Model-informed Dosing Research Articles

P0869 Model-informed dosage de-escalation of infliximab in patients with inflammatory bowel diseases

Abstract Background Extending the infliximab (IFX) infusion interval has been attempted in patients with Crohn’s disease (CD) and ulcerative colitis (UC) who maintained response following an earlier interval shortening. We compared therapeutic drug monitoring (TDM) and model-informed precision dosing (MIPD) for successful de-escalation of infliximab dosage in these patients. Methods MODIFI is a monocentric, open-label, historically controlled Phase 4 trial (NCT04982172). Eligible patients with steroid-free clinical (two-item patient-reported outcome [PRO2] ≤1 for UC and ≤8 for CD) and biological remission(C-reactive protein <5 mg/L and faecal calprotectin <250 mg/kg) on accelerated IFX dosing underwent interval extension to every eight weeks. The extension was guided by either TDM (historical control) or MIPD targeting an infliximab trough concentration (Ctrough) of 5 mg/L (prospective intervention). Bayesian forecasting was performed using the TDMx Infliximab module (www.TDMx.eu). Steroid-free clinical and biological remission and IFX concentrations were measured at each study visit over 52 weeks. The primary endpoint was sustained steroid-free clinical and biological remission over one year. Results The study enrolled 52 patients: 21 patients (11 CD, 10 UC) in the TDM arm and 31 patients (19 CD, 12 UC) in the MIPD arm (Table 1). Although the proportion of patients achieving the primary endpoint was numerically higher in the MIPD arm (52%, n=16/31) compared to the TDM arm (29%, n=6/21), this was not significantly different p=0.17; Figure 1A). However, the proportion achieving sustained steroid-free clinical remission over one year was significantly higher in the MIPD arm compared to the TDM arm (81%, n=25/31, vs. 38%, n=8/21, p<0.01), particularly in the UC subpopulation (100% in the MIPD arm, n=12/12, vs. 30% in the MIPD arm, n=3/10, p<0.01; Figure 1D&F). The proportion of patients achieving sustained steroid-free biological remission over one year did not statistically differ between the MIPD and TDM arms, both in the total population and in the subpopulation analyses (p>0.50; Figure 1G–I). Following a single model-informed dose at baseline, the IFX Ctrough decreased to median 5.9 mg/L [interquartile range IQR 4.4–7.5 mg/L] with 65% of patients having an IFX Ctrough >5 mg/L. The IFX dose per infusion over the one year period was comparable between the TDM arm (7.5 [IQR 7.4–8.2] mg/kg) and the MIPD arm (8.8 [IQR 6.6–10.9] mg/kg). Conclusion MIPD-guided IFX dosage de-escalation resulted in numerically higher rates of sustained steroid-free clinical and biological remission compared to dose de-escalation based on TDM. Future confirmatory studies with randomization and larger sample size are needed to validate our findings.

External evaluation of intravenous vancomycin population pharmacokinetic models in adults receiving high-flux intermittent haemodialysis.

Patients undergoing haemodialysis (HD) are at greater risk of methicillin-resistant Staphylococcus aureus infections requiring intravenous vancomycin. Close vancomycin therapeutic drug monitoring is warranted in HD patients as renal clearance is the primary elimination pathway. Clinically, population pharmacokinetics (popPK) model-informed dosing is commonly used. This study aimed to perform an external evaluation of published vancomycin popPK models developed for adults undergoing high-flux intermittent HD, and to create a dosing nomogram derived from the model that performed best. A literature review was conducted through PubMed and EMBASE to identify relevant popPK models. an external dataset was collected retrospectively from patients of 2 healthcare centres in Quebec, Canada. Selected models were implemented in NONMEM (v7.5; ICON Development Solutions). Predictive performance was assessed through prediction and simulation-based diagnostics. In total, 2386 vancomycin concentrations were collected from 274 patients and 476 antibiotic courses. Four vancomycin popPK models were selected for evaluation. None of the models demonstrated overall satisfactory or clinically acceptable predictive performance. Nonetheless, Bae et al.'s model performed best with a median prediction error of 16.25% and median absolute prediction error of 34.66%. Different predictive performance was also observed for vancomycin concentrations from samples collected during and between HD sessions. All evaluated models presented poor overall predictive performance. Further studies are required, through existing popPK model parameter re-estimation or new model development, to adequately describe vancomycin pharmacokinetics for our high-flux intermittent HD patient cohort.

Improving cefazolin exposure in critically ill children using a population pharmacokinetic model.

Population pharmacokinetics (PK) models may be effective in improving antibiotic exposure with individualized dosing. The aim of the study is to assess cefazolin exposure using a population PK model in critically ill children. We conducted a single-centre observational study including children under 18 years old who had cefazolin plasma monitoring before and after a cefazolin model implementation. The first concentration at steady state of each cefazolin course was analysed. The optimal exposure was defined by concentration values ranging from free concentration over four times the minimal inhibitory concentration (MIC) for 100% of the dosing interval to total trough or plateau concentration under 100 mg. L-1. A total of 58 patients were included, of whom 39 and 19 children received conventional dosing or model-informed dosing, respectively. Median [range] age was 2.3 [0.1-17]years old, and median weight was 14.2 [2.9-72]kg. There were more continuous infusions (CI) in the model group than in the conventional group (n= 19/19 [100%] vs. n= 23/39 [59%]). Compared to conventional dosing, model-informed dosing provided more optimal exposure (n= 17/39 [44%] vs. n= 15/19 [79%], P= .01) and less underexposure (n= 18/39 [46%] vs. n= 2/19 [10%], P= .008), without increasing overexposure (n= 4/39 [10%] vs. n= 2/19 [11%], P= 1). Moreover, the time to C-reactive protein decrease by 50% was significantly shorter in the model group than the conventional group (3 [0.5-13] vs. 4 [1-34]; P= .045). Use of individualized cefazolin model-informed dosing improves critically ill children's exposure. Further studies are needed to assess the clinical benefit of cefazolin PK model application.

Model-informed dose optimization of mycophenolic acid in pediatric kidney transplant patients

PurposeWe aimed to develop and evaluate a population PK model of mycophenolic acid (MPA) in pediatric kidney transplant patients to aid MPA dose optimization.MethodsData were collected from pediatric kidney transplant recipients from a Dutch academic hospital (Radboudumc, the Netherlands). Pharmacokinetic model-building and model-validation analyses were performed using NONMEM. Subsequently, we externally evaluated the final model using data from another academic hospital. The final model was used to develop an optimized dosing regimen.ResultsThirty pediatric patients were included of whom 266 measured MPA plasma concentrations, including 20 full pharmacokinetic (PK) curves and 24 limited sampling curves, were available. A two-compartment model with a transition compartment for Erlang-type absorption best described the data. The final population PK parameter estimates were Ktr (1.48 h−1; 95% CI, 1.15–1.84), CL/F (16.0 L h−1; 95% CI, 10.3–20.4), Vc/F (24.9 L; 95% CI, 93.0–6.71E25), Vp/F (1590 L; 95% CI, 651–2994), and Q/F (36.2 L h−1; 95% CI, 9.63–74.7). The performance of the PK model in the external population was adequate. An optimized initial dose scheme based on bodyweight was developed. With the licensed initial dose, 35% of patients were predicted to achieve the target AUC, compared to 42% using the optimized scheme.ConclusionWe have successfully developed a pharmacokinetic model for MPA in pediatric renal transplant patients. The optimized dosing regimen is expected to result in better target attainment early in treatment. It can be used in combination with model-informed follow-up dosing to further individualize the dose when PK samples become available.

Evaluating the evidence for genotype-informed Bayesian dosing of tacrolimus in children undergoing solid organ transplantation: A systematic literature review.

Tacrolimus, a calcineurin inhibitor, is a highly effective immunosuppressant used in solid organ transplantation (SOT). However, it is characterized by a narrow therapeutic range and high inter-patient variability in pharmacokinetics. Standard weight-based dosing followed by empiric dose titration is suboptimal in controlling drug concentrations, increasing risk of rejection or toxicity, particularly in the initial months post transplantation. This review explores the potential of combined pre-transplant genotyping and pharmacokinetic (PK) modelling to improve tacrolimus dosing in paediatric SOT recipients. A systematic search of Medline, Embase and Cochrane databases identified studies published between March 2013 and March 2023 that investigated genotype- and PK model-informed tacrolimus dosing in children post-SOT. The Newcastle-Ottawa Scale assessed study quality. Seven studies encompassing paediatric kidney, heart, liver and lung transplants reported using genotype and model-informed dosing. A combination of clinical and genetic factors significantly impacts tacrolimus clearance and thus initial dose recommendation. Body size, transplant organ and co-medications were consistently important, while either time post-transplant or haematocrit emerged in some studies. Several models were identified, however, with limitations evident in some and with absence of evidence for their effectiveness in optimizing initial and subsequent dosing. This review highlights the development of PK models in paediatric SOT that integrate genotype and clinical covariates to personalize early tacrolimus dosing. While promising, prospective studies are needed to validate and confirm their effectiveness in improving time to therapeutic concentrations and reducing under- or overexposure. This approach has the potential to optimize tacrolimus therapy in paediatric SOT, thereby improving outcomes.

Model-Informed Dosing Optimization of Tacrolimus for Concomitant Administration with Itraconazole to Japanese Lung Transplant Recipients.

Tacrolimus is an immunosuppressant administered to patients undergoing lung transplantation. Itraconazole is often concomitantly used with tacrolimus to prevent fungal infections and increase tacrolimus concentration. However, the pharmacokinetics of tacrolimus in Japanese lung transplant recipients and the effect of itraconazole on its pharmacokinetics have not been adequately evaluated. Population pharmacokinetic analysis was conducted to develop an optimal dose adjustment method for use upon itraconazole initiation in Japanese lung transplant recipients. This study comprised Japanese lung transplant recipients whose blood tacrolimus and itraconazole concentrations were measured between January 2017 and December 2019. A nonlinear mixed-effects modeling program was used to explore the covariates of tacrolimus pharmacokinetics and effects of concomitant itraconazole use. Using the model, the optimal initial tacrolimus dose was calculated and a dose adjustment method comprising concomitant itraconazole use was developed. A total of 1693 tacrolimus trough blood concentrations and 85 itraconazole trough plasma concentrations were obtained from 43 patients. Postoperative day, albumin level, and administration route were extracted as covariates for tacrolimus pharmacokinetics. The drug-drug interaction between tacrolimus and itraconazole could be predicted more accurately by considering the concentration-dependent inhibition of itraconazole. The optimal initial tacrolimus dose was 2.0 mg twice daily for tube and 1.5 mg twice daily for oral administration. To maintain the target concentration, the tacrolimus dose was reduced by 60% upon itraconazole initiation. This study is the first to use population pharmacokinetic analysis to assess the interaction between tacrolimus and itraconazole in patients who underwent lung transplantation. These results provide useful insights for optimizing the initial tacrolimus dose for concomitant itraconazole use.

Perceived barriers and facilitators for model-informed dosing in pregnancy: a qualitative study across healthcare practitioners and pregnant women

BackgroundMost women use medication during pregnancy. Pregnancy-induced changes in physiology may require antenatal dose alterations. Yet, evidence-based doses in pregnancy are missing. Given historically limited data, pharmacokinetic models may inform pregnancy-adjusted doses. However, implementing model-informed doses in clinical practice requires support from relevant stakeholders.PurposeTo explore the perceived barriers and facilitators for model-informed antenatal doses among healthcare practitioners (HCPs) and pregnant women.MethodsOnline focus groups and interviews were held among healthcare practitioners (HCPs) and pregnant women from eight countries across Europe, Africa and Asia. Purposive sampling was used to identify pregnant women plus HCPs across various specialties prescribing or providing advice on medication to pregnant women. Perceived barriers and facilitators for implementing model-informed doses in pregnancy were identified and categorised using a hybrid thematic analysis.ResultsFifty HCPs and 11 pregnant women participated in 12 focus groups and 16 interviews between January 2022 and March 2023. HCPs worked in the Netherlands (n = 32), the UK (n = 7), South Africa (n = 5), Uganda (n = 4), Kenya, Cameroon, India and Vietnam (n = 1 each). All pregnant women resided in the Netherlands. Barriers and facilitators identified by HCPs spanned 14 categories across four domains whereas pregnant women described barriers and facilitators spanning nine categories within the same domains. Most participants found current antenatal dosing information inadequate and regarded model-informed doses in pregnancy as a valuable and for some, much-needed addition to antenatal care. Although willingness-to-follow model-informed antenatal doses was high across both groups, several barriers for implementation were identified. HCPs underlined the need for transparent model validation and endorsement of the methodology by recognised institutions. Foetal safety was deemed a critical knowledge gap by both groups. HCPs’ information needs and preferred features for model-informed doses in pregnancy varied. Several pregnant women expressed a desire to access information and partake in decisions on antenatal dosing.ConclusionsGiven the perceived limitations of current pharmacotherapy for pregnant women and foetuses, model-informed dosing in pregnancy was seen as a promising means to enhance antenatal care by pregnant women and healthcare practitioners.

Optimization of the betamethasone and dexamethasone dosing regimen during pregnancy: a combined placenta perfusion and pregnancy physiologically based pharmacokinetic modeling approach

BackgroundAntenatal betamethasone and dexamethasone are prescribed to women who are at high risk of premature birth to prevent neonatal respiratory distress syndrome. The current treatment regimens, effective to prevent neonatal RDS, may be suboptimal. Recently, concerns have been raised regarding possible adverse long-term neurological outcomes due to high fetal drug exposures. Data from non-human primates and sheep suggest maintaining a fetal plasma concentration above 1 ng/mL for 48 hours to retain efficacy, while avoiding undesirable high fetal plasma levels. ObjectiveWe aimed to re-evaluate the current betamethasone and dexamethasone dosing strategies to assess estimated fetal exposure and provide new dosing proposals that meet the efficacy target but avoid excessive peak exposures. Study designA pregnancy physiologically-based pharmacokinetic model was used to predict fetal drug exposures. To allow prediction of the extent of betamethasone and dexamethasone exposure in the fetus, placenta perfusion experiments were conducted to determine placental transfer. Placental transfer rates were integrated in the PBPK model to predict fetal exposure and model performance was verified using published maternal and fetal PK data. The verified pregnancy physiologically-based pharmacokinetic models were then used to simulate alternative dosing regimens to establish a model-informed dose. ResultsEx vivo data showed that both drugs extensively cross the placenta. For betamethasone 15.7 ± 1.7% and for dexamethasone 14.4 ± 1.5% of the initial maternal perfusate concentration reached the fetal circulations at the end of the 3-hour perfusion period. Pregnancy physiologically-based pharmacokinetic models that include these ex vivo-derived placental transfer rates, accurately predicted maternal and fetal exposures resulting from current dosing regimens. The dose simulations suggest that for betamethasone intramuscular a dose reduction from 2 dosages 11.4 mg, 24 hours apart, to 4 dosages 1.425 mg, 12 hours apart would avoid excessive peak exposures and still meet the fetal response threshold. For dexamethasone, the dose may be reduced from four times 6 mg every 12 hours to 8 times 1.5 mg every 6 hours. ConclusionA combined placenta perfusion and pregnancy physiologically-based pharmacokinetic modeling approach adequately predicted both maternal and fetal drug exposures of two antenatal corticosteroids. Strikingly, our PBPK simulations suggest that drug doses might be reduced drastically to still meet earlier proposed efficacy targets and minimize peak exposures. We propose the provided model-informed dosing regimens are used to support further discussion on an updated antenatal corticosteroid scheme and design of clinical trials to confirm the effectiveness and safety of lower doses.

Using Real-World Data to Externally Evaluate Population Pharmacokinetic Models of Dexmedetomidine in Children and Infants.

Dexmedetomidine is a sedative used in both adults and off-label in children with considerable reported pharmacokinetic (PK) interindividual variability affecting drug exposure across populations. Several published models describe the population PKs of dexmedetomidine in neonates, infants, children, and adolescents, though very few have been externally evaluated. A prospective PK dataset of dexmedetomidine plasma concentrations in children and young adults aged 0.01-19.9 years was collected as part of a multicenter opportunistic PK study. A PubMed search of studies reporting dexmedetomidine PK identified five population PK models developed with data from demographically similar children that were selected for external validation. A total of 168 plasma concentrations from 102 children were compared with both population (PRED) and individualized (IPRED) predicted values from each of the five published models by quantitative and visual analyses using NONMEM (v7.3) and R (v4.1.3). Mean percent prediction errors from observed values ranged from -1% to 120% for PRED, and -24% to 60% for IPRED. The model by James et al, which was developed using similar "real-world" data, nearly met the generalizability criteria from IPRED predictions. Other models developed using clinical trial data may have been limited by inclusion/exclusion criteria and a less racially diverse population than this study's opportunistic dataset. The James model may represent a useful, but limited tool for model-informed dosing of hospitalized children.

Towards More Robust Evaluation of the Predictive Performance of Physiologically Based Pharmacokinetic Models: Using Confidence Intervals to Support Use of Model-Informed Dosing in Clinical Care.

With the rise in the use of physiologically based pharmacokinetic (PBPK) modeling over the past decade, the use of PBPK modeling to underpin drug dosing for off-label use in clinical care has become an attractive option. In order to use PBPK models for high-impact decisions, thorough qualification and validation of the model is essential to gain enough confidence in model performance. Currently, there is no agreed method for model acceptance, while clinicians demand a clear measure of model performance before considering implementing PBPK model-informed dosing. We aim to bridge this gap and propose the use of a confidence interval for the predicted-to-observed geometric mean ratio with predefined boundaries. This approach is similar to currently accepted bioequivalence testing procedures and can aid in improved model credibility and acceptance. Two different methods to construct a confidence interval are outlined, depending on whether individual observations or aggregate data are available from the clinical comparator data sets. The two testing procedures are demonstrated for an example evaluation of a midazolam PBPK model. In addition, a simulation study is performed to demonstrate the difference between the twofold criterion and our proposed method. Using midazolam adult pharmacokinetic data, we demonstrated that creating a confidence interval yields more robust evaluation of the model than a point estimate, such as the commonly used twofold acceptance criterion. Additionally, we showed that the use of individual predictions can reduce the number of required test subjects. Furthermore, an easy-to-implement software tool was developed and is provided to make our proposed method more accessible. With this method, we aim to provide a tool to further increase confidence in PBPK model performance and facilitate its use for directly informing drug dosing in clinical care.

Model-Informed Dose Selection for a Novel Human Immunoglobulin G4 Derived Monoclonal Antibody Targeting Proprotein Convertase Kwashiorkor Type 9: Insights from Population Pharmacokinetics-Pharmacodynamics and Systems Pharmacology.

Monoclonal antibody drugs targeting proprotein convertase kwashiorkor type 9 (PCSK9) have recently demonstrated remarkable success in lipid-lowering therapies. Specifically, antibodies derived from immunoglobulin G1 (IgG1, alirocumab) and IgG2 (evolocumab) have been successfully utilized for this purpose. Recently, a novel recombinant fully human anti-PCSK9 monoclonal antibody, originally derived from IgG4 and designated as SAL003, was developed. This study aimed to explore the pharmacokinetics, efficacy, and safety of SAL003 in both single and multiple administrations. The investigation included both healthy individuals and individuals with hyperlipidemia. To comprehensively grasp the pharmacokinetic (PK) and pharmacodynamic (PD) attributes of SAL003, this study employed population PK-PD (popPK-PD) and mechanistic systems pharmacology (MSP) modeling. These models were employed for predicting low-density lipoprotein cholesterol (LDLc) concentrations and appropriate dosages across diverse potential clinical scenarios. The research results indicated that SAL003 demonstrated comparable pharmacokinetic properties to evolocumab, exhibited notable effectiveness in reducing lipid levels, and was confirmed to be safe and well-tolerated in both healthy individuals and individuals with hyperlipidemia. Notably, SAL003 displayed differing effectiveness between patients and healthy populations. This discrepancy was observed in the popPK-PD model, with a positive population influence on Emax, and the MSP model, indicating elevated PCSK9 clearance and LDLr-related LDLc clearance in the healthy group. Simulation results from the popPK-PD and MSP models indicated a dosage of 140 mg of Q4W and 420 mg of Q8W for phase II/III clinical trials. Reducing the drug dose or extending the dosing intervals may result in treatment failure. Additionally, the simultaneous use of statins led to elevated PCSK9 levels and intensified fluctuations in steady-state LDLc levels during SAL003 treatment.

Toward Model-Based Informed Precision Dosing of Vancomycin in Hematologic Cancer Patients: A First Step.

There is no consensus on the optimal vancomycin dose to achieve pharmacokinetic/pharmacodynamic (PK/PD) target in patients with hematologic cancer or in hematopoietic stem cell transplant (HSCT) recipients. A 24-h area under the concentration-time curve (AUC) >400 mg*h/L must be achieved early for successful treatment of severe methicillin-resistant Staphylococcus aureus (MRSA) infections. Current nomograms derived from general population data are not sufficiently accurate to allow AUC-based model-informed precision dosing. The objective of this study was to characterize vancomycin PK in patients with hematologic cancer or in HSCT recipients and to develop a model-informed dosing tool based on PK/PD target requirements. Pooled retrospective and prospective vancomycin serum concentrations were analyzed using NONMEM® to evaluate the performance of previously published population PK (popPK) models built from hematologic cancer datasets and to develop a novel Bayesian PK model. Patients' characteristics and clinical data were tested as potential covariates. The popPK model was validated internally and externally. Predictions of vancomycin concentrations for different dosing regimens were made using Monte-Carlo simulations, and a nomogram strategy was proposed according to selected probability of target attainment (PTA). The predictive performance of the published popPK models was found to be suboptimal for our population. A novel popPK model was developed using 240 vancomycin concentrations (60 patients). A two-compartment structural model with an additive error model best described the data. Ideal body weight and estimated glomerular filtration rate (eGFR) [Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI)] were selected as covariates for volume of distribution (V) and clearance (CL). Bootstrapping confirmed the stability and precision of the popPK parameters. The volume of distribution was V1=46.8 L and V2=56.1L, while CL=5.63L/h. External validation using 107 vancomycin concentrations (24 patients) demonstrated the predictivity of the model. A nomogram was developed to reach minimally PTA >50% for 400 < AUC < 600 mg*h/L. To our knowledge, this study provides the first model-informed AUC-based strategy in North American hematologic cancer patients with or without HSCT. The resulting nomogram generated provides a simplified approach to improving the accuracy of initial vancomycin dosing in this population.

Pragmatic physiologically-based pharmacokinetic modeling to support clinical implementation of optimized gentamicin dosing in term neonates and infants: proof-of-concept.

Modeling and simulation can support dosing recommendations for clinical practice, but a simple framework is missing. In this proof-of-concept study, we aimed to develop neonatal and infant gentamicin dosing guidelines, supported by a pragmatic physiologically-based pharmacokinetic (PBPK) modeling approach and a decision framework for implementation. An already existing PBPK model was verified with data of 87 adults, 485 children and 912 neonates, based on visual predictive checks and predicted-to-observed pharmacokinetic (PK) parameter ratios. After acceptance of the model, dosages now recommended by the Dutch Pediatric Formulary (DPF) were simulated, along with several alternative dosing scenarios, aiming for recommended peak (i.e., 8-12 mg/L for neonates and 15-20 mg/L for infants) and trough (i.e., <1 mg/L) levels. We then used a decision framework to weigh benefits and risks for implementation. The PBPK model adequately described gentamicin PK. Simulations of current DPF dosages showed that the dosing interval for term neonates up to 6 weeks of age should be extended to 36-48 h to reach trough levels <1 mg/L. For infants, a 7.5 mg/kg/24 h dose will reach adequate peak levels. The benefits of these dose adaptations outweigh remaining uncertainties which can be minimized by routine drug monitoring. We used a PBPK model to show that current DPF dosages for gentamicin in term neonates and infants needed to be optimized. In the context of potential uncertainties, the risk-benefit analysis proved positive; the model-informed dose is ready for clinical implementation.

Model-Informed Dosing Regimen of Ticagrelor in Chinese Patients With Acute Coronary Syndrome.

The exposure to ticagrelor (BRILINTA) is higher in the East Asian population compared with the White population, thus, East Asians have an increased risk of bleeding. We developed a population pharmacokinetic (PopPK) model of ticagrelor based on a randomized 3 × 3 crossover study in healthy subjects. The area under the concentration-time curve (AUC) of Chinese patients with acute coronary syndrome was simulated based on this model. Following this, eight machine learning (ML) methods were used to construct bleeding risk models. Variables included in the final bleeding risk model were age, hypertension, body weight, AUC, drinking status, calcium channel blockers, antidiabetic medications, β-blockers, peripheral vascular disease, diabetes, transient ischemic attack, sex, and proton pump inhibitor. In terms of F1 scores and area under the curve of receiver operating characteristic curve (ROC-AUC), the Random Forest model performed best among all models, with an F1 score of 0.73 and ROC-AUC of 0.81. Moreover, the PopPK model and ML algorithm were used to bridge the real-world data to build a bleeding risk prediction model based on drug exposure and clinical information. Using this model, a ticagrelor regimen that is associated with a lower risk of bleeding in individuals can be obtained. This model should be further validated prospectively in clinical settings.

PBPK modeling for drug dosing in pediatric patients: a tutorial for a pragmatic approach in clinical care.

It is well accepted that off-label drug dosing recommendations for pediatric patients should be based on the best available evidence. However, the available traditional evidence is often low. To bridge this gap, physiologically-based pharmacokinetic (PBPK) modeling is a scientifically well-founded tool that can be used to enable model-informed dosing (MID) recommendations in children in clinical practice. In this tutorial, we provide a pragmatic, PBPK-based pediatric modeling workflow. For this approach to be successfully implemented in pediatric clinical practice, a thorough understanding of the model assumptions and limitations is required. More importantly, careful evaluation of a MID approach within the context of overall benefits and the potential risks is crucial. The tutorial is aimed to help modelers, researchers and clinicians, to effectively employ PBPK simulations to support pediatric drug dosing.

Various Factors Influencing the Enantiomers of Warfarin Pharmacokinetics: A Systematic Review of Population Pharmacokinetics

Warfarin is the most commonly prescribed anti-coagulant medication. Warfarin’s pharmacokinetics (PK) in its enantiomeric form have been reported to be highly variable. Five population pharmacokinetic model studies for warfarin were identified in this systematic review. This review summarized these studies and reported on various factors affecting warfarin PK. Most studies reported a one-compartment model with first-order absorption and elimination for both S-warfarin and R-warfarin. Warfarin disposition has been reported to be influenced by various factors, including gender, age, genetic variation, body surface area (BSA), concurrent drug, weight, and ethnicity. So, all of these factors must be considered when addressing this pharmacokinetic variability. These models should undergo an external evaluation to confirm their generalizability and to support model-informed dosing in clinical settings.

Precision sirolimus dosing in children: The potential for model-informed dosing and novel drug monitoring.

The mTOR inhibitor sirolimus is prescribed to treat children with varying diseases, ranging from vascular anomalies to sporadic lymphangioleiomyomatosis to transplantation (solid organ or hematopoietic cell). Precision dosing of sirolimus using therapeutic drug monitoring (TDM) of sirolimus concentrations in whole blood drawn at the trough (before the next dose) time-point is the current standard of care. For sirolimus, trough concentrations are only modestly correlated with the area under the curve, with R 2 values ranging from 0.52 to 0.84. Thus, it should not be surprising, even with the use of sirolimus TDM, that patients treated with sirolimus have variable pharmacokinetics, toxicity, and effectiveness. Model-informed precision dosing (MIPD) will be beneficial and should be implemented. The data do not suggest dried blood spots point-of-care sampling of sirolimus concentrations for precision dosing of sirolimus. Future research on precision dosing of sirolimus should focus on pharmacogenomic and pharmacometabolomic tools to predict sirolimus pharmacokinetics and wearables for point-of-care quantitation and MIPD.

Predicting in vivo Target Occupancy (TO) Profiles via PBPK-TO Modeling of Warfarin Pharmacokinetics in Blood: Importance of Low Dose Data and Prediction of Stereoselective Target Interactions.

Warfarin is well-recognized for its high-affinity and capacity-limited binding to the pharmacological target and undergoes target-mediated drug disposition (TMDD). Here, we developed a physiologically-based pharmacokinetic (PBPK) model that incorporated saturable target binding and other reported hepatic disposition components of warfarin. The PBPK model parameters were optimized by fitting to the reported blood PK profiles of warfarin with no stereoisomeric separation following oral dosing of racemic warfarin (0.1, 2, 5, or 10 mg) using the Cluster Gauss-Newton Method (CGNM). The CGNM-based analysis yielded multiple "accepted" sets for six optimized parameters, which were then used to simulate the warfarin blood PK and in vivo target occupancy (TO) profiles. When further analyses examined the impact of dose selection on uncertainty in parameter estimation by the PBPK modeling, the PK data from 0.1 mg dose (well below target saturation) was important in practically identifying the target binding-related parameters in vivo When stereoselective differences were incorporated for both hepatic disposition and target interactions, our PBPK modeling predicted that R-warfarin (of slower clearance and lower target affinity than S-warfarin) contributes to TO prolongation following oral dosing of racemic warfarin. Our results extend the validity of the approach by which the PBPK-TO modeling of blood PK profiles can yield TO prediction in vivo (applicable to the drugs with targets of high affinity and abundance and limited distribution volume via non-target interactions). Our findings support that model-informed dose selection and PBPK-TO modeling may aid in TO and efficacy assessment in preclinical and clinical phase-1 studies. Significance Statement The current PBPK modeling incorporated the reported hepatic disposition components and target binding of warfarin and analyzed the blood PK profiles from varying warfarin doses, practically identifying target binding-related parameters in vivo By implementing the stereoselective differences between R- and S-warfarin, our analysis predicted the role of R-warfarin in prolonging overall target occupancy. Our results extend the validity of analyzing blood PK profiles to predict target occupancy in vivo, which may guide efficacy assessment in preclinical and clinical phase-1 studies.

Concepts and applications of pharmacokinetic parameters: A guide for the classroom

Context: Pharmacokinetic studies play a fundamental role in making informed decisions during the drug development stage and fulfilling regulatory agencies' requirements for drug approval. Disposition profiles of plasma drug concentrations over time can be characterized by using non-compartmental analysis, compartmental and physiological-based modeling. These models allow us to determine the pharmacokinetic parameters that best describe the absorption, distribution, metabolism, and excretion (ADME) processes. Aims: To develop a conceptual and practical guide for the classroom on the most relevant pharmacokinetic parameters and their applications. Results: The apparent volume of distribution (Vd), systemic drug clearance (CL), bioavailability (F) and elimination half-life (t1/2) are among the most relevant pharmacokinetic parameters discussed in this article. The Vd describes the relationship at equilibrium between the amount of drug in the body and its plasma concentrations after distribution, used to calculate the initial dose to reach the target drug concentration. The CL describes the relationship between plasma drug concentrations and the rate of elimination from the body, allowing calculation of a maintenance dosing rate to maintain an average target concentration at steady-state. The t1/2 is the time required to halve the plasma drug concentration, whereas F is critical to understand the biological performance of the drug formulation. Conclusions: In this teacher's topic text, we emphasize the importance of these parameters for optimizing strategies of model-informed dose individualization. Indeed, they are crucial for predicting systemic drug exposures and how long the drug will last in the body, as well as time to steady state after multiple-dosing regimens.

Factors Influencing Pharmacokinetics of Tamoxifen in Breast Cancer Patients: A Systematic Review of Population Pharmacokinetic Models.

Tamoxifen is useful in managing breast cancer and it is reported to have significant variability in its pharmacokinetics. This review aimed to summarize reported population pharmacokinetics studies of tamoxifen and to identify the factors affecting the pharmacokinetics of tamoxifen in adult breast cancer patients. A systematic search was undertaken in Scopus, Web of Science, and PubMed for papers published in the English language from inception to 20 August 2022. Studies were included in the review if the population pharmacokinetic modeling was based on non-linear mixed-effects modeling with a parametric approach for tamoxifen in breast cancer patients. After initial selection, 671 records were taken for screening. A total of five studies were selected from Scopus, Web of Science, PubMed, and by manual searching. The majority of the studies were two-compartment models with first-order absorption and elimination to describe tamoxifen and its metabolites' disposition. The CYP2D6 phenotype and CYP3A4 genotype were the main covariates that affected the metabolism of tamoxifen and its metabolites. Other factors influencing the drug's pharmacokinetics included age, co-medication, BMI, medication adherence, CYP2B6, and CYP2C19 genotype. The disposition of tamoxifen and its metabolites varies primarily due to the CYP2D6 phenotype and CYP3A4 genotype. However, other factors, such as anthropometric characteristics and menopausal status, should also be addressed when accounting for this variability. All these studies should be externally evaluated to assess their applicability in different populations and to use model-informed dosing in the clinical setting.