Proteomics-based method to comprehensively model the removal of host cell protein impurities.

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.

Current state of implementation of in silico tools in the biopharmaceutical industry-Proceedings of the 5th modeling workshop.

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.

High-throughput in silico workflow for optimization and characterization of multimodal chromatographic processes.

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.

A Combination of Machine Learning and PBPK Modeling Approach for Pharmacokinetics Prediction of Small Molecules in Humans.

PURPOSE: 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. CONCLUSION: 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.

Mechanistic Investigation of Enzyme Triggered Release from a Xyloglucan Matrix Tablet for Controlled Colonic Drug Delivery.

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.

Deciphering oxygen distribution and hypoxia profiles in the tumor microenvironment: a data-driven mechanistic modeling approach.

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.

Implementation of mechanistic modeling and global sensitivity analysis (GSA) for design, optimization, and scale-up of a roller compaction process.

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.

Mechanistic modeling of in vitro transcription incorporating effects of magnesium pyrophosphate crystallization.

The in vitro transcription (IVT) reaction used in the production of messenger RNA vaccines and therapies remains poorly quantitatively understood. Mechanistic modeling of IVT could inform reaction design, scale-up, and control. In this work, we develop a mechanistic model of IVT to include nucleation and growth of magnesium pyrophosphate crystals and subsequent agglomeration of crystals and DNA. To help generalize this model to different constructs, a novel quantitative description is included for the rate of transcription as a function of target sequence length, DNA concentration, and T7 RNA polymerase concentration. The model explains previously unexplained trends in IVT data and quantitatively predicts the effect of adding the pyrophosphatase enzyme to the reaction system. The model is validated on additional literature data showing an ability to predict transcription rates as a function of RNA sequence length.

Bringing a scalable adaptive hybrid modeling framework closer to industrial use: Application on a multiscale fungal fermentation.

Digitalization has paved the way for new paradigms such as digital shadows and digital twins for fermentation processes, opening the door for real-time process monitoring, control, and optimization. With a digital shadow, real-time model adaptation to accommodate complex metabolic phenomena such as metabolic shifts of a process can be monitored. Despite the many benefits of digitalization, the potential has not been fully reached in the industry. This study investigates the development of a digital shadow for a very complex fungal fermentation process in terms of microbial physiology and fermentation operation on pilot-scale at Novonesis and the challenges thereof. The process has historically been difficult to optimize and control due to a lack of offline measurements and an absence of biomass measurements. Pilot-scale and lab-scale fermentations were conducted for model development and validation. With all available pilot-scale data, a data-driven soft sensor was developed to estimate the main substrate concentration (glucose) with a normalized root mean squared error (N-RMSE) of 2%. This robust data-driven soft sensor was able to estimate accurately in lab-scale (volume < 20× pilot) with a N-RMSE of 7.8%. A hybrid soft sensor was developed by combining the data-driven soft sensor with a mass balance to estimate the glycerol and biomass concentrations on pilot-scale data with N-RMSEs of 11% and 21%, respectively. A digital shadow modeling framework was developed by coupling a mechanistic model (MM) with the hybrid soft sensor. The digital shadow modeling framework significantly improved the predictability compared with the MM. The contribution of this study brings the application of digital shadows closer to industrial implementation. It demonstrates the high potential of using this type of modeling framework for scale-up and leads the way to a new generation of in silico-based process development.

Development of a Semi-Mechanistic Modeling Framework for Wet Bead Milling of Pharmaceutical Nanosuspensions.

This study aimed to develop a practical semi-mechanistic modeling framework to predict particle size evolution during wet bead milling of pharmaceutical nanosuspensions over a wide range of process conditions and milling scales. The model incorporates process parameters, formulation parameters, and equipment-specific parameters such as rotor speed, bead type, bead size, bead loading, active pharmaceutical ingredient (API) mass, temperature, API loading, maximum bead volume, blade diameter, distance between blade and wall, and an efficiency parameter. The characteristic particle size quantiles, i.e., x10, x50, and x90, were transformed to obtain a linear relationship with time, while the general functional form of the apparent breakage rate constant of this relationship was derived based on three models with different complexity levels. Model A, the most complex and general model, was derived directly from microhydrodynamics. Model B is a simpler model based on a power-law function of process parameters. Model C is the simplest model, which is the pre-calibrated version of Model B based on data collected from different mills across scales, formulations, and drug products. Being simple and computationally convenient, Model C is expected to reduce the amount of experimentation needed to develop and optimize the wet bead milling process and streamline scale-up and/or scale-out.

Cross-evaluation of E. coli's operon structures via a whole-cell model suggests alternative cellular benefits for low- versus high-expressing operons.

Many bacteria use operons to coregulate genes, but it remains unclear how operons benefit bacteria. We integrated E. coli's 788 polycistronic operons and 1,231 transcription units into an existing whole-cell model and found inconsistencies between the proposed operon structures and the RNA-seq read counts that the model was parameterized from. We resolved these inconsistencies through iterative, model-guided corrections to both datasets, including the correction of RNA-seq counts of short genes that were misreported as zero by existing alignment algorithms. The resulting model suggested two main modes by which operons benefit bacteria. For 86% of low-expression operons, adding operons increased the co-expression probabilities of their constituent proteins, whereas for 92% of high-expression operons, adding operons resulted in more stable expression ratios between the proteins. These simulations underscored the need for further experimental work on how operons reduce noise and synchronize both the expression timing and the quantity of constituent genes. A record of this paper's transparent peer review process is included in the supplemental information.

Use of the Same Model or Modeling Strategy Across Multiple Submissions: Focus on Complex Drug Products.

Evidence shows that there is an increasing use of modeling and simulation to support product development and approval for complex generic drug products in the USA, which includes the use of mechanistic modeling and model-integrated evidence (MIE). The potential for model reuse was the subject of a workshop session summarized in this review, where the session included presentations and a panel discussion from members of the U.S. Food and Drug Administration (FDA), academia, and the generic drug product industry. Concepts such as platform performance assessment and MIE standardization were introduced to provide potential frameworks for model reuse related to mechanistic models and MIE, respectively. The capability of models to capture formulation and product differences was explored, and challenges with model validation were addressed for drug product classes including topical, orally inhaled, ophthalmic, and long-acting injectable drug products. An emphasis was placed on the need for communication between FDA and the generic drug industry to continue to foster maturation of modeling and simulation that may support complex generic drug product development and approval, via meetings and published guidance from FDA. The workshop session provided a snapshot of the current state of modeling and simulation for complex generic drug products and offered opportunities to explore the use of such models across multiple drug products.

Using Mechanistic Modeling Approaches to Support Bioequivalence Assessments for Oral Products.

This report summarizes the proceedings for Day 1 Session 3 of the 2-day public workshop entitled "Best Practices for Utilizing Modeling Approaches to Support Generic Product Development," a jointly sponsored workshop by the US Food and Drug Administration (FDA) and the Center for Research on Complex Generics (CRCG) in the year 2022. The aims of this workshop were to discuss how to modernize approaches for efficiently demonstrating bioequivalence (BE), to establish their role in modern paradigms of generic drug development, and to explore and develop best practices for the use of modeling and simulation approaches in regulatory submissions and approval. The theme of this session is mechanistic modeling approaches supporting BE assessments for oral drug products. As a summary, with more successful cases of PBPK absorption modeling being developed and shared, the general strategies/frameworks on using PBPK for oral products are being formed; this will help further evolvement of this area. In addition, the early communications between the industry and the agency through appropriate pathways (e.g., pre-abbreviated new drug applications (pre-ANDA) meetings) are encouraged, and this will speed up the successful development and utility of PBPK modeling for oral products.

Predicting gene-level sensitivity to JAK-STAT signaling perturbation using a mechanistic-to-machine learning framework.

The Janus kinase (JAK)-signal transducer and activator of transcription (STAT) pathway integrates complex cytokine signals via a limited number of molecular components, inspiring numerous efforts to clarify the diversity and specificity of STAT transcription factor function. We developed a computational framework to make global cytokine-induced gene predictions from STAT phosphorylation dynamics, modeling macrophage responses to interleukin (IL)-6 and IL-10, which signal through common STATs, but with distinct temporal dynamics and contrasting functions. Our mechanistic-to-machine learning model identified cytokine-specific genes associated with late pSTAT3 time frames and a preferential pSTAT1 reduction upon JAK2 inhibition. We predicted and validated the impact of JAK2 inhibition on gene expression, identifying genes that were sensitive or insensitive to JAK2 variation. Thus, we successfully linked STAT signaling dynamics to gene expression to support future efforts targeting pathology-associated STAT-driven gene sets. This serves as a first step in developing multi-level prediction models to understand and perturb gene expression outputs from signaling systems. A record of this paper's transparent peer review process is included in the supplemental information.

Mechanistic modeling of empty-full separation in recombinant adeno-associated virus production using anion-exchange membrane chromatography.

Recombinant adeno-associated viral vectors (rAAVs) have become an industry-standard technology in the field of gene therapy, but there are still challenges to be addressed in their biomanufacturing. One of the biggest challenges is the removal of capsid species other than that which contains the gene of interest. In this work, we develop a mechanistic model for the removal of empty capsids-those that contain no genetic material-and enrichment of full rAAV using anion-exchange membrane chromatography. The mechanistic model was calibrated using linear gradient experiments, resulting in good agreement with the experimental data. The model was then applied to optimize the purification process through maximization of yield studying the impact of mobile phase salt concentration and pH, isocratic wash and elution length, flow rate, percent full (purity) requirement, loading density (challenge), and the use of single-step or two-step elution modes. A solution from the optimization with purity of 90% and recovery yield of 84% was selected and successfully validated, as the model could predict the recovery yield with remarkable fidelity and was able to find process conditions that led to significant enrichment. This is, to the best of our knowledge, the first case study of the application of de novo mechanistic modeling for the enrichment of full capsids in rAAV manufacturing, and it serves as demonstration of the potential of mechanistic modeling in rAAV process development.

A Review of Mechanistic Models for Predicting Adverse Effects in Sediment Toxicity Testing.

Since recognizing the importance of bioavailability for understanding the toxicity of chemicals in sediments, mechanistic modeling has advanced over the last 40 years by building better tools for estimating exposure and making predictions of probable adverse effects. Our review provides an up-to-date survey of the status of mechanistic modeling in contaminated sediment toxicity assessments. Relative to exposure, advances have been most substantial for non-ionic organic contaminants (NOCs) and divalent cationic metals, with several equilibrium partitioning-based (Eq-P) models having been developed. This has included the use of Abraham equations to estimate partition coefficients for environmental media. As a result of the complexity of their partitioning behavior, progress has been less substantial for ionic/polar organic contaminants. When the EqP-based estimates of exposure and bioavailability are combined with water-only effects measurements, predictions of sediment toxicity can be successfully made for NOCs and selected metals. Both species sensitivity distributions and toxicokinetic and toxicodynamic models are increasingly being applied to better predict contaminated sediment toxicity. Furthermore, for some classes of contaminants, such as polycyclic aromatic hydrocarbons, adverse effects can be modeled as mixtures, making the models useful in real-world applications, where contaminants seldomly occur individually. Despite the impressive advances in the development and application of mechanistic models to predict sediment toxicity, several critical research needs remain to be addressed. These needs and others represent the next frontier in the continuing development and application of mechanistic models for informing environmental scientists, managers, and decisions makers of the risks associated with contaminated sediments. Environ Toxicol Chem 2023;00:1-17. © 2023 SETAC. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.

Mathematical modeling of cardio-oncology: Modeling the systemic effects of cancer therapeutics on the cardiovascular system.

Cardiotoxicity is a common side-effect of many cancer therapeutics; however, to-date there has been very little push to understand the mechanisms underlying this group of pathologies. This has led to the emergence of cardio-oncology, a field of medicine focused on understanding the effects of cancer and its treatment on the human heart. Here, we describe how mechanistic modeling approaches have been applied to study open questions in the cardiovascular system and how these approaches are being increasingly applied to advance knowledge of the underlying effects of cancer treatments on the human heart. A variety of mechanistic, mathematical modeling techniques have been applied to explore the link between common cancer treatments, such as chemotherapy, radiation, targeted therapy, and immunotherapy, and cardiotoxicity, nevertheless there is limited coverage in the different types of cardiac dysfunction that may be associated with these treatments. Moreover, cardiac modeling has a rich heritage of mathematical modeling and is well suited for the further development of novel approaches for understanding the cardiotoxicities associated with cancer therapeutics. There are many opportunities to combine mechanistic, bottom-up approaches with data-driven, top-down approaches to improve personalized, precision oncology to better understand, and ultimately mitigate, cardiac dysfunction in cancer patients.

Emerging Process Modeling Capabilities for Dry Powder Operations for Inhaled Formulations.

Dry powder inhaler (DPI) products are commonly formulated as a mixture of micronized drug particles and large carrier particles, with or without additional fine particle excipients, followed by final powder filling into dose containment systems such as capsules, blisters, or reservoirs. DPI product manufacturing consists of a series of unit operations, including particle size reduction, blending, and filling. This review provides an overview of the relevant critical process parameters used for jet milling, high-shear blending, and dosator/drum capsule filling operations across commonly utilized instruments. Further, this review describes the recent achievements regarding the application of empirical and mechanistic models, especially discrete element method (DEM) simulation, in DPI process development. Although to date only limited modeling/simulation work has been accomplished, in the authors' perspective, process design and development are destined to be more modeling/simulation driven with the emphasis on evaluating the impact of material attributes/process parameters on process performance. The advancement of computational power is expected to enable modeling/simulation approaches to tackle more complex problems with better accuracy when dealing with real-world DPI process operations.

The Comparison of Machine Learning and Mechanistic In Vitro-In Vivo Extrapolation Models for the Prediction of Human Intrinsic Clearance.

Accurate prediction of human pharmacokinetics (PK) remains one of the key objectives of drug metabolism and PK (DMPK) scientists in drug discovery projects. This is typically performed by using in vitro-in vivo extrapolation (IVIVE) based on mechanistic PK models. In recent years, machine learning (ML), with its ability to harness patterns from previous outcomes to predict future events, has gained increased popularity in application to absorption, distribution, metabolism, and excretion (ADME) sciences. This study compares the performance of various ML and mechanistic models for the prediction of human IV clearance for a large (645) set of diverse compounds with literature human IV PK data, as well as measured relevant in vitro end points. ML models were built using multiple approaches for the descriptors: (1) calculated physical properties and structural descriptors based on chemical structure alone (classical QSAR/QSPR); (2) in vitro measured inputs only with no structure-based descriptors (ML IVIVE); and (3) in silico ML IVIVE using in silico model predictions for the in vitro inputs. For the mechanistic models, well-stirred and parallel-tube liver models were considered with and without the use of empirical scaling factors and with and without renal clearance. The best ML model for the prediction of in vivo human intrinsic clearance (CLint) was an in vitro ML IVIVE model using only six in vitro inputs with an average absolute fold error (AAFE) of 2.5. The best mechanistic model used the parallel-tube liver model, with empirical scaling factors resulting in an AAFE of 2.8. The corresponding mechanistic model with full in silico inputs achieved an AAFE of 3.3. These relative performances of the models were confirmed with the prediction of 16 Pfizer drug candidates that were not part of the original data set. Results show that ML IVIVE models are comparable to or superior to their best mechanistic counterparts. We also show that ML IVIVE models can be used to derive insights into factors for the improvement of mechanistic PK prediction.

Model based process optimization of an industrial chromatographic process for separation of lactoferrin from bovine milk.

Model based process development using predictive mechanistic models is a powerful tool for in-silico downstream process development. It allows to obtain a thorough understanding of the process reducing experimental effort. While in pharma industry, mechanistic modeling becomes more common in the last years, it is rarely applied in food industry. This case study investigates risk ranking and possible optimization of the industrial process of purifying lactoferrin from bovine milk using SP Sepharose Big Beads with a resin particle diameter of 200 µm, based on a minimal number of lab-scale experiments combining traditional scale-down experiments with mechanistic modeling. Depending on the location and season, process water pH and the composition of raw milk can vary, posing a challenge for highly efficient process development. A predictive model based on the general rate model with steric mass action binding, extended for pH dependence, was calibrated to describe the elution behavior of lactoferrin and main impurities. The gained model was evaluated against changes in flow rate, step elution conditions, and higher loading and showed excellent agreement with the observed experimental data. The model was then used to investigate the critical process parameters, such as water pH, conductivity of elution steps, and flow rate, on process performance and purity. It was found that the elution behavior of lactoferrin is relatively consistent over the pH range of 5.5 to 7.6, while the elution behavior of the main impurities varies greatly with elution pH. As a result, a significant loss in lactoferrin is unavoidable to achieve desired purities at pH levels below pH 6.0. Optimal process parameters were identified to reduce water and salt consumption and increase purity, depending on water pH and raw milk composition. The optimal conductivity for impurity removal in a low conductivity elution step was found to be 43 mS/cm, while a conductivity of 95 mS/cm leads to the lowest overall salt usage during lactoferrin elution. Further increasing the conductivity during lactoferrin elution can only slightly lower the elution volume thus can also lead to higher total salt usage. Low flow rates during elution of 0.2 column volume per minute are beneficial compared to higher flow rates of 1 column volume per minute. The, on lab-scale, calibrated model allows predicting elution volume and impurity removal for large-scale experiments in a commercial plant processing over 106 liters of milk per day. The successful model extrapolation was possible without recalibration or detailed knowledge of the manufacturing plant. This study therefore provides a possible pathway for rapid process development of chromatographic purification in the food industries combining traditional scale-down experiments with mechanistic modeling.