Multifactorial Experimental Design Research Articles

A Study of the Influence of Ion-Ozonized Water on the Properties of Pasta Dough Made from Wheat Flour and Pumpkin Powder.

Water treated with ion ozone improves the technological qualities of food products. Therefore, ion-ozonated water was used in the work, and whole-grain flour from soft wheat of the Almaly variety and pumpkin powder were used as raw materials to improve the quality and nutritional value of the pasta. This study investigated the effects of ion-ozone concentration in ion-ozonated water Cio, water temperature tw, pumpkin powder content Cpp and drying temperature td on various characteristics affecting the quality of pasta, including its organoleptic physical, chemical, and rheological properties. These characteristics were assessed by conducting multiple experiments, a total of 25 indicators were determined, such as humidity, acidity, cooking properties, deformation, and other basic quality indicators. To reduce the number of experiments and obtain a reliable assessment of the influence of individual factors on the quality indicators of pasta, methods involving the multifactorial design of experiments were applied. Data processing and all necessary calculations were carried out using the PLAN sequential regression analysis program. Consequently, our findings indicate that minimizing dry water (DM) loss in cooking water requires a dual approach: increasing ion-ozone concentration and optimizing pasta composition and drying conditions, specifically by reducing pumpkin powder content and drying temperature. As a result, it was established that to obtain high-quality pasta from whole-grain flour with high quality and rheological properties, it is necessary to use the following optimal production modes: ion-ozone concentration in ion-ozonated water Cio = 2.5 × 10-6 mg/cm3, water temperature tw = 50 °C, pumpkin powder content Cpp = 3.0%, and pasta drying temperature td = 50 °C. The resulting pasta is an environmentally friendly product with a high content of biologically active substances.

Non-conventional endpoints show higher sulfoxaflor toxicity to Chironomus riparius than conventional endpoints in a multistress environment

Evidence grows that standard toxicity testing might underestimate the environmental risk of neurotoxic insecticides. Behavioural endpoints such as locomotion and mobility have been suggested as sensitive and ecologically relevant additions to the standard tested endpoints. Possible interactive effects of chemicals and additional stressors are typically overlooked in standardised testing. Therefore, we aimed to investigate how concurrent exposure to environmental stressors (increased temperature and predation cues) and a nicotinic acetylcholine receptor (nAChR)-modulating insecticide (‘sulfoxaflor’) impact Chironomus riparius across a range of conventional and non-conventional endpoints. We used a multifactorial experimental design encompassing three stressors, sulfoxaflor (2.0–110 µg/L), predation risk (presence/absence of predatory cues), and elevated temperature (20 °C and 23 °C), yielding a total of 24 distinct treatment conditions. Additional stressors did not change the sensitivity of C. riparius to sulfoxaflor. To assess potential additive effects, we applied an Independent Action (IA) model to predict the impact on eight endpoints, including conventional endpoints (growth, survival, total emergence, and emergence time) and less conventional endpoints (the size of the adults, swimming abilities and exploration behaviour). For the conventional endpoints, observed effects were either lower than expected or well-predicted by the IA model. In contrast, we found greater than predicted effects of predation cues and temperature in combination with sulfoxaflor on adult size, larval exploration, and swimming behaviour. However, in contrast to the non-conventional endpoints, no conventional endpoints detected interactive effects of the neurotoxic insecticide and the environmental stressors. Acknowledging these interactions, increasing ecological context of ecotoxicological test systems may, therefore, advance environmental risk analysis and interpretation as the safe environmental concentrations of neurotoxic insecticides depend on the context of both the test organism and its environment.

Environmental aging influence on mechanical properties of additive manufactured polyamide parts: A statistical approach

AbstractThis study explores the influence of environmental factors and printing orientation on the aging of additive manufactured polyamide 12 (PA12). A multifactorial experimental design was employed to age specimens under controlled conditions. Analysis of variance revealed that temperature, irradiance, relative humidity, and time significantly impact the ultimate tensile strength (UTS) and Young's modulus (Y) of PA12. Applying multiple linear regression methods produced predictive models with R2 values of 0.9075 for the UTS and 0.5226 for the Y, indicating substantial explanatory power for the specimen's UTS. These models enhance understanding of PA12 aging, aiding in the design of more durable mechanical parts for applications exposed to environmental conditions, such as footwear, automotive components, and medical devices. As next steps, future research could empirically deploy these developed predictive models and explore longer time scales to further elucidate the long‐term aging behavior of additively manufactured PA12.

Automated multifactorial design of experiment and Bayesian optimisation algorithm approaches to method development for the green analysis by supercritical fluid chromatography of a pharmaceutical ingredient

During drug development, chromatography is frequently used for purity and stability testing of both drug substance and drug product. Reversed phase liquid chromatography (RPLC) is one of the most widely used methodologies due to its wide scope of application. In the later stages of drug development, the specified impurities and degradation products that define the critical quality attribute of the final API, also known as Key Predictive Sample Set (KPSS), are usually well defined and controlled. At this point, a method review enables selecting the most appropriate technique which should be the one providing optimal robustness (ICH-Q14[1]), with the support of Quality by Design (QbD) approaches. Supercritical Fluid Chromatography (SFC) is a preferred technique for its proven diversity in selectivity. The adoption of a technique which presents the most favourable environmental impact, such as, but not limited to, SFC, is also becoming increasingly important as laboratories strive to reduce carbon footprint. Re-developing a method requires high resource-demands in terms of staff, materials, and time. Any step of the process that can be automated can facilitate this approach, speeding up the delivery of the method whilst preserving robustness.In this article we describe how an SFC method was developed for the purity profiling of a late-stage oncology candidate, taking advantage of the superior selectivity of SFC towards structurally similar analytes, owed to the high orthogonality with R2 as low as 0.014 towards the KPSS. We describe two approaches to automate the method development. Firstly, a multifactorial design of experiments (DoE) and secondly, an optimization via a Bayesian algorithm, which was completed in one night, highlighting the potential and limitations, with an insight into the robustness. Both methods achieved baseline separation with varying levels of automation embedded into the process and a large reduction of the resource demands when compared to traditional optimisation methods. Finally, we describe the beneficial environmental impact that implementing SFC methods can yield, with a calculated green score reduced to a value between 17 and 30 % compared to RPLC, depending on the number of runs per sequence.

“Quantifying the Impacts of Multiple Stressors” (QIMS)—a new experimental platform for robust multifactorial experiments in benthic ecosystems

AbstractTo predict the ecological consequences of expected global change, we need to understand the independent and combined effects of multiple stressors. Multiple experimental treatments are required to simultaneously test for effects of multiple stressors at different levels of intensity, independently and combined, and at different levels of biological organization. Most marine multiple stressors studies to date are conducted on assembled communities in mesocosms with a low number of treatments or low replication of treatments or both. These limitations prevent (1) robust data analyses, (2) characterization of single and combined effects of multiple stressors, and (3) identification of mechanisms underpinning biological responses. We present a new mesocosm‐based experimental platform for benthic communities: Quantifying the Impacts of Multiple Stressors (QIMS). Here, 96 independent mesocosms facilitate multifactorial and multilevel experimental designs with the high replication required for robust tests of multiple stressors and biological interactions. For example, three distinct pH levels are achieved by manipulating CO2 concentrations in the air supply, and three water temperature levels are provided by a cooling system in a fully crossed design that is required to identify all potential interactions, from which all combinations can be replicated 10 times (i.e., 90 experimental units). We demonstrate clearly how different levels of temperature and pCO2/pH can be manipulated precisely and maintained for at least 7 weeks. QIMS complements the limited number of permanently installed marine mesocosm facilities worldwide that simulate ocean warming and/or acidification and expedites multiple stressor research by providing an unprecedented level of replication for statistical robustness.

Experimental manipulation of food availability and macroparasite prevalence reveal differential effects on space use in wild rodents.

Research Highlight: Mistrick, J., Veitch, J. S. M., Kitchen, S. M., Clague, S., Newman, B. C., Hall, R. J., Budischak, S. A., Forbes, K. M., & Craft, M. E. (2024). Effects of food supplementation and helminth removal on space use and spatial overlap in wild rodent populations. Journal of Animal Ecology. http://doi.org/10.1111/1365-2656.14067. The spread of pathogens has been of long-standing interest, even before dramatic outbreaks of avian influenza and the coronavirus pandemic spiked broad public interest. However, the dynamics of pathogen spread in wild populations are complex, with multiple effects shaping where animals go (their space use), population density and, more fundamentally, the resultant patterns of contacts (direct or indirect) among individuals. Thus, experimental studies exploring the dynamics of contact under different sets of conditions are needed. In the current field study, Mistrick etal. (2024) used a multifactorial experimental design, manipulating food availability and individual pathogen infection state in wild bank voles (Clethrionomys glareolus). They found that while food availability, individual traits and seasonality can affect how far individual voles moved, the degree of overlap between individual voles remained largely the same despite a high variation in population density-which itself was affected by food availability. These results highlight how biotic and abiotic factors can shape patterns of space use and balance the level of spatial overlap through multiple pathways.

Turning discarded blue shark (Prionace glauca) skin into a valuable nutraceutical resource: An enzymatic collagen hydrolysate

Marine-derived collagen, particularly from blue shark (Prionace glauca) skin, represents a sustainable resource for the nutraceutical industry, yet its effective utilization remains underexplored. This study aims to optimise the enzymatic hydrolysis of blue shark skin collagen using alcalase and bromelain to enhance the bioactive properties of the resultant hydrolysates. We employed a multifactorial experimental design to determine the optimal hydrolysis conditions, assessing factors including enzyme concentration, pH, and temperature. The alcalase-treated hydrolysates demonstrated superior antioxidant and anti-inflammatory activities compared to those treated with bromelain, with increased solubilised proteins and a higher degree of hydrolysis. Notably, peptide profiles indicated that alcalase hydrolysates favoured the production of smaller peptides, suggesting enhanced bioavailability and digestive stability. In vitro gastrointestinal simulations demonstrated the functional stability of these peptides, indicating that while they undergo structural changes during digestion, their potential for gastrointestinal health remains significant. Our findings highlight the feasibility of converting blue shark skin, a commonly discarded by-product, into valuable nutraceutical ingredients, thus contributing to marine sustainability and waste reduction This research advances the biotechnological application of marine collagen and opens avenues for developing functional foods and pharmaceuticals.

Bayesian Analysis of Multi-Factorial Experimental Designs Using SEM

Latent repeated measures ANOVA (L-RM-ANOVA) has recently been proposed as an alternative to traditional repeated measures ANOVA. L-RM-ANOVA builds upon structural equation modeling and enables researchers to investigate interindividual differences in main/interaction effects, examine custom contrasts, incorporate a measurement model, and account for missing data. However, L-RM-ANOVA uses maximum likelihood and thus cannot incorporate prior information and can have poor statistical properties in small samples. We show how L-RM-ANOVA can be used with Bayesian estimation to resolve the aforementioned issues. We demonstrate how to place informative priors on model parameters that constitute main and interaction effects. We further show how to place weakly informative priors on standardized parameters which can be used when no prior information is available. We conclude that Bayesian estimation can lower Type 1 error and bias, and increase power and efficiency when priors are chosen adequately. We demonstrate the approach using a real empirical example and guide the readers through specification of the model. We argue that ANOVA tables and incomplete descriptive statistics are not sufficient information to specify informative priors, and we identify which parameter estimates should be reported in future research; thereby promoting cumulative research.

Quantifying the impact of AI recommendations with explanations on prescription decision making

The influence of AI recommendations on physician behaviour remains poorly characterised. We assess how clinicians’ decisions may be influenced by additional information more broadly, and how this influence can be modified by either the source of the information (human peers or AI) and the presence or absence of an AI explanation (XAI, here using simple feature importance). We used a modified between-subjects design where intensive care doctors (N = 86) were presented on a computer for each of 16 trials with a patient case and prompted to prescribe continuous values for two drugs. We used a multi-factorial experimental design with four arms, where each clinician experienced all four arms on different subsets of our 24 patients. The four arms were (i) baseline (control), (ii) peer human clinician scenario showing what doses had been prescribed by other doctors, (iii) AI suggestion and (iv) XAI suggestion. We found that additional information (peer, AI or XAI) had a strong influence on prescriptions (significantly for AI, not so for peers) but simple XAI did not have higher influence than AI alone. There was no correlation between attitudes to AI or clinical experience on the AI-supported decisions and nor was there correlation between what doctors self-reported about how useful they found the XAI and whether the XAI actually influenced their prescriptions. Our findings suggest that the marginal impact of simple XAI was low in this setting and we also cast doubt on the utility of self-reports as a valid metric for assessing XAI in clinical experts.

A unified strategy to rebalance multifactorial designs with unequal group sizes: application to analysis of variance multiblock orthogonal partial least squares

BackgroundAdequately handling unbalanced groups remains one of the major challenges for the analysis of multivariate data collected from multifactorial experimental designs. While partial least squares-based methods, such as analysis of variance multiblock orthogonal partial least squares (AMOPLS), can offer better discrimination between factor levels, they can be more heavily affected by this issue, and unbalanced designs of experiments may lead to a substantial confusion of the effects. Even state-of-the-art analysis of variance (ANOVA) decomposition methodologies using general linear models (GLM) lack the ability to efficiently disentangle these sources of variation when combined with AMOPLS. ResultsA versatile solution developed as an extension of a prior rebalancing strategy is proposed for the first decomposition step based on ANOVA. This approach has the advantage of yielding an unbiased estimation of the parameters and retaining the within-group variation in the rebalanced design, while preserving the orthogonality of effect matrices, even in presence of unequal group sizes. This property is of utmost importance for model interpretation because it avoids mixing sources of variation related to the different effects in the design. A real case study involving metabolomic data from in vitro toxicological experiments was used to demonstrate the potential of this strategy to handle unequal group sizes using a supervised approach. Primary 3D rat neural cell cultures were exposed to trimethyltin following a multifactorial design of experiments involving three fixed effect factors. Significance and NoveltyThe rebalancing strategy was demonstrated as a novel and potent solution to handle unbalanced experimental designs by offering unbiased parameter estimators and orthogonal submatrices, thus avoiding confusion of the effects and facilitating model interpretation. Moreover, it can be combined with any multivariate method used for the analysis of high-dimensional data collected from multifactorial designs.

Optimization of the Dose Rate Effect in Tetrazolium Gellan Gel Dosimeters.

Tetrazolium salts provide an appealing candidate for 3D gel dosimeters as they exhibit a low intrinsic color, no signal diffusion and excellent chemical stability. However, a previously developed commercial product (the ClearView 3D Dosimeter) based on a tetrazolium salt dispersed within a gellan gum matrix presented a noticeable dose rate effect. The goal of this study was to find out whether ClearView could be reformulated in order to minimize the dose rate effect by optimizing of the tetrazolium salt and gellan gum concentrations and by the addition a thickening agent, ionic crosslinkers, and radical scavengers. To that goal, a multifactorial design of experiments (DOE) was conducted in small-volume samples (4-mL cuvettes). It showed that the dose rate could be effectively minimized without sacrificing the integrity, chemical stability, or dose sensitivity of the dosimeter. The results from the DOE were used to prepare candidate formulations for larger-scale testing in 1-L samples to allow for fine-tuning the dosimeter formulation and conducting more detailed studies. Finally, an optimized formulation was scaled-up to a clinically relevant volume of 2.7 L and tested against a simulated arc treatment delivery with three spherical targets (diameter 3.0 cm), requiring different doses and dose rates. The results showed excellent geometric and dosimetric registration, with a gamma passing rate (at 10% minimum dose threshold) of 99.3% for dose difference and distance to agreement criteria of 3%/2 mm, compared to 95.7% in the previous formulation. This difference may be of clinical importance, as the new formulation may allow the quality assurance of complex treatment plans, relying on a variety of doses and dose rates; thus, expanding the potential practical application of the dosimeter.

Applying the Artistic Psychological Painting Course to Improve the Mental Health of Middle School Students

Purpose: This study aims to verify the significance of the Artistic Psychological Painting program for developing middle school students' psychological well-being through an educational program and a multi-factorial experimental design based on feedback from teachers and students.
 Methodology: This paper presents the format and content of the Artistic Psychological Painting program to test the program's effectiveness. The subjects were 12-15-year-old middle school students in the first and second grades of Southwest University South China Town Middle School (middle school) in Chongqing, China, and the Artistic Psychological Painting course they took at school.
 Findings: The study showed that the Artistic Psychological Painting program positively affected middle school students' emotional intelligence and self-identity. Accordingly, based on the initial definition and exploration of the connotation and extension of the Artistic Psychological Painting curriculum, we have communicated with school leaders, subject experts, in-service teachers, and students to propose the implementation of the "Artistic Psychological Painting".
 Recommendation: The program is designed to complement the psychological education curriculum in Chinese public schools and to provide a vision for school-based and community-based curricula.
 

Enhancing surface properties and corrosion resistance of API 5L X52 steel through orthogonal ball burnishing

Surface engineering involves using various technologies to modify the surface and near-surface areas of a material to enable it to perform different functions from the bulk of the material. Two primary methods of surface engineering are surface coatings and surface modification. Orthogonal ball burnishing is a process that improves certain surface integrity properties of materials through superficial plastic deformation. This study focuses on the effect of this treatment and the influence of its parameters on the physico-geometric surface characteristics of API 5L X52 steel. Burnishing tests were conducted with four main parameters: burnishing force (Py), feed rate (f), spindle speed (N), and number of passes (i). The analysis revealed an appropriate combination of burnishing parameters that led to a 98% reduction in surface roughness and a 42% increase in surface microhardness. A multi-objective optimization of roughness and microhardness after burnishing yielded optimal parameters. Furthermore, a multifactorial experimental design demonstrated that corrosion resistance increased by 318%.

Advancing Microbial Electrolysis Technology via Impedance Spectroscopy and Multi-Variate Analysis

In this study, EIS data collected from three electrode half-cell configurations was used to qualitatively identify and quantitatively determine the responses of ohmic, kinetic, and mass transfer impedances to buffer concentration, flow rate, and applied potential in an MEC consisting of a bioanode and an abiotic nickel-mesh cathode separated by a microporous membrane. EIS measurements were collected during startup and growth (including an abiotic run) at closed circuit and open circuit conditions to accurately match portions of the EIS spectra with the corresponding physical processes and to quantify kinetic changes as the biofilm matured. Once the MEC reached a target current density of 10 A/m2, a multifactorial experimental design formulated as a Taguchi array was executed to assess the impact of flow rate, buffer concentration, and applied voltage on EIS and performance response variables. Multivariate analysis was conducted to ascertain the relative importance of the independent variables and identify any correlations between process conditions and system response. The liquid flow through the anode was found to be strongly correlated with the impedance parameters and the MEC performance, while applied voltage influenced them to a lesser degree. The results are important from an industrial application perspective and provide insights into parameters important for process optimization.

Encouraging bystander helping behaviour in a violent incident: a virtual reality study using reinforcement learning

Virtual reality (VR) affords the study of the behaviour of people in social situations that would be logistically difficult or ethically problematic in reality. The laboratory-controlled setup makes it straightforward to collect multi-modal data and compare the responses across different experimental conditions. However, the scenario is typically fixed and the resulting data are usually analysed only once the VR experience has ended. Here we describe a method that allows adaptation of the environment to the behaviours of participants and where data is collected and processed during the experience. The goal was to examine the extent to which helping behaviour of participants towards the victim of a violent aggression might be encouraged, with the use of reinforcement learning (RL). In the scenario, a virtual human character represented as a supporter of the Arsenal Football Club, was attacked by another with the aggression escalating over time. (In some countries football is referred to as ‘soccer’, but we will use ‘football’ throughout). Each participant, a bystander in the scene, might intervene to help the victim or do nothing. By varying the extent to which some actions of the virtual characters during the scenario were determined by the RL we were able to examine whether the RL resulted in a greater number of helping interventions. Forty five participants took part in the study divided into three groups: with no RL, a medium level of RL, or full operation of the RL. The results show that the greater extent to which the RL operated the greater the number of interventions. We suggest that this methodology could be an alternative to full multi-factorial experimental designs, and more importantly as a way to produce adaptive VR scenarios that encourage participants towards a particular line of action.

Die wear reduction by multifactorial Design of Experiments applied to forging simulations

Both wear and fracture mechanisms of 32CrMoV12 dies after hot forging were investigated through SEM and metallographic tests. In order to contain wear mechanisms and to prevent fracture occurrence, a multifactorial Design of Experiments (DoE) has been executed on the Finite Element simulated process. Considered design factors have been limited to process parameters directly modifiable in the actual industrial process. Two step forging cycle has been accounted in the evaluation of stress distribution on the dies and in the estimate of die wear. Despite high process variability, the possibility to effectively hinder fracture and to control most of the wear sources was demonstrated.

The effect of synthesis parameters on density and thermal conductivity of MMT based aerogel. Application of experimental design model

Aerogels are a class of materials largely composed of air with very low bulk densities. Due to their extremely porous nature, aerogels are very promising materials for thermal and acoustic insulation applications. While their majority described in the literature are silica-based, there are a limited number of references to clay-based aerogels. The abundance of clay and its limited toxicity make them particularly attractive. Clay aerogels formation involves mixing of clay with water, freezing of the suspension and freeze-drying of the frozen gel. This work concerns the use of montmorillonite as a raw material for the synthesis of aeroclays with low density. Clay aerogels were synthesized by mixing montmorillonite clay and water and their density was measured. Various agents were used for the reinforcement of the brittle structure of clay aerogel. A multifactorial experimental design model is applied in order to study the combined effect of synthesis parameters on the density of aeroclays. The thermal conductivity of the optimized sample was determined by means of the Heat Flow method, on a Netzsch HFM 436 lamda Heat Flow Meter. As it is concluded, aeroclays, derived from a solution of montmorillonite clay and water alone were brittle so the addition of reinforcement agent is crucial. Depending on the synthesis conditions, a density range from 0.05 to 0.4 g/cm3 and thermal conductivity in the range of 0.04 W/mK was achieved. The factors with the highest impact on the density of the final product were the content of MMT and reinforcement agent in the precursor solution. The optimized synthesis in terms of density and thermal conductivity was proved to be inflammable.

Effective adsorption of Tartrazine by modified biomaterial from wheat residues

The use of adsorbents from wheat residues (WR), wheat cellulose (WC) and wheat cellulose (MWC) treated with Cetyl trimethyl ammonium chloride (CTAC) was studied in the removal of tartrazine in an aqueous solution. The effect of adsorbent dose (15, 25 and 35 mg) and initial concentration (40, 70 and 100 mg/L) was evaluated. WC was obtained by double alkaline extraction, and the modification was performed with CTAC at 25 %w. Adsorption tests were performed following a multifactorial experimental design 33, placing 5 mL of solution in contact with the bioadsorbent for 24 h, at 30 °C and 250 rpm. UV-Vis determined the concentration of tartrazine at 500 nm. FTIR analysis showed that the prepared adsorbents present a diverse structure, with functional groups such as OH, carboxyl, amino and hydrocarbon compounds. Decreasing the adsorbent dose and increasing the initial concentration positively affect the removal efficiency, achieving the removal of 97.65 % with MWC. Adsorption kinetics showed that equilibrium was reached at 480 min when MWC and WC were used. The Freundlich model fitted the adsorption equilibrium data, showing that removal occurs in multilayers due to chemical interactions. CTAC-modified wheat cellulose is a good adsorbent for tartrazine in an aqueous solution.

Responses to Offense at Work and the Impact of Hierarchical Status: The Fault of the Leader, Causal Attributions, and Social Support During the Covid-19 Pandemic Crisis.

The study explores the mechanism by which unadapted causal attributions and the perception of social support stimulate revenge and reconciliation at the social and professional level in the context of the current pandemic. In particular, the purpose of the study is to investigate the relationship between the accused, the victim and offender status and the search for revenge or reconciliation following a personal offense. To test the suggested research model, we analyzed the data collected by 167 (m = 28.52; SD = 8.98) employees in different organizations using a multifactorial experimental design. The results support the influence of attributional predictions in forming revenge and reconciliation and show that they are involved in the decision to carry out revenge, but especially in the way the employee interprets the trigger situation. In conclusion, the revenge is based on a negative attributional mechanism that produces the greatest deficit of adaptation to the situation and a weakening of the perception of social support, while reconciliation seems to be based on a much more complex socio-occupational mechanism. Leaders should pay attention to organizational communication during a crisis as they could encourage hopelessness depression. Adjusting crisis communication is crucial to ensuring job satisfaction that could mitigate negative effects.

Abstract 295: Implementing fully automated kinase inhibitor characterization using a robotic system

Abstract About 50 kinase inhibitors have been approved by the FDA for different indications so far. On the way from target validation to approval, the biochemical characterisation of a novel kinase inhibitor is a challenging, yet absolutely critical task. High resolution, kinetic molecular profiling can enable better data-driven decision making early on in the drug discovery process, not only saving time and resources, but also leading to superior molecular design. Arctoris developed a robotics-enabled process for fully automated kinase inhibitor characterisation, providing an unparalleled depth of data capture, going beyond the current state-of-the-art of biochemical assay setup. We validated our technology platform establishing assays against four members of the Janus Kinase family (JAK1, JAK2, JAK3, TYK2), profiling a set of JAK inhibitors. Of note, several JAK inhibitors with prior FDA approval for other indications entered clinical trials for COVID-19 treatment, making this target class particularly relevant for an in-depth study. Reagent validation, assay development, calibration, and optimization were expedited through systematic multifactorial experimental design, high density assay plate formats and versatile automated liquid handling. The Arctoris Ulysses platform affords 9 orders of magnitude range in liquid volume handling, with picolitre precision and contact-free digital dispensing for true, non-serial, independent experimentation. Fully automated protocols were optimized, validated, versioned, and explicitly encoded. Robust potency measurements of all inhibitors were established against each of the JAK targets, revealing molecules with distinct isoform selectivity. Designing and selecting molecules with specific activity profiles enables the fine tuning of pharmacology and the avoidance of unwanted off-target toxicity. Our unique platform, assay design, and deep expertise enabled the identification of molecules within the JAK inhibitor set that exhibit a range of kinetic properties. Our mechanistic analyses can help to elucidate the mode of inhibition (competitive, allosteric, synergistic etc.) as well as provide information pertaining to the kinetic selectivity that may be present but missed by focusing solely on potency. Citation Format: Daniel A. Thomas, Tom A. Fleming, Martin-Immanuel Bittner. Implementing fully automated kinase inhibitor characterization using a robotic system [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 295.