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Background For decades, vitamin K antagonists and specifically warfarin, have been the sole agents used orally to manage thromboembolic conditions, including stroke and venous thromboembolism (VTE). Several factors lead to warfarin dose variability, including genetic and non-genetic factors which made warfarin management challenging especially at the initiation phase. To overcome the challenges with warfarin dosing at initiation, strategies other than conventional or fixed dosing were introduced and explored. Aim In this narrative review, we aim to discuss and critique the different dosing strategies for warfarin at initiation with more focus on genotype-guided warfarin dosing and the most recent supporting evidence for and against its use. Method Medline database was searched from 1965 to July 2021. Articles addressing different warfarin dosing methods were screened for inclusion. Results A number of methods exist for warfarin initiation. Studies comparing different dosing methods for initiation yielded conflicting outcomes due to differences in study design, population studied, comparator, and outcomes measured. Conclusions Looking at the big picture, the use of genetic dosing for warfarin initiation can lead to better outcomes. Whether these better outcomes are clinically or economically beneficial remains controversial.

Serplulimab is a fully humanized anti–PD‐1 monoclonal antibody approved for small‐cell lung cancer and other malignancies. The initial dosing strategy was based on body weight (WT‐based); however, flat‐dosing offers greater convenience and reduced variability. This study characterized the population pharmacokinetics (PopPK) of serplulimab using data from 11 clinical trials and quantitatively evaluated the appropriateness of transitioning from WT‐based to flat‐dose regimens. Serplulimab concentration–time data from 2110 patients were analyzed using nonlinear mixed‐effects modeling. A previously developed two‐compartment model with time‐varying clearance best described the pharmacokinetic (PK) profile. A stepwise forward‐addition and backward‐elimination procedure was used to evaluate covariate effects on PK parameters. Model adequacy was confirmed by diagnostic plots, prediction‐corrected visual predictive checks (pcVPCs), and bootstrap analysis. Simulations compared exposures between 3 mg/kg every 2 weeks (Q2W), 4.5 mg/kg every 3 weeks (Q3W), 200 mg Q2W, and 300 mg Q3W regimens. Body weight and albumin were the main predictors of exposure. Although statistically significant, covariate effects were modest (≤ 20%) and not clinically meaningful, supporting a unified dosing strategy. Simulations showed that flat‐dose regimens achieved exposure comparable to WT‐based dosing, with < 25% differences in median Cavg and Cmax across body‐weight ranges. Exposure–response (E–R) analyses revealed no meaningful association between exposure and outcomes. The PopPK model adequately described serplulimab PK and supports transitioning from WT‐based to flat‐dosing, demonstrating comparable exposure, efficacy, and safety across regimens. Study Highlights What is the current knowledge on the topic? ○Flat‐dosing of monoclonal antibodies can provide comparable exposure as WT‐based dosing while simplifying their clinical use, but quantitative justification is needed for each specific agent. What question did this study address? ○Can flat‐dose regimens of serplulimab achieve similar PK exposure, efficacy, and safety as WT‐based dosing across broad patient populations? What does this study add to our knowledge? ○PopPK and E‐R analyses showed comparable exposure and clinical outcomes between flat and WT‐based dosing, with covariate effects ≤ 20% and not clinically meaningful. How might this change clinical pharmacology or translational science? ○The findings quantitatively support flat‐dosing of serplulimab, reinforcing the shift toward simplified, standardized dosing strategies for monoclonal antibodies in oncology.

BackgroundAdherence to a therapy, though a key factor for successful treatment, is low among patients with chronic conditions such as migraine. Dose frequency plays a major role in adherence. This study evaluated the impact of having flexible dosing options on acceptance of and adherence to a new migraine preventive therapy class among adults with migraine.MethodsIn this observational study, two 20-min online surveys were completed: one by physicians currently treating adult patients with migraine and the other by adults with migraine. Both surveys presented the participants with three scenarios: 1) only monthly, 2) only quarterly, and 3) both dosing options of the new medication are available. Physicians estimated the proportion of their migraine patients who would receive the new medication in each scenario. Patients were asked about their dosing preference when either or both options are available. Respondents were asked to rate the likelihood of their acceptance of and adherence to the therapy.Results400 physicians and 417 US adults with migraine completed the surveys. The availability of both dosing options yielded a significant increase in the proportion of patients expected to receive the new medication. The overall proportion of patients favoring monthly dosing (35%) was similar to the proportion favoring quarterly dosing (40%). Among those who preferred monthly dosing (n = 147), a greater proportion indicated they are more likely to fill the prescription (77% vs 56%, P < 0.05) and remain adherent (80% vs 57%, P < 0.05) when only monthly is available versus when only quarterly is available. Similarly, among those who preferred quarterly dosing (n = 166), a greater proportion indicated they are likely to fill (63% vs 55%, P < 0.05) and remain adherent (62% vs 54%, P < 0.05) when only quarterly is available compared with when only monthly is available.ConclusionsPhysicians anticipated that the proportion of patients to receive the new medication would increase when both dosing options are available. Patients stated that they are more likely to fill the prescription and adhere to the new therapy when their preferred dosing regimen is available.

BackgroundPrevious studies demonstrated the superiority of a genotype-guided warfarin dosing (GWD) method for warfarin initiation. International GWD models such as Gage et al model were validated in diverse populations but Arab patients were not well represented in these studies.ObjectiveTo derive and validate a GWD model in an Arab population and compare the outcomes to Gage et al model.MethodsIn this cross-sectional study, DNA was collected through saliva kits from a cohort of recruited Arab patients on warfarin Clinical factors and demographics were recorded and genotyping for     , , and was performed. Subjects were randomly divided to derivation and validation cohorts. Simple and Multiple linear regression analyses were used to identify factors associated with warfarin dose and derive a warfarin dosing model. The warfarin dose was also calculated using Gage et al model. Accuracy of the 2 models were compared through the mean absolute error (MAE) and percentage of predicted warfarin doses within 20% of the actual warfarin doseResultsThe Arab cohort included 405 patients. In the derivation cohort (n = 270), multiple regression analysis showed a dosing model consisting of     , & genotypes, along with other clinical factors (R  = 51.6%) (P < 0.05). When compared to Gage et al model, the Arab model had a significantly lower MAE of weekly warfarin dose (9.3 ± 7.6 mg/week vs 12.4 ± 10.4 mg/week,  = 0.03)ConclusionA model derived and validated in an Arab population had better prediction accuracy compared to an internationally validated one.

The administered dose of a drug modulates whether patients will experience optimal effectiveness, toxicity including death, or no effect at all. Dosing is particularly important for diseases and/or drugs where the drug can decrease severe morbidity or prolong life. Likewise, dosing is important where the drug can cause death or severe morbidity. Since we believe there are many examples where more precise dosing could benefit patients, it is worthwhile to consider how to prioritize drug-disease targets. One key consideration is the quality of information available from which more precise dosing recommendations can be constructed. When a new more precise dosing scheme is created and differs significantly from the approved label, it is important to consider the level of proof necessary to either change the label and/or change clinical practice. The cost and effort needed to provide this proof should also be considered in prioritizing drug-disease precision dosing targets. Although precision dosing is being promoted and has great promise, it is underutilized in many drugs and disease states. Therefore, we believe it is important to consider how more precise dosing is going to be delivered to high priority patients in a timely manner. If better dosing schemes do not change clinical practice resulting in better patient outcomes, then what is the use? This review paper discusses variables to consider when prioritizing precision dosing candidates while highlighting key examples of precision dosing that have been successfully used to improve patient care.

Recently, using artificial intelligence (AI) in drug discovery has received much attention since it significantly shortens the time and cost of developing new drugs. Deep learning (DL)-based approaches are increasingly being used in all stages of drug development as DL technology advances, and drug-related data grows. Therefore, this paper presents a systematic Literature review (SLR) that integrates the recent DL technologies and applications in drug discovery Including, drug–target interactions (DTIs), drug–drug similarity interactions (DDIs), drug sensitivity and responsiveness, and drug-side effect predictions. We present a review of more than 300 articles between 2000 and 2022. The benchmark data sets, the databases, and the evaluation measures are also presented. In addition, this paper provides an overview of how explainable AI (XAI) supports drug discovery problems. The drug dosing optimization and success stories are discussed as well. Finally, digital twining (DT) and open issues are suggested as future research challenges for drug discovery problems. Challenges to be addressed, future research directions are identified, and an extensive bibliography is also included.

To develop and validate a machine-learning/population-pharmacokinetic (ML-PPK) hybrid model that predicts individual vancomycin clearance (CL) and volume of distribution (V ) before the first dose, thereby informing initial dosing in critically ill children. We retrospectively analysed children from two tertiary centers in China (2013-2023). A previously published one-compartment PPK model was re-estimated with the pooled dataset and used as a Bayesian prior to derive individual CL and V as training targets. Ten machine-learning and deep-learning algorithms were trained, and an XGBoost-based sequential forward-selection procedure was applied to identify a minimal predictor set. Model performance was evaluated on a held-out test set and an external cohort. Data from 821 children and 1,767 vancomycin concentrations were included. 29 candidate variables were screened, and six high-impact predictors - body weight, cardiothoracic surgery, estimated glomerular filtration rate, sex, ICU admission, and post-menstrual-age class - maximized performance. CatBoost outperformed the other evaluated algorithms and, under this study design, more closely approximated PPK-Bayesian posterior PK estimates than the original parametric PPK covariate model, yielding for CL: R = 0.89, and 81.8% of predictions within ±30%; and for V : R = 0.95, with 92.1% within ±30%. Performance remained robust in both the test set and the external validation cohort, with external validation R values of 0.85 for CL and 0.95 for V . SHAP analysis highlighted body weight, renal function, and cardiothoracic surgery status as the main determinants of CL, consistent with covariate effects in the PPK model. An interpretable CatBoost-based ML-PPK hybrid can estimate CL and V pre-dose using routinely available data, enabling patient-specific initial vancomycin regimens and reducing early under- or overexposure in pediatric critical care.

Background Correct dosing and therapeutic drug monitoring (TDM) practices are essential when aiming for optimal vancomycin treatment. Objective To assess target attainment after initial dosing and dose adjustments, and to determine compliance to dosing and TDM guidelines. Setting Tertiary care university hospital in Belgium. Method A chart review was performed in 150 patients, ranging from preterm infants to adults, treated intravenously with vancomycin. Patient characteristics, dosing and TDM data were compared to evidence-based hospital guidelines. Main outcome measures Target attainment of vancomycin after initial dosing and dose adjustments. Results Subtherapeutic concentrations were measured in 68% of adults, in 76% of children and in 52% of neonates after treatment initiation. Multiple dose adaptations (median 2, Q1 1–Q3 2) were required for target attainment, whilst more than 20% of children and neonates never reached targeted concentrations. Regarding compliance to the hospital guideline, some points of improvement were identified: omitted dose adjustment in adults with decreased renal function (53%), delayed sampling (16% in adults, 31% in children) and redundant sampling (34% of all samples in adults, 12% in children, 13% in neonates). Conclusion Target attainment for vancomycin with current dosing regimens and TDM is poor in all age groups. Besides, human factors should not be ignored when aiming for optimal treatment. This study reflects an ongoing challenge in clinical practice and highlights the need for optimization of vancomycin dosing strategies and improvement of awareness of all health care professionals involved.

In the COVID-19 pandemic era, safety concerns have been raised regarding the risk of severe infection following administration of ocrelizumab (OCR), a B-cell-depleting therapy. We enrolled all relapsing remitting multiple sclerosis (RRMS) patients who received maintenance doses of OCR from January 2020 to June 2021. Data were extracted in December 2021. Standard interval dosing (SID) was defined as a regular maintenance interval of OCR infusion every 6 months, whereas extended interval dosing (EID) was defined as an OCR infusion delay of at least 4 weeks. Three infusions were considered in defining SID vs. EID (infusions A, B, and C). Infusion A was the last infusion before January 2020. The primary study outcome was a comparison of disease activity during the A-C interval, which was defined as either clinical (new relapses) or radiological (new lesions on T1-gadolinium or T2-weighted magnetic resonance imaging (MRI) sequences). Second, we aimed to assess confirmed disability progression (CDP). A total cohort of 278 patients (174 on SID and 104 on EID) was enrolled. Patients who received OCR on EID had a longer disease duration and a higher rate of vaccination against severe acute respiratory syndrome-coronavirus 2 (p < 0.05). EID was associated with an increased risk of MRI activity during the A-C interval (OR 5.373, 95% CI 1.203–24.001, p = 0.028). Being on SID or EID did not influence CDP (V-Cramer 0.47, p = 0.342). EID seemed to be associated with a higher risk of MRI activity in our cohort. EID needs to be carefully considered for OCR-treated patients.