Electronic cigarettes can generate multiple carcinogenic substances and damage respiratory epithelial cells. The absorption mechanisms of e-cigarette toxicants throughout the puffing session remain poorly understood owing to ethical constraints associated with subjective experiments. This study provides an alternative computational method that integrates computational fluid dynamics and physiological pharmacokinetic models to predict respiratory retention rates (R_i). The results show that the arithmetic average retention of 35 substances is 62.2 %, whereas the mass-weighted average retention rate is 86.7 %, which is induced by high-mass-fraction toxicants such as glycerol, nicotine, formaldehyde, and acetaldehyde. This suggests that a considerable proportion of e-cigarette compounds is exhaled, thus reflecting the risk of passive smoking. Diffusivity in air (D_a) is not a universal predictor of R_i but is highly relevant for soluble compounds. However, solubility in the watery mucus layer is the primary determinant of R_i for all examined constituents, thus reflecting the logarithmic correlation between R_i and the partition coefficient between mucus and air (P_m:a). We demonstrate the nonlinear relationship between physicochemical properties and respiratory uptake by combining D_a and P_m:a, thereby facilitating the prediction of R_i. Simulation of vaping behavioral factors reveals that exhalation through the nostrils can increase R_i by 7 %–12 % compared with oral-only exhalation owing to more significant substance–tissue interactions in the complex passages of the nasal cavity. This model is promising for future health-risk assessments and regulatory decisions aimed at limiting e-cigarette usage.