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Simple Summary: Poor adherence to trametinib, an oral anticancer drug, may be the consequence of side effects that severely impact the patient's quality of life. The significant interindividual variability associated with poor adherence results in suboptimal drug exposure and consequently in unfavourable patient outcomes. By characterizing the pharmacokinetics of trametinib, this study aims to assess (i) the adequacy of recommended doses to achieve efficacy thresholds and (ii) the impact of non-adherence on drug exposure. The latter was assessed by simulating different scenarios of missing one or more doses per week to highlight the risk of treatment failure associated with poor adherence. These results promote interprofessional collaboration and patient partnership to address patients' needs in order to ensure adherence to trametinib and in fine therapeutic success. Trametinib is a targeted therapy used for the treatment of solid tumours, with significant variability reported in real-life studies. This variability increases the risk of suboptimal exposure, which can lead to treatment failure or increased toxicity. Using model-based simulation, this study aims to characterize and investigate the pharmacokinetics and the adequacy of the currently recommended doses of trametinib. Additionally, the simulation of various suboptimal adherence scenarios allowed for an assessment of the impact of patients' drug adherence on the treatment outcome. The population data collected in 33 adult patients, providing 113 plasmatic trametinib concentrations, were best described by a two-compartment model with linear absorption and elimination. The study also identified a significant positive effect of fat-free mass and a negative effect of age on clearance, explaining 66% and 21% of the initial associated variability, respectively. Simulations showed that a maximum dose of 2 mg daily achieved the therapeutic target in 36% of male patients compared to 72% of female patients. A dose of 1.5 mg per day in patients over 65 years of age achieved similar rates, with 44% and 79% for male and female patients, respectively, reaching the therapeutic target. Poor adherence leads to a significant drop in concentrations and a high risk of subtherapeutic drug levels. These results underline the importance of interprofessional collaboration and patient partnership along the patient's journey to address patients' needs regarding trametinib and support medication adherence. [ABSTRACT FROM AUTHOR]

Background: Oral anticancer therapies such as protein kinase inhibitors (PKIs) are increasingly prescribed in cancer care. We aimed to evaluate the impact of a pharmacist-led interprofessional medication adherence program (IMAP) on patient implementation (dosing history), persistence (time until premature cessation of the treatment) and adherence to 27 PKIs prescribed for various solid cancers, as well as the impact on patients' beliefs about medicines (BAM) and quality of life (QoL). Methods: Patients (n = 118) were randomized 1:1 into two arms. In the intervention arm, pharmacists supported patient adherence through monthly electronic and motivational feedback, including educational, behavioral and affective components, for 12 months. The control arm received standard care plus EM without intervention. All PKIs were delivered in electronic monitors (EMs). Medication implementation and adherence were compared between groups using generalized estimating equation models, in which relevant covariables were included; persistence was compared with Kaplan‒Meier curves. Information on all treatment interruptions was compiled for the analysis. Questionnaires to evaluate BAM and QoL were completed among patients who refused and those who accepted to participate at inclusion, 6 and 12 months post-inclusion or at study exit. Results: Day-by-day PKI implementation was consistently higher and statistically significant in the intervention arm (n = 58) than in the control arm (n = 60), with 98.1% and 95.0% (Δ3.1%, 95% confidence interval (CI) of the difference 2.5%; 3.7%) implementation at 6 months, respectively. The probabilities of persistence and adherence were not different between groups, and no difference was found between groups for BAM and QoL scores. No difference in BAM or QoL was found among patients who refused versus those who participated. The intervention benefited mostly men (at 6 months, Δ4.7%, 95% CI 3.4%; 6.0%), those younger than 60 years (Δ4.0%, 95% CI 3.1%; 4.9%), those who had initiated PKI more than 60 days ago before inclusion (Δ4.5%, 95% CI 3.6%; 5.4%), patients without metastasis (Δ4.5%, 95% CI 3.4%; 5.7%), those who were diagnosed with metastasis more than 2 years ago (Δ5.3%, 95% CI 4.3%; 6.4%) and those who had never used any adherence tool before inclusion (Δ3.8%, 95% CI 3.1%; 4.5%). Conclusions: The IMAP, led by pharmacists in the context of an interprofessional collaborative practice, supported adherence, specifically implementation, to PKIs among patients with solid cancers. To manage adverse drug events, PKI transient interruptions are often mandated as part of a strategy for treatment and adherence optimization according to guidelines. Implementation of longer-term medication adherence interventions in the daily clinic may contribute to the improvement of progression-free survival. Trial registration: ClinicalTrials.gov NCT04484064. [ABSTRACT FROM AUTHOR]

Background: Toxicity or treatment failure related to drug-drug interactions (DDIs) are known to significantly affect morbidity and hospitalization rates. Despite the availability of numerous databases for DDIs identification and management, their information often differs. Oral anticoagulants are deemed at risk of DDIs and a leading cause of adverse drug events, most of which being preventable. Although many databases include DDIs involving anticoagulants, none are specialized in them. Aim and method: This study aims to compare the DDIs information content of four direct oral anticoagulants and two vitamin K antagonists in three major DDI databases used in Switzerland: Lexi-Interact, Pharmavista, and MediQ. It evaluates the consistency of DDIs information in terms of differences in severity rating systems, mechanism of interaction, extraction and documentation processes and transparency. Results: This study revealed 2'496 DDIs for the six anticoagulants, with discrepant risk classifications. Only 13.2% of DDIs were common to all three databases. Overall concordance in risk classification (high, moderate, and low risk) was slight (Fleiss' kappa = 0.131), while high-risk DDIs demonstrated a fair agreement (Fleiss' kappa = 0.398). The nature and the mechanism of the DDIs were more consistent across databases. Qualitative assessments highlighted differences in the documentation process and transparency, and similarities for availability of risk classification and references. Discussion: This study highlights the discrepancies between three commonly used DDI databases and the inconsistency in how terminology is standardised and incorporated when classifying these DDIs. It also highlights the need for the creation of specialised tools for anticoagulant-related interactions. [ABSTRACT FROM AUTHOR]

When used in real‐world conditions, substantial interindividual variations in direct oral anticoagulant (DOAC) plasma concentrations are observed for a given dose, leading to a risk of over‐ or under‐exposure and clinically significant adverse events. Physiologically‐based pharmacokinetic (PBPK) models could help physicians to tailor DOAC prescriptions in vulnerable patient populations, such as those in the hospital setting. The present study aims to validate prospectively PBPK models for rivaroxaban and apixaban in a large cohort of elderly, polymorbid, and hospitalized patients. In using a model of geriatric population integrating appropriate physiological parameters into models first optimized with healthy volunteer data, observed plasma concentration collected in hospitalized patients on apixaban (n = 100) and rivaroxaban (n = 100) were adequately predicted (ratio predicted/observed area under the concentration curve for a dosing interval [AUCtau] = 0.97 [0.96–0.99] geometric mean, 90% confidence interval, ratio predicted/observed AUCtau = 1.03 [1.02–1.05]) for apixaban and rivaroxaban, respectively. Validation of the present PBPK models for rivaroxaban and apixaban in in‐patients represent an additional step toward the feasibility of bedside use. [ABSTRACT FROM AUTHOR]

This study aimed to characterize apixaban pharmacokinetics (PKs) and its variability in a real‐world clinical setting of hospitalized patients using a population PK (PopPK) approach. Model‐based simulations helped to identify factors that affect apixaban exposure and their clinical significance. A classic stepwise strategy was applied to determine the best PopPK model for describing typical apixaban PKs in hospitalized patients from the OptimAT study (n = 100) and evaluating the associated variability and influencing factors. Apixaban exposure under specific conditions was assessed using the final model. A two‐compartment model with first‐order absorption and elimination best described the data. The developed PopPK model revealed a major role of renal function and a minor role of P‐glycoprotein phenotypic (P‐gp) activity in explaining apixaban variability. The final model indicated that a patient with stage 4 chronic kidney disease (creatinine clearance [CLcr] = 15–29 mL/min) would have a 45% higher drug exposure than a patient with normal renal function (CLcr >90 mL/min), with a further 12% increase if the patient was also a poor metabolizer of P‐gp. A high interindividual variability in apixaban PKs was observed in a real‐life setting, which was partially explained by renal function and by P‐gp phenotypic activity. Target apixaban concentrations are reached under standard dosage regimens, but overexposure can rapidly occur in the presence of cumulative factors warranting the development of a predictive tool for tailoring apixaban exposure and its clinical utility in at‐risk patients. [ABSTRACT FROM AUTHOR]

The development of immune checkpoint inhibitors (ICIs) has revolutionized cancer therapy but only a fraction of patients benefits from this therapy. Model‐informed drug development can be used to assess prognostic and predictive clinical factors or biomarkers associated with treatment response. Most pharmacometric models have thus far been developed using data from randomized clinical trials, and further studies are needed to translate their findings into the real‐world setting. We developed a tumor growth inhibition model based on real‐world clinical and imaging data in a population of 91 advanced melanoma patients receiving ICIs (i.e., ipilimumab, nivolumab, and pembrolizumab). Drug effect was modeled as an ON/OFF treatment effect, with a tumor killing rate constant identical for the three drugs. Significant and clinically relevant covariate effects of albumin, neutrophil to lymphocyte ratio, and Eastern Cooperative Oncology Group (ECOG) performance status were identified on the baseline tumor volume parameter, as well as NRAS mutation on tumor growth rate constant using standard pharmacometric approaches. In a population subgroup (n = 38), we had the opportunity to conduct an exploratory analysis of image‐based covariates (i.e., radiomics features), by combining machine learning and conventional pharmacometric covariate selection approaches. Overall, we demonstrated an innovative pipeline for longitudinal analyses of clinical and imaging RWD with a high‐dimensional covariate selection method that enabled the identification of factors associated with tumor dynamics. This study also provides a proof of concept for using radiomics features as model covariates. [ABSTRACT FROM AUTHOR]

PURPOSE: A semiautomated pipeline for the collection and curation of free-text and imaging real-world data (RWD) was developed to quantify cancer treatment outcomes in large-scale retrospective real-world studies. The objectives of this article are to illustrate the challenges of RWD extraction, to demonstrate approaches for quality assurance, and to showcase the potential of RWD for precision oncology. METHODS: We collected data from patients with advanced melanoma receiving immune checkpoint inhibitors at the Lausanne University Hospital. Cohort selection relied on semantically annotated electronic health records and was validated using process mining. The selected imaging examinations were segmented using an automatic commercial software prototype. A postprocessing algorithm enabled longitudinal lesion identification across imaging time points and consensus malignancy status prediction. Resulting data quality was evaluated against expert-annotated ground-truth and clinical outcomes obtained from radiology reports. RESULTS: The cohort included 108 patients with melanoma and 465 imaging examinations (median, 3; range, 1-15 per patient). Process mining was used to assess clinical data quality and revealed the diversity of care pathways encountered in a real-world setting. Longitudinal postprocessing greatly improved the consistency of image-derived data compared with single time point segmentation results (classification precision increased from 53% to 86%). Image-derived progression-free survival resulting from postprocessing was comparable with the manually curated clinical reference (median survival of 286 v 336 days, P =.89). CONCLUSION: We presented a general pipeline for the collection and curation of text- and image-based RWD, together with specific strategies to improve reliability. We showed that the resulting disease progression measures match reference clinical assessments at the cohort level, indicating that this strategy has the potential to unlock large amounts of actionable retrospective real-world evidence from clinical records. Our semiautomated pipeline captures real-world tumor evolution from retrospective PET-CT scans. [ABSTRACT FROM AUTHOR]

Imatinib is a targeted cancer therapy that has significantly improved the care of patients with chronic myeloid leukemia (CML) and gastrointestinal stromal tumor (GIST). However, it has been shown that the recommended dosages of imatinib are associated with trough plasma concentration (Cmin) lower than the target value in many patients. The aims of this study were to design a novel model-based dosing approach for imatinib and to compare the performance of this method with that of other dosing methods. Three target interval dosing (TID) methods were developed based on a previously published PK model to optimize the achievement of a target Cmin interval or minimize underexposure. We compared the performance of those methods to that of traditional model-based target concentration dosing (TCD) as well as fixed-dose regimen using simulated patients (n = 800) as well as real patients' data (n = 85). Both TID and TCD model-based approaches were effective with about 65% of Cmin achieving the target imatinib Cmin interval of 1000–2000 ng/mL in 800 simulated patients and more than 75% using real data. The TID approach could also minimize underexposure. The standard 400 mg/24 h dosage of imatinib was associated with only 29% and 16.5% of target attainment in simulated and real conditions, respectively. Some other fixed-dose regimens performed better but could not minimize over- or underexposure. Model-based, goal-oriented methods can improve initial dosing of imatinib. Combined with subsequent TDM, these approaches are a rational basis for precision dosing of imatinib and other drugs with exposure–response relationships in oncology. [ABSTRACT FROM AUTHOR]

Simple Summary: Medication adherence to CDK4/6 inhibitors such as palbociclib, prescribed as a cyclic oral anticancer therapy in women diagnosed with advanced breast cancer, may be suboptimal. We evaluated adherence to palbociclib and its impact on pharmacokinetic and pharmacodynamic (PK-PD) profiles. Patients included in the OpTAT study used an electronic monitor to register each drug intake event and were randomized into the intervention (i.e., interprofessional medication adherence program) or control group (i.e., usual care). Patients in the intervention group (n = 19) had a higher and more stable adherence than control patients (n = 19). The intervention had a larger effect on patients older than 65 and in patients with longer treatment and disease experience. The PK-PD analysis showed that catching up on a missed dose at the end of the cycle increases the risk of severe neutropenia in the next cycle. The interprofessional healthcare team should closely monitor patients' cycle management to improve prescriptions and decrease toxicity. The cyclin-dependent kinase 4/6 inhibitor (CDK4/6i) palbociclib is administered orally and cyclically, causing medication adherence challenges. We evaluated components of adherence to palbociclib, its relationship with pharmacokinetics (PK), and drug-induced neutropenia. Patients with metastatic breast cancer (MBC) receiving palbociclib, delivered in electronic monitors (EM), were randomized 1:1 to an intervention and a control group. The intervention was a 12-month interprofessional medication adherence program (IMAP) along with monthly motivational interviews by a pharmacist. Implementation adherence was compared between groups using generalized estimating equation models, in which covariates were included. Model-based palbociclib PK and neutrophil profiles were simulated under real-life implementation scenarios: (1) optimal, (2) 2 doses omitted and caught up at cycle end. At 6 months, implementation was slightly higher and more stable in the intervention (n = 19) than in the control (n = 19) group, 99.2% and 97.3% (Δ1.95%, 95% CI 1.1–2.9%), respectively. The impact of the intervention was larger in patients diagnosed with MBC for >2 years (Δ3.6%, 95% CI 2.1–5.4%), patients who received >4 cycles before inclusion (Δ3.1%, 95% CI 1.7–4.8%) and patients >65 (Δ2.3%, 95% CI 0.8–3.6%). Simulations showed that 25% of patients had neutropenia grade ≥3 during the next cycle in scenario 1 versus 30% in scenario 2. Education and monitoring of patient CDK4/6i cycle management and adherence along with therapeutic drug monitoring can help clinicians improve prescription and decrease toxicity. [ABSTRACT FROM AUTHOR]

Aims: Locally advanced rectal cancer (LARC) is an area of unmet medical need with one third of patients dying from their disease. With response to neoadjuvant chemo‐radiotherapy being a major prognostic factor, trial SAKK 41/16 assessed potential benefits of adding regorafenib to capecitabine‐amplified neoadjuvant radiotherapy in LARC patients. Methods: Patients received regorafenib at three dose levels (40/80/120 mg once daily) combined with capecitabine 825 mg/m2 bidaily and local radiotherapy. We developed population pharmacokinetic models from plasma concentrations of capecitabine and its metabolites 5′‐deoxy‐5‐fluorocytidine and 5′‐deoxy‐5‐fluorouridine as well as regorafenib and its metabolites M‐2 and M‐5 as implemented into SAKK 41/16 to assess potential drug–drug interactions (DDI). After establishing parent‐metabolite base models, drug exposure parameters were tested as covariates within the respective models to investigate for potential DDI. Simulation analyses were conducted to quantify their impact. Results: Plasma concentrations of capecitabine, regorafenib and metabolites were characterized by one and two compartment models and absorption was described by parallel first‐ and zero‐order processes and transit compartments, respectively. Apparent capecitabine clearance was 286 L/h (relative standard error [RSE] 14.9%, interindividual variability [IIV] 40.1%) and was reduced by regorafenib cumulative area under the plasma concentration curve (median reduction of 45.6%) as exponential covariate (estimate −4.10 × 10−4, RSE 17.8%). Apparent regorafenib clearance was 1.94 L/h (RSE 12.1%, IIV 38.1%). Simulation analyses revealed significantly negative associations between capecitabine clearance and regorafenib exposure. Conclusions: This work informs the clinical development of regorafenib and capecitabine combination treatment and underlines the importance of studying potential DDI with new anticancer drug combinations. [ABSTRACT FROM AUTHOR]

Lower respiratory tract infections lead to significant morbidity and mortality. They are increasingly caused by multidrug-resistant pathogens, notably in individuals with cystic fibrosis, hospital-acquired pneumonia and lung transplantation. The use of bacteriophages (phages) to treat bacterial infections is gaining growing attention, with numerous published cases of compassionate treatment over the last few years. Although the use of phages appears safe, the lack of standardisation, the significant heterogeneity of published studies and the paucity of robust efficacy data, alongside regulatory hurdles arising from the existing pharmaceutical legislation, are just some of the challenges phage therapy has to overcome. In this review, we discuss the lessons learned from recent clinical experiences of phage therapy for the treatment of pulmonary infections. We review the key aspects, opportunities and challenges of phage therapy regarding formulations and administration routes, interactions with antibiotics and the immune system, and phage resistance. Building upon the current knowledge base, future pre-clinical studies using emerging technologies and carefully designed clinical trials are expected to enhance our understanding and explore the therapeutic potential of phage therapy. [ABSTRACT FROM AUTHOR]

Aims: A recent review identified 19 anticholinergic burden scales (ABSs) but no study has yet compared the impact of all 19 ABSs on delirium. We evaluated whether a high anticholinergic burden as classified by each ABS is associated with incident delirium. Method: We performed a retrospective cohort study in a Swiss tertiary teaching hospital using data from 2015–2018. Included were patients aged ≥65, hospitalised ≥48 hours with no stay >24 hours in intensive care. Delirium was defined twofold: (i) ICD‐10 or CAM and (ii) ICD‐10 or CAM or DOSS. Patients' cumulative anticholinergic burden score, calculated within 24 hours after admission, was classified using a binary (<3: low, ≥3: high burden) and a categorical approach (0: no, 0.5–3: low, ≥3: high burden). Association was analysed using multivariable logistic regression. Results: Over 25 000 patients (mean age 77.9 ± 7.6 years) were included. Of these, (i) 864 (3.3%) and (ii) 2770 (11.0%) developed delirium. Depending on the evaluated ABS, 4–63% of the patients were exposed to at least one anticholinergic drug. Out of 19 ABSs, (i) 14 and (ii) 16 showed a significant association with the outcomes. A patient with a high anticholinergic burden score had odds ratios (ORs) of 1.21 (95% confidence interval [CI]: 1.03–1.42) to 2.63 (95% CI: 2.28–3.03) for incident delirium compared to those with low or no burden. Conclusion: A high anticholinergic burden within 24 hours after admission was significantly associated with incident delirium. Although prospective studies need to confirm these results, discontinuing or substituting drugs with a score of ≥3 at admission might be a targeted intervention to reduce incident delirium. [ABSTRACT FROM AUTHOR]

Background: One-third of older inpatients experience adverse drug events (ADEs), which increase their mortality, morbidity, and health care use and costs. In particular, antithrombotic drugs are among the most at-risk medications for this population. Reporting systems have been implemented at the national, regional, and provider levels to monitor ADEs and design prevention strategies. Owing to their well-known limitations, automated detection technologies based on electronic medical records (EMRs) are being developed to routinely detect or predict ADEs. Objective: This study aims to develop and validate an automated detection tool for monitoring antithrombotic-related ADEs using EMRs from 4 large Swiss hospitals. We aim to assess cumulative incidences of hemorrhages and thromboses in older inpatients associated with the prescription of antithrombotic drugs, identify triggering factors, and propose improvements for clinical practice. Methods: This project is a multicenter, cross-sectional study based on 2015 to 2016 EMR data from 4 large hospitals in Switzerland: Lausanne, Geneva, and Zürich university hospitals, and Baden Cantonal Hospital. We have included inpatients aged =65 years who stayed at 1 of the 4 hospitals during 2015 or 2016, received at least one antithrombotic drug during their stay, and signed or were not opposed to a general consent for participation in research. First, clinical experts selected a list of relevant antithrombotic drugs along with their side effects, risks, and confounding factors. Second, administrative, clinical, prescription, and laboratory data available in the form of free text and structured data were extracted from study participants' EMRs. Third, several automated rule-based and machine learning-based algorithms are being developed, allowing for the identification of hemorrhage and thromboembolic events and their triggering factors from the extracted information. Finally, we plan to validate the developed detection tools (one per ADE type) through manual medical record review. Performance metrics for assessing internal validity will comprise the area under the receiver operating characteristic curve, F1-score, sensitivity, specificity, and positive and negative predictive values. Results: After accounting for the inclusion and exclusion criteria, we will include 34,522 residents aged =65 years. The data will be analyzed in 2022, and the research project will run until the end of 2022 to mid-2023. Conclusions: This project will allow for the introduction of measures to improve safety in prescribing antithrombotic drugs, which today remain among the drugs most involved in ADEs. The findings will be implemented in clinical practice using indicators of adverse events for risk management and training for health care professionals; the tools and methodologies developed will be disseminated for new research in this field. The increased performance of natural language processing as an important complement to structured data will bring existing tools to another level of efficiency in the detection of ADEs. Currently, such systems are unavailable in Switzerland. [ABSTRACT FROM AUTHOR]

Busulfan, a drug used in conditioning prior to hematopoietic stem cell transplantation (HSCT) in children, has a narrow therapeutic margin. The model-informed precision dosing (MIPD) of busulfan is desirable, but there is a lack of validated tools. The objective of this study was to implement and cross-validate a population pharmacokinetic (PK) model in the Tucuxi software for busulfan MIPD in HSCT children. A search of the literature was performed to identify candidate population PK models. The goodness of fit of three selected models was assessed in a dataset of 178 children by computing the mean error (ME) and root-mean-squared error of prediction (RMSE). The best model was implemented in Tucuxi. The individual predicted concentrations, the area under the concentration-time curve (AUC), and dosage requirements were compared between the Tucuxi model and a reference model available in the BestDose software in a subset of 61 children. The model from Paci et al. best fitted the data in the full dataset. In a subset of 61 patients, the predictive performance of Tucuxi and BestDose models was comparable with ME values of 6.4% and −2.5% and RMSE values of 11.4% and 13.6%, respectively. The agreement between the estimated AUC and the predicted dose was good, with 6.6% and 4.9% of the values being out of the 95% limits of agreement, respectively. To conclude, a PK model for busulfan MIPD was cross-validated and is now available in the Tucuxi software. [ABSTRACT FROM AUTHOR]

High interindividual variability (IIV) of the clinical response to epidermal growth factor receptor (EGFR) inhibitors such as osimertinib in non-small-cell lung cancer (NSCLC) might be related to the IIV in plasma exposure. The aim of this study was to evaluate the exposure–response relationship for toxicity and efficacy of osimertinib in unselected patients with advanced EGFR-mutant NSCLC. This retrospective analysis included 87 patients treated with osimertinib. Exposure–toxicity analysis was performed in the entire cohort and survival analysis only in second-line patients (n = 45). No significant relationship between occurrence of dose-limiting toxicity and plasma exposure was observed in the entire cohort (p = 0.23, n = 86). The median overall survival (OS) was approximately two-fold shorter in the 4th quartile (Q4) of osimertinib trough plasma concentration (>235 ng/mL) than in the Q1–Q3 group (12.2 months [CI95% = 8.0–not reached (NR)] vs. 22.7 months [CI95% = 17.1–34.1]), but the difference was not statistically significant (p = 0.15). To refine this result, the exposure–survival relationship was explored in a cohort of 41 NSCLC patients treated with erlotinib. The Q4 erlotinib exposure group (>1728 ng/mL) exhibited a six-fold shorter median OS than the Q1–Q3 group (4.8 months [CI95% = 3.3-NR] vs. 22.8 months (CI95% = 10.6–37.4), p = 0.00011). These results suggest that high exposure to EGFR inhibitors might be related to worse survival in NSCLC patients. [ABSTRACT FROM AUTHOR]

Available data have shown an association between direct oral anticoagulant (DOAC) plasma concentration and clinical, particularly bleeding, events. Factors that may influence DOAC plasma concentration are therefore the focus of particular attention. Population pharmacokinetic (PopPK) analyses can help in identifying such factors while providing predictive models. The main aim of the present study was to identify all the PopPK models to date for the four most frequently used DOACs (dabigatran, apixaban, rivaroxaban, and edoxaban). The secondary aim was to use these models to simulate different DOAC plasma concentration–time profiles in relevant clinical scenarios. The results of our model‐based simulations confirm the clinical relevance of the known major factors influencing DOAC exposure and support the current approved dose adaptation, at least for atrial fibrillation. They also highlight how the accumulation of covariates, not currently considered for dose adaptation due to their seemingly minor influence on DOAC exposure, lead to supratherapeutic blood concentrations and could thus enhance the risk of major bleeding. The present results therefore question DOAC dose adaptation in the presence of these covariates, such as drug–drug interaction or genotypes, alongside the known existing covariates. As the overall effect of accumulation of several covariates could be difficult to apprehend for the clinicians, PopPK modeling could represent an interesting approach for informed precision dosing and to improve personalized prescription of DOACs. [ABSTRACT FROM AUTHOR]