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Recurrent respiratory papillomatosis (RRP) is a chronic condition primarily affecting children, known as juvenile onset RRP (JORRP), caused by a viral infection. Antiviral medications have been used to reduce the need for frequent surgeries, slow the growth of papillomata, and prevent disease spread. Effective treatment of JORRP necessitates targeted drug delivery (TDD) to ensure that inhaled aerosolized drugs reach specific sites, such as the larynx and glottis, without harming healthy tissues. Using computational fluid particle dynamics (CFPD) and machine learning (ML), this study (1) investigated how drug properties and individual factors influence TDD efficiency for JORRP treatment and (2) developed personalized inhalation therapy using an ML-empowered smart inhaler control algorithm for precise medication release. This algorithm optimizes the inhaler nozzle position and diameter based on drug and patient-specific data, enhancing drug delivery to the larynx and glottis. CFPD simulations show that particle size significantly affects deposition fractions in the upper airway, emphasizing the importance of particle size selection. Additionally, optimal nozzle diameter and delivery efficiency depend on particle size, inhalation flow rate, and release time. The ML-based TDD strategy, employing a classification and regression tree model, outperforms conventional inhalation therapy by achieving a higher delivery efficiency to the larynx and glottis. This innovative concept of an ML-empowered smart inhaler represents a promising step toward personalized and precise pulmonary healthcare through inhalation therapy. It demonstrates the potential of AI-driven smart inhalers for improving the treatment outcomes of lung diseases that require TDD at designated lung sites. 

Tracheobronchial tumors, while uncommon, are often malignant in adults. Surgical removal is the primary therapy for non-metastatic lung malignancies, but it is only possible in a small percentage of non-small-cell lung cancer patients and is limited by the number and location of tumors, as well as the patient’s overall health. This study proposes an alternative treatment: administering aerosolized chemotherapeutic particles via the pulmonary route using endotracheal catheters to target lung tumors. To improve delivery efficiency to the lesion, it is essential to understand local drug deposition and particle transport dynamics. This study uses an experimentally validated computational fluid particle dynamics (CFPD) model to simulate the transport and deposition of inhaled chemotherapeutic particles in a 3-dimensional tracheobronchial tree with 10 generations (G). Based on the particle release maps, targeted drug delivery strategies are proposed to enhance particle deposition at two lung tumor sites in G10. Results indicate that controlled drug release can improve particle delivery efficiencies at both targeted regions. The use of endotracheal catheters significantly affects particle delivery efficiencies in targeted tumors. The parametric analysis shows that using smaller catheters can deliver more than 74% of particles to targeted tumor sites, depending on the location of the tumor and the catheter diameter used, compared to less than 1% using conventional particle administration methods. Furthermore, the results indicate that particle release time has a significant impact on particle deposition under the same inhalation profile. This study serves as a first step in understanding the impact of catheter diameter on localized endotracheal injection for targeting tumors in small lung airways.