Initial dosage optimization of olanzapine in patients with bipolar disorder based on model-informed precision dosing: a study from the real world.

Objectives: Olanzapine is used for treating bipolar disorder (BPD); however, the optimal initial dosing regimen is unclear. The present study aimed to investigate the optimal olanzapine initial dosage in patients with BPD via model-informed precision dosing (MIPD) based on a real-world study. Methods: Thirty-nine patients with BPD from the real-world study were collected to construct the MIPD model. Results: Weight, combined used quetiapine influenced olanzapine clearances in patients with BPD, where the clearance rates were 0.152:1 in patients with or without quetiapine under the same weight. We simulated olanzapine doses once a day or twice a day, of which twice a day was optimal. Without quetiapine, for twice-a-day olanzapine doses, 0.80, 0.70, and 0.60 mg/kg/day were suitable for 40- to 56-kg BPD patients, 56- to 74-kg BPD patients, and 74- to 100-kg BPD patients, respectively. With quetiapine, for twice-a-day olanzapine doses, 0.05 mg/kg/day was suitable for 40- to 100-kg BPD patients. Conclusion: This study was the first to investigate the optimal olanzapine initial dosage in patients with BPD via MIPD based on a real-world study, providing clinical reference for the precision medication of olanzapine in BPD patients.

Model-informed precision dosing: State of the art and future perspectives.

Model-informed precision dosing (MIPD) stands as a significant development in personalized medicine to tailor drug dosing to individual patient characteristics. MIPD moves beyond traditional therapeutic drug monitoring (TDM) by integrating mathematical predictions of dosing, and considering patient-specific factors (patient characteristics, drug measurements) as well as different sources of variability. For this purpose, rigorous model qualification is required for the application of MIPD in patients. This review delves into new methods in model selection and validation, also highlighting the role of machine learning in improving MIPD, the utilization of biosensors for real-time monitoring, as well as the potential of models integrating biomarkers for efficacy or toxicity for precision dosing. The clinical evidence of TDM and MIPD is discussed for various medical fields including infection medicine, oncology, transplant medicine, and inflammatory bowel diseases, thereby underscoring the role of pharmacokinetics/pharmacodynamics and specific biomarkers. Further research, particularly randomized clinical trials, is warranted to corroborate the value of MIPD in enhancing patient outcomes and advancing personalized medicine.

Hepatic Metabolic Enzyme Activity with Endogenous Substances-current Status, Challenges and Limitations.

Precision dosing is essential in improving drug efficacy and minimizing adverse reactions, especially in liver impaired patients. However, there is no objective index to directly evaluate the body's ability to metabolize specific drugs. Many factors affect the activity of enzymes, and alter the systemic exposure of substrate drugs, like genetic polymorphism, drug-drug interactions and physiological/pathological state. So, quantifying the activities of enzymes dynamically would be helpful to make precision dosing. Recently, some endogenous substrates of enzymes, such as 6ß-hydroxycortisol (6ß-OH-cortisol)/cortisol and 6ß-hydroxycortisone, have been identified to investigate variations in drug enzymes in humans. Clinical data obtained support their performance as surrogate probes in terms of reflecting the activities of corresponding enzyme. Therefore, a group of Monitored endogenous biomarkers in multiple points can address the uncertainty in drug metabolization in the preclinical phase and have the potential to fulfill precision dosing. This review focuses on recent progress in the contribution of endogenous substances to drug precision dosing, factors that influence enzyme activities, and drug exposure in vivo.

Physiologically based pharmacokinetic modeling in obesity: applications and challenges.

INTRODUCTION: Rising global obesity rates pose a threat to people's health. Obesity causes a series of pathophysiologic changes, making the response of patients with obesity to drugs different from that of nonobese, thus affecting the treatment efficacy and even leading to adverse events. Therefore, understanding obesity's effects on pharmacokinetics is essential for the rational use of drugs in patients with obesity. AREAS COVERED: Articles related to physiologically based pharmacokinetic (PBPK) modeling in patients with obesity from inception to October 2023 were searched in PubMed, Embase, Web of Science and the Cochrane Library. This review outlines PBPK modeling applications in exploring factors influencing obesity's effects on pharmacokinetics, guiding clinical drug development and evaluating and optimizing clinical use of drugs in patients with obesity. EXPERT OPINION: Obesity-induced pathophysiologic alterations impact drug pharmacokinetics and drug-drug interactions (DDIs), altering drug exposure. However, there is a lack of universal body size indices or quantitative pharmacology models to predict the optimal for the patients with obesity. Therefore, dosage regimens for patients with obesity must consider individual physiological and biochemical information, and clinically individualize therapeutic drug monitoring for highly variable drugs to ensure effective drug dosing and avoid adverse effects.

Individualized sublingual immunotherapy with dynamic maintenance dose ascending for house dust mite-induced allergic rhinitis.

BACKGROUND: Precision dosing in sublingual immunotherapy (SLIT) has become a hotspot gradually, yet no standardized dose adjustment pattern for house dust mite (HDM)-SLIT. This study aims to investigate the clinical feasibility of the dynamic maintenance dose ascending regimen for individualized SLIT. METHODS: A total of 258 allergic rhinitis (AR) patients treated with HDM-SLIT were included in this retrospective study. Patients were divided into the regular dose (RD) group (n = 101) and the high dose (HD) group (n = 157) according to different maintenance dosages of SLIT. In the RD group, patients received the fixed dose recommended by the manufacturer. In the HD group, patients received a maximum tolerance dose determined by dynamic dose ascending. The clinical efficacy was evaluated by combined symptom and medication score (CSMS) and visual analogue scale score (VAS) at the baseline, 0.5-year, 1-year, and 2-year. The safety was evaluated by adverse events (AEs). RESULTS: Significant reductions of CSMS and VAS at 0.5-year, 1-year, and 2-year were observed in both the RD group and the HD group compared to the baseline (P < 0.05). In addition, greater improvements in these clinical parameters from 0.5- to 2-year were found in the HD group compared to the RD group (P < 0.05). For subgroup analysis in the HD group, no significant differences in CSMS and VAS were observed among subgroups of patients <14 years old and patients ≥14 years old (P > 0.05). No serious AEs in the two groups and no significant differences were observed between the AE incidence rate of the RD group and HD group during the incremental and maintenance phases. CONCLUSIONS: The 2-year HDM-SLIT with dynamic maintenance dose ascending regimen offers an "optimal" treatment for AR patients while maintaining safety. This study introduced a pattern for individualized dose adjustment in clinical practice, offering potential benefits for AR patients.

Predictive performance of multi-model approaches for model-informed precision dosing of piperacillin in critically ill patients.

Piperacillin (PIP)/tazobactam is a frequently prescribed antibiotic; however, over- or underdosing may contribute to toxicity, therapeutic failure, and development of antimicrobial resistance. An external evaluation of 24 published PIP-models demonstrated that model-informed precision dosing (MIPD) can enhance target attainment. Employing various candidate models, this study aimed to assess the predictive performance of different MIPD-approaches comparing (i) a single-model approach, (ii) a model selection algorithm (MSA) and (iii) a model averaging algorithm (MAA). Precision, accuracy and expected target attainment, considering either initial (B1) or initial and secondary (B2) therapeutic drug monitoring (TDM)-samples per patient, were assessed in a multicenter dataset (561 patients, 11 German centers, 3654 TDM-samples). The results demonstrated a slight superiority in predictive performance using MAA in B1, regardless of the candidate models, compared to MSA and the best single models (MAA, MSA, single models: inaccuracy ±3%, ±10%, ±8%; imprecision: <25%, <31%, <28%; expected target attainment >77%, >71%, >73%). The inclusion of a second TDM-sample notably improved precision and target attainment for all MIPD-approaches, particularly within the context of MSA and most of the single models. The expected target attainment is maximized (up to >90%) when the TDM-sample is integrated within 24 hours. In conclusion, MAA streamlines MIPD by reducing the risk of selecting an inappropriate model for specific patients. Therefore, MIPD of PIP using MAA implicates further optimization of antibiotic exposure in critically ill patients, by improving predictive performance with only one sample available for Bayesian forecasting, safety, and usability in clinical practice.

Acute kidney injury is associated with abnormal cefepime exposure among critically ill children and young adults.

BACKGROUND: Elevated cefepime blood concentrations can cause neurotoxicity in adults. The consequences of elevated cefepime concentrations among pediatric patients are unknown. Future exploration of such effects requires first identifying patients at risk for elevated cefepime exposure. We investigated the role of acute kidney injury as a risk factor for increased cefepime concentrations in critically ill children. METHODS: This was a retrospective analysis at a single pediatric intensive care unit. Analyzed patients received at least 24 h of cefepime and had at least two opportunistic samples collected for total cefepime concentration measurement. Individual pharmacokinetic (PK) profiles during treatment courses were reconstructed using Bayesian estimation with an established population PK model. Elevated trough concentration (Cmin) was defined as ≥ 30 mg/L based on adult toxicity studies. The effect of kidney dysfunction on cefepime PK profiles was interrogated using a mixed-effect model. RESULTS: Eighty-seven patients were included, of which 13 (14.9%) had at least one estimated Cmin ≥ 30 mg/L. Patients with elevated Cmin were more likely to have acute kidney injury (AKI) during their critical illness (92% vs. 57%, p = 0.015 for any AKI; 62% vs. 26%, p = 0.019 for severe AKI). Patients who had AKI during critical illness had significantly higher cefepime exposure, as quantified by the area under the concentration-time curve over 24 h (AUC24h) and Cmin. CONCLUSIONS: Among critically ill children, AKI is associated with elevated cefepime concentrations. Identifying these high-risk patients is the first step toward evaluating the clinical consequences of such exposures.

Development and application of a population pharmacokinetic model repository for caffeine dose tailoring in preterm infants.

BACKGROUND: Considerable interindividual variability for the pharmacokinetics of caffeine in preterm infants has been demonstrated, emphasizing the importance of personalized dosing. This study aimed to develop and apply a repository of currently published population pharmacokinetic (PopPK) models of caffeine in preterm infants to facilitate model-informed precision dosing (MIPD). RESEARCH DESIGN AND METHODS: Literature search was conducted using PubMed, Embase, Scopus, and Web of Science databases. Relevant publications were screened, and their quality was assessed. PopPK models were reestablished to develop the model repository. Covariate effects were evaluated and the concentration-time profiles were simulated. An online simulation and calculation tool was developed as an instance. RESULTS: Twelve PopPK models were finally included in the repository. Preterm infants' age and body size, especially the postnatal age and current weight, were identified as the most clinically critical covariates. Simulated blood concentration-time profiles across these models were comparable. Caffeine citrate-dose regimen should be adjusted according to the age and body size of preterm infants. The developed online tool can be used to facilitate clinical decision-making. CONCLUSIONS: The first developed repository of PopPK models for caffeine in preterm infants has a wide range of potential applications in the MIPD of caffeine.

Dose optimization of ß-lactam antibiotics in children: from population pharmacokinetics to individualized therapy.

INTRODUCTION: ß-Lactams are the most widely used antibiotics in children. Their optimal dosing is essential to maximize their efficacy, while minimizing the risk for toxicity and the further emergence of antimicrobial resistance. However, most ß-lactams were developed and licensed long before regulatory changes mandated pharmacokinetic studies in children. As a result, pediatric dosing practices are poorly harmonized and off-label use remains common today. AREAS COVERED: ß-Lactam pharmacokinetics and dose optimization strategies in pediatrics, including fixed dose regimens, therapeutic drug monitoring, and model-informed precision dosing are reviewed. EXPERT OPINION/COMMENTARY: Standard pediatric doses can result in subtherapeutic exposure and non-target attainment for specific patient subpopulations (neonates, critically ill children, e.g.). Such patients could benefit greatly from more individualized approaches to dose optimization, beyond a relatively simple dose adaptation based on weight, age, or renal function. In this context, Therapeutic Drug Monitoring (TDM) and Model-Informed Precision Dosing (MIPD) emerge as particularly promising avenues. Obstacles to their implementation include the lack of strong evidence of clinical benefit due to the paucity of randomized clinical trials, of standardized assays for monitoring concentrations, or of adequate markers for renal function. The development of precision medicine tools is urgently needed to individualize therapy in vulnerable pediatric subpopulations.

A prediction method for the individual serum concentration and therapeutic effect for optimizing adalimumab therapy in inflammatory bowel disease.

OBJECTIVES: Adalimumab (ADM) therapy is effective for inflammatory bowel disease (IBD), but a significant number of IBD patients lose response to ADM. Thus, it is crucial to devise methods to enhance ADM's effectiveness. This study introduces a strategy to predict individual serum concentrations and therapeutic effects to optimize ADM therapy for IBD during the induction phase. METHODS: We predicted the individual serum concentration and therapeutic effect of ADM during the induction phase based on pharmacokinetic and pharmacodynamic (PK/PD) parameters calculated using the empirical Bayesian method. We then examined whether the predicted therapeutic effect, defined as clinical remission or treatment failure, matched the observed effect. RESULTS: Data were obtained from 11 IBD patients. The therapeutic effect during maintenance therapy was successfully predicted at 40 of 47 time points. Moreover, the predicted effects at each patient's final time point matched the observed effects in 9 of the 11 patients. CONCLUSION: This is the inaugural report predicting the individual serum concentration and therapeutic effect of ADM using the Bayesian method and PK/PD modelling during the induction phase. This strategy may aid in optimizing ADM therapy for IBD.

Impact of CYP2D6*2, CYP2D6*35, rs5758550, and related haplotypes on risperidone clearance in vivo.

PURPOSE: The CYP2D6 gene exhibits significant polymorphism, contributing to variability in responses to drugs metabolized by CYP2D6. While CYP2D6*2 and CYP2D6*35 are presently designated as alleles encoding normal metabolism, this classification is based on moderate level evidence. Additionally, the role of the formerly called "enhancer" single nucleotide polymorphism (SNP) rs5758550 is unclear. In this study, the impacts of CYP2D6*2, CYP2D6*35 and rs5758550 on CYP2D6 activity were investigated using risperidone clearance as CYP2D6 activity marker. METHODS: A joint parent-metabolite population pharmacokinetic model was used to describe 1,565 serum concentration measurements of risperidone and 9-hydroxyrisperidone in 512 subjects. Risperidone population clearance was modeled as the sum of a CYP2D6-independent clearance term and the partial clearances contributed from each individually expressed CYP2D6 allele or haplotype. In addition to the well-characterized CYP2D6 alleles (*3-*6, *9, *10 and *41), *2, *35 and two haplotypes assigned as CYP2D6*2-rs5758550G and CYP2D6*2-rs5758550A were evaluated. RESULTS: Each evaluated CYP2D6 allele was associated with significantly lower risperidone clearance than the reference normal function allele CYP2D6*1 (p < 0.001). Further, rs5758550 differentiated the effect of CYP2D6*2 (p = 0.005). The haplotype-specific clearances for CYP2D6*2-rs5758550A, CYP2D6*2-rs5758550G and CYP2D6*35 were estimated to 30%, 66% and 57%, respectively, relative to the clearance for CYP2D6*1. Notably, rs5758550 is in high linkage disequilibrium (R2 > 0.85) with at least 24 other SNPs and cannot be assigned as a functional SNP. CONCLUSION: CYP2D6*2 and CYP2D6*35 encode reduced risperidone clearance, and the extent of reduction for CYP2D6*2 is differentiated by rs5758550. Genotyping of these haplotypes might improve the precision of genotype-guided prediction of CYP2D6-mediated clearance.

Pharmacokinetic-pharmacodynamic modelling and simulation of methotrexate dosing in patients with rheumatoid arthritis.

AIMS: To develop a non-linear mixed-effects population pharmacokinetic and pharmacodynamic (PK-PD) model describing the change in the concentration of methotrexate polyglutamates in erythrocytes (ery-MTX-PGn with "n" number of glutamate, representing PK component) and how this relates to modified 28-joint Disease Activity Score incorporating erythrocyte sedimentation rate (DAS-28-3) for rheumatoid arthritis (RA), representing PD component. METHODS: An existing PK model was fitted to data from a study consisting of 117 RA patients. The estimation of population PK-PD parameters was performed using stochastic approximation expectation maximisation algorithm in Monolix 2021R2. The model was used to perform Monte Carlo simulations of a loading dose regimen (50mg subcutaneous methotrexate as loading doses, then 20mg weekly oral methotrexate) compared to a standard dosing regimen (10mg weekly oral methotrexate for 2 weeks, then 20mg weekly oral methotrexate). RESULTS: Every 40 nmol/L increase in ery-MTX-PG3-5 total concentration correlated with 1-unit reduction in DAS-28-3. Significant covariate effects on the therapeutic response of methotrexate included the use of prednisolone in the first 4 weeks (positive use correlated with 25% reduction in DAS-28-3 when other variables were constant) and patient age (every 10-year increase in age correlated with 3.4% increase in DAS-28-3 when other variables were constant). 4 methotrexate loading doses led to a higher percentage of patients achieving a good/moderate response compared to the standard regimen (Week 4: 87.6% vs. 39.8%; Week 10: 64.7% vs. 57.0%). CONCLUSIONS: A loading dose regimen was more likely to achieve higher ery-MTX-PG concentration and better therapeutic response after 4 weeks of methotrexate treatment.

The potential for therapeutic drug monitoring of belatacept and other biologicals in solid organ transplantation.

In solid organ transplantation (SOT), biologicals such as recombinant therapeutic proteins, monoclonal antibodies, fusion proteins and conjugates are increasingly used for immunosuppression, desensitization, ABO (blood group) incompatibility, antibody-mediated rejections and atypical haemolytic uremic syndrome. In this paper, we review the medical evidence available for biologicals used in SOT and the potential for improvement by the application of therapeutic drug monitoring (TDM) and model-informed precision dosing. Biologicals are used for off-label indications within the field of SOT, building on the experience from their use on labelled indications. Dosing is currently mostly standard, and experience vs. effect and toxicity is limited. Pharmacokinetic characteristics of these large, partly also immunogenic molecules differ from those of traditional small molecules. Individualization by concentration measurements and modelling has mostly been proof-of-concept or feasibility studies that lack the power to provide evidence for improvement in clinical outcome. For some drugs such as alemtuzumab, eculizumab, rituximab, tocilizumab and belatacept, studies have demonstrated significant interindividual variability in pharmacokinetics. Variability in absorption from subcutaneous administration may increase interindividual variability. There is also an economic aspect of appropriate dosing that needs to be pursued. Available assays and models to refine interpretation are in place, but trials of adequate size to document the usefulness of TDM and MIPD are scarce. Collaboration within the TDM community seems mandatory to establish studies of sufficient strength to provide evidence for the use of biologicals that are currently used off-label in SOT and furthermore to identify the settings where TDM may be beneficial.

External validation and updating of the infusion rate individualization of soybean oil-based intravenous lipid emulsion: A descriptive cohort study.

BACKGROUND: Safe and efficient provision of intravenous lipid emulsion (ILE) requires a strategy to individualize infusion rates. Estimating the maximum acceptable infusion rate (MaxInfRate) of soybean oil-based ILE (SO-ILE) in individuals by using a triglyceride (TG) kinetic model was reported to be feasible. In this study, we aimed to externally validate and, if needed, update the MaxInfRate estimation. METHODS: The maximum TG concentration (TGmax) in patients receiving SO-ILE at MaxInfRate was evaluated to determine if it met the definition of being <400 mg/dl for 90th percentile of patients. The TG kinetic model was evaluated through prediction performance checks and was subsequently updated using the data set of both the previous model development and present validation studies. RESULTS: Out of 83 patients, 74 had TGmax <400 mg/dl, corresponding to a probability of 89.2% (95% CI, 81.9%-95.2%), and the 90th percentile of TGmax was 400 mg/dl (95% CI, 328-490 mg/dl), closely aligned with the theoretical values. However, the individual TGmax values were biased by the infusion rate because the covariate effects were overestimated in the TG kinetic model, requiring a minor revision. The updated MaxInfRate with the combined data set showed unbiased and more accurate predictions. CONCLUSION: The MaxInfRate was validated in external inpatients and updated with all available data. MaxInfRate estimation for individuals could be an option for the safe and efficient provision of SO-ILE.

Prospective validation of a model-informed precision dosing tool for vancomycin treatment in neonates.

We recruited 48 neonates (50 vancomycin treatment episodes) in a prospective study to validate a model-informed precision dosing (MIPD) software. The initial vancomycin dose was based on a population pharmacokinetic model and adjusted every 36-48 h. Compared with a historical control group of 53 neonates (65 episodes), the achievement of a target trough concentration of 10-15 mg/L improved from 37% in the study to 62% in the MIPD group (P = 0.01), with no difference in side effects.

Model-based precision dosing and remedial dosing recommendations for delayed or missed doses of isoniazid in Chinese patients with tuberculosis.

AIMS: Isoniazid (INH) has been used as a first-line drug to treat tuberculosis (TB) for more than 50 years. However, large interindividual variability was found in its pharmacokinetics, and effects of nonadherence to INH treatment and corresponding remedy regime remain unclear. This study aimed to develop a population pharmacokinetic (PPK) model of INH in Chinese patients with TB to provide model-informed precision dosing and explore appropriate remedial dosing regimens for nonadherent patients. METHODS: In total, 1012 INH observations from 736 TB patients were included. A nonlinear mixed-effects modelling was used to analyse the PPK of INH. Using Monte Carlo simulations to determine optimal dosage regimens and design remedial dosing regimens. RESULTS: A 2-compartmental model, including first-order absorption and elimination with allometric scaling, was found to best describe the PK characteristics of INH. A mixture model was used to characterize dual rates of INH elimination. Estimates of apparent clearance in fast and slow eliminators were 28.0 and 11.2 L/h, respectively. The proportion of fast eliminators in the population was estimated to be 40.5%. Monte Carlo simulations determined optimal dosage regimens for slow and fast eliminators with different body weight. For remedial dosing regimens, the missed dose should be taken as soon as possible when the delay does not exceed 12 h, and an additional dose is not needed. delay for an INH dose exceeds 12 h, the patient only needs to take the next single dose normally. CONCLUSION: PPK modelling and simulation provide valid evidence on the precision dosing and remedial dosing regimen of INH.

Physiologically-Based Pharmacokinetic Modeling and Dosing Optimization of Cefotaxime in Preterm and Term Neonates.

BACKGROUND: Cefotaxime is commonly used in treating bacterial infections in neonates. To characterize the pharmacokinetic process in neonates and evaluate different recommended dosing schedules of cefotaxime, a physiologically-based pharmacokinetic (PBPK) model of cefotaxime was established in adults and scaled to neonates. METHODS: A whole-body PBPK model was built in PK-SIM® software. Three elimination pathways are composed of enzymatic metabolism in the liver, passive filtration through glomerulus, and active tubular secretion mediated by renal transporters. The ontogeny information was applied to account for age-related changes in cefotaxime pharmacokinetics. The established models were verified with realistic clinical data in adults and pediatric populations. Simulations in neonates were conducted and 100 % of the dosing interval where the unbound concentration in plasma was above the minimum inhibitory concentration (fT>MIC) was selected as the target index for dosing regimen evaluation. RESULTS: The developed PBPK models successfully described the pharmacokinetic process of cefotaxime in adults and were scaled to the pediatric population. Good verification results were achieved in both adults' and neonates' PBPK models, indicating a good predictive performance. The optimal dosage regimen of cefotaxime was proposed according to the postnatal age (PNA) and gestational age (GA) of neonates. For preterm neonates (GA < 36 weeks), dosages of 25 mg/kg every 8 h in PNA 0-6 days and 25 mg/kg every 6 h in PNA 7-28 days were suggested. For term neonates (GA ≥ 36 weeks), dosages of 33 mg/kg every 8 h in PNA 0-6 days and 33 mg/kg every 6 h in PNA 7-28 days were recommended. CONCLUSIONS: Our study may provide useful experience in practicing PBPK model-informed precision dosing in the pediatric population.

A machine learning approach to predict daptomycin exposure from two concentrations based on Monte Carlo simulations.

Daptomycin is a concentration-dependent lipopeptide antibiotic for which exposure/effect relationships have been shown. Machine learning (ML) algorithms, developed to predict the individual exposure to drugs, have shown very good performances in comparison to maximum a posteriori Bayesian estimation (MAP-BE). The aim of this work was to predict the area under the blood concentration curve (AUC) of daptomycin from two samples and a few covariates using XGBoost ML algorithm trained on Monte Carlo simulations. Five thousand one hundred fifty patients were simulated from two literature population pharmacokinetics models. Data from the first model were split into a training set (75%) and a testing set (25%). Four ML algorithms were built to learn AUC based on daptomycin blood concentration samples at pre-dose and 1 h post-dose. The XGBoost model (best ML algorithm) with the lowest root mean square error (RMSE) in a 10-fold cross-validation experiment was evaluated in both the test set and the simulations from the second population pharmacokinetic model (validation). The ML model based on the two concentrations, the differences between these concentrations, and five other covariates (sex, weight, daptomycin dose, creatinine clearance, and body temperature) yielded very good AUC estimation in the test (relative bias/RMSE = 0.43/7.69%) and validation sets (relative bias/RMSE = 4.61/6.63%). The XGBoost ML model developed allowed accurate estimation of daptomycin AUC using C0, C1h, and a few covariates and could be used for exposure estimation and dose adjustment. This ML approach can facilitate the conduct of future therapeutic drug monitoring (TDM) studies.

Maximum a posteriori Bayesian methods out-perform non-compartmental analysis for busulfan precision dosing.

Dose personalization improves patient outcomes for many drugs with a narrow therapeutic index and high inter-individuality variability, including busulfan. Non-compartmental analysis (NCA) and model-based methods like maximum a posteriori Bayesian (MAP) approaches are two methods routinely used for dose optimization. These approaches vary in how they estimate patient-specific pharmacokinetic parameters to inform a dose and the impact of these differences is not well-understood. Using busulfan as an example application and area under the concentration-time curve (AUC) as a target exposure metric, these estimation methods were compared using retrospective patient data (N = 246) and simulated precision dosing treatment courses. NCA was performed with or without peak extension, and MAP Bayesian estimation was performed using either the one-compartment Shukla model or the two-compartment McCune model. All methods showed good agreement on real-world data (correlation coefficients of 0.945-0.998) as assessed by Bland-Altman plots, although agreement between NCA and MAP methods was higher during the first dosing interval (0.982-0.994) compared to subsequent dosing intervals (0.918-0.938). In dose adjustment simulations, both NCA and MAP estimated high target attainment (> 98%) although true simulated target attainment was lower for NCA (63-66%) versus MAP (91-93%). The largest differences in AUC estimation were due to different assumptions for the shape of the concentration curve during the infusion phase, followed by how the methods considered time-dependent clearance and concentration-time points collected in earlier intervals. In conclusion, although AUC estimates between the two methods showed good correlation, in a simulated study, MAP lead to higher target attainment. When changing from one method to another, or changing infusion duration and other factors, optimum estimated exposure targets may require adjusting to maintain a consistent exposure.