1 Division of Undergraduate Medicine, University of Toronto , Toronto, Ontario, Canada .
J Aerosol Med Pulm Drug Deliv. 2013 Dec;26(6):387-96. doi: 10.1089/jamp.2012.0984. Epub 2013 Mar 19.
Predictable delivery of aerosol medication for a given patient and drug-device combination is crucial, both for therapeutic effect and to avoid toxicity. The gold standard for measuring pulmonary drug deposition (PDD) is gamma scintigraphy. However, these techniques expose patients to radiation, are complicated, and are relevant for only one patient and drug-device combination, making them less available. Alternatively, in vitro experiments have been used as a surrogate to estimate in vivo performance, but this is time-consuming and has few "in vitro to in vivo" correlations for therapeutics delivered by inhalation. An alternative method for determining inhaled mass and PDD is proposed by deriving and validating a mathematical model, for the individual breathing patterns of normal subjects and drug-device operating parameters. This model was evaluated for patients with cystic fibrosis (CF).
This study is comprised of three stages: mathematical model derivation, in vitro testing, and in vivo validation. The model was derived from an idealized patient's respiration cycle and the steady-state operating characteristics of a drug-device combination. The model was tested under in vitro dynamic conditions that varied tidal volume, inspiration-to-expiration time, and breaths per minute. This approach was then extended to incorporate additional physiological parameters (dead space, aerodynamic particle size distribution) and validated against in vivo nuclear medicine data in predicting PDD in both normal subjects and those with CF.
The model shows strong agreement with in vitro testing. In vivo testing with normal subjects yielded good agreement, but less agreement for patients with chronic obstructive lung disease and bronchiectasis from CF.
The mathematical model was successful in accommodating a wide range of breathing patterns and drug-device combinations. Furthermore, the model has demonstrated its effectiveness in predicting the amount of aerosol delivered to "normal" subjects. However, challenges remain in predicting deposition in obstructive lung disease.
对于给定的患者和药物设备组合，可预测地输送气溶胶药物至关重要，这既关系到治疗效果，也关系到避免毒性。测量肺部药物沉积（PDD）的金标准是伽马闪烁照相术。然而，这些技术会使患者暴露在辐射下，操作复杂，并且只与一个患者和药物设备组合相关，因此可用性较低。或者，已经使用体外实验作为替代方法来估计体内性能，但这既耗时，而且对于通过吸入给予的治疗药物，其“体外到体内”相关性也很少。提出了一种替代方法，通过推导和验证针对正常受试者的个体呼吸模式和药物设备操作参数的数学模型来确定吸入质量和 PDD。该模型在囊性纤维化（CF）患者中进行了评估。
本研究包括三个阶段：数学模型推导、体外测试和体内验证。该模型是从理想化患者的呼吸周期和药物设备组合的稳态操作特性推导出来的。模型在变化潮气量、吸气至呼气时间和每分钟呼吸次数的体外动态条件下进行了测试。然后，将该方法扩展到纳入其他生理参数（死腔、空气动力学颗粒大小分布），并根据核医学数据验证了该模型在预测正常受试者和 CF 患者的 PDD 方面的预测能力。
该模型与体外测试具有很强的一致性。在正常受试者进行的体内测试中，该模型具有很好的一致性，但对于患有慢性阻塞性肺疾病和支气管扩张症的 CF 患者，一致性较差。
该数学模型成功地适应了广泛的呼吸模式和药物设备组合。此外，该模型已证明其在预测递送到“正常”受试者的气溶胶量方面的有效性。然而，在预测阻塞性肺病中的沉积方面仍然存在挑战。
