Simulation-based approaches for drug delivery systems: Navigating advancements, opportunities, and challenges

Journal of Molecular Liquids, Год журнала: 2023, Номер 395, С. 123888 - 123888

Опубликована: Дек. 27, 2023

Efficient drug delivery systems (DDSs) play a pivotal role in ensuring pharmaceuticals' targeted and effective administration. However, the intricate interplay between formulations poses challenges their design optimization. Simulations have emerged as indispensable tools for comprehending these interactions enhancing DDS performance to address this complexity. This comprehensive review explores latest advancements simulation techniques provides detailed analysis. The encompasses various methodologies, including molecular dynamics (MD), Monte Carlo (MC), finite element analysis (FEA), computational fluid (CFD), density functional theory (DFT), machine learning (ML), dissipative particle (DPD). These are critically examined context of research. article presents illustrative case studies involving liposomal, polymer-based, nano-particulate, implantable DDSs, demonstrating influential simulations optimizing systems. Furthermore, addresses advantages limitations It also identifies future directions research development, such integrating multiple techniques, refining validating models greater accuracy, overcoming limitations, exploring applications personalized medicine innovative DDSs. employing like MD, MC, FEA, CFD, DFT, ML, DPD offer crucial insights into behaviour, aiding Despite advantages, rapid cost-effective screening, require validation addressing limitations. Future should focus on models, enhance outcomes. paper underscores contribution emphasizing providing valuable facilitating development optimization ultimately patient As we continue explore impact advancing discovery improving DDSs is expected be profound.

Язык: Английский

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Targeting the undruggables—the power of protein degraders

Science Bulletin, Год журнала: 2024, Номер 69(11), С. 1776 - 1797

Опубликована: Март 29, 2024

Undruggable targets typically refer to a class of therapeutic that are difficult target through conventional methods or have not yet been targeted, but great clinical significance. According statistics, over 80% disease-related pathogenic proteins cannot be targeted by current treatment methods. In recent years, with the advancement basic research and new technologies, development various technologies mechanisms has brought perspectives overcome challenging drug targets. Among them, protein degradation technology is breakthrough strategy for This can specifically identify directly degrade utilizing inherent pathways within cells. form includes types such as proteolysis targeting chimera (PROTAC), molecular glue, lysosome-targeting Chimaera (LYTAC), autophagosome-tethering compound (ATTEC), autophagy-targeting (AUTAC), (AUTOTAC), degrader-antibody conjugate (DAC). article systematically summarizes application in degraders Finally, looks forward future direction prospects technology.

Язык: Английский

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Degraders in epigenetic therapy: PROTACs and beyond

Theranostics, Год журнала: 2024, Номер 14(4), С. 1464 - 1499

Опубликована: Янв. 1, 2024

Epigenetics refers to the reversible process through which changes in gene expression occur without changing nucleotide sequence of DNA. The is currently gaining prominence as a pivotal objective treatment cancers and other ailments. Numerous drugs that target epigenetic mechanisms have obtained approval from Food Drug Administration (FDA) for therapeutic intervention diverse diseases; many drawbacks, such limited applicability, toxicity, resistance. Since discovery first proteolysis-targeting chimeras (PROTACs) 2001, studies on targeted protein degradation (TPD)-encompassing PROTACs, molecular glue (MG), hydrophobic tagging (HyT), TAG (dTAG), Trim-Away, specific non-genetic inhibitor apoptosis (IAP)-dependent eraser (SNIPER), antibody-PROTACs (Ab-PROTACs), lysosome-based strategies-have achieved remarkable progress. In this review, we comprehensively highlight small-molecule degraders beyond PROTACs could achieve proteins (including bromodomain-containing protein-related targets, histone acetylation/deacetylation-related methylation/demethylation related targets) via proteasomal or lysosomal pathways. present difficulties forthcoming prospects domain are also deliberated upon, may be valuable medicinal chemists when developing more potent, selective, drug-like clinical applications.

Язык: Английский

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Efficient synthesis and molecular docking analysis of quinazoline and azole hybrid derivatives as promising agents for anti-cancer and anti-tuberculosis activities

Journal of Molecular Structure, Год журнала: 2024, Номер 1310, С. 138289 - 138289

Опубликована: Апрель 9, 2024

Язык: Английский

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Innovative, combinatorial and high-throughput approaches to degrader synthesis

Chemical Society Reviews, Год журнала: 2024, Номер 53(10), С. 4838 - 4861

Опубликована: Янв. 1, 2024

In this review we highlight how the synthesis of degraders has evolved in recent years, particular application high-throughput chemistry and screening approaches such as D2B DEL technologies to expedite discovery timelines.

Язык: Английский

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Finding a needle in the haystack: ADME and pharmacokinetics/pharmacodynamics characterization and optimization toward orally available bifunctional protein degraders

Expert Opinion on Drug Discovery, Год журнала: 2025, Номер unknown

Опубликована: Фев. 16, 2025

Degraders are an increasingly important sub-modality of small molecules as illustrated by ever-expanding number publications and clinical candidate in human trials. Nevertheless, their preclinical optimization ADME PK/PD properties has remained challenging. Significant research efforts being directed to elucidate underlying principles derive rational strategies. In this review the authors summarize current best practices terms vitro assays vivo experiments. Furthermore, collate comment on understanding optimal physicochemical characteristics impact absorption, distribution, metabolism excretion including knowledge Drug-Drug interactions. Finally, describe Pharmacokinetic prediction Pharmacokinetic/Pharmacodynamic -concepts unique degraders how implement these projects. Despite many recent advances field, continued will further our design regarding degrader optimization. Machine-learning computational approaches become once larger, more robust datasets available. tissue-targeting (particularly Central Nervous System be studied efficacious drug regimens that capitalize catalytic mode action. additional specialized (e.g. covalent degraders, LOVdegs) can enrich field offer interesting alternative approaches.

Язык: Английский

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Structural Basis of Conformational Dynamics in the PROTAC‐Induced Protein Degradation

ChemMedChem, Год журнала: 2024, Номер 19(14)

Опубликована: Апрель 24, 2024

Abstract Pronounced conformational dynamics is unveiled upon analyzing multiple crystal structures of the same proteins recruited to E3 ligases by PROTACs, and yet, largely permissive for targeted protein degradation due intrinsic mobility assemblies creating a large ubiquitylation zone. Mathematical modelling ternary on probability confirms experimental finding that complex rigidification need not correlate with enhanced degradation. Salt bridges are found prevail in PROTAC‐induced complexes, may contribute positive cooperativity prolonged half‐life. The analysis highlights importance presenting lysines close active site E2 enzyme while constraining PROTAC design achieve high efficiency.

Язык: Английский

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Application of machine learning models for property prediction to targeted protein degraders

Nature Communications, Год журнала: 2024, Номер 15(1)

Опубликована: Июль 9, 2024

Abstract Machine learning (ML) systems can model quantitative structure-property relationships (QSPR) using existing experimental data and make property predictions for new molecules. With the advent of modalities such as targeted protein degraders (TPD), applicability QSPR models is questioned ML usage in TPD-centric projects remains limited. Herein, are developed evaluated TPDs’ predictions, including passive permeability, metabolic clearance, cytochrome P450 inhibition, plasma binding, lipophilicity. Interestingly, performance on TPDs comparable to that other modalities. Predictions glues heterobifunctionals often yield lower higher errors, respectively. For CYP3A4 human rat microsomal misclassification errors into high low risk categories than 4% 15% heterobifunctionals. all modalities, range from 0.8% 8.1%. Investigated transfer strategies improve This first comprehensive evaluation prediction absorption, distribution, metabolism, excretion (ADME) physicochemical properties TPD molecules, heterobifunctional molecular glue sub-modalities. Taken together, our investigations show ML-based applicable support design, potentially accelerate drug discovery.

Язык: Английский

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Development of PROTACs using computational approaches

Trends in Pharmacological Sciences, Год журнала: 2024, Номер unknown

Опубликована: Ноя. 1, 2024

Язык: Английский

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Modeling PROTAC degradation activity with machine learning

Artificial Intelligence in the Life Sciences, Год журнала: 2024, Номер 6, С. 100104 - 100104

Опубликована: Июль 14, 2024

PROTACs are a promising therapeutic modality that harnesses the cell's built-in degradation machinery to degrade specific proteins. Despite their potential, developing new is challenging and requires significant domain expertise, time, cost. Meanwhile, machine learning has transformed drug design development. In this work, we present strategy for curating open-source PROTAC data an deep tool predicting activity of novel molecules. The curated dataset incorporates important information such as pDC50, Dmax, E3 ligase type, POI amino acid sequence, experimental cell type. Our model architecture leverages learned embeddings from pretrained models, in particular encoding protein sequences type information. We assessed quality generalization ability our against targets via three tailored studies, which recommend other researchers use evaluating models. each study, models predict majority vote setting, reaching top test accuracy 82.6% 0.848 ROC AUC, 61% 0.615 AUC when generalizing targets. results not only comparable state-of-the-art prediction, but also part implementation easily reproducible less computationally complex than existing approaches.

Язык: Английский

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