Prior knowledge What it is and How we use it. Josefine Persson

Transcription

1 Prior knowledge What it is and How we use it Josefine Persson

2 What Knowledge do we have? Process Development Process Validation Development Knowledge Vendor Knowledge Manufacturing Knowledge Literature Public Knowledge Prior Validation Modeling & Knowledge Prediction Tools Product Specific Knowledge

3 How do we increase our Knowledge? PV data PD data = Process Validation = Process Development PV data Prior Knowledge PD data Not enough Knowledge Need to perform PV studies Have critical Knowledge Limited need for PV studies Extensive Process Knowledge PV studies not needed (???)

4 How can we systematically drive to minimize risk for product and process Functional areas are summarizing knowledge (Prior Validation Knowledge) to enable systematic use of knowledge for each unit operation Prior (Validation) Knowledge and computational modelling is currently used for Risk Ranking and Filtering 1. Filtering out pcqas that don t need to be tested (again) 2. Filtering out parameters that don t need to be studied (again) 3. Filter out full PV studies (e.g. centrifugation) Historical data can be used to create statistical models that are applicable for related products 4

5 Our Definition of Prior Knowledge Summary of Prior Validation data from one unit operation/study the unit operation has showed very comparable results between project Example: Affinity Chromatography for monoclonal antibodies Validation results from the affinity chromatography steps for 11 monoclonal antibodies were compared. While each process was individually optimized to attain the highest purity and yield, there are several commonalities across many mabs However, no two processes are identical The Prior Knowledge reveals similar low-risk and high-risk parameters (including claimed CPPs) across diverse mab formats and affinity resins.

6 Example of Prior (Validation) Knowledge for a specific output for the Affinity Chromatography step Process Parameter Molecule PP1 PP2 PP3 PP4 PP5 PP6 PP7 PP8 PP9 PP10 PP11 PP12 PP13 PP14 mab1 mab2 mab3 mab4 mab5 mab6 mab7 mab8 mab9 mab10 mab11 Not studied Low-Impact High-Impact Non-CPP Non-CPP Claimed range supported by platform data or no expected impact by parameter or change to process output Low impact CPP High impact CPP Minor effect - detectable variation within normal process No impact - no detectable variation or response Key

7 How is Prior Knowledge used for Risk Assessments for Validation Study Design Use Risk Ranking and Filtering (RRF) to rank the impact of each potential Critical Process Parameter (pcpp) Severity score impact of process parameter Uncertainty score degree of confidence in severity score, based on availability prior and product specific knowledge Risk Score = Severity x Uncertainty: Thresholds selected to enroll identify parameters as pcpps if their effects may be detectable Severity Definition Score No Impact Minor Impact Major Impact Variation in process input across the proposed characterization range alone, or if affected by an interaction, causes variation in process output which is not expected to be detectable (within assay variability) Variation in process input across the proposed characterization range alone, or if affected by an interaction, causes variation in process output which is detectable but expected to be within expected output range Variation in process input across the proposed characterization range alone, or if affected by an interaction, causes variation in process output which is expected to be outside expected output range Uncertainty Definition Score Low Product specific process development data available 2 Medium Prior Knowledge. Generally accepted scientific principle 4 High No public, prior or product specific knowledge available

8 Example of Buffer Stability Prior Knowledge Stability untested Stable to >28d Any new buffer within the previously validated range will be leveraging prior knowledge instead of performing product specific validation

9 Advantages with leveraging Prior Knowledge The validation phase to commercialization is faster and drugs can reach the patients faster The validation studies are smaller and of higher resolution for the important parameters and outputs Resulting in more deep knowledge about the important factors for the process, e.g. better process understanding 9

10 Reordering Activities and leverage Prior Knowledge Traditional Bioprocess Analytics Plan! DOE #1 Wait/Assess DOE #2 Wait/Assess Linkage Wait! Test Wait some more Test Wait Test Bioprocess Prior Knowledge Analytics Plan! Linkage DOE Wait! Test Time 10

11 Mechanistic modeling and prediction tools Long-term vision: describe all parts of the process with mathematic models and prediction tools Current Status: using Mechanistic Chromatography modeling for RRF Faster RRF process, less experiments needed Run high resolution DoE in-silico to understand/assess interactions Greater process knowledge across range (not just edges and center-points)

12 Model Application Calibrated Model Monomer K eq, Z, K kin, Shielding, Film Diffusion, Pore Diffusion HMWS K eq, Z, K kin, Shielding, Film Diffusion, Pore Diffusion vhmws K eq, Z, K kin, Shielding, Film Diffusion, Pore Diffusion Model Verification How well does it predict a new set of experimental data? In-Silico Model Predictions Use model to predict impact of process parameters on KPIs/CQAs Study interactions between process parameters Understand feedstock variability and process capability 12

13 Modeling Example: PV Study and Modeling Comparison Pool Volume (CV) CQA Experiment DOE Model-Simulated DOE Yield (%) CQA 2 CQA 3 Acidic (%) Basic (%) CQA 4 CQA 5 C-Terminal Lysine (%) HMWS (%) CQA Load Density Load ph Load Conductivity Elution ph PP1 PP2 PP3 PP4 PP5 PP6 Elution Acetate Molarity End Pooling OD

14 Conclusions Prior Knowledge: One unit operation often shows high degree of similarity in performance between projects, e.g. same parameters and outputs that are important PV studies can be smaller more focused and give greater and deeper information about your process More efficient RRF procedure Modeling: Gain greater knowledge of your process Have knowledge outside of the characterized range Predictive capabilities

15 Doing now what patients need next