Machine Learning Approach in Dosage Individualization of Isoniazid for Tuberculosis.

INTRODUCTION: Isoniazid is a first-line antituberculosis agent with high variability, which would profit from individualized dosing. Concentrations of isoniazid at 2 h (C2h), as an indicator of safety and efficacy, are important for optimizing therapy. OBJECTIVE: The objective of this study was to establish machine learning (ML) models to predict the C2h, that can be used for establishing an individualized dosing regimen in clinical practice. METHODS: Published population pharmacokinetic (PopPK) models for adults were searched based on PubMed and ultimately four reliable models were selected for simulating individual C2h datasets under different conditions (demographics, genotype, ethnicity, etc.). Machine learning models were trained on simulated C2h obtained from the four PopPK models. Five different algorithms were used for ML model building to predict C2h. Real-world data were used for predictive performance evaluations. Virtual trials were used to compare ML-optimized doses with PopPK model-optimized doses. RESULTS: Categorical boosting (CatBoost) exhibited the highest prediction ability. Target C2h can be predicted using the ML model combined with the dosing regimen and three covariates (N-acetyltransferase 2 [NAT2] genotypes, weight and race [Asians and Africans]). Real-world data validation results showed that the ML model can achieve an overall prediction accuracy of 93.4%. Using the final ML model, the mean absolute prediction error value decreased by 45.7% relative to the average of PopPK models. Using the ML-optimized dosing regimen, the probability of target attainment increased by 43.7% relative to the PopPK model-optimized dosing regimens. CONCLUSION: Machine learning models were developed with great predictive performance, which can be used to determine the individualized initial dose of isoniazid in adult patients.

Optimal use of ß-lactams in neonates: machine learning-based clinical decision support system.

BACKGROUND: Accurate prediction of the optimal dose for ß-lactam antibiotics in neonatal sepsis is challenging. We aimed to evaluate whether a reliable clinical decision support system (CDSS) based on machine learning (ML) can assist clinicians in making optimal dose selections. METHODS: Five ß-lactam antibiotics (amoxicillin, ceftazidime, cefotaxime, meropenem and latamoxef), commonly used to treat neonatal sepsis, were selected. The CDSS was constructed by incorporating the drug, patient, dosage, pharmacodynamic, and microbiological factors. The CatBoost ML algorithm was used to build the CDSS. Real-world studies were used to evaluate the CDSS performance. Virtual trials were used to compare the CDSS-optimized doses with guideline-recommended doses. FINDINGS: For a specific drug, by entering the patient characteristics and pharmacodynamic (PD) target (50%/70%/100% fraction of time that the free drug concentration is above the minimal inhibitory concentration [fT > MIC]), the CDSS can determine whether the planned dosing regimen will achieve the PD target and suggest an optimal dose. The prediction accuracy of all five drugs was >80.0% in the real-world validation. Compared with the PopPK model, the overall accuracy, precision, recall, and F1-Score improved by 10.7%, 22.1%, 64.2%, and 43.1%, respectively. Using the CDSS-optimized doses, the average probability of target concentration attainment increased by 58.2% compared to the guideline-recommended doses. INTERPRETATION: An ML-based CDSS was successfully constructed to assist clinicians in selecting optimal ß-lactam antibiotic doses. FUNDING: This work was supported by the National Natural Science Foundation of China; Distinguished Young and Middle-aged Scholar of Shandong University; National Key Research and Development Program of China.

Machine Learning: A Potential Therapeutic Tool to Facilitate Neonatal Therapeutic Decision Making.

Bacterial infection is one of the major causes of neonatal morbidity and mortality worldwide. Finding rapid and reliable methods for early recognition and diagnosis of bacterial infections and early individualization of antibacterial drug administration are essential to eradicate these infections and prevent serious complications. However, this is often difficult to perform due to non-specific clinical presentations, low accuracy of current diagnostic methods, and limited knowledge of neonatal pharmacokinetics. Although neonatal medicine has been relatively late to embrace the benefits of machine learning (ML), there have been some initial applications of ML for the early prediction of neonatal sepsis and individualization of antibiotics. This article provides a brief introduction to ML and discusses the current state of the art in diagnosing and treating neonatal bacterial infections, gaps, potential uses of ML, and future directions to address the limitations of current studies. Neonatal bacterial infections involve a combination of physiologic development, disease expression, and treatment response outcomes. To address this complex relationship, future models could consider appropriate ML algorithms to capture time series features while integrating influences from the host, microbes, and drugs to optimize antimicrobial drug use in neonates. All models require prospective clinical trials to validate their clinical utility before clinical use.

Population pharmacokinetics of daptomycin in critically ill patients receiving extracorporeal membrane oxygenation.

BACKGROUND: Daptomycin is widely used in critically ill patients for Gram-positive bacterial infections. Extracorporeal membrane oxygenation (ECMO) is increasingly used in this population and can potentially alter the pharmacokinetic (PK) behaviour of antibiotics. However, the effect of ECMO has not been evaluated in daptomycin. Our study aims to explore the effect of ECMO on daptomycin in critically ill patients through population pharmacokinetic (PopPK) analysis and to determine optimal dosage regimens based on both efficacy and safety considerations. METHODS: A prospective, open-label PK study was carried out in critically ill patients with or without ECMO. The total concentration of daptomycin was determined by UPLC-MS/MS. NONMEM was used for PopPK analysis and Monte Carlo simulations. RESULTS: Two hundred and ninety-three plasma samples were collected from 36 critically ill patients, 24 of whom received ECMO support. A two-compartment model with first-order elimination can best describe the PK of daptomycin. Creatinine clearance (CLCR) significantly affects the clearance of daptomycin while ECMO has no significant effect on the PK parameters. Monte Carlo simulations showed that, when the MICs for bacteria are  ≥1 mg/L, the currently recommended dosage regimen is insufficient for critically ill patients with CLCR > 30 mL/min. Our simulations suggest 10 mg/kg for patients with CLCR between 30 and 90 mL/min, and 12 mg/kg for patients with CLCR higher than 90 mL/min. CONCLUSIONS: This is the first PopPK model of daptomycin in ECMO patients. Optimal dosage regimens considering efficacy, safety, and pathogens were provided for critical patients based on pharmacokinetic-pharmacodynamic analysis.

Efficacy, Safety, and Population Pharmacokinetics of Eltrombopag in Children with Different Severities of Aplastic Anemia.

Eltrombopag was approved as a first-line treatment for patients older than 2 years old with severe aplastic anemia (SAA). However, data on eltrombopag in children with different types of aplastic anemia (AA), especially non-severe AA (NSAA), are limited. We performed a prospective, single-arm, and observational study to investigate eltrombopag's efficacy, safety, and pharmacokinetics in children with NSAA, SAA, and very severe AA (VSAA). The efficacy and safety were assessed every 3 months. The population pharmacokinetic (PPK) model was used to depict the pharmacokinetic profile of eltrombopag. Twenty-three AA children with an average age of 7.9 (range of 3.0-14.0) years were enrolled. The response (complete and partial response) rate was 12.5%, 50.0%, and 100.0% after 3, 6, and 12 months in patients with NSAA. For patients with SAA and VSAA, these response rates were 46.7%, 61.5%, and 87.5%. Hepatotoxicity occurred in one patient. Fifty-three blood samples were used to build the PPK model. Body weight was the only covariate for apparent clearance (CL/F) and volume of distribution. The allele-T carrier of adenosine triphosphate-binding cassette transporter G2 was found to increase eltrombopag's clearance. However, when normalized by weight, the clearance between the wild-type and variant showed no statistical difference. In patients with response, children with NSAA exhibited lower area under the curve from time zero to infinity, higher CL/F, and higher weight-adjusted CL/F than those with SAA or VSAA. However, the differences were not statistically significant. The results may support further individualized treatment of eltrombopag in children with AA.

Machine Learning: A New Approach for Dose Individualization.

The application of machine learning (ML) has shown promising results in precision medicine due to its exceptional performance in dealing with complex multidimensional data. However, using ML for individualized dosing of medicines is still in its early stage, meriting further exploration. A systematic review of study designs and modeling details of using ML for individualized dosing of different drugs was performed. We have summarized the status of the study populations, predictive targets, and data sources for ML modeling, the selection of ML algorithms and features, and the evaluation and validation of their predictive performance. We also used the Prediction model Risk of Bias Assessment Tool (PROBAST) to assess the risk of bias of included studies. Currently, ML can be used for both a priori and a posteriori dose selection and optimization, and it can also assist the implementation of therapeutic drug monitoring. However, studies are mainly focused on drugs with narrow therapeutic windows, predominantly immunosuppressants (N = 23, 35.9%) and anti-infectives (N = 21, 32.8%), and there is currently only very limited attention for special populations, such as children (N = 22, 34.4%). Most studies showed poor methodological quality and a high risk of bias. The lack of external validation and clinical utility evaluation currently limits the further clinical implementation of ML for dose individualization. We therefore have proposed several ways to improve the clinical relevance of the studies and facilitate the translation of ML models into clinical practice.

Population pharmacokinetics of antibacterial agents in the older population: a literature review.

INTRODUCTION: Older individuals face an elevated risk of developing bacterial infections. The optimal use of antibacterial agents in this population is challenging because of age-related physiological alterations, changes in pharmacokinetics (PK) and pharmacodynamics (PD), and the presence of multiple underlying diseases. Therefore, population pharmacokinetics (PPK) studies are of great importance for optimizing individual treatments and prompt identification of potential risk factors. AREA COVERED: Our search involved keywords such as 'elderly,' 'old people,' and 'geriatric,' combined with 'population pharmacokinetics' and 'antibacterial agents.' This comprehensive search yielded 11 categories encompassing 28 antibacterial drugs, including vancomycin, ceftriaxone, meropenem, and linezolid. Out of 127 studies identified, 26 (20.5%) were associated with vancomycin, 14 (11%) with meropenem, and 14 (11%) with piperacillin. Other antibacterial agents were administered less frequently. EXPERT OPINION: PPK studies are invaluable for elucidating the characteristics and relevant factors affecting the PK of antibacterial agents in the older population. Further research is warranted to develop and validate PPK models for antibacterial agents in this vulnerable population.

A first-in-human phase 1 study of simnotrelvir, a 3CL-like protease inhibitor for treatment of COVID-19, in healthy adult subjects.

Safe and efficacious antiviral therapeutics are in urgent need for the treatment of coronavirus disease 2019. Simnotrelvir is a selective 3C-like protease inhibitor that can effectively inhibit severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). We evaluated the safety, tolerability, and pharmacokinetics of dose escalations of simnotrelvir alone or with ritonavir (simnotrelvir or simnotrelvir/ritonavir) in healthy subjects, as well as the food effect (ClinicalTrials.gov Identifier: NCT05339646). The overall incidence of adverse events (AEs) was 22.2% (17/72) and 6.3% (1/16) in intervention and placebo groups, respectively. The simnotrelvir apparent clearance was 135-369 L/h with simnotrelvir alone, and decreased significantly to 19.5-29.8 L/h with simnotrelvir/ritonavir. The simnotrelvir exposure increased in an approximately dose-proportional manner between 250 and 750 mg when co-administered with ritonavir. After consecutive twice daily dosing of simnotrelvir/ritonavir, simnotrelvir had a low accumulation index ranging from 1.39 to 1.51. The area under the curve of simnotrelvir increased 44.0 % and 47.3 % respectively, after high fat and normal diet compared with fasted status. In conclusion, simnotrelvir has adequate safety and tolerability. Its pharmacokinetics indicated a trough concentration above the level required for 90 % inhibition of SARS-CoV-2 in vitro at 750 mg/100 mg simnotrelvir/ritonavir twice daily under fasted condition, supporting further development using this dosage as the clinically recommended dose regimen.

Use of Machine Learning for Dosage Individualization of Vancomycin in Neonates.

BACKGROUND AND OBJECTIVE: High variability in vancomycin exposure in neonates requires advanced individualized dosing regimens. Achieving steady-state trough concentration (C0) and steady-state area-under-curve (AUC0-24) targets is important to optimize treatment. The objective was to evaluate whether machine learning (ML) can be used to predict these treatment targets to calculate optimal individual dosing regimens under intermittent administration conditions. METHODS: C0 were retrieved from a large neonatal vancomycin dataset. Individual estimates of AUC0-24 were obtained from Bayesian post hoc estimation. Various ML algorithms were used for model building to C0 and AUC0-24. An external dataset was used for predictive performance evaluation. RESULTS: Before starting treatment, C0 can be predicted a priori using the Catboost-based C0-ML model combined with dosing regimen and nine covariates. External validation results showed a 42.5% improvement in prediction accuracy by using the ML model compared with the population pharmacokinetic model. The virtual trial showed that using the ML optimized dose; 80.3% of the virtual neonates achieved the pharmacodynamic target (C0 in the range of 10-20 mg/L), much higher than the international standard dose (37.7-61.5%). Once therapeutic drug monitoring (TDM) measurements (C0) in patients have been obtained, AUC0-24 can be further predicted using the Catboost-based AUC-ML model combined with C0 and nine covariates. External validation results showed that the AUC-ML model can achieve an prediction accuracy of 80.3%. CONCLUSION: C0-based and AUC0-24-based ML models were developed accurately and precisely. These can be used for individual dose recommendations of vancomycin in neonates before treatment and dose revision after the first TDM result is obtained, respectively.

Epithelial lining fluid concentrations of ceftriaxone in children with community-acquired pneumonia.

Ceftriaxone is widely used in children with community-acquired pneumonia. Currently, there are no available data regarding epithelial lining fluid (ELF) concentrations of ceftriaxone in children. Thus, blood and bronchoalveolar lavage fluids samples were collected by using an opportunistic sampling design, then we determined plasma and ELF concentrations in 22 children (0.5-11.7 years), with a total of 36 plasma and 22 ELF samples available for analysis. Ceftriaxone plasma and ELF concentrations ranged from 1.07 to 138.71 mg/L and from 0.61 to 26.69 mg/L, respectively. Ceftriaxone concentration in ELF was 12.18 ± 5.15 (mean ± standard deviation) times higher than that in plasma, ranging from 1.29 to 20.44.

Fabrication of anti-icing/de-icing surfaces by femtosecond laser.

In this minireview, we comprehensively reviewed recent progress on fabricating anti-icing/de-icing surfaces by femtosecond laser technologies. Typical bioinspired micro-/nano-structures fabrication strategies, superhydrophobic surfaces with anti-icing properties, and photothermal surfaces with de-icing properties are summarized. At last, we discussed challenges and prospects in anti-icing/de-icing surfaces fabricated by femtosecond laser technologies.

Meropenem for children with severe pneumonia: Protocol for a randomized controlled trial.

Background: Pneumonia, caused by infection or other factors, seriously endangers the health of children. Meropenem is an effective broad-spectrum antibiotic using in the treatment of infectious diseases. In the therapy of pneumonia, meropenem is mostly employed for the treatment of moderate to severe pneumonia. Previously, we established a population pharmacokinetics (PPK) model for meropenem in pediatric severe infection and simulated the control rate of the time during which the free plasma concentration of meropenem exceeds the minimum inhibitory concentration (MIC) is 70% of the dosing interval (70% fT > MIC). Therefore, we plan to conduct a multicenter randomized controlled trial (RCT) to compare the efficacy and safety between conventional regimen and model regimen for meropenem in pediatric severe pneumonia. Methods: One hundred patients (aged 3 months to 15 years) will be recruited in this RCT. They will be assigned randomly (at a 1:1 ratio) to a conventional treatment group (20 mg/kg, q8h, with 0.5-1 h infusion) and a model treatment group (20 mg/kg, q8 h, with 4 h infusion). The primary outcome will be 70% fT > MIC. Secondary outcomes will be the prevalence of meropenem therapy failure, duration of antibiotic therapy, changes in levels of inflammatory indicators, changes in imaging examination results, and prevalence of adverse events. Ethical approval of our clinical trial has been granted by the ethics committee of Beijing Children's Hospital ([2022]-E-133-Y). This trial has been registered in the Chinese Clinical Trial Registry (ChiCTR2200061207). Discussion: Based on our previous PPK data, we have designed this RCT. It is hoped that it will promote rational use of antibacterial drugs in children suffering from severe pneumonia. Clinical Trial Registration: http://www.chictr.org.cn identifier, ChiCTR2200061207.

Developmental Population Pharmacokinetics-Pharmacodynamics of Meropenem in Chinese Neonates and Young Infants: Dosing Recommendations for Late-Onset Sepsis.

The pharmacokinetic (PK) studies of meropenem in Chinese newborns with late-onset sepsis (LOS) are still lacking. Causative pathogens of LOS and their susceptibility patterns in China differ from the data abroad. We, therefore, conducted a developmental population pharmacokinetic−pharmacodynamic analysis in Chinese newborns with the goal to optimize meropenem dosing regimens for LOS therapy. An opportunistic sampling strategy was used to collect meropenem samples, followed by model building and validation. A Monte Carlo simulation was performed to show the probability of target attainment (PTA) for various dosages. The information from 78 newborns (postmenstrual age: 27.4−46.1 weeks) was compiled and had a good fit to a 1-compartment model that had first order elimination. The median (range) values of estimated weight−normalized volume of distribution (V)and clearance (CL) were 0.60 (0.51−0.69) L/kg and 0.16 (0.04−0.51) L/h/kg, respectively. Covariate analysis revealed that postnatal age (PNA), gestational age (GA) and current weight (CW) were the most important factors in describing meropenem PK. Simulation results showed for LOS with a minimal inhibitory concentration (MIC) of 8 mg/L, the doses of 30 mg/kg 3 times daily (TID) as a 1-h infusion for newborns with GA ≤ 37 weeks and 40 mg/kg TID as a 3-h infusion for those with GA > 37 weeks were optimal, with PTA of 71.71% and 75.08%, respectively. In conclusion, we proposed an evidence-based dosing regimen of meropenem for LOS in Chinese newborns by using the population pharmacokinetic−pharmacodynamic analysis, based on domestic common pathogens and their susceptibility patterns.

Clinical utility of a model-based amoxicillin dosage regimen in neonates with early-onset sepsis.

Early-onset sepsis (EOS) is one of the most significant causes of morbidity and mortality in neonates. Currently, amoxicillin is empirically used to treat neonates with EOS. However, data on its effectiveness in neonates with EOS are still limited. Therefore, we aimed to evaluate the pharmacodynamics (PD) target attainment and effectiveness of a model-based amoxicillin dosage regimen in these neonates. We used a previously developed model and collected additional clinical data from the EOS neonates who used the model-based dosage regimen (25 mg/kg every 12 h). The primary outcomes were PD target attainment (free drug concentration above minimum inhibitory concentration during 70% of the dosing interval) and treatment failure rate. The secondary endpoints were length of amoxicillin treatment, duration of hospitalization etc. Seventy-five neonates (postmenstrual age 28.4-41.6 wk) were enrolled. A total of 70 (93.3%) neonates reached their PD target using 1 mg/L as the minimum inhibitory concentration breakpoint. The treatment failure rate was 10.7%.

Population pharmacokinetics and dosing optimization of mezlocillin in neonates and young infants.

OBJECTIVES: Mezlocillin is used in the treatment of neonatal infectious diseases. However, due to the absence of population pharmacokinetic studies in neonates and young infants, dosing regimens differ considerably in clinical practice. Hence, this study aimed to describe the pharmacokinetic characteristics of mezlocillin in neonates and young infants, and propose the optimal dosing regimen based on the population pharmacokinetic model of mezlocillin. METHODS: A prospective, open-label pharmacokinetic study of mezlocillin was carried out in newborns. Blood samples were collected using an opportunistic sampling method. HPLC was used to measure the plasma drug concentrations. A population pharmacokinetic model was developed using NONMEM software. RESULTS: Ninety-five blood samples from 48 neonates and young infants were included. The ranges of postmenstrual age and birth weight were 29-40 weeks and 1200-4000 g, respectively, including term and preterm infants. A two-compartment model with first-order elimination was developed to describe the population pharmacokinetics of mezlocillin. Postmenstrual age, current weight and serum creatinine concentration were the most important covariates. Monte Carlo simulation results indicated that the current dose of 50 mg/kg q12h resulted in 89.2% of patients achieving the therapeutic target, when the MIC of 4 mg/L was used as the breakpoint. When increasing the dosing frequency to q8h, a dose of 20 mg/kg resulted in 74.3% of patients achieving the therapeutic target. CONCLUSIONS: A population pharmacokinetic model of mezlocillin in neonates and young infants was established. Optimal dosing regimens based on this model were provided for use in neonatal infections.

Predictive Performance of Pharmacokinetic Model-Based Virtual Trials of Vancomycin in Neonates: Mathematics Matches Clinical Observation.

BACKGROUND AND OBJECTIVE: Vancomycin is frequently used to treat Gram-positive bacterial infections in neonates. However, there is still no consensus on the optimal initial dosing regimen. This study aimed to assess the performance of pharmacokinetic model-based virtual trials to predict the dose-exposure relationship of vancomycin in neonates. METHODS: The PubMed database was searched for clinical trials of vancomycin in neonates that reported the percentage of target attainment. Monte Carlo simulations were performed using nonlinear mixed-effect modeling to predict the dose-exposure relationship, and the differences in outcomes between virtual trials and real-world data in clinical studies were calculated. RESULTS: A total of 11 studies with 14 dosing groups were identified from the literature to evaluate dose-exposure relationships. For the ten dosing groups where the surrogate marker for exposure was the trough concentration, the mean ± standard deviation (SD) for the target attainment between original studies and virtual trials was 3.0 ± 7.3%. Deviations between - 10 and 10% accounted for 80% of the included dosing groups. For the other four dosing groups where the surrogate marker for exposure was concentration during continuous infusion, all deviations were between - 10 and 10%, and the mean ± SD value was 2.9 ± 4.5%. CONCLUSION: The pharmacokinetic model-based virtual trials of vancomycin exhibited good predictive performance for dose-exposure relationships in neonates. These results might be used to assist the optimization of dosing regimens in neonatal practice, avoiding the need for trial and error.

Effect of drug combination on tacrolimus target dose in renal transplant patients with different CYP3A5 genotypes.

Various factors, including genetic polymorphisms, drug-drug interactions, and patient characteristics influence the blood concentrations of tacrolimus in renal transplant patients. In the present study, we established a population pharmacokinetic model to explore the effect of combined use of Wuzhi capsules/echinocandins and the patients' biochemical parameters such as haematocrit on blood concentrations and target doses of tacrolimus in renal transplant patients with different CYP3A5 genotypes. The aim of the study was to propose an individualised tacrolimus administration regimen for early renal transplant recipients.In this retrospective cohort study, we included 240 renal transplant recipients within 21 days of surgery (174 males and 66 females, mean age 39.4 years), who received tacrolimus alone (n = 54), in combination with Wuzhi capsules (99) or caspofungin (57) or micafungin (30). We collected demographic characteristics, clinical indicators, CYP3A5 genotypes, and 1950 steady-state concentrations of tacrolimus and included them in population pharmacokinetic model. An additional 110 renal transplant recipients and 625 steady-state concentrations of tacrolimus were included for external validation of the model. The population pharmacokinetic model was established and Monte Carlo was used to simulate probabilities for achieving the target concentration for individual tacrolimus administration.A two-compartment model of first-order absorption and elimination was developed to describe the population pharmacokinetics of tacrolimus. CYP3A5 genotypes and co-administration of Wuzhi capsules, as well as time after renal transplantation and haematocrit, were important factors affecting the clearance of tacrolimus. We found no obvious change in trend in the scatter plot of tacrolimus clearance rate vs. haematocrit. The Monte Carlo simulation indicated the following recommended doses of tacrolimus alone: 0.14 mg⋅kg-1⋅d-1 for genotype CYP3A5*1*1, 0.12 mg⋅kg-1⋅d-1 for CYP3A5*1*3, and 0.10 mg⋅kg-1⋅d-1 for CYP3A5*3*3. For patients receiving the combination with Wuzhi capsules, the recommended doses of tacrolimus were 0.10 mg⋅kg-1⋅d-1 for CYP3A5*1*1, 0.08 mg⋅kg-1⋅d-1 for CYP3A5*1*3, and 0.06 mg⋅kg-1⋅d-1 for CYP3A5*3*3 genotypes. Caspofungin or micafungin had no effect on the clearance of tacrolimus in renal transplant recipients.The population pharmacokinetics of tacrolimus in renal transplant patients was evaluated and the individual administration regimen of tacrolimus was simulated. For early kidney transplant recipients receiving tacrolimus treatment, not only body weight, but also CYP3A5 genotypes and drugs used in combination should be considered when determining the target dose of tacrolimus.

Optimal Sample Size for Use in Neonatal Pharmacokinetic Studies.

BACKGROUND: In recent years, population pharmacokinetics (PK) has been widely used in neonatal pharmacology. However, the sample size selection for neonatal PK studies has been highly variable and without clear consensus, especially for drugs with large individual variability. Therefore, this study's objective was to investigate the optimal sample size for use in neonatal PK studies. METHODS: A comprehensive and reliable population PK model (1631 neonates) of vancomycin was selected as a reference model. The original sparse PK dataset was divided into several sub-datasets according to different sample sizes. NONMEM was used for sub-datasets PK analysis. Statistical powers were calculated to evaluate different sample sizes (> 80% was expected). RESULTS: During population clearance estimations, the average power was 40%, 85%, 100%, and 100% for sample sizes of 10, 25, 50, and 100 neonates, respectively. And the frequency of model-estimated median clearance values within ± 10% (relative errors) of target value (0.057 L/h) were 75.0%, 68.8%, 57.8%, and 35.0%, respectively. Regarding age sub-groups (postmenstrual age (PMA) < or ≥ 37 weeks) clearance estimation, a sample size of 50 was better to complete the assessment of the neonatal age sub-group even in some cases of unbalanced age distribution. CONCLUSION: A sample size of 25 neonates provided a consistent estimation of the overall population (PMA: 23.3-52.4 weeks) clearance for a drug with high individual variability using a sparse PK sampling design. A sample size of 50 was recommended to complete neonatal age sub-group assessments.

Population Pharmacokinetics and Safety of Dasatinib in Chinese Children with Core-Binding Factor Acute Myeloid Leukemia.

BACKGROUND: Dasatinib, an orally administered Src-family kinase inhibitor, is combined with the standard chemotherapeutic regimen to enhance antineoplastic activity against core-binding factor acute myeloid leukemia (CBF-AML) in adults; however, limited data are available for use in children. In the present study, we studied the pharmacokinetics and safety of dasatinib in children. METHODS: Dasatinib (60 or 80 mg/m2 once daily) was administered to 20 children with CBF-AML. Blood samples were collected and drug concentrations were quantified by high-performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS). Population pharmacokinetic analysis and Monte-Carlo simulations were performed using NONMEM software, and safety analyses were assessed according to the National Cancer Institute Common Terminology Criteria for Adverse Events (NCI-CTCAE) version 4.0 (NCT03844360). RESULTS: Twenty pediatric patients (3.3-14.4 years of age) were included, and a total of 40 dasatinib concentrations were available for population pharmacokinetic analysis. The mean (standard deviation) of the estimated area under the concentration-time curve extrapolated to steady state (AUCss) of dasatinib 60 and 80 mg/m2 was 366.1 (146.6) ng·h/mL and 425.3 (150.7) ng·h/mL, respectively. The majority of adverse events were grade 1/2 in severity, including thrombocytopenia, rash, and pain in the extremities. The estimated cumulative incidence of complete remission and complete molecular response were 95.0% and 75.5%, respectively. CONCLUSIONS: The population pharmacokinetics of orally administered dasatinib were evaluated in pediatric CBF-AML patients. The AUCss of dasatinib (80 mg/m2) in CBF-AML pediatric patients was similar to those of dasatinib (100 mg) in adult patients. Dasatinib is well-tolerated in pediatric patients with CBF-AML.

Clinical utiliy of a model-based piperacillin dose in neonates with early-onset sepsis.

AIMS: Early-onset sepsis (EOS) is a common disease in neonates with a high morbidity and mortality rate. Piperacillin/tazobactam has been used extensively and empirically for EOS treatment without clinically validated dosing regimens, although the population pharmacokinetics (PPK) of piperacillin in neonates has been reported. Therefore, we wanted to study the effectiveness and tolerance of a PPK model-based dosing regimen of piperacillin/tazobactam in EOS patients. METHODS: A prospective, single-centre, phase II clinical study of piperacillin/tazobactam in neonates with EOS was conducted. The dosing regimen (90 mg·kg-1 , q8h) was determined based on a previous piperacillin PPK model in young infants using NONMEM v7.4. The pharmacodynamics (PD) target (70%fT > MIC, free drug concentration above MIC during 70% of the dosing interval) attainment was calculated using NONMEM combined with an opportunistic sampling design. The clinical treatment data were collected. RESULTS: A total of 52 neonates were screened and 49 neonates completed their piperacillin/tazobactam treatment course and were included in this analysis. The median (range) values of postmenstrual age were 33.57 (range 26.14-41.29) weeks. Forty-seven (96%) neonates reached their PD target. Eight (16%) neonates experienced treatment failure clinically. The mean (SD, range) duration of treatment and length of hospitalization were 100.1 (62.2, 36.2-305.8) hours and 31 (30, 5-123) days. There were no obvious adverse events and no infection-related deaths occurred in the first month of life. CONCLUSIONS: A model-based dosing regimen of piperacillin/tazobactam was evaluated clinically, was tolerated well and was determined to be effective for EOS treatment.