Pressurized metered dose inhalers (pMDIs) are widely utilized to manage diseases of the lungs, such as asthma and chronic obstructive pulmonary disease. They can be formulated such that the drug and/or nonvolatile excipients are dissolved or dispersed in the formulation, rendering a solution or suspension formulation, respectively. While the formulation process for solution pMDIs is well defined, the formulation process of pMDIs with any type of suspended entity can be lengthy and empirical. The use of suspended drug or the addition of a second drug or excipient in a suspension pMDI formulation may non-linearly impact the product performance of the drug of interest in the formulation; this requires iterative testing of a series of pMDIs in order to identify a formulation with the most potential for success. One of the primary attributes used to characterize the product performance and quality control of inhaled medications is the residual aerodynamic particle size distribution (APSD) of the aerosolized drug. Along with clinical factors, formulation and device parameters have a significant impact on APSD. In this study, a computational model was developed using the principles of statistics and physical chemistry to predict the residual APSD generated by suspension pMDIs based on formulation, device, and raw drug or excipient substance considerations. The formulations modeled and experimentally evaluated consist of a suspended drug or excipient with/without a dissolved drug or excipient in a cosolvent-propellant system. The in silico model enables modeling a process that is difficult to delineate experimentally and contributes to understanding the link between pMDI formulation and device to product performance. The ability to identify and understand the variables that affect atomization and/or aerosol disposition , such as initial droplet size, suspended micronized drug or excipient size, and drug or excipient concentration, facilitates defining the design space for suspension pMDIs during development and improves recognizing the sensitive of the APSD is on each hardware and formulation variable. This model can later be applied to limit batch-to-batch variation in the manufacturing process and selecting plausible suspension pMDI formulations with quality design as the end goal.