Rapid Development and Manufacture of ADC's- Integration of Antibody and ADC Process Development and Optimisation"

Transcription

1 Rapid Development and Manufacture of ADC's- Integration of Antibody and ADC Process Development and Optimisation" Bo Kara, Director Science and Technology Fujifilm Diosynth Biotechnologies, UK Biomanufacturing Innovation Group: Improving Process Knowledge, Dublin, 27 th February, 2014

2 Outline ADC s Platform Approaches Process risk assessments Antibody Conjugate-ability mab production screening High throughput Learning Extraction Tools Data to so what Conjugation Heterogeneity extent and control DAR optimisation and characterisation Optimisation and control of reaction steps

3 Antibody Drug Conjugates (ADC s) Targeted therapy Antibody (or fragment) linked via stable chemical linker with labile bond to biologically active cytotoxic (anti-cancer) (Gemtuzumab ozogamicin (Mylotarg ): Approved Anti-CD33 antibody conjugated to cytotoxin calicheamicin: withdrawn 2010) Brentuximab vedotin (Adcetris ): anti-cd30 antibody conjugated via cleavable linker to cytotoxic monoethyl auristatin (MMAE) Hodgkins lymphoma and anaplastic large cell lymphoma s Ado-trastuzumab emtansine (Kadcyla ): anti-her2 antibody conjugated via non-cleavable linker to cytotoxic DM-1 (derivative of maytansine) Her2 metastatic breast cancer Market predicted to grow significantly E.g. $9Bn by 2023, (Roots Analysis) 30+ molecules in clinical trials (mid 2013), significant increase in pre-clinical molecules over 2013/2014 mab Linker Cytotoxic Drug 3

4 ADC Capabilities Fujifilm Diosynth-Piramal: Integrated Antibody + ADC process development and GMP manufacturing mab cell line development mab and ADC process optimisation Small scale and toxicology supply of mab and ADC GMP: Phase I/II/III, Process Characterisation, Process Validation, Launch and Commercial supply Experience: Example ADC platforms

5 Antibody Drug Conjugates: Increased Complexity mab Linker Typical ADC Schematic Multiple Sites : Drug-Antibody-Ratio Drug-Linker DAR (average) Drug >100x smaller than mab: But influences ADC considerably Antibody generally track record of stability BUT: Many small molecule drugs relatively hydrophobic: potential for hydrophobicity driven aggregation? Conjugation vs lysine potential to reduce number of protein surface charges hydrophilicity? Impact on ADC stability or solubility vs naked antibody?

6 ADC Design Complexity Connection : specific, selectively reactive handle Many (growing) approaches/chemistries Spacer : if trigger enzymatic may need to move away from sterically dense antibody Can engineer polarity vs aggregation and/or efflux Trigger : releasable (if used) but serum stability. Another polarity engineering option Self-immolative spacer : separate DRUG from Trigger. Enzyme access Drug : potency, permeability and mechanism of action

7 ADC Manufacturing: Schematic Linker DRUG MODIFICATION CONJUGATION REACTION Antibody Antibody-Linker Molecule Antibody-Drug Conjugate

8 Streamlining Development Integrated development Single project team that delivers: Cell line development (if required) o Representative material: for early conjugation development studies: 10mg to g s ADC s mab titre and quality (vs. conjugation) optimisation o Representative material from lab scale proving runs: 10 s g quantities ADC ADC development and optimisation o Representative material supply 100 s g quantities ADC mab and ADC analytical characterisation: Toolkit Management of raw materials supply chain o mab, Linker(s), drug(s) mab and final ADC scale up and manufacturing o Non-GMP pilot scale material: ~350g ADC scale o GMP material: >1kg scale ADC

9 Evolution and Incorporation of Process Knowledge Example: Client requirements: 3 antibody variants Conjugation chemistry options: A/B Formulation: to be developed Need 1: Early PoC material Need 2: In-vivo studies Need 3: Preclinical tox material Need 4: GMP material Phase I clinical trials Need 5: Flexible but fast track as possible programme Approach: Holistic process definition/optimisation to achieve final mab/adc CoG targets Evolving process understanding

10 Fast track programme Cell Line Dev mab2 + Conjugation A + B Process Development mab2 + ConjA: Scale-up demo batch Complete Analytical Qualification Transfection pools: Expand small scale ~100mg s to g representative mab 1, mab2, mab3 Rapid production low ETX conjugates mab2 selected to continue CLD mab/adc Formulation Dev Client: in-vitro and in-vivo characterisation ConjA selected mab2-a mab2-b Material Supply 200g mab2-a Demo Material Supply GMP Preparation Client: non- GMP tox studies Interim Reference GMP mab2 > 2kg mab2 Intermediate GMP mab2 ADC 1kg scale ADC for Phase I 1kg mab2 ADC Client: tox and Phase I Month 1,2,3 Month 4,5, months

11 ADC Process Understanding Design Space Tools Mechanistic modelling Experimental data and mechanistic knowledge of chemistry, physics, and engineering to model and predict performance Design of Experiments (DoE) Effect of multidimensional combinations and interactions of input variables and process parameters on final mab and ADC quality Characterise and establish the ranges for mab and ADC critical process parameters providing assurance of quality HT- approaches Scale-up correlations Semi-empirical approach to translate operating conditions between different scales or pieces of equipment Ensure relevance of small scale conjugation to scale up Combination of the above

12 Risk Assessment: RAPTA Risk Assessment Process Template Application o Risk Assessments based on ICH guidelines o Thorough, structured, standardised o Inbuilt lists of variables based on previous experiences in process characterisation and manufacturing o Automated visual plots (heat maps, sorted score plots) o Emphasis on impact of each individual parameter on CQA s o Impact, Detection and Occurrence within the unit operation o Links the various options of Mitigation Strategy relevant to our mab and ADC manufacturing facilities

13 RAPTA: Features Inbuilt lists of variables based on previous experiences in process characterisation and manufacturing Links the various options of Mitigation Strategy relevant to our manufacturing facilities Links process parameters to Critical Quality and Process Attributes The 'Sev', 'Occ', 'Det' and 'RPN' scores are colour coded to give visual Heat Map view Sorted RPN score with cut-off line to highlight parameter needing further Mitigation strategy & segregate parameters into highmedium-low risk category Cut-off line to separate into high, medium and low risk parameters Sorted OxD plot to assess the control of the process Sorted Severity plot to highlight parameter needing further evaluation and characterization through formal process ranging Plot and List of actions for further process improvement & control

14 Risk Assessment: RAPTA output for entire process Seed Stage Shake flask 2 Stage Seed fermenter age Upstream Production Stage Using RAPTA: distil down, assess, rationalise and prioritise what s important for the entire process Shake flask Stage Harvest Stage High Occurrence & Detectability Mitigation through control strategy, facility/equipment improvements, PAT. May require limited / one-off studies High Severity & RPN score Needs further evaluation and characterization through formal process ranging. Requires univariate (OFAT) &/or multivariate studies (DoE) Shake flask Stage Column Stage-2 Parameters needing further mitigation strategy (DOE/OFAT?) Shake flask Stage Etc

15 Improving process knowledge: high throughput upstream/downstream scale down systems Expt Design 48 Parallel Bioreactors mab intermediate.. Initial screen vs. conjugate-ability of material produced 10 s mg establish aggregation propensity pre/post conjugation

16 Need: Fast data transformation and analysis Tools developed: Enable scientist to quickly transform vast amounts data collected (5-10 mins) Interactive platform: help scientist to probe any data set, identify root cause and link to final DoE output Simplify further analysis - cluster analysis, multivariate analysis, etc Single core platform: adapted for other HT-experimental platforms

17 Data to Process Understanding in 5-10 mins HT-microbioreactor data file (+30MB) Online data ph DO Temp Gas data, etc., At line data Cell count, Viability, etc., At line data Product Concentration, Glucose User specify time stamp, location of file & align bioreactor codes with DoE run order Visualisation Script Dash board Visual Output (Time vs. VCC, Viability, ph, DO, etc) DoE Output Processing Script Estimates IVCC, Qp, peak VCC, etc DoE Script

18 ADC Process Development Rapid mab process development mab CQA as mab and vs conjugation-ability ADC process development parameters Partial reduction/modification ph, ionic strength/buffering Linker, reductant equivalents and kinetics Protein concentration Agitation, hold time stability Conjugation Co-solvent, e.g. DMSO, etc Stabilising exipients Drug XS Quenching

19 Example: Reduction Kinetics Reduction conditions ph - important parameter vs. reduction condition screening Reactivity disulphide to reduction - ph dependent Higher ph increases the rate of reaction May impact the overall conversion of reductant into free thiols on the antibody Some antibodies - more problematic to reduce May require higher excesses of reductant, elevated temperatures and higher ph s Can risk more damage to the antibody Controlled through careful selection of basic parameters

20 Example: TCEP: mab ratio and DAR Reduction reaction - critical to the process Translates directly into the final DAR Very tight control required over the charge of TCEP (tris(2- carboxyethyl)phosphine) and the total protein present vs. right molar ratio. Theoretically, one molecule of TCEP should generate two free thiols Often side reactions - consume the TCEP so this is not always seen. Reproducible reaction vs delivery of consistent DAR (average)

21 DAR Example: DAR Optimisation (DoE) DoE approach Particularly useful vs. difficult to conjugate antibodies Example screen: Protein concentration/ph vs. impact on DAR Understanding of the robustness of the process steps DESIGN-EXPERT Plot DAR X = B: ph Y = D: [mab] Actual Factors A: reducing agent = 3.00 C: Temperature = DAR D: [mab] B: ph ph [mab]

22 Summary ADC s are complex molecules Understand the molecule mab and DC influence on ADC process understanding ADC development requires integration of team understanding the biologic (mab) and the chemistry (linker, drug, ADC) and the interplay Integrated team Fast track but robust delivery Whole (holistic) process development approach Risk assessment tools HT- approaches Data analysis tools Process understanding quickly