Multivariate data analysis to assess dry powder inhalers performance from powder properties

Co-spray-dried poly-L-lysine with L-leucine as dry powder inhalations for the treatment of pulmonary infection: Moisture-resistance and desirable aerosolization performance

Poly-L-lysine (PLL) is a promising candidate for the treatment of pulmonary infection with lower occurrence of drug-resistance due to its unique antibacterial mechanisms. Dry powder inhalations (DPIs) are considered as the first choice for formulating PLL to treat pulmonary infection on account of direct delivery and satisfactory stability. However, hygroscopicity of PLL limited its therapeutic effect on pulmonary infection when PLL developed into DPIs. The hygroscopicity caused two obstacles including the low drug deposition in the lower respiratory tract and undesirable aerosolization performance deterioration. In this study, PLL was co-spray-dried with L-leucine (LL) to achieve moisture-resistance and desirable aerosolization performance. The ratio of PLL and LL was optimized to obtain particles with different morphology, hygroscopicity and aerodynamic properties. The obtained PLL DPIs were suitable for inhalation with a corrugated surface formed by hydrophobic LL. The anti-hygroscopicity, aerosolization performance and rheological properties of P₂ DPIs were optimal when PLL:LL = 85:15. The DPIs particles were stable after being stored at high relative humidity (60 ± 5%), and their superiority in treating pulmonary infections was also proved by in vitro and in vivo experiments. The established PLL DPIs were proved to be a feasible and desirable approach to treat pulmonary infections.

A real-time and modular approach for quick detection and mechanism exploration of DPIs with different carrier particle sizes

Dry powder inhalers (DPIs) had been widely used in lung diseases on account of direct pulmonary delivery, good drug stability and satisfactory patient compliance. However, an indistinct understanding of pulmonary delivery processes (PDPs) hindered the development of DPIs. Most current evaluation methods explored the PDPs with over-simplified models, leading to uncompleted investigations of the whole or partial PDPs. In the present research, an innovative modular process analysis platform (MPAP) was applied to investigate the detailed mechanisms of each PDP of DPIs with different carrier particle sizes (CPS). The MPAP was composed of a laser particle size analyzer, an inhaler device, an artificial throat and a pre-separator, to investigate the fluidization and dispersion, transportation, detachment and deposition process of DPIs. The release profiles of drug, drug aggregation and carrier were monitored in real-time. The influence of CPS on PDPs and corresponding mechanisms were explored. The powder properties of the carriers were investigated by the optical profiler and Freeman Technology four powder rheometer. The next generation impactor was employed to explore the aerosolization performance of DPIs. The novel MPAP was successfully applied in exploring the comprehensive mechanism of PDPs, which had enormous potential to be used to investigate and develop DPIs.

Probabilistic modeling of an injectable aqueous crystalline suspension using influence networks

Probabilistic modeling using influence networks is an efficient, intuitive, and easy to communicate strategy in the development of complex pharmaceutical products. This study was aimed to use a risk-based approach to explore the complex interactions between product and process design parameters affecting size and shape of the particles in injectable aqueous crystalline suspensions (ACS). Based on a risk assessment, a design of experiments (DOE) was applied to evaluate the most important parameters, i.e., four critical material attributes and two critical process parameters. A model hydrophobic drug (carbamazepine) was milled and homogenized in a multistep process (dispersion and milling steps). The final formulations were characterized with automated at-line image analysis of thousands of individual particles. The particle size and shape distributions were summarized with descriptive parameters, and the relationship of these parameters and the DOE was modeled using influence networks (INs). This approach was compared and contrasted with a classical modeling approach based on multivariate linear regression (MVLR). INs had a superior visual interpretation capability of the complex and multivariate ACS system making the risk-based decision making more accessible. The probability and causality were included in the IN, i.e., the relationships between size and shape. Moreover, IN allowed to incorporate prior knowledge in a systematic way by implementing a 'black and white list'. An IN based model was created with the following model performance: a mean absolute percentage error of 1.7% and 1.1% for the size and 6.2% and 5.0% for the shape, respectively for dispersed and milled ACS. Parameters with the highest and lowest probability to control the critical quality attributes of ACS could be identified. Consequently, the parameter settings giving the optimum particle size and shape could be predicted using a Monte Carlo simulation to calculate the probability of success including the uncertainty of the model. The cubic MVLR model for the size of milled ACS was comparable to the IN in terms of the mean absolute percentage error, i.e., 1.1%. However, IN was more efficient in visualizing the complex and multivariate data set, including all the critical quality attributes and formulation/process parameters of the ACS at the same time. Moreover, the prior knowledge used in probabilistic modeling of IN could be systematically documented.

Exploring the potential influence of drug charge on downstream deposition behaviour of DPI powders

Dry powder inhaler (DPI) development is limited by the time- and labor-consuming in vitro lung deposition test. It's highly desirable to find an easy tool for DPI formulation screening. Dynamic powder rheological properties seem to present many advantages, however, the adoptability needs to be verified. Drug charge is an important parameter especially for DPI formulation design but how it affects the process of pulmonary drug delivery is unavailable. Therefore, the objective of this study is to explore the influence of drug charge on DPI powders, further testing the potentials of powder properties for downstream deposition behavior prediction. Taking five differently charged drugs as model, influence of drug charge on uniformity, rheological and aerodynamic properties of the mixtures were investigated systemically. It was found that mometasone furoate with near neutral charge presented better content homogeneity, while significantly decreased recovery was noted for charged drugs, such as positively charged drug (salbutamol sulphate and indacaterol maleate) mixtures and negatively charged drug (budesonide and fluticasone propionate) mixtures. Moreover, drug charge also influenced flowability and cohesion of their admixture with lactose. As for the downstream deposition, neutral drugs presented higher fine particle fraction (FPF), followed by positively charged drugs and negatively charged drugs. Good correlations between basic flowability energy, aeration energy, Permeability and FPF were established irrespective of different drugs. Principal component analysis results suggested flowability had a greater influence on FPF when mixtures were less cohesive. In conclusion, this study demonstrated drug charge can influence physicochemical, rheological and aerodynamic properties of the admixture, and DPIs' dynamic properties could be used as potential tools to predict downstream deposition with good accuracy.

Partial Least Square Model (PLS) as a Tool to Predict the Diffusion of Steroids Across Artificial Membranes

One of the most challenging goals in modern pharmaceutical research is to develop models that can predict drugs’ behavior, particularly permeability in human tissues. Since the permeability is closely related to the molecular properties, numerous characteristics are necessary in order to develop a reliable predictive tool. The present study attempts to decode the permeability by correlating the apparent permeability coefficient (P_app) of 33 steroids with their properties (physicochemical and structural). The P_app of the molecules was determined by in vitro experiments and the results were plotted as Y variable on a Partial Least Squares (PLS) model, while 37 pharmacokinetic and structural properties were used as X descriptors. The developed model was subjected to internal validation and it tends to be robust with good predictive potential (R²Y = 0.902, RMSEE = 0.00265379, Q²Y = 0.722, RMSEP = 0.0077). Based on the results specific properties (logS, logP, logD, PSA and VD_ss) were proved to be more important than others in terms of drugs P_app. The models can be utilized to predict the permeability of a new candidate drug avoiding needless animal experiments, as well as time and material consuming experiments.

Fragmented particles containing octreotide acetate prepared by spray drying technique for dry powder inhalation

Dry powder inhalers (DPIs) have been proposed as an alternative administration route for protein and peptide drugs. However, DPI particles are easy to aggregate due to the strong interactions between the particles, leading to poor aerosolization performance. In this study, fragmented particles containing octreotide acetate (OA) were prepared by spray drying technique for dry powder inhalation, which were expected to decrease the particle-particle interaction by reducing the contact sites. Mannitol and ammonium carbonate were used as protein stabilizer and fragment-forming agent, respectively. The obtained fragmented particles presented larger particle size, lower density, better dispersibility, and well in vitro aerodynamic behavior (emitted dose > 97%, fine particle fraction ≈ 40%). The circular dichroism spectrum results indicated that OA maintained the stability throughout the spray drying process. The relative bioavailability of dry powder inhalation (DPI) compared with subcutaneous injection of commercial product was up to 88.0%, demonstrating the feasibility of DPI for OA delivery. These results confirmed that the proposed fragmented particles had great potential for pulmonary delivery of protein and peptide drugs in a painless, rapid, and convenient manner.