## 1 Introduction  
Current trends in pharmaceutical formulation development, reflected in guidelines for industry [1; 2], indicate the need for a better understanding of manufacturing processes. The quality of pharmaceutical formulation depends on the qualitative and quantitative composition, applied production process, and its parameters. In-depth knowledge of production processes is crucial for quality assurance of the final product. Complexity, nonlinearity, and lack of full understanding of the processes are the main obstacles to the high-quality models development. Application of the artificial intelligence (AI) tools, such as evolutionary algorithms (EA), in the knowledge discovery of the manufacturing process, is beneficial for both the optimization of pharmaceutical formulationsand quality control purposes. The knowledge-based drug development is in accordance with process analytical technology (PAT) paradigm of drugs manufacturing advocated by the U.S. Food and Drug Administration agency (FDA). Moreover, the FDA pushes forward modeling as an important strategy for drug development and endorses terms \"model informed drug development\" (MIDD) and \"model-based drug development\" (MBDD). MIDD/MBDD extend substantially the classical PAT concept of drug manufacturing and navigates modern pharmaceutical industry towards Industry 4.0 and smart plants. Thus, it positions Information Communication Technologies (ICT) as pivotal elements of the successful development of new medicinal products. Since the pharmaceutical industry is highly regulated, to ensure this course the FDA announced that MIDD is going to be a subject of regulations and new guidelines for the industry in the future [3]. Spray drying is a single step process which converts a liquid feed into dry particles. It is widely used for producing inhalable microparticles including small molecules like salbutamol [4], ciprofloxacin hydrochloride [5] and macromolecules like cyclosporine A [6] or salmon calcitonin [7]. Spray-dried particles' properties depend on the type of equipment, working conditions, and feedstock characteristics [8]. Quality control procedures for pulmonary drug delivery systems imply the determination of the emitted dose (ED) and fine particle fraction (FPF) through the use of impactors and liquid impingers. Both parameters (ED and FPF) highly depend on assay conditions like apparatus, flow rate, inhaler type [9], and are determined in vitro by standardized procedures [10]. Multiple factors influence inhalable powder performances, such as particle shape, size, surface, charge, which determine inter-particle interaction [11]. Despite the wide use of the spray drying technology in the pharmaceutical industry, understanding of the process and the final product characteristics is still limited. [PERSON] et al. [12] proposed an approach which allows prediction of outlet temperature and relative humidity of the exhausted air leaving the drying chamber. The first parameter was calculated based on heat loss caused by conduction and an evaporation process, while the second one was estimated by applying the Antoine equation and the ideal gas law. Simulations were consistent with experimental results. Furthermore, models were applied to optimize spray drying conditions for inulin and trehalose solutions. Another example is the application of computational fluid dynamics (CFD) to simulate the course of the spray drying process and prediction of outlet temperature, as well as residue accumulation during the operation [13]. The development of data-driven model presented by [PERSON] et al. focused on final product characteristics [14]. The statistical approach presented was successfully applied to predict particles size, and surface area for spray-dried trehalose and raffinose. Soon enough, the pharmaceutical industry will be in the need for good quality, transparent (no black box allowed) predictive, and/or explanatory models, susceptible to validation performed at the highest level of pharmaceutical standards.