1
|
Kalejaye L, Wu IE, Terry T, Lai PK. DeepSP: Deep learning-based spatial properties to predict monoclonal antibody stability. Comput Struct Biotechnol J 2024; 23:2220-2229. [PMID: 38827232 PMCID: PMC11140563 DOI: 10.1016/j.csbj.2024.05.029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2024] [Revised: 05/15/2024] [Accepted: 05/16/2024] [Indexed: 06/04/2024] Open
Abstract
Therapeutic antibody development faces challenges due to high viscosities and aggregation tendencies. The spatial charge map (SCM) and spatial aggregation propensity (SAP) are computational techniques that aid in predicting viscosity and aggregation, respectively. These methods rely on structural data derived from molecular dynamics (MD) simulations, which are computationally demanding. DeepSCM, a deep learning surrogate model based on sequence information to predict SCM, was recently developed to screen high-concentration antibody viscosity. This study further utilized a dataset of 20,530 antibody sequences to train a convolutional neural network deep learning surrogate model called Deep Spatial Properties (DeepSP). DeepSP directly predicts SAP and SCM scores in different domains of antibody variable regions based solely on their sequences without performing MD simulations. The linear correlation coefficient between DeepSP scores and MD-derived scores for 30 properties achieved values between 0.76 and 0.96 with an average of 0.87. DeepSP descriptors were employed as features to build machine learning models to predict the aggregation rate of 21 antibodies, and the performance is similar to the results obtained from the previous study using MD simulations. This result demonstrates that the DeepSP approach significantly reduces the computational time required compared to MD simulations. The DeepSP model enables the rapid generation of 30 structural properties that can also be used as features in other research to train machine learning models for predicting various antibody stability using sequences only. DeepSP is freely available as an online tool via https://deepspwebapp.onrender.com and the codes and parameters are freely available at https://github.com/Lailabcode/DeepSP.
Collapse
Affiliation(s)
- Lateefat Kalejaye
- Department of Chemical Engineering and Materials Science, Stevens Institute of Technology, Hoboken 07030, NJ, United States
| | - I-En Wu
- Department of Chemical Engineering and Materials Science, Stevens Institute of Technology, Hoboken 07030, NJ, United States
| | - Taylor Terry
- Department of Chemical Engineering and Materials Science, Stevens Institute of Technology, Hoboken 07030, NJ, United States
| | - Pin-Kuang Lai
- Department of Chemical Engineering and Materials Science, Stevens Institute of Technology, Hoboken 07030, NJ, United States
| |
Collapse
|
2
|
Armstrong GB, Shah V, Sanches P, Patel M, Casey R, Jamieson C, Burley GA, Lewis W, Rattray Z. A framework for the biophysical screening of antibody mutations targeting solvent-accessible hydrophobic and electrostatic patches for enhanced viscosity profiles. Comput Struct Biotechnol J 2024; 23:2345-2357. [PMID: 38867721 PMCID: PMC11167247 DOI: 10.1016/j.csbj.2024.05.041] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2024] [Revised: 05/23/2024] [Accepted: 05/23/2024] [Indexed: 06/14/2024] Open
Abstract
The formulation of high-concentration monoclonal antibody (mAb) solutions in low dose volumes for autoinjector devices poses challenges in manufacturability and patient administration due to elevated solution viscosity. Often many therapeutically potent mAbs are discovered, but their commercial development is stalled by unfavourable developability challenges. In this work, we present a systematic experimental framework for the computational screening of molecular descriptors to guide the design of 24 mutants with modified viscosity profiles accompanied by experimental evaluation. Our experimental observations using a model anti-IL8 mAb and eight engineered mutant variants reveal that viscosity reduction is influenced by the location of hydrophobic interactions, while targeting positively charged patches significantly increases viscosity in comparison to wild-type anti-IL-8 mAb. We conclude that most predicted in silico physicochemical properties exhibit poor correlation with measured experimental parameters for antibodies with suboptimal developability characteristics, emphasizing the need for comprehensive case-by-case evaluation of mAbs. This framework combining molecular design and triage via computational predictions with experimental evaluation aids the agile and rational design of mAbs with tailored solution viscosities, ensuring improved manufacturability and patient convenience in self-administration scenarios.
Collapse
Affiliation(s)
- Georgina B. Armstrong
- Drug Substance Development, GlaxoSmithKline, Gunnels Wood Road, Stevenage, UK
- Strathclyde Institute of Pharmacy and Biomedical Sciences, University of Strathclyde, Glasgow, UK
| | - Vidhi Shah
- Large Molecule Discovery, GlaxoSmithKline, Gunnels Wood Road, Stevenage, UK
| | - Paula Sanches
- Drug Substance Development, GlaxoSmithKline, Gunnels Wood Road, Stevenage, UK
| | - Mitul Patel
- Drug Substance Development, GlaxoSmithKline, Gunnels Wood Road, Stevenage, UK
| | - Ricky Casey
- Drug Substance Development, GlaxoSmithKline, Gunnels Wood Road, Stevenage, UK
| | - Craig Jamieson
- Department of Pure and Applied Chemistry, University of Strathclyde, Glasgow, UK
| | - Glenn A. Burley
- Department of Pure and Applied Chemistry, University of Strathclyde, Glasgow, UK
| | - William Lewis
- Drug Substance Development, GlaxoSmithKline, Gunnels Wood Road, Stevenage, UK
| | - Zahra Rattray
- Strathclyde Institute of Pharmacy and Biomedical Sciences, University of Strathclyde, Glasgow, UK
| |
Collapse
|
3
|
Estes B, Jain M, Jia L, Whoriskey J, Bennett B, Hsu H. Sequence-Based Viscosity Prediction for Rapid Antibody Engineering. Biomolecules 2024; 14:617. [PMID: 38927021 PMCID: PMC11202045 DOI: 10.3390/biom14060617] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2024] [Revised: 05/18/2024] [Accepted: 05/21/2024] [Indexed: 06/28/2024] Open
Abstract
Through machine learning, identifying correlations between amino acid sequences of antibodies and their observed characteristics, we developed an internal viscosity prediction model to empower the rapid engineering of therapeutic antibody candidates. For a highly viscous anti-IL-13 monoclonal antibody, we used a structure-based rational design strategy to generate a list of variants that were hypothesized to mitigate viscosity. Our viscosity prediction tool was then used as a screen to cull virtually engineered variants with a probability of high viscosity while advancing those with a probability of low viscosity to production and testing. By combining the rational design engineering strategy with the in silico viscosity prediction screening step, we were able to efficiently improve the highly viscous anti-IL-13 candidate, successfully decreasing the viscosity at 150 mg/mL from 34 cP to 13 cP in a panel of 16 variants.
Collapse
Affiliation(s)
- Bram Estes
- Amgen Research, Protein Therapeutics, Thousand Oaks, CA 91320, USA; (M.J.); (L.J.)
| | - Mani Jain
- Amgen Research, Protein Therapeutics, Thousand Oaks, CA 91320, USA; (M.J.); (L.J.)
| | - Lei Jia
- Amgen Research, Protein Therapeutics, Thousand Oaks, CA 91320, USA; (M.J.); (L.J.)
| | - John Whoriskey
- Amgen Research, Inflammation, Thousand Oaks, CA 91320, USA; (J.W.); (B.B.); (H.H.)
| | - Brian Bennett
- Amgen Research, Inflammation, Thousand Oaks, CA 91320, USA; (J.W.); (B.B.); (H.H.)
| | - Hailing Hsu
- Amgen Research, Inflammation, Thousand Oaks, CA 91320, USA; (J.W.); (B.B.); (H.H.)
| |
Collapse
|
4
|
Hao X, Fan L. ProtT5 and random forests-based viscosity prediction method for therapeutic mAbs. Eur J Pharm Sci 2024; 194:106705. [PMID: 38246432 DOI: 10.1016/j.ejps.2024.106705] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Revised: 01/01/2024] [Accepted: 01/15/2024] [Indexed: 01/23/2024]
Abstract
Viscosity is a key characteristic of therapeutic antibodies for subcutaneous administration which requires low volume and high concentration formulations. It would be highly beneficial to accurately predict the viscosity of newly developed therapeutic antibodies in the early stages of development. In this work, a ProtT5-XL-UniRef50 (ProtT5) and Random Forests (RF)-based prediction method was proposed for accurately predicting the viscosity of monoclonal antibodies, with only corresponding sequences needed. Starting from the given heavy and light chain V-region sequences, corresponding features were first extracted from the ProtT5 pretrained model. Kernel principal analysis (Kernel-PCA) was then used for reducing the extracted 2048-D (1024-D for each sequence) feature vector to a reasonable level for efficient training of the RF-regressor. Then, the RF model was constructed on 40 commercially available therapeutic antibodies and tested with 3-folds cross-validation. Test results show that the model could reproduce the viscosity value at a high level (Pearson correlation coefficient (PCC) = 0.928). Performance on classifying high (>30 cP) and low (<30 cP) viscosity is much more satisfactory, the Accuracy (ACC) and the area under precision-recall curve (AUC) of the classification model from validation tests are 0.975 and 1.000, respectively. Compared to 5 existing state-of-the-art viscosity prediction methods, the proposed method performs best which would facilitate high concentration antibody viscosity high-throughput screening.
Collapse
Affiliation(s)
- Xiaohu Hao
- Production and R&D Center I of LSS (Life Science Service), GenScript Biotech Corporation, No. 28, Yongxi Rd., Nanjing, 211110, Jiangsu, China
| | - Long Fan
- Production and R&D Center I of LSS (Life Science Service), GenScript Biotech Corporation, No. 28, Yongxi Rd., Nanjing, 211110, Jiangsu, China; Production and R&D Center I of LSS (Life Science Service), GenScript (Shanghai) Biotech Corporation, No. 186, Hedan Rd., Shanghai, 200100, China.
| |
Collapse
|
5
|
Mock M, Langmead CJ, Grandsard P, Edavettal S, Russell A. Recent advances in generative biology for biotherapeutic discovery. Trends Pharmacol Sci 2024; 45:255-267. [PMID: 38378385 DOI: 10.1016/j.tips.2024.01.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Revised: 12/22/2023] [Accepted: 01/05/2024] [Indexed: 02/22/2024]
Abstract
Generative biology combines artificial intelligence (AI), advanced life sciences technologies, and automation to revolutionize the process of designing novel biomolecules with prescribed properties, giving drug discoverers the ability to escape the limitations of biology during the design of next-generation protein therapeutics. Significant hurdles remain, namely: (i) the inherently complex nature of drug discovery, (ii) the bewildering number of promising computational and experimental techniques that have emerged in the past several years, and (iii) the limited availability of relevant protein sequence-function data for drug-like molecules. There is a need to focus on computational methods that will be most practically effective for protein drug discovery and on building experimental platforms to generate the data most appropriate for these methods. Here, we discuss recent advances in computational and experimental life sciences that are most crucial for impacting the pace and success of protein drug discovery.
Collapse
Affiliation(s)
- Marissa Mock
- Amgen Research, Amgen Inc., One Amgen Center Drive, Thousand Oaks, CA 91320, USA
| | | | - Peter Grandsard
- Amgen Research, Amgen Inc., One Amgen Center Drive, Thousand Oaks, CA 91320, USA
| | - Suzanne Edavettal
- Amgen Research, Amgen Inc., One Amgen Center Drive, Thousand Oaks, CA 91320, USA
| | - Alan Russell
- Amgen Research, Amgen Inc., One Amgen Center Drive, Thousand Oaks, CA 91320, USA.
| |
Collapse
|
6
|
Park E, Izadi S. Molecular surface descriptors to predict antibody developability: sensitivity to parameters, structure models, and conformational sampling. MAbs 2024; 16:2362788. [PMID: 38853585 PMCID: PMC11168226 DOI: 10.1080/19420862.2024.2362788] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Accepted: 05/29/2024] [Indexed: 06/11/2024] Open
Abstract
In silico assessment of antibody developability during early lead candidate selection and optimization is of paramount importance, offering a rapid and material-free screening approach. However, the predictive power and reproducibility of such methods depend heavily on the selection of molecular descriptors, model parameters, accuracy of predicted structure models, and conformational sampling techniques. Here, we present a set of molecular surface descriptors specifically designed for predicting antibody developability. We assess the performance of these descriptors by benchmarking their correlations with an extensive array of experimentally determined biophysical properties, including viscosity, aggregation, hydrophobic interaction chromatography, human pharmacokinetic clearance, heparin retention time, and polyspecificity. Further, we investigate the sensitivity of these surface descriptors to methodological nuances, such as the choice of interior dielectric constant, hydrophobicity scales, structure prediction methods, and the impact of conformational sampling. Notably, we observe systematic shifts in the distribution of surface descriptors depending on the structure prediction method used, driving weak correlations of surface descriptors across structure models. Averaging the descriptor values over conformational distributions from molecular dynamics mitigates the systematic shifts and improves the consistency across different structure prediction methods, albeit with inconsistent improvements in correlations with biophysical data. Based on our benchmarking analysis, we propose six in silico developability risk flags and assess their effectiveness in predicting potential developability issues for a set of case study molecules.
Collapse
Affiliation(s)
- Eliott Park
- Pharmaceutical Development, Genentech Inc, South San Francisco, CA, USA
| | - Saeed Izadi
- Pharmaceutical Development, Genentech Inc, South San Francisco, CA, USA
| |
Collapse
|
7
|
Heisler J, Kovner D, Izadi S, Zarzar J, Carter PJ. Modulation of the high concentration viscosity of IgG 1 antibodies using clinically validated Fc mutations. MAbs 2024; 16:2379560. [PMID: 39028186 PMCID: PMC11262234 DOI: 10.1080/19420862.2024.2379560] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2024] [Accepted: 07/09/2024] [Indexed: 07/20/2024] Open
Abstract
The self-association of therapeutic antibodies can result in elevated viscosity and create problems in manufacturing and formulation, as well as limit delivery by subcutaneous injection. The high concentration viscosity of some antibodies has been reduced by variable domain mutations or by the addition of formulation excipients. In contrast, the impact of Fc mutations on antibody viscosity has been minimally explored. Here, we studied the effect of a panel of common and clinically validated Fc mutations on the viscosity of two closely related humanized IgG1, κ antibodies, omalizumab (anti-IgE) and trastuzumab (anti-HER2). Data presented here suggest that both Fab-Fab and Fab-Fc interactions contribute to the high viscosity of omalizumab, in a four-contact model of self-association. Most strikingly, the high viscosity of omalizumab (176 cP) was reduced 10.7- and 2.2-fold by Fc modifications for half-life extension (M252Y:S254T:T256E) and aglycosylation (N297G), respectively. Related single mutations (S254T and T256E) each reduced the viscosity of omalizumab by ~6-fold. An alternative half-life extension Fc mutant (M428L:N434S) had the opposite effect in increasing the viscosity of omalizumab by 1.5-fold. The low viscosity of trastuzumab (8.6 cP) was unchanged or increased by ≤ 2-fold by the different Fc variants. Molecular dynamics simulations provided mechanistic insight into the impact of Fc mutations in modulating electrostatic and hydrophobic surface properties as well as conformational stability of the Fc. This study demonstrates that high viscosity of some IgG1 antibodies can be mitigated by Fc mutations, and thereby offers an additional tool to help design future antibody therapeutics potentially suitable for subcutaneous delivery.
Collapse
Affiliation(s)
- Joel Heisler
- Department of Antibody Engineering, Genentech, Inc, South San Francisco, CA, USA
| | - Daniel Kovner
- Department of Pharmaceutical Development, Genentech, Inc, South San Francisco, CA, USA
| | - Saeed Izadi
- Department of Pharmaceutical Development, Genentech, Inc, South San Francisco, CA, USA
| | - Jonathan Zarzar
- Department of Pharmaceutical Development, Genentech, Inc, South San Francisco, CA, USA
| | - Paul J. Carter
- Department of Antibody Engineering, Genentech, Inc, South San Francisco, CA, USA
| |
Collapse
|
8
|
Makowski EK, Chen HT, Wang T, Wu L, Huang J, Mock M, Underhill P, Pelegri-O’Day E, Maglalang E, Winters D, Tessier PM. Reduction of monoclonal antibody viscosity using interpretable machine learning. MAbs 2024; 16:2303781. [PMID: 38475982 PMCID: PMC10939158 DOI: 10.1080/19420862.2024.2303781] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Accepted: 01/05/2024] [Indexed: 03/14/2024] Open
Abstract
Early identification of antibody candidates with drug-like properties is essential for simplifying the development of safe and effective antibody therapeutics. For subcutaneous administration, it is important to identify candidates with low self-association to enable their formulation at high concentration while maintaining low viscosity, opalescence, and aggregation. Here, we report an interpretable machine learning model for predicting antibody (IgG1) variants with low viscosity using only the sequences of their variable (Fv) regions. Our model was trained on antibody viscosity data (>100 mg/mL mAb concentration) obtained at a common formulation pH (pH 5.2), and it identifies three key Fv features of antibodies linked to viscosity, namely their isoelectric points, hydrophobic patch sizes, and numbers of negatively charged patches. Of the three features, most predicted antibodies at risk for high viscosity, including antibodies with diverse antibody germlines in our study (79 mAbs) as well as clinical-stage IgG1s (94 mAbs), are those with low Fv isoelectric points (Fv pIs < 6.3). Our model identifies viscous antibodies with relatively high accuracy not only in our training and test sets, but also for previously reported data. Importantly, we show that the interpretable nature of the model enables the design of mutations that significantly reduce antibody viscosity, which we confirmed experimentally. We expect that this approach can be readily integrated into the drug development process to reduce the need for experimental viscosity screening and improve the identification of antibody candidates with drug-like properties.
Collapse
Affiliation(s)
- Emily K. Makowski
- Department of Pharmaceutical Sciences, University of Michigan, Ann Arbor, MI, USA
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI, USA
| | - Hsin-Ting Chen
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI, USA
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI, USA
| | - Tiexin Wang
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI, USA
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI, USA
| | - Lina Wu
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI, USA
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI, USA
| | - Jie Huang
- Department of Pharmaceutical Sciences, University of Michigan, Ann Arbor, MI, USA
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI, USA
| | - Marissa Mock
- Therapeutic Discovery, Research, Amgen Inc, Thousand Oaks, CA, USA
| | - Patrick Underhill
- Department of Chemical and Biological Engineering, Rensselaer Polytechnic Institute, Troy, NY, USA
| | | | - Erick Maglalang
- Drug Product Technologies, Amgen Inc, Thousand Oaks, CA, USA
| | - Dwight Winters
- Therapeutic Discovery, Research, Amgen Inc, Thousand Oaks, CA, USA
| | - Peter M. Tessier
- Department of Pharmaceutical Sciences, University of Michigan, Ann Arbor, MI, USA
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI, USA
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI, USA
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA
| |
Collapse
|
9
|
Bhandari K, Wei Y, Amer BR, Pelegri-O’Day EM, Huh J, Schmit JD. Prediction of Antibody Viscosity from Dilute Solution Measurements. Antibodies (Basel) 2023; 12:78. [PMID: 38131800 PMCID: PMC10740665 DOI: 10.3390/antib12040078] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2023] [Revised: 11/20/2023] [Accepted: 11/28/2023] [Indexed: 12/23/2023] Open
Abstract
The high antibody doses required to achieve a therapeutic effect often necessitate high-concentration products that can lead to challenging viscosity issues in production and delivery. Predicting antibody viscosity in early development can play a pivotal role in reducing late-stage development costs. In recent years, numerous efforts have been made to predict antibody viscosity through dilute solution measurements. A key finding is that the entanglement of long, flexible complexes contributes to the sharp rise in antibody viscosity at the required dosing. This entanglement model establishes a connection between the two-body binding affinity and the many-body viscosity. Exploiting this insight, this study connects dilute solution measurements of self-association to high-concentration viscosity profiles to quantify the relationship between these regimes. The resulting model has exhibited success in predicting viscosity at high concentrations (around 150 mg/mL) from dilute solution measurements, with only a few outliers remaining. Our physics-based approach provides an understanding of fundamental physics, interpretable connections to experimental data, the potential to extrapolate beyond training conditions, and the capacity to effectively explain the physical mechanics behind these outliers. Conducting hypothesis-driven experiments that specifically target the viscosity and relaxation mechanisms of outlier molecules may allow us to unravel the intricacies of their behavior and, in turn, enhance the performance of our model.
Collapse
Affiliation(s)
- Kamal Bhandari
- Department of Physics, Kansas State University, Manhattan, KS 66506, USA;
| | - Yangjie Wei
- Amgen Inc., Thousand Oaks, CA 91320, USA; (Y.W.); (B.R.A.); (E.M.P.-O.); (J.H.)
| | - Brendan R. Amer
- Amgen Inc., Thousand Oaks, CA 91320, USA; (Y.W.); (B.R.A.); (E.M.P.-O.); (J.H.)
| | | | - Joon Huh
- Amgen Inc., Thousand Oaks, CA 91320, USA; (Y.W.); (B.R.A.); (E.M.P.-O.); (J.H.)
| | - Jeremy D. Schmit
- Department of Physics, Kansas State University, Manhattan, KS 66506, USA;
| |
Collapse
|
10
|
Chowdhury AA, Manohar N, Lanzaro A, Kimball WD, Witek MA, Woldeyes MA, Majumdar R, Qian KK, Xu S, Gillilan RE, Huang Q, Truskett TM, Johnston KP. Characterizing Protein-Protein Interactions and Viscosity of a Monoclonal Antibody from Low to High Concentration Using Small-Angle X-ray Scattering and Molecular Dynamics Simulations. Mol Pharm 2023; 20:5563-5578. [PMID: 37782765 DOI: 10.1021/acs.molpharmaceut.3c00484] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/04/2023]
Abstract
Understanding protein-protein interactions and formation of reversible oligomers (clusters) in concentrated monoclonal antibody (mAb) solutions is necessary for designing stable, low viscosity (η) concentrated formulations for processing and subcutaneous injection. Here we characterize the strength (K) of short-range anisotropic attractions (SRA) for 75-200 mg/mL mAb2 solutions at different pH and cosolute conditions by analyzing structure factors (Seff(q)) from small-angle X-ray scattering (SAXS) using coarse-grained molecular dynamics simulations. Best fit simulations additionally provide cluster size distributions, fractal dimensions, cluster occluded volume, and mAb coordination numbers. These equilibrium properties are utilized in a model to account for increases in viscosity caused by occluded volume in the clusters (packing effects) and dissipation of stress across lubricated fractal clusters. Seff(q) is highly sensitive to K at 75 mg/mL where mAbs can mutually align to form SRA contacts but becomes less sensitive at 200 mg/mL as steric repulsion due to packing becomes dominant. In contrast, η at 200 mg/mL is highly sensitive to SRA and the average cluster size from SAXS/simulation, which is observed to track the cluster relaxation time from shear thinning. By analyzing the distribution of sub-bead hot spots on the 3D mAb surface, we identify a strongly attractive hydrophobic patch in the complementarity determining region (CDR) at pH 4.5 that contributes to the high K and consequently large cluster sizes and high η. Adding NaCl screens electrostatic interactions and increases the impact of hydrophobic attraction on cluster size and raises η, whereas nonspecific binding of Arg attenuates all SRA, reducing η. The hydrophobic patch is absent at higher pH values, leading to smaller K, smaller clusters, and lower η. This work constitutes a first attempt to use SAXS and CG modeling to link both structural and rheological properties of concentrated mAb solutions to the energetics of specific hydrophobic patches on mAb surfaces. As such, our work opens an avenue for future research, including the possibility of designing coarse-grained models with physically meaningful interacting hot spots.
Collapse
Affiliation(s)
- Amjad A Chowdhury
- McKetta Department of Chemical Engineering, The University of Texas at Austin, Austin, Texas 78712, United States
| | - Neha Manohar
- McKetta Department of Chemical Engineering, The University of Texas at Austin, Austin, Texas 78712, United States
| | - Alfredo Lanzaro
- McKetta Department of Chemical Engineering, The University of Texas at Austin, Austin, Texas 78712, United States
| | - William D Kimball
- McKetta Department of Chemical Engineering, The University of Texas at Austin, Austin, Texas 78712, United States
| | - Marta A Witek
- Eli Lilly and Company, Indianapolis, Indiana 46225, United States
| | | | - Ranajoy Majumdar
- Eli Lilly and Company, Indianapolis, Indiana 46225, United States
| | - Ken K Qian
- Eli Lilly and Company, Indianapolis, Indiana 46225, United States
| | - Shifeng Xu
- McKetta Department of Chemical Engineering, The University of Texas at Austin, Austin, Texas 78712, United States
| | - Richard E Gillilan
- Center for High Energy X-ray Sciences at CHESS, Cornell University, Ithaca, New York 14853, United States
| | - Qingqiu Huang
- Center for High Energy X-ray Sciences at CHESS, Cornell University, Ithaca, New York 14853, United States
| | - Thomas M Truskett
- McKetta Department of Chemical Engineering, The University of Texas at Austin, Austin, Texas 78712, United States
- Department of Physics, The University of Texas at Austin, Austin, Texas 78712, United States
| | - Keith P Johnston
- McKetta Department of Chemical Engineering, The University of Texas at Austin, Austin, Texas 78712, United States
| |
Collapse
|
11
|
Zhou Y, Huang Z, Li W, Wei J, Jiang Q, Yang W, Huang J. Deep learning in preclinical antibody drug discovery and development. Methods 2023; 218:57-71. [PMID: 37454742 DOI: 10.1016/j.ymeth.2023.07.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Revised: 03/20/2023] [Accepted: 07/10/2023] [Indexed: 07/18/2023] Open
Abstract
Antibody drugs have become a key part of biotherapeutics. Patients suffering from various diseases have benefited from antibody therapies. However, its development process is rather long, expensive and risky. To speed up the process, reduce cost and improve success rate, artificial intelligence, especially deep learning methods, have been widely used in all aspects of preclinical antibody drug development, from library generation to hit identification, developability screening, lead selection and optimization. In this review, we systematically summarize antibody encodings, deep learning architectures and models used in preclinical antibody drug discovery and development. We also critically discuss challenges and opportunities, problems and possible solutions, current applications and future directions of deep learning in antibody drug development.
Collapse
Affiliation(s)
- Yuwei Zhou
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Ziru Huang
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Wenzhen Li
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Jinyi Wei
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Qianhu Jiang
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Wei Yang
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Jian Huang
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China.
| |
Collapse
|
12
|
Bauer J, Rajagopal N, Gupta P, Gupta P, Nixon AE, Kumar S. How can we discover developable antibody-based biotherapeutics? Front Mol Biosci 2023; 10:1221626. [PMID: 37609373 PMCID: PMC10441133 DOI: 10.3389/fmolb.2023.1221626] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Accepted: 07/10/2023] [Indexed: 08/24/2023] Open
Abstract
Antibody-based biotherapeutics have emerged as a successful class of pharmaceuticals despite significant challenges and risks to their discovery and development. This review discusses the most frequently encountered hurdles in the research and development (R&D) of antibody-based biotherapeutics and proposes a conceptual framework called biopharmaceutical informatics. Our vision advocates for the syncretic use of computation and experimentation at every stage of biologic drug discovery, considering developability (manufacturability, safety, efficacy, and pharmacology) of potential drug candidates from the earliest stages of the drug discovery phase. The computational advances in recent years allow for more precise formulation of disease concepts, rapid identification, and validation of targets suitable for therapeutic intervention and discovery of potential biotherapeutics that can agonize or antagonize them. Furthermore, computational methods for de novo and epitope-specific antibody design are increasingly being developed, opening novel computationally driven opportunities for biologic drug discovery. Here, we review the opportunities and limitations of emerging computational approaches for optimizing antigens to generate robust immune responses, in silico generation of antibody sequences, discovery of potential antibody binders through virtual screening, assessment of hits, identification of lead drug candidates and their affinity maturation, and optimization for developability. The adoption of biopharmaceutical informatics across all aspects of drug discovery and development cycles should help bring affordable and effective biotherapeutics to patients more quickly.
Collapse
Affiliation(s)
- Joschka Bauer
- Early Stage Pharmaceutical Development Biologicals, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach/Riss, Germany
- In Silico Team, Boehringer Ingelheim, Hannover, Germany
| | - Nandhini Rajagopal
- In Silico Team, Boehringer Ingelheim, Hannover, Germany
- Biotherapeutics Discovery, Boehringer Ingelheim Pharmaceuticals Inc., Ridgefield, CT, United States
| | - Priyanka Gupta
- In Silico Team, Boehringer Ingelheim, Hannover, Germany
- Biotherapeutics Discovery, Boehringer Ingelheim Pharmaceuticals Inc., Ridgefield, CT, United States
| | - Pankaj Gupta
- In Silico Team, Boehringer Ingelheim, Hannover, Germany
- Biotherapeutics Discovery, Boehringer Ingelheim Pharmaceuticals Inc., Ridgefield, CT, United States
| | - Andrew E. Nixon
- Biotherapeutics Discovery, Boehringer Ingelheim Pharmaceuticals Inc., Ridgefield, CT, United States
| | - Sandeep Kumar
- In Silico Team, Boehringer Ingelheim, Hannover, Germany
- Biotherapeutics Discovery, Boehringer Ingelheim Pharmaceuticals Inc., Ridgefield, CT, United States
| |
Collapse
|
13
|
Mieczkowski CA. The Evolution of Commercial Antibody Formulations. J Pharm Sci 2023; 112:1801-1810. [PMID: 37037341 DOI: 10.1016/j.xphs.2023.03.026] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Revised: 03/29/2023] [Accepted: 03/29/2023] [Indexed: 04/12/2023]
Abstract
It has been nearly four decades since the first therapeutic monoclonal antibodies were approved and made available for widespread human use. Herein, US and EU approved antibody formulations are reviewed, and their nature and compositions are evaluated over time. From 1986 through Jan 2023, significant formulation trends have occurred and to represent this, 165 commercial antibody therapeutic formulations were binned into 5 different periods of time. Overall, we have observed the following: 1) The average formulation pH has decreased in recent years by over 0.5 units along with a decrease in variability that is largely driven by non-high concentration liquid in vial presentations for IV administration, 2) The use of certain excipients and buffers such as histidine, sucrose, metal chelators, arginine and methionine has become significantly more common, whereas formulations that contain phosphate, salt, no sugar or no surfactant have fallen out of favor, 3) Overall formulation space has increasingly become more homogenous and has converged in terms of formulation pH and excipient preferences regardless of formulation concentration, drug product presentation, and route of administration, 4) The average calculated isoelectric point (pI) has decreased 0.26 units, and 5) Overall, the average formulation pH and calculated pI for all commercial antibodies surveyed was 6.0 and 8.4, respectively. These trends and formulation convergence may be driven by multiple factors such as advancements in high-throughput computational and analytical technologies, the increased emphasis and understanding of certain developability attributes and formulation principles during lead selection and formulation development, and the adoption of low-risk development platform approaches.
Collapse
|
14
|
Mills BJ, Godamudunage MP, Ren S, Laha M. Predictive Nature of High-Throughput Assays in ADC Formulation Screening. J Pharm Sci 2023; 112:1821-1831. [PMID: 37037342 DOI: 10.1016/j.xphs.2023.03.021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Revised: 03/28/2023] [Accepted: 03/29/2023] [Indexed: 04/12/2023]
Abstract
Utilization of high-throughput biophysical screening techniques during early screening studies is warranted due to the limited amount of material and large number of samples. But the predictability of the data to longer-term storage stability is critical as the high-throughput methods assist in defining the design space for the longer-term studies. In this study, the biophysical properties of two ADCs in 16 formulation conditions were evaluated using high-throughput techniques. Conformational stability and colloidal stability were evaluated by determining Tm values, kD, B22, and Tagg. In addition, the samples were placed on stability and the extent of aggregate formation over the 8-week interval was determined. The rank order of the 16 different formulations in the high-throughput assays was compared to the rank order observed during the stability studies to assess the predictive capabilities of the screening methods. It was demonstrated that similar rank orders can be expected between high-throughput physical stability indicating assays such as Tagg and B22 and traditional aggregation by SEC data, whereas conformational stability read-outs (Tm) are less predictive. In addition, the high-throughput assays appropriately identified the poor performing formulation conditions, which is ultimately what is desired of screening assays.
Collapse
Affiliation(s)
- Brittney J Mills
- Biologics CMC Drug Product Development, AbbVie Inc., 1 N Waukegan Road, North Chicago, IL 60064, United States.
| | - Malika P Godamudunage
- Biologics CMC Drug Product Development, AbbVie Inc., 1 N Waukegan Road, North Chicago, IL 60064, United States
| | - Siyuan Ren
- Biologics CMC Drug Product Development, AbbVie Inc., 1 N Waukegan Road, North Chicago, IL 60064, United States
| | - Malabika Laha
- Biologics CMC Drug Product Development, AbbVie Inc., 1 N Waukegan Road, North Chicago, IL 60064, United States
| |
Collapse
|
15
|
Chowdhury AA, Manohar N, Witek MA, Woldeyes MA, Majumdar R, Qian KK, Kimball WD, Xu S, Lanzaro A, Truskett TM, Johnston KP. Subclass Effects on Self-Association and Viscosity of Monoclonal Antibodies at High Concentrations. Mol Pharm 2023. [PMID: 37191356 DOI: 10.1021/acs.molpharmaceut.3c00023] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/17/2023]
Abstract
The effects of a subclass of monoclonal antibodies (mAbs) on protein-protein interactions, formation of reversible oligomers (clusters), and viscosity (η) are not well understood at high concentrations. Herein, we quantify a short-range anisotropic attraction between the complementarity-determining region (CDR) and CH3 domains (KCDR-CH3) for vedolizumab IgG1, IgG2, or IgG4 subclasses by fitting small-angle X-ray scattering (SAXS) structure factor Seff(q) data with an extensive library of 12-bead coarse-grained (CG) molecular dynamics simulations. The KCDR-CH3 bead attraction strength was isolated from the strength of long-range electrostatic repulsion for the full mAb, which was determined from the theoretical net charge and a scaling parameter ψ to account for solvent accessibility and ion pairing. At low ionic strength (IS), the strongest short-range attraction (KCDR-CH3) and consequently the largest clusters and highest η were observed with IgG1, the subclass with the most positively charged CH3 domain. Furthermore, the trend in KCDR-CH3 with the subclass followed the electrostatic interaction energy between the CDR and CH3 regions calculated with the BioLuminate software using the 3D mAb structure and molecular interaction potentials. Whereas the equilibrium cluster size distributions and fractal dimensions were determined from fits of SAXS with the MD simulations, the degree of cluster rigidity under flow was estimated from the experimental η with a phenomenological model. For the systems with the largest clusters, especially IgG1, the inefficient packing of mAbs in the clusters played the largest role in increasing η, whereas for other systems, the relative contribution from stress produced by the clusters was more significant. The ability to relate η to short-range attraction from SAXS measurements at high concentrations and to theoretical characterization of electrostatic patches on the 3D surface is not only of fundamental interest but also of practical value for mAb discovery, processing, formulation, and subcutaneous delivery.
Collapse
Affiliation(s)
- Amjad A Chowdhury
- McKetta Department of Chemical Engineering, The University of Texas at Austin, Austin, Texas 78712, United States
| | - Neha Manohar
- McKetta Department of Chemical Engineering, The University of Texas at Austin, Austin, Texas 78712, United States
| | - Marta A Witek
- Eli Lilly and Company, Indianapolis, Indiana 46225, United States
| | | | - Ranajoy Majumdar
- Eli Lilly and Company, Indianapolis, Indiana 46225, United States
| | - Ken K Qian
- Eli Lilly and Company, Indianapolis, Indiana 46225, United States
| | - William D Kimball
- McKetta Department of Chemical Engineering, The University of Texas at Austin, Austin, Texas 78712, United States
| | - Shifeng Xu
- McKetta Department of Chemical Engineering, The University of Texas at Austin, Austin, Texas 78712, United States
| | - Alfredo Lanzaro
- McKetta Department of Chemical Engineering, The University of Texas at Austin, Austin, Texas 78712, United States
| | - Thomas M Truskett
- McKetta Department of Chemical Engineering, The University of Texas at Austin, Austin, Texas 78712, United States
- Department of Physics, The University of Texas at Austin, Austin, Texas 78712, United States
| | - Keith P Johnston
- McKetta Department of Chemical Engineering, The University of Texas at Austin, Austin, Texas 78712, United States
| |
Collapse
|
16
|
Chen Z, Wang X, Chen X, Huang J, Wang C, Wang J, Wang Z. Accelerating therapeutic protein design with computational approaches toward the clinical stage. Comput Struct Biotechnol J 2023; 21:2909-2926. [PMID: 38213894 PMCID: PMC10781723 DOI: 10.1016/j.csbj.2023.04.027] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Revised: 04/11/2023] [Accepted: 04/27/2023] [Indexed: 01/13/2024] Open
Abstract
Therapeutic protein, represented by antibodies, is of increasing interest in human medicine. However, clinical translation of therapeutic protein is still largely hindered by different aspects of developability, including affinity and selectivity, stability and aggregation prevention, solubility and viscosity reduction, and deimmunization. Conventional optimization of the developability with widely used methods, like display technologies and library screening approaches, is a time and cost-intensive endeavor, and the efficiency in finding suitable solutions is still not enough to meet clinical needs. In recent years, the accelerated advancement of computational methodologies has ushered in a transformative era in the field of therapeutic protein design. Owing to their remarkable capabilities in feature extraction and modeling, the integration of cutting-edge computational strategies with conventional techniques presents a promising avenue to accelerate the progression of therapeutic protein design and optimization toward clinical implementation. Here, we compared the differences between therapeutic protein and small molecules in developability and provided an overview of the computational approaches applicable to the design or optimization of therapeutic protein in several developability issues.
Collapse
Affiliation(s)
- Zhidong Chen
- Department of Pathology, The Eighth Affiliated Hospital, Sun Yat-sen University, Shenzhen 518033, China
- School of Pharmaceutical Sciences, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China
| | - Xinpei Wang
- School of Pharmaceutical Sciences, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China
| | - Xu Chen
- School of Pharmaceutical Sciences, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China
| | - Juyang Huang
- School of Pharmaceutical Sciences, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China
| | - Chenglin Wang
- Shenzhen Qiyu Biotechnology Co., Ltd, Shenzhen 518107, China
| | - Junqing Wang
- School of Pharmaceutical Sciences, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China
| | - Zhe Wang
- Department of Pathology, The Eighth Affiliated Hospital, Sun Yat-sen University, Shenzhen 518033, China
| |
Collapse
|
17
|
Li T, Li Y, Zhu X, He Y, Wu Y, Ying T, Xie Z. Artificial intelligence in cancer immunotherapy: Applications in neoantigen recognition, antibody design and immunotherapy response prediction. Semin Cancer Biol 2023; 91:50-69. [PMID: 36870459 DOI: 10.1016/j.semcancer.2023.02.007] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Revised: 02/13/2023] [Accepted: 02/28/2023] [Indexed: 03/06/2023]
Abstract
Cancer immunotherapy is a method of controlling and eliminating tumors by reactivating the body's cancer-immunity cycle and restoring its antitumor immune response. The increased availability of data, combined with advancements in high-performance computing and innovative artificial intelligence (AI) technology, has resulted in a rise in the use of AI in oncology research. State-of-the-art AI models for functional classification and prediction in immunotherapy research are increasingly used to support laboratory-based experiments. This review offers a glimpse of the current AI applications in immunotherapy, including neoantigen recognition, antibody design, and prediction of immunotherapy response. Advancing in this direction will result in more robust predictive models for developing better targets, drugs, and treatments, and these advancements will eventually make their way into the clinical setting, pushing AI forward in the field of precision oncology.
Collapse
Affiliation(s)
- Tong Li
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Yupeng Li
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Xiaoyi Zhu
- MOE/NHC Key Laboratory of Medical Molecular Virology, Shanghai Institute of Infectious Disease and Biosecurity, School of Basic Medical Sciences, Shanghai Medical College, Fudan University, Shanghai, China; Shanghai Engineering Research Center for Synthetic Immunology, Shanghai, China
| | - Yao He
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Yanling Wu
- MOE/NHC Key Laboratory of Medical Molecular Virology, Shanghai Institute of Infectious Disease and Biosecurity, School of Basic Medical Sciences, Shanghai Medical College, Fudan University, Shanghai, China; Shanghai Engineering Research Center for Synthetic Immunology, Shanghai, China
| | - Tianlei Ying
- MOE/NHC Key Laboratory of Medical Molecular Virology, Shanghai Institute of Infectious Disease and Biosecurity, School of Basic Medical Sciences, Shanghai Medical College, Fudan University, Shanghai, China; Shanghai Engineering Research Center for Synthetic Immunology, Shanghai, China.
| | - Zhi Xie
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China; Center for Precision Medicine, Sun Yat-sen University, Guangzhou, China.
| |
Collapse
|
18
|
Low-data interpretable deep learning prediction of antibody viscosity using a biophysically meaningful representation. Sci Rep 2023; 13:2917. [PMID: 36806303 PMCID: PMC9941094 DOI: 10.1038/s41598-023-28841-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Accepted: 01/25/2023] [Indexed: 02/22/2023] Open
Abstract
Deep learning, aided by the availability of big data sets, has led to substantial advances across many disciplines. However, many scientific problems of practical interest lack sufficiently large datasets amenable to deep learning. Prediction of antibody viscosity is one such problem where deep learning methods have not yet been explored due to the relative scarcity of relevant training data. In this work, we overcome this limitation using a biophysically meaningful representation that enables us to develop generalizable models even under limited training data. We present, PfAbNet-viscosity, a 3D convolutional neural network architecture, to predict high-concentration viscosity of therapeutic antibodies. We show that with the electrostatic potential surface of the antibody variable region as the only input to the network, the models trained on as few as couple dozen datapoints can generalize with high accuracy. Our feature attribution analysis shows that PfAbNet-viscosity has learned key biophysical drivers of viscosity. The applicability of our approach to other biological systems is discussed.
Collapse
|
19
|
Desai PG, Garidel P, Gbormittah FO, Kamen DE, Mills BJ, Narasimhan CN, Singh S, Stokes ESE, Walsh ER. An Intercompany Perspective on Practical Experiences of Predicting, Optimizing and Analyzing High Concentration Biologic Therapeutic Formulations. J Pharm Sci 2023; 112:359-369. [PMID: 36442683 DOI: 10.1016/j.xphs.2022.11.020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Revised: 11/18/2022] [Accepted: 11/18/2022] [Indexed: 11/27/2022]
Abstract
Developing high-dose biologic drugs for subcutaneous injection often requires high-concentration formulations and optimizing viscosity, solubility, and stability while overcoming analytical, manufacturing, and administration challenges. To understand industry approaches for developing high-concentration formulations, the Formulation Workstream of the BioPhorum Development Group, an industry-wide consortium, conducted an inter-company collaborative exercise which included several surveys. This collaboration provided an industry perspective, experience, and insight into the practicalities for developing high-concentration biologics. To understand solubility and viscosity, companies desire predictive tools, but experience indicates that these are not reliable and experimental strategies are best. Similarly, most companies prefer accelerated and stress stability studies to in-silico or biophysical-based prediction methods to assess aggregation. In addition, optimization of primary container-closure and devices are pursued to mitigate challenges associated with high viscosity of the formulation. Formulation strategies including excipient selection and application of studies at low concentration to high-concentration formulations are reported. Finally, analytical approaches to high concentration formulations are presented. The survey suggests that although prediction of viscosity, solubility, and long-term stability is desirable, the outcome can be inconsistent and molecule dependent. Significant experimental studies are required to confirm robust product definition as modeling at low protein concentrations will not necessarily extrapolate to high concentration formulations.
Collapse
Affiliation(s)
- Preeti G Desai
- Bristol Myers Squibb, Sterile Product Development, 556 Morris Avenue, Summit, NJ 07901, USA
| | - Patrick Garidel
- Boehringer Ingelheim Pharma GmbH Co KG, Innovation Unit, PDB-TIP, 88397 Biberach an der Riss, Germany
| | - Francisca O Gbormittah
- GlaxoSmithKline, Strategic External Development, 1000 Winter Street North, Waltham, MA 02451, USA
| | - Douglas E Kamen
- Regeneron Pharmaceuticals Inc., Formulation Development, 777 Old Saw Mill River Road, Tarrytown, NY 10591, USA
| | - Brittney J Mills
- AbbVie, NBE Drug Product Development, 1 N Waukegan Road, North Chicago, IL 60064, USA
| | | | - Shubhadra Singh
- GlaxoSmithKline R&D, Biopharmaceutical Product Sciences, Collegeville, PA 19426, USA
| | - Elaine S E Stokes
- BioPhorum, The Gridiron Building, 1 Pancras Square, London N1C 4AG UK.
| | - Erika R Walsh
- Merck & Co., Inc., Sterile and Specialty Products, Rahway, NJ, USA
| |
Collapse
|
20
|
Jain T, Boland T, Vásquez M. Identifying developability risks for clinical progression of antibodies using high-throughput in vitro and in silico approaches. MAbs 2023; 15:2200540. [PMID: 37072706 PMCID: PMC10114995 DOI: 10.1080/19420862.2023.2200540] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Revised: 04/03/2023] [Accepted: 04/04/2023] [Indexed: 04/20/2023] Open
Abstract
With the growing significance of antibodies as a therapeutic class, identifying developability risks early during development is of paramount importance. Several high-throughput in vitro assays and in silico approaches have been proposed to de-risk antibodies during early stages of the discovery process. In this review, we have compiled and collectively analyzed published experimental assessments and computational metrics for clinical antibodies. We show that flags assigned based on in vitro measurements of polyspecificity and hydrophobicity are more predictive of clinical progression than their in silico counterparts. Additionally, we assessed the performance of published models for developability predictions on molecules not used during model training. We find that generalization to data outside of those used for training remains a challenge for models. Finally, we highlight the challenges of reproducibility in computed metrics arising from differences in homology modeling, in vitro assessments relying on complex reagents, as well as curation of experimental data often used to assess the utility of high-throughput approaches. We end with a recommendation to enable assay reproducibility by inclusion of controls with disclosed sequences, as well as sharing of structural models to enable the critical assessment and improvement of in silico predictions.
Collapse
Affiliation(s)
| | - Todd Boland
- Computational Biology, Adimab LLC, Lebanon, NH, USA
| | | |
Collapse
|
21
|
Schmitt J, Razvi A, Grapentin C. Predictive modeling of concentration-dependent viscosity behavior of monoclonal antibody solutions using artificial neural networks. MAbs 2023; 15:2169440. [PMID: 36705325 PMCID: PMC9888472 DOI: 10.1080/19420862.2023.2169440] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023] Open
Abstract
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.
Collapse
Affiliation(s)
| | - Abbas Razvi
- Lonza AG/Ltd, Drug Product Services, Basel, Switzerland
| | - Christoph Grapentin
- Lonza AG/Ltd, Drug Product Services, Basel, Switzerland,CONTACT Christoph Grapentin
| |
Collapse
|
22
|
Fernández-Quintero ML, Ljungars A, Waibl F, Greiff V, Andersen JT, Gjølberg TT, Jenkins TP, Voldborg BG, Grav LM, Kumar S, Georges G, Kettenberger H, Liedl KR, Tessier PM, McCafferty J, Laustsen AH. Assessing developability early in the discovery process for novel biologics. MAbs 2023; 15:2171248. [PMID: 36823021 PMCID: PMC9980699 DOI: 10.1080/19420862.2023.2171248] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Accepted: 01/18/2023] [Indexed: 02/25/2023] Open
Abstract
Beyond potency, a good developability profile is a key attribute of a biological drug. Selecting and screening for such attributes early in the drug development process can save resources and avoid costly late-stage failures. Here, we review some of the most important developability properties that can be assessed early on for biologics. These include the influence of the source of the biologic, its biophysical and pharmacokinetic properties, and how well it can be expressed recombinantly. We furthermore present in silico, in vitro, and in vivo methods and techniques that can be exploited at different stages of the discovery process to identify molecules with liabilities and thereby facilitate the selection of the most optimal drug leads. Finally, we reflect on the most relevant developability parameters for injectable versus orally delivered biologics and provide an outlook toward what general trends are expected to rise in the development of biologics.
Collapse
Affiliation(s)
- Monica L. Fernández-Quintero
- Center for Molecular Biosciences Innsbruck (CMBI), Department of General, Inorganic and Theoretical Chemistry, University of Innsbruck, Innsbruck, Austria
| | - Anne Ljungars
- Department of Biotechnology and Biomedicine, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Franz Waibl
- Center for Molecular Biosciences Innsbruck (CMBI), Department of General, Inorganic and Theoretical Chemistry, University of Innsbruck, Innsbruck, Austria
| | - Victor Greiff
- Department of Immunology, University of Oslo, Oslo, Norway
| | - Jan Terje Andersen
- Department of Immunology, University of Oslo, Oslo University Hospital Rikshospitalet, Oslo, Norway
- Institute of Clinical Medicine and Department of Pharmacology, University of Oslo, Oslo, Norway
| | | | - Timothy P. Jenkins
- Department of Biotechnology and Biomedicine, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Bjørn Gunnar Voldborg
- National Biologics Facility, Department of Biotechnology and Biomedicine, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Lise Marie Grav
- Department of Biotechnology and Biomedicine, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Sandeep Kumar
- Biotherapeutics Discovery, Boehringer Ingelheim Pharmaceuticals Inc, Ridgefield, CT, USA
| | - Guy Georges
- Roche Pharma Research and Early Development, Large Molecule Research, Roche Innovation Center Munich, Penzberg, Germany
| | - Hubert Kettenberger
- Roche Pharma Research and Early Development, Large Molecule Research, Roche Innovation Center Munich, Penzberg, Germany
| | - Klaus R. Liedl
- Center for Molecular Biosciences Innsbruck (CMBI), Department of General, Inorganic and Theoretical Chemistry, University of Innsbruck, Innsbruck, Austria
| | - Peter M. Tessier
- Department of Chemical Engineering, Pharmaceutical Sciences and Biomedical Engineering, Biointerfaces Institute, University of Michigan, Ann Arbor, Michigan, USA
| | - John McCafferty
- Department of Medicine, Cambridge Institute of Therapeutic Immunology and Infectious Disease, University of Cambridge, Cambridge, UK
- Maxion Therapeutics, Babraham Research Campus, Cambridge, UK
| | - Andreas H. Laustsen
- Department of Biotechnology and Biomedicine, Technical University of Denmark, Kongens Lyngby, Denmark
| |
Collapse
|
23
|
Thorsteinson N, Comeau SR, Kumar S. Structure-Based Optimization of Antibody-Based Biotherapeutics for Improved Developability: A Practical Guide for Molecular Modelers. Methods Mol Biol 2023; 2552:219-235. [PMID: 36346594 DOI: 10.1007/978-1-0716-2609-2_11] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
A great effort to avoid known developability risks is now more often being made earlier during the lead candidate discovery and optimization phase of biotherapeutic drug development. Predictive computational strategies, used in the early stages of antibody discovery and development, to mitigate the risk of late-stage failure of antibody candidates, are highly valuable. Various structure-based methods exist for accurately predicting properties critical to developability, and, in this chapter, we discuss the history of their development and demonstrate how they can be used to filter large sets of candidates arising from target affinity screening and to optimize lead candidates for developability. Methods for modeling antibody structures from sequence and detecting post-translational modifications and chemical degradation liabilities are also discussed.
Collapse
Affiliation(s)
- Nels Thorsteinson
- Scientific Services Manager, Biologics, Chemical Computing Group ULC, Montreal, QC, Canada
| | - Stephen R Comeau
- Computational Biochemistry and Bioinformatics Group, Biotherapeutics Discovery, Boehringer Ingelheim Pharmaceutical Inc., Ridgefield, CT, USA
| | - Sandeep Kumar
- Computational Biochemistry and Bioinformatics Group, Biotherapeutics Discovery, Boehringer Ingelheim Pharmaceutical Inc., Ridgefield, CT, USA.
| |
Collapse
|
24
|
Zarzar J, Khan T, Bhagawati M, Weiche B, Sydow-Andersen J, Alavattam S. High concentration formulation developability approaches and considerations. MAbs 2023; 15:2211185. [PMID: 37191233 DOI: 10.1080/19420862.2023.2211185] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/17/2023] Open
Abstract
The growing need for biologics to be administered subcutaneously and ocularly, coupled with certain indications requiring high doses, has resulted in an increase in drug substance (DS) and drug product (DP) protein concentrations. With this increase, more emphasis must be placed on identifying critical physico-chemical liabilities during drug development, including protein aggregation, precipitation, opalescence, particle formation, and high viscosity. Depending on the molecule, liabilities, and administration route, different formulation strategies can be used to overcome these challenges. However, due to the high material requirements, identifying optimal conditions can be slow, costly, and often prevent therapeutics from moving rapidly into the clinic/market. In order to accelerate and derisk development, new experimental and in-silico methods have emerged that can predict high concentration liabilities. Here, we review the challenges in developing high concentration formulations, the advances that have been made in establishing low mass and high-throughput predictive analytics, and advances in in-silico tools and algorithms aimed at identifying risks and understanding high concentration protein behavior.
Collapse
Affiliation(s)
- Jonathan Zarzar
- Pharmaceutical Development, Genentech Inc, South San Francisco, CA, USA
| | - Tarik Khan
- Pharma Technical Development Europe, F. Hoffmann-La Roche Ltd, Basel, Switzerland
| | - Maniraj Bhagawati
- Large Molecule Research, Pharma Research and Early Development (pRED), Roche Diagnostics GmbH, Penzberg, Germany
| | - Benjamin Weiche
- Large Molecule Research, Pharma Research and Early Development (pRED), Roche Diagnostics GmbH, Penzberg, Germany
| | - Jasmin Sydow-Andersen
- Large Molecule Research, Pharma Research and Early Development (pRED), Roche Diagnostics GmbH, Penzberg, Germany
| | | |
Collapse
|
25
|
Waight AB, Prihoda D, Shrestha R, Metcalf K, Bailly M, Ancona M, Widatalla T, Rollins Z, Cheng AC, Bitton DA, Fayadat-Dilman L. A machine learning strategy for the identification of key in silico descriptors and prediction models for IgG monoclonal antibody developability properties. MAbs 2023; 15:2248671. [PMID: 37610144 PMCID: PMC10448975 DOI: 10.1080/19420862.2023.2248671] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Revised: 07/28/2023] [Accepted: 08/11/2023] [Indexed: 08/24/2023] Open
Abstract
Identification of favorable biophysical properties for protein therapeutics as part of developability assessment is a crucial part of the preclinical development process. Successful prediction of such properties and bioassay results from calculated in silico features has potential to reduce the time and cost of delivering clinical-grade material to patients, but nevertheless has remained an ongoing challenge to the field. Here, we demonstrate an automated and flexible machine learning workflow designed to compare and identify the most powerful features from computationally derived physiochemical feature sets, generated from popular commercial software packages. We implement this workflow with medium-sized datasets of human and humanized IgG molecules to generate predictive regression models for two key developability endpoints, hydrophobicity and poly-specificity. The most important features discovered through the automated workflow corroborate several previous literature reports, and newly discovered features suggest directions for further research and potential model improvement.
Collapse
Affiliation(s)
- Andrew B. Waight
- Discovery Biologics, Protein Sciences, Merck & Co., Inc, South San Francisco, CA, USA
| | - David Prihoda
- Discovery Informatics, MSD Czech Republic s.r.o, Prague, Czech Republic
| | - Rojan Shrestha
- Discovery Biologics, Protein Sciences, Merck & Co., Inc, South San Francisco, CA, USA
| | - Kevin Metcalf
- Discovery Biologics, Protein Sciences, Merck & Co., Inc, South San Francisco, CA, USA
| | - Marc Bailly
- Discovery Biologics, Protein Sciences, Merck & Co., Inc, South San Francisco, CA, USA
| | - Marco Ancona
- Discovery Informatics, MSD Czech Republic s.r.o, Prague, Czech Republic
| | - Talal Widatalla
- Computational and Structural Chemistry, Merck & Co., Inc, South San Francisco, CA, USA
| | - Zachary Rollins
- Computational and Structural Chemistry, Merck & Co., Inc, South San Francisco, CA, USA
| | - Alan C Cheng
- Computational and Structural Chemistry, Merck & Co., Inc, South San Francisco, CA, USA
| | - Danny A. Bitton
- Discovery Informatics, MSD Czech Republic s.r.o, Prague, Czech Republic
| | | |
Collapse
|
26
|
Mieczkowski C, Zhang X, Lee D, Nguyen K, Lv W, Wang Y, Zhang Y, Way J, Gries JM. Blueprint for antibody biologics developability. MAbs 2023; 15:2185924. [PMID: 36880643 PMCID: PMC10012935 DOI: 10.1080/19420862.2023.2185924] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/08/2023] Open
Abstract
Large-molecule antibody biologics have revolutionized medicine owing to their superior target specificity, pharmacokinetic and pharmacodynamic properties, safety and toxicity profiles, and amenability to versatile engineering. In this review, we focus on preclinical antibody developability, including its definition, scope, and key activities from hit to lead optimization and selection. This includes generation, computational and in silico approaches, molecular engineering, production, analytical and biophysical characterization, stability and forced degradation studies, and process and formulation assessments. More recently, it is apparent these activities not only affect lead selection and manufacturability, but ultimately correlate with clinical progression and success. Emerging developability workflows and strategies are explored as part of a blueprint for developability success that includes an overview of the four major molecular properties that affect all developability outcomes: 1) conformational, 2) chemical, 3) colloidal, and 4) other interactions. We also examine risk assessment and mitigation strategies that increase the likelihood of success for moving the right candidate into the clinic.
Collapse
Affiliation(s)
- Carl Mieczkowski
- Department of Protein Sciences, Hengenix Biotech, Inc, Milpitas, CA, USA
| | - Xuejin Zhang
- Department of Protein Sciences, Hengenix Biotech, Inc, Milpitas, CA, USA
| | - Dana Lee
- Department of Protein Sciences, Hengenix Biotech, Inc, Milpitas, CA, USA
| | - Khanh Nguyen
- Department of Protein Sciences, Hengenix Biotech, Inc, Milpitas, CA, USA
| | - Wei Lv
- Department of Protein Sciences, Hengenix Biotech, Inc, Milpitas, CA, USA
| | - Yanling Wang
- Department of Protein Sciences, Hengenix Biotech, Inc, Milpitas, CA, USA
| | - Yue Zhang
- Department of Protein Sciences, Hengenix Biotech, Inc, Milpitas, CA, USA
| | - Jackie Way
- Department of Protein Sciences, Hengenix Biotech, Inc, Milpitas, CA, USA
| | - Jean-Michel Gries
- President, Discovery Research, Hengenix Biotech, Inc, Milpitas, CA, USA
| |
Collapse
|
27
|
Licari G, Martin KP, Crames M, Mozdzierz J, Marlow MS, Karow-Zwick AR, Kumar S, Bauer J. Embedding Dynamics in Intrinsic Physicochemical Profiles of Market-Stage Antibody-Based Biotherapeutics. Mol Pharm 2022; 20:1096-1111. [PMID: 36573887 PMCID: PMC9906779 DOI: 10.1021/acs.molpharmaceut.2c00838] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
Adequate stability, manufacturability, and safety are crucial to bringing an antibody-based biotherapeutic to the market. Following the concept of holistic in silico developability, we introduce a physicochemical description of 91 market-stage antibody-based biotherapeutics based on orthogonal molecular properties of variable regions (Fvs) embedded in different simulation environments, mimicking conditions experienced by antibodies during manufacturing, formulation, and in vivo. In this work, the evaluation of molecular properties includes conformational flexibility of the Fvs using molecular dynamics (MD) simulations. The comparison between static homology models and simulations shows that MD significantly affects certain molecular descriptors like surface molecular patches. Moreover, the structural stability of a subset of Fv regions is linked to changes in their specific molecular interactions with ions in different experimental conditions. This is supported by the observation of differences in protein melting temperatures upon addition of NaCl. A DEvelopability Navigator In Silico (DENIS) is proposed to compare mAb candidates for their similarity with market-stage biotherapeutics in terms of physicochemical properties and conformational stability. Expanding on our previous developability guidelines (Ahmed et al. Proc. Natl. Acad. Sci. 2021, 118 (37), e2020577118), the hydrodynamic radius and the protein strand ratio are introduced as two additional descriptors that enable a more comprehensive in silico characterization of biotherapeutic drug candidates. Test cases show how this approach can facilitate identification and optimization of intrinsically developable lead candidates. DENIS represents an advanced computational tool to progress biotherapeutic drug candidates from discovery into early development by predicting drug properties in different aqueous environments.
Collapse
Affiliation(s)
- Giuseppe Licari
- Early
Stage Pharmaceutical Development, Pharmaceutical Development Biologicals
& In silico Team, Boehringer Ingelheim
International GmbH & Co. KG, Biberach/Riss 88397, Germany
| | - Kyle P. Martin
- Biotherapeutics
Discovery & In silico Team, Boehringer
Ingelheim Pharmaceuticals Inc., Ridgefield, Connecticut 06877, United States
| | - Maureen Crames
- Biotherapeutics
Discovery & In silico Team, Boehringer
Ingelheim Pharmaceuticals Inc., Ridgefield, Connecticut 06877, United States
| | - Joseph Mozdzierz
- Biotherapeutics
Discovery & In silico Team, Boehringer
Ingelheim Pharmaceuticals Inc., Ridgefield, Connecticut 06877, United States
| | - Michael S. Marlow
- Biotherapeutics
Discovery & In silico Team, Boehringer
Ingelheim Pharmaceuticals Inc., Ridgefield, Connecticut 06877, United States
| | - Anne R. Karow-Zwick
- Early
Stage Pharmaceutical Development, Pharmaceutical Development Biologicals
& In silico Team, Boehringer Ingelheim
International GmbH & Co. KG, Biberach/Riss 88397, Germany
| | - Sandeep Kumar
- Biotherapeutics
Discovery & In silico Team, Boehringer
Ingelheim Pharmaceuticals Inc., Ridgefield, Connecticut 06877, United States,
| | - Joschka Bauer
- Early
Stage Pharmaceutical Development, Pharmaceutical Development Biologicals
& In silico Team, Boehringer Ingelheim
International GmbH & Co. KG, Biberach/Riss 88397, Germany,
| |
Collapse
|
28
|
Advanced Formulations/Drug Delivery Systems for Subcutaneous Delivery of Protein-Based Biotherapeutics. J Pharm Sci 2022; 111:2968-2982. [PMID: 36058255 DOI: 10.1016/j.xphs.2022.08.036] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Revised: 08/29/2022] [Accepted: 08/29/2022] [Indexed: 12/14/2022]
Abstract
Multiple advanced formulations and drug delivery systems (DDSs) have been developed to deliver protein-based biotherapeutics via the subcutaneous (SC) route. These formulations/DDSs include high-concentration solution, co-formulation of two or more proteins, large volume injection, protein cluster/complex, suspension, nanoparticle, microparticle, and hydrogel. These advanced systems provide clinical benefits related to efficacy and safety, but meanwhile, have more complicated formulations and manufacturing processes compared to conventional solution formulations. To develop a fit-for-purpose formulation/DDS for SC delivery, scientists need to consider multiple factors, such as the primary indication, targeted site, immunogenicity, compatibility, biopharmaceutics, patient compliance, etc. Next, they need to develop appropriate formulation (s) and manufacturing processes using the QbD principle and have a control strategy. This paper aims to provide a comprehensive review of advanced formulations/DDSs recently developed for SC delivery of proteins, as well as some knowledge gaps and potential strategies to narrow them through future research.
Collapse
|
29
|
Discovering Novel Small Molecule Compound for Prevention of Monoclonal Antibody Self-Association. Antibodies (Basel) 2022; 11:antib11020040. [PMID: 35735359 PMCID: PMC9219649 DOI: 10.3390/antib11020040] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Revised: 05/30/2022] [Accepted: 05/30/2022] [Indexed: 11/17/2022] Open
Abstract
Designing an antibody with the desired affinity to the antigen is challenging, often achieved by lengthening the hydrophobic CDRs, which can lead to aggregation and cause major hindrance to the development of successful biopharmaceutical products. Aggregation can cause immunogenicity, viscosity and stability issues affecting both the safety and quality of the product. As the hydrophobic residues on the CDR are required for direct binding to antigens, it is not always possible to substitute these residues for aggregation-reduction purposes. Therefore, discovery of specific excipients to prevent aggregation is highly desirable for formulation development. Here, we used a combination of in silico screening methods to identify aggregation-prone regions on an aggregation-prone therapeutic antibody. The most aggregation-prone region on the antibody was selected to conduct virtual screening of compounds that can bind to such regions and act as an aggregation breaker. The most promising excipient candidate was further studied alongside plain buffer formulations and formulations with trehalose using coarse-grained molecular dynamics (CGMD) simulations with MARTINI force field. Mean interaction value between two antibody molecules in each formulation was calculated based on 1024 replicates of 512 ns of such CGMD simulations. Corresponding formulations with an excipient:antibody ratio of 1:5 were compared experimentally by measuring the diffusion interaction parameter kD and accelerated stability studies. Although the compound with the highest affinity score did not show any additional protective effects compared with trehalose, this study proved using a combination of in silico tools can aid excipient design and formulation development.
Collapse
|
30
|
Jacobitz AW, Rodezno W, Agrawal NJ. Utilizing cross-product prior knowledge to rapidly de-risk chemical liabilities in therapeutic antibody candidates. AAPS OPEN 2022. [DOI: 10.1186/s41120-022-00057-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
AbstractThere is considerable pressure in the pharmaceutical industry to advance better molecules faster. One pervasive concern for protein-based therapeutics is the presence of potential chemical liabilities. We have developed a simple methodology for rapidly de-risking specific chemical concerns in antibody-based molecules using prior knowledge of each individual liability at a specific position in the molecule’s sequence. Our methodology hinges on the development of sequence-aligned chemical liability databases of molecules from different stages of commercialization and on sequence-aligned experimental data from prior molecules that have been developed at Amgen. This approach goes beyond the standard practice of simply flagging all instances of each motif that fall in a CDR. Instead, we de-risk motifs that are common at a specific site in commercial mAb-based molecules (and therefore did not previously pose an insurmountable barrier to commercialization) and motifs at specific sites for which we have prior experimental data indicating acceptably low levels of modification. We have used this approach successfully to identify candidates in a discovery phase program with exclusively very low risk potential chemical liabilities. Identifying these candidates in the discovery phase allowed us to bypass protein engineering and accelerate the program’s timeline by 6 months.
Collapse
|
31
|
Lai PK. DeepSCM: An efficient convolutional neural network surrogate model for the screening of therapeutic antibody viscosity. Comput Struct Biotechnol J 2022; 20:2143-2152. [PMID: 35832619 PMCID: PMC9092385 DOI: 10.1016/j.csbj.2022.04.035] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2022] [Revised: 04/26/2022] [Accepted: 04/26/2022] [Indexed: 11/24/2022] Open
Abstract
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.
Collapse
Affiliation(s)
- Pin-Kuang Lai
- Department of Chemical Engineering and Materials Science, Stevens Institute of Technology, Hoboken 07030, NJ, United States
| |
Collapse
|
32
|
Spassov VZ, Kemmish H, Yan L. Two physics‐based models for
pH
‐dependent calculations of protein solubility. Protein Sci 2022; 31:e4299. [PMID: 35481654 PMCID: PMC8996476 DOI: 10.1002/pro.4299] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Revised: 02/01/2022] [Accepted: 02/28/2022] [Indexed: 11/11/2022]
Abstract
When engineering a protein for its biological function, many physicochemical properties are also optimized throughout the engineering process, and the protein's solubility is among the most important properties to consider. Here, we report two novel computational methods to calculate the pH-dependent protein solubility, and to rank the solubility of mutants. The first is an empirical method developed for fast ranking of the solubility of a large number of mutants of a protein. It takes into account electrostatic solvation energy term calculated using Generalized Born approximation, hydrophobic patches, protein charge, and charge asymmetry, as well as the changes of protein stability upon mutation. This method has been tested on over 100 mutations for 17 globular proteins, as well as on 44 variants of five different antibodies. The prediction rate is over 80%. The antibody tests showed a Pearson correlation coefficient, R, with experimental data from .83 to .91. The second method is based on a novel, completely force-field-based approach using CHARMm program modules to calculate the binding energy of the protein to a part of the crystal lattice, generated from X-ray structure. The method predicted with very high accuracy the solubility of Ribonuclease SA and its 3K and 5K mutants as a function of pH without any parameter adjustments of the existing BIOVIA Discovery Studio binding affinity model. Our methods can be used for rapid screening of large numbers of design candidates based on solubility, and to guide the design of solution conditions for antibody formulation.
Collapse
Affiliation(s)
- Velin Z. Spassov
- BIOVIA Dassault Systemes, 5005 Wateridge Vista Drive San Diego California USA
| | - Helen Kemmish
- BIOVIA Dassault Systemes, 5005 Wateridge Vista Drive San Diego California USA
| | - Lisa Yan
- BIOVIA Dassault Systemes, 5005 Wateridge Vista Drive San Diego California USA
| |
Collapse
|
33
|
Blanco MA. Computational models for studying physical instabilities in high concentration biotherapeutic formulations. MAbs 2022; 14:2044744. [PMID: 35282775 PMCID: PMC8928847 DOI: 10.1080/19420862.2022.2044744] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022] Open
Abstract
Computational prediction of the behavior of concentrated protein solutions is particularly advantageous in early development stages of biotherapeutics when material availability is limited and a large set of formulation conditions needs to be explored. This review provides an overview of the different computational paradigms that have been successfully used in modeling undesirable physical behaviors of protein solutions with a particular emphasis on high-concentration drug formulations. This includes models ranging from all-atom simulations, coarse-grained representations to macro-scale mathematical descriptions used to study physical instability phenomena of protein solutions such as aggregation, elevated viscosity, and phase separation. These models are compared and summarized in the context of the physical processes and their underlying assumptions and limitations. A detailed analysis is also given for identifying protein interaction processes that are explicitly or implicitly considered in the different modeling approaches and particularly their relations to various formulation parameters. Lastly, many of the shortcomings of existing computational models are discussed, providing perspectives and possible directions toward an efficient computational framework for designing effective protein formulations.
Collapse
Affiliation(s)
- Marco A. Blanco
- Materials and Biophysical Characterization, Analytical R & D, Merck & Co., Inc, Kenilworth, NJ USA
| |
Collapse
|
34
|
Sankar K, Trainor K, Blazer L, Adams J, Sidhu S, Day T, Meiering E, Maier J. A Descriptor set for Quantitative Structure-Property Relationship Prediction in Biologics. Mol Inform 2022; 41:e2100240. [PMID: 35277930 DOI: 10.1002/minf.202100240] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Accepted: 03/11/2022] [Indexed: 11/12/2022]
Abstract
In addition to attaining the desired binding to their targets, a crucial aspect in the development of biotherapeutics is 'developability', which includes several desirable properties such as high solubility, low viscosity and aggregation, physico-chemical stability and low immunogenicity. The lack of any of these properties can lead to significant obstacles in advancing them to clinic; thus in silico methods capable of raising warning flags in earlier stages of development are highly beneficial. We have developed a computational framework based on a large and diverse set of protein specific descriptors ideal for making liability predictions using a machine-learning approach. This set offers a high degree of feature diversity classifiable by sequence, structure and surface patches. We assess the sensitivity and applicability of these descriptors in four dedicated case studies that are believed to be representative of biophysical characterizations commonly employed during the development process. In addition to data sets obtained from public sources, we have validated the descriptors on novel experimental data sets in order to address antibody developability and to generate prospective predictions on Adnectins. The results demonstrate that the descriptors are well suited to assist in the improvement of properties of systems that exhibit poor solubility or aggregation.
Collapse
|
35
|
Lai PK, Gallegos A, Mody N, Sathish HA, Trout BL. Machine learning prediction of antibody aggregation and viscosity for high concentration formulation development of protein therapeutics. MAbs 2022; 14:2026208. [PMID: 35075980 PMCID: PMC8794240 DOI: 10.1080/19420862.2022.2026208] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
Machine learning has been recently used to predict therapeutic antibody aggregation rates and viscosity at high concentrations (150 mg/ml). These works focused on commercially available antibodies, which may have been optimized for stability. In this study, we measured accelerated aggregation rates at 45°C and viscosity at 150 mg/ml for 20 preclinical and clinical-stage antibodies. Features obtained from molecular dynamics simulations of the full-length antibody and sequences were used for machine learning model construction. We found a k-nearest neighbors regression model with two features, spatial positive charge map on the CDRH2 and solvent-accessible surface area of hydrophobic residues on the variable fragment, gives the best performance for predicting antibody aggregation rates (r = 0.89). For the viscosity classification model, the model with the highest accuracy is a logistic regression model with two features, spatial negative charge map on the heavy chain variable region and spatial negative charge map on the light chain variable region. The accuracy and the area under precision recall curve of the classification model from validation tests are 0.86 and 0.70, respectively. In addition, we combined data from another 27 commercial mAbs to develop a viscosity predictive model. The best model is a logistic regression model with two features, number of hydrophobic residues on the light chain variable region and net charges on the light chain variable region. The accuracy and the area under precision recall curve of the classification model are 0.85 and 0.6, respectively. The aggregation rates and viscosity models can be used to predict antibody stability to facilitate pharmaceutical development.
Collapse
Affiliation(s)
- Pin-Kuang Lai
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.,Department of Chemical Engineering and Materials Science, Stevens Institute of Technology, Hoboken, New Jersey, USA
| | - Austin Gallegos
- Dosage Form Design and Development, AstraZeneca, Gaithersburg, Maryland, USA
| | - Neil Mody
- Dosage Form Design and Development, AstraZeneca, Gaithersburg, Maryland, USA
| | - Hasige A Sathish
- Dosage Form Design and Development, AstraZeneca, Gaithersburg, Maryland, USA
| | - Bernhardt L Trout
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| |
Collapse
|
36
|
Assessment of Therapeutic Antibody Developability by Combinations of In Vitro and In Silico Methods. METHODS IN MOLECULAR BIOLOGY (CLIFTON, N.J.) 2022; 2313:57-113. [PMID: 34478132 DOI: 10.1007/978-1-0716-1450-1_4] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Although antibodies have become the fastest-growing class of therapeutics on the market, it is still challenging to develop them for therapeutic applications, which often require these molecules to withstand stresses that are not present in vivo. We define developability as the likelihood of an antibody candidate with suitable functionality to be developed into a manufacturable, stable, safe, and effective drug that can be formulated to high concentrations while retaining a long shelf life. The implementation of reliable developability assessments from the early stages of antibody discovery enables flagging and deselection of potentially problematic candidates, while focussing available resources on the development of the most promising ones. Currently, however, thorough developability assessment requires multiple in vitro assays, which makes it labor intensive and time consuming to implement at early stages. Furthermore, accurate in vitro analysis at the early stage is compromised by the high number of potential candidates that are often prepared at low quantities and purity. Recent improvements in the performance of computational predictors of developability potential are beginning to change this scenario. Many computational methods only require the knowledge of the amino acid sequences and can be used to identify possible developability issues or to rank available candidates according to a range of biophysical properties. Here, we describe how the implementation of in silico tools into antibody discovery pipelines is increasingly offering time- and cost-effective alternatives to in vitro experimental screening, thus streamlining the drug development process. We discuss in particular the biophysical and biochemical properties that underpin developability potential and their trade-offs, review various in vitro assays to measure such properties or parameters that are predictive of developability, and give an overview of the growing number of in silico tools available to predict properties important for antibody development, including the CamSol method developed in our laboratory.
Collapse
|
37
|
Akbar R, Bashour H, Rawat P, Robert PA, Smorodina E, Cotet TS, Flem-Karlsen K, Frank R, Mehta BB, Vu MH, Zengin T, Gutierrez-Marcos J, Lund-Johansen F, Andersen JT, Greiff V. Progress and challenges for the machine learning-based design of fit-for-purpose monoclonal antibodies. MAbs 2022; 14:2008790. [PMID: 35293269 PMCID: PMC8928824 DOI: 10.1080/19420862.2021.2008790] [Citation(s) in RCA: 29] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2021] [Revised: 11/04/2021] [Accepted: 11/17/2021] [Indexed: 12/15/2022] Open
Abstract
Although the therapeutic efficacy and commercial success of monoclonal antibodies (mAbs) are tremendous, the design and discovery of new candidates remain a time and cost-intensive endeavor. In this regard, progress in the generation of data describing antigen binding and developability, computational methodology, and artificial intelligence may pave the way for a new era of in silico on-demand immunotherapeutics design and discovery. Here, we argue that the main necessary machine learning (ML) components for an in silico mAb sequence generator are: understanding of the rules of mAb-antigen binding, capacity to modularly combine mAb design parameters, and algorithms for unconstrained parameter-driven in silico mAb sequence synthesis. We review the current progress toward the realization of these necessary components and discuss the challenges that must be overcome to allow the on-demand ML-based discovery and design of fit-for-purpose mAb therapeutic candidates.
Collapse
Affiliation(s)
- Rahmad Akbar
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Habib Bashour
- School of Life Sciences, University of Warwick, Coventry, UK
| | - Puneet Rawat
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, India
| | - Philippe A. Robert
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Eva Smorodina
- Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, Russia
| | | | - Karine Flem-Karlsen
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
- Institute of Clinical Medicine, Department of Pharmacology, University of Oslo and Oslo University Hospital, Norway
| | - Robert Frank
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Brij Bhushan Mehta
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Mai Ha Vu
- Department of Linguistics and Scandinavian Studies, University of Oslo, Norway
| | - Talip Zengin
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
- Department of Bioinformatics, Mugla Sitki Kocman University, Turkey
| | | | | | - Jan Terje Andersen
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
- Institute of Clinical Medicine, Department of Pharmacology, University of Oslo and Oslo University Hospital, Norway
| | - Victor Greiff
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
| |
Collapse
|
38
|
Han X, Shih J, Lin Y, Chai Q, Cramer SM. Development of QSAR models for in silico screening of antibody solubility. MAbs 2022; 14:2062807. [PMID: 35442164 PMCID: PMC9037471 DOI: 10.1080/19420862.2022.2062807] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022] Open
Abstract
Although monoclonal antibodies (mAbs) have been shown to be extremely effective in treating a number of diseases, they often suffer from poor developability attributes, such as high viscosity and low solubility at elevated concentrations. Since experimental candidate screening is often materials and labor intensive, there is substantial interest in developing in silico tools for expediting mAb design. Here, we present a strategy using machine learning-based QSAR models for the a priori estimation of mAb solubility. The extrapolated protein solubilities of a set of 111 antibodies in a histidine buffer were determined using a high throughput PEG precipitation assay. 3D homology models of the antibodies were determined, and a large set of in house and commercially available molecular descriptors were then calculated. The resulting experimental and descriptor data were then used for the development of QSAR models of mAb solubilities. After feature selection and training with different machine learning algorithms, the models were evaluated with external test sets. The resulting regression models were able to estimate the solubility values of external test set data with R2 of 0.81 and 0.85 for the two regression models developed. In addition, three class and binary classification models were developed and shown to be good estimators of mAb solubility behavior, with overall test set accuracies of 0.70 and 0.95, respectively. The analysis of the selected molecular descriptors in these models was also found to be informative and suggested that several charge-based descriptors and isotype may play important roles in mAb solubility. The combination of high throughput relative solubility experimental techniques in concert with efficient machine learning QSAR models offers an opportunity to rapidly screen potential mAb candidates and to design therapeutics with improved solubility characteristics.
Collapse
Affiliation(s)
- Xuan Han
- Department of Chemical and Biological Engineering and Center for Biotechnology and interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, New York, USA
| | - James Shih
- Biotechnology Discovery Research, Eli Lilly Biotechnology Center, San Diego, California, USA
| | - Yuhao Lin
- Research Information & Digital Solutions, Eli Lilly Biotechnology Center, San Diego, California, USA
| | - Qing Chai
- Biotechnology Discovery Research, Eli Lilly Biotechnology Center, San Diego, California, USA
| | - Steven M Cramer
- Department of Chemical and Biological Engineering and Center for Biotechnology and interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, New York, USA
| |
Collapse
|
39
|
Discovery of compounds with viscosity-reducing effects on biopharmaceutical formulations with monoclonal antibodies. Comput Struct Biotechnol J 2022; 20:5420-5429. [PMID: 36212536 PMCID: PMC9529560 DOI: 10.1016/j.csbj.2022.09.035] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Revised: 09/22/2022] [Accepted: 09/22/2022] [Indexed: 11/24/2022] Open
Abstract
Computational screening yielded 44 new viscosity-reducing agents on two model mAbs. Dual excipients for viscosity reduction and solution buffering were discovered. Compounds with three or more charges reduce the viscosity of model mAb formulations. Filtering based on physicochemical properties can be applied to other mAb formulations.
For the development of concentrated monoclonal antibody formulations for subcutaneous administration, the main challenge is the high viscosity of the solutions. To compensate for this, viscosity reducing agents are commonly used as excipients. Here, we applied two computational chemistry approaches to discover new viscosity-reducing agents: fingerprint similarity searching, and physicochemical property filtering. In total, 94 compounds were selected and experimentally evaluated on two model monoclonal antibodies, which led to the discovery of 44 new viscosity-reducing agents. Analysis of the results showed that using a simple filter that selects only compounds with three or more charge groups is a good ‘rule of thumb’ for selecting potential viscosity-reducing agents for two model monoclonal antibody formulations.
Collapse
|
40
|
Tang Y, Cain P, Anguiano V, Shih JJ, Chai Q, Feng Y. Impact of IgG subclass on molecular properties of monoclonal antibodies. MAbs 2021; 13:1993768. [PMID: 34763607 PMCID: PMC8726687 DOI: 10.1080/19420862.2021.1993768] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/31/2022] Open
Abstract
Immunoglobulin G-based monoclonal antibodies (mAbs) have become a dominant class of biotherapeutics in recent decades. Approved antibodies are mainly of the subclasses IgG1, IgG2, and IgG4, as well as their derivatives. Over the decades, the selection of IgG subclass has frequently been based on the needs of Fc gamma receptor engagement and effector functions for the desired mechanism of action, while the effect on drug product developability has been less thoroughly characterized. One of the major reasons is the lack of systematic understanding of the impact of IgG subclass on the molecular properties. Several efforts have been made recently to compare molecular property differences among these IgG subclasses, but the conclusions from these studies are sometimes obscured by the interference from variable regions. To further establish mechanistic understandings, we conducted a systematic study by grafting three independent variable regions onto human IgG1, an IgG1 variant, IgG2, and an IgG4 variant constant domains and evaluating the impact of subclass and variable regions on their molecular properties. Structural and computational analysis revealed specific molecular features that potentially account for the differential behavior of the IgG subclasses observed experimentally. Our data indicate that IgG subclass plays a significant role on molecular properties, either through direct effects or via the interplay with the variable region, the IgG1 mAbs tend to have higher solubility than either IgG2 or IgG4 mAbs in a common pH 6 buffer matrix, and solution behavior relies heavily on the charge status of the antibody at the desirable pH.
Collapse
Affiliation(s)
- Yu Tang
- Pharmaceutical Development, Syndax Pharmaceuticals, Waltham, Massachusetts, USA
| | - Paul Cain
- Biotechnology Discovery Research, Lilly Research Laboratories, Lilly Technology Center North, Indianapolis, Indiana, USA
| | - Victor Anguiano
- Bioproduct Research & Development, Lilly Research Laboratories, Lilly Technology Center North, Indianapolis, Indiana, USA
| | - James J Shih
- Biotechnology Discovery Research, Lilly Research Laboratories, Lilly Biotechnology Center, San Diego, California, USA
| | - Qing Chai
- Biotechnology Discovery Research, Lilly Research Laboratories, Lilly Biotechnology Center, San Diego, California, USA
| | - Yiqing Feng
- Biotechnology Discovery Research, Lilly Research Laboratories, Lilly Technology Center North, Indianapolis, Indiana, USA
| |
Collapse
|
41
|
Lai PK, Ghag G, Yu Y, Juan V, Fayadat-Dilman L, Trout BL. Differences in human IgG1 and IgG4 S228P monoclonal antibodies viscosity and self-interactions: Experimental assessment and computational predictions of domain interactions. MAbs 2021; 13:1991256. [PMID: 34747330 PMCID: PMC8583000 DOI: 10.1080/19420862.2021.1991256] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022] Open
Abstract
Human/humanized IgG4 antibodies have reduced effector function relative to IgG1 antibodies, which is desirable for certain therapeutic purposes. However, the developability and biophysical properties for IgG4 antibodies are not well understood. This work focuses on the head-to-head comparison of key biophysical properties, such as self-interaction and viscosity, for 14 human/humanized, and chimeric IgG1 and IgG4 S228P monoclonal antibody pairs that contain the identical variable regions. Experimental measurements showed that the IgG4 S228P antibodies have similar or higher self-interaction and viscosity than that of IgG1 antibodies in 20 mM sodium acetate, pH 5.5. We report sequence and structural drivers for the increased viscosity and self-interaction detected in IgG4 S228P antibodies through a combination of experimental data and computational models. Further, we applied and extended a previously established computational model for IgG1 antibodies to predict the self-interaction and viscosity behavior for each antibody pair, providing insight into the structural characteristics and differences of these two isotypes. Interestingly, we observed that the IgG4 S228P swapped variants, where the CH3 domain was swapped for that of an IgG1, showed reduced self-interaction behavior. These domain swapped IgG4 S228P molecules also showed reduced viscosity from experiment and coarse-grained simulations. We also observed that experimental diffusion interaction parameter (kD) values have a high correlation with computational diffusivity prediction for both IgG1 and IgG4 S228P isotypes. Abbreviations: AHc, constant region Hamaker constant; AHv, variable region Hamaker constant; CDRs, Complementarity-determining regions; CG, Coarse-grained model; CH1, Constant heavy chain 1; CH2 Constant heavy chain 2; CH3 Constant heavy chain 3; chgCH3 Effective charge on the CH3 region; CL Constant light chain; cP, Centipoise; DLS, Dynamic light scattering; Fab, Fragment antigen-binding; Fc, Fragment crystallizable; Fv, Variable domaing; (r) Radial distribution function; H1 CDR1 of Heavy Chain; H2 CDR2 of Heavy Chain; H3 CDR3 of Heavy Chain; HVI, High viscosity index; IgG1 human immunoglobulin of IgG1 subclass; IgG4 human immunoglobulin of IgG4 subclass; kD, Diffusion interaction parameter; L1 CDR1 of Light Chain; L2 CDR2 of Light Chain; L3 CDR3 of Light Chain; mAb, Monoclonal antibody; MD, Molecular dynamics; PPI Protein–protein interactions; SCM, Spatial charge map; UP-SEC, Ultra-high-performance size-exclusion chromatography; VH, Variable domain of Heavy Chain; VL, Variable domain of Light Chain
Collapse
Affiliation(s)
- Pin-Kuang Lai
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts USA.,Current Address: Department of Chemical Engineering and Materials Science, Stevens Institute of Technology, Hoboken, New Jersey USA
| | - Gaurav Ghag
- Merck & Co, Discovery Biologics, Protein Sciences Department, South San Francisco, CA , USA
| | - Yao Yu
- Merck & Co, Discovery Biologics, Protein Sciences Department, South San Francisco, CA , USA
| | - Veronica Juan
- Merck & Co, Discovery Biologics, Protein Sciences Department, South San Francisco, CA , USA
| | | | - Bernhardt L Trout
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts USA
| |
Collapse
|
42
|
Thorsteinson N, Gunn JR, Kelly K, Long W, Labute P. Structure-based charge calculations for predicting isoelectric point, viscosity, clearance, and profiling antibody therapeutics. MAbs 2021; 13:1981805. [PMID: 34632944 PMCID: PMC8510563 DOI: 10.1080/19420862.2021.1981805] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022] Open
Abstract
The effect of hydrophobicity on antibody aggregation is well understood, and it has been shown that charge calculations can be useful for high-concentration viscosity and pharmacokinetic (PK) clearance predictions. In this work, structure-based charge descriptors are evaluated for their predictive performance on recently published antibody pI, viscosity, and clearance data. From this, we devised four rules for therapeutic antibody profiling which address developability issues arising from hydrophobicity and charged-based solution behavior, PK, and the ability to enrich for those that are approved by the U.S. Food and Drug Administration. Differences in strategy for optimizing the solution behavior of human IgG1 antibodies versus the IgG2 and IgG4 isotypes and the impact of pH alterations in formulation are discussed.
Collapse
Affiliation(s)
- Nels Thorsteinson
- Research and Development, Chemical Computing Group ULC, Montreal, Quebec, Canada
| | - John R Gunn
- Research and Development, Chemical Computing Group ULC, Montreal, Quebec, Canada
| | - Kenneth Kelly
- Research and Development, Chemical Computing Group ULC, Montreal, Quebec, Canada
| | - Will Long
- Research and Development, Chemical Computing Group ULC, Montreal, Quebec, Canada
| | - Paul Labute
- Research and Development, Chemical Computing Group ULC, Montreal, Quebec, Canada
| |
Collapse
|
43
|
Prabakaran R, Rawat P, Yasuo N, Sekijima M, Kumar S, Gromiha MM. Effect of charged mutation on aggregation of a pentapeptide: Insights from molecular dynamics simulations. Proteins 2021; 90:405-417. [PMID: 34460128 DOI: 10.1002/prot.26230] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2021] [Revised: 06/30/2021] [Accepted: 08/24/2021] [Indexed: 12/14/2022]
Abstract
Aggregation of therapeutic monoclonal antibodies (mAbs) can negatively affect their chemistry, manufacturing, and control attributes and lead to undesirable immune responses in patients. Therefore, optimization of lead mAb drug candidates during discovery stages to mitigate aggregation is increasingly becoming an integral part of their developability assessments. The disruption of short sequence motifs called aggregation prone regions (APRs) found in amino acid sequences of mAb candidates can potentially mitigate their aggregation. In this work, we have performed molecular dynamics simulations to study the aggregation of an APR (VLVIY) found in λ light chains of human antibodies and its single point mutant KLVIY. Eighteen different multicopy peptide simulation systems of "VLVIY" and "KLVIY" were constructed by varying their concentrations, temperatures, termini capping, and flanking gate-keeper regions. Within 20 ns of the simulation, peptide "VLVIY" formed an aggregate of 100 peptides at ~0.1 M concentration with a 60% reduction in solvent accessible surface area (SASA). Furthermore, analysis of the SASA change, peptide cluster distribution, and water residence time demonstrated how Val ➔ Lys mutation resists aggregation and improves solubility. Presence of Lys slows down aggregation kinetics via charge-charge repulsions and by raising the kinetic barrier to formation of large oligomers. However, the effect of the Val ➔ Lys mutation is dependent on sequence and structural contexts around the APR. This mutation also alters the solvation shell around the peptide by favoring solute-solvent interactions, thereby increasing its solubility. This work has provided a detailed mechanistic explanation of how APR disruption can mitigate aggregation in biotherapeutics and improve their developability.
Collapse
Affiliation(s)
- R Prabakaran
- Protein Bioinformatics Laboratory, Department of Biotechnology, Indian Institute of Technology Madras, Chennai, India
| | - Puneet Rawat
- Protein Bioinformatics Laboratory, Department of Biotechnology, Indian Institute of Technology Madras, Chennai, India
| | - Nobuaki Yasuo
- Department of Computer Science, Tokyo Institute of Technology, Yokohama, Japan
| | - Masakazu Sekijima
- Department of Computer Science, Tokyo Institute of Technology, Yokohama, Japan
| | - Sandeep Kumar
- Biotherapeutics Discovery, Boehringer Ingelheim Pharmaceutical Inc., Ridgefield, Connecticut, USA
| | - M Michael Gromiha
- Protein Bioinformatics Laboratory, Department of Biotechnology, Indian Institute of Technology Madras, Chennai, India.,Department of Computer Science, Tokyo Institute of Technology, Yokohama, Japan
| |
Collapse
|
44
|
Cloutier TK, Sudrik C, Mody N, Hasige SA, Trout BL. Molecular computations of preferential interactions of proline, arginine.HCl, and NaCl with IgG1 antibodies and their impact on aggregation and viscosity. MAbs 2021; 12:1816312. [PMID: 32938318 PMCID: PMC7531574 DOI: 10.1080/19420862.2020.1816312] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
Preferential interactions of excipients with the antibody surface govern their effect on the stability of antibodies in solution. We probed the preferential interactions of proline, arginine.HCl (Arg.HCl), and NaCl with three therapeutically relevant IgG1 antibodies via experiment and simulation. With simulations, we examined how excipients interacted with different types of surface patches in the variable region (Fv). For example, proline interacted most strongly with aromatic surfaces, Arg.HCl was included near negative residues, and NaCl was excluded from negative residues and certain hydrophobic regions. The differences in interaction of different excipients with the same surface patch on an antibody may be responsible for variations in the antibody's aggregation, viscosity, and self-association behaviors in each excipient. Proline reduced self-association for all three antibodies and reduced aggregation for the antibody with an association-limited aggregation mechanism. The effects of Arg.HCl and NaCl on aggregation and viscosity were highly dependent on the surface charge distribution and the extent of exclusion from highly hydrophobic patches. At pH 5.5, both tended to increase the aggregation of an antibody with a strongly positive charge on the Fv, while only NaCl reduced the aggregation of the antibody with a large negative charge patch on the Fv. Arg.HCl reduced the viscosities of antibodies with either a hydrophobicity-driven mechanism or a charge-driven mechanism. Analysis of this data presents a framework for understanding how amino acid and ionic excipients interact with different protein surfaces, and how these interactions translate to the observed stability behavior.
Collapse
Affiliation(s)
- Theresa K Cloutier
- Department of Chemical Engineering, Massachusetts Institute of Technology , Cambridge, Maryland, USA
| | - Chaitanya Sudrik
- Department of Chemical Engineering, Massachusetts Institute of Technology , Cambridge, Maryland, USA
| | - Neil Mody
- Dosage Form Design and Development, AstraZeneca , Gaithersburg, Maryland, USA
| | - Sathish A Hasige
- Dosage Form Design and Development, AstraZeneca , Gaithersburg, Maryland, USA
| | - Bernhardt L Trout
- Department of Chemical Engineering, Massachusetts Institute of Technology , Cambridge, Maryland, USA
| |
Collapse
|
45
|
Chaturvedi SK, Parupudi A, Juul-Madsen K, Nguyen A, Vorup-Jensen T, Dragulin-Otto S, Zhao H, Esfandiary R, Schuck P. Measuring aggregates, self-association, and weak interactions in concentrated therapeutic antibody solutions. MAbs 2021; 12:1810488. [PMID: 32887536 PMCID: PMC7531506 DOI: 10.1080/19420862.2020.1810488] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023] Open
Abstract
Monoclonal antibodies are a class of biotherapeutics used for an increasing variety of disorders, including cancer, autoimmune, neurodegenerative, and viral diseases. Besides their antigen specificity, therapeutic use also mandates control of their solution interactions and colloidal properties in order to achieve a stable, efficacious, non-immunogenic, and low viscosity antibody solution at concentrations in the range of 50–150 mg/mL. This requires characterization of their reversible self-association, aggregation, and weak attractive and repulsive interactions governing macromolecular distance distributions in solution. Simultaneous measurement of these properties, however, has been hampered by solution nonideality. Based on a recently introduced sedimentation velocity method for measuring macromolecular size distributions in a mean-field approximation for hydrodynamic interactions, we demonstrate simultaneous measurement of polydispersity and weak and strong solution interactions in a panel of antibodies with concentrations up to 45 mg/mL. By allowing approximately an order of magnitude higher concentrations than previously possible in sedimentation velocity size distribution analysis, this approach can substantially improve efficiency and sensitivity for characterizing polydispersity and interactions of therapeutic antibodies at or close to formulation conditions.
Collapse
Affiliation(s)
- Sumit K Chaturvedi
- Dynamics of Macromolecular Assembly Section, Laboratory of Cellular Imaging and Macromolecular Biophysics, National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health , Bethesda, MD, USA
| | - Arun Parupudi
- Department of Dosage Form Design and Development, Biopharmaceuticals R&D, AstraZeneca , Gaithersburg, MD, USA
| | - Kristian Juul-Madsen
- Dynamics of Macromolecular Assembly Section, Laboratory of Cellular Imaging and Macromolecular Biophysics, National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health , Bethesda, MD, USA.,Biophysical Immunology Laboratory, Department of Biomedicine, Aarhus University , Aarhus, Denmark
| | - Ai Nguyen
- Dynamics of Macromolecular Assembly Section, Laboratory of Cellular Imaging and Macromolecular Biophysics, National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health , Bethesda, MD, USA
| | - Thomas Vorup-Jensen
- Biophysical Immunology Laboratory, Department of Biomedicine, Aarhus University , Aarhus, Denmark
| | - Sonia Dragulin-Otto
- Department of Dosage Form Design and Development, Biopharmaceuticals R&D, AstraZeneca , Gaithersburg, MD, USA
| | - Huaying Zhao
- Dynamics of Macromolecular Assembly Section, Laboratory of Cellular Imaging and Macromolecular Biophysics, National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health , Bethesda, MD, USA
| | - Reza Esfandiary
- Department of Dosage Form Design and Development, Biopharmaceuticals R&D, AstraZeneca , Gaithersburg, MD, USA
| | - Peter Schuck
- Dynamics of Macromolecular Assembly Section, Laboratory of Cellular Imaging and Macromolecular Biophysics, National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health , Bethesda, MD, USA
| |
Collapse
|
46
|
Grinshpun B, Thorsteinson N, Pereira JN, Rippmann F, Nannemann D, Sood VD, Fomekong Nanfack Y. Identifying biophysical assays and in silico properties that enrich for slow clearance in clinical-stage therapeutic antibodies. MAbs 2021; 13:1932230. [PMID: 34116620 PMCID: PMC8204999 DOI: 10.1080/19420862.2021.1932230] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022] Open
Abstract
Understanding the pharmacokinetic (PK) properties of a drug, such as clearance, is a crucial step for evaluating efficacy. The PK of therapeutic antibodies can be complex and is influenced by interactions with the target, Fc-receptors, anti-drug antibodies, and antibody intrinsic factors. A growing body of literature has linked biophysical properties of antibodies, particularly nonspecific-binding propensity, hydrophobicity and charged regions to rapid clearance in preclinical species and selected human PK studies. A clear understanding of the connection between biophysical properties and their impact on PK would allow for early selection and optimization of antibodies and reduce costly attrition during clinical trials due to sub-optimal human clearance. Due to the difficulty in obtaining large and unbiased human PK data, previous studies have focused mostly on preclinical PK. For this study, we obtained and curated the most comprehensive clinical PK dataset to date and calculated accurate estimates of linear clearance for 64 monoclonal antibodies ranging from investigational candidates in Phase 2 trials to marketed products. This allows for the first time a deep analysis of the influence of biophysical and sequence-based in silico properties directly on human clearance. We use statistical analysis and a Random Forest classifier to identify properties that have the greatest influence in our dataset. Our findings indicate that in vitro poly-specificity assay and in silico estimated isoelectric point can discriminate fast and slow clearing antibodies, extending previous observations on preclinical clearance. This provides a simple yet powerful approach to select antibodies with desirable PK during early-stage screening.
Collapse
Affiliation(s)
- Boris Grinshpun
- Discovery & Development Technologies, Drug Disposition & Design, EMD Serono Research & Development Institute, Inc, Billerica, MA, USA
| | - Nels Thorsteinson
- Department of Scientific Services, Chemical Computing Group ULC, Montreal, Quebec, Canada
| | - Joao Ns Pereira
- Discovery & Development Technologies, Drug Disposition & Design, Merck Healthcare KGaA, Darmstadt, Germany
| | - Friedrich Rippmann
- Discovery & Development Technologies, Drug Disposition & Design, Merck Healthcare KGaA, Darmstadt, Germany
| | - David Nannemann
- Discovery & Development Technologies, Drug Disposition & Design, EMD Serono Research & Development Institute, Inc, Billerica, MA, USA
| | - Vanita D Sood
- Discovery & Development Technologies, Drug Disposition & Design, EMD Serono Research & Development Institute, Inc, Billerica, MA, USA
| | - Yves Fomekong Nanfack
- Discovery & Development Technologies, Drug Disposition & Design, EMD Serono Research & Development Institute, Inc, Billerica, MA, USA
| |
Collapse
|
47
|
Golinski AW, Mischler KM, Laxminarayan S, Neurock NL, Fossing M, Pichman H, Martiniani S, Hackel BJ. High-throughput developability assays enable library-scale identification of producible protein scaffold variants. Proc Natl Acad Sci U S A 2021; 118:e2026658118. [PMID: 34078670 PMCID: PMC8201827 DOI: 10.1073/pnas.2026658118] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023] Open
Abstract
Proteins require high developability-quantified by expression, solubility, and stability-for robust utility as therapeutics, diagnostics, and in other biotechnological applications. Measuring traditional developability metrics is low throughput in nature, often slowing the developmental pipeline. We evaluated the ability of 10 variations of three high-throughput developability assays to predict the bacterial recombinant expression of paratope variants of the protein scaffold Gp2. Enabled by a phenotype/genotype linkage, assay performance for 105 variants was calculated via deep sequencing of populations sorted by proxied developability. We identified the most informative assay combination via cross-validation accuracy and correlation feature selection and demonstrated the ability of machine learning models to exploit nonlinear mutual information to increase the assays' predictive utility. We trained a random forest model that predicts expression from assay performance that is 35% closer to the experimental variance and trains 80% more efficiently than a model predicting from sequence information alone. Utilizing the predicted expression, we performed a site-wise analysis and predicted mutations consistent with enhanced developability. The validated assays offer the ability to identify developable proteins at unprecedented scales, reducing the bottleneck of protein commercialization.
Collapse
Affiliation(s)
- Alexander W Golinski
- Department of Chemical Engineering and Materials Science, University of Minnesota, Minneapolis, MN 55455
| | - Katelynn M Mischler
- Department of Chemical Engineering and Materials Science, University of Minnesota, Minneapolis, MN 55455
| | - Sidharth Laxminarayan
- Department of Chemical Engineering and Materials Science, University of Minnesota, Minneapolis, MN 55455
| | - Nicole L Neurock
- Department of Chemical Engineering and Materials Science, University of Minnesota, Minneapolis, MN 55455
| | - Matthew Fossing
- Department of Chemical Engineering and Materials Science, University of Minnesota, Minneapolis, MN 55455
| | - Hannah Pichman
- Department of Chemical Engineering and Materials Science, University of Minnesota, Minneapolis, MN 55455
| | - Stefano Martiniani
- Department of Chemical Engineering and Materials Science, University of Minnesota, Minneapolis, MN 55455
| | - Benjamin J Hackel
- Department of Chemical Engineering and Materials Science, University of Minnesota, Minneapolis, MN 55455
| |
Collapse
|
48
|
Mieczkowski C, Cheng A, Fischmann T, Hsieh M, Baker J, Uchida M, Raghunathan G, Strickland C, Fayadat-Dilman L. Characterization and Modeling of Reversible Antibody Self-Association Provide Insights into Behavior, Prediction, and Correction. Antibodies (Basel) 2021; 10:antib10010008. [PMID: 33671864 PMCID: PMC7931086 DOI: 10.3390/antib10010008] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Revised: 12/24/2020] [Accepted: 02/01/2021] [Indexed: 12/20/2022] Open
Abstract
Reversible antibody self-association, while having major developability and therapeutic implications, is not fully understood or readily predictable and correctable. For a strongly self-associating humanized mAb variant, resulting in unacceptable viscosity, the monovalent affinity of self-interaction was measured in the low μM range, typical of many specific and biologically relevant protein-protein interactions. A face-to-face interaction model extending across both the heavy-chain (HC) and light-chain (LC) Complementary Determining Regions (CDRs) was apparent from biochemical and mutagenesis approaches as well as computational modeling. Light scattering experiments involving individual mAb, Fc, Fab, and Fab'2 domains revealed that Fabs self-interact to form dimers, while bivalent mAb/Fab'2 forms lead to significant oligomerization. Site-directed mutagenesis of aromatic residues identified by homology model patch analysis and self-docking dramatically affected self-association, demonstrating the utility of these predictive approaches, while revealing a highly specific and tunable nature of self-binding modulated by single point mutations. Mutagenesis at these same key HC/LC CDR positions that affect self-interaction also typically abolished target binding with notable exceptions, clearly demonstrating the difficulties yet possibility of correcting self-association through engineering. Clear correlations were also observed between different methods used to assess self-interaction, such as Dynamic Light Scattering (DLS) and Affinity-Capture Self-Interaction Nanoparticle Spectroscopy (AC-SINS). Our findings advance our understanding of therapeutic protein and antibody self-association and offer insights into its prediction, evaluation and corrective mitigation to aid therapeutic development.
Collapse
Affiliation(s)
- Carl Mieczkowski
- Discovery Biologics, Protein Sciences, Merck & Co., Inc., South San Francisco, CA 94080, USA; (C.M.); (M.H.); (J.B.); (M.U.); (G.R.); (L.F.-D.)
| | - Alan Cheng
- Discovery Chemistry, Modeling and Informatics, Merck & Co., Inc., South San Francisco, CA 94080, USA
- Correspondence: ; Tel.: +1-650-496-4834
| | - Thierry Fischmann
- Department of Chemistry, Modeling and Informatics, Merck & Co., Inc., Kenilworth, NJ 07033, USA; (T.F.); (C.S.)
| | - Mark Hsieh
- Discovery Biologics, Protein Sciences, Merck & Co., Inc., South San Francisco, CA 94080, USA; (C.M.); (M.H.); (J.B.); (M.U.); (G.R.); (L.F.-D.)
| | - Jeanne Baker
- Discovery Biologics, Protein Sciences, Merck & Co., Inc., South San Francisco, CA 94080, USA; (C.M.); (M.H.); (J.B.); (M.U.); (G.R.); (L.F.-D.)
| | - Makiko Uchida
- Discovery Biologics, Protein Sciences, Merck & Co., Inc., South San Francisco, CA 94080, USA; (C.M.); (M.H.); (J.B.); (M.U.); (G.R.); (L.F.-D.)
| | - Gopalan Raghunathan
- Discovery Biologics, Protein Sciences, Merck & Co., Inc., South San Francisco, CA 94080, USA; (C.M.); (M.H.); (J.B.); (M.U.); (G.R.); (L.F.-D.)
| | - Corey Strickland
- Department of Chemistry, Modeling and Informatics, Merck & Co., Inc., Kenilworth, NJ 07033, USA; (T.F.); (C.S.)
| | - Laurence Fayadat-Dilman
- Discovery Biologics, Protein Sciences, Merck & Co., Inc., South San Francisco, CA 94080, USA; (C.M.); (M.H.); (J.B.); (M.U.); (G.R.); (L.F.-D.)
| |
Collapse
|
49
|
Bailly M, Mieczkowski C, Juan V, Metwally E, Tomazela D, Baker J, Uchida M, Kofman E, Raoufi F, Motlagh S, Yu Y, Park J, Raghava S, Welsh J, Rauscher M, Raghunathan G, Hsieh M, Chen YL, Nguyen HT, Nguyen N, Cipriano D, Fayadat-Dilman L. Predicting Antibody Developability Profiles Through Early Stage Discovery Screening. MAbs 2021; 12:1743053. [PMID: 32249670 PMCID: PMC7153844 DOI: 10.1080/19420862.2020.1743053] [Citation(s) in RCA: 116] [Impact Index Per Article: 38.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Abstract
Monoclonal antibodies play an increasingly important role for the development of new drugs across multiple therapy areas. The term 'developability' encompasses the feasibility of molecules to successfully progress from discovery to development via evaluation of their physicochemical properties. These properties include the tendency for self-interaction and aggregation, thermal stability, colloidal stability, and optimization of their properties through sequence engineering. Selection of the best antibody molecule based on biological function, efficacy, safety, and developability allows for a streamlined and successful CMC phase. An efficient and practical high-throughput developability workflow (100 s-1,000 s of molecules) implemented during early antibody generation and screening is crucial to select the best lead candidates. This involves careful assessment of critical developability parameters, combined with binding affinity and biological properties evaluation using small amounts of purified material (<1 mg), as well as an efficient data management and database system. Herein, a panel of 152 various human or humanized monoclonal antibodies was analyzed in biophysical property assays. Correlations between assays for different sets of properties were established. We demonstrated in two case studies that physicochemical properties and key assay endpoints correlate with key downstream process parameters. The workflow allows the elimination of antibodies with suboptimal properties and a rank ordering of molecules for further evaluation early in the candidate selection process. This enables any further engineering for problematic sequence attributes without affecting program timelines.
Collapse
Affiliation(s)
- Marc Bailly
- Discovery Biologics, Protein Sciences, South San Francisco, CA, USA
| | - Carl Mieczkowski
- Discovery Biologics, Protein Sciences, South San Francisco, CA, USA
| | - Veronica Juan
- Discovery Biologics, Protein Sciences, South San Francisco, CA, USA
| | - Essam Metwally
- Computation and Structural Chemistry, South San Francisco, CA, USA
| | - Daniela Tomazela
- Discovery Biologics, Protein Sciences, South San Francisco, CA, USA
| | - Jeanne Baker
- Discovery Biologics, Protein Sciences, South San Francisco, CA, USA
| | - Makiko Uchida
- Discovery Biologics, Protein Sciences, South San Francisco, CA, USA
| | - Ester Kofman
- Discovery Biologics, Protein Sciences, South San Francisco, CA, USA
| | - Fahimeh Raoufi
- Discovery Biologics, Protein Sciences, South San Francisco, CA, USA
| | - Soha Motlagh
- Discovery Biologics, Protein Sciences, South San Francisco, CA, USA
| | - Yao Yu
- Discovery Biologics, Protein Sciences, South San Francisco, CA, USA
| | - Jihea Park
- Discovery Biologics, Protein Sciences, South San Francisco, CA, USA
| | - Smita Raghava
- Pharmaceutical Sciences, Sterile FormulationSciences, Kenilworth, NJ, USA
| | - John Welsh
- Downstream Process Development andEngineering, Kenilworth, NJ, USA
| | - Michael Rauscher
- Downstream Process Development andEngineering, Kenilworth, NJ, USA
| | | | - Mark Hsieh
- Discovery Biologics, Protein Sciences, South San Francisco, CA, USA
| | - Yi-Ling Chen
- Discovery Biologics, Protein Sciences, South San Francisco, CA, USA
| | - Hang Thu Nguyen
- Discovery Biologics, Protein Sciences, South San Francisco, CA, USA
| | - Nhung Nguyen
- Discovery Biologics, Protein Sciences, South San Francisco, CA, USA
| | - Dan Cipriano
- Discovery Biologics, Protein Sciences, South San Francisco, CA, USA
| | | |
Collapse
|
50
|
Prabakaran R, Rawat P, Thangakani AM, Kumar S, Gromiha MM. Protein aggregation: in silico algorithms and applications. Biophys Rev 2021; 13:71-89. [PMID: 33747245 PMCID: PMC7930180 DOI: 10.1007/s12551-021-00778-w] [Citation(s) in RCA: 36] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2020] [Accepted: 01/01/2021] [Indexed: 01/08/2023] Open
Abstract
Protein aggregation is a topic of immense interest to the scientific community due to its role in several neurodegenerative diseases/disorders and industrial importance. Several in silico techniques, tools, and algorithms have been developed to predict aggregation in proteins and understand the aggregation mechanisms. This review attempts to provide an essence of the vast developments in in silico approaches, resources available, and future perspectives. It reviews aggregation-related databases, mechanistic models (aggregation-prone region and aggregation propensity prediction), kinetic models (aggregation rate prediction), and molecular dynamics studies related to aggregation. With a multitude of prediction models related to aggregation already available to the scientific community, the field of protein aggregation is rapidly maturing to tackle new applications.
Collapse
Affiliation(s)
- R. Prabakaran
- Department of Biotechnology, Indian Institute of Technology Madras, Chennai, Tamil Nadu India
| | - Puneet Rawat
- Department of Biotechnology, Indian Institute of Technology Madras, Chennai, Tamil Nadu India
| | - A. Mary Thangakani
- Department of Biotechnology, Indian Institute of Technology Madras, Chennai, Tamil Nadu India
| | - Sandeep Kumar
- Biotherapeutics Discovery, Boehringer Ingelheim Pharmaceutical Inc., Ridgefield, CT USA
| | - M. Michael Gromiha
- Department of Biotechnology, Indian Institute of Technology Madras, Chennai, Tamil Nadu India
- School of Computing, Institute of Innovative Research, Tokyo Institute of Technology, Yokohama, Kanagawa Japan
| |
Collapse
|