Ongoing Challenges to Develop High Concentration Monoclonal Antibody-based Formulations for Subcutaneous Administration: Quo Vadis?

Jiskoot, Wim; Hawe, Andrea; Menzen, Tim; Volkin, David B.; Crommelin, Daan J.A.

doi:10.1016/j.xphs.2021.11.008

Cited by 53 publications

(50 citation statements)

References 68 publications

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“…There are, however, certain limitations with s.c. administration. By general consensus, a single injection volume should be limited to < 2 mL, determined by the available subcutaneous space and sensation of tolerable pain by the patient, 2 although investigation of injection volumes >2.5 mL has been suggested. 3 …”

Section: Introductionmentioning

confidence: 99%

“… Neergaard, 2013 20 3 IgG1 and IgG4 at 200 mg/mL in diverse buffers at pH 4–9 plus NaCl kD (DLS), Zeta potential for effective pI; Calculated values: Net charge and theoretical pI kD (if negative) and net charge (if low at pI) indicate potential high viscosity. Zeta potential predicts pH of maximum viscosity Binabaji, 2015 19 1 IgG1 at max 269 mg/mL, pH 5–7 in diverse buffers; plus excipients sucrose, proline, arginine-HCl, NaCl Net protein charge and zeta potential by (electrophoretic light scattering); B2, B3 (osmotic pressure data) B3 shows a strong linear correlation (R 2 =0.97) with the viscosity parameter from the Mooney equation Woldeyes, 2019 28 4 mAbs at 1–150 mg/mL, pH 5–8 in diverse buffers, plus NaCl A2 (SLS), S q=0 (SLS), kD (DLS), H q=0 (DLS/SLS) kD and A2, determined at low mAb concentration, are often not qualitative predictive of viscosity at high concentration. PPI are complex especially at high concentration and influence viscosity in nonlinear fashion.…”

Section: Introductionmentioning

confidence: 99%

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Predictive modeling of concentration-dependent viscosity behavior of monoclonal antibody solutions using artificial neural networks

Schmitt

Razvi

Grapentin

2023

mAbs

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Solutions of monoclonal antibodies (mAbs) can show increased viscosity at high concentration, which can be a disadvantage during protein purification, filling, and administration. The viscosity is determined by protein-protein-interactions, which are influenced by the antibody’s sequence as well as solution conditions, like pH, buffer type, or the presence of salts and other excipients. To predict viscosity, experimental parameters, like the diffusion interaction parameter (kD), or computational tools harnessing information derived from primary sequence, are often used, but a reliable predictive tool is still missing. We present a modeling approach employing artificial neural networks (ANNs) using experimental factors combined with simulation-derived parameters plus viscosity data from 27 highly concentrated (180 mg/mL) mAbs. These ANNs can be used to predict if mAbs exhibit problematic viscosity at distinct concentrations or to model viscosity-concentration-curves.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Predictive modeling of concentration-dependent viscosity behavior of monoclonal antibody solutions using artificial neural networks

Schmitt

Razvi

Grapentin

2023

mAbs

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“…Immanent to the at times high individual dose levels, compared to mAbs in immunology for example (refer to disease area archetype 1), developing subcutaneous injection regimens was initially complicated due to a number of technical challenges. With the introduction of methodologies to achieve high-concentration solutions (Mahler et al 2009;Jiskoot et al 2022) and the co-administration of the dispersion enhancer hyaluronidase (Frost 2007), the first moves were made to reduce the overall dosing volume and to facilitate spreading of an injected fluid in the interstitial space. Approved high-volume subcutaneous treatments that apply these technologies can maintain the infrequent dosing regimen of the initially marketed intravenous presentations.…”

Section: Disease Area Archetype 3-market With Variety Of Established ...mentioning

confidence: 99%

Customer-centric product presentations for monoclonal antibodies

Bittner

2023

AAPS Open

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Delivering customer-centric product presentations for biotherapeutics, such as monoclonal antibodies (mAbs), represents a long-standing and paramount area of engagement for pharmaceutical scientists. Activities include improving experience with the dosing procedure, reducing drug administration-related expenditures, and ultimately shifting parenteral treatments outside of a controlled healthcare institutional setting. In times of increasingly cost-constrained markets and reinforced with the coronavirus pandemic, this discipline of “Product Optimization” in healthcare has gained momentum and changed from a nice-to-have into a must.This review summarizes latest trends in the healthcare ecosystem that inform key strategies for developing customer-centric products, including the availability of a wider array of sustainable drug delivery options and treatment management plans that support dosing in a flexible care setting. Three disease area archetypes with varying degree of implementation of customer-centric concepts are introduced to highlight relevant market differences and similarities. Namely, rheumatoid arthritis and inflammatory bowel disease, multiple sclerosis, and oncology have been chosen due to differences in the availability of subcutaneously dosed and ready-to-use self-administration products for mAb medicines and their follow-on biologics.Different launch scenarios are described from a manufacturer’s perspective highlighting the necessity of platform approaches. To unfold the full potential of customer-centric care, value-based healthcare provider reimbursement schemes that incentivize the efficiency of care need to be broadly implemented.

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“…(1, 2, 3), numbers of filters (32,64,128,256) and kernel sizes (3,5,7). The MAE values of the best validation model were used to evaluate the model performance.…”

Section: Machine and Deep Learning Model Constructionmentioning

confidence: 99%

“…1,2 At high protein concentrations, some antibodies might exhibit elevated viscosity beyond the syringeability limit. 3 However, most antibodies have low viscosity at low concentrations. 4 It is a challenge to identify potential problematic antibodies during discovery.…”

Section: Introductionmentioning

confidence: 99%

DeepSCM: an efficient convolutional neural network surrogate model for the screening of therapeutic antibody viscosity

Lai

2022

Preprint

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Predicting high concentration antibody viscosity is essential for developing subcutaneous administration. Computer simulations provide promising tools to reach this aim. One such model is the spatial charge map (SCM) proposed by Agrawal and coworkers (mAbs. 2015, 8(1):43–48). SCM applies molecular dynamics simulations to calculate a score for the screening of antibody viscosity at high concentrations. However, molecular dynamics simulations are computationally costly and require structural information, a significant application bottleneck. In this work, high throughput computing was performed to calculate the SCM scores for 6596 nonredundant antibody variable regions. A convolutional neural network surrogate model, DeepSCM, requiring only sequence information, was then developed based on this dataset. The linear correlation coefficient of the DeepSCM and SCM scores achieved 0.9 on the test set (N=1320). The DeepSCM model was applied to screen the viscosity of 38 therapeutic antibodies that SCM correctly classified and resulted in only one misclassification. The DeepSCM model will facilitate high concentration antibody viscosity screening. The code and parameters are freely available at https://github.com/Lailabcode/DeepSCM.

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Ongoing Challenges to Develop High Concentration Monoclonal Antibody-based Formulations for Subcutaneous Administration: Quo Vadis?

Cited by 53 publications

References 68 publications

Predictive modeling of concentration-dependent viscosity behavior of monoclonal antibody solutions using artificial neural networks

Predictive modeling of concentration-dependent viscosity behavior of monoclonal antibody solutions using artificial neural networks

Customer-centric product presentations for monoclonal antibodies

DeepSCM: an efficient convolutional neural network surrogate model for the screening of therapeutic antibody viscosity

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