Theoretical and applied concepts of nanocarriers for the treatment of Parkinson's diseases

Bhosale, Anjali; Paul, Gajanan; Mazahir, Farhan; Yadav, Awesh K.

doi:10.1016/j.onano.2022.100111

Cited by 8 publications

(4 citation statements)

References 157 publications

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“…Therefore, what is observed in the documents is that nanoparticles, nanomaterials, quantum rods, liposomes, chitosan, borneol and modi ed lactoferrin are common compounds that have been investigated in their interaction with Selegiline [91,92]. Also, several valuable works of molecular dynamics simulations, and ab initio and DFT calculations on Selegiline and its energetic interaction in various chemical environments have been reported in the literature [93][94][95][96]. In this matter, accurate calculations of molecular PESs and intramolecular interaction studies are essential, and the accuracy of these calculations is the biggest challenge for pharmaceutical computational scientists.…”

Section: Introductionmentioning

confidence: 99%

Quantum-level machine learning calculations to predict the PES of Selegiline

Shirani,

Hashemianzadeh

2024

Preprint

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Many drug molecules contain functional groups, resulting in a torsional barrier corresponding to rotation around the bond linking the fragments. In medicinal chemistry and pharmaceutical sciences, inclusive of drug design studies, the exact calculation of the potential energy surface of these molecular torsions is extremely important and precious. Machine learning, including deep learning, is currently one of the most rapidly evolving tools in computer-aided drug discovery and molecular simulations. In this work, we used ANI-1x neural network potential as a quantum-level machine learning to predict the PESs of the Selegiline antiparkinsonian drug molecule. Also, DFT calculations at the wB97X/6-31G(d) level of theory have been used to study the structural parameters and vibrational normal modes of the Selegiline molecule. We succeeded in calculating the vibrational frequencies, electronic energy and optimization of the molecular structure of the Selegiline using the ANI-1x dataset in a very short computing cost. From this perspective, we expect the ANI-1x dataset applied in this work to be appreciably efficient and effective in computational structure-based drug design studies.

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Section: Introductionmentioning

confidence: 99%

Quantum-level machine learning calculations to predict the PES of Selegiline

Shirani,

Hashemianzadeh

2024

Preprint

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“…One such approach involves enhancing the effectiveness of therapeutics by attaching them to nanocarriers that are functionalized with appropriate ligands. This strategy enables targeted delivery to speci c sites in the body, particularly the brain, thereby improving the treatment of Parkinson's disease and minimizing any adverse effects [10].…”

Section: Introductionmentioning

confidence: 99%

Entacapone Nanoemulsion: Formulation Design, Optimization, and Evaluation

Garla,

Shaik,

Gugulothu

et al. 2024

Preprint

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Entacapone is utilized as an adjunct to levodopa therapy for the treatment of Parkinson's syndrome. According to the Biopharmaceutics Classification System, it is classified as a class IV drug. The solubility of a substance can be enhanced by utilizing nanoemulsion, which can also effectively traverse the blood-brain barrier owing to its nanoscale dimensions. The formulation utilized various proportions of Capmul Medium Chain Mono and Diglycerides as the oil phase, Pluronic F127, and Phospholipin 90 as hydrophilic and lipophilic surfactants. Optimization was performed using 32 factorial designs in Design Expert®8.0.5.2 software, incorporating mixing speed, time, and sonication parameters. The nanoemulsion exhibited an average particle size of 120.8±1.9nm, with a low polydispersity index of 0.144, indicating a uniform globule size. The zeta potential suggested good stability, while the XRD pattern indicated decreased drug crystallinity. The TEM images confirm that the size of the particles falls within the range of 120–150 nm and there is no evidence of aggregation. The drug was released at a rate of 80.33±0.92% for 8 hours. The current study demonstrates enhancements in the solubility and stability of formulated nanoemulsions designed for oral delivery.

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“…Hence, nanotechnology is an emerging approach in the battle against PD, both in transforming the delivery of therapeutics and creating an entirely new class of therapies able to target canonical challenges of this complex disorder. 16 Graphene is investigated as a potential nanomaterial in biomedical applications, stem cell research, cell imaging, and drug/gene delivery due to its excellent conductivity, stability, and biocompatibility. 17 Graphene-based nanomaterials (GBNs) with large surface area, high electrical conductivity, and excellent mechanical performance have made their way to the forefront of nanomedicine.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%