Assessment Paradigm Research Articles

The Efficacy, Effectiveness, and Efficiency of Integrated QTc Assessment: Rationalizing Approaches to New Drug Modalities.

After nearly 3 decades of regulatory activity concerning new drugs' potential for delayed cardiac repolarization an integrated risk assessment paradigm for small molecule drugs has been established. Regulatory guidance also suggests that for large, targeted proteins and monoclonal antibodies no quantitative clinical QTc assessment is necessary. The expansion of new drug modalities prompts the question: "Should these new modalities be treated like small molecule drugs or like monoclonal antibodies?"

Use of human–computer interactive games for the dynamic assessment of language skills of children with autism spectrum disorder

AbstractThis study explored the influence of the dynamic assessment of human–computer interactive games on children's language potential. Thirty‐seven special children aged 3–7 years were selected to participate in the study. They were divided into three groups according to their scores on the Autism Behaviour Checklist: (1) a non‐autism group, (2) a suspected autism group and (3) an autism group. In two stages of static and dynamic assessment, all three groups of children completed language tests under the conditions of no mediated prompt and mediated prompts, and the data generated in the game interactions were processed and analysed. The findings suggest the following: (1) static assessment may ignore children's language potential, while dynamic assessment can measure and differentiate children's potential language development ability; (2) children with ASD need more mediation prompts and are more likely to complete tasks with explicit mediation prompts; (3) children with ASD have weaker language potential, and children with suspected ASD or non‐ASD children have homogeneous language potential; and (4) assessors should pay timely attention to and maintain children's learning status and psychological changes. This study demonstrates the importance of human–computer interactive games as a dynamic assessment tool to tap into children's language potential and to determine their proximal developmental zone, which has important implications for teachers in understanding groups with different language abilities. Practitioner notesWhat is already known about the topic? The traditional language assessment paradigm (static assessment) can only evaluate the learners' current language development ability, while the short‐term development potential may not be observed. Different prompts in the mediation strategy can be used as a quantitative assessment paradigm in dynamic assessment. Much of the dynamic assessment in children with ASD focuses on language vocabulary learning outcomes and lacks measurement or quantification of language potential. What this paper adds? Presents a method for the dynamic assessment of a human–computer interactive language game suitable for children aged 3–7 years with ASD. Quantifies the potential level of language skills in children with varying degrees of ASD. Objectively and dynamically compares differences in language development potential between different groups. In dynamic assessment, children's learning status and psychological changes should be paid attention to and maintained in time. Implications for practice, research or policy This paper provides a concrete example of how human–computer interactive games can be used to dynamically assess the language potential of children with ASD. These findings highlight the importance of progressive scaffolds (mediated prompts) in children's language learning, which can help children identify and solve problems in a timely manner. For educational developers, language intervention materials should be prepared in a way that encompasses the different levels of language skills of children with different disabilities, that is, within the individual's zone of proximal development.

The Paradigm Shift in Education in Big Data Era: Exploring the Intersection of Historical Assessment Frameworks and AI-Powered Assessment Methods in Education

This paper delves into the dynamic intersection between traditional assessment processes and AI-powered technology assessment models within the expansive landscape of the big data era. Tracing the origins of large-scale standardized testing, from Francis Galton's pioneering intelligence testing proposals to the subsequent development of norm-referenced testing (NRT) by Alfred Binet and Theodore Simon, the historical trajectory underscores the evolution towards authentic assessment paradigms such as computerized adaptive testing (CAT) and performance-based assessment (PBA). These shifts reflect a broader aim of equipping educators with tools to measure student learning effectively and foster continuous learning beyond the confines of the classroom. However, despite the proliferation of methods integrating big data and AI technologies in educational settings, a discernible gap persists between technological capabilities and their practical implementation in education, notably in the realm of student assessment and evaluation. This gap highlights challenges in enhancing assessment accuracy and reliability, evaluating non-cognitive skills, and delivering personalized feedback. The introduction of AI-powered technologies like ChatGPT raises ethical considerations regarding fairness, privacy, and bias, necessitating responsible and equitable deployment.This paper addresses fundamental research questions concerning the historical influences on assessment frameworks, the role of AI-powered technology in driving paradigm shifts, and the ethical considerations surrounding its application. Through a comprehensive literature review, it explores the potential of AI technology to enhance assessment accuracy and reliability while advocating for its responsible use. Despite the challenges posed by traditional assessment processes and AI-powered technology assessment models, educators continually strive to improve student learning and readiness for lifelong learning.By celebrating the invaluable contributions of educators, we can shift the narrative surrounding assessment practices towards a more positive and supportive environment, fostering ethical and innovative uses of AI-powered technology. This celebration serves as inspiration for future research and development in assessment, emphasizing the importance of building upon educators’ innovative practices to shape the future of assessment in the big data era.

Artificial intelligence (AI)-it's the end of the tox as we know it (and I feel fine).

The rapid progress of AI impacts diverse scientific disciplines, including toxicology, and has the potential to transform chemical safety evaluation. Toxicology has evolved from an empirical science focused on observing apical outcomes of chemical exposure, to a data-rich field ripe for AI integration. The volume, variety and velocity of toxicological data from legacy studies, literature, high-throughput assays, sensor technologies and omics approaches create opportunities but also complexities that AI can help address. In particular, machine learning is well suited to handle and integrate large, heterogeneous datasets that are both structured and unstructured-a key challenge in modern toxicology. AI methods like deep neural networks, large language models, and natural language processing have successfully predicted toxicity endpoints, analyzed high-throughput data, extracted facts from literature, and generated synthetic data. Beyond automating data capture, analysis, and prediction, AI techniques show promise for accelerating quantitative risk assessment by providing probabilistic outputs to capture uncertainties. AI also enables explanation methods to unravel mechanisms and increase trust in modeled predictions. However, issues like model interpretability, data biases, and transparency currently limit regulatory endorsement of AI. Multidisciplinary collaboration is needed to ensure development of interpretable, robust, and human-centered AI systems. Rather than just automating human tasks at scale, transformative AI can catalyze innovation in how evidence is gathered, data are generated, hypotheses are formed and tested, and tasks are performed to usher new paradigms in chemical safety assessment. Used judiciously, AI has immense potential to advance toxicology into a more predictive, mechanism-based, and evidence-integrated scientific discipline to better safeguard human and environmental wellbeing across diverse populations.

Preoperative validation of edema-corrected tractography in neurosurgical practice: translating surgeon insights into novel software implementation.

Peritumoral edema alters diffusion anisotropy, resulting in false negatives in tractography reconstructions negatively impacting surgical decision-making. With supratotal resections tied to survival benefit in glioma patients, advanced diffusion modeling is critical to visualize fibers within the peritumoral zone to prevent eloquent fiber transection thereafter. A preoperative assessment paradigm is therefore warranted to systematically evaluate multi-subject tractograms along clinically meaningful parameters. We propose a novel noninvasive surgically-focused survey to evaluate the benefits of a tractography algorithm for preoperative planning, subsequently applied to Synaptive Medical's free-water correction algorithm developed for clinically feasible single-shell DTI data. Ten neurosurgeons participated in the study and were presented with patient datasets containing histological lesions of varying degrees of edema. They were asked to compare standard (uncorrected) tractography reconstructions overlaid onto anatomical images with enhanced (corrected) reconstructions. The raters assessed the datasets in terms of overall data quality, tract alteration patterns, and the impact of the correction on lesion definition, brain-tumor interface, and optimal surgical pathway. Inter-rater reliability coefficients were calculated, and statistical comparisons were made. Standard tractography was perceived as problematic in areas proximal to the lesion, presenting with significant tract reduction that challenged assessment of the brain-tumor interface and of tract infiltration. With correction applied, significant reduction in false negatives were reported along with additional insight into tract infiltration. Significant positive correlations were shown between favorable responses to the correction algorithm and the lesion-to-edema ratio, such that the correction offered further clarification in increasingly edematous and malignant lesions. Lastly, the correction was perceived to introduce false tracts in CSF spaces and - to a lesser degree - the grey-white matter interface, highlighting the need for noise mitigation. As a result, the algorithm was modified by free-water-parameterizing the tractography dataset and introducing a novel adaptive thresholding tool for customizable correction guided by the surgeon's discretion. Here we translate surgeon insights into a clinically deployable software implementation capable of recovering peritumoral tracts in edematous zones while mitigating artifacts through the introduction of a novel and adaptive case-specific correction tool. Together, these advances maximize tractography's clinical potential to personalize surgical decisions when faced with complex pathologies.

Assessing the Environmental Impact of Plastic Waste using Life Cycle Assessment

This research use a life cycle assessment (LCA) paradigm to investigate the environmental effects of plastic waste management practices. The environmental impacts of these processes are measured using experimental data. The acquisition of raw materials, particularly in plastic manufacturing, results in considerable environmental consequences, including an energy expenditure of 1200 MJ and the release of 300 kg of CO2. Likewise, waste processing activities, such as plastic shredding and molding, need 1500 MJ of energy and produce 400 kg of CO2 emissions. The operational lifespan of the product is underscored in its usage phase, wherein Plastic Product A and Plastic Component B exhibit cumulative energy consumption of 100 MJ/year and 120 MJ/year, alongside emissions of 20 kg CO2/year and 25 kg CO2/year, respectively, thereby accentuating the significance of a product’s lifecycle. The end-of-life phase underscores the variety in recycling rates, emphasizing the need for more effective recycling techniques. This comprehensive LCA methodology delineates critical areas for improvement, directing sustainable plastic waste management methods and fostering environmentally responsible decision-making within the sector. The results provide a more sustainable method for handling plastic garbage and diminishing its ecological impact.

Leveraging biomarkers and translational medicine for preclinical safety - Lessons for advancing the validation of alternatives to animal testing.

This article explores the potential of principles established in translational medicine for the use of bio-markers to advance the validation of alternatives to animal testing in preclinical safety assessment. It examines especially how such principles can enhance the predictive power, mechanistic under-standing, and human relevance of new approach methodologies (NAMs). Key concepts from translational medicine, such as fit-for-purpose validation, evidence-based approaches, and inte-grated testing strategies, are already being applied to the development and validation of NAMs. The article discusses challenges in implementing biomarker-based approaches, including standardi-zation, demonstration of relevance, regulatory acceptance, and addressing biological complexity. It also highlights opportunities for advancement through collaborative efforts, technological inno-vations, and regulatory evolution. Case studies demonstrate successful applications of biomarkers in preclinical safety, while future perspectives explore emerging trends like multi-omics integration, microphysiological systems, and artificial intelligence. The article emphasizes the potential of bio-markers and translational science approaches in creating more predictive, efficient, and ethical preclinical safety assessment paradigms in the use of NAMs. Use of biomarkers can enable the mechanistic validation of human-relevant models and provide a means to relate changes in NAMs to animal or clinical study results. By leveraging these tools, the field can work towards reducing reliance on animal testing while improving the accuracy and human relevance of safety predictions.

Relationships between hourly cognitive variability and risk of Alzheimer's disease revealed with mixed-effects location scale models.

Observational studies on aging and Alzheimer's disease (AD) typically focus on mean-level changes in cognitive performance over relatively long periods of time (years or decades). Additionally, some studies have examined how trial-level fluctuations in speeded reaction time are related to both age and AD. The aim of the current project was to describe patterns of variability across repeated days of testing as a function of AD risk in cognitively normal older adults. The current project examined the performance of the Ambulatory Research in Cognition (ARC) smartphone application, a high-frequency remote cognitive assessment paradigm, that administers brief tests of episodic memory, spatial working memory, and processing speed. Bayesian mixed-effects location scale models were used to explore differences in mean cognitive performance and intraindividual variability across 28 repeated sessions over a 1-week assessment interval as function of age and genetic risk of AD, specifically the presence of at least one apolipoprotein E (APOE) ε4 allele. Mean performance on processing speed and working memory was negatively related to age and APOE status. More importantly, e4 carriers exhibited increased session-level variability on a test of processing speed compared to noncarriers. Age and education did not consistently relate to cognitive variability, contrary to expectations. Preclinical AD risk, defined as possessing at least one APOE ε4 allele, is not only associated with mean-level performance differences, but also with increases in variability across repeated testing occasions particularly on a test of processing speed. Thus, cognitive variability may serve as an additional and important indicator of AD risk. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Investigating conditioned pain modulation in horses: can the lip-twitch be used as a conditioning stimulus?

Study objective was to evaluate whether the application of a lip twitch could be proposed as conditioning stimulus in the context of a novel Conditioned Pain Modulation (CPM) assessment paradigm for use in horses. The study was a prospective, experimental, randomized trial. Twelve healthy horses were evaluated in two experimental sessions. The lip twitch was used as the conditioning stimulus in both sessions; electrical stimulation was used as the test stimulus in one session, while mechanical and thermal stimulations were used in the other. Differences between thresholds recorded before and during twitching (Δ) as well as their percent (%) change were computed for each stimulation modality as a measure of CPM. Heart rate and respiratory rate were recorded throughout the experiments to monitor physiological reactions, while the general level of stress and aversiveness toward twitching were scored using ad hoc behavioural scales. Based on these scores, interruption criteria were defined. Ten and seven horses completed the electrical and mechanical/thermal experimental sessions respectively. For electrical stimulation, median (IQR) Δ was -2.8 (-3.9, -1.1) mA and% change 87.9 (65.7-118.2)%; for mechanical stimulation, Δ was -18.2 (-6.4, -21.4) N and% change 343.5 (140, 365.3)%; for thermal stimulation, Δ was -3.1 (-9.2, -2.1)°C, while% change was not calculated. Heart rate and respiratory rates varied significantly over time, with higher values recorded during twitching. Median stress and aversion scores did not differ between the two sessions. As lip twitching consistently affected thresholds to all stimulation modalities, it can be proposed as effective conditioning method for CPM assessment in horses. The exclusion of subjects due to severe aversion shows that this paradigm cannot be indistinctively applied to all horses and that stringent interruption criteria are necessary to guarantee adequate welfare during testing.

Toward a New Flood Assessment Paradigm: From Exceedance Probabilities to the Expected Maximum Floods and Damages

AbstractTo assess flood risks, we seek to estimate the probability distribution of the worst possible single‐event over a contiguous period of N years rather than the cumulative losses expected over a planning horizon. For this we use the probability distribution FN of extreme flood events over a multi‐year period, which is different from using the conventional single‐valued exceedance probability of 1/N years. FN can be used to estimate the hazard and then proceed to the estimation of risk, which we define as the “largest expected damage” over the set period. It also allows for a more coherent determination of design values, which descend from fully acknowledging the aleatoric uncertainty of the underlying natural river flow process. The epistemic uncertainty is removed by marginalizing the aleatoric‐epistemic uncertainty joint distribution over the parameter space. The advantage of the proposed Bayesian approach, which can be summarized in 12 steps, is demonstrated for the 2021 River Ahr flood in Germany, which caused casualties and huge material damage. Adopting the multi‐year maxima extreme value distribution can potentially lead to the reclassification of vulnerability levels for flood‐prone areas and reconsideration of current policy‐making and flood risk communication.

Arrhythmia classification based on multi-feature multi-path parallel deep convolutional neural networks and improved focal loss.

Early diagnosis of abnormal electrocardiogram (ECG) signals can provide useful information for the prevention and detection of arrhythmia diseases. Due to the similarities in Normal beat (N) and Supraventricular Premature Beat (S) categories and imbalance of ECG categories, arrhythmia classification cannot achieve satisfactory classification results under the inter-patient assessment paradigm. In this paper, a multi-path parallel deep convolutional neural network was proposed for arrhythmia classification. Furthermore, a global average RR interval was introduced to address the issue of similarities between N vs. S categories, and a weighted loss function was developed to solve the imbalance problem using the dynamically adjusted weights based on the proportion of each class in the input batch. The MIT-BIH arrhythmia dataset was used to validate the classification performances of the proposed method. Experimental results under the intra-patient evaluation paradigm and inter-patient evaluation paradigm showed that the proposed method could achieve better classification results than other methods. Among them, the accuracy, average sensitivity, average precision, and average specificity under the intra-patient paradigm were 98.73%, 94.89%, 89.38%, and 98.24%, respectively. The accuracy, average sensitivity, average precision, and average specificity under the inter-patient paradigm were 91.22%, 89.91%, 68.23%, and 95.23%, respectively.

A (not) new Rx: Quality Improvement for NAM

New Approach Methodologies (NAMs) are a rapidly growing set of tools/methods for use food, drug, consumer product, or chemical safety assessment paradigms. The massive growth in NAMs tech development, publication, and legislation has been paralleled by a growing sense of frustration. The challenge of realizing the systems-level changes needed to catalyze the broad-scale adoption and use of NAMs is substantial. This Commentary asserts that these challenges may be less unique than perceived to date, and points to specific opportunities to learn from decades of experience (both positive and negative) from the Quality Improvement (QI) movement in the public health and healthcare arenas. Specific recommendations to inform and guide NAMs development are offered.

A System Dynamics-Based Interactive Learning Environment for Online Formative (Self-)Assessment of Wanna-Be Entrepreneurs’ Performance Management Capabilities

Background: Many scholars emphasize the way in which sustained organizational development requires a shift in focus from the individual traits of the entrepreneur to the building of an organization that utilizes the collective contributions of its employees. To achieve this, entrepreneurs must adopt the role of a facilitator and empower their employees to perform at their best. There are numerous factors that influence one’s decision to pursue a career in entrepreneurship, such as the beliefs and desires that individuals possess. Entrepreneurs possess diverse self-perceptions, and this plays a crucial role in their motivation to start a new business. Studies on entrepreneurial self-perception examine the elements, such as personal identity and capabilities—particularly the ability to effectively manage company drivers—which ultimately shape one’s decision to embark on a new venture. Launching a startup is not merely an act; rather, it represents a substantial reflection of an individual’s self-concept and identity. Consequently, an individual’s perceived social identity has a substantial influence on their choice to pursue entrepreneurship, as they view the venture as an extension of themselves. Methods: An online interactive learning environment (ILE) designed to assess the performance management capabilities of wanna-be entrepreneurs, in accordance with the formative assessment paradigm, has been developed. Results: The procedures for carrying out the formative (self-) assessment of wanna-be entrepreneurs’ performance management capabilities will be detailed. Two concrete assessment cases, with the aim of making clearer what kind of outcomes the ILE can generate, will be presented. Conclusions: The ILE could contribute in the encouragement of wanna-be entrepreneurs to participate in entrepreneurship educational programs.

How does language assessment impart for the Teaching of English as Second Language!

English language teaching can never achieve the goal without assessment. The language assessment is inevitable for teaching and learning. This paper argues about the co-ordination between the assessment and teaching. India, the classrooms are often considered as the symbol of pluralism indeed. The student hails from the multilingual and multicultural background, and these features can be used as resource rather than seen as an obstacle. The student learns English as second language and the teacher assesses the language skills. The paradigm of language assessment is explicit to the worth of teaching and learning. It is feasible to measure the language skills of language learners and its influence on the phenomenon of teaching and learning.

Estimation of Average and Maximum Daily-Life Mobility Performance Using the Timed Up-and-Go: Exploring the Added Value of an Instrumented Timed Up-and-Go

Introduction: The association between specific motor capacity variables obtained in a laboratory and parameters of daily-life mobility performance (MP) obtained via wearables is still unclear. The Timed Up-and-Go (TUG) test is a widely used motor capacity tests available either as traditional hand-stopped TUG or as instrumented TUG (iTUG), providing specific information about its subphases. This study aimed to: (1) estimate the association between the TUG and specific parameters reflecting average and maximum daily-life MP, (2) estimate the benefits of the iTUG in terms of explaining MP in daily life compared to the TUG. Methods: The present study was a cross-sectional analysis using baseline data of 294 older persons (mean age: 76.7 ± 5.3 years). Univariate linear regression analysis was performed to delineate the coefficient of determination between TUG time and participants’ MP. MP variables containing mean cadence (MCA) to represent average performance and the 95th percentile of mean cadence of walks with more than three steps (p95>3stepsMCA) to represent maximum performance. To determine whether the iTUG variables give more information about MP, a stepwise multivariate regression analysis between iTUG variables and the p95>3stepsMCA variable to represent maximum performance was conducted. Results: The univariate regression models revealed associations of the TUG with MCA (adjusted R2 = 0.078, p < 0.001) and p95>3stepsMCA (adjusted R2 = 0.199, p < 0.001). The multivariate stepwise regression models revealed a total explanation of maximum daily-life MP (p95>3stepsMCA) of the TUG (adjusted R2 = 0.199, p < 0.001) versus iTUG (adjusted R2 = 0.278, p < 0.010). Discussion/Conclusion: This study shows that the TUG better reflects maximum daily-life MP than average daily-life MP. Moreover, we demonstrate the added value of the iTUG for a more accurate estimation of daily MP compared to the traditional TUG. The iTUG is recommended to estimate maximum daily-life MP in fall-prone older adults. The study is a step toward a specific assessment paradigm using capacity variables from the iTUG to estimate maximum daily-life MP.

Shifting assessment paradigms in South African higher education: Evolving towards transformative approaches to policy development.

Assessment policies facilitate the optimisation of learning and academic performance through the provision of fair, equitable, and standardised criteria for evaluation. In recent years, the assessment policies of three South African universities have been reviewed. These policies, in comparison to their previous iterations, are indicative of transformative approaches taken in assessment practices in South Africa amid broader disruptions to higher education. This article explores shifts in assessment paradigms in South African higher education using a conceptual framework of positivist and behaviourist, interpretative and constructivist, and socio-constructivist views. Document analysis identifies several notable trends, including a shift in the purpose of assessment away from assessment of learning to assessment for learning and assessment as learning. The paradigm shift is characterised by changes in the approach from a rules-based, with specific prescriptions, to principles and values-based approaches. The growing use of alternative and technology-enhanced online assessment methods, along with the need for flexibility, are more prominent additions to these new policies post-COVID-19. However, being mindful of the South African context, culturally sensitive and fair assessment practices, that consider the diversity of South African learners, and the role of assessment in promoting equity, social justice, and quality learning, are highlighted.

Learning from the Experience of the COVID-19 Pandemic: A New Paradigm for Occupational Biohazard Assessment and Management.

The COVID-19 pandemic has affected workplaces in many different aspects. In this scenario, Occupational Physicians played a crucial role in assessing and managing the risk of SARS-CoV-2 infection and associated diseases to guarantee workers' health and the safety of workplaces. However, the pandemic experience has drawn attention to several critical issues in overall biohazard prevention and management strategies, originating from important knowledge gaps in our scientific understanding. An extensive analysis of the relevant hurdles that have emerged in our medical field can bring valuable lessons for the post pandemic future, not only in preparation for possible new pathogens with pandemic potential but also with principles and concepts applicable to managing all biological agents. In particular, a paradigm shift is needed to properly approach occupational diseases caused by infective agents, accurately define the "case", assess exposure and possible causal relationship with work appropriately, and effectively manage the specific risk through implementing appropriate preventive and protective measures. In this framework, the Occupational Physician should expand his contribution based on his unique expertise and specific competencies, confirming his role as the go-to consultant in all occupational health matters, but also in a multidisciplinary approach, considering different scientific expertise and evidence.

AAPM Task Group Report 311: Guidance for performance evaluation of fluorescence-guided surgery systems.

The last decade has seen a large growth in fluorescence-guided surgery (FGS) imaging and interventions. With the increasing number of clinical specialties implementing FGS, the range of systems with radically different physical designs, image processing approaches, and performance requirements is expanding. This variety of systems makes it nearly impossible to specify uniform performance goals, yet at the same time, utilization of different devices in new clinical procedures and trials indicates some need for common knowledge bases and a quality assessment paradigm to ensure that effective translation and use occurs. It is feasible to identify key fundamental image quality characteristics and corresponding objective test methods that should be determined such that there are consistent conventions across a variety of FGS devices. This report outlines test methods, tissue simulating phantoms and suggested guidelines, as well as personnel needs and professional knowledge bases that can be established. This report frames the issues with guidance and feedback from related societies and agencies having vested interest in the outcome, coming from an independent scientific group formed from academics and international federal agencies for the establishment of these professional guidelines.

HTA100 Health Technology Assessment Paradigm in Rare Diseases: Critical Determinants and Recommendations

HTA100 Health Technology Assessment Paradigm in Rare Diseases: Critical Determinants and Recommendations

Acetylcholinesterase Inhibition in Rats and Humans Following Acute Fenitrothion Exposure Predicted by Physiologically Based Kinetic Modeling-Facilitated Quantitative In Vitro to In Vivo Extrapolation.

Worldwide use of organophosphate pesticides as agricultural chemicals aims to maintain a stable food supply, while their toxicity remains a major public health concern. A common mechanism of acute neurotoxicity following organophosphate pesticide exposure is the inhibition of acetylcholinesterase (AChE). To support Next Generation Risk Assessment for public health upon acute neurotoxicity induced by organophosphate pesticides, physiologically based kinetic (PBK) modeling-facilitated quantitative in vitro to in vivo extrapolation (QIVIVE) approach was employed in this study, with fenitrothion (FNT) as an exemplary organophosphate pesticide. Rat and human PBK models were parametrized with data derived from in silico predictions and in vitro incubations. Then, PBK model-based QIVIVE was performed to convert species-specific concentration-dependent AChE inhibition obtained from in vitro blood assays to corresponding in vivo dose-response curves, from which points of departure (PODs) were derived. The obtained values for rats and humans were comparable with reported no-observed-adverse-effect levels (NOAELs). Humans were found to be more susceptible than rats toward erythrocyte AChE inhibition induced by acute FNT exposure due to interspecies differences in toxicokinetics and toxicodynamics. The described approach adequately predicts toxicokinetics and acute toxicity of FNT, providing a proof-of-principle for applying this approach in a 3R-based chemical risk assessment paradigm.