In the quest for a formidable weapon against the SARS-CoV-2 pandemic, mRNA therapeutics have stolen the spotlight. mRNA vaccines are a prime example of the benefits of mRNA approaches towards a broad array of clinical entities and druggable targets. Amongst these benefits is the rapid cycle “from design to production” of an mRNA product compared to their peptide counterparts, the mutability of the production line should another target be chosen, the side-stepping of safety issues posed by DNA therapeutics being permanently integrated into the transfected cell’s genome and the controlled precision over the translated peptides. Furthermore, mRNA applications are versatile: apart from vaccines it can be used as a replacement therapy, even to create chimeric antigen receptor T-cells or reprogram somatic cells. Still, the sudden global demand for mRNA has highlighted the shortcomings in its industrial production as well as its formulation, efficacy and applicability. Continuous, smart mRNA manufacturing 4.0 technologies have been recently proposed to address such challenges. In this work, we examine the lab and upscaled production of mRNA therapeutics, the mRNA modifications proposed that increase its efficacy and lower its immunogenicity, the vectors available for delivery and the stability considerations concerning long-term storage.

Continuous mRNA drugs manufacturing is perceived to nurture flow processes featuring quality by design, controlled automation, real time validation, robustness, and reproducibility, pertaining to regulatory harmonization. However, the actual adaptation of the latter remains elusive, hence batch-to-continuous transition would a priori necessitate holistic process understanding. In addition, the cost related to experimental, pilot manufacturing lines development and operations thereof renders such venture prohibitive. Systems-based Pharmaceutics 4.0 digital design enabling tools, i.e., converging mass and energy balance simulations, Monte-Carlo machine learning iterations, and spatial arrangement analysis were recruited herein to overcome the aforementioned barriers. The primary objective of this work is to hierarchically design the related bioprocesses, embedded in scalable devices, compatible with continuous operation. Our secondary objective is to harvest the obtained technological data and conduct resource commitment analysis. We herein demonstrate for first time the feasibility of the continuous, end-to-end production of sterile mRNA formulated into lipid nanocarriers, defining the equipment specifications and the desired operational space. Moreover, we find that the cell lysis modules and the linearization enzymes ascend as the principal resource-intensive model factors, accounting for 40% and 42% of the equipment and raw material, respectively. We calculate MSPD 1.30–1.45 €, demonstrating low margin lifecycle fluctuation.

Comminution of BCS II APIs below the 1 μm threshold followed by solidification of the obtained nanosuspensions improves their dissolution properties. The breakage process reveals new crystal faces, thus creating altered crystal habits of improved wettability, facilitated by the adsorption of stabilizing polymers. However, process-induced transformations remain unpredictable, mirroring the current limitations of our atomistic level of understanding. Moreover, conventional equations of estimating dissolution, such as Noyes–Whitney and Nernst–Brunner, are not suitable to quantify the solubility enhancement due to the nanoparticle formation; hence, neither the complex stabilizer contribution nor the adsorption influence on the interfacial tension occurring between the water and APIs is accounted for. For such ternary mixtures, no numeric method exists to correlate the mechanical properties with the interfacial energy, capable of informing the key process parameters and the thermodynamic stability assessment of nanosuspensions. In this work, an elastic tensor analysis was performed to quantify the API stability during process implementation. Moreover, a novel thermodynamic model, described by the stabilizer-coated nanoparticle Gibbs energy anisotropic minimization, was structured to predict the material’s system solubility quantified by the application of PC-SAFT modeling. Comprehensively merging elastic tensor and PC-SAFT analysis into the systems-based Pharma 4.0 algorithm provided a validated, multi-level, built-in method capable of predicting the critical material quality attributes and corresponding key process parameters.

Digital twins capacitate the industry 4.0 paradigm by predicting and optimizing the performance of physical assets of interest, mirroring a realistic in-silico representation of their functional behaviour. Although advanced digital twins set forth disrupting opportunities by delineating the in-service product and the related process dynamic performance, they have yet to be adopted by the pharma sector. The latter, currently struggles more than ever before to improve solubility of BCS II i.e., hard-to-dissolve active pharmaceutical ingredients by micronization and subsequent stabilization. Herein we construct and functionally validate the first artificially intelligent digital twin thread, capable of describing the course of manufacturing of such solidified nanosuspensions given a defined lifecycle starting point and predict and optimize the relevant process outcomes. To this end, we referenced experimental data as the sampling source, which we then augmented via pattern recognition utilizing neural network propagations. The zeta-dynamic potential metrics of the nanosuspensions were correlated to the interfacial Gibbs energy, while the density and heat capacity of the material system was calculated via the Saft-γ-Mie statistical fluid theory. The curated data was then fused to physical and empirical laws to choose the appropriate theory and numeric description, respectively, before being polished by tuning the critical parameters to achieve the best fit with reality.