COSMIC-dFBA: A novel multi-scale hybrid framework for bioprocess modeling.

Metabolism governs cell performance in biomanufacturing, as it fuels growth and productivity. However, even in well-controlled culture systems, metabolism is dynamic, with shifting objectives and resources, thus limiting the predictive capability of mechanistic models for process design and optimization. Here, we present Cellular Objectives and State Modulation In bioreaCtors (COSMIC)-dFBA, a hybrid multi-scale modeling paradigm that accurately predicts cell density, antibody titer, and bioreactor metabolite concentration profiles. Using machine-learning, COSMIC-dFBA decomposes the instantaneous metabolite uptake and secretion rates in a bioreactor into weighted contributions from each cell state (growth or antibody-producing state) and integrates these with a genome-scale metabolic model. A major strength of COSMIC-dFBA is that it can be parameterized with only metabolite concentrations from spent media, although constraining the metabolic model with other omics data can further improve its capabilities. Using COSMIC-dFBA, we can predict the final cell density and antibody titer to within 10% of the measured data, and compared to a standard dFBA model, we found the framework showed a 90% and 72% improvement in cell density and antibody titer prediction, respectively. Thus, we demonstrate our hybrid modeling framework effectively captures cellular metabolism and expands the applicability of dFBA to model the dynamic conditions in a bioreactor.

Guidelines for extracting biologically relevant context-specific metabolic models using gene expression data.

Genome-scale metabolic models comprehensively describe an organism's metabolism and can be tailored using omics data to model condition-specific physiology. The quality of context-specific models is impacted by (i) choice of algorithm and parameters and (ii) alternate context-specific models that equally explain the -omics data. Here we quantify the influence of alternate optima on microbial and mammalian model extraction using GIMME, iMAT, MBA, and mCADRE. We find that metabolic tasks defining an organism's phenotype must be explicitly and quantitatively protected. The scope of alternate models is strongly influenced by algorithm choice and the topological properties of the parent genome-scale model with fatty acid metabolism and intracellular metabolite transport contributing much to alternate solutions in all models. mCADRE extracted the most reproducible context-specific models and models generated using MBA had the most alternate solutions. There were fewer qualitatively different solutions generated by GIMME in E. coli, but these increased substantially in the mammalian models. Screening ensembles using a receiver operating characteristic plot identified the best-performing models. A comprehensive evaluation of models extracted using combinations of extraction methods and expression thresholds revealed that GIMME generated the best-performing models in E. coli, whereas mCADRE is better suited for complex mammalian models. These findings suggest guidelines for benchmarking -omics integration algorithms and motivate the development of a systematic workflow to enumerate alternate models and extract biologically relevant context-specific models.

Dropwise additive manufacturing of pharmaceutical products for amorphous and self emulsifying drug delivery systems.

The improvements in healthcare systems and the advent of the precision medicine initiative have created the need to develop more innovative manufacturing methods for the delivery and production of individualized dosing and personalized treatments. In accordance with the changes observed in healthcare systems towards more innovative therapies, this paper presents dropwise additive manufacturing of pharmaceutical products (DAMPP) for small scale, distributed manufacturing of individualized dosing as an alternative to conventional manufacturing methods A dropwise additive manufacturing process for amorphous and self-emulsifying drug delivery systems is reported, which utilizes drop-on-demand printing technology for automated and controlled deposition of melt-based formulations onto inert tablets. The advantages of drop on demand technology include reproducible production of droplets with adjustable sizing and high placement accuracy, which enable production of individualized dosing even for low dose and high potency drugs. Flexible use of different formulations, such as lipid-based formulations, allows enhancement of the solubility of poorly water soluble and highly lipophilic drugs with DAMPP. Here, DAMPP is used to produce solid oral dosage forms from melts of an active pharmaceutical ingredient and a surfactant. The dosage forms are analyzed to show the amorphous nature, self-emulsifying drug delivery system characteristics and dissolution behavior of these formulations.

Drop-on-Demand System for Manufacturing of Melt-based Solid Oral Dosage: Effect of Critical Process Parameters on Product Quality.

The features of a drop-on-demand-based system developed for the manufacture of melt-based pharmaceuticals have been previously reported. In this paper, a supervisory control system, which is designed to ensure reproducible production of high quality of melt-based solid oral dosages, is presented. This control system enables the production of individual dosage forms with the desired critical quality attributes: amount of active ingredient and drug morphology by monitoring and controlling critical process parameters, such as drop size and product and process temperatures. The effects of these process parameters on the final product quality are investigated, and the properties of the produced dosage forms characterized using various techniques, such as Raman spectroscopy, optical microscopy, and dissolution testing. A crystallization temperature control strategy, including controlled temperature cycles, is presented to tailor the crystallization behavior of drug deposits and to achieve consistent drug morphology. This control strategy can be used to achieve the desired bioavailability of the drug by mitigating variations in the dissolution profiles. The supervisor control strategy enables the application of the drop-on-demand system to the production of individualized dosage required for personalized drug regimens.

Dropwise additive manufacturing of pharmaceutical products for melt-based dosage forms.

The US Food and Drug Administration introduced the quality by design approach and process analytical technology guidance to encourage innovation and efficiency in pharmaceutical development, manufacturing, and quality assurance. As part of this renewed emphasis on the improvement of manufacturing, the pharmaceutical industry has begun to develop more efficient production processes with more intensive use of online measurement and sensing, real-time quality control, and process control tools. Here, we present dropwise additive manufacturing of pharmaceutical products (DAMPP) as an alternative to conventional pharmaceutical manufacturing methods. This mini-manufacturing process for the production of pharmaceuticals utilizes drop on demand printing technology for automated and controlled deposition of melt-based formulations onto edible substrates. The advantages of drop-on-demand technology, including reproducible production of small droplets, adjustable drop sizing, high placement accuracy, and flexible use of different formulations, enable production of individualized dosing even for low-dose and high-potency drugs. In this work, DAMPP is used to produce solid oral dosage forms from hot melts of an active pharmaceutical ingredient and a polymer. The dosage forms are analyzed to show the reproducibility of dosing and the dissolution behavior of different formulations.