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AcceleratingR&D
Maximizing the Benefits of PEGylation by Optimizing the Conjugation
ProcessThe Crossroad of Biotechnology
Montreal, February 9, 2005
Guus ScheefhalsVP Business Development
Avantium Technologies
Optimizing PEGylation
• Avantium Technologies• PEGylation• PEGylation downside• PEGylation optimization• Case study
Benefits of high-throughput experimentation
• Increase in R&D productivity• Increased speed, significant time savings• Better data consistency• Better coverage of experimental space • Higher knowledge return from R&D investments
PEGylation
• Chemically coupling PEG-agent to protein drug
• Marketed products:– Neulasta/Amgen– Somavert/Pfizer– Pegasys/Roche– PEG-Intron/Schering-Plough
PEGylation
• With the aim to:– Prolong circulation half-life (lower dosage frequency)– Decrease immune reactions and other side-effects– Reduce the amount of protein needed for the same
therapeutic effect
PEGylation downside
• Losses during the PEGylation process of 25% in late stage/commercial production to 50-75% in early clinical production are not uncommon
• Proteins as well as PEG agents are expensive
PEGylation downside
• Example of Marketed Product: Annual sales of $250 million and COGS of 20% => Each 10% yield improvement represents a value of $5 million per year.
• Example Early Clinical Product: Price per batch of $2.5 million =>Each 10% yield improvement represents a $250K cost reduction per batch.
PEGylation downside
• Maximizing the benefits requires Yield improvement!but usually not performed due to time constraints
PEGylation Process Optimization
Screening50-500 exp@1-5 mL Optimization
25-75 exp@20-50 mL
3-5 [email protected] LVerification
Start to Finish in 2-3 months
AcceleratingR&D
Yield / Cycle time optimization
Case studyCase study
The Process
• Pegylation of a polypeptide (NH2 specific conjugation)
mPEG-O linkerO
H2N Polypeptide NH
PolypeptidemPEG-O linker
+
reducing agentco-solvent
pH 7.525°C>100 h
The Problems• Process step has long cycle times and low yields
– Reaction time >100 h
– Yield 76%
– Conversion 80%
• R&D at the customer’s site:– Cycle time reduced to 40 h
– Yield reduced to 73%
– Conversion increased to 85%
– Undesired impurity profile
– pH important for impurity profile
The Objectives
• Reduce cycle time (<40 hrs)
• Minimize impurities (not exceed current process)
• Maximize yield (>85%)
Project Scope
• Phase 1: Rational screening of co-solvents and reducing agents
• Phase 2: Optimize process parameters
Rational screening
• How to select the co-solvents (=categorical variable)?
• How to ensure diversity?
• Topological• Use of the
connection table representation of the chemical structure.
• Employ methods drawn from mathematical graph theory.
• Geometrical• Calculated from 3D molecular models.
• Electronic• Calculated from semi-empirical or ab-initio calculations.
• Hybrid Representations• Encode the molecule's ability to interact with other compounds.• Encode the molecule's ability to form other species (e.g. complexes).
• Measured• Physicochemical (e.g. based on Spectroscopy or Thermodynamic)
The rational HT screening approach
The rational HT screening approach
• Co-solvents represented as points in ‘property space’• Selection based on existing knowledge
Rational Screening
• Experiments:
– All combinations of 15 solvents and 10 reducing agents
– Other conditions fixed based on current process conditions
– 150 pegylation reactions within 2 weeks (including analysis)
Rational screening• Screening results (Yield)
Reagent
40.3 39.7 21 24.2 50.6 49.6 22 37.4 8.5 14.1
75.9 71.8 35.4 43.2 70.8 70.6 37.5 42.6 20.1 61.6
34.8 30 11.4 18.7 32.2 29.7 8 16.4 14.1 43.4
69.4 73.2 45.1 32.6 71 69.6 34.8 35.4 26.3 20.5
72.2 68 17.9 46.3 72.6 62.3 22.7 54.1 23.5 63
53.9 49.7 28.5 25.4 50.6 45.2 26.9 24.9 3.7 24.2
64.2 66 36 22 61.2 62.8 31.5 25.1 21.4 33
72.8 66.4 35.7 39.9 71.6 69.8 30.9 44.2 13.7 72.3
46.8 51.5 46 59.9 45 48.2 38.6 64.3 15.1 59.1
57.5 40.1 26 15.5 43 45.6 20.2 19.5 6 44.3
64.1 67.8 29.7 24.3 66.3 68.3 27.3 29.6 27.3 28.2
66.5 70.1 19.9 53.5 66.2 61.3 13.8 48 23.5 71.8
78.5 76.4 32.1 48.5 78.1 74.2 39.1 54.7 30.7 75.9
75 75.5 20.3 41.9 78.7 74.3 36.7 50.5 46.8 73.7
64.5 66.9 14.1 42.3 70.1 68.7 18.5 49.5 17.4 73.5
REA 01 REA 02 REA 03 REA 04 REA 05 REA 06 REA 07 REA 08 REA 09 REA 10
SOL 01
SOL 02
SOL 03
SOL 04
SOL 05
SOL 06
SOL 07
SOL 08
SOL 09
SOL 10
SOL 11
SOL 12
SOL 13
SOL 14
SOL 15
Conclusions Phase 1
• Highlights:
– Yield improved to 78% with encouraging impurity profile (conversion of 84%)
– Co-solvent and reducing agent selected for optimization phase
– Time frame screening phase: 2 weeks
Scope
• Phase 1: Rational screening of co-solvents / reducing agent
• Phase 2: Optimize process parameters
Response surface modeling
• Evaluate process parameters (main effects study) – Equivalents of reducing agent
– Equivalents of PEG reagent
– Reducing agent addition rate
– Reaction concentration
– Stirring speed
– Stirring method (mechanical vs. orbital shaking)
– Reaction time
– Temperature
– pH
– Ionic strength
Response surface modeling
• Optimize significant process parameters and their interactions using statistical design of experiments (DoE)
– Reaction concentration
– Reaction time
– Equivalents of PEG reagent
– pH
• I-optimal design (48 reactions including replicates)
• Results in a response surface model (RSM)
Response surface modeling
Response surface modeling
Conclusions Case study
• Optimal co-solvent/reagent combination and 4 main process conditions identified and explored
• Response Surface Model indicating achievable yield of 92% within process time of 50 hrs (instead of target 40 hrs)
• Potential yield improvement of >15% representing a COGS reduction of > $5 mio per annum
• Acceptable impurity profile
• Time frame: <2 months
Summary
• PEGylation benefit of lower amount needed for same therapeutic effect can be optimized by yield optimization
• PEGylation yield improvement represents significant value
• Rational design, high-throughput experimentation is a very effective and efficient way to optimize PEGylation process
