Evolution of the Computational Pharmaceutics Approaches in the Modeling and Prediction of Drug Payload in Lipid and Polymeric Nanocarriers

Abd-algaleel, Shaymaa A.; Abdel-Bar, Hend Mohamed; Metwally, Abdelkader A.; Hathout, Rania M.

doi:10.3390/ph14070645

Cited by 14 publications

(11 citation statements)

References 73 publications

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“…Higher negative docking score would enhance solubility, dissolution, and stability of the compound in carrier medium for its circulation. 24,25 The compound was also found to be interacting with the hydrophobic core (Figure 1) of the carrier, suggesting that Miglyol is superior to other biocompatible carriers for potential GMFBI.1 delivery. Moreover, the ESP map revealed GMFBI.1's amphipathic nature as it interacts with polar components and hydrophobic molecules with its hydrophobic indazole core.…”

Section: ■ Discussionmentioning

confidence: 98%

“…In silico molecular docking, ESP, and MD simulation studies on GMFBI.1 in complex with Miglyol revealed that with repeated extension of this biocompatible carrier having nine units, Miglyol showed higher binding affinity for our compound with a maximum docking score of 5.3 kcal/mol (Table S1). Higher negative docking score would enhance solubility, dissolution, and stability of the compound in carrier medium for its circulation. , The compound was also found to be interacting with the hydrophobic core (Figure ) of the carrier, suggesting that Miglyol is superior to other biocompatible carriers for potential GMFBI.1 delivery. Moreover, the ESP map revealed GMFBI.1’s amphipathic nature as it interacts with polar components and hydrophobic molecules with its hydrophobic indazole core.…”

Section: Discussionmentioning

confidence: 99%

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Translational Significance of GMF-β Inhibition by Indazole-4-yl-methanol in Enteric Glial Cells for Treating Multiple Sclerosis

Subhramanian

Ariyath

Sabhi

et al. 2022

ACS Chem. Neurosci.

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In the emerging context of gut−brain control of multiple sclerosis (MS), developing therapeutics targeting proinflammatory proteins controlling the gut−brain immunomodulation is welcoming. One such immunomodulator is glia maturation factor-β (GMF-β). GMF-β activation following GMFβ-ser-83 phosphorylation upregulates proinflammatory responses and exacerbates experimental autoimmune encephalomyelitis (EAE). Notably, GMF-β −/− mice exhibited no EAE symptoms. Thus, we identified 1H-indazole-4-yl-methanol (GMFBI.1) inhibitor which blocked GMF-β-ser-83 phosphorylation critical in EAE suppression. To establish gut GMF-β′s role in EAE in the context of gut−brain involvement in neurodegenerative diseases, we altered gut GMFBI.1 bioavailability as an index of EAE suppression. At first, we identified Miglyol 812N as a suitable biocompatible GMFBI.1 carrier compared to other FDA-approved carriers using in silico molecular docking analysis. GMFBI.1 administration in Miglyol 812N enhanced its retention/brain permeability. Subsequently, we administered GMFBI.1-Miglyol 812N by subcutaneous/oral routes at different doses with differential GMFBI.1 bioavailability in gut and brain to assess the role of local GMFBI.1 bioavailability in EAE reversal by a pharmacokinetic approach. Deprival of gut GMFBI.1 bioavailability led to partial EAE suppression despite having sufficient GMFBI.1 in circulation to inhibit brain GMF-β activity. Restoration of gut GMFBI.1 bioavailability led to complete EAE reversal. Molecular pathology behind partial/full EAE reversal was associated with differential GMF-β-Ser-83 phosphorylation/GM-CSF expression levels in enteric glial cells owing to GMFBI.1 bioavailability. In addition, we observed leaky gut reversal, tight junction protein ZO-1 restoration, beneficial gut microbiome repopulation, recovery from gut dysbiosis, and upregulation of Treg cells. GMFBI.1's dual gut/brain targeting of GMF-β has therapeutical/translational potential in controlling autoimmunity in MS.

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Section: ■ Discussionmentioning

confidence: 98%

Section: Discussionmentioning

confidence: 99%

Translational Significance of GMF-β Inhibition by Indazole-4-yl-methanol in Enteric Glial Cells for Treating Multiple Sclerosis

Subhramanian

Ariyath

Sabhi

et al. 2022

ACS Chem. Neurosci.

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“…162,163 Despite the ability to stimulate the structure of molecules precisely, dynamic simulation is time-consuming and too expensive to be used in large databases. 164,165 AI and ML generally have been utilized in various fields of medicine, such as medical imaging and nanomedicine. 31 Integrating nanotechnology and AI leads to more innovative technologies 174,175 and gives a chance for targeted delivery to specific cells and even personalized medicine.…”

Section: Artificial Intelligencementioning

confidence: 99%

“…It utilizes a complex and large number of data sets and accurately makes predictions without needing biological tests. The use of AI in nanoparticles was preliminary and based on molecular dynamic simulation to estimate nanoparticle-cell membrane interaction. , Despite the ability to stimulate the structure of molecules precisely, dynamic simulation is time-consuming and too expensive to be used in large databases. , Table summarizes most AI technologies that are used in the field of nanomaterials.…”

Section: Artificial Intelligencementioning

confidence: 99%

Knowledge Gaps in Generating Cell-Based Drug Delivery Systems and a Possible Meeting with Artificial Intelligence

Mozafari

Dehshahri

et al. 2023

Mol. Pharmaceutics

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Cell-based drug delivery systems are new strategies in targeted delivery in which cells or cell-membrane-derived systems are used as carriers and release their cargo in a controlled manner. Recently, great attention has been directed to cells as carrier systems for treating several diseases. There are various challenges in the development of cell-based drug delivery systems. The prediction of the properties of these platforms is a prerequisite step in their development to reduce undesirable effects. Integrating nanotechnology and artificial intelligence leads to more innovative technologies. Artificial intelligence quickly mines data and makes decisions more quickly and accurately. Machine learning as a subset of the broader artificial intelligence has been used in nanomedicine to design safer nanomaterials. Here, how challenges of developing cell-based drug delivery systems can be solved with potential predictive models of artificial intelligence and machine learning is portrayed. The most famous cell-based drug delivery systems and their challenges are described. Last but not least, artificial intelligence and most of its types used in nanomedicine are highlighted. The present Review has shown the challenges of developing cells or their derivatives as carriers and how they can be used with potential predictive models of artificial intelligence and machine learning.

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“…The noninvasive and fast sample scan in microplate formats enables HTS with minimal off-line sample processing. It may also very well present an opportunity to combine the high-content spectral data with modeling and machine learning, which have been emerging to shorten the development cycle of pharmaceutical formulations in recent years. − Among different modeling approaches, locally weighted regression (LWR) has been chosen here as an adaptive method that selects calibration samples with similar features as a query sample and builds prediction models tailored for the query sample. , For high-dimensional data, principal component analysis (PCA) helps simplify the complexity while still retaining the data trends . Both LWR and PCA have been investigated to process spectroscopic data and predict attributes of pharmaceutical formulations. − …”

mentioning

confidence: 99%

Spectroscopy-Based Local Modeling Method for High-Throughput Quantification of Nucleic Acid Loading in Lipid Nanoparticles

Fan¹,

Shi²,

Ma³

et al. 2022

Anal. Chem.

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Lipid nanoparticles (LNPs) are the most widely investigated delivery systems for nucleic acid-based therapeutics and vaccines. Loading efficiency of nucleic acids may vary with formulation conditions, and it is considered one of the critical quality attributes of LNP products. Current analytical methods for quantification of cargo loading in LNPs often require external standard preparations and preseparation of unloaded nucleic acids from LNPs; therefore, they are subject to tedious and lengthy procedures, LNP stability, and unpredictable recovery rates of the separated analytes. Here, we developed a modeling approach, which was based on locally weighted regression (LWR) of ultraviolet (UV) spectra of unpurified samples, to quantify the loading of nucleic acid cargos in LNPs in-situ. We trained the model to automatically tune the training library space according to the spectral features of a query sample so as to robustly predict the nucleic acid cargo concentration and rank loading capacity with similar performance as the more complicated experimental approaches. Furthermore, we successfully applied the model to a wide range of nucleic acid cargo species, including antisense oligonucleotides, single-guided RNA, and messenger RNA, in varied lipid matrices. The LWR modeling approach significantly saved analytical time and efforts by facile UV scans of 96-well sample plates within a few minutes and with minimal sample preprocessing. Our proof-of-concept study presented the very first data mining and modeling strategy to quantify nucleic acid loading in LNPs and is expected to better serve high-throughput screening workflows, thereby facilitates early-stage optimization and development of LNP formulations.

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Evolution of the Computational Pharmaceutics Approaches in the Modeling and Prediction of Drug Payload in Lipid and Polymeric Nanocarriers

Cited by 14 publications

References 73 publications

Translational Significance of GMF-β Inhibition by Indazole-4-yl-methanol in Enteric Glial Cells for Treating Multiple Sclerosis

Translational Significance of GMF-β Inhibition by Indazole-4-yl-methanol in Enteric Glial Cells for Treating Multiple Sclerosis

Knowledge Gaps in Generating Cell-Based Drug Delivery Systems and a Possible Meeting with Artificial Intelligence

Spectroscopy-Based Local Modeling Method for High-Throughput Quantification of Nucleic Acid Loading in Lipid Nanoparticles

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