Predicting the Fine Particle Fraction of Dry Powder Inhalers Using Artificial Neural Networks

Pulmonary Delivery of Antibiotics to the Lungs: Current State and Future Prospects

This paper presents a comprehensive review of the current literature, clinical trials, and products approved for the delivery of antibiotics to the lungs. While there are many literature reports describing potential delivery systems, few of these have translated into marketed products. Key challenges remaining are the high doses required and, for powder formulations, the ability of the inhaler and powder combination to deliver the dose to the correct portion of the respiratory tract for maximum effect. Side effects, safety concerns, and disappointing clinical trial results remain barriers to regulatory approval. In this review, we describe some possible approaches to address these issues and highlight prospects in this area.

Advancing Pharmaceutical Science with Artificial Neural Networks: A Review on Optimizing Drug Delivery Systems Formulation

Drug Delivery Systems (DDS) have been developed to address the challenges associated with traditional drug delivery methods. These DDS aim to improve drug administration, enhance patient compliance, reduce side effects, and optimize target therapy. To achieve these goals, it is crucial to design DDS with optimal performance characteristics. The final properties of a DDS are determined by several factors that go into formulating a pharmaceutical preparation. Thus, optimizing these factors can lead to the ideal DDS formulation. Artificial Neural Networks (ANN) are computational models that mimic the function of biological neurons and neural networks and perform mathematical operations on inputs to generate outputs. ANN is widely used in medical sciences for modeling disease diagnosis and treatment, dose adjustment in combination therapy, medical education, and other fields. In the pharmaceutical sciences, ANN has gained significant attention for designing and optimizing pharmaceutical formulations. This article reviews the use of ANN in the design and optimization of pharmaceutical formulations, specifically DDS. Since DDS is highly diverse, different factors are examined for each type of DDS. These factors are considered independent and dependent parameters for each ANN model, and various examples are provided. By utilizing ANN, it is possible to establish the relationship between the formulation factors and the resulting DDS characteristics, ultimately leading to the development of optimized DDS.

Dry Powder Inhaler with the technical and practical obstacles, and forthcoming platform strategies

A Dry Powder Inhaler (DPI) is a technique as well as a device used to inhale formulation which is in the form of dry powder, and is inhaled through the nose or mouth. It was developed for the purpose of treating conditions like chronic obstructive pulmonary disease (COPD), Asthma, and even cystic fibrosis etc. The aim of the review is to discuss the different methods of preparation of dry powders along with the characterization of DPI. Here we present the outline of different methods like supercritical fluid extraction (SCF), spray drying, and milling. The review focussed on various devices including single and multi-dose devices used in the DPI. It also highlights on recent advances in the DPI including nano particulate system, siRNA-based medication, liposomes, and pro-liposomes based delivery. In COVID-19 silver nanoparticles-based DPIs provide very prominent results in the infected lungs. Moreover, this review states that the AI-based DPI development provides and improvement in the bioavailability and effectiveness of the drug along with the role of artificial neural networks (ANN). The study also showed that nasally administered drugs (nose to brain) can easily cross the blood-brain barrier (BBB) and enter the central nervous system (CNS) through the olfactory and trigeminal pathway which provides effective CNS concentrations at lower dosage. It is suggested that DPIs not only target respiratory complications but also treat CNS complications too. This review provides support and guides the researcher in the recent development and evaluation of DPI.

The effects of grid design on the performance of 3D-printed dry powder inhalers

The grid structure is an indispensable part of most dry powder inhalers, but the effects of grid geometry on inhaler performance are rarely reported. This study aims to systemically investigate the influence of grid design on the aerosolization performance of capsule-based inhalers through experiments and computational analysis. In-vitro aerosolization and deposition performance of commercial and 3D-printed customized inhalers with different grid mesh designs were experimentally studied using a Next Generation Impactor (NGI). Flow fields in the inhalers were generated, and average turbulence kinetic energy (TKE) and airstream trajectories were obtained through Computational Fluid Dynamics (CFD) analysis, delineating the effects of the different grid designs. Comparative studies using the commercial inhalers and the 3D-printed inhalers show a slightly better performance for the latter, probably due to the different materials used for the inhalers, confirming the suitability of 3D printing. Experimental results show that intensive grid meshes with a relatively small aperture size are beneficial to enhancing inhaler performance. Computational results illustrate that the intensive grid meshes can reduce vortexed airstreams and increase turbulent kinetic energy at the grids in general, which also supports the experimental results. In summary, inhalers with intensive grid meshes are preferred for capsule-based inhalers to enhance aerosolization performance. These findings have significant implications for the comprehensive understanding of how grid designs influence inhaler performance.

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.

In vitro-in vivo correlation of cascade impactor data for orally inhaled pharmaceutical aerosols

In vitro-in vivo correlation is the establishment of a predictive relationship between in vitro and in vivo data. In the context of cascade impactor results of orally inhaled pharmaceutical aerosols, this involves the linking of parameters such as the emitted dose, fine particle dose, fine particle fraction, and mass median aerodynamic diameter to in vivo lung deposition from scintigraphy data. If the dissolution and absorption processes after deposition are adequately understood, the correlation may be extended to the pharmacokinetics and pharmacodynamics of the delivered drugs. Correlation of impactor data to lung deposition is a relatively new research area that has been gaining recent interest. Although few in number, experiments and meta-analyses have been conducted to examine such correlations. An artificial neural network approach has also been employed to analyse the complex relationships between multiple factors and responses. However, much research is needed to generate more data to obtain robust correlations. These predictive models will be useful in improving the efficiency in product development by reducing the need of expensive and lengthy clinical trials.

Analysis of microparticle deposition in the human lung by taguchi method and response surface methodology

The deposition phenomenon of microparticle and SAR-CoV-2 laced bioaerosol in human airways is studied by Taguchi methods and response surface methodology (RSM). The data used herein is obtained from simulations of airflow dynamics and deposition fractions of drug particle aerosols in the downstream airways of asthma patients using computational fluid dynamics (CFD) and discrete particle motion (DPM). Three main parameters, including airflow rate, drug dose, and particle size, affecting aerosol deposition in the lungs of asthma patients are examined. The highest deposition fraction (DF) is obtained at the flow rate of 45 L min^-1, the drug dose of 200 μg·puff^-1, and the particle diameter of 5 μm. The optimized combination of levels for the three parameters for maximum drug deposition is performed via the Taguchi method. The importance of the influencing factors rank as particle size > drug dose > flow rate. RSM reveals that the combination of 30 L min^-1, 5 μm, 200 μg·puff^- has the highest deposition fraction. In part, this research also studied the deposition of bioaerosols contaminated with the SAR-CoV-2 virus, and their lowest DF is 1.15%. The low DF of bioaerosols reduces the probability of the SAR-CoV-2 virus transmission.

Comprehensive evaluation of the combined extracts of Epimedii Folium and Ligustri Lucidi Fructus for PMOP in ovariectomized rats based on MLP-ANN methods

ETHNOPHARMACOLOGICAL RELEVANCE

Kidney deficiency is the main pathogenesis of osteoporosis based on the theory of "kidney governing bones" in traditional Chinese medicine (TCM). Osteoporosis is a systemic disease; kidney deficiency influences the growth, aging and reproduction of human body, reflecting in endocrine, nerve, immunity, metabolism and other functions. Multi-target drugs composed of natural non-toxic products from kidney-reinforcing herbs, are being investigated for the treatment of osteoporosis. Therefore, it is necessary and imperative to develop an objective and comprehensive method to evaluate and compare the effects of herbs with listed drugs.

AIM OF THE STUDY

This study was designed to evaluate and compare the therapeutic effects and the underlying molecular mechanism of the combined extracts of Epimedii Folium and Ligustri Lucidi Fructus (EL) with Raloxifene hydrochloride (RH) in ovariectomy (OVX)-induced postmenopausal osteoporosis (PMOP) rats based on the multi-layer perception (MLP)-artificial neural network (ANN) model.

MATERIALS AND METHODS

Female SD rats were subjected to either sham surgery (n = 8) or bilateral OVX (n = 48). One week after recovering from surgery, the OVX-induced rats were randomly divided into three groups: OVX model group (n = 32, every 8 rats were killed at the end of the 5th, 9th, 11th or 13th week after OVX), EL group (treated with EL 0.35 g/kg, n = 8), and RH group (treated with RH 6.25 mg/kg, n = 8). The rats in the treatment groups were administrated once a day for 12 weeks, then sacrificed. We observed bone mass and quality, bone remodeling, the function of estrogen and TGF-β1/Smads pathway in all rats.

RESULTS

Both EL and RH could increase bone mineral density, enhance bone strength, relieve bone micro-structure degeneration, re-balance bone remodeling, regulate estrogen dysfunction, and up-regulate TGF-β1 expression. The evaluation of the MLP-ANN model showed that EL and RH had markedly anti-PMOP effects, and there was no significant difference in the comprehensive evaluation of anti-osteoporosis between the two drugs. However, RH had better effects on bone mass and quality and TGF-β1/Smads pathway than EL; EL had better effects on estrogen function than RH.

CONCLUSION

Combined extracts of Epimedii Folium and Ligustri Lucidi Fructus (EL) exhibited bone-protective effects on PMOP. The MLP-ANN method evaluated the efficacy of drugs more comprehensively, which provided a new direction for the evaluation and comparison of drugs.

Development of solid lipid microparticles by melt-emulsification/spray-drying processes as carriers for pulmonary drug delivery

The aim of this study was to optimize the parameters of the complex melt-emulsification process coupled with the spray-drying, in order to maintain the balance between solid lipid microparticles (SLMs) powders aerodynamic performance and salbutamol sulfate release rate. Quality target product profile was identified and risk management and principal component analysis were used to guide formulation development. Obtained dry powders for inhalation (DPIs) were evaluated in terms of SLMs size distribution, morphology, true density, drug content, solid state characterization studies, in vitro aerosol performance and in vitro drug release. SLMs micrographs indicated spherical, porous particles. Selected powders showed satisfactory aerosol performance with a mean mass aerodynamic diameter of around 3 μm and acceptable fine particle fraction (FPF). Addition of trehalose positively affected SLMs aerodynamic properties. The results of in vitro dissolution testing indicated that salbutamol sulfate release from the tested SLMs formulations was modified, in comparison to the raw drug release. In conclusion, SLMs in a form of DPIs were successfully developed and numerous factors that affects SLMs properties were identified in this study. Further research is required for full understanding of each factor's influence on SLMs properties and optimization of DPIs with maximized FPFs.

Novel Approaches for the Treatment of Pulmonary Tuberculosis

Tuberculosis (TB) is a contagious airborne disease caused by Mycobacterium tuberculosis, which primarily affects human lungs. The progression of drug-susceptible TB to drug-resistant strains, MDR-TB and XDR-TB, has become worldwide challenge in eliminating TB. The limitations of conventional TB treatment including frequent dosing and prolonged treatment, which results in patient’s noncompliance to the treatment because of treatment-related adverse effects. The non-invasive pulmonary drug administration provides the advantages of targeted-site delivery and avoids first-pass metabolism, which reduced the dose requirement and systemic adverse effects of the therapeutics. With the modification of the drugs with advanced carriers, the formulations may possess sustained released property, which helps in reducing the dosing frequency and enhanced patients’ compliances. The dry powder inhaler formulation is easy to handle and storage as it is relatively stable compared to liquids and suspension. This review mainly highlights the aerosolization properties of dry powder inhalable formulations with different anti-TB agents to understand and estimate the deposition manner of the drug in the lungs. Moreover, the safety profile of the novel dry powder inhaler formulations has been discussed. The results of the studies demonstrated that dry powder inhaler formulation has the potential in enhancing treatment efficacy.

A dry powder inhalable formulation of salvianolic acids for the treatment of pulmonary fibrosis: safety, lung deposition, and pharmacokinetic study

Salvianolic acids (SAL), the main bioactive component extracted from Salvia miltiorrhiza, is a natural product with a reported anti-pulmonary fibrosis (PF) effect. SAL is commonly administrated orally; however, it has a low oral bioavailability (less than 5%). The objective of this work was to develop a new dry powder inhalable formulation intended to facilitate the access of SAL to the target place. We prepared the new SAL powder formulation containing L-arginine and 2% of lecithin using the ball milling technique. L-arginine was used to regulate the strong acidity of the SAL solution, and lecithin was added to disperse the powder and improve the flowability. The resulting powder had a content in salvianolic acid B (SALB, the main active principle of SAL) of 66.67%, a particle size of less than 5 μm and a good flowability. In vivo fluorescence imaging showed that the powder could be successfully aerosolized and delivered to the lung. The acute lung irritation study proved that the presence of L-arginine improved the biocompatibility of the powder. Finally, according to the pharmacokinetic study, the new SAL powder formulation was found to significantly increase drug concentration in the lung and the bioavailability. In conclusion, the new dry powder inhalable formulation of SAL developed in this study could be a strategy to enhance the performance of SAL at the lung level. Graphical abstract.

Contemporary Formulation Development for Inhaled Pharmaceuticals

Pulmonary delivery has gained increased interests over the past few decades. For respiratory conditions, targeted drug delivery directly to the site of action can achieve a high local concentration for efficacy with reduced systemic exposure and adverse effects. For systemic conditions, the unique physiology of the lung evolutionarily designed for rapid gaseous exchange presents an entry route for systemic drug delivery. Although the development of inhaled formulations has come a long way over the last few decades, many aspects of it remain to be elucidated. In particular, a reliable and well-understood method for in vitro-in vivo correlations remains to be established. With the rapid and ongoing advancement of technology, there is much potential to better utilise computational methods including different types of modelling and simulation approaches to support inhaled formulation development. This review intends to provide an introduction on some fundamental concepts in pulmonary drug delivery and inhaled formulation development followed by discussions on some challenges and opportunities in the translation of inhaled pharmaceuticals from preclinical studies to clinical development. The review concludes with some recent advancements in modelling and simulation approaches that could play an increasingly important role in modern formulation development of inhaled pharmaceuticals.

Prediction of Aerosol Deposition in the Human Respiratory Tract via Computational Models: A Review with Recent Updates

The measurement of deposited aerosol particles in the respiratory tract via in vivo and in vitro approaches is difficult due to those approaches’ many limitations. In order to overcome these obstacles, different computational models have been developed to predict the deposition of aerosol particles inside the lung. Recently, some remarkable models have been developed based on conventional semi-empirical models, one-dimensional whole-lung models, three-dimensional computational fluid dynamics models, and artificial neural networks for the prediction of aerosol-particle deposition with a high accuracy relative to experimental data. However, these models still have some disadvantages that should be overcome shortly. In this paper, we take a closer look at the current research trends as well as the future directions of this research area.

Delineating the effects of hot-melt extrusion on the performance of a polymeric film using artificial neural networks and an evolutionary algorithm

The aim of this study was to utilize an artificial neural network (ANN) in conjunction with an evolutionary algorithm to investigate the relationship between hot melt extrusion (HME) process parameters and vaginal film performance. Investigated HME process parameters were: barrel temperature, screw speed, and feed rate. Investigated film performance attributes were: percent dissolution at 30 min, puncture strength, and drug content. An ANN model was successfully developed and validated with a root mean squared error of 0.043 and 0.098 for training and validation, respectively. Of all three assessed process parameters, the model revealed that barrel temperature has a significant impact on film performance. An increase in barrel temperature resulted in increased dissolution and punctures strength and decreased drug content. Additionally, a successful implementation of an evolutionary algorithm was carried out in order to demonstrate the potential applicability of the developed ANN model in film formulation optimization. In this analysis, the values predicted of film performance attributes were within 1% error of the experimental data. The findings of this study provide a quantitative framework to understand the relationship between HME parameters and film performance. This quantitative framework has the potential to be used for film formulation development and optimization.

Predicting postoperative facial swelling following impacted mandibular third molars extraction by using artificial neural networks evaluation

Patients' postoperative facial swelling following third molars extraction may have both biological impacts and social impacts. The purpose of this study was to evaluate the accuracy of artificial neural networks in the prediction of the postoperative facial swelling following the impacted mandibular third molars extraction. The improved conjugate grads BP algorithm combining with adaptive BP algorithm and conjugate gradient BP algorithm together was used. In this neural networks model, the functional projective relationship was established among patient's personal factors, anatomy factors of third molars and factors of surgical procedure to facial swelling following impacted mandibular third molars extraction. This neural networks model was trained and tested based on the data from 400 patients, in which 300 patients were made as the training samples, and another100 patients were assigned as the test samples. The improved conjugate grads BP algorithm was able to not only avoid the problem of local minimum effectively, but also improve the networks training speed greatly. 5-fold cross-validation was used to get a better sense of the predictive accuracy of the neural network and early stopping was used to improve generalization. The accuracy of this model was 98.00% for the prediction of facial swelling following impacted mandibular third molars extraction. This artificial intelligence model is approved as an accurate method for prediction of the facial swelling following impacted mandibular third molars extraction.