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9/22/17
1
Formula(on and Process Science for Freeze Drying: Past, Present, Future
Michael J. Pikal School of Pharmacy
University of Connec(cut
Topic Areas
• Formula(on – Design – Characteriza(on of Formula(on
• Process – Design – Control – Scale Up
9/22/17
2
Formula(on-‐Past • Freeze Dry Pure Drug
• S(ll the choice when no stability issue and dose is moderate to high
• Freeze Dry with mannitol • Because that’s what you do! • Problem: mannitol is a good bulking agent but a poor stabilizer
• Freeze Dry with Lactose • Tradi(on! • Problem: Lactose is a reducing sugar
– With hGH, dry product 1 mo/25°C = half adduct degrada(on product
• OVen did not differen(ate between “bulking agent” and stabilizer func(on.
• Problem: good bulking agent (Mannitol) is poor stabilizer – Mannitol crystallizes therefore removing it’s role as a stabilizer
Formula(on-‐Present • Recognize the desirable proper(es of a stabilizer
– Remain amorphous and in same phase as drug – Have Tg well above desired storage and test condi(ons – Have Tg’ well above -‐40° for ease in processing – Be chemically inert –( no reducing sugars) – “Immobilize” the drug and reactants
• Mechanism not completely clear – For proteins, maintain high level of na(ve structure – Stabilizer to drug weight ra(o is important
• Beaer stability as level of stabilizer increases – Recognize that not all stabilizers are equivalent
• Mechanisms not completely clear • Recognize that buffers may shiV pH drama(cally during freezing – Use only enough buffer necessary for the capacity needed
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3
Some Data
Degrada(on Kine(cs is Not “First Order” Square Root of Time Kine(cs prevails for amorphous solids
2.01.51.00.50.00
1
2
3
4
5
6Freeze Dried AmorphousCrystalline
Dimer Formation During 40°C Storage in Dry Solid Forms of Insulin
|months
% D
imer
by
SEC
HPL
C
Note: Here, crystalline is less stable than amorphous!!
Dimer Formation in Crystalline and Amorphous Insulin
321093
94
95
96
97
98
99
100
Aggregation (SEC HPLC)Chemical Degradation (RP)
Examples of Square Root Time Kinetics for Degradation of Freeze Dried hGH at 40°C
|months
% M
onom
er o
r %
Rev
erse
Pha
se P
urity
monomer, trehalose-1
RP purity, trehalose-3
Aggregation and Chemical Degradation in hGH
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4
Storage Stability is Very Sensi(ve to Formula(on
Rate Constants, k(√t), at 40°C (40°C << Tg)
* residual water in range 0.7% to 2.5%; stability not correlated with %H2O * all formula(ons except Gly:Mann are glassy ** Trend is same for both chemical degrada(on and aggrega(on!
Why?
Human Growth Hormone Formula(ons
Storage Stability of hGH Formulations at 40°C
0.01
0.10
1.00
10.00
None
HES(1
)
Gly
(1):M
ann(5
)
Stach
yose
(1)
Treh
alose
(1)
Treh
alose
(3)
Treh
alose
(6)
Sucrose
(1)
Sucrose
(3)
Sucrose
(6)
Stabilizer System
Rate
Co
nsta
nt,
%/√
mo
kRP,%/√lmo
kSEC,%/√mo
*
*
STD NEW
Why is hGH More Stable in Sucrose Formula(ons?
• Structure? – More “na(ve” structure in sucrose?
• No, at least not secondary structure!
• Dynamics? – Less molecular mobility in sucrose?
• No, at least not if mobility is measured by proximity to Tg and/or “structural relaxa(on (me”
9/22/17
5
Is the Difference Structure? FTIR Structure of 1:6 hGH:Disaccharide Formula(ons
-0.070
-0.060
-0.050
-0.040
-0.030
-0.020
-0.010
0.000
0.010
160016201640166016801700
wavenumber
Sucrose 1:6
Trehalose 1:6
No Difference between Sucrose and Trehalose! Is FTIR the appropriate measure of structure?
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
Suc, 40 Treh, 40 Suc, 50 Treh, 50
ln(tb)'or'1
0*kagg'
System, Temperature
hGH'Stability'and'TAM'Relaxa=on'Time:'Comparison'of'Disaccharides'
All'formula=ons'are'6:1'Disaccharide:hGH'(w/w)'ln(tb). TAM
10*k, agg
The trehalose formula(on has longer relaxa(on (me (lower mobility) than the sucrose formula(on, but the sucrose formula(on is more stable!
9/22/17
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Fast Dynamics: hGH in Sucrose and Trehalose
1:6 hGH : Sugar
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0 50 100 150 200 250 300 350 400 450T [K]
Trehalose u^2Sucrose u^2
Tg sucrose
Tg trehalose
Amplitude of nano-‐second mo(ons from neutron scaaering
• less mobility (I.e., lower amplitude) in sucrose systems, un(l well above Tg.
Stability and Fast Dynamics • Reciprocal of mean amplitude of fast motion by
neutron scattering, 1/<u2>
12 M. Cicerone et al. Soft Matter, 8, 2012
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Relationship between the normalized aggregation rate constant and fast local mobility (1/<u2>) at 50 oC for five different proteins
Increasing Sucrose level
-0.50
1.50
3.50
5.50
2 2.2 2.4 2.6 2.8 3 3.2 3.4 3.6 3.8
1/<u2>
Ln (
k/X
p) +
2
A
B
C
D
E
Linear (A)Linear (E)Linear (D)
• Excellent correlation between stability & “Fast Dynamics”.
Stability and “Fast Dynamics”
13
New Technique for Characteriza(on of “Fast Dynamics” mean square amplitude, <u2>
Fluorescence Red Edge Effect
Lines = neutron ScaAering; Symbols = Fluorescence Blue = trehalose
Red = Sucrose
• Good agreement between techniques!
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Surface Effects Maybe Protein at Surface is “ReacDve”
• Interac(on with ice during freezing – unfolding
• Protein at surface is “concentrated” – Par(al separa(on from stabilizer
• Protein at surface is in “reac(ve environment” – Greater mobility!
15
Surface diffusion is as much as six orders of magnitude faster!
Stability (50°C) Correla(ons in IgG1:Sucrose (1:4)
• Fair correla(on of stability with % of protein on surface -‐addi(onal variable (not discussed) is thermal history varia(on giving mobility
varia(on
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0
0.5
1
1.5
2
2.5
3
3.5
4
Estim
ated
% (w
/w) o
f the
tota
l pr
otei
n on
the
surf
ace
k'ag
greg
atio
n at
50o
C (%
agg
./mon
th0.
5 )
Estimated % of the total protein on the surface k' aggregation (50C)
Spray dried Foam dried
LYO ANNLYO
9/22/17
9
Comparison of Kine(c Model with Data: AggregaDon aHer 16 weeks at 50°C
• Most degrada(on occurs in the surface region!! • very low protein (0.1%) & 5% saccharide = large heterogeneity effect (>20x)
• Some difference with stabilizer (HES poor stabilizer) 17
Formulation-Future • Specific Effects do seem to be present-‐Inves(gate
– Different proteins behave differently: Why? • Sucrose stabilizes hGH beaer than trehalose, but with KGF, no real difference
• Answer Ques(ons: – Other cri(cl factors, what are they?
• Likely that “coupling” between matrix and protein varies between stabilizers
– How to measure? • Is protein structure really driving stability differences?
– What is role of ter(ary structure: how to measure in solid?
• Specific surface area measurement and <u2> by fluorescence will see much greater use in characterizing formula(ons
• Develop stabilizer systems (that work) that have much higher collapse temperatures than sucrose or trehalose
9/22/17
10
Process-‐Past • Distant past-‐No pressure control • Revela(on!-‐ Pressure is important
– “spoiling the vacuum” increases drying rate • The nitrogen bleed “sweeps” out the water vapor
– NOT REALLY!!! • Process monitoring by thermocouples (TC) in product
– Operate by fixed (me, based on “some” lab studies, or on TC response
– Problems: significant scale up problems, and TC response guarantees the TC vials are OK, but the other 40,000 vials dry differently
– Scale up based on “experience”, and luck! • Interdependence of formula(on and process (phase chemistry)
oVen not recognized – Eutec(c temperature recognized and “melt-‐back” must be avoided
• OVen insufficient laboratory studies – No “Quality by Design”or QbD: rather QbA “quality by accident”
Process-‐Present • Chamber pressure control recognized as cri(cal
– “spoiling the vacuum” accelerates drying because heat transfer is more efficient and product dries at higher temperatures.
• Measurement of collapse temperature recognized as important • Importance of varia(on in ice nuclea(on temperature recognized
– Control of ice nuclea(on now possible – Is Scale-‐Up problem, but implementa(on in mfg slow
• End Point of 1° Drying now can be easily detected – However, these techniques are not rou(nely used in mfg
• Temperature in secondary drying can impact stability; high T, beaer stability • Qualifica(on of dryer for representa(ve heat and mass transfer now
recognized as important – However, quan(ta(ve qualifica(on data commonly not obtained
• Quality by Design (QbD) philosophy presented, and execu(on of QbD being discussed – However, not universally applied to product and process development
9/22/17
11
Some Data
Predic(ve Collapse Temperature Measurement OpDcal Coherence Tomography
• Looks at collapse in a vial rather than thin film • Data for 5% Sucrose:
– DSC: Tg’ = -‐ 34°C – Conven(onal FD Microscopy: Tc = -‐32°C – OCT: Tc = -‐28°C
• Data for 1:3 BSA:Sucrose – Tg’ = -‐28°C – Conven(onal FD Microscopy: Tc =-‐26°C to -‐28°C – OCT: No Collapse! (only shrinkage) – Product freeze dried in lab freeze dryer, Tp(max) ≈ -‐21°C. No collapse observed!
• 4 hr primary drying with OCT driven cycle; • 20 hr primary drying with FD Microscopy cycle • SEM and SSA suggest “micro-‐collapse” • No difference visual
22
9/22/17
12
Ice Nuclea(on is Scale-‐Up Issue • Observa(on: In laboratory, supercooling much less than in manufacturing (non-‐TC vials) – lower level of ice-‐nuclea(ng par(culates in manufacturing?
• Result: product runs warmer (≈1-‐2°C) and 1° drying is longer (≈10-‐30%) in manufacturing!
• Solu(ons: – Set shelf colder (≈3°C) in manufacturing and run about 30% longer in 1°
drying. – Anneal to increase size of ice crystals and decrease difference between
lab and produc(on. – Nucleate to fix degree of super-‐cooling
Video of Ice Nuclea(on courtesy of Praxair
1. Uncontrolled Nucleation Nucleation over long time and over large temperature range.
2. Controlled Nucleation Near instantaneous nucleation, at fixed temperature (-5°)
9/22/17
13
• Run ≈ “constant” product temperature 2°-‐5° below collapse temperature; this is the TARGET PRODUCT TEMPERATURE
-‐ must know what collapse temperature is!
• Maintain chamber pressure 10-‐30% of P(H2O) -‐ near upper limit of 30% for low collapse temperature (i.e., ≈ -‐30°C) -‐ near lower limit of 10% for high collapse temperature (i.e., ≈ -‐15°C)
• Heat input must decrease with (me to hold at “target” product temperature
-‐ may oVen tolerate small (i.e., 2°C-‐3°C) increase in product temperature -‐ if so, maintain constant heat input for simplicity in process design
-‐ if need to hold constant product temperature, must decrease heat input -‐ decrease shelf temperature or decrease chamber pressure
• Determine shelf temperature vs (me program (by experiment or calcula(on) -‐ Do experiments: use fill volume and containers of interest! -‐ Find appropriate shelf temperature to maintain target product temperature
Guidelines for Process Development: Primary Drying Primary Drying: General Principles
9/22/17
14
Modeling of Primary Drying • Guide Formula(on and Process Op(miza(on Efforts – Help design and interpret experiments – Facilitate defini(on of “Design Space”
• More later!
• Assist in Trouble Shoo(ng Problems – Quan(fy the effects of heat transfer varia(ons on product temperature history
• container effects • vial posi(on effects
Steady State Models: The “LyoCalculator”
• Advantages: – very quickly, and easily, can inves(gate ice temperature and drying (me for primary drying
• with minimal suitable mass and heat transfer input data – Normally, about as accurate as experiment, some(mes beaer!
• Useful in “what if” experiments and an aid in process design
• Limita(ons – Cannot provide informa(on during periods of shelf temperature increase (i.e., during non-‐steady state)
– Cannot provide informa(on on residual moisture in “dry” layer. • With current “LyoCalculator”
– Is normally limited to one-‐dimensional problems (i.e., slab geometry)
• cannot inves(gate impact of intra-‐vial heterogeneity in heat transfer or material proper(es.
9/22/17
15
Simple Steady State Heat and Mass Transfer Theory
Mass Transfer : dmdt
= ApP0 T( ) − Pc( )
ˆ R ps
; lnP0 =−6144.96
T+ 24.01849
Heat Transfer : dQdt
= Av ⋅ Kv Pc( ) ⋅ Ts − T − ΔT( ); ΔT → function of dm/dt
Coupling : dQdt
= ΔH s ⋅dmdt
ΔH s Ap / Av( ) ⋅ P0 T( ) − Pc( )ˆ R ps
- K v Ts − T − ΔT( )= 0
• One Equation, one unknown (T): Solve for T, get dm/dt. and then calculate drying time- Basis of the “Lyo-Calculator”
The LyoCalculator
9/22/17
16
Experiment and Calculations Agree Well: Blue = Exp., Red = Calc.
Product 5% w/w
Vial Fill cc
Shelf, interior, °C
Pc, Torr
1° Drying Time, hr
Shelf Surface,Ts
Mean Tp
Max Tp
PVP W5816 8 -5 0.1 25.8 -9.6 -27.8 -25.3 26.9 -9.9 -27.3 -24.6 Mannitol W5816 8 -5 0.1 33.4 -8.6 -22.4 -20.2 34.8 -8.9 -22.9 -18.5 Mannitol W5816 8 +15 0.1 19.2 +6.2 -17.0 -14.2 19.1 +8.0 -17.0 -11.8 Mannitol W5816 8 +15 0.4 14.0 +5.7 -13.0 -11.9 15.8 +6.6 -11.8 -8.0 Mannitol 5303 20 +15 0.4 19.2 +6.1 -14.5 -12.8 19.0 +8.1 -13.5 -9.7
OLD METHODOLGY:M. Pikal, PDA Journal, 39, 115-138 (1985)
Now available as “Lyo-Calculator”
Role of “Design of Experiments” (DOE) in Primary Drying Design
• Virtually, no role at all – DOE is useful when mechanistic understanding is
poor – The physics of primary drying is well understood (i.e. “Lyo-Calculator)
• General statistics dogma: DOE is an efficient way to generate a “response surface” (or Design Space) – Not true for freeze drying in general, and is very
inefficient for primary drying.
9/22/17
17
DOE: Box-Behnken Design Independent variables: (3) chamber Pressure, shelf
temperature, Ice nucleation temperature Responses:(3) 1° drying time (hr), mean product temp. maximum product temp in 1° drying, sublimation rate
• 15 freeze drying experiments, average 2 days per experiment---> 30 days run time • Physics Driven: do runs in green, 4 runs--> 8 days
Exp. # Pattern Pchamber T shelf Ice Nucl. Temp 1° dry hr Tp mean Tp(max) mean dm/dt1 0.4 -5 -12.5 33.9 -18.7 -15.1 0.2282 0.25 -5 -5 31.4 -21.3 -17.2 0.2463 /++0 0.4 15 -12.5 16 -13.8 -8.2 0.4834 0.1 -5 -12.5 35.5 -23.6 -18.6 0.2185 /000 0.25 5 -12.5 22.1 -17.8 -12.6 0.3506 0.25 15 -20 17.3 -14.4 -8.2 0.4477 0.1 15 -12.5 19.4 -18.4 -11.8 0.3988 0.25 -5 -20 35 -19.9 -15.6 0.2219 0.1 5 -20 26.4 -19.8 -13.8 0.29310 /000 0.25 5 -12.5 22.4 -18.3 -13.4 0.34511 /+0+ 0.4 5 -5 21.1 -16.8 -12.3 0.36612 0.4 5 -20 23 -15.2 -10.4 0.33613 /000 0.25 5 -12.5 21.2 -17.2 -12 0.36514 /0++ 0.25 15 -5 16.1 -16.5 -10.6 0.48015 0.1 5 -5 24.1 -21.9 -16.1 0.321
Using Physics • Vial Heat Transfer Coefficients
– Previously determined for all vials used by company, vs. Pressure-3 days required for each vial type
• Dry Layer Resistance- 4 experiments! – Unique to formulation and ice nucleation temperature – Need runs at three ice nucleation temperatures
• See GREEN on previous slide – Rp evaluated from MTM data and/or cycle product temperatures.
– Prudent to do one of the runs that give high product temperature to compare with center point temp.
• Provides two replicate runs for Rp @ center point ice nucl. • Provides validation of calculations in extreme case
– Resistance normally independent of temperature, but not near collapse temperature!
• Total Run Time of 8 days, save 22 d, $66MM
9/22/17
18
The Non-‐Steady State Model in Two Dimensions
(three dimensions with an axis of symmetry) “Passage FD or equivalent”
• Based upon a set of coupled differen(al equa(ons (M. J. Millman, A. I. Liapis, and J. M. Marchello, AIChE J. 31, 1594-‐1604(1985)
– conserva(on of mass – conserva(on of energy – input data for mass and heat transfer coefficients – flexible boundary condi(ons
• allows a variety of problems to be studied • Uses Finite Element Analysis
– allows extension to 2-‐D & study of complex geometries
• Employs a “Modular SoVware Package” – for ease and flexibility of use
36
PAT: DeterminaDon of End Point of 1° Drying
5% Sucrose
Needs 1. Use the
technology 2. Confront the
“can’t steam sterilize myth”
3. Stop QbA (quality by accident) -‐fixed (me cycle
4. Start QbD
Compara(ve Pressure: Pirani vs MKS
9/22/17
19
Advanced Freeze Drying PAT Tunable Diode Laser AbsorpDon Spectroscopy (TDLAS) Flux Monitoring
for LyophilizaDon via Doppler ShiH ! TDLAS has been used for real-time, in-line freeze-dryer monitoring
– Water vapor concentrations– Gas flow velocity
– (< 5 to >200 m/s – Mass Flux determinations
(<3x10-4 grams/sec to– >4x10-2 grams/sec)– Determination of mass flow rate for determination of choked flow
! TDLAS mass flux determinations in satisfactory agreement with:
! gravimetric mass loss determinations! MTM mass flux
! TDLAS can be used to measure product temperature (with Kv input)_
TDLAS Product Temperature– Sucrose 5%
-50
-45
-40
-35
-30
-25
-20
-15
-10
0 2 4 6 8 10 12 14 16 18
Hours
t°C
T shelf surface
TC Front
TC Center 1
TC Center 2
TC Center 3
TC Back
TDLAS Product Temperature
9/22/17
20
Stability and Thermal History: Annealing Impacts Stability!
• ANNEALING: (accident or planned) – Hold sample at T<Tg for given (me(s), as in secondary drying – Energy decreases, Structure Increases, Free Volume Decreases, Mobility Decreases, Stability improves!
• Means that terminal secondary drying temperature high, storage stability improves
3
3.5
4
4.5
5
5.5
6
0 0.5 1 1.5 2 2.5 3
(Months)^0.5
DK
P A
rea%
Unannealed Annealed @ 60C for 20 hours
Aggrega(on: small molecule & protein Aspartame: sucrose (1:10) formulation
0
2
4
6
8
10
ECA:Sucrose(1:10) ECA:Trehalose(1:10) IgG1:Sucrose(1:1)
System
Rate
Co
nsta
nt
for A
gg
reg
ati
on
(√
t)
Fresh Freeze Dried
Annealed 10 hr
Scale-‐Up Issues
• Freezing (ice-‐nuclea(on) Differences* – causes mass transfer differences: impacts drying temperature and (me
• Heat and Mass Transfer Differences – lab and manufacturing dryers not always same
• Timing is Different – “everything” takes longer in produc(on!
• Measurement Differences – temperature, pressure measured same way?
9/22/17
21
Scale-‐Up: Correc(ng for Difference in Ice Nuclea(on Temperature
1. Lab runs at ≥ 2 ice nucleation temperatures
-measure Specific Surface Area (SSA)
-measure dry layer resistance Rp (MTM or from cycle data)
2. Measure SSA of product produced in sterile run (Clinical trial batch).
3. Estimate resistance of “sterile batch” using SSA vs. <Rp> correlation.
- <Rp> is linear in SSA
4. Use simple steady state heat and mass transfer theory to estimate lab to mfg cycle difference (LyoCalculator).
Process Implica(ons of Posi(on Effects and Ice Nuclea(on temperature: Product Tmax 10°C lower ice nucleaDon temperature in Mfg
-35
-30
-25
-20
-15
-10
-5
0
Mannitol/Lab Mannitol/Mfg Protein-Suc/Lab
Protein-Suc/Mfg
Sucrose/Lab Sucrose/Mfg
Tmax
, °C
Compare Tp(max), Lab vs. Mfg: Including Vial position effects AND Ice Nucleation temperature (10° difference Mfg to Lab)
Tmax, center
Tmax, side Tchg
• 2°C to 4°C difference in max. product T, lab to mfg (significant)• Effects can be calculated with Rp and Kv input to LyoCalclator
9/22/17
22
43
Freeze Drying in Syringes and Cartridges Bad Heat Transfer
Sublima(on Rate (dm/dt) in Plexiglass Holder Close Packed Array
0.0E+00
5.0E-06
1.0E-05
1.5E-05
2.0E-05
2.5E-05
3.0E-05
3.5E-05
4.0E-05
4.5E-05
0 50 100 150 200 250 300
Chamber Pressure, P c (mTorr)
Sub
limat
ion
Rat
e, d
m/d
t (g
/s)
Center Syringes Edge Syringes
Note: 1. Sublima(on Rate decreases as Pressure Decreases 2. ≈70% faster sublima(on for edge syringes (hoaer)
• Need beaer heat transfer system (Al block)
QbD-‐ Should it be MORE WORK? • Answer: No, not in the “long run”!
– Development (me and $$$ counts with delays due to problems that surface!
• Efficiencies: – Make use of “Pla}orm Technologies”
• First (me through, lots of work, but then …
– Do what is necessary-‐”meaningful risk analysis” – Do not overdo DOE!
• QbD does NOT demand DOE, only demands good science!
9/22/17
23
Role of “Design of Experiments” (DOE) in Primary Drying Design
• Virtually, no role at all – DOE is useful when mechanistic
understanding is poor – The physics of primary drying is well
understood (i.e. “Lyo-Calculator) • General statistics dogma: DOE is an
efficient way to generate a “response surface” (or Design Space) – Not true for freeze drying in general, and is
very inefficient for primary drying.
DOE: Box-Behnken Design Independent variables: (3) chamber Pressure, shelf
temperature, Ice nucleation temperature Responses:(3) 1° drying time (hr), mean product temp. maximum product temp in 1° drying, sublimation rate
• 15 freeze drying experiments, average 2 days per experiment---> 30 days run time • Physics Driven: do runs in green, 4 runs--> 8 days
Exp. # Pattern Pchamber T shelf Ice Nucl. Temp 1° dry hr Tp mean Tp(max) mean dm/dt1 0.4 -5 -12.5 33.9 -18.7 -15.1 0.2282 0.25 -5 -5 31.4 -21.3 -17.2 0.2463 /++0 0.4 15 -12.5 16 -13.8 -8.2 0.4834 0.1 -5 -12.5 35.5 -23.6 -18.6 0.2185 /000 0.25 5 -12.5 22.1 -17.8 -12.6 0.3506 0.25 15 -20 17.3 -14.4 -8.2 0.4477 0.1 15 -12.5 19.4 -18.4 -11.8 0.3988 0.25 -5 -20 35 -19.9 -15.6 0.2219 0.1 5 -20 26.4 -19.8 -13.8 0.29310 /000 0.25 5 -12.5 22.4 -18.3 -13.4 0.34511 /+0+ 0.4 5 -5 21.1 -16.8 -12.3 0.36612 0.4 5 -20 23 -15.2 -10.4 0.33613 /000 0.25 5 -12.5 21.2 -17.2 -12 0.36514 /0++ 0.25 15 -5 16.1 -16.5 -10.6 0.48015 0.1 5 -5 24.1 -21.9 -16.1 0.321
9/22/17
24
Process-‐Future • Implement what we now know how to do! (hopefully)
• Perfect our ability to predict impact of natural process varia(ons on thermal history
• Develop beaer holders for syringes & cartridges • Develop TDLAS for use in single vials • Develop Con(nuous Freeze Drying Technology
– Several efforts claim (par(al) success • Advantages Suggested
– Beaer control – Beaer quality – Faster freeze drying
– Claimed advantages need “valida(on” by comparison with “best technology” for batch freeze drying with publica(on in the literature!
