Comparison of response surface methodology and artificial neural network to optimize novel ophthalmic flexible nano-liposomes: Characterization, evaluation, in vivo pharmacokinetics and molecular dynamics simulation

Nano-composite system of traditional Chinese medicine for ocular applications: molecular docking and three-dimensional modeling insight for intelligent drug evaluation

The absorption of drugs was impeded in the posterior part of the eye due to the special structure. In addition, it was crucial to comprehend transport laws of molecules in ocular drug delivery for designing effective strategies. However, the current quality evaluation methods of the eye were backward and lack of dynamic monitoring of drug processes in vivo. Herein, nano-drug delivery system and three-dimensional (3D) model were combined to overcome the problems of low bioavailability and diffusion law. The model drugs were screened by molecular docking. The flexible nano-liposome (FNL) and temperature-sensitive gel (TSG) composite formulation was characterized through comprehensive evaluation. COMSOL software was utilized to build 3D eyeball to predict the bioavailability of drugs. The size of the preparation was about 98.34 nm which is relatively optimal for the enhanced permeability of the eyes. The formulation showed a stronger safety and non-irritant. The pharmacokinetics results of aqueous humor showed that the AUC of two drugs in this system increased by 3.79 and 3.94 times, respectively. The results of 3D calculation model proved that the concentrations of drugs reaching the retina were 1.90×10-5 mol/m3 and 6.37×10-6 mol/m3. In conclusion, the FNL-TSG markedly improved the bioavailability of multiple components in the eye. More importantly, a simplified 3D model was developed to preliminarily forecast the bioavailability of the retina after drug infusion, providing technical support for the accurate evaluation of ocular drug delivery. It provided new pattern for the development of intelligent versatile ophthalmic preparations.

Optimizing nanoliposomal formulations: Assessing factors affecting entrapment efficiency of curcumin-loaded liposomes using machine learning

BACKGROUND

Curcumin faces challenges in clinical applications due to its low bioavailability and poor water solubility. Liposomes have emerged as a promising delivery system for curcumin. This study aims to apply ensemble learning, a machine learning technique, to determine the most effective experimental conditions for formulating stable curcumin-loaded liposomes with a high entrapment efficiency (EE).

METHODS

Two liposomal formulations composed of HSPC:DPPG:Chol:DSPE-mPEG2000 and HSPC:Chol:DSPE-mPEG2000 at 55:5:35:5 and 55:40:5 M ratios, respectively, were prepared using the remote loading method, and their particle size and polydispersity index (PDI) were determined using Dynamic Light Scattering. To model the impact of five factors (molar ratios, particle size, sonication time, pH, and PDI) on EE%, the Least-squares boosting (LSBoost) ensemble learning algorithm was employed due to its capability to effectively handle nonlinear and non-stationary problems. The implementation and optimization of LSBoost were performed using MATLAB R2020a. The dataset was randomly split into training and testing sets, with 70% allocated for training. The mean absolute error (MAE) was used as the cost function to evaluate model performance. Additionally, a novel approach was employed to visualize the results using 3D plots, facilitating practical interpretation.

RESULTS

The optimal model exhibited an MAE of 3.61, indicating its robust predictive capability. The study identified several optimal conditions for achieving the highest EE value of 100%. However, to ensure both the highest EE value and a suitable particle size, it is recommended to set the following conditions: a molar ratio of 55:5:35:5, a PDI within the range of 0.09-0.13, a particle size of approximately 130 nm, a sonication time of 30 min, and a pH within the range of 7.2-8. It is worth mentioning that adjusting the molar ratio to 55:40:5 resulted in a maximum EE of 88.38%.

CONCLUSION

These findings underscore the high performance of ensemble learning in accurately predicting and optimizing the EE of the curcumin-loaded liposomes. The application of this technique provides valuable insights and holds promise for the development of efficient drug delivery systems.

Application of ensemble machine learning approach to assess the factors affecting size and polydispersity index of liposomal nanoparticles

Liposome nanoparticles have emerged as promising drug delivery systems due to their unique properties. Assessing particle size and polydispersity index (PDI) is critical for evaluating the quality of these liposomal nanoparticles. However, optimizing these parameters in a laboratory setting is both costly and time-consuming. This study aimed to apply a machine learning technique to assess the impact of specific factors, including sonication time, extrusion temperature, and compositions, on the size and PDI of liposomal nanoparticles. Liposomal solutions were prepared and subjected to sonication with varying values for these parameters. Two compositions: (A) HSPC:DPPG:Chol:DSPE-mPEG2000 at 55:5:35:5 molar ratio and (B) HSPC:Chol:DSPE-mPEG2000 at 55:40:5 molar ratio, were made using remote loading method. Ensemble learning (EL), a machine learning technique, was employed using the Least-squares boosting (LSBoost) algorithm to accurately model the data. The dataset was randomly split into training and testing sets, with 70% allocated for training. The LSBoost algorithm achieved mean absolute errors of 1.652 and 0.0105 for modeling the size and PDI, respectively. Under conditions where the temperature was set at approximately 60 °C, our EL model predicted a minimum particle size of 116.53 nm for composition (A) with a sonication time of approximately 30 min. Similarly, for composition (B), the model predicted a minimum particle size of 129.97 nm with sonication times of approximately 30 or 55 min. In most instances, a PDI of less than 0.2 was achieved. These results highlight the significant impact of optimizing independent factors on the characteristics of liposomal nanoparticles and demonstrate the potential of EL as a decision support system for identifying the best liposomal formulation. We recommend further studies to explore the effects of other independent factors, such as lipid composition and surfactants, on liposomal nanoparticle characteristics.

Nano transdermal system combining mitochondria-targeting cerium oxide nanoparticles with all-trans retinoic acid for psoriasis

Psoriasis is an inflammatory skin disease that is intricately linked to oxidative stress. Antioxidation and inhibition of abnormal proliferation of keratinocytes are pivotal strategies for psoriasis. Delivering drugs with these effects to the site of skin lesions is a challenge that needs to be solved. Herein, we reported a nanotransdermal delivery system composed of all-trans retinoic acid (TRA), triphenylphosphine (TPP)-modified cerium oxide (CeO2) nanoparticles, flexible nanoliposomes and gels (TCeO2-TRA-FNL-Gel). The results revealed that TCeO2 synthesized by the anti-micelle method, with a size of approximately 5 nm, possessed excellent mitochondrial targeting ability and valence conversion capability related to scavenging reactive oxygen species (ROS). TCeO2-TRA-FNL prepared by the film dispersion method, with a size of approximately 70 nm, showed high drug encapsulation efficiency (>96%). TCeO2-TRA-FNL-Gel further showed sustained drug release behaviors, great transdermal permeation ability, and greater skin retention than the free TRA. The results of in vitro EGF-induced and H2O2-induced models suggested that TCeO2-TRA-FNL effectively reduced the level of inflammation and alleviated oxidative stress in HaCat cells. The results of in vivo imiquimod (IMQ)-induced model indicated that TCeO2-TRA-FNL-Gel could greatly alleviate the psoriasis symptoms. In summary, the transdermal drug delivery system designed in this study has shown excellent therapeutic effects on psoriasis and is prospective for the safe and accurate therapy of psoriasis.

Comparative study of cyclosporine A liposomes and emulsions for ophthalmic drug delivery: Process optimization through response surface methodology (RSM) and biocompatibility evaluation

Herein, cyclosporine A loaded liposomes (CsA-Lips) were fabricated aimed at improving the biocompatibility of the ophthalmic formulation and getting rid of the direct contact of ocular tissues with irritant excipients. Response surface methodology was exploited in order to investigate the influence of miscellaneous factors on the key characteristics of CsA-Lips. Ratio of EPC:CsA, ratio of EPC:Chol, and stirring speed were selected as the independent variables, while size, drug-loading content (DL), and drug-loading content (DL) loss rate were applied as the response variables. In case of the maximal lack-of-fit p-value and minimum sequential p-value, quadratic model was regarded as the fittest model to analyze the data. The correlation of independent variables with response variables was described by three-dimension surface figures. Optimized formulation for CsA-Lips was obtained with ratio of EPC:CsA set as 15, ratio of EPC:Chol set as 2, and stirring speed set as 800 rpm. The particle size of CsA-Lips was 129.2 nm after optimalization while their TEM images exhibited spherical unilamellar vesicles with clearly shell-core structure. CsA released more rapidly from CsA-Lips in comparison with self-made emulsion and Restasis®. Besides, minimum cytotoxicity of CsA-Lips was perceived via both MTT method and LDH method, indicating the excellent compatibility of the ophthalmic formulation. Simultaneously, CsA-Lips showed enhanced nonspecific internalization in the cytoplasm with a time-dose-dependent manner. In conclusion, CsA-Lips could be adhibited as the hopeful ophthalmic drug delivery system clinically for dry eye syndrome (DES).

Nano-based eye drop: Topical and noninvasive therapy for ocular diseases

Eye drops are the most accessible therapy for ocular diseases, while inevitably suffering from their lower bioavailability which highly restricts the treatment efficacy. The introduction of nanotechnology has attracted considerable interest as it has advantages over conventional ones such as prolonged ocular surface retention time and enhanced ocular barrier penetrating properties, and achieving higher bioavailability and improved treatment efficacy. This review describes various ocular diseases treated with eye drops as well as the physiological and anatomical ocular barriers faced with through drug administration. It also summarizes the recent advances regarding the utilization of nanotechnology in developing eye drops, and how to optimize the nanocarrier-based ocular drug delivery systems. The prospective future research directions for nano-based eye drops are also discussed here.

A Strategy for the Effective Optimization of Pharmaceutical Formulations Based on Parameter-Optimized Support Vector Machine Model

Engineering pharmaceutical formulations is governed by a number of variables, and the finding of the optimal preparation is intricately linked to the exploration of a multiparametric space through a variety of optimization tasks. As a result, making such optimization activities simpler is a significant undertaking. For the purposes of this study, we suggested a prediction model that was based on least square support vector machine (LSSVM) and whose parameters were optimized using the particle swarm optimization algorithm (PSO-LSSVM model). Other in silico optimization methods were used and compared, including the LSSVM and the back propagation (BP) neural networks algorithm. PSO-LSSVM demonstrated the highest performance on the test dataset, with the lowest mean square error. In addition, two dosage forms, quercetin solid dispersion and apigenin nanoparticles, were selected as model formulations due to the wide range of formulation compositions and manufacturing factors used in their production. Three different models were used to predict the ideal formulations of two different dosage forms, and in real world, the Taguchi orthogonal design arrays were used to optimize the formulations of each dosage form. It is clear that the predicted performance of two formulations using PSO-LSSVM was both consistent with the outcomes of the Taguchi orthogonal planned experiment, demonstrating the model's good reliability and high usefulness. Together, our PSO-LSSVM prediction model has the potential to accurately predict the best possible formulations, reduce the reliance on experimental effort, accelerate the process of formulation design, and provide a low-cost solution to drug preparation optimization.

State-of-the-Art Review of Artificial Neural Networks to Predict, Characterize and Optimize Pharmaceutical Formulation

During the development of a pharmaceutical formulation, a powerful tool is needed to extract the key points from the complicated process parameters and material attributes. Artificial neural networks (ANNs), a promising and more flexible modeling technique, can address real intricate questions in a high parallelism and distributed pattern in the manner of biological neural networks. The data mined and analyzing based on ANNs have the ability to replace hundreds of trial and error experiments. ANNs have been used for data analysis by pharmaceutics researchers since the 1990s and it has now become a research method in pharmaceutical science. This review focuses on the latest application progress of ANNs in the prediction, characterization and optimization of pharmaceutical formulation to provide a reference for the further interdisciplinary study of pharmaceutics and ANNs.

Neural Network Modelling for Prediction of Zeta Potential

The study is focused on monitoring the influence of selected parameters on the zeta potential values of titanium dioxide nanoparticles. The influence of pH, temperature, ionic strength, and mass content of titanium dioxide in the suspension was assessed. More than a thousand samples were measured by combining these variables. On the basis of results, the model of artificial neural network was proposed and tested. The authors have rich experiences with neural networks applications and this case shows that the neural network model works with a very high prediction success rate of zeta potential. Clearly, pH has the greatest effect on zeta potential values. The influence of other variables is not so significant. However, it can be said that increasing temperature results in an increase in the value of the zeta potential of titanium dioxide nanoparticles. The ionic force affects the zeta potential depending on the pH; in the vicinity of the isoelectric point, its effect is negligible. The effect of the mass content of titanium dioxide in the suspension is absolutely minor.

Design of experiments (DoE) to develop and to optimize nanoparticles as drug delivery systems

Nanotechnology has been widely applied to develop drug delivery systems to improve therapeutic performance. The effectiveness of these systems is intrinsically related to their physicochemical properties, so their biological responses are highly susceptible to factors such as the type and quantity of each material that is employed in their synthesis and to the method that is used to produce them. In this context, quality-oriented manufacturing of nanoparticles has been an important strategy to understand and to optimize the factors involved in their production. For this purpose, Design of Experiment (DoE) tools have been applied to obtain enough knowledge about the process and hence achieve high-quality products. This review aims to set up the bases to implement DoE as a strategy to improve the manufacture of nanocarriers and to discuss the main factors involved in the production of the most common nanocarriers employed in the pharmaceutical field.

Nanomedicines for the treatment of glaucoma: Current status and future perspectives

Glaucoma is the global leading cause of irreversible blindness. It is a chronic progressive disorder and, therefore, often requires long-term management with drugs on patients' discretion. However, there is a shortage of antiglaucoma drugs in the current market due to their low bioavailability. This is because there are multiple biological barriers of the human eyes, thereby leading to increased demands for frequent dosage regimen per day of these drugs, which could result in concomitant side effects and eventually reduced patient compliance. Recently, nanomedicines have become optimized alternatives to conventional ophthalmic formulations due to advantages of improved barrier permeability, sustained drug release, tissue targeting, and lowered systemic absorption of instilled medications. These merits provide the active ingredients in these nanomedicines an effective manner to reach the ideal concentrations at sites of damaged nerves, offering a promising platform for neuroprotective treatment of these conditions. In this study, nanomedicines and nanomedicine-based novel strategies for pharmacotherapy of glaucoma were reviewed, including liposomes, niosomes, nanoparticles, and dendrimers. This article intends to offer a comprehensive review of frontier progresses as well as hotspots and issues that appeared in the field of nanomedicines, which may enable a practical flourish in the future. STATEMENT OF SIGNIFICANCE: Recent novel pharmaceutical strategies toward glaucoma, a chronic blinding ocular disease that currently requires frequent daily dosage regimen, based on nanomedicines and nanomaterials have been comprehensively reviewed in this manuscript. The collection of field hotspots and issues in the late years should offer a quick grasp of the general concept and up-to-date threads upon the refinement of existing treatment patterns for glaucoma. Meanwhile, the Conclusion and Future Perspective section given at the end of the text brings out the possible shortages and opinions in terms of ideal research direction, which hopefully could facilitate a future practical flourish in the area.

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.

Digital Pharmaceutical Sciences

Artificial intelligence (AI) and machine learning, in particular, have gained significant interest in many fields, including pharmaceutical sciences. The enormous growth of data from several sources, the recent advances in various analytical tools, and the continuous developments in machine learning algorithms have resulted in a rapid increase in new machine learning applications in different areas of pharmaceutical sciences. This review summarizes the past, present, and potential future impacts of machine learning technologies on different areas of pharmaceutical sciences, including drug design and discovery, preformulation, and formulation. The machine learning methods commonly used in pharmaceutical sciences are discussed, with a specific emphasis on artificial neural networks due to their capability to model the nonlinear relationships that are commonly encountered in pharmaceutical research. AI and machine learning technologies in common day-to-day pharma needs as well as industrial and regulatory insights are reviewed. Beyond traditional potentials of implementing digital technologies using machine learning in the development of more efficient, fast, and economical solutions in pharmaceutical sciences are also discussed.

Chitosan and its Derivatives for Ocular Delivery Formulations: Recent Advances and Developments

Chitosan (CS) is a hemi-synthetic cationic linear polysaccharide produced by the deacetylation of chitin. CS is non-toxic, highly biocompatible, and biodegradable, and it has a low immunogenicity. Additionally, CS has inherent antibacterial properties and a mucoadhesive character and can disrupt epithelial tight junctions, thus acting as a permeability enhancer. As such, CS and its derivatives are well-suited for the challenging field of ocular drug delivery. In the present review article, we will discuss the properties of CS that contribute to its successful application in ocular delivery before reviewing the latest advances in the use of CS for the development of novel ophthalmic delivery systems. Colloidal nanocarriers (nanoparticles, micelles, liposomes) will be presented, followed by CS gels and lenses and ocular inserts. Finally, instances of CS coatings, aiming at conferring mucoadhesiveness to other matrixes, will be presented.

Formulation of Chitosan Polymeric Vesicles of Ciprofloxacin for Ocular Delivery: Box-Behnken Optimization, In Vitro Characterization, HET-CAM Irritation, and Antimicrobial Assessment

Ciprofloxacin is a commonly used antibiotic for treatment of bacterial conjunctivitis. The conventional eye drop dosage form is the widely used mode of treatment, but it has low corneal residence time. This drawback can be overcome by developing a bioadhesive noisome system (chitosan-coated) for enhanced corneal residence time. The niosomes were prepared by thin-film hydration technique and optimized by using Box-Behnken statistical design software. Cholesterol (A), Span 60 (B), and sonication time (C) were selected as independent variables, whereas vesicle size (Y₁ in nm), entrapment efficiency (Y₂ in %), and drug release (Y₃ in %) were chosen as dependent variables. The vesicle size, entrapment efficiency, and drug release of optimized CIP niosomes (CIP-NSMopt) were found to be 180.34 ± 5.13 nm, 78.32 ± 4.49%, and 82.87 ± 4.01% (in 12 h), respectively. Further CIP-NSMopt was coated with different chitosan concentrations (0.1 to 0.3%) to enhance mucoadhesion. Finally, optimized chitosan-coated niosomes (chitosomes; CIP-CHTopt) showed a vesicle size of 210.65 ± 2.76 nm, zeta potential of - 35.17 ± 2.25Mv, and PDI of 0.221. CIP-CHTopt exhibited sustained release profile (75.31% in 12 h) with the Korsmeyer-Peppas kinetic model (R² = 0.980). The permeation study showed 1.79-fold enhancements in corneal permeation compared with marketed CIP eye drop. The hen's egg chorioallantoic membrane (HET-CAM) study showed 0 scores (no irritation), and it was further confirmed by corneal hydration and histopathology study. The antimicrobial study exhibited a significant high zone (P < 0.05) of inhibition against tested organism. Our findings demonstrated that chitosan-coated niosomes are a promising drug carrier to enhance corneal contact time and treatment of bacterial conjunctivitis.

Preparation and study of two kinds of ophthalmic nano-preparations of everolimus

Objective: To prepare everolimus nanoformulations and increase their solubility to suit their application in the eye. Methods: The everolimus micelles was prepared by thin film dispersion method using Tween-80 (P80) and polyoxyethylene stearate (P40S) as carriers. In addition, the everolimus nanosuspension was prepared by injection method using poloxamer 407 (P407), hydroxypropyl methylcellulose (HPMC) and polyvinyl alcohol (PVA) as stabilizers. It was characterized in terms of particle size, PDI and encapsulation efficiency or drug loading. The in vitro release and in vitro rabbit scleral permeability characteristics were investigated, and the pharmacokinetics of anterior chamber drug in rabbit eyes were studied. Results: The average particle size of the micelles was (8.74 ± 0.21) nm, the encapsulation efficiency and drug loading were (90.12 ± 1.18)% and (2.14 ± 0.028)%, while the average particle size of the nanosuspension was (156.47 ± 1.10) nm, and the drug loading was (16.51 ± 0.21)%, respectively. Both in vitro release and rabbit scleral permeation models were consistent with the Higuchi equation. The pharmacokinetic experiments of aqueous humor showed that area under the curve of everolimus nanosuspension was about 3 times higher than that of micelles. Micelles could be achieved in the eye and maintained for a long time. Conclusion: The preparation of everolimus micelles or nanosuspension for eye are suitable for ocular administration and expected to be new dosage form for corneal transplantation immunological rejection or other ocular disease.

Phenylboronic acid-tethered chondroitin sulfate-based mucoadhesive nanostructured lipid carriers for the treatment of dry eye syndrome

Dry eye syndrome is a common eye disease that affects many people worldwide. It is usually treated with eye drops, which has low bioavailability owing to rapid clearance from the ocular surface and leads to poor patient compliance and side effects. For the purpose of improving the therapeutic efficacy, nanostructured lipid carrier (NLC)-loaded dexamethasone (DEX) was prepared and functionalized with (3-aminomethylphenyl)boronic acid-conjugated chondroitin sulfate (APBA-ChS). As APBA has a boronic acid group, it can form a high-affinity complex with sialic acids present in the ocular mucin, which contributes to extension of corneal retention time and improvement of drug delivery. Compared with eye drops, Rhodamine B (RhB)-labeled APBA-ChS-NLC could significantly prolong the residence time on the corneal surface. Moreover, the DEX-APBA-ChS-NLC showed no irritation to the rabbit eye as indicated in irritation studies and histological images. The pharmacodynamics study indicated that DEX-APBA-ChS-NLC could relieve symptoms of dry eye disease in rabbits. These results demonstrated that the developed mucoadhesive drug carrier could improve the delivery of drugs and have promising potential to treat anterior eye diseases. STATEMENT OF SIGNIFICANCE: In this research, (3-aminomethylphenyl)boronic acid-conjugated chondroitin sulfate (APBA-ChS)-based nanostructured lipid carriers (NLCs) including dexamethasone (DEX) were designed and constructed. APBA-ChS, which is present on the surface of DEX-NLC and contains the boronic acid group, can form complex with sialic acids in the ocular mucin, hence leading to prolonged precorneal retention. This affinity between boronic acid and sialic acids was used to develop a mucoadhesive drug delivery system. The developed mucoadhesive drug carrier demonstrated prolonged retention time and alleviation of dry eye syndrome. APBA-ChS-based NLC may be considered a promising ocular drug delivery system for treating anterior eye diseases.