Montelukast Nanocrystals for Transdermal Delivery with Improved Chemical Stability

Im, Sung Hyun; Jung, Hoe Taek; Ho, Myoung Jin; Lee, Jeong Eun; Kim, Hyun Jae; Kim, Dong Yoon; Lee, Hyo Chun; Choi, Yong Seok; Kang, Myung Joo

doi:10.3390/pharmaceutics12010018

Cited by 21 publications

(11 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Beck et al 2020 conducted a study to predict commercially available antiviral drugs as a potential therapeutic agent against novel coronavirus (SARS-CoV-2) through DeepDTA [ 316 ]. Similarly, Lee and Kim 2019 predicted the drug-target interactions by DNN based on large-scale drug-induced transcriptome data using PADME [ 317 ]. Another DL model that uses both RNN and CNN was constructed to predict drug-target binding affinity, which is called as DeepAffinity ( https://github.com/Shen-Lab/DeepAffinity ) [ 318 ].…”

Section: Applications Of Artificial Intelligence In Drug Development mentioning

confidence: 99%

Artificial intelligence to deep learning: machine intelligence approach for drug discovery

et al. 2021

View full text Add to dashboard Cite

Graphic abstract Drug designing and development is an important area of research for pharmaceutical companies and chemical scientists. However, low efficacy, off-target delivery, time consumption, and high cost impose a hurdle and challenges that impact drug design and discovery. Further, complex and big data from genomics, proteomics, microarray data, and clinical trials also impose an obstacle in the drug discovery pipeline. Artificial intelligence and machine learning technology play a crucial role in drug discovery and development. In other words, artificial neural networks and deep learning algorithms have modernized the area. Machine learning and deep learning algorithms have been implemented in several drug discovery processes such as peptide synthesis, structure-based virtual screening, ligand-based virtual screening, toxicity prediction, drug monitoring and release, pharmacophore modeling, quantitative structure–activity relationship, drug repositioning, polypharmacology, and physiochemical activity. Evidence from the past strengthens the implementation of artificial intelligence and deep learning in this field. Moreover, novel data mining, curation, and management techniques provided critical support to recently developed modeling algorithms. In summary, artificial intelligence and deep learning advancements provide an excellent opportunity for rational drug design and discovery process, which will eventually impact mankind. The primary concern associated with drug design and development is time consumption and production cost. Further, inefficiency, inaccurate target delivery, and inappropriate dosage are other hurdles that inhibit the process of drug delivery and development. With advancements in technology, computer-aided drug design integrating artificial intelligence algorithms can eliminate the challenges and hurdles of traditional drug design and development. Artificial intelligence is referred to as superset comprising machine learning, whereas machine learning comprises supervised learning, unsupervised learning, and reinforcement learning. Further, deep learning, a subset of machine learning, has been extensively implemented in drug design and development. The artificial neural network, deep neural network, support vector machines, classification and regression, generative adversarial networks, symbolic learning, and meta-learning are examples of the algorithms applied to the drug design and discovery process. Artificial intelligence has been applied to different areas of drug design and development process, such as from peptide synthesis to molecule design, virtual screening to molecular docking, quantitative structure–activity relationship to drug repositioning, protein misfolding to protein–protein interactions, and molecular pathway identification to polypharmacology. Artificial intelligence principles have been applied to the classification of active and inactive, monitoring drug release, pre-clinical and clinical development, primary and secondary drug screeni...

show abstract

Section: Applications Of Artificial Intelligence In Drug Development mentioning

confidence: 99%

Artificial intelligence to deep learning: machine intelligence approach for drug discovery

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Nanocrystals (NCY) are colloidal carriers of nano-sized particles stabilized in a dispersion medium by least polymeric and/or amphiphilic stabilizing agents. NCY was designed to enhance the absorption and permeation of lipophilic drugs [ 47 ]. Baicalin (BCN) cannot be used in topical applications due to its lipophilicity and poor permeation.…”

Section: Hydrogels Prepared With Natural Polymersmentioning

confidence: 99%

Novel Hydrogels for Topical Applications: An Updated Comprehensive Review Based on Source

Almoshari

2022

Gels

View full text Add to dashboard Cite

Active pharmaceutical ingredients (API) or drugs are normally not delivered as pure chemical substances (for the prevention or the treatment of any diseases). APIs are still generally administered in prepared formulations, also known as dosage forms. Topical administration is widely used to deliver therapeutic agents locally because it is convenient and cost-effective. Since earlier civilizations, several types of topical semi-solid dosage forms have been commonly used in healthcare society to treat various skin diseases. A topical drug delivery system is designed primarily to treat local diseases by applying therapeutic agents to surface level parts of the body such as the skin, eyes, nose, and vaginal cavity. Nowadays, novel semi-solids can be used safely in pediatrics, geriatrics, and pregnant women without the possibility of causing any allergy reactions. The novel hydrogels are being used in a wide range of applications. At first, numerous hydrogel research studies were carried out by simply adding various APIs in pure form or dissolved in various solvents to the prepared hydrogel base. However, numerous research articles on novel hydrogels have been published in the last five to ten years. It is expected that novel hydrogels will be capable of controlling the APIs release pattern. Novel hydrogels are made up of novel formulations such as nanoparticles, nanoemulsions, microemulsions, liposomes, self-nano emulsifying drug delivery systems, cubosomes, and so on. This review focus on some novel formulations incorporated in the hydrogel prepared with natural and synthetic polymers.

show abstract

“…20 Recently, drug nanocrystal suspension (NS) has been in the spotlight as a promising topical as well as transdermal delivery system for lipophilic pharmaceuticals and cosmetic substances. [21][22][23] Drug nanocrystals are colloidal dispersions of sub-micronized drug particles stabilized using minimal polymeric or amphiphilic stabilizers in a continuous phase. 24,25 A decrease in particle size leads to an increase in surface area, which is favorable for the adhesion of drug particles to biological membranes.…”

Section: Introductionmentioning

confidence: 99%

High-Payload Nanosuspension of Centella asiatica Extract for Improved Skin Delivery with No Irritation

Kim¹,

Park²,

Kim³

et al. 2021

IJN

Self Cite

View full text Add to dashboard Cite

Montelukast Nanocrystals for Transdermal Delivery with Improved Chemical Stability

Cited by 21 publications

References 40 publications

Artificial intelligence to deep learning: machine intelligence approach for drug discovery

Artificial intelligence to deep learning: machine intelligence approach for drug discovery

Novel Hydrogels for Topical Applications: An Updated Comprehensive Review Based on Source

High-Payload Nanosuspension of Centella asiatica Extract for Improved Skin Delivery with No Irritation

Contact Info

Product

Resources

About