Mechanistic modeling—the ultimate HTPD tool?

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Experimental and analysis Analytical support

Stephan Menzel

Modeling (GoSilico)

Acknowledgements

Anna Edman-Örlefors

Ulrika Knutsson

Tobias Hahn

Nora Geng

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Evolution of process development methodology

One-factor-at-a-time (OFAT )

Design of experiments

(DoE)

High-throughput

process development

(HTPD)

Process robustnessQuality by

Design (QbD)

Mechanistic modeling

Evolution of process development methodology

Today Future

Clinical safety and efficacy

Quality target product profile

Critical quality attributes

Critical process parameters and raw material attributes

Design space

Current QbD paradigm

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TODAY TOMORROW: DRIVEN BY DATA AND SIMULATION

• Decision making

enhanced by

simulation

• Lower risk in

scale-up and

manufacture

• Potentially

greater flexibility

• Trial and error

• Process

understanding

limited by

experiment

cost and time

Inte

gra

ted

da

ta

Disco

nn

ecte

d d

ata

Manufacture

Optimize

Calibrate

Screen

Verify

Scale up

Optimize

Screen

Process development workflow

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Evolution of scale down models for chromatography

Prepacked, prequalifiedTime saving

Prepacked for QbD-compliant PDRobust process outcome

Prepacked for modelingSpeed up PD with data driven decisions

Process Characterization Kit

packed into 3 Validation columns

3 ligand densities, representing a low,

average, and high value in the

manufacturing envelope.

Mechanistic modeling columns

Prepacked column with data• internal column dead volume

• particle porosity

• interstitial porosity

• ligand density

• axial dispersion

• particle size (d50v)

Validation columns

Prepacked 1 cm i.d. Tricorn™ column

20 cm bed height (or custom)

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Today Future

Scale-down

models from GE

Mechanistic modeling:Opportunities and consequences

Mechanistic modeling opportunities throughout drug development

Pre-clinical Phase I Phase II Phase III Commercial

Accelerate

process

development

for toxicity runs

Reduce number

of runs to 5–8

per step

Support scale-up

and tech transfer

activities

Reduce process

characterization

wet lab work

Support root

cause analysis

and release

decisions for

deviations

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Reducing number of experiments vs fractions to analyze

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Experiment typeNumber

of runs

Number of

fractions

Breakthrough curve 1 10

Three factor DoE (CCF) 17 17 × 3

Sum 18 61

Experiment typeNumber of

runs

Number of

fractions

Column calibration 2 0

Breakthrough curve 1 10

Model calibration (gradient runs) 4 93

Sum 7 103

Mechanistic modeling (case study) DoE study (hypothetical)

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Each fraction is analyzed for charge variants, HMW/LMW, HCP, Protein A

Analytical support is crucial!

CCF = Central composite face centered, HMW = High molecular weights, LMW = Low molecular weights, HCP = Host cell proteins

9

Modeling landscape

𝐶 = 𝑘 ∗ 𝑃𝐺𝐴𝑆

1st principle models Mechanistic models Statistical modelsArtificial intelligence

models

𝐶𝑖 =𝑄𝑖

𝐾𝑆𝑀𝐴∗

𝐶𝑠𝑎𝑙𝑡Λ − 𝜎𝑖 + 𝜈𝑖 𝑄𝑖

𝜈𝑖𝐻𝑀𝑊% = 𝛽0 + 𝛽1𝑝𝐻 +

+ 𝛽11𝑝𝐻2 + 𝛽2 𝑁𝑎𝐶𝑙 + 𝜀

Steric Mass Action (SMA) Henry’s law Design of experiments Deep learning

Process understanding

Dependency on assumptions

General applicability

Size of training set

Risk if extrapolating

Amount of parameter fitting

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Modeling mAb separations—A tricky business with ”double bookkeeping”

How to model a monoclonal antibody peak?

Shark-fin peak shape: difficult to model as single species

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Capto™ S ImpAct

Charge variants

Single species vs charge variants The charge variants aren’t single species either

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Load material:

Acidic 51%, Main 22%, Basic 27%

Aggregates and fragments

Three species? At least multiple LMW species

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LMW and HMW

separate scale

Load material:

HMW 3.3%, LMW 3.8%LMW = Low molecular weights

HMW = High molecular weights

SEC = Size exclusion chromatography

Host cell proteins (HCP)

Certainly many species CHO cell total protein Cy™3 labelled (archive picture)

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HCP

separate scale

1494 spots detected

(not all them will be in the Protein A eluate)

Load material:

6035 ng/mL, 293 ppm

Protein A leakage

Leakage is not necessarily only intact Protein A ligand

Finally something that behaves like a single species! Protein A consists of multiple domains

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Protein A

separate

scale

Load material

893 ng/mL, 43.4 ppm

”Double bookkeeping”

Charge variants by analytical IEX HMW/LMW by analytical SEC

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Are we accounting for some species twice?IEX = Ion exchange chromatography

LMW = Low molecular weights

HMW = High molecular weights

SEC = Size exclusion chromatography

LMW and HMW

separate scale

mAb modeling case study

Case study setup

mAb protein A eluate (20.6 mg/mL)

Capto™ S ImpAct• Ionic capacity 0.046 mmol/mL resin• 0.561 mmol/mL solid phase

Tricorn™ 5/100 column (1.9 mL CV)

0.5 CV fractions collected• Charge variants• HMW/LMW• Leached Protein A• HCP

Total porosity by acetone

Interstitial porosity by KNO3• Donnan exclusion

Breakthrough curve

Four gradient runs• 0 CV/10 CV/25 CV/30 CV• Load ratio varied across gradient runs

Separation Calibration

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Modeling approach:

Generalized IEX isotherm (Mollerup) | Equilibrium dispersive mass transfer model

LMW = Low molecular weights

HMW = High molecular weights

HCP = Host cell proteins

0 CV gradient (97 g/L) 10 CV gradient (60 g/L)

25 CV gradient (121 g/L) 30 CV gradient (97 g/L)

Calibration runs UV300

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Breakthrough curve

Different X-axis

DBC = 121 g/L

DBC = Dynamic binding capacity at 10% breakthrough

0 CV gradient 10 CV gradient

25 CV gradient 30 CV gradient

Modeling results: calibration runs UV300

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Measured

Simulated

Salt

No fractions collected during early breakthrough

Factors investigated in optimization:• Step salt concentration• Gradient salt concentration• Pooling criteria (UV levels)

Optimize conditions for impurity removal (monomer yield >80%)

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Selected optimum:Lowest HMW with

yield > 80%

HMW = High molecular weights, LMW = Low molecular weights

Verifying optimized conditions: prediction vs reality

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UV peak shape only

qualitatively predicted

Load 60 g/L

Prediction vs reality

HMW/LMW removal prediction HMW/LMW removal verification

Unexpectedly high HMW concentration in first fractionsLMW = Low molecular weights

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Prediction vs reality

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Charge variant prediction Charge variant verification

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Basic variant(s) eluting earlier than expected

while the main variant is eluting slightly later

Prediction vs reality

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HCP prediction HCP verification

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Prediction vs reality

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Leached Protein A prediction Leached Protein A verification

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Summary

Summary

The case: tricky mAb with non-optimized upstream conditions

Challenging (=interesting) peak shapes to model• Not completely successful for charge variants and high molecular weights

– Need to include more data to improve model fit

Good analytical support is crucial for mechanistic modeling• Do we need better analysis to resolve the ”double bookkeeping”?

– Risk for overfitting and/or excessive computational times with more parameters to fit

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The ultimate HTPD tool?

Mechanistic modeling and HTPD• Steep learning curve and entry barrier—need to build knowledge (and infrastructure)• Analytical bottleneck comparable• Data management• Not mutually exclusive—HTPD workflow facilitated by modeling

– Kp screens and scale-down model qualification

Mechanistic modeling as high throughput• Rather long time to obtain a useful model• Very high throughput once you have a working model

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