Machine Learning-Guided Prediction of Formulation Performance in Inhalable Ciprofloxacin-Bile Acid Dispersions with Antimicrobial and Toxicity Evaluation.

Ciprofloxacin (CFX) is a potent antibiotic for respiratory infections, but its poor solubility and high crystallinity limit its effectiveness in dry powder inhaler (DPI) delivery. Although soluble forms such as CFX hydrochloride are available, their rapid dissolution may lead to systemic absorption, undermining localized lung targeting. To address this, we developed solid dispersions of CFX with primary bile acids, namely, cholic acid (CA) and chenodeoxycholic acid (CDA), using spray drying and ball milling to enhance solubility in a controlled manner while maintaining deposition in the lungs. Differential scanning calorimetry showed glass-transition temperature (T_g) values were elevated for both bile acids, with CA dispersions showing slightly higher absolute values (114.16-131.77 °C vs 109.13-120.67 °C). However, Fourier transform infrared and dissolution data indicated that CDA formed stronger directional hydrogen bonding with CFX. X-ray diffraction confirmed partially amorphous dispersions with minimal residual crystallinity. Solubility enhancement was observed for both bile acids, showing slightly higher values with CA dispersions. Aerodynamic assessments using an Andersen cascade impactor revealed improved lung deposition with CFX-CDA, with a higher fine particle fraction (FPF: 30.81%) and lower mass median aerodynamic diameter (MMAD: 5.89 μm) compared to CFX-CA (FPF: 26.93%, MMAD: 6.19 μm). The emitted dose was highest in CDA with nearly 5 mg compared to CA dispersions (∼3 mg). In vitro antimicrobial studies showed that dispersions maintained comparable antimicrobial activity to pure CFX, while in vivo toxicology in rats indicated mild, dose-dependent hepatic changes. CDA formulations showed AST elevation at a low dose and ALP increase at a high dose, consistent with the known hepatic effects of this bile acid, while CA formulations were broadly comparable to pure CFX. Machine learning algorithms, including tree-based models and neural networks, were used to predict the formulation performance and identify critical variables. Feature selection was achieved using recursive elimination, and permutation analysis showed that the bile acid type, inlet temperature, and molar ratio were the most influential predictors of solubility and lung deposition. Models such as gradient boosting and elastic net showed a high predictive accuracy (R² > 0.85). Overall, this study highlights the potential of primary bile acid-based DPI formulations as effective inhalable antibiotic therapies.