Early Stages Of Development Process Research Articles

Prediction models for the flux decay profile and initial flux of microfiltration for therapeutic proteins.

Microfiltration (MF) is an essential step during biopharmaceutical manufacturing. However, unexpected flux decay can occur. Although the flux decay profile and initial flux are important factors determining MF filterability, predicting them accurately is challenging, as the root cause of unexpected flux decay remains elusive. In this study, the methodology for developing a prediction model of flux decay profiles was established. First, the filtration profiles of different monodisperse polystyrene latex and silica beads of various sizes were evaluated. These results revealed that the size and surface electrostatic properties of the beads affect the flux decay profile. Taking the size and surface electrostatic properties of protein aggregates into account, we constructed a predictive model using model bead filtration profiles. We showed that this methodology was applicable to two different MF filters to predict the flux decay profile of therapeutic proteins. Because our proposed prediction model is based on normalized flux, the initial flux is required to predict the overall filtration profile. Then, we applied the Hagen-Poiseuille equation using sample viscosity values to estimate the initial flux. The developed prediction models can be used for effective MF scale-up assessment during the early stages of process development.

Mimicking CHO large-scale effects in the single multicompartment bioreactor: A new approach to access scale-up behavior.

During the scale-up of biopharmaceutical production processes, insufficiently predictable performance losses may occur alongside gradients and heterogeneities. To overcome such performance losses, tools are required to explain, predict, and ultimately prohibit inconsistencies between laboratory and commercial scale. In this work, we performed CHO fed-batch cultivations in the single multicompartment bioreactor (SMCB), a new scale-down reactor system that offers new access to study large-scale heterogeneities in mammalian cell cultures. At volumetric power inputs of 20.4-1.5 W m-3, large-scale characteristics like long mixing times and dissolved oxygen (DO) heterogeneities were mimicked in the SMCB. Compared to a reference bioreactor (REFB) set-up, the conditions in the SMCB provoked an increase in lactate accumulation of up to 87%, an increased glucose uptake, and reduced viable cell concentrations in the stationary phase. All are characteristic for large-scale performance. The unique possibility to distinguish between the effects of changing power inputs and observed heterogeneities provided new insights into the potential reasons for altered product quality attributes. Apparently, the degree of galactosylation in the evaluated glycan patterns changed primarily due to the different power inputs rather than the provoked heterogeneities. The SMCB system could serve as a potent tool to provide new insights into scale-up behavior and to predict cell line-specific drawbacks at an early stage of process development.

Development of a chemically defined medium for Paenibacillus polymyxa by parallel online monitoring of the respiration activity in microtiter plates

BackgroundOne critical parameter in microbial cultivations is the composition of the cultivation medium. Nowadays, the application of chemically defined media increases, due to a more defined and reproducible fermentation performance than in complex media. In order, to improve cost-effectiveness of fermentation processes using chemically defined media, the media should not contain nutrients in large excess. Additionally, to obtain high product yields, the nutrient concentrations should not be limiting. Therefore, efficient medium optimization techniques are required which adapt medium compositions to the specific nutrient requirements of microorganisms.ResultsSince most Paenibacillus cultivation protocols so far described in literature are based on complex ingredients, in this study, a chemically defined medium for an industrially relevant Paenibacillus polymyxa strain was developed. A recently reported method, which combines a systematic experimental procedure in combination with online monitoring of the respiration activity, was applied and extended to identify growth limitations for Paenibacillus polymyxa. All cultivations were performed in microtiter plates. By systematically increasing the concentrations of different nutrient groups, nicotinic acid was identified as a growth-limiting component. Additionally, an insufficient buffer capacity was observed. After optimizing the growth in the chemically defined medium, the medium components were systematically reduced to contain only nutrients relevant for growth. Vitamins were reduced to nicotinic acid and biotin, and amino acids to methionine, histidine, proline, arginine, and glutamate. Nucleobases/-sides could be completely left out of the medium. Finally, the cultivation in the reduced medium was reproduced in a laboratory fermenter.ConclusionIn this study, a reliable and time-efficient high-throughput methodology was extended to investigate limitations in chemically defined media. The interpretation of online measured respiration activities agreed well with the growth performance of samples measured in parallel via offline analyses. Furthermore, the cultivation in microtiter plates was validated in a laboratory fermenter. The results underline the benefits of online monitoring of the respiration activity already in the early stages of process development, to avoid limitations of medium components, oxygen limitation and pH inhibition during the scale-up.

Bayesian Optimization as a Sustainable Strategy for Early-Stage Process Development? A Case Study of Cu-Catalyzed C–N Coupling of Sterically Hindered Pyrazines

Bayesian Optimization as a Sustainable Strategy for Early-Stage Process Development? A Case Study of Cu-Catalyzed C–N Coupling of Sterically Hindered Pyrazines

PreHAZOP: Graph‐Based Safety Analysis for Early Integration into Automated Engineering Workflows

AbstractThe increasing digitalization and standardization within the process industry lead to a high availability of digital, machine‐readable processes and plant descriptions. In particular, the publication of the DEXPI standard provides a digital representation of plant topologies including a complete description of all specifications. In early planning phases, this can be used as the basis for an automated safety assessment since digital availability significantly simplifies accessibility for smart search algorithms. This paper presents the preHAZOP search algorithm, which was developed to analyze P&IDs in DEXPI format and to detect safety‐critical deviations regarding their risk according to a classical HAZOP analysis. The preHAZOP is of particular interest in early process development stages and can be easily integrated into modern, digital engineering workflows.

An inherently safer design approach based on process safety time for batch chemical reaction processes

The economic attractiveness of an industrial process depends a large extent on its safe and reliable operation. The inherently safer design can help reduce the potential hazard on a fundamental level and eliminate escalation at the early stage of process development. In this work, an inherently safer design method for batch chemical reactors is proposed, which maximizes the yield of target product while minimizes Dow’s fire and explosion index. Process safety time is introduced as criterion to respond to the thermal runaway risk risen by unexpected failures. Finally, two examples for specific reaction systems with given kinetics are performed to demonstrate the feasibility and validity of the proposed method. The results indicate that the product yield optimized with the proposed method is significantly higher than that obtained from literature approach involving temperature limit or divergence criterion under the same PST. With the same yield, the PST optimized with the proposed method is longer.

Industrial batch monitoring for cell culture processes during process scale-ups: data challenges and solutions

Cell culture process scale-ups are common in biologics manufacturing as the process moves from the early stages of process development to commercial manufacturing. Statistical batch process monitoring (BPM) is frequently deployed for efficient real-time monitoring and control of cell culture processes. A BPM platform relies on historical process data to train in-control model for real-time monitoring of future batches. Deploying a BPM platform during or immediately after process scale-up is nontrivial due to limited batch campaigns at the commercial scale. In the absence of available at-scale commercial data during scale-ups, we propose an approach to reuse data sets generated at bench or pilot-scale for training BPM models at the commercial scale. The proposed method uses a scaling model to learn complex data scaling relations between bench/pilot-commercial scales. The model is then used to synthesise data sets at a commercial scale using existing in-control bench/pilot data. Using synthesised at-scale commercial scale data, a BPM model can be trained and deployed soon after the scale-up, which would not have been possible otherwise. The efficacy of the proposed approach is illustrated in an industrial cell culture process.

Model-based solvent selection for the synthesis and crystallisation of pharmaceutical compounds

The pharmaceutical industry needs design tools to identify greener and more resource-efficient process routes. Current model-based solvent selection methodologies often focus on the choice of solvent in a single unit operation, with fixed operating conditions. In particular, the two key stages of synthesis and separation are usually treated independently. This often results in the use of different solvents for each processing task, which typically requires energy-intensive solvent swap operations. In the current paper, we present a novel computer-aided approach based on computer-aided mixture/blend design (CAMbD) that couples property prediction with simple process models and optimisation to simultaneously identify optimal solvents and anti-solvents, compositions and process conditions for integrated synthesis and crystallisation. Solvents are chosen using key performance indicators (KPIs) that quantify mass efficiency and product quality. The proposed methodology is illustrated by identifying promising reaction and crystallisation solvents for the synthesis of mefenamic acid from 2,3-dimethylaniline and 2-chlorobenzoic acid. Furthermore, multi-objective optimisation is deployed to highlight the trade-offs between the solvent or process E-factor and safety indicators, and between the solvent E-factor and crystal yield. The inclusion of mass-based KPIs and safety specifications ensures that only high-performance solvents are chosen. The findings of our approach are expected to guide the rational selection of solvents for greener pharmaceutical manufacturing during early-stage process development.

Predicting Spray Dried Dispersion Particle Size Via Machine Learning Regression Methods

Spray dried dispersion particle size is a critical quality attribute that impacts bioavailability and manufacturability of the spray drying process and final dosage form. Substantial experimentation has been required to relate formulation and process parameters to particle size with the results limited to a single active pharmaceutical ingredient (API). This is the first study that demonstrates prediction of particle size independent of API for a wide range of formulation and process parameters at pilot and commercial scale. Additionally we developed a strategy with formulation and target particle size as inputs to define a set of “first to try” process parameters. An ensemble machine learning model was created to predict dried particle size across pilot and production scale spray dryers, with prediction errors between −7.7% and 18.6% (25th/75th percentiles) for a hold-out evaluation set. Shapley additive explanations identified how changes in formulation and process parameters drove variations in model predictions of dried particle size and were found to be consistent with mechanistic understanding of the particle formation process. Additionally, an optimization strategy used the predictive model to determine initial estimates for process parameter values that best achieve a target particle size for a provided formulation. The optimization strategy was employed to estimate process parameters in the hold-out evaluation set and to illustrate selection of process parameters during scale-up. The results of this study illustrate how trained regression models can reduce the experimental effort required to create an in-silico design space for new molecules during early-stage process development and subsequent scale-up.

A Collaborative Design Method for Safety and Security Engineers

The number of cyberattacks has been increasing not only on information systems but also on physical systems. Safety must be considered as an influence of cyberattacks. Vulnerabilities exploited in cyberattacks continue to occur day by day even if systems were developed securely. Security engineers must eliminate vulnerabilities even if the vulnerabilities occur after the developed systems are released. Vulnerabilities must be managed throughout system life cycle. But it takes time to apply its security patch. Safety engineers are required to ensure safety even when vulnerabilities exist. Therefore, collaboration between safety and security (S&S) engineers is necessary to manage corresponding S&S in operation process. S&S should be considered simultaneously in early stage of development process. Collaborative discussion is useful to mitigating risk of reworks. It is an example of reworks by inadequate S&S discussion that the braking system might be redesigned to promote the response in order to compensate for the delay caused by encryption. Therefore, this paper proposes common models effective for the collaboration throughout system life cycle. A management approach using the models is also proposed. Common model is represented by data flow diagram (DFD) because a module under cyberattacks can adversely affect other modules only through data flows. In the proposed method, the three improvements contribute to supporting management throughout system life cycle. Firstly, the models are applied to safety analysis and security analysis. Secondly, vulnerability occurrence is managed at the level of modules. System structures are designed based on modules. Module abnormalities caused by cyberattacks on the vulnerabilities are managed as causes of safety corruption. To indicate critical points for system to be considered, the points from a safety perspective must be identified. Processes and information are traced from the points in DFD. Finally, a module, which performs sets of functions, is outsourced. For each module, it must be considered who will manage vulnerabilities. The proposed method is illustrated using a development of a self-driving wheelchair as an example. In this paper, the collaborative design method for S&S engineers of products and their management based on modules are described to ensure safety even when unexpected vulnerabilities exist.

Predicting the Solubility of Nonelectrolyte Solids Using a Combination of Molecular Simulation with the Solubility Parameter Method MOSCED: Application to the Wastewater Contaminants Monuron, Diuron, Atrazine and Atenolol

Methods to predict the equilibrium solubility of nonelectrolyte solids are indispensable for early-stage process development, design, and feasibility studies. Conventional analytic methods typically require reference data to regress parameters, which may not be available or limited for novel systems. Molecular simulation is a promising alternative, but is computationally intensive. Here, we demonstrate the ability to use a small number of molecular simulation free energy calculations to generate reference data to regress model parameters for the analytical MOSCED (modified separation of cohesive energy density) model. The result is an efficient analytical method to predict the equilibrium solubility of nonelectrolyte solids. The method is demonstrated for the wastewater contaminants monuron, diuron, atrazine and atenolol. Predictions for monuron, diuron and atrazine are in reasonable agreement with MOSCED parameters regressed using experimental solubility data. Predictions for atenolol are inferior, suggesting a potential limitation in the adopted molecular models, or the solvents selected to generate the necessary reference data.

Device for respiration activity measurement enables the determination of oxygen transfer rates of microbial cultures in shaken 96-deepwell microtiter plates.

Mini-bioreactors with integrated online monitoring capabilities are well established in the early stages of process development. Mini-bioreactors fulfil the demand for high-throughput-applications and a simultaneous reduction of material costs and total experimental time. One of the most essential online monitored parameters is the oxygen transfer rate (OTR). OTR-monitoring allows fast characterization of bioprocesses and process transfer to larger scales. Currently, OTR-monitoring on a small-scale is limited to shake flasks and 48-well microtiter plates (MTP). Especially, 96-deepwell MTP are used for high-throughput-experiments during early-stage bioprocess development. However, a device for OTR monitoring in 96-deepwell MTP is still not available. To determine OTR values, the measurement of the gas composition in each well of a MTP is necessary. Therefore, a new micro(µ)-scale Transfer rate Online Measurement device (µTOM) was developed. The µTOM includes 96 parallel oxygen-sensitive sensors and a single robust sealing mechanism. Different organisms (Escherichia coli, Hansenula polymorpha, and Ustilago maydis) were cultivated in the µTOM. The measurement precision for 96 parallel cultivations was 0.21 mmol·L-1 ·h-1 (pooled standard deviation). In total, a more than 15-fold increase in throughput and an up to a 50-fold decrease in media consumption, compared with the shake flask RAMOS-technology, was achieved using the µTOM for OTR-monitoring.

Breakage Study of the Urchinlike Crystal Clusters of Ibrutinib

In this work, we investigate the impact of stirring and tableting on the breakup and deformation of the urchinlike crystal clusters of ibrutinib prepared by cooling crystallization. The breakup of clusters was induced directly in the dispersion unit of the light scattering device, typically used for the characterization of particulate materials in the pharmaceutical industry. This allows us to perform the experiment only with several milligrams of the sample. Computational fluid dynamics simulation was used to estimate the stresses to which the clusters were exposed to during the breakup experiment in the dispersion unit. The obtained results confirm the strong impact of the morphology of initial clusters on their breakup kinetics and steady-state sizes, with the strength of tested clusters covering the range from 180 to 1300 Pa. These values are significantly below the ibrutinib crystal hardness as determined by the nano- and microindentation experiment, revealing values above 253 MPa and leading to the breakup of the urchinlike crystal clusters of ibrutinib into individual crystals. Subsequently, the same ibrutinib crystal clusters were exposed to compaction pressures in the range of 38–565 MPa, using lactose monohydrate as a water-soluble model excipient having a hardness of 535 ± 59 MPa. It was found that below the ibrutinib crystal hardness, clusters break into smaller crystals, but when exceeding the ibrutinib crystal hardness, they deform plastically. A dissolution study demonstrates a significant impact of the abovementioned transformations on the dissolution profile of ibrutinib crystals. The developed methodology can provide information about the impact of various conditions used in drug product formulation, utilizing only a few milligrams of the sample, thus being suitable for the early stage of process development.

Integrating life cycle assessment in early process development stage: The case of extracting starch from mango kernel

An important issue in the development of new processes is how to perform environmental impact assessments that support decisions at an early stage of process development. In this study, a methodological procedure was applied to insert life cycle assessment (LCA) in early design of two alternative processes (A and B) to extract starch from mango kernel. This procedure allowed to perform LCAs at technology readiness levels (TRL) 4 and 5 considering different functional units, production scales and alternative scenarios. The analysis of process A and B identified the phase of starch purification as one of the most impacting one. After modifications were implemented, the comparison of Process A with B showed that process A, characterized by extracting only starch, performed better in all situations, being recommended for the implementation in a pilot plant, the next level in the technology maturity scale. Process B still requires improvements to reduce the impacts on climate change and human toxicity. This case study showed reductions in energy and water use, and life cycle impacts, when moving production from lab to industrial scale, through simulation. It also showed that crop production should be considered when evaluating processes that use biomass waste as raw material. The lessons learned from this case study allowed the simplification and detail of the applied procedure for inserting LCA at early research stage. This procedure can be applied to perform ex-ante LCA of new processes.

Time-Resolved Monitoring of the Oxygen Transfer Rate of Chinese Hamster Ovary Cells Provides Insights Into Culture Behavior in Shake Flasks

Cultivations of mammalian cells are routinely conducted in shake flasks. In contrast to instrumented bioreactors, reliable options for non-invasive, time-resolved monitoring of the culture status in shake flasks are lacking. The Respiration Activity Monitoring Respiration Activity Monitoring System system was used to determine the oxygen transfer rate (OTR) in shake flasks. It was proven that the OTR could be regarded as equal to the oxygen uptake rate as the change of the dissolved oxygen concentration in the liquid phase over time was negligibly small. Thus, monitoring the oxygen transfer rate (OTR) was used to increase the information content from shake flask experiments. The OTR of a Chinese hamster ovary cell line was monitored by applying electrochemical sensors. Glass flasks stoppered with cotton plugs and polycarbonate flasks stoppered with vent-caps were compared in terms of mass transfer characteristics and culture behavior. Similar mass transfer resistances were determined for both sterile closures. The OTR was found to be well reproducible within one experiment (standard deviation <10%). It correlated with changes in cell viability and depletion of carbon sources, thus, giving more profound insights into the cultivation process. Culture behavior in glass and polycarbonate flasks was identical. Monitoring of the OTR was applied to a second culture medium. Media differed in the maximum OTR reached during cultivation and in the time when all carbon sources were depleted. By applying non-invasive, parallelized, time-resolved monitoring of the OTR, the information content and amount of data from shake flask experiments was significantly increased compared to manual sampling and offline analysis. The potential of the technology for early-stage process development was demonstrated.

Economic assessment of the conversion of bleached eucalyptus Kraft pulp into cellulose nanocrystals in a stand‐alone facility via acid and enzymatic hydrolysis

AbstractCellulose nanocrystals (CNCs) have become valued bionanomaterials with enormous potential to bring fundamental changes and benefits to society. We evaluated the techno‐economic viability of using sulfuric acid and enzymatic hydrolysis technologies to produce CNCs from bleached eucalyptus Kraft pulp (BEKP) in stand‐alone facilities. Experiments were performed on the enzymatic and acid hydrolysis of BEKP and the separation and isolation of the generated CNCs. The results obtained were used to determine reaction yields and compositions through the process stages and to build mass balances for the production of CNCs via acid and enzymatic hydrolysis. We used the process data that were generated to simulate and scale up process models of the production of CNCs using Aspen Plus. Techno‐economic analyses of the simulated processes were performed using Aspen Process Economic Analyzer to generate capital and operating cost estimates. At an estimated minimum selling price (MSP) of $10 031/dry tonne of CNCs, the production of CNCs via acid hydrolysis can be technically and economically competitive. Further research and process optimization efforts should focus on low‐cost technologies that minimize water use and on sulfuric acid recovery technologies that lead to lower production costs. The production of CNCs via enzymatic hydrolysis requires a low capital investment. However, due to its low reaction yield, a reflection of the still early stage of process development, the production cost of the enzymatic CNCs, with an MSP of $65 740 dry tonne of CNCs t–1, is too high to be commercially attractive. Nonetheless, the low capital cost of producing CNCs by using enzymatic hydrolysis indicates that the process may be profitable if the enzymatic hydrolysis yield is drastically improved. Research efforts towards developing an enzyme cocktail designed and optimized specifically for CNCs production and the introduction of an efficient and low‐cost pretreatment stage prior to enzymatic hydrolysis to increase the accessibility of enzymes to cellulose may improve the hydrolysis yield. © 2021 Society of Chemical Industry and John Wiley &amp; Sons, Ltd

Advancing a rapid, high throughput screening platform for optimization of lentivirus production.

Lentiviral vectors (LVVs) hold great promise as delivery tools for gene therapy and chimeric antigen receptor T cell (CAR-T) therapy. Their ability to target difficult to transfect cells and deliver genetic payloads that integrate into the host genome makes them ideal delivery candidates. However, several challenges remain to be addressed before LVVs are more widely used as therapeutics including low viral vector concentrations and the absence of suitable scale-up methods for large-scale production. To address these challenges, we have developed a high throughput microscale HEK293 suspension culture platform that enables rapid screening of conditions for improving LVV productivity. High density culture (40 million cells mL-1 ) of HEK293 suspension cells in commercially available media was achieved in microscale 96-deep well plate platform at liquid volumes of 200μL. Comparable transfection and LVV production efficiencies were observed at the microscale, in conventional shake flasks and a 1-L bioreactor, indicating that significant scale-down does not affect LVV concentrations and predictivity of scale-up. Optimization of production step allowed for final yields of LVVs to reach 1.5×107 TU mL-1 . The ability to test a large number of conditions simultaneously with minimal reagent use allows for the rapid optimization of LVV production in HEK293 suspension cells. Therefore, such a system may serve as a valuable tool in early stage process development and can be used as a screening tool to improve LVV concentrations for both batch and perfusion based systems.

Challenges in transfer of gas-liquid reactions from batch to continuous operation: dimensional analysis and simulations for aerobic oxidation

The transfer of gas-liquid reactions from conventional batch processes into continuous operation using milli and micro reactors is claimed as an important step towards process intensification. Importantly, this transfer step should be realized in an early phase of process development, already, in order to minimize research efforts towards the undesired operation strategy. The main challenge of this approach, therefore, arises from lack of knowledge in the early stage of process development and the resulting system with high degrees of freedom. This contribution presents an approach to tackle this challenge by means of mathematical modelling and simulation for the aerobic oxidation of 9,10-dihydroanthracene (DHA) catalyzed by polyoxometalates (POMs) being used as example for gas-liquid reactions. The reaction was chosen as it provides sufficient complexity, since it consists of three consecutive oxidation steps of DHA and a parallel catalytic redox-cycle according to a Mars-van-Krevelen mechanism. It also provides the challenge of unknown reaction kinetics, which have been estimated in this contribution. The dimensionless balance equations for reactor modeling are derived and parametrized based on early stage experimental results obtained in batch operation mode. The discrimination between batch and continuous operation was performed by means of characteristic dimensionless numbers using the identical mathematical model for comparability reasons. The model was used to perform sensitivity studies with emphasis on the interplay between mass transfer characteristics and reaction kinetics for both the batch and continuous operation mode. The simulation results show that the performance of both operation modes mainly depend on the oxidation state of the POM catalyst, which is caused by the differences in oxygen availability. Therefore, results obtained in batch operation mode are prone to be masked by mass transfer issues, which affects catalyst and reactor development at the same time and may thus cause maldevelopments. With respect to process development it can thus be concluded that the transfer from batch to continuous operation together with mathematical modeling is important in an early phase, already, in order to detect limitations misleading the development. Finally, even simple models with roughly estimated parameters from preliminary experiments are shown to be sufficient in the early phase and can systematically be improved, in the subsequent phases.Graphical abstract

A hybrid NIR-soft sensor method for real time in-process control during continuous direct compression manufacturing operations

Near Infrared (NIR) spectroscopy is commonly utilized for continuous manufacturing as Process Analytical Technology (PAT) tool. This paper focus on a continuous direct compression manufacturing process, in which an NIR PAT probe is integrated into the tablet press feed frame and into the tablet diversion control system to ensure continuous monitoring of the potency and homogeneity of the blend within the process line. The quantification of NIR spectra is achieved through Partial Least-Squares (PLS) modeling, calibrated with offline analyzed tablet cores at different potency levels. Because the NIR measurements are often sensitive to sample physical properties caused by raw materials or process conditions, etc., adopting a data-driven approach will require a large amount of representative data throughout the method lifecycle. During the early stages of process development, whenever new uncaptured source of variability in the model space are encountered, the chemometric predictions can deviate from the offline reference, requiring frequent model updates. These deviations can be reduced by integrating process and physico-chemical knowledge in the on-line potency estimation. This paper presents a novel hybrid method combining the online NIR PLS and a potency soft sensor estimation, enabling a robust potency prediction whilst minimizing maintenance downtimes and facilitating cross-site method transfer.

OPTIMIZATION OF PRESS-FIT PROCESSES

The press-fit process is an efficient, low-cost method for joining parts. The parts that must be joined interfere with each other’s occupation of space; therefore, contact dimensions and their tolerances influence the quality of the assembly. The traditional method for the selection of contact dimensions and their tolerances is based on engineering experience. The idea of the research work presented in this paper is to optimize the press-fit process at an early stage of development process, involving prediction and optimization of the joining force and consequently the prediction and minimization of the rejection rate. Accordingly, several finite-element (FE) simulations of the press-fit process for predicting the joining forces were conducted, considering input-parameter variations (material properties: yield stress, hardening exponent; geometry: shaft diameter, guide diameter of the core, functional diameter of the core; friction coefficient). Based on FE simulations and 47 different input-parameter-variation results, the empirical model for predicting the joining force using the response-surface methodology (RSM) was obtained. By using RSM and a stochastic Monte Carlo simulation, the rejection rate was also determined. The predicted and the actual rejection rates for selected process parameters were 1.4 % and 1.5 %, respectively. Consequently, the press-fit process can also be optimized to reduce the rejection rate using the same Monte Carlo simulation. The results of the analysis show that the rejection rate can be reduced from 1.4 % to 0.2 %.