Development and qualification of 3 L scale-down model for large scale vaccine process on vero cell culture using microcarriers.

Scale-down models (SDM) are pivotal tools for process understanding and improvement to accelerate the development of vaccines from laboratory research to global commercialization. In this study, a 3 L SDM representing a 50 L scale Vero cell culture process of a live-attenuated virus vaccine using microcarriers was developed and qualified based on the constant impeller power per volume principle. Both multivariate data analysis (MVDA) and the traditional univariate data analysis showed comparable and equivalent cell growth, metabolic activity, and product quality results across scales. Computational fluid dynamics simulation further confirmed similar hydrodynamic stress between the two scales.

Effects of process parameters on tablet critical quality attributes in continuous direct compression: a case study of integrating data-driven statistical models and mechanistic compaction models.

Continuous manufacturing of oral-dosage drug products is increasing the need for rigorous process understanding both from a process design and control perspective. The purpose of this study is to develop a methodology that analyzes the effects of upstream process parameters on continuous tablet compaction and then correlates associated upstream variables to the final tablet attributes (e.g. relative density and hardness). The impact of three process parameters (system throughput, blender speed, and compaction force) on tablet attributes is investigated using a full factorial experimental design. As expected, the compaction force was found to be the most significant process parameter. However, importantly, throughput was discovered to have a non-negligible impact which was previously unaccounted for. This impact is proposed to be related to differing levels of powder pre-compression. An empirical model for this relationship is regressed and incorporated into a flowsheet model. The flowsheet model is then used to develop an in silico design space which is compared favorably to that built from experiments. Moreover, in the future, the in silico design space based on the validated flowsheet model can provide better manufacturing flexibility and make control strategy development simpler.

Assessment of spatial heterogeneity in continuous twin screw wet granulation process using three-compartmental population balance model.

In this study, a novel three-compartmental population balance model (PBM) for a continuous twin screw wet granulation process is developed, combining the techniques of PBM and regression process modeling. The developed model links screw configuration, screw speed, and blend throughput with granule properties to predict the granule size distribution (GSD) and volume-average granule diameter. The granulator screw barrel was divided into three compartments along barrel length: wetting compartment, mixing compartment, and steady growth compartment. Different granulation mechanisms are assumed in each compartment. The proposed model therefore considers spatial heterogeneity, improving model prediction accuracy. An industrial data set containing 14 experiments is applied for model development. Three validation experiments show that the three-compartmental PBM can accurately predict granule diameter and size distribution at randomly selected operating conditions. Sixteen combinations of aggregation and breakage kernels are investigated in predicting the experimental GSD to best judge the granulation mechanism. The three-compartmental model is compared with a one-compartmental model in predicting granule diameter at different experimental conditions to demonstrate its advantage. The influence of the screw configuration, screw speed and blend throughput on the volume-average granule diameter is analyzed based on the developed model.

Multiscale prediction of patient-specific platelet function under flow.

During thrombotic or hemostatic episodes, platelets bind collagen and release ADP and thromboxane A(2), recruiting additional platelets to a growing deposit that distorts the flow field. Prediction of clotting function under hemodynamic conditions for a patient's platelet phenotype remains a challenge. A platelet signaling phenotype was obtained for 3 healthy donors using pairwise agonist scanning, in which calcium dye-loaded platelets were exposed to pairwise combinations of ADP, U46619, and convulxin to activate the P2Y(1)/P2Y(12), TP, and GPVI receptors, respectively, with and without the prostacyclin receptor agonist iloprost. A neural network model was trained on each donor's pairwise agonist scanning experiment and then embedded into a multiscale Monte Carlo simulation of donor-specific platelet deposition under flow. The simulations were compared directly with microfluidic experiments of whole blood flowing over collagen at 200 and 1000/s wall shear rate. The simulations predicted the ranked order of drug sensitivity for indomethacin, aspirin, MRS-2179 (a P2Y(1) inhibitor), and iloprost. Consistent with measurement and simulation, one donor displayed larger clots and another presented with indomethacin resistance (revealing a novel heterozygote TP-V241G mutation). In silico representations of a subject's platelet phenotype allowed prediction of blood function under flow, essential for identifying patient-specific risks, drug responses, and novel genotypes.

Simulation of aggregating particles in complex flows by the lattice kinetic Monte Carlo method.

We develop and validate an efficient lattice kinetic Monte Carlo (LKMC) method for simulating particle aggregation in laminar flows with spatially varying shear rate, such as parabolic flow or flows with standing vortices. A contact time model was developed to describe the particle-particle collision efficiency as a function of the local shear rate, G, and approach angle, θ. This model effectively accounts for the hydrodynamic interactions between approaching particles, which is not explicitly considered in the LKMC framework. For imperfect collisions, the derived collision efficiency [ɛ=1 - ∫(0)(π/2) sinθ exp(-2cotθΓ(agg)/G)dθ] was found to depend only on Γ(agg)∕G, where Γ(agg) is the specified aggregation rate. For aggregating platelets in tube flow, Γ(agg)=0.683 s(-1) predicts the experimentally measured ε across a physiological range (G = 40-1000 s(-1)) and is consistent with α(2b)ß(3)-fibrinogen bond dynamics. Aggregation in parabolic flow resulted in the largest aggregates forming near the wall where shear rate and residence time were maximal, however intermediate regions between the wall and the center exhibited the highest aggregation rate due to depletion of reactants nearest the wall. Then, motivated by stenotic or valvular flows, we employed the LKMC simulation developed here for baffled geometries that exhibit regions of squeezing flow and standing recirculation zones. In these calculations, the largest aggregates were formed within the vortices (maximal residence time), while squeezing flow regions corresponded to zones of highest aggregation rate.

Lattice kinetic Monte Carlo simulations of convective-diffusive systems.

Diverse phenomena in physical, chemical, and biological systems exhibit significant stochasticity and therefore require appropriate simulations that incorporate noise explicitly into the dynamics. We present a lattice kinetic Monte Carlo approach to simulate the trajectories of tracer particles within a system in which both diffusive and convective transports are operational. While diffusive transport is readily accounted for in a kinetic Monte Carlo simulation, we demonstrate that the inclusion of bulk convection by simply biasing the rate of diffusion with the rate of convection creates unphysical, shocklike behavior in concentrated systems due to particle pile up. We report that elimination of shocklike behavior requires the proper passing of blocked convective rates along nearest-neighbor chains to the first available particle in the direction of flow. The resulting algorithm was validated for the Taylor-Aris dispersion in parallel plate flow and multidimensional flows. This is the first generally applicable lattice kinetic Monte Carlo simulation for convection-diffusion and will allow simulations of field-driven phenomena in which drift is present in addition to diffusion.

Systems biology of platelet-vessel wall interactions.

Blood systems biology seeks to quantify outside-in signaling as platelets respond to numerous external stimuli, typically under flow conditions. Platelets can activate via GPVI collagen receptor and numerous G-protein coupled receptors (GPCRs) responsive to ADP, thromboxane, thrombin, and prostacyclin. A bottom-up ODE approach allowed prediction of platelet calcium and phosphoinositides following P2Y1 activation with ADP, either for a population average or single cell stochastic behavior. The homeostasis assumption (i.e., a resting platelet stays resting until activated) was particularly useful in finding global steady states for these large metabolic networks. Alternatively, a top-down approach involving Pairwise Agonist Scanning (PAS) allowed large data sets of measured calcium mobilization to predict an individual's platelet responses. The data was used to train neural network (NN) models of signaling to predict patient-specific responses to combinatorial stimulation. A kinetic description of platelet signaling then allows prediction of inside-out activation of platelets as they experience the complex biochemical milieu at the site of thrombosis. Multiscale lattice kinetic Monte Carlo (LKMC) utilizes these detailed descriptions of platelet signaling under flow conditions where released soluble species are solved by finite element method and the flow field around the growing thrombus is updated using computational fluid dynamics or lattice Boltzmann method. Since hemodynamic effects are included in a multiscale approach, thrombosis can then be predicted under arterial and venous thrombotic conditions for various anatomical geometries. Such systems biology approaches accommodate the effect of anti-platelet pharmacological intervention where COX1 pathways or ADP signaling are modulated in a patient-specific manner.