Artificial Neural Networks: Comparison of Two Programs for Modeling a Process of Nanoparticle Preparation

Advanced optical imaging for the rational design of nanomedicines

Despite the enormous potential of nanomedicines to shape the future of medicine, their clinical translation remains suboptimal. Translational challenges are present in every step of the development pipeline, from a lack of understanding of patient heterogeneity to insufficient insights on nanoparticle properties and their impact on material-cell interactions. Here, we discuss how the adoption of advanced optical microscopy techniques, such as super-resolution optical microscopies, correlative techniques, and high-content modalities, could aid the rational design of nanocarriers, by characterizing the cell, the nanomaterial, and their interaction with unprecedented spatial and/or temporal detail. In this nanomedicine arena, we will discuss how the implementation of these techniques, with their versatility and specificity, can yield high volumes of multi-parametric data; and how machine learning can aid the rapid advances in microscopy: from image acquisition to data interpretation.

Effectiveness of carbonic anhydrase inhibitor loaded nanoparticles in the treatment of diabetic retinopathy

Diabetic Retinopathy (DRP) is a disease consisting of all the structural and functional changes that develop in the retinal layer of the eye due to diabetes. DRP is the most important cause of blindness between the ages of 20-74 in the world, and the most successful standard treatment option in the treatment of DRP is intravitreal injections. To synthesize acetazolamide loaded nanoparticles to be applied intravitreal treatment of DRP and to examine thein vitroefficacy of the nanoparticles. ACZ loaded PHBV nanoparticles (PHBV-ACZ NPs) formulations were prepared. Nanoparticles with a particle size of 253.20 ± 0.55 nm. A DRP model was established and characterized in HRMEC cells. The effect of the nanoparticles on permeability has been investigated and carrier proteins in BRB due to the development of DRP has been investigated. To establish thein vitroDRP model, HRMEC was stimulated with Recombinant human 165 Vascular Endothelial Growth Factor (VEGF), thereby temporarily reducing the expression levels of endothelial junction proteins, increasing the number of intercellular spaces in the monolayers of HRMECs. It was determined that after the cells were exposed to Carbonic anhydrase inhibitors (CAI) loaded nanoparticles, permeability decreased and protein expression increased.

Potential of Raman spectroscopy in facilitating pharmaceutical formulations development - An AI perspective

Drug development is time-consuming and inherently possesses a high failure rate. Pharmaceutical formulation development is the bridge that links a new chemical entity (NCE) to pre-clinical and clinical trials, and has a high impact on the efficacy and safety of the final drug product. Further, the time required for this process is escalating as formulation techniques are becoming more complicated due to the rising demands for drug products with better efficacy and patient compliance, as well as the inherent difficulties of addressing the unfavorable properties of NCEs such as low water solubility. The advent of artificial intelligence (AI) provides possibilities to accelerate the drug development process. In this review, we first examine applications of AI methods in different types of pharmaceutical formulations and formulation techniques. Moreover, as availability of data is the engine for the advancement of AI, we then suggest a potential way (i.e. applying Raman spectroscopy) for faster high-quality data gathering from formulations. Raman techniques have the capability of analyzing the composition and distribution of components and the physicochemical properties thereof within formulations, which are prominent factors governing drug dissolution profiles and subsequently bioavailability. Thus, useful information can be obtained bridging formulation development to the final product quality.

Noninvasive and fast measurement of blood glucose in vivo by near infrared (NIR) spectroscopy

This research was to develop a method for noninvasive and fast blood glucose assay in vivo. Near-infrared (NIR) spectroscopy, a more promising technique compared to other methods, was investigated in rats with diabetes and normal rats. Calibration models are generated by two different multivariate strategies: partial least squares (PLS) as linear regression method and artificial neural networks (ANN) as non-linear regression method. The PLS model was optimized individually by considering spectral range, spectral pretreatment methods and number of model factors, while the ANN model was studied individually by selecting spectral pretreatment methods, parameters of network topology, number of hidden neurons, and times of epoch. The results of the validation showed the two models were robust, accurate and repeatable. Compared to the ANN model, the performance of the PLS model was much better, with lower root mean square error of validation (RMSEP) of 0.419 and higher correlation coefficients (R) of 96.22%.

Effect of various formulation ingredients on thermal characteristics of PVC/clay nanocomposite foams: experimental and modeling

The effect of various ingredients – nanoclay (NC), azodicarbonamaide (ADCA), paraloeid K-400 and calcium stearate (Ca.St) – on thermal behavior of melt blended poly (vinyl chloride)/NC nanocomposites was investigated. Combinations of two artificial intelligence algorithms were performed for modeling of the systems. The formulation ingredients affect the thermal features of the compounds. Upon increasing the effect of various ingredients, i.e. NC, ADCA, K-400 and Ca.St, an increase in ash content was observed, hence, the thermal stability increased. The effect of ingredients on initial degradation temperature (T5%) and half degradation temperature (T50%) was not obvious. The artificial neural network (model) and the clonal selection algorithm (optimizer) were performed for proper modeling of the systems. These models were used to determine the role of T5%, T50% and char based on the contents of ingredients. The result successfully modeled the process parameters.

Rapid Determination of Puerarin by Near-infrared Spectroscopy During Percolation and Concentration Process of Puerariae Lobatae Radix

BACKGROUND

Gegen (Puerariae Labatae Radix) is one of the important medicines in Traditional Chinese Medicine. The studies showed that Gegen and its preparation had effective actions for atherosclerosis.

OBJECTIVE

Near-infrared (NIR) was used to develop a method for rapid determination of puerarin during percolation and concentration process of Gegen.

MATERIALS AND METHODS

About ten batches of samples were collected with high-performance liquid chromatography analysis values as reference, calibration models are generated by partial least-squares (PLS) regression as linear regression, and artificial neural networks (ANN) as nonlinear regression.

RESULTS

The root mean square error of prediction for the PLS and ANN model was 0.0396 and 0.0365 and correlation coefficients (r (2)) was 97.79% and 98.47%, respectively.

CONCLUSIONS

The NIR model for the rapid analysis of puerarin can be used for on-line quality control in the percolation and concentration process.

SUMMARY

Near-infrared was used to develop a method for on-line quality control in the percolation and concentration process of GegenCalibration models are generated by partial least-squares (PLS) regression as linear regression and artificial neural networks (ANN) as non-linear regressionThe root mean square error of prediction for the PLS and ANN model was 0.0396 and 0.0365 and correlation coefficients (r (2)) was 97.79% and 98.47%, respectively. Abbreviations used: NIR: Near-Infrared Spectroscopy; Gegen: Puerariae Loabatae Radix; TCM: Traditional Chinese Medicine; PLS: Partial least-squares; ANN: Artificial neural networks; RMSEP: Root mean square error of validation; R2: Correlation coefficients; PAT: Process analytical technology; FDA: The Food and Drug Administration; Rcal: Calibration set; RMSECV: Root mean square errors of cross-validation; RPD: Residual predictive deviation; SLS: Straight Line Subtraction; MLP: Multi-Layer Perceptron; MSE: Mean square error.

Determination of factors controlling the particle size and entrapment efficiency of noscapine in PEG/PLA nanoparticles using artificial neural networks

In this study, di- and triblock copolymers based on polyethylene glycol and polylactide were synthesized by ring-opening polymerization and characterized by proton nuclear magnetic resonance and gel permeation chromatography. Nanoparticles containing noscapine were prepared from these biodegradable and biocompatible copolymers using the nanoprecipitation method. The prepared nanoparticles were characterized for size and drug entrapment efficiency, and their morphology and size were checked by transmission electron microscopy imaging. Artificial neural networks were constructed and tested for their ability to predict particle size and entrapment efficiency of noscapine within the formed nanoparticles using different factors utilized in the preparation step, namely polymer molecular weight, ratio of polymer to drug, and number of blocks that make up the polymer. Using these networks, it was found that the polymer molecular weight has the greatest effect on particle size. On the other hand, polymer to drug ratio was found to be the most influential factor on drug entrapment efficiency. This study demonstrated the ability of artificial neural networks to predict not only the particle size of the formed nanoparticles but also the drug entrapment efficiency. This may have a great impact on the design of polyethylene glycol and polylactide-based copolymers, and can be used to customize the required target formulations.

Release modeling and comparison of nanoarchaeosomal, nanoliposomal and pegylated nanoliposomal carriers for paclitaxel

Breast cancer is the most prevalent cancer among women. Recently, delivering by nanocarriers has resulted in a remarkable evolution in treatment of numerous cancers. Lipid nanocarriers are important ones while liposomes and archaeosomes are common lipid nanocarriers. In this work, paclitaxel was used and characterized in nanoliposomal and nanoarchaeosomal form to improve efficiency. To increase stability, efficiency and solubility, polyethylene glycol 2000 (PEG 2000) was added to some samples. MTT assay confirmed effectiveness of nanocarriers on MCF-7 cell line and size measuring validated nano-scale of particles. Nanoarchaeosomal carriers demonstrated highest encapsulation efficiency and lowest release rate. On the other hand, pegylated nanoliposomal carrier showed higher loading efficiency and less release compared with nanoliposomal carrier which verifies effect of PEG on improvement of stability and efficiency. Additionally, release pattern was modeled using artificial neural network (ANN) and genetic algorithm (GA). Using ANN modeling for release prediction, resulted in R values of 0.976, 0.989 and 0.999 for nanoliposomal, pegylated nanoliposomal and nanoarchaeosomal paclitaxel and GA modeling led to values of 0.954, 0.951 and 0.976, respectively. ANN modeling was more successful in predicting release compared with the GA strategy.

Artificial intelligence in nanotechnology

During the last decade there has been increasing use of artificial intelligence tools in nanotechnology research. In this paper we review some of these efforts in the context of interpreting scanning probe microscopy, the study of biological nanosystems, the classification of material properties at the nanoscale, theoretical approaches and simulations in nanoscience, and generally in the design of nanodevices. Current trends and future perspectives in the development of nanocomputing hardware that can boost artificial-intelligence-based applications are also discussed. Convergence between artificial intelligence and nanotechnology can shape the path for many technological developments in the field of information sciences that will rely on new computer architectures and data representations, hybrid technologies that use biological entities and nanotechnological devices, bioengineering, neuroscience and a large variety of related disciplines.

Effect of different formulation variables on some particle characteristics of poly (DL-lactide-co-glycolide) nanoparticles

This study investigates the effect of some formulation variables on particulate characteristics of poly (DL-lactide-co-glycolide) (PLGA) copolymer nanoparticles by applying 2(3) factorial design and response surface methodology (RSM). Nanoparticles were prepared by solvent displacement technique. Initially, appropriate formulation factors for elaboration of polymeric particles were selected by screening. A 2(3) full factorial design was employed to evaluate the influence of three formulation variables, polymer concentration (X(1)), dispersant concentration (X(2)) and phase volume ratio (X(3)) on the percentage of total particles at submicron range (Y(1)), mean diameter (Y(2)) and specific surface area (Y(3)) as particle characteristics. The results showed that all the three variables had significant influence on mean diameter of particles and amount of particles at submicron range. Simultaneous change of polymer concentration and dispersant concentration had significant effect on specific surface area of particles. Span value as an index of polydispersity indicated uniformity in particle size distribution.