Artificial Intelligence in Pharmaceutical Technology and Drug Delivery Design

Pharmaceutics, Journal Year: 2023, Volume and Issue: 15(7), P. 1916 - 1916

Published: July 10, 2023

Artificial intelligence (AI) has emerged as a powerful tool that harnesses anthropomorphic knowledge and provides expedited solutions to complex challenges. Remarkable advancements in AI technology machine learning present transformative opportunity the drug discovery, formulation, testing of pharmaceutical dosage forms. By utilizing algorithms analyze extensive biological data, including genomics proteomics, researchers can identify disease-associated targets predict their interactions with potential candidates. This enables more efficient targeted approach thereby increasing likelihood successful approvals. Furthermore, contribute reducing development costs by optimizing research processes. Machine assist experimental design pharmacokinetics toxicity capability prioritization optimization lead compounds, need for costly animal testing. Personalized medicine approaches be facilitated through real-world patient leading effective treatment outcomes improved adherence. comprehensive review explores wide-ranging applications delivery form designs, process optimization, testing, pharmacokinetics/pharmacodynamics (PK/PD) studies. an overview various AI-based utilized technology, highlighting benefits drawbacks. Nevertheless, continued investment exploration industry offer exciting prospects enhancing processes care.

Language: Английский

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Assessing developability early in the discovery process for novel biologics

mAbs, Journal Year: 2023, Volume and Issue: 15(1)

Published: Feb. 23, 2023

Beyond potency, a good developability profile is key attribute of biological drug. Selecting and screening for such attributes early in the drug development process can save resources avoid costly late-stage failures. Here, we review some most important properties that be assessed on biologics. These include influence source biologic, its biophysical pharmacokinetic properties, how well it expressed recombinantly. We furthermore present silico, vitro, vivo methods techniques exploited at different stages discovery to identify molecules with liabilities thereby facilitate selection optimal leads. Finally, reflect relevant parameters injectable versus orally delivered biologics provide an outlook toward what general trends are expected rise

Language: Английский

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Applications of machine learning in antibody discovery, process development, manufacturing and formulation: Current trends, challenges, and opportunities

Computers & Chemical Engineering, Journal Year: 2024, Volume and Issue: 182, P. 108585 - 108585

Published: Jan. 11, 2024

While machine learning (ML) has made significant contributions to the biopharmaceutical field, its applications are still in early stages terms of providing direct support for quality-by-design based development and manufacturing biologics, hindering enormous potential bioprocesses automation from their manufacturing. However, adoption ML-based models instead conventional multivariate data analysis methods is significantly increasing due accumulation large-scale production data. This trend primarily driven by real-time monitoring process variables quality attributes products through implementation advanced analytical technologies. Given complexity multidimensionality a bioproduct design, bioprocess development, product data, approaches increasingly being employed achieve accurate, flexible, high-performing predictive address problems analytics, monitoring, control within biopharma field. paper aims provide comprehensive review current ML solutions control, optimisation upstream, downstream, formulation processes monoclonal antibodies. Finally, this thoroughly discusses main challenges related themselves, use antibody Moreover, it offers further insights into innovative novel trends new digital solutions.

Language: Английский

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Assessing antibody and nanobody nativeness for hit selection and humanization with AbNatiV

Nature Machine Intelligence, Journal Year: 2024, Volume and Issue: 6(1), P. 74 - 91

Published: Jan. 15, 2024

Abstract Monoclonal antibodies have emerged as key therapeutics. In particular, nanobodies, small, single-domain that are naturally expressed in camelids, rapidly gaining momentum following the approval of first nanobody drug 2019. Nonetheless, development these biologics therapeutics remains a challenge. Despite availability established vitro directed-evolution technologies relatively fast and cheap to deploy, gold standard for generating therapeutic discovery from animal immunization or patients. Immune-system-derived tend favourable properties vivo, including long half-life, low reactivity with self-antigens toxicity. Here we present AbNatiV, deep learning tool assessing nativeness is, their likelihood belonging distribution immune-system-derived human camelid nanobodies. AbNatiV is multipurpose accurately predicts Fv sequences any source, synthetic libraries computational design. It provides an interpretable score immunogenicity, residue-level profile can guide engineering nanobodies indistinguishable ones. We further introduce automated humanization pipeline, which applied two Laboratory experiments show AbNatiV-humanized retain binding stability at par better than wild type, unlike humanized using conventional structural residue-frequency analysis. make available downloadable software webserver.

Language: Английский

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Machine-designed biotherapeutics: opportunities, feasibility and advantages of deep learning in computational antibody discovery

Briefings in Bioinformatics, Journal Year: 2022, Volume and Issue: 23(4)

Published: July 13, 2022

Antibodies are versatile molecular binders with an established and growing role as therapeutics. Computational approaches to developing designing these molecules being increasingly used complement traditional lab-based processes. Nowadays, in silico methods fill multiple elements of the discovery stage, such characterizing antibody-antigen interactions identifying developability liabilities. Recently, computational tackling problems have begun follow machine learning paradigms, many cases deep specifically. This paradigm shift offers improvements areas structure or binding prediction opens up new possibilities language-based modeling antibody repertoires machine-learning-based generation novel sequences. In this review, we critically examine recent developments (deep) therapeutic design implications for fully design.

Language: Английский

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Understanding the Stabilizing Effect of Histidine on mAb Aggregation: A Molecular Dynamics Study

Molecular Pharmaceutics, Journal Year: 2022, Volume and Issue: 19(9), P. 3288 - 3303

Published: Aug. 10, 2022

Histidine, a widely used buffer in monoclonal antibody (mAb) formulations, is known to reduce aggregation. While experimental studies suggest nonelectrostatic, nonstructural (relating secondary structure preservation) origin of the phenomenon, underlying microscopic mechanism behind histidine action still unknown. Understanding this will help evaluate and predict stabilizing effect under different conditions for mAbs. We have all-atom molecular dynamics simulations contact-based free energy calculations investigate molecular-level interactions between mAbs, which lead observed stability therapeutic formulations presence histidine. reformulate Spatial Aggregation Propensity index by including buffer–protein interactions. The adsorption on protein surface leads lower exposure hydrophobic regions water. Our analysis indicates that connected shielding solvent-exposed molecules.

Language: Английский

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The evolutionary and functional significance of germline immunoglobulin gene variation

Trends in Immunology, Journal Year: 2022, Volume and Issue: 44(1), P. 7 - 21

Published: Dec. 2, 2022

The recombination between immunoglobulin (IG) gene segments determines an individual's naïve antibody repertoire and, consequently, (auto)antigen recognition. Emerging evidence suggests that mammalian IG germline variation impacts humoral immune responses associated with vaccination, infection, and autoimmunity – from the molecular level of epitope specificity, up to profound changes in architecture repertoires. These links variants immunophenotype raise question on evolutionary causes consequences diversity within loci. We discuss why extreme loci remains a mystery, resolving this is important for design more effective vaccines therapeutics, how recent multiple lines inquiry may help us do so.

Language: Английский

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Evolution of phage display libraries for therapeutic antibody discovery

mAbs, Journal Year: 2023, Volume and Issue: 15(1)

Published: May 24, 2023

Monoclonal antibodies (mAbs) and their derivatives have emerged as one of the most important classes biotherapeutics in recent decades. The success mAb is due to high versatility, target specificity, excellent clinical safety profile, efficacy. Antibody discovery, upstream stage antibody development pipeline, plays a pivotal role determination outcome an product. Phage display technology, originally developed for peptide directed evolution, has been extensively applied discovery fully human its unprecedented advantages. value phage technology proven by number approved mAbs, including several top-selling drugs, derived from technology. Since was first established over 30 years ago, platforms generate mAbs targeting difficult-to-target antigens tackle drawbacks present vivo approaches. More recently, new generation libraries optimized with "drug-like" properties. This review will summarize principles design three generations libraries.

Language: Английский

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Artificial intelligence for compound pharmacokinetics prediction

Current Opinion in Structural Biology, Journal Year: 2023, Volume and Issue: 79, P. 102546 - 102546

Published: Feb. 15, 2023

Language: Английский

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Linguistically inspired roadmap for building biologically reliable protein language models

Nature Machine Intelligence, Journal Year: 2023, Volume and Issue: 5(5), P. 485 - 496

Published: April 6, 2023

Language: Английский

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