Implementing Fundamental Pharmaceutical Science and Materials/Engineer Expertise in Scale-up 2 nd FDA/PQRI Conference on Advancing Product Quality Session: The Science of Tech Transfer/Scale-up North Bethesda, Maryland, October 05-07, 2015 Ecevit Bilgili (E-mail: bilgece@njit.edu, Phone: 973-596-2998) Associate Professor & Associate Chair Department of Chemical, Biological, & Pharmaceutical Engineering New Jersey Institute of Technology Newark, NJ
Outline A chemical engineering perspective to unit ops. scale-up Art or science/engineering or maybe both? Scale-up or down? What to scale-up? Fundamental, first-principle-based models (DEM/PBM/CFD) Criticality of understanding the key physical transformations, measuring relevant response variables and using scale-up rules/heuristics & PAT/simulators Case Study with Fluidized Bed Granulation (FBG) Scale-up Brief intro to FBG Demonstration of scale-up Do scaling rules/PAT/surrogate tools work? Conclusions and Outlook
The Concept of (FBG) Scale-up in Batch Processes (~100 g) (10-15 kg) (100-150 kg) Product volume, batch size, and capacity increase with scale.
Elements of a QbD Program D. Ventura, American Association of Pharmaceutical Scientists Workshop, Sept. 2006 Robust Product Formulation & Equipment Materials Process The same elements are needed for successful scale-up! Scale-up/down is an integral part of product (process/formulation) development.
Fact: Scale-up still entails a marriage between science/engineering and the art of making! Unit ops. scale-up has evolved from traditional trial-error approach to a creative activity involving scientific/engineering principles More use of scale-up rules based on fundamental dimensionless #s and empirical studies More use of first-principle-based models based on continuum theories or discrete particle interactions (DEM/PBM/CFD/FEM) & their combination More use of PAT and data-driven process models But the art of making/manufacturing did not disappear: Scale-up: a creative process also requiring skills based on experience (personal skills, company internal knowhow/culture) and observation of process, equipment, and operational aspects as well as economics Upon more use of scientific/engineering principles, the involvement of the art component will be less significant.
Scale-up or down? What to scale-up? Scale-up is an integral part of product development. Process development at small/pilot scale equipment must consider eventual scale-up. In the selection of smaller scale equipment/process, we use Scale-up rules for approximate scale-down In-house experience/expertise, equipment knowledge, etc. Retrospective studies of prior development activities We cannot perform DOEs at every scale. Hence, understanding the key physical transformations and considering equipment- independent, “key response variables ” for scale-up/down is critical. Design space grows automatically if extensive process variables vs. dimensionless or key response variables are used. Commercial Scale Scale-up Lab Scale Pilot Scale
On Various Process Modeling Approaches DEM Simulation: a milling ball on particles CFD Simulation of Multiphase Flow in an FBG: Volume fraction of powder DEM-PBM Multi-Scale Modeling Approach for Dry Milling (Capece et al., 2015, Chem. Eng. Sci.)
Case Study: Scale-up of Fluidized Bed Granulation (FBG) Process ABC of FBG
What is Fluid Bed Granulation? Definition: A wet granulation process in which API(s) and excipient powders, which are set in fluidization by a heated gas, are bound together by binder droplets originating from a two-fluid nozzle Objective: Form granules that allow or improve successful down-stream processing of pharmaceutical materials (from blending to tabletting) Materials: API(s), excipients, binder (usually dissolved in a solvent prior to atomization) Equipment: An FBG processer equipped with an air handling unit (AHU), two-fluid atomizing nozzle, and spray pump
How does FBG Work? Exhaust Air Exhaust Fan Police Filters Expansion Chamber Filter Bags Conical binder spray (droplets) Two-fluid Nozzle Assembly Product Bowl Powder Bed Air Ambient Air Pump Handling Sucked In Unit (AHU) Binder Solution Inlet Plenum Air Filters
Fluid Bed Granulation Parameters Equipment Process Formulation Hydrodynamic Behavior: Particle: Inlet air flow rate Gas Distributor Plate: Density, size, shape, type, nominal & open Area surface characteristics, Binder Soln. Dispersion & porosity, friction, terminal Droplet Size Distribution: Bowl-Expansion Chamber: velocity, initial moisture, dimensionless flux number, diameter, height, cone angle dissolution, hydrophilicity, spray rate, atomization Two-fluid Nozzle: wettability, mechanical air pressure and flow rate location on the column properties Product Bed Moisture Content number of nozzle heads & Temperature: liquid tip, air cap size Bulk/Powder: Spray rate, excess air velocity, relative position of tip/air cap Bulk/tap density, cohesion, inlet air flow rate, temperature, minimum fluidization and Air Handling Unit (AHU) and humidity bubbling velocity Filter Bag/Cartridge: Bed height: batch size type, pore size, permeability Binder and Binder Solution: Fines Incorporation: one-side vs. two-side shake, Level, concentration, shake duration & frequency, Pulsation pressure viscosity, surface tension inlet air flowrate 11
What is Fluidization? Fundamentals (I) Fluidization Regimes as Determined by Superficial Air Velocity & Material Characteristics Vigorous bubbling/turbulent fluidization is key to a successful FBG process. Schematic from a Lecture by Prof. J. Werther, 5 th World Cong. on Particle Technol. 2006
What is Fluidization? Fundamentals (II) Geldart’s Classification of Powders Lecture by Prof. J. Werther, 5 th World Cong. on Particle Technol. 2006
Case Study: Scale-up of Fluidized Bed Granulation Process Scale-up to Ensure Key Response Variables Remain Scale-Invariant
Process Scale-up (I): What to Maintain? Scaling rules based on theory/modeling/heuristics/ Key Input experiments are needed!!! Variables Air Flow Rate, Q Key Response (Output) Variables Distributor Plate Area, A Hydrodynamic Product Spray Rate, S Behavior Characteristics Inlet Air Bed moisture and PSD Temperature/RH temperature Drying-end-point Atomization Air Droplet size Pressure, P moisture distribution Granule Number of nozzle Binder/saturation Morphology heads, N distribution Granule porosity Spray foot-print area, A f
Process Scale-up (II): Scaling Rules for FBG Scaling Rules Key Input (Connecting Input Variables to Response) Key Response Air Flow Rate, Q (Output) Variables Distributor Plate Area, Q Hydrodynamic A = − = − u e u u u mf mf A Behavior Spray Rate, S Bed moisture and S , , T RH Inlet Air Temperature temperature in Q and Humidity, T in &RH S S Droplet size Atomization Air Flow Mehta (1988), 2 or 2 NM NP or Pressure, M a or P a Rambali (2003) distribution a a 3 S Dimensionless Spray Number of nozzle ψ = 2 Px heads, N Flux (Litster, 2001) or d Akkermans Flux #, ρ u A log p e f = FN Spray foot-print area, FN (1988) 10 S A f
Granule PSD upon Scale-up 100 Cumulative Mass Percent Retained (%) Peak LOD 45 L, 0219151:0042, 2.1% 80 45 L, 0219151:0043, 5.2% 45 L, 0219151:0044, 1.3% 420 L Scale Batch A, 1.6% 420 L Scale Batch B, 2.2% 60 40 20 0 0 200 400 600 800 1000 Sieve Opening Size ( µ m) Similar granule PSD achieved at 420L scale in Batch B, after slightly adjusting the spray rate from that in Batch A (Basis for scale-up: Batch 0042 at 45 L scale).
Conclusions & Outlook More science/engineering vs. the art More scale-up rules and modeling for process scale-up; no more trial-error A fundamental understanding of the underlying physical transformations as opposed to “black-box” treatment of processes To DesignOE or not to DesignOE upon scale-up? Too expensive, impractical, …Not needed with establishment of good process understanding at smal/pilot scales. Design space in terms of scale-independent parameters May provide regulatory flexibility for tech transfer Instead of reestablishing the design space at each scale, confirm the “relatively fixed design” space at larger scales
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