Multi-criteria manufacturability indices for ranking high-concentration monoclonal antibody formulations

Abstract

The need for high-concentration formulations for subcutaneous delivery of therapeutic monoclonal antibodies (mAbs) can present manufacturability challenges for the final ultrafiltration/diafiltration (UF/DF) step. Viscosity levels and the propensity to aggregate are key considerations for high-concentration formulations. This work presents novel frameworks for deriving a set of manufacturability indices related to viscosity and thermostability to rank high-concentration mAb formulation conditions in terms of their ease of manufacture. This is illustrated by analyzing published high-throughput biophysical screening data that explores the influence of different formulation conditions (pH, ions, and excipients) on the solution viscosity and product thermostability. A decision tree classification method, CART (Classification and Regression Tree) is used to identify the critical formulation conditions that influence the viscosity and thermostability. In this work, three different multi-criteria data analysis frameworks were investigated to derive manufacturability indices from analysis of the stress maps and the process conditions experienced in the final UF/DF step. Polynomial regression techniques were used to transform the experimental data into a set of stress maps that show viscosity and thermostability as functions of the formulation conditions. A mathematical filtrate flux model was used to capture the time profiles of protein concentration and flux decay behavior during UF/DF. Multi-criteria decision-making analysis was used to identify the optimal formulation conditions that minimize the potential for both viscosity and aggregation issues during UF/DF. Biotechnol. Bioeng. 2017;114: 2043-2056. © 2017 The Authors. Biotechnology and Bioengineering Published by Wiley Perodicals, Inc.

Keywords: aggregation; data mining; developability assessment; high-concentration mAb formulation; manufacturability index; viscosity.

Figure 1

Data mining results for formulation conditions driving viscosity and thermostability. a) CART tree for viscosity factors analysis. Rectangular nodes are branch nodes (e.g., concentration, pH) which represent the formulation factors leading to split. Values on branches are the threshold levels of the split points for the corresponding split conditions. Circular nodes are leaves representing subsets with different class labels for viscosity (solution viscosity is higher or not than 6 cP). b) Impact of different combinations of formulation factors on viscosity with sucrose excipient or proline/no excipient. The blue area represents formulations with low solution viscosity (<6 cP), and the red area represents formulations with high solution viscosity (≥6 cP). c) CART tree for thermostability analysis. Circular nodes are leaves representing subsets with different class labels for thermostability (T _h is higher or not than 50 °C). b) Impact of different combinations of formulation factors on thermostability, with no excipient and with excipient (sucrose or proline excipient). The red area represents formulations unstable at 50 ^°C (T _h < 50 °C), blue area represents formulations stable at 50 °C (T _h ≥ 50 °C).

Figure 2

Methodology of quantified overlay region framework to generate manufacturability index for formulation of no ion with no excipient. (a) Generate viscosity and thermostability stress maps as functions of pH and protein concentration. (b) Validate the regression models for viscosity and thermostability stress map. Plots of actual values against the predicted values from regression models of viscosity and thermostability for formulation of no ion with no excipient. The blue triangles represent actual values while red dots represent predicted values. (c) Calculate manufacturability index for formulation of no ion with no excipient to meet both viscosity and thermostability requirements. The blue line is the contour line of viscosity of 6 cP, while the green line is the contour line of thermostability of 50 °C. The shaded area is the overlapping window to meet both requirements of viscosity <6 cP and thermostability >50 °C. The manufacturing index (0.35 in this example) is calculated as the ratio of the overlapping window area over the whole stress map area.

Figure 3

Manufacturability indices by quantified overlay region for all formulation conditions. The higher manufacturability index values indicate the higher tolerance capability to meet both requirements of viscosity <6 cP and thermostability >50 °C. The best formulation condition is no ion with sucrose, followed by no ion with proline and no ion with no excipient. For the formulation of Ca²⁺ with no excipient, the manufactuability index is 0 since there is no overlapping window for this formulation to meet both requirements.

Figure 4

Methodology of temporal operating window framework to generate manufactuability index for formulation of no ion with no excipient. (a) Locate window of operation using the thermostability criterion. In the viscosity stress map for formulation of no ion with no excipient, the red line shows thermostability criterion T _h = 50 °C, while the white line shows the final product concentration of 100 g/L. The intersection of the red and white lines represents the pH for final product to meet T _h = 50 °C, which is 5.3. The operating range of pH is set to ±0.2, so the pH range of window of operation is 5.1–5.5. (b) Capture the time profile of product stream during overconcentration stage and flush stage in the final UF/DF step captured by the flux decay model. (c) Calculate the viscosity score as a pH and time weighted viscosity score. The viscosity score of formulation of no ion with no excipient is 5.2 cP. (d) Calculate manufacturability index as standard the viscosity scores into [0, 1]. The manufacturing index of formulation of no ion with no excipient is 0.5.

Figure 5

Manufacturability indices by temporal operating window for all formulation conditions. The index indicates the average viscosity value of the product stream during the UF/DF step taking into account the time profiles of pH and concentration. Each set of formulation conditions can be ranked according to their manufacturability indices where a higher index value indicates a more desirable outcome. The best formulation is Ca²⁺ with proline, followed by no ion with proline and no ion with sucrose. The three formulations in the red window are the worst three formulation conditions of all.

Figure 6

Methodology of temporal multi‐criteria weighted score framework to generate manufacturability index for formulation of no ion with no excipient. (a) Step 1: Standardize the real values of both viscosity and thermostability into [0, 1] to derive scores for viscosity and aggregation. Then, polynomial regression combined with the flux decay model was applied to the scores to generate stress maps for viscosity and aggregation as a function of time. In this framework, thermostability acts as an indicator of aggregation. (b) Step 2: Combine viscosity and aggregation scores using weighted sum method (WSM). Manufacturability, defined as weighted sum of viscosity and aggregation scores, is a function of pH and processing time. Different colors indicate different manufacturability levels. From blue to red, manufacturability is from easy to hard. Average manufacturability value for each pH calculated and the pH with the maximum average manufacturability value is the optimal pH value. When the weights for viscosity and aggregation are 0.5 and 0.5 separately, the optimal pH value for formulation no ion with no excipient is 5.9 and the maximum manufacturability is 0.73. (c) Step 3: Derive a multi‐criteria weighted manufacturability index for UF/DF averaged over time at the optimal pH value. The red lines indicate the optimal pH values for maximum manufacturability through the final UF/DF step. The optimal pH value changes with the weights. When a = 0.5, b = 0.5, the optimal pH is 5.9 and the maximum manufacturablity is 0.73; when a = 0.2, b = 0.8, the optimal pH is 6.0 and the maximum manufacturability is 0.76; when a = 0.8, b = 0.2, the optimal pH is 5.4 and the maximum manufacturability is 0.79.

Figure 7

Manufacturability indices by temporal multi‐criteria weighted score framework for all formulation conditions. Comparison of all formulation conditions according to their manufacturability indices using multi‐criteria decisions with different weights on viscosity and aggregation. Each set of formulation conditions has been ranked when the weights on viscosity and aggregation are 0.2 and 0.8, 0.5 and 0.5, 0.8 and 0.2, respectively. Different colors indicate different ranking orders where 1 is the best formulation condition with maximum manufacturability index value and 9 is the worst.