Development of a generalized pharmacokinetic model to characterize clinical pharmacokinetics of monomethyl auristatin E-based antibody-drug conjugates.

AIMS: This study aims to develop a generalized pharmacokinetic (PK) model for monomethyl auristatin E (MMAE)-based antibody-drug conjugates (ADCs) that can simultaneously capture the PK of multiple ADC analytes commonly measured in the clinic. METHODS: A comprehensive literature review was conducted to collect PK data on MMAE-based ADCs from clinical trials. From each study, PK profiles of total antibody, the ADC, conjugated MMAE, and unconjugated MMAE, were extracted. These data were pooled and dose-normalized to evaluate the generalizability of PK across various ADCs and dose levels. Upon confirming PK generalizability, a generalized PK model for MMAE-based ADCs was developed using the entire dataset. Furthermore, exposure metrics ( C max and AUC) reported across the range of doses were combined to establish linear relationships between dose and exposure metrics for MMAE-based ADCs. RESULTS: A total of 109 PK profiles from 18 distinct MMAE-based ADCs were gathered. The dose-normalized PK profiles supported the generalizability of PK for MMAE-based ADCs. A generalized PK model was developed, which enabled capturing the PK data for 4 ADC analytes across all collected MMAE-based ADCs. A linear relationship between dose and PK exposure metrics was established, enabling the prediction of typical exposure values across different doses for MMAE-based ADCs. CONCLUSIONS: This study comprehensively analysed clinical PK data from different valine-citrulline (vc)-MMAE-based ADCs. The generalized PK model developed here serves as an important tool for a priori prediction of the PK for multiple ADC analytes in clinical settings and lays the foundation for establishing generalized exposure-response and exposure-toxicity correlations for MMAE-based ADCs.

Predictors, Mediators and Moderators of Police Work-Related Stress: A Scoping Review.

Owing to the complication in organisation, the dangerous job nature and the rise of demonstrations and protests across the world in the past decade, police work-related stress has become a topic of global concern. This review aimed to provide an understanding of predictors, mediators and moderators of police work-related stress from a multi-level perspective. Using a scoping review approach underpinned by the six-stage methodological framework, studies were found from six electronic databases (MEDLINE, Web of Science, Sociological Abstracts, Scopus, PsycINFO and PsychiatryOnline) and grey literature sources. Thirty studies were yielded across 35,446 participants from 12 locations. This review contributes to a systematic understanding of the factors affecting police work-related stress by identifying six predictors, four mediators and three moderators. It then discusses limitations and future research.

Work-Family Conflicts, Stress, and Turnover Intention Among Hong Kong Police Officers Amid the COVID-19 Pandemic.

Although work stress, turnover intention, and work-family conflicts among police officers have been extensively investigated, no studies have explored these issues simultaneously under the context of the coronavirus pandemic. Clearly, both work and family domains have been drastically affected by this global health crisis, and it is likely that each domain has a distinctive impact on work outcomes. Using survey data based on a representative random sample of 335 police officers in Hong Kong, this study examines the impacts of resource losses and gains across family and work domains on occupational stress and turnover intention amid the pandemic. A multiple regression indicates that both family-to-work and work-to-family conflicts lead to work stress and turnover intention among police officers. Among officers, supervisory support is negatively associated with turnover intention and moderates the impact of work-to-family conflicts on turnover intention. Finally, measures to mitigate work stress during public health disasters are discussed.

Whole-Body Pharmacokinetics and Physiologically Based Pharmacokinetic Model for Monomethyl Auristatin E (MMAE).

Monomethyl auristatin E (MMAE) is one of the most commonly used payloads for developing antibody-drug conjugates (ADC). However, limited studies have comprehensively evaluated the whole-body disposition of MMAE. Consequently, here, we have investigated the whole-body pharmacokinetics (PK) of MMAE in tumor-bearing mice. We show that while MMAE is rapidly eliminated from the plasma, it shows prolonged and extensive distribution in tissues, blood cells, and tumor. Highly perfused tissues (e.g., lung, kidney, heart, liver, and spleen) demonstrated tissue-to-plasma area under the concentration curve (AUC) ratios > 20, and poorly perfused tissues (e.g., fat, pancreas, skin, bone, and muscle) had ratios from 1.3 to 2.4. MMAE distribution was limited in the brain, and tumor had 8-fold higher exposure than plasma. A physiological-based pharmacokinetic (PBPK) model was developed to characterize the whole-body PK of MMAE, which accounted for perfusion/permeability-limited transfer of drug in the tissue, blood cell distribution of the drug, tissue/tumor retention of the drug, and plasma protein binding. The model was able to characterize the PK of MMAE in plasma, tissues, and tumor simultaneously, and model parameters were estimated with good precision. The MMAE PBPK model presented here can facilitate the development of a platform PBPK model for MMAE containing ADCs and help with their preclinical-to-clinical translation and clinical dose optimization.

The Hexaco-60, the Dark Triad and Scholastic Cheating.

Previous studies have found inconsistent results regarding the personality predictors of scholastic cheating. This study investigated whether personality was a predictor of scholastic cheating using the HEXACO-60 personality inventory and the Dark Triad (DT). A sample of 252 students completed the online questionnaire. Results from a one-way ANOVA showed that scholastic cheating was more common in associate degree/diploma/foundation students and undergraduate students than postgraduate students. Year of study or student status (local or international students) had no effect on scholastic cheating. MANOVA showed that academic qualification, year of study, and student status had no effect on reasons for cheating. A structural equation model (SEM) found that scholastic cheating was positively predicted by unmitigated achievement and psychopathy. Psychopathy emerged as the strongest significant predictor of scholastic cheating. These results supported the view that dark personality is relevant for understanding scholastic cheating.