scholarly journals Microfluidic Synthesis of Indomethacin-Loaded PLGA Microparticles Optimized by Machine Learning

2021 ◽  
Vol 8 ◽  
Author(s):  
Safa A. Damiati ◽  
Samar Damiati
Keyword(s):  
Machine Learning ◽  
Sustained Release ◽  
Low Cost ◽  
Drug Loading ◽  
Microfluidic System ◽  
Ann Model ◽  
Droplet Sizes ◽  
Poorly Soluble ◽  
Polymeric Microparticles ◽  
Poorly Soluble Drug

Several attempts have been made to encapsulate indomethacin (IND), to control its sustained release and reduce its side effects. To develop a successful formulation, drug release from a polymeric matrix and subsequent biodegradation need to be achieved. In this study, we focus on combining microfluidic and artificial intelligence (AI) technologies, alongside using biomaterials, to generate drug-loaded polymeric microparticles (MPs). Our strategy is based on using Poly (D,L-lactide-co-glycolide) (PLGA) as a biodegradable polymer for the generation of a controlled drug delivery vehicle, with IND as an example of a poorly soluble drug, a 3D flow focusing microfluidic chip as a simple device synthesis particle, and machine learning using artificial neural networks (ANNs) as an in silico tool to generate and predict size-tunable PLGA MPs. The influence of different polymer concentrations and the flow rates of dispersed and continuous phases on PLGA droplet size prediction in a microfluidic platform were assessed. Subsequently, the developed ANN model was utilized as a quick guide to generate PLGA MPs at a desired size. After conditions optimization, IND-loaded PLGA MPs were produced, and showed larger droplet sizes than blank MPs. Further, the proposed microfluidic system is capable of producing monodisperse particles with a well-controllable shape and size. IND-loaded-PLGA MPs exhibited acceptable drug loading and encapsulation efficiency (7.79 and 62.35%, respectively) and showed sustained release, reaching approximately 80% within 9 days. Hence, combining modern technologies of machine learning and microfluidics with biomaterials can be applied to many pharmaceutical applications, as a quick, low cost, and reproducible strategy.

scholarly journals Novel application of mixed solvency concept in the development of oral liquisolid system of a poorly soluble drug, cefixime and its evaluation

2018 ◽  
Vol 8 (6-s) ◽  
pp. 5-8 ◽  
Author(s):  
Rinshi Agrawal ◽  
RK Maheshwari
Keyword(s):  
Drug Loading ◽  
Research Work ◽  
Water Soluble ◽  
Dsc Studies ◽  
Water Soluble Drug ◽  
Poorly Soluble ◽  
Poorly Water Soluble ◽  
Poorly Soluble Drug ◽  
Model Drug ◽  
Poorly Water Soluble Drug

Application of mixed solvency has been employed in the present research work to develop a liquisolid system (Powder formulation) of poorly water soluble drug, cefixime (as model drug). Material and Methods: For poorly water soluble drug cefixime, combination of solubilizers such as sodium acetate, sodium caprylate and propylene glycol as mixed solvent systems were used to decrease the overall concentration of solubilizers required to produce substantial increase in solubility and thereby resulting in enhanced drug loading capacity of cefixime. The procured sample of cefixime was characterized by melting point, IR, UV and DSC studies. Stability studies of liquisolid system of cefixime were performed for two months at room temperature, 30˚C and 40˚C. All the formulations were physically, chemically, and microbiologically stable. Conclusion: Mixed solvency concept has been successfully employed for enhancing the drug loading of poorly water soluble drug, cefixime. Keywords: Solubility, cefixime, liquisolid system, mixed solvency concept.

scholarly journals Colorimetric detection on paper analytical device using machine learning

2021 ◽  
Author(s):  
Bidur Khanal ◽  
Pravin Pokhrel ◽  
Bishesh Khanal ◽  
Basant Giri
Keyword(s):  
Machine Learning ◽  
Enzyme Inhibition ◽  
Prediction Accuracy ◽  
Low Cost ◽  
Colorimetric Detection ◽  
Field Testing ◽  
Support Vector ◽  
Ann Model ◽  
Fine Grained ◽  
Imaging Conditions

Paper-based analytical devices (PADs) employing colorimetric detection and smartphone images have gained wider acceptance in a variety of measurement applications. The PADs are primarily meant to be used in field settings where assay and imaging conditions greatly vary resulting in less accurate results. Recently, machine learning (ML) assisted models have been used in image analysis. We evaluated a combinations of four ML models - logistic regression, support vector machine, random forest, and artificial neural network, and three image color spaces - RGB, HSV, and LAB for their ability to accurately predict analyte concentrations. We used images of PADs taken at varying lighting conditions, with different cameras, and users for food color and enzyme inhibition assays to create training and test datasets. Prediction accuracy was higher for food color than enzyme inhibition assays in most of the ML model and colorspace combinations. All models better predicted coarse level classification than fine grained concentration labels. ML models using sample color along with a reference color increased the models’ ability in predicting the result in which the reference color may have partially factored out the variation in ambient assay and imaging conditions. The best concentration label prediction accuracy obtained for food color was 0.966 when using ANN model and LAB colorspace. The accuracy for enzyme inhibition assay was 0.908 when using SVM model and LAB colorspace. Appropriate model and colorspace combinations can be useful to analyze large numbers of samples on PADs as a powerful low-cost quick field-testing tool.

scholarly journals Formulation and development of fast dissolving oral film of a poorly soluble drug, frusemide with improved drug loading using mixed solvency concept and its evaluation

2018 ◽  
Vol 8 (6) ◽  
pp. 132-141 ◽  
Author(s):  
Garima Carpenter ◽  
R. K. Maheshwari
Keyword(s):  
Polyethylene Glycol ◽  
Sodium Benzoate ◽  
Sodium Citrate ◽  
Drug Loading ◽  
Research Work ◽  
Water Soluble ◽  
Polyethylene Glycol 200 ◽  
Poorly Soluble ◽  
Sodium Caprylate ◽  
Poorly Soluble Drug

The aim of the present research work is to explore the application of mixed solvency concept to formulate and develop a fast dissolving oral film of furosemide with improved drug loading. In the present study, poorly soluble drug, furosemide was tried to be solubilized by employing the combination of physiologically compatible water-soluble additives (solubilizers) to formulate its fast dissolving formulations. For the development of fast dissolving oral film, firstly, different film forming polymers were tested for their film properties. The second fast dissolving layer was also formed and optimized. Solubility studies were conducted to select water-soluble additives for formulation of fast dissolving drug layer. Keeping the total concentration less than 40 % w/v of mixed blends, different aqueous blends were prepared employing solubilizers from among sodium benzoate, sodium acetate, sodium citrate, urea, niacinamide, glycerin, propylene glycol, polyethylene glycol 200, polyethylene glycol 400, polyethylene glycol 600, and PVP K 30. Maximum solubility of furosemide was found in blends- F5 (10% sodium caprylate +2.5%sodium benzoate+ 2.5% niacinamide) and in blend F7 (10% sodium caprylate +2.5%sodium benzoate +2.5% sodium citrate + 2.5% niacinamide). Prepared films were evaluated for drug content, thickness, folding endurance, tensile strength and hydration ratio. Keywords: Furosemide, fast dissolving oral film, mixed solvency concept.

scholarly journals FORMULATION DEVELOPMENT OF IMMEDIATE RELEASE PELLETS OF TADALAFIL: SOLIDIFICATION APPROACH FOR NANOSUSPENSION

2019 ◽  
pp. 124-131 ◽  
Author(s):  
SHOBHA UBGADE ◽  
VAISHALI KILOR ◽  
VIDYA BAHEKAR ◽  
ABHAY ITTADWAR
Keyword(s):  
Dissolution Rate ◽  
Drug Loading ◽  
Sodium Dodecyl ◽  
Immediate Release ◽  
In Vitro Dissolution ◽  
Formulation Development ◽  
Saturation Solubility ◽  
Improved Stability ◽  
Poorly Soluble ◽  
Poorly Soluble Drug

Objective: Nanosuspension is known to enhance the saturation solubility and dissolution velocity of poorly soluble drugs owing to the increased surface area of nanosized particles. Stability of these solubility enhancing systems can be improved by converting them into solidified forms. To simultaneously achieve enhanced dissolution and improved stability, an attempt has been made to increase the dissolution rate of poorly soluble drug tadalafil by formulating immediate release pellets of its nanosuspension. Methods: Tadalafil nanosuspensions were prepared using high shear homogenization technique and hydroxypropyl methylcellulose (HPMC) E 15, sodium dodecyl sulphate (SDS) as stabilizers. Prepared nanosuspensions were subjected to the characterization of particle size distribution, zeta potential, drug loading and saturation solubility. Optimized nanosuspension was solidified by preparing immediate release pellets: for improved stability, where tadalafil nanosuspension was used as a binder. Pellets were prepared by extrusion-spheronization technique using κ-carrageenan as a pelletizing aid. Results: Prepared immediate release pellets disintegrated within 03 min. In vitro dissolution studies showed 85% drug release within 45 min in pH 1.2 buffer from immediate release pellets containing tadalafil nanosuspension. Conclusion: It can be concluded that formulation of nanosuspension of poorly soluble drug and its use as a binder for the preparation of immediate release pellets markedly improved the dissolution rate.

CLAY NANOTUBES FOR ENCAPSULATION AND SUSTAINED RELEASE OF DRUGS

NANO ◽  
2007 ◽  
Vol 02 (02) ◽  
pp. 115-120 ◽  
Author(s):  
NALINKANTH G. VEERABADRAN ◽  
RONALD R. PRICE ◽  
YURI M. LVOV
Keyword(s):  
Drug Delivery ◽  
Sustained Release ◽  
Drug Loading ◽  
Poorly Soluble Drugs ◽  
Simple Loading ◽  
Alcohol Water ◽  
Poorly Soluble ◽  
Clay Nanotubes ◽  
Water Ratio ◽  
Loading Procedure

50-nm diameter halloysite clay nanotubules with 15 nm lumen were used for loading poorly soluble drugs and sustaining their release. Loading was optimized by varying pH and alcohol/water ratio in the solvent with a maximum drug loading of 12 volume%. Near linear release of Dexamethasone, Furosemide, and Nifedipine was demonstrated for 5–10 hours. Its capacity for the time release of drugs, along with its simple loading procedure and the biocompatibility of halloysite nanotubules makes this method a prospective drug delivery system.

Design and Evaluation of Hydrophilic Matrix System for pH-Independent Sustained Release of Weakly Acidic Poorly Soluble Drug

2018 ◽  
Vol 19 (5) ◽  
pp. 2144-2154 ◽  
Author(s):  
Jinheng Huang ◽  
Huaqing Lin ◽  
Bingxin Peng ◽  
Qianfeng Huang ◽  
Fangzhou Shuai ◽  
...  
Keyword(s):  
Sustained Release ◽  
Matrix System ◽  
Hydrophilic Matrix ◽  
Hydrophilic Matrix System ◽  
Poorly Soluble ◽  
Poorly Soluble Drug

Machine Learning for Acute Toxicity Prediction Using High-Throughput Enzyme-Reaction Chip

2019 ◽  
Author(s):  
Qiannan Duan ◽  
Jianchao Lee ◽  
Jinhong Gao ◽  
Jiayuan Chen ◽  
Yachao Lian ◽  
...  
Keyword(s):  
Machine Learning ◽  
Acute Toxicity ◽  
Model Building ◽  
Low Cost ◽  
Enzyme Reaction ◽  
Chemical Effect ◽  
Technological Innovations ◽  
Acute Toxicity Test ◽  
Toxicity Prediction ◽  
Traditional Understanding

<p>Machine learning (ML) has brought significant technological innovations in many fields, but it has not been widely embraced by most researchers of natural sciences to date. Traditional understanding and promotion of chemical analysis cannot meet the definition and requirement of big data for running of ML. Over the years, we focused on building a more versatile and low-cost approach to the acquisition of copious amounts of data containing in a chemical reaction. The generated data meet exclusively the thirst of ML when swimming in the vast space of chemical effect. As proof in this study, we carried out a case for acute toxicity test throughout the whole routine, from model building, chip preparation, data collection, and ML training. Such a strategy will probably play an important role in connecting ML with much research in natural science in the future.</p>

Studies on the Preparation, Characterization and Solubility of -Cyclodextrin-Roxythromycin Inclusion Complexes

2009 ◽  
Vol 2 (2) ◽  
pp. 523-530
Author(s):  
D. Nagasamy Venkatesh ◽  
S. Karthick ◽  
M. Umesh ◽  
G. Vivek ◽  
R.M. Valliappan ◽  
...  
Keyword(s):  
Dissolution Rate ◽  
Inclusion Complexes ◽  
Phase Solubility ◽  
X Ray Diffraction ◽  
Scanning Calorimetry ◽  
X Ray ◽  
Ir Studies ◽  
Poorly Soluble ◽  
Poorly Soluble Drug

Roxythromycin/ β-cyclodextrin (Roxy/ β-CD) dispersions were prepared with a view to study the influence of β-CD on the solubility and dissolution rate of this poorly soluble drug. Phase-solubility profile indicated that the solubility of roxythromycin was significantly increased in the presence of β-cyclodextrin and was classified as AL-type, indicating the 1:1 stoichiometric inclusion complexes. Physical characterization of the prepared systems was carried out by differential scanning calorimetry (DSC), X-ray diffraction studies (XRD) and IR studies. Solid state characterization of the drug β-CD binary system using XRD, FTIR and DSC revealed distinct loss of drug crystallinity in the formulation, ostensibly accounting for enhancement of dissolution rate.

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