 = 0

𝑔(𝑦, 𝑥, 𝑢, 𝜃) ≤ 0

Data reconciliation principle

𝑧𝑚 vector of measured process variables

𝑧 vector of reconciled measurement variables

W weighting matrix

y process output variables

x process state variables

u process input variables

𝜃 process model parameters

f (·) process equality equations

g (·) process inequality equations

Su Q, Bommireddy Y, Gonzalez M, Reklaitis GV, Nagy ZK. Data reconciliation in the Quality-by-Design (QbD) implementation of pharmaceutical continuous tablet manufacturing. 2018, in preparation.

Data reconciliation method and application

Valle ECd, Kalid RdA, Secchi AR, Kiperstok A. Collection of benchmark test problems for data reconciliation and gross error detection and identification. Computers and Chemical Engineering. 2018; 111: 134-148.

Data reconciliation in industrial applications

Rafiee A, Behrouzshad F. Data reconciliation with application to a natural gas processing plant. Journal of Natural Gas

Science and Engineering. 2016; 31: 538-545.

The equality constraints for the total mass balance of the plant

Based on the measured data, the mass flow of output streams is greater than the mass flow of the raw feed to the plant which leads to an imbalance of about 7%.

Data reconciliation with gross error test indicates there is no gross error in the measurements.

Data reconciliation in industrial application

Weiss GH, Romagnoli JA, Islam KA. Data reconciliation-An industrial case study. Computers and Chemical

Engineering. 1996; 20(12): 1441-1449.

Based on simplified mass and energy balances, the overall heat transfer coefficient, estimated using reconciled measurement data could be used to determine better regeneration cycle time of the reactor.

Pharmaceutical continuous manufacturing

crystallization

Secondary manufacturing

UK EPSRC Center for Continuous Manufacturing and Crystallisation (CMAC)

▪ Continuous tablet manufacturing

Pfizer, the Portable Continuous Miniature and Modular (PCMM) design, 2016 Facility of the Year

Awards

Vertex, FDA approved in July 2015Orkambi (lumacaftor/ivacaftor)

Janssen, FDA approved in April 2016darunavir

▪ In the last decade, the FDA’s Quality by Design (QbD) and Process Analytical Technologies (PAT) initiatives have been supporting the development of continuous manufacturing based on scientific process understanding.

▪ The pharmaceutical continuous manufacturing has recently been moving from conceptual designs to pilot/production processes, specifically in secondary manufacturing.

[1] http://blogs.fda.gov/fdavoice/index.php/2016/04/continuous-manufacturing-has-a-strong-impact-on-drug-quality/

Pharmaceutical continuous manufacturing (PCM) is identified as an emerging technology.

CMA CQA CPP

Quality by Design (QbD)

CQA monitoring

CQA control

Process Analytical Technology (PAT)

Risk Assessment

RiskEvaluation

Risk management

▪ Controllability▪ Stability▪ Robustness▪ Control design▪ Level 1 PID loops▪ Level 2 MPC▪ Evaluation

▪ Risk scoring▪ Risk mapping▪ Risk assessing▪ Risk planning

▪ Design space: A state-space model

The Continuous manufacturing pilot plant in Purdue University

▪ API composition▪ Mixing uniformity▪ Powder flowrate

Rotation speed

Tablet hardness

Tensile strength

Dissolution rate

Tablets mass flowrate

Tablet weight

Main compression force

Pharmaceutical continuous manufacturing pilot plant at Purdue University

Process disturbance or failure

Schenck AccuRate PureFeed® AP-300 loss-in-weight

feeders

“Rat hole”

Sensor measurement fault

Innopharma EyeConTM for particle size analyzer

‘’fouling’’

Sensor measurement uncertainty

Mass flowrate =Δ Gain in mass

ΔTime

Sensor measurement uncertainty

Tablet weight =Mass flowrate

Turret speed ∗ Station No

Main Comp. kN

Tablet production rate, kg/hour

Averaged tablet weight, mg

Turret, RPM

Dosing, mm

Sotax Auto Test 4

* Feb 26, 2018, API% = 10.0, MCC% = 89.8%, SiO2% = 0.2

Continuous tableting process: no quality control

Natoli BLP-16

Tablets

Sotax Auto Test 4

▪ The built-in Level 0 control in Natoli press BLP16 can only maintain critical process parameters (CPPs) of dosing position, turret speed. With newer version of NP-400, additional control of main compression force, also one of the CPPs, is possible.

▪ However, no critical quality attributes (CQAs), e.g., the tablet weight, hardness, is real-time monitored and controlled.

▪ Variations in material properties, e.g., particle size, bulk density, or the interactions between dosing and turret speed, can affect the die filling and thus the tablet weight, main compression force, hardness, etc.

▪ Turret speed▪ Dosing position▪ Feeder speed

Die filling Pre-comp. Main-comp.

▪ Pre-compression force▪ Turret speed

▪ Main-compression force▪ Turret speed▪ Punch displacement

▪ Powder bulk density▪ Powder flowability

▪ Powder bulk density▪ Powder/tablet weight

▪ Powder compressibility▪ Powder/tablet weight

▪ Main compression force is one of the critical process parameters (CPPs) that determines the final drug properties, e.g., hardness, dissolution rate, etc.

▪ Tablet weight is a critical quality attribute (CQA) concerning about the potency of the drug, but also a good process indicator of tableting process operation in die filling and compressing. Furthermore, tablet weight will also reflect the changes in material properties. The interlink between main compression force and tablet weight play a critical role in tablet press process control and quality assurance.

▪ The lock symbol here also means that if you use the relationship between tablet weight and main compression force to reconciliate the measurement of tablet weight, it would not create any process control stability issue, since the turret speed and dosing position are not included in the data reconciliation.

▪ Data reconciliation for tablet weight

Natoli BLP-16

Mettler Toledo ME4001E

Main comp. force

Kawakita model

Dell Laptop

Formulation characterization: compressibility

Data reconciliation, the power to combine knowledge, speed, accuracy, and precision

Tablets

Sotax Auto Test 4

* Feb 19, 2018, API% = 10.0, MCC% = 89.8%, SiO2% = 0.2

Changes in material properties -> compressibility

𝜌𝐶 = 0.2499

𝜌𝐶 = 0.2499

𝜌𝐶 = 0.2577

𝜌𝐶 = 0.2607

Tablet press data reconciliation with open-loop operation

Tablet press data reconciliation with closed-loop operation

▪ No process dynamic information was removed or filtered out, as the data reconciliation using tablet weight and main compression force was not interacting with the Level 2 MPC process control loop.

▪ The use of reconciliated tablet weight measurement in process control loop should be possible, improving the process control performance.

▪ NOTE: At this stage, the Level 2 MPC process

control and the data reconciliation were running independently.

Feb 5, 2018, API% = 10.0, MCC% = 89.8%, SiO2% = 0.2

▪ No process dynamic information was removed or filtered out, as the data reconciliation using tablet weight and main compression force was not interacting with the Level 2 MPC process control loop.

▪ The use of reconciliated tablet weight measurement in process control loop should be possible, improving the process control performance.

Tablet press data reconciliation with closed-loop operation

▪ NOTE: At this stage, the Level 2 MPC process

control and the data reconciliation were running independently.

Feb 5, 2018, API% = 10.0, MCC% = 89.8%, SiO2% = 0.2

Data reconciliation under different tuning strategies

Slow update with good initial value of 𝜌𝐶

Slow update with rough initial value of 𝜌𝐶

Fast update with rough initial value of 𝜌𝐶

0.2600

0.2600

0.2600

Level 1 control scheme for Natoli Tablet Press

▪ Process control development

Level 2 control scheme for Natoli Tablet Press

Feb 26, 2018, API% =10, MCC% =89.8, SiO2% = 0.2

▪ Open loop operation for the tablet press to reach steady state

▪ At the mean time, the model in the Data Reconciliation was also automatically calibrated and verified with Auto Test (AT4) measurement of tablet weight

▪ The Level 1 control showed promising performance with the reconciliated measurements of tablet weight and tablet production rate.

▪ Disturbance due to the longer duration of AT4 gate opening to take tablet samples was introduced, the Level 1 control was capable to bring the process back to normal after the abnormal disturbance.

▪ The interaction between dosing and turret speed under Level 1 control can also be observed at time 1600s, when the reduced turret speed causing variations in the dosing, main compression force, and turret speed.

Integration data reconciliation with close-loop operation (1)

Open loop Level 1 close loop

Integration data reconciliation with close-loop operation (2)

Open loop Level 1 close loop

Feb 27, 2018, API% =10, MCC% =89.8, SiO2% = 0.2

▪ Set-point changes for both tablet weight and production rate were taken, the Level 1 control also showed promising control performance.

Open loop Level 1 closed loop

AT4 samplingBucket changingTurret speed changing

Feb 27, 2018, API% =10, MCC% =89.8, SiO2% = 0.2

Data reconciliation to reject process error and filter process noise

Feb 27, 2018, API% =10, MCC% =89.8, SiO2% = 0.2

𝜌𝐶 = 0.2575

𝜌𝐶 = 0.2610

Data reconciliation to update the model parameter: critical density

▪ The initial value of critical density was obtained from previous run with raw materials from the same tanks.

Adaptive moving window time to collect 100 tablets: turret 13 RPM

Adaptive moving window time to collect 100 tablets: turret 20 RPM

Operation panel for tablet press Level 1 control

Comparison of Level 0 and Level 1 control

Level 0 control

Level 1 control

Mar 12, 2018, API% =10, MCC% =89.8, SiO2% = 0.2

▪ Relative density 𝜌𝑐 updated by data reconciliation▪ Both updated from initial value of 0.2575 to 0.2645▪ Auto calibration of the tablet weight measurement▪ Almost consistent material properties of the two runs

Comparison of Level 1 and Level 2 control performance

Better control performance was obtained with the Level 2 MPC control for the process set point change to the tablet weight while maintaining the same tablet flow. Unknown disturbance occurred to the tablet weight and main compression force, as highlighted in red, could be due to the feeding of the powder into die. After its removal, the system was back to normal and under control.

Level 1 Level 2

04/09/2018, API 10%, MCC 89.8%, SiO2 0.2%

Rate Severity Probability Controllability

1 No effect None < 1 occurrence during the continuous production in 5 years (e.g., control instrument failure)

Very rare All the applicablemeasurements &controls are in place

Always undercontrol

3 No patient effectProcess performance decreasing (e.g., divert of non-conformingproduct)

Low 1 occurrence during the continuous production in 1 to 2years (e.g., calibration errors)

Rare Most of the applicablemeasurements & controls are in place

High credibility of under control

5 No patient effectProcess performance decreasing(e.g., large amount of non-conforming product, process shut-down)

Medium < 1 occurrence during a single continuous production campaign (e.g., variance in raw material properties)

Sometimes Some of the applicablemeasurement &controls are in place

Moderate credibility of under control

7 Potential patient effect (e.g., large variance in critical quality attributes)

High > 1 occurrence during a single continuous production campaign (e.g., raw material reloading)

Frequent/No information

Few of the applicable measurements & controls are in place

Remote credibility of under control

9 Significant patient effect (e.g., large variance in target product quality profile, loss of clinicalperformance)

Very high

At any time during the continuous operation (e.g., machinery vibration)

High frequent None of the applicablemeasurements & controls are in place

No control

[1] Potter C. PQLI application of science- and risk-based approaches (ICH Q8, Q9, and Q10) to existing products. Journal of Pharmaceutical Innovation. 2009;4(1):4-23.

▪ Risk analysis and assessment

A risk mapping, from the perspective of process control, will be designed based on scores of severity and probability of risks during continuous manufacturing, upon which the proposed real-time release control strategies will be evaluated to give corresponding controllability scores for each risk.

45 < R227 <R1 <451< R0 < 27

Severity

1None

3Low

5Medium

7High

9Very high

Pro

bab

ility

9High frequent

7Frequent

5Sometimes

3Rare

1Very rare

1 Always under control

3 High credibility of under control

5 Moderate credibility of under control

7 Remote credibility of under control

9 No control

Machinery vibration

Raw material propertyvariation

System interaction

Level 0 control risk analysis in tablet press

Sensor noise

Sluggish Level 0 or machine control

Bulk densitychanges by shear

45 < R227 <R1 <451< R0 < 27

Severity

1None

3Low

5Medium

7High

9Very high

Pro

bab

ility

9High frequent

7Frequent

5Sometimes

3Rare

1Very rare





9 No control

Machinery vibration


System interaction

Sensor noise




▪ Conclusions and future work

▪ The importance of data reconciliation in reducing data imperfection was demonstrated for the real-time tablet weight measurement in the pharmaceutical continuous manufacturing.

▪ The current data reconciliation strategy is implemented to a subsystem of the tablet press based on the Kawakita model for main compression force and tablet weight.

▪ The future work would consider the implementation of data reconciliation to the feeding-blending system, as well as to the plant-wide system to reconcile more critical process parameters and critical quality attributes.

Acknowledgement

Prof. G.V. ReklaitisProf. Z.K. Nagy

M. Moreno J. Liu S. Ganesh

Prof. M. Gonzalez

Y. Bommireddy Dr. A. Giridhar Dr. D. Pai

real time release in continuous solid dose manufacturingreal time release in continuous solid dose...

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