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Journal of Aerosol Medicine and... Feb 2021Patterns of regional aerosol deposition within the lungs are known to vary in a predictable manner with a number of factors, most notably aerodynamic particle size and...
Patterns of regional aerosol deposition within the lungs are known to vary in a predictable manner with a number of factors, most notably aerodynamic particle size and inhalation pattern. Targeting deposition involves the intentional manipulation of one or more of these factors to promote aerosol deposition in certain locations within the respiratory tract. This section will begin by exploring existing evidence supporting the need to target regional deposition. Thereafter, various approaches to targeting will be introduced. In addition to control of aerodynamic particle size and inhalation pattern, a collection of approaches are available through which to passively target deposition to more central or peripheral lung regions. These include the delivery of short aerosol boluses at prescribed time points in inhalation, control of transient hygroscopic aerosol size changes during transport through the respiratory tract, and use of alternative carrier gas mixtures such as helium/oxygen mixtures. Comparatively, targeting aerosol deposition locally to very precise, spatially-defined lung regions is in its infancy. Early, exploratory techniques used for local targeting will be described. The continued evolution of deposition targeting towards ever more specific locations within the lungs is required to explore fundamental research questions in aerosol medicine: namely, how precise does targeting need to be before additional refinement fails to produce appreciably different therapeutic effects, and which nascent applications of aerosols in medicine might benefit from more selective regional targeting?
Topics: Administration, Inhalation; Aerosols; Lung; Particle Size
PubMed: 33325789
DOI: 10.1089/jamp.2021.29033.am -
Recent Advances in Drug Delivery and... 2022
Topics: Powders; Particle Size; Chemistry, Pharmaceutical; Pharmacy
PubMed: 35794746
DOI: 10.2174/2667387816666220704124635 -
International Journal of Pharmaceutics Sep 2021A continuous polymeric micelle processing platform was successfully developed, which eliminated batch-to-batch variation in critical quality attributes (for example,...
A continuous polymeric micelle processing platform was successfully developed, which eliminated batch-to-batch variation in critical quality attributes (for example, size and polydispersity that are typically associated with batch processing). A continuous precipitation process was achieved via coaxial turbulent jet in co-flow technology allowing precise control of particle size with average particle size in the range 15 to 70 nm and low polydispersity. Critical relationships between material attributes (e.g., block copolymer design), process parameters (e.g., polymer concentration, organic to aqueous flow rate ratios, and temperature), and critical quality attributes (e.g., size and polydispersity) of the polymeric micelles were realized via multiple designs of experiments studies. Both polymer molecular weight and concentration were shown to influence the micelle polydispersity index. Notably, higher molecular weight polymer required higher processing temperatures to produce monodispersed particles and were generally of larger size. Using optimized conditions, paclitaxel polymeric micelles that are qualitatively and quantitatively equivalent to commercial Genexol PM were produced, exhibiting comparable quality attributes including particle size, size distribution, morphology, drug loading, release characteristics, and stability. Lastly, a dynamic light scattering method was adapted to determine the critical micelle concentration and aggregation number of the block copolymers, providing useful information about the raw material.
Topics: Dynamic Light Scattering; Micelles; Paclitaxel; Particle Size; Polymers
PubMed: 34333023
DOI: 10.1016/j.ijpharm.2021.120946 -
International Journal of Pharmaceutics Jun 2022Poor bioavailability and aqueous solubility represent a major constraint during the development of new API molecules and can influence the impact of new medicines or... (Review)
Review
Poor bioavailability and aqueous solubility represent a major constraint during the development of new API molecules and can influence the impact of new medicines or halt their approval to the market. Cocrystals offer a novel and competitive advantage over other conventional methods with respect towards the substantial improvement in solubility profiles relative to the single-API crystals. Furthermore, the production of such cocrystals through atomization-based methods allow for greater control, with respect to particle size reduction, to further increase the solubility of the API. Such atomization-based methods include supercritical fluid methods, conventional spray drying and electrohydrodynamic atomization/electrospraying. The influence of process parameters such as solution flow rates, pressure and solution concentration, in controlling the solid-state and final particle size are discussed in this review with respect to atomization-based methods. For the last decade, literature has been attempting to catch-up with new regulatory rulings regarding the classification of cocrystals, due in part to data sparsity. In recent years, there has been an increase in cocrystal publications, specifically employing atomization-based methods. This review considers the benefits to employing atomization-based methods for the generation of pharmaceutical cocrystals, examines the most recent regulatory changes regarding cocrystals and provides an outlook towards the future of this field.
Topics: Biological Availability; Crystallization; Particle Size; Pharmaceutical Preparations; Solubility
PubMed: 35525471
DOI: 10.1016/j.ijpharm.2022.121798 -
The Science of the Total Environment May 2022There are strong indications that exposure to ultrafine particles (UFP) (mobility diameters ≤100 nm) can induce adverse health effects. UFP can be present in the... (Review)
Review
There are strong indications that exposure to ultrafine particles (UFP) (mobility diameters ≤100 nm) can induce adverse health effects. UFP can be present in the atmosphere through direct emissions such as from motor vehicles or through new particle formation events. To be able to develop control strategies or to provide source specific exposure metrics, it is possible to perform source apportionments using particle number size distributions. Thus, this study has searched the literature for all papers reporting source apportionments based on particle size distributions and compiled them into a database of all published studies. Typically reported sources include nucleation, several traffic sources, space heating, secondary inorganic aerosol, and particles associated with oxidants as represented by ozone. Nucleation and traffic typically dominated the particle number concentrations.
Topics: Air Pollutants; Environmental Monitoring; Particle Size; Particulate Matter; Vehicle Emissions
PubMed: 35038523
DOI: 10.1016/j.scitotenv.2022.153104 -
PDA Journal of Pharmaceutical Science... 2021Traditional statistical analyses of subvisible particle data are usually based on either descriptive statistics, normal-based methods, or standard Poisson models. These...
Traditional statistical analyses of subvisible particle data are usually based on either descriptive statistics, normal-based methods, or standard Poisson models. These methods often do not adequately describe the counts or particle size distribution. They usually ignore relevant information represented in the data, such as count correlation. Therefore, any meaningful analyses of subvisible particle data require a reasonable representation of counts and particle size distribution and the correlation in the data. Such comprehensive approaches are not widely available or used when analyzing subvisible particle data. In this article, we propose the use of generalized linear mixed models to analyze the counts and the particle size distribution of subvisible particle data. These models make optimal use of the information in the data and allow flexible approaches for the analyses of a wide range of data structures. They are readily accessible to practitioners through the use of modern statistical software. These models are demonstrated with two numerical examples using two different data structures.
Topics: Linear Models; Models, Statistical; Particle Size
PubMed: 33199515
DOI: 10.5731/pdajpst.2020.011510 -
Journal of Aerosol Medicine and... Dec 2021Particle size measurement of aerosolized particles from orally inhaled and nasal drug products (OINDPs) can be used to assess the likely deposition distribution in the...
Particle size measurement of aerosolized particles from orally inhaled and nasal drug products (OINDPs) can be used to assess the likely deposition distribution in the human respiratory tract (HRT). Size is normally expressed in terms of aerodynamic diameter, since this scale directly relates to the mechanics of particle transport from inhaler to deposition locations. The multistage cascade impactor (CI) is the principal apparatus used to size fractionate aerosols in terms of their aerodynamic particle size distributions (APSDs). Clinically meaningful metrics, such as fine and coarse particle mass fractions, can be determined from the cumulative mass-weighted APSD. In effective data analysis (EDA), CI data are reduced to small and large particle mass. The sum and ratio of these metrics are used to characterize impactor-sized mass, without the need for stage groupings or other APSD interpretation. Aerosol characterization by full-resolution CI is complex, and so, an abbreviated impactor measurement has recently come to prominence. Here, multiple stages of the CI are reduced to just one or two size fractionating stages so that measures of fine (and extrafine) particle mass from a two-stage system can be directly determined without the need to group the mass of active pharmaceutical ingredient (API) on adjacent stages. Time-of-flight-based methods determine APSD more rapidly but require refinements such as single-particle mass spectroscopy to relate size measurements to API content. Alternatives for size characterizing OINDP aerosols are few; laser diffractometry is by far the most important, especially for nasal sprays and solution-based orally inhaled formulations in which there is no confounding of data from suspended excipient(s). Laser-phase Doppler anemometry (L-PDA) has also been shown to be useful for nasal sprays. If aerodynamic size-related information is not a priority, optical microscopy combined with Raman chemical imaging offers prospects for separate determination of API components in combination product-generated aerosols.
Topics: Administration, Inhalation; Aerosolized Particles and Droplets; Aerosols; Equipment Design; Humans; Particle Size; Quality Control; Technology, Pharmaceutical
PubMed: 34860563
DOI: 10.1089/jamp.2021.29047.jpm -
Journal of Aerosol Medicine and... Aug 2021Of the various particle properties that affect deposition in the respiratory tract, particle diameter and particle density are the most commonly considered, since their... (Review)
Review
Of the various particle properties that affect deposition in the respiratory tract, particle diameter and particle density are the most commonly considered, since their effect on deposition is well known and important, as has been discussed earlier in this chapter. However, there are several other particle properties that can affect particle deposition in the lungs. These include: 1) electrostatic charge on the particle, which can cause electrostatic forces to enhance deposition; 2) the shape of the particle, which can cause its trajectory to differ from that of a spherical particle and thereby alter its deposition; and 3) volatility of the particle i.e., its ability to condense or evaporate at its surface, which can change its diameter and in turn affect its deposition. In this section, we examine each of these three factors individually.
Topics: Administration, Inhalation; Aerosols; Lung; Particle Size
PubMed: 34264776
DOI: 10.1089/jamp.2021.29040.whf -
ACS Nano Jul 2019Living cells achieve precise control of shape and size through sophisticated biochemical machinery. However, such precision is extremely challenging to emulate in... (Review)
Review
Living cells achieve precise control of shape and size through sophisticated biochemical machinery. However, such precision is extremely challenging to emulate in artificial cellular compartments. So far, various physicochemical and mechanical interventions have been employed to tailor the dimensions of model systems such as liposomes, emulsions, coacervates, and polymer capsules. In this Perspective, we discuss the state of the art in artificial cell research in controlling shape and size and the challenges that need to be addressed.
Topics: Artificial Cells; Particle Size; Surface Properties
PubMed: 31298028
DOI: 10.1021/acsnano.9b05112 -
Journal of Controlled Release :... Jun 2020Nanocrystals have exhibited great advantage for enhancing the dissolution rate of water insoluble drugs due to the reduced size to nanoscale. However, current...
Nanocrystals have exhibited great advantage for enhancing the dissolution rate of water insoluble drugs due to the reduced size to nanoscale. However, current pharmaceutical approaches for nanocrystals formulation development highly depend on the expert experience and trial-and-error attempts which remain time and resource consuming. In this research, we utilized machine learning techniques to predict the particle size and polydispersity index (PDI) of nanocrystals. Firstly, 910 nanocrystal size data and 341 PDI data by three preparation methods (ball wet milling (BWM) method, high-pressure homogenization (HPH) method and antisolvent precipitation (ASP) method) were collected for the construction of the prediction models. The results demonstrated that light gradient boosting machine (LightGBM) exhibited well performance for the nanocrystals size and PDI prediction with BWM and HPH methods, but relatively poor predictions for ASP method. The possible reasons for the poor prediction refer to low quality of data because of the poor reproducibility and instability of nanocrystals by ASP method, which also confirm that current commercialized products were mainly manufactured by BWM and HPH approaches. Notably, the contribution of the influence factors was ranked by the LightGBM, which demonstrated that milling time, cycle index and concentration of stabilizer are crucial factors for nanocrystals prepared by BWM, HPH and ASP, respectively. Furthermore, the model generalizations and prediction accuracies of LightGBM were confirmed experimentally by the newly prepared nanocrystals. In conclusion, the machine learning techniques can be successfully utilized for the predictions of nanocrystals prepared by BWM and HPH methods. Our research also reveals a new way for nanotechnology manufacture.
Topics: Drug Compounding; Machine Learning; Nanoparticles; Particle Size; Pharmaceutical Preparations; Reproducibility of Results; Solubility
PubMed: 32234511
DOI: 10.1016/j.jconrel.2020.03.043