Biopharmaceutical products are often produced in Chinese hamster ovary (CHO) cell cultures that are vulnerable to virus infections. Therefore, it is a regulatory requirement that downstream purification steps for biopharmaceuticals can remove viruses from feedstocks. Anion exchange chromatography (AEX) is one of the downstream unit operations that is most frequently used for this purpose and claimed for its capability to remove viruses. However, the impact of various process parameters on virus removal by AEX is still not fully understood. Mechanistic modeling could be a promising way to approach this gap, as these models require comparatively few experiments for calibration. This makes them a valuable tool to improve understanding of viral clearance, especially since virus spiking studies are costly and time consuming. In this study, we present how the virus clearance of a MVM mock virus particle by Q Sepharose FF resin can be described by mechanistic modeling. A lumped kinetic model was combined with a steric mass action model and calibrated at micro scale using three linear gradient experiments and an incremental step elution gradient. The model was subsequently verified for its capability to predict the effect of different sodium chloride concentrations, as well as residence times, on virus clearance and was in good agreement with the LRVs of the verification runs. Overall, models like this could enhance the mechanistic understanding of viral clearance mechanisms and thereby contribute to the development of more efficient and safer biopharmaceutical downstream processes.

Recombinant adeno-associated virus (rAAV) is a commonly used in vivo gene therapy vector because of its nonpathogenicity, long-term transgene expression, broad tropism, and ability to transduce both dividing and nondividing cells. However, rAAV vector production via transient transfection of mammalian cells typically yields a low fraction of filled-to-total capsids (~1%-30% of total capsids produced). Analysis of our previously developed mechanistic model for rAAV2/5 production attributed these low fill fractions to a poorly coordinated timeline between capsid synthesis and viral DNA replication and the repression of later phase capsid formation by Rep proteins. Here, we extend the model by quantifying the expression dynamics of total Rep proteins and their influence on the key steps of rAAV2/5 production using a multiple dosing transfection of human embryonic kidney 293 (HEK293) cells. We report that the availability of preformed empty capsids and viral DNA copies per cell are not limiting to the capsid-filling reaction. However, optimal expression of Rep proteins (<240 ± 13 ag per cell) enables enrichment of the filled capsid population (>12% of total capsids/cell) upstream. Our analysis suggests increased enrichment of filled capsids via regulating the expression of Rep proteins is possible but at the expense of per cell capsid titer in a triple plasmid transfection. Our study reveals an intrinsic limitation of scaling rAAV2/5 vector genome (vg) production and underscores the need for approaches that allow for regulating the expression of Rep proteins to maximize vg titer per cell upstream.

Microplastics have numerous different shapes, affecting the fate and transport of these particles in the environment. However, theoretical models generally assume microplastics to be spherical. This study aims to develop a modeling approach that incorporates the shapes of microplastics to investigate the vertical transport of microplastics in rivers and simulate the effect of particle and flow characteristics on settling and resuspension. To achieve these aims, a mechanistic model was developed utilizing the mass-balance and hydrodynamic equations. Scenario analysis was implemented assigning different values to model parameters, such as bed shear stress, shape factor and particle size to simulate the effect of flow patterns and particle properties. The model outcomes revealed that the residence time of microplastics in the water column was longest in medium bed shear stress, whilst it was shortest in low bed shear stress. This suggests that the influence of turbulence is not unidirectional; it can both increase and decrease microplastic concentrations and residence time in the water column. According to the scenario analysis, the settling flux of microplastics was the highest for near-spherical particles and increased with the size of the particles, as well as with increasing bed shear stress. However, the resuspension of particles was primarily influenced by increasing bed shear stress, but the ranking of resuspension flux values for different shaped and sized microplastics exhibited alterations with changing flow patterns. Turbulent conditions predominantly influenced the resuspension of near-spheres and large microplastics. On the contrary, the settling of fibers and small microplastics were significantly influenced by changing flow patterns, whereas near-spheres and largest particles were least affected. The model results were sensitive to changes in shape factor developed for this model, therefore this parameter should be improved in future studies.

Mechanistic models mostly focus on the target protein and some selected process- or product-related impurities. For a better process understanding, however, it is advantageous to describe also reoccurring host cell protein impurities. Within the purification of biopharmaceuticals, the binding of host cell proteins to a chromatographic resin is far from being described comprehensively. For a broader coverage of the binding characteristics, large-scale proteomic data and systems level knowledge on protein interactions are key. However, a method for determining binding parameters of the entire host cell proteome to selected chromatography resins is still lacking. In this work, we have developed a method to determine binding parameters of all detected individual host cell proteins in an Escherichia coli harvest sample from large-scale proteomics experiments. The developed method was demonstrated to model abundant and problematic proteins, which are crucial impurities to be removed. For these 15 proteins covering varying concentration ranges, the model predicts the independently measured retention time during the validation gradient well. Finally, we optimized the anion exchange chromatography capture step in silico using the determined isotherm parameters of the persistent host cell protein contaminants. From these results, strategies can be developed to separate abundant and problematic impurities from the target antigen.

OBJECTIVE: Recently, there has been rapid development in model-informed drug development, which has the potential to reduce animal experiments and accelerate drug discovery. Physiologically based pharmacokinetic (PBPK) and machine learning (ML) models are commonly used in early drug discovery to predict drug properties. However, basic PBPK models require a large number of molecule-specific inputs from in vitro experiments, which hinders the efficiency and accuracy of these models. To address this issue, this paper introduces a new computational platform that combines ML and PBPK models. The platform predicts molecule PK profiles with high accuracy and without the need for experimental data.
METHODS: This study developed a whole-body PBPK model and ML models of plasma protein fraction unbound ( f up ), Caco-2 cell permeability, and total plasma clearance to predict the PK of small molecules after intravenous administration. Pharmacokinetic profiles were simulated using a \"bottom-up\" PBPK modeling approach with ML inputs. Additionally, 40 compounds were used to evaluate the platform\'s accuracy.
RESULTS: Results showed that the ML-PBPK model predicted the area under the concentration-time curve (AUC) with 65.0 % accuracy within a 2-fold range, which was higher than using in vitro inputs with 47.5 % accuracy.
CONCLUSIONS: The ML-PBPK model platform provides high accuracy in prediction and reduces the number of experiments and time required compared to traditional PBPK approaches. The platform successfully predicts human PK parameters without in vitro and in vivo experiments and can potentially guide early drug discovery and development.

While high-throughput (HT) experimentation and mechanistic modeling have long been employed in chromatographic process development, it remains unclear how these techniques should be used in concert within development workflows. In this work, a process development workflow based on HT experiments and mechanistic modeling was constructed. The integration of HT and modeling approaches offers improved workflow efficiency and speed. This high-throughput in silico (HT-IS) workflow was employed to develop a Capto MMC polishing step for mAb aggregate removal. High-throughput batch isotherm data was first generated over a range of mobile phase conditions and a suite of analytics were employed. Parameters for the extended steric mass action (SMA) isotherm were regressed for the multicomponent system. Model validation was performed using the extended SMA isotherm in concert with the general rate model of chromatography using the CADET modeling software. Here, step elution profiles were predicted for eight RoboColumn runs across a range of ionic strength, pH, and load density. Optimized processes were generated through minimization of a complex objective function based on key process metrics. Processes were evaluated at lab-scale using two feedstocks, differing in composition. The results confirmed that both processes obtained high monomer yield (>85%) and removed ∼ 50 % $$ \\sim 50\\% $$ of aggregate species. Column simulations were then carried out to determine sensitivity to a wide range of process inputs. Elution buffer pH was found to be the most critical process parameter, followed by resin ionic capacity. Overall, this study demonstrated the utility of the HT-IS workflow for rapid process development and characterization.

The fifth modeling workshop (5MW) was held in June 2023 at Favrholm, Denmark and sponsored by Recovery of Biological Products Conference Series. The goal of the workshop was to assemble modeling practitioners to review and discuss the current state, progress since the last fourth mini modeling workshop (4MMW), gaps and opportunities for development, deployment and maintenance of models in bioprocess applications. Areas of focus were four categories: biophysics and molecular modeling, mechanistic modeling, computational fluid dynamics (CFD) and plant modeling. Highlights of the workshop included significant advancements in biophysical/molecular modeling to novel protein constructs, mechanistic models for filtration and initial forays into modeling of multiphase systems using CFD for a bioreactor and mapped strategically to cell line selection/facility fit. A significant impediment to more fully quantitative and calibrated models for biophysics is the lack of large, anonymized datasets. A potential solution would be the use of specific descriptors in a database that would allow for detailed analyzes without sharing proprietary information. Another gap identified was the lack of a consistent framework for use of models that are included or support a regulatory filing beyond the high-level guidance in ICH Q8-Q11. One perspective is that modeling can be viewed as a component or precursor of machine learning (ML) and artificial intelligence (AI). Another outcome was alignment on a key definition for \"mechanistic modeling.\" Feedback from participants was that there was progression in all of the fields of modeling within scope of the conference. Some areas (e.g., biophysics and molecular modeling) have opportunities for significant research investment to realize full impact. However, the need for ongoing research and development for all model types does not preclude the application to support process development, manufacturing and use in regulatory filings. Analogous to ML and AI, given the current state of the four modeling types, a prospective investment in educating inter-disciplinary subject matter experts (e.g., data science, chromatography) is essential to advancing the modeling community.

Objective. The distribution of hypoxia within tissues plays a critical role in tumor diagnosis and prognosis. Recognizing the significance of tumor oxygenation and hypoxia gradients, we introduce mathematical frameworks grounded in mechanistic modeling approaches for their quantitative assessment within a tumor microenvironment. By utilizing known blood vasculature, we aim to predict hypoxia levels across different tumor types.Approach. Our approach offers a computational method to measure and predict hypoxia using known blood vasculature. By formulating a reaction-diffusion model for oxygen distribution, we derive the corresponding hypoxia profile.Main results. The framework successfully replicates observed inter- and intra-tumor heterogeneity in experimentally obtained hypoxia profiles across various tumor types (breast, ovarian, pancreatic). Additionally, we propose a data-driven method to deduce partial differential equation models with spatially dependent parameters, which allows us to comprehend the variability of hypoxia profiles within tissues. The versatility of our framework lies in capturing diverse and dynamic behaviors of tumor oxygenation, as well as categorizing states of vascularization based on the dynamics of oxygen molecules, as identified by the model parameters.Significance. The proposed data-informed mechanistic method quantitatively assesses hypoxia in the tumor microenvironment by integrating diverse histopathological data and making predictions across different types of data. The framework provides valuable insights from both modeling and biological perspectives, advancing our comprehension of spatio-temporal dynamics of tumor oxygenation.

The objective of this study was to investigate the mechanisms underlying drug release from a controlled colonic release (CCR) tablet formulation based on a xyloglucan polysaccharide matrix and identify the factors that control the rate of release for the purpose of fundamentally substantiating the concept and demonstrating its robustness for colonic drug delivery. Previous work demonstrated in vitro limited release of 5-aminosalicylic acid (5-ASA) and caffeine from these tablets in small intestinal environment and significant acceleration of release by xyloglucanase, an enzyme of the colonic microbiome. Targeted colonic drug delivery was verified in an animal study in vivo. In the present work, interaction of the xyloglucan matrix tablets with aqueous dissolution media containing xyloglucanase was found to lead to the spontaneous formation of a hydrated highly viscous gummy layer at the surface of the matrix which had a reduced drug content compared to the underlying regions and persisted with a nearly constant thickness that was inversely correlated to the enzyme concentration throughout the duration of the release process. Enzymatic hydrolysis of xyloglucan was determined to take place at the surface of the matrix leading to matrix erosion and a relation for the rate of enzymatic reaction as a function of bulk enzyme concentration and the concentration of dissolved xyloglucan in the gummy layer was derived. A mathematical model was developed encompassing aqueous medium ingress, matrix metamorphosis due to xyloglucan dissolution and matrix swelling, enzymatic hydrolysis of the polysaccharide and concomitant drug release due to matrix erosion and simultaneous drug diffusion. The model was fitted to data of reducing sugar equivalents in the medium reflecting matrix erosion and released drug amount. Enzymatic reaction parameters and reasonable values of medium ingress velocity, xyloglucan dissolution rate constant and drug diffusion coefficient were deduced that provided an adequate approximation of the data. Erosion was shown to be the overwhelmingly dominant drug release mechanism while the role of diffusion marginally increased at low enzyme concentration and high drug solubility. Changing enzyme concentration had a rather weak effect on matrix erosion and drug release rate as demonstrated by model simulations supported by experimental data, while xyloglucan dissolution was slow and had a stronger effect on the rate of the process. Therefore, reproducible colonic drug delivery not critically influenced by inter- and intra-individual variation of microbial enzyme activity may be projected.

The pharmaceutical industry has been shifting towards the application of mechanistic modeling to improve process robustness, enable scale-up, and reduce time to market. Modeling approaches have been well-developed for processes such as roller compaction, a continuous dry granulation process. Several mechanistic models/approaches have been documented with limited application to high drug-loaded formulations. In this study, the Johanson model was employed to optimize roller compaction processing and guide its scale-up for a high drug loaded formulation. The model was calibrated using a pilot-scale Minipactor and was validated for a commercial-scale Macropactor. Global sensitivity analysis (GSA) was implemented to determine the impact of process parameter variations (roll force, gap, speed) on a quality attribute [solid fraction (SF)]. The throughput method, which estimates SF values of ribbons using granule production rate, was also studied. The model predicted SF values for all 14 Macropactor batches within ± 0.04 SF. The throughput method estimated SF with ± 0.06 SF for 7 out of 11 batches. GSA confirmed that roll force had the largest impact on SF. This case study represents a process modeling approach to build quality into the products/processes and expands the use of mechanistic modeling during drug product development.