Predicting the clinical effect of a short acting bronchodilator in individual patients using artificial neural networks

The Application of Artificial Intelligence in Magnetic Hyperthermia Based Research

The development of nanomedicine involves complex nanomaterial research involving magnetic nanomaterials and their use in magnetic hyperthermia. The selection of the optimal treatment strategies is time-consuming, expensive, unpredictable, and not consistently effective. Delivering personalized therapy that obtains maximal efficiency and minimal side effects is highly important. Thus, Artificial Intelligence (AI) based algorithms provide the opportunity to overcome these crucial issues. In this paper, we briefly overview the significance of the combination of AI-based methods, particularly the Machine Learning (ML) technique, with magnetic hyperthermia. We considered recent publications, reports, protocols, and review papers from Scopus and Web of Science Core Collection databases, considering the PRISMA-S review methodology on applying magnetic nanocarriers in magnetic hyperthermia. An algorithmic performance comparison in terms of their types and accuracy, data availability taking into account their amount, types, and quality was also carried out. Literature shows AI support of these studies from the physicochemical evaluation of nanocarriers, drug development and release, resistance prediction, dosing optimization, the combination of drug selection, pharmacokinetic profile characterization, and outcome prediction to the heat generation estimation. The papers reviewed here clearly illustrate that AI-based solutions can be considered as an effective supporting tool in drug delivery, including optimization and behavior of nanocarriers, both in vitro and in vivo, as well as the delivery process. Moreover, the direction of future research, including the prediction of optimal experiments and data curation initiatives has been indicated.

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.

Pivotal Role of Quantum Dots in the Advancement of Healthcare Research

The quantum dot is a kind of nanoparticle whose dimension is smaller than the size of a typical nanoparticle ranging from tens of nanometers to a few hundredths of nanometers. The quantum mechanical behavior associated with the quantum dot displays different optical and electronic properties, enabling the quantum dot to find potential applications in a multitude of areas such as solar cells, light-emitting diodes, lasers, and biomedical applications. The objective of this investigation is to explore its fundamentals, synthesis, and applications, especially in the healthcare domain. We have discussed the quantum dot synthesis techniques using chemical methods, namely, wet-chemical methods and vapor-phase methods and plasma processing methods, namely, an ion sputtering method and plasma-enhanced chemical vapor deposition method. We have thoroughly investigated the application of quantum dots in imaging, diagnostics, and gene therapy areas. A significant outcome of this review is to propose quantum dots as a new modality in the treatment of cancer and gene therapeutics in the healthcare domain and the potentials of artificial intelligence to improve their performance via the applications of neural networks.

Predicting Lung Deposition of Extrafine Inhaled Corticosteroid-Containing Fixed Combinations in Patients with Chronic Obstructive Pulmonary Disease Using Functional Respiratory Imaging: An In Silico Study

Background: Functional respiratory imaging (FRI) is a computational fluid dynamics-based technique using three-dimensional models of human lungs and formulation profiles to simulate aerosol deposition. Methods: FRI was used to evaluate lung deposition of extrafine beclomethasone dipropionate (BDP)/formoterol fumarate (FF)/glycopyrronium bromide (GB) and extrafine BDP/FF delivered through pressurized metered dose inhalers and to compare results with reference gamma scintigraphy data. FRI combined high-resolution computed tomography scans of 20 patients with moderate-to-severe chronic obstructive pulmonary disease (mean forced expiratory volume in 1 second 42% predicted) with in silico computational flow simulations, and incorporated drug delivery parameters to calculate aerosol airway deposition. Inhalation was simulated using profiles obtained from real-life measurements. Results: Total lung deposition (proportion deposited in intrathoracic region) was similarly high for both products, with mean ± standard deviation (SD) values of 31.0% ± 5.7% and 28.1% ± 5.2% (relative to nominal dose) for BDP/FF/GB and BDP/FF, respectively. Pairwise comparison of the deposition of BDP and FF gave a mean intrathoracic BDP/FF/GB:BDP/FF deposition ratio of 1.10 (p = 0.0405). Mean intrathoracic, central and peripheral deposition ratios for BDP were 1.09 (95% confidence interval [CI]: 1.05-1.14), 0.92 (95% CI: 0.89-0.96), and 1.20 (95% CI: 1.15-1.26), respectively, and for FF were 1.11 (95% CI: 1.07-1.15), 0.94 (95% CI: 0.91-0.98), and 1.21 (95% CI: 1.15-1.27), within the bioequivalence range (0.80-1.25) for intrathoracic and central regions, and slightly exceeding the upper boundary in the peripheral region. Mean ± SD central:peripheral deposition (C:P) was 0.48 ± 0.13 for BDP/FF/GB and 0.62 ± 0.17 for BDP/FF, indicating a higher proportion of drug deposition in the small airways than in the large airways. Conclusion: FRI demonstrated similar deposition patterns for extrafine BDP/FF/GB and BDP/FF, with both having a high lung deposition. Moreover, the deposition patterns of BDP and FF were similar in both products. Furthermore, the C:P ratios of both products indicated a high peripheral deposition, supporting small airway targeting and delivery of these two extrafine fixed combinations, with a small difference in ratios potentially due to mass median aerodynamic diameters.

Enhancing Clinical Translation of Cancer Using Nanoinformatics

Application of drugs in high doses has been required due to the limitations of no specificity, short circulation half-lives, as well as low bioavailability and solubility. Higher toxicity is the result of high dosage administration of drug molecules that increase the side effects of the drugs. Recently, nanomedicine, that is the utilization of nanotechnology in healthcare with clinical applications, has made many advancements in the areas of cancer diagnosis and therapy. To overcome the challenge of patient-specificity as well as time- and dose-dependency of drug administration, artificial intelligence (AI) can be significantly beneficial for optimization of nanomedicine and combinatorial nanotherapy. AI has become a tool for researchers to manage complicated and big data, ranging from achieving complementary results to routine statistical analyses. AI enhances the prediction precision of treatment impact in cancer patients and specify estimation outcomes. Application of AI in nanotechnology leads to a new field of study, i.e., nanoinformatics. Besides, AI can be coupled with nanorobots, as an emerging technology, to develop targeted drug delivery systems. Furthermore, by the advancements in the nanomedicine field, AI-based combination therapy can facilitate the understanding of diagnosis and therapy of the cancer patients. The main objectives of this review are to discuss the current developments, possibilities, and future visions in naoinformatics, for providing more effective treatment for cancer patients.

The significance of artificial intelligence in drug delivery system design

Over the last decade, increasing interest has been attracted towards the application of artificial intelligence (AI) technology for analyzing and interpreting the biological or genetic information, accelerated drug discovery, and identification of the selective small-molecule modulators or rare molecules and prediction of their behavior. Application of the automated workflows and databases for rapid analysis of the huge amounts of data and artificial neural networks (ANNs) for development of the novel hypotheses and treatment strategies, prediction of disease progression, and evaluation of the pharmacological profiles of drug candidates may significantly improve treatment outcomes. Target fishing (TF) by rapid prediction or identification of the biological targets might be of great help for linking targets to the novel compounds. AI and TF methods in association with human expertise may indeed revolutionize the current theranostic strategies, meanwhile, validation approaches are necessary to overcome the potential challenges and ensure higher accuracy. In this review, the significance of AI and TF in the development of drugs and delivery systems and the potential challenging issues have been highlighted.

Biophysical model to predict lung delivery from a dual bronchodilator dry-powder inhaler

A biophysical lung model was designed to predict inhaled drug deposition in patients with obstructive airway disease, and quantitatively investigate sources of deposition variability. Different mouth-throat anatomies at varying simulated inhalation flows were used to calculate the lung dose of indacaterol/glycopyrronium [IND/GLY] 110/50 µg (QVA149) from the dry-powder inhaler Breezhaler^®. Sources of variability in lung dose were studied using computational fluid dynamics, supported by aerosol particle sizing measurements, particle image velocimetry and computed tomography. Anatomical differences in mouth-throat geometries were identified as a major source of inter-subject variability in lung deposition. Lung dose was similar across inhalation flows of 30–120 L/min with a slight drop in calculated delivery at high inspiratory flows. Delivery was relatively unaffected by inhaler inclination angle. The delivered lung dose of the fixed-dose combination IND/GLY matched well with corresponding monotherapy doses. This biophysical model indicates low extra-thoracic drug loss and consistent lung delivery of IND/GLY, independent of inhalation flows. This is an important finding for patients across various ages and lung disease severities. The model provides a quantitative, mechanistic simulation of inhaled therapies that could provide a test system for estimating drug delivery to the lung and complement traditional clinical studies.

The soft computing-based approach to investigate allergic diseases: a systematic review

BACKGROUND

Early recognition of inflammatory markers and their relation to asthma, adverse drug reactions, allergic rhinitis, atopic dermatitis and other allergic diseases is an important goal in allergy. The vast majority of studies in the literature are based on classic statistical methods; however, developments in computational techniques such as soft computing-based approaches hold new promise in this field.

OBJECTIVE

The aim of this manuscript is to systematically review the main soft computing-based techniques such as artificial neural networks, support vector machines, bayesian networks and fuzzy logic to investigate their performances in the field of allergic diseases.

METHODS

The review was conducted following PRISMA guidelines and the protocol was registered within PROSPERO database (CRD42016038894). The research was performed on PubMed and ScienceDirect, covering the period starting from September 1, 1990 through April 19, 2016.

RESULTS

The review included 27 studies related to allergic diseases and soft computing performances. We observed promising results with an overall accuracy of 86.5%, mainly focused on asthmatic disease. The review reveals that soft computing-based approaches are suitable for big data analysis and can be very powerful, especially when dealing with uncertainty and poorly characterized parameters. Furthermore, they can provide valuable support in case of lack of data and entangled cause-effect relationships, which make it difficult to assess the evolution of disease.

CONCLUSIONS

Although most works deal with asthma, we believe the soft computing approach could be a real breakthrough and foster new insights into other allergic diseases as well.

Predicting the physiological response of Tivela stultorum hearts with digoxin from cardiac parameters using artificial neural networks

Multi-layer perceptron artificial neural networks (MLP-ANNs) were used to predict the concentration of digoxin needed to obtain a cardio-activity of specific biophysical parameters in Tivela stultorum hearts. The inputs of the neural networks were the minimum and maximum values of heart contraction force, the time of ventricular filling, the volume used for dilution, heart rate and weight, volume, length and width of the heart, while the output was the digoxin concentration in dilution necessary to obtain a desired physiological response. ANNs were trained, validated and tested with the dataset of the in vivo experiment results. To select the optimal network, predictions for all the dataset for each configuration of ANNs were made, a maximum 5% relative error for the digoxin concentration was set and the diagnostic accuracy of the predictions made was evaluated. The double-layer perceptron had a barely higher performance than the single-layer perceptron; therefore, both had a good predictive ability. The double-layer perceptron was able to obtain the most accurate predictions of digoxin concentration required in the hearts of T. stultorum using MLP-ANNs.

Defining the critical material attributes of lactose monohydrate in carrier based dry powder inhaler formulations using artificial neural networks

The study aimed to establish a function-based relationship between the physical and bulk properties of pre-blended mixtures of fine and coarse lactose grades with the in vitro performance of an adhesive active pharmaceutical ingredient (API). Different grades of micronised and milled lactose (Lactohale (LH) LH300, LH230, LH210 and Sorbolac 400) were pre-blended with coarse grades of lactose (LH100, LH206 and Respitose SV010) at concentrations of 2.5, 5, 10 and 20 wt.%. The bulk and rheological properties and particle size distributions were characterised. The pre-blends were formulated with micronised budesonide and in vitro performance in a Cyclohaler device tested using a next-generation impactor (NGI) at 90 l/min. Correlations between the lactose properties and in vitro performance were established using linear regression and artificial neural network (ANN) analyses. The addition of milled and micronised lactose fines with the coarse lactose had a significant influence on physical and rheological properties of the bulk lactose. Formulations of the different pre-blends with budesonide directly influenced in vitro performance attributes including fine particle fraction, mass median aerodynamic diameter and pre-separator deposition. While linear regression suggested a number of physical and bulk properties may influence in vitro performance, ANN analysis suggested the critical parameters in describing in vitro deposition patterns were the relative concentrations of lactose fines % < 4.5 μm and % < 15 μm. These data suggest that, for an adhesive API, the proportion of fine particles below % < 4.5 μm and % < 15 μm could be used in rational dry powder inhaler formulation design.