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Manning MC, Holcomb RE, Payne RW, Stillahn JM, Connolly BD, Katayama DS, Liu H, Matsuura JE, Murphy BM, Henry CS, Crommelin DJA. Stability of Protein Pharmaceuticals: Recent Advances. Pharm Res 2024; 41:1301-1367. [PMID: 38937372 DOI: 10.1007/s11095-024-03726-x] [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: 03/25/2024] [Accepted: 06/03/2024] [Indexed: 06/29/2024]
Abstract
There have been significant advances in the formulation and stabilization of proteins in the liquid state over the past years since our previous review. Our mechanistic understanding of protein-excipient interactions has increased, allowing one to develop formulations in a more rational fashion. The field has moved towards more complex and challenging formulations, such as high concentration formulations to allow for subcutaneous administration and co-formulation. While much of the published work has focused on mAbs, the principles appear to apply to any therapeutic protein, although mAbs clearly have some distinctive features. In this review, we first discuss chemical degradation reactions. This is followed by a section on physical instability issues. Then, more specific topics are addressed: instability induced by interactions with interfaces, predictive methods for physical stability and interplay between chemical and physical instability. The final parts are devoted to discussions how all the above impacts (co-)formulation strategies, in particular for high protein concentration solutions.'
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Affiliation(s)
- Mark Cornell Manning
- Legacy BioDesign LLC, Johnstown, CO, USA.
- Department of Chemistry, Colorado State University, Fort Collins, CO, USA.
| | - Ryan E Holcomb
- Legacy BioDesign LLC, Johnstown, CO, USA
- Department of Chemistry, Colorado State University, Fort Collins, CO, USA
| | - Robert W Payne
- Legacy BioDesign LLC, Johnstown, CO, USA
- Department of Chemistry, Colorado State University, Fort Collins, CO, USA
| | - Joshua M Stillahn
- Legacy BioDesign LLC, Johnstown, CO, USA
- Department of Chemistry, Colorado State University, Fort Collins, CO, USA
| | | | | | | | | | | | - Charles S Henry
- Department of Chemistry, Colorado State University, Fort Collins, CO, USA
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Ghosh D, Biswas A, Radhakrishna M. Advanced computational approaches to understand protein aggregation. BIOPHYSICS REVIEWS 2024; 5:021302. [PMID: 38681860 PMCID: PMC11045254 DOI: 10.1063/5.0180691] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Accepted: 03/18/2024] [Indexed: 05/01/2024]
Abstract
Protein aggregation is a widespread phenomenon implicated in debilitating diseases like Alzheimer's, Parkinson's, and cataracts, presenting complex hurdles for the field of molecular biology. In this review, we explore the evolving realm of computational methods and bioinformatics tools that have revolutionized our comprehension of protein aggregation. Beginning with a discussion of the multifaceted challenges associated with understanding this process and emphasizing the critical need for precise predictive tools, we highlight how computational techniques have become indispensable for understanding protein aggregation. We focus on molecular simulations, notably molecular dynamics (MD) simulations, spanning from atomistic to coarse-grained levels, which have emerged as pivotal tools in unraveling the complex dynamics governing protein aggregation in diseases such as cataracts, Alzheimer's, and Parkinson's. MD simulations provide microscopic insights into protein interactions and the subtleties of aggregation pathways, with advanced techniques like replica exchange molecular dynamics, Metadynamics (MetaD), and umbrella sampling enhancing our understanding by probing intricate energy landscapes and transition states. We delve into specific applications of MD simulations, elucidating the chaperone mechanism underlying cataract formation using Markov state modeling and the intricate pathways and interactions driving the toxic aggregate formation in Alzheimer's and Parkinson's disease. Transitioning we highlight how computational techniques, including bioinformatics, sequence analysis, structural data, machine learning algorithms, and artificial intelligence have become indispensable for predicting protein aggregation propensity and locating aggregation-prone regions within protein sequences. Throughout our exploration, we underscore the symbiotic relationship between computational approaches and empirical data, which has paved the way for potential therapeutic strategies against protein aggregation-related diseases. In conclusion, this review offers a comprehensive overview of advanced computational methodologies and bioinformatics tools that have catalyzed breakthroughs in unraveling the molecular basis of protein aggregation, with significant implications for clinical interventions, standing at the intersection of computational biology and experimental research.
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Affiliation(s)
- Deepshikha Ghosh
- Department of Biological Sciences and Engineering, Indian Institute of Technology (IIT) Gandhinagar, Palaj, Gujarat 382355, India
| | - Anushka Biswas
- Department of Chemical Engineering, Indian Institute of Technology (IIT) Gandhinagar, Palaj, Gujarat 382355, India
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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.
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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
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Li D, Hu J, Zhang L, Li L, Yin Q, Shi J, Guo H, Zhang Y, Zhuang P. Deep learning and machine intelligence: New computational modeling techniques for discovery of the combination rules and pharmacodynamic characteristics of Traditional Chinese Medicine. Eur J Pharmacol 2022; 933:175260. [PMID: 36116517 DOI: 10.1016/j.ejphar.2022.175260] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2022] [Revised: 08/15/2022] [Accepted: 09/05/2022] [Indexed: 11/19/2022]
Abstract
It has been increasingly accepted that Multi-Ingredient-Based interventions provide advantages over single-target therapy for complex diseases. With the growing development of Traditional Chinese Medicine (TCM) and continually being refined of a holistic view, "multi-target" and "multi-pathway" integration characteristics of which are being accepted. However, its effector substances, efficacy targets, especially the combination rules and mechanisms remain unclear, and more powerful strategies to interpret the synergy are urgently needed. Artificial intelligence (AI) and computer vision lead to a rapidly expanding in many fields, including diagnosis and treatment of TCM. AI technology significantly improves the reliability and accuracy of diagnostics, target screening, and new drug research. While all AI techniques are capable of matching models to biological big data, the specific methods are complex and varied. Retrieves literature by the keywords such as "artificial intelligence", "machine learning", "deep learning", "traditional Chinese medicine" and "Chinese medicine". Search the application of computer algorithms of TCM between 2000 and 2021 in PubMed, Web of Science, China National Knowledge Infrastructure (CNKI), Elsevier and Springer. This review concentrates on the application of computational in herb quality evaluation, drug target discovery, optimized compatibility and medical diagnoses of TCM. We describe the characteristics of biological data for which different AI techniques are applicable, and discuss some of the best data mining methods and the problems faced by deep learning and machine learning methods applied to Chinese medicine.
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Affiliation(s)
- Dongna Li
- State Key Laboratory of Component-based Chinese Medicine, Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, China
| | - Jing Hu
- State Key Laboratory of Component-based Chinese Medicine, Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, China
| | - Lin Zhang
- State Key Laboratory of Component-based Chinese Medicine, Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, China
| | - Lili Li
- State Key Laboratory of Component-based Chinese Medicine, Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, China
| | - Qingsheng Yin
- State Key Laboratory of Component-based Chinese Medicine, Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, China
| | - Jiangwei Shi
- First Teaching Hospital of Tianjin University of Traditional Chinese Medicine, Tianjin, China; National Clinical Research Center for Chinese Medicine Acupuncture and Moxibustion, China
| | - Hong Guo
- State Key Laboratory of Component-based Chinese Medicine, Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, China
| | - Yanjun Zhang
- State Key Laboratory of Component-based Chinese Medicine, Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, China; First Teaching Hospital of Tianjin University of Traditional Chinese Medicine, Tianjin, China; National Clinical Research Center for Chinese Medicine Acupuncture and Moxibustion, China.
| | - Pengwei Zhuang
- State Key Laboratory of Component-based Chinese Medicine, Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, China.
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Zbacnik NJ, Manning MC, Henry CS. Chemometric Study of the Relative Aggregation Propensity of Position 19
Mutants of Aβ(1-42). Curr Protein Pept Sci 2022; 23:52-60. [DOI: 10.2174/1389203723666220128105334] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2021] [Revised: 12/12/2021] [Accepted: 12/13/2021] [Indexed: 11/22/2022]
Abstract
Background:
The importance of aromaticity vs. hydrophobicity of the central hydrophobic
core (CHC, residues 17-20) in governing fibril formation in Aβ(1-42) has been the focus of an ongoing
debate in the literature.
Introduction:
Mutations in the CHC (especially at Phe19 and Phe20) have been used to examine the
relative impact of hydrophobicity and aromaticity on the degree of aggregation of Aβ(1-42). However,
the results have not been conclusive.
Methods:
Partial least squares (PLS) modeling of aggregation rates, using reduced properties of a series
of position 19 mutants, was employed to identify the physicochemical properties that had the
greatest impact on the extent of aggregation.
Results:
The PLS models indicate that hydrophobicity at position 19 of Aβ(1-42) appears to be the
primary and dominant factor in controlling Aβ(1-42) aggregation, with aromaticity having little effect.
Conclusions:
This study illustrates the value of using reduced properties of amino acids in conjunction
with PLS modeling to investigate mutational effects in peptides and proteins, as the reduced properties
can capture in a quantitative manner the different physicochemical properties of the amino acid side
chains. In this particular study, hydrophobicity at position 19 was determined to be the dominant property
controlling aggregation, while size, charge, and aromaticity had little impact.
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Affiliation(s)
| | - Mark Cornell Manning
- Legacy BioDesign LLC, Johnstown, CO 80534, USA
- Department of Chemistry, Colorado State University, Fort Collins, CO 80523, USA
| | - Charles S. Henry
- Department of Chemistry, Colorado State University, Fort Collins, CO 80523, USA
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Ptak-Kaczor M, Banach M, Stapor K, Fabian P, Konieczny L, Roterman I. Solubility and Aggregation of Selected Proteins Interpreted on the Basis of Hydrophobicity Distribution. Int J Mol Sci 2021; 22:ijms22095002. [PMID: 34066830 PMCID: PMC8125953 DOI: 10.3390/ijms22095002] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Revised: 05/03/2021] [Accepted: 05/06/2021] [Indexed: 11/30/2022] Open
Abstract
Protein solubility is based on the compatibility of the specific protein surface with the polar aquatic environment. The exposure of polar residues to the protein surface promotes the protein’s solubility in the polar environment. The aquatic environment also influences the folding process by favoring the centralization of hydrophobic residues with the simultaneous exposure to polar residues. The degree of compatibility of the residue distribution, with the model of the concentration of hydrophobic residues in the center of the molecule, with the simultaneous exposure of polar residues is determined by the sequence of amino acids in the chain. The fuzzy oil drop model enables the quantification of the degree of compatibility of the hydrophobicity distribution observed in the protein to a form fully consistent with the Gaussian 3D function, which expresses an idealized distribution that meets the preferences of the polar water environment. The varied degrees of compatibility of the distribution observed with the idealized one allow the prediction of preferences to interactions with molecules of different polarity, including water molecules in particular. This paper analyzes a set of proteins with different levels of hydrophobicity distribution in the context of the solubility of a given protein and the possibility of complex formation.
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Affiliation(s)
- Magdalena Ptak-Kaczor
- Department of Bioinformatics and Telemedicine, Jagiellonian University—Medical College, Medyczna 7, 30-688 Kraków, Poland; (M.P.-K.); (M.B.)
- Faculty of Physics, Astronomy and Applied Computer Science, Jagiellonian University, Łojasiewicza 11, 30-348 Kraków, Poland
| | - Mateusz Banach
- Department of Bioinformatics and Telemedicine, Jagiellonian University—Medical College, Medyczna 7, 30-688 Kraków, Poland; (M.P.-K.); (M.B.)
| | - Katarzyna Stapor
- Institute of Computer Science, Silesian University of Technology, Akademicka 16, 44-100 Gliwice, Poland; (K.S.); (P.F.)
| | - Piotr Fabian
- Institute of Computer Science, Silesian University of Technology, Akademicka 16, 44-100 Gliwice, Poland; (K.S.); (P.F.)
| | - Leszek Konieczny
- Chair of Medical Biochemistry—Jagiellonian University—Medical College, Kopernika 7, 31-034 Kraków, Poland;
| | - Irena Roterman
- Department of Bioinformatics and Telemedicine, Jagiellonian University—Medical College, Medyczna 7, 30-688 Kraków, Poland; (M.P.-K.); (M.B.)
- Faculty of Physics, Astronomy and Applied Computer Science, Jagiellonian University, Łojasiewicza 11, 30-348 Kraków, Poland
- Correspondence:
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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.
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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
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Personalized Body Constitution Inquiry Based on Machine Learning. JOURNAL OF HEALTHCARE ENGINEERING 2020; 2020:8834465. [PMID: 33274038 PMCID: PMC7676967 DOI: 10.1155/2020/8834465] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/28/2020] [Accepted: 10/30/2020] [Indexed: 02/01/2023]
Abstract
Background Body constitution (BC) is the abstract concept indicating the state of a person's health in Traditional Chinese Medicine (TCM). The doctor identifies the body constitution of the patient through inspection and inquiry. Previous research simulates doctors to identify BC types according to a patient's objective physical indicators. However, the lack of subjective feeling information can reduce the accuracy of the machine to imitate the doctor's diagnosis. The Constitution in Chinese Medicine Questionnaire (CCMQ) is used to collect subjective information but suffers from low acquisition efficiency. Methods This paper presents a personalized body constitution inquiry method based on a machine learning technique. It employs a random generator, a feature extractor, and a classifier to simulate the doctor inquiry and generate a personalized questionnaire. Specifically, the feature extractor evaluates and sorts the question of the constitution in the CCMQ based on the recognition results of the tongue coating image of patients. The sorted questions and relevant BC label are inputted into the classifier; the best questions are screened out for patients. Results The experimental results show that our method can select personalized questions from the CCMQ for the patients, significantly reducing the time and the number of questions to answer. It also improves the accuracy of recognizing BC. Compared with the CCMQ, patients had 68.3% fewer questions to answer and the time occupied by answering is reduced by 80.3%. Conclusions The proposed method can simulate the doctor's inquiry and pick out personalized questions for patients. It can act as auxiliary diagnosis tools to collect subjective patient feelings and help make further judgments on the patient's BC types.
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Yang W, Peng L, Zhu Y, Hong L. When machine learning meets multiscale modeling in chemical reactions. J Chem Phys 2020; 153:094117. [DOI: 10.1063/5.0015779] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022] Open
Affiliation(s)
- Wuyue Yang
- Yau Mathematical Sciences Center, Tsinghua University, Beijing 100084, People’s Republic of China
| | - Liangrong Peng
- College of Mathematics and Data Science, Minjiang University, Fuzhou 350108, People’s Republic of China
| | - Yi Zhu
- Yau Mathematical Sciences Center, Tsinghua University, Beijing 100084, People’s Republic of China
| | - Liu Hong
- School of Mathematics, Sun Yat-sen University, Guangzhou 510275, People’s Republic of China
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