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.

Model-informed drug development in pediatric, pregnancy and geriatric drug development: States of the art and future.

The challenges of drug development in pediatric, pregnant and geriatric populations are a worldwide concern shared by regulatory authorities, pharmaceutical companies, and healthcare professionals. Model-informed drug development (MIDD) can integrate and quantify real-world data of physiology, pharmacology, and disease processes by using modeling and simulation techniques to facilitate decision-making in drug development. In this article, we reviewed current MIDD policy updates, reflected on the integrity of physiological data used for MIDD and the effects of physiological changes on the drug PK, as well as summarized current MIDD strategies and applications, so as to present the state of the art of MIDD in pediatric, pregnant and geriatric populations. Some considerations are put forth for the future improvements of MIDD including refining regulatory considerations, improving the integrity of physiological data, applying the emerging technologies, and exploring the application of MIDD in new therapies like gene therapies for special populations.

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.

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.

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.

Population pharmacokinetics and dose optimization of levofloxacin in elderly patients with pneumonia.

AIMS: Levofloxacin is a quinolone antibiotic with a broad antibacterial spectrum. It is frequently used in elderly patients with pneumonia. The pharmacokinetic profile of elderly patients changes with age, but data on the pharmacokinetics of levofloxacin in these patients are limited. The aim of this study was to establish a population pharmacokinetic model of levofloxacin in elderly patients with pneumonia and to optimize individualized dosing regimens based on this newly developed model. METHODS: This is a prospective, open-label pharmacokinetic study in elderly patients with pneumonia. Blood samples were collected using an opportunistic approach. The plasma concentrations of levofloxacin were determined by high-performance liquid chromatography. A population pharmacokinetic model was established using nonlinear mixed-effect model software. Monte Carlo simulations were used for dose simulation and dose optimization. RESULTS: Data from 51 elderly patients with pneumonia were used for the population pharmacokinetic analysis. A one-compartment model with first-order elimination was most suitable for describing the data, and the estimated glomerular filtration rate was the only covariate that had a significant impact on the model. The final model estimated that the mean clearance of levofloxacin in elderly patients with pneumonia was 5.26 L/h. Monte Carlo simulation results showed that the optimal dosing regimen for levofloxacin was 750 mg once a day in elderly patients with pneumonia, with a minimum inhibitory concentration of 2 mg/L. CONCLUSIONS: The population pharmacokinetic model of levofloxacin in elderly patients with pneumonia was established, and the dose optimization of levofloxacin was completed through Monte Carlo simulation.

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.

Accuracy of antibiotic concentrations in drug dispensing in neonates: a laboratory-based study.

BACKGROUND: Antibacterial therapy plays a crucial role in neonatal infections. The efficacy of antibacterial agents is closely related to the actual dose given to neonates. So we evaluated factors potentially affecting the actual dose of intravenous antibiotics during dispensing process in neonates. METHODS: Meropenem, cefoperazone/sulbactam and piperacillin/tazobactam with two strengths were used to evaluate three methods. Method A (MA) was diluted once and the volumes of 5% glucose for MA were meropenem 4.00 mL, cefoperazone/sulbactam 3.00 mL, piperacillin/tazobactam 9.00 mL. Method B (MB) differed by doubling the volume of 5% glucose. The difference in method C (MC) involved diluting with 5% glucose twice. The concentrations were measured by high-performance liquid chromatography. Relative error (RE) was used to evaluate the preparation accuracy. RESULTS: The RE values using MA/MB/MC were: (1) meropenem 0.5 g: 15.1%, 8.0%, 10.4%; 0.25 g: 7.8%, 3.1%, 6.0%; (2) cefoperazone/sulbactam 1.5 g: 13.6%, 4.2%, 3.4%; 0.75 g: 8.8%, 3.5%, 4.0%; (3) piperacillin/tazobactam 4.5 g: 18.2%, 8.7%, 6.3%; 562.5 mg: 8.1%, 2.8%, 6.1%. MB was better than MA in all three drugs. No difference in RE values was found between single and double dilution, except meropenem with 0.25 g. Using MB, meropenem and piperacillin/tazobactam with small drug strength had higher accuracy in preparation. CONCLUSIONS: MB was suitable for neonatal drug dispensing because of its high accuracy and simple operation. Drugs with small strength were promoted due to the high accuracy.

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.

Optimizing ganciclovir and valganciclovir dosing regimens in pediatric patients with cytomegalovirus infection: a spotlight on therapeutic drug monitoring.

INTRODUCTION: Infants and immunocompromised children with cytomegalovirus (CMV) infection have significant morbidity and mortality. Ganciclovir (GCV) and its oral prodrug valganciclovir (VGCV) are the major antiviral options of choice for the prophylaxis and treatment of CMV infection. However, with the currently recommended dosing regimens used in pediatric patients, large intra- and inter-individual variability of pharmacokinetic (PK) parameters and exposure are observed. AREAS COVERED: This review describes the PK and pharmacodynamic (PD) characteristics of GCV and VGCV in pediatrics. Moreover, the role of therapeutic drug monitoring (TDM) and current clinical practice for GCV and VGCV dosing regimens optimization in pediatrics are discussed. EXPERT OPINION: GCV/VGCV TDM has shown the potential value to improve the benefit/risk ratio in pediatrics when using the therapeutic ranges derived from adults. However, well-designed studies are required to evaluate the relationship of TDM with clinical outcomes. Furthermore, studies to explore the children-specific dose-response-effect relationships will be helpful to facilitate the TDM practice. In the clinical setting, optimal sampling methods such as limited sampling strategies for pediatrics can be used in TDM and intracellular ganciclovir triphosphate may be used as an alternative TDM marker.

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.

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.

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.

Optimal dose of meropenem for the treatment of neonatal sepsis: Dosing guideline variations and clinical practice deviations.

AIMS: Meropenem is increasingly used to treat neonatal sepsis. There are several guidelines recommending different dosing regimens of meropenem in neonates. Furthermore, deviations from these guidelines regularly occur in daily clinical practice. Therefore, the current study aimed to evaluate the variations of meropenem dosing guidelines and compare the difference between guideline and clinical practice in terms of the probability of target attainment. METHODS: This study is based on a population pharmacokinetic model. After defining the predictive performance of the model, Monte Carlo simulations were used to calculate the probability of target attainment of the currently existing dosing guidelines of meropenem and their use in daily clinical practice. RESULTS: Two guidelines and two labels were included in the Monte Carlo simulations. For 70% fT>MIC (fraction of time when the free meropenem concentration exceeded the minimum inhibitory concentration during the dosing interval), the probability of target attainment of four recommended doses ranged from 59% to 88% (MIC = 2 mg·L-1 ) and from 17% to 47% (MIC = 8 mg·L-1 ). At the clinical practice evaluation, only 20% of patients attained target exposure for the MIC of 8 mg·L-1 with 70% fT>MIC , which was much less than those found in the Food and Drug Administration labels (40%). CONCLUSION: This model-based population pharmacokinetics simulation showed that improper guidelines and/or clinical practice deviations will result in low probability of target attainment for patients infected with resistant bacteria and critically ill patients. It is important to develop and adhere to evidence-based and clinically pragmatic guidelines.

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.

LPS-Induced Inflammation Affects Midazolam Clearance in Juvenile Mice in an Age-Dependent Manner.

PURPOSE: Inflammation has a significant impact on CYP3A activity. We hypothesized that this effect might be age dependent. Our objective was to conduct a population pharmacokinetic study of midazolam in mice at different developmental stages with varying degrees of inflammation to verify our hypothesis. METHODS: Different doses (2 and 5 mg/kg) of lipopolysaccharide (LPS) were used to induce different degrees of systemic inflammation in Swiss mice (postnatal age 9-42 days, n = 220). The CYP3A substrate midazolam was selected as the pharmacological probe to study CYP3A activity. Postnatal age, current body weight, serum amyloid A protein 1 (SAA1) levels and LPS doses were collected as covariates to perform a population pharmacokinetic analysis using NONMEM 7.2. RESULTS: A population pharmacokinetic model of midazolam in juvenile and adult mice was established. Postnatal age and current body weight were the most significant and positive covariates for clearance and volume of distribution. LPS dosage was the most significant and negative covariate for clearance. LPS dosage can significantly reduce the clearance of midazolam by 21.8% and 38.7% with 2 mg/kg and 5 mg/kg, respectively. Moreover, the magnitude of the reduction was higher in mice with advancing postnatal age. CONCLUSION: Both inflammation and ontogeny have an essential role in CYP3A activity in mice. The effect of LPS-induced systemic inflammation on midazolam clearance in mice is dependent on postnatal age.

Developmental Pharmacogenetics of CYP2D6 in Chinese Children: Loratadine as a Substrate Drug.

Objective: The elucidation of CYP2D6 developmental pharmacogenetics in children has improved, however, these findings have been largely limited to studies of Caucasian children. Given the clear differences in CYP2D6 pharmacogenetic profiles in people of different ancestries, there remains an unmet need to better understand the developmental pharmacogenetics in populations of different ancestries. We sought to use loratadine as a substrate drug to evaluate the effects of ontogeny and pharmacogenetics on the developmental pattern of CYP2D6 in Chinese pediatric patients. Methods: Chinese children receiving loratadine treatment were enrolled in the present study. The metabolite-to-parent ratio (M/P ratio), defined as the molar ratio of desloratadine to loratadine of trough concentrations samples at steady-state condition, was used as a surrogate of CYP2D6 activity. Loratadine and desloratadine were determined by LC/MS/MS method. Variants of CYP2D6 were genotyped by polymerase chain reaction for CYP2D6 *4, *10, *41 and long polymerase chain reaction for CYP2D6 *5. Results: A total of 40 patients were available for final analysis. The mean age was 4.50 (range 0.50-9.00) years and the mean weight was 19.64 (range 7.00-42.00) kg. The M/P ratio was significantly lower in intermediate metabolizers (IMs) compared to normal metabolizers (NMs) (10.18 ± 7.97 vs. 18.80 ± 15.83, p = 0.03). Weight was also found to be significantly associated with M/P ratio (p = 0.03). Conclusion: The developmental pharmacogenetics of CYP2D6 in Chinese children was evaluated using loratadine as a substrate drug. This study emphasizes the importance of evaluating the developmental pharmacogenetics in populations of different ancestries.

Downregulation of Renal MRPs Transporters in Acute Lymphoblastic Leukemia Mediated by the IL-6/STAT3/PXR Signaling Pathway.

PURPOSE: Considering prior investigations on reductions of renal multidrug resistance-associated protein (MRP) 2 and 4 transporters in mice with acute lymphoblastic leukemia (ALL), we sought to characterize the underlying mechanisms responsible for IL-6/STAT3/PXR-mediated changes in the expression of MRP2 and MRP4 in ALL. SUBJECTS AND METHODS: ALL xenograft models were established and intravenously injected with methotrexate (MTX) of MRPs substrate in NOD/SCID mice. Protein expression of MRPs and associated mechanisms were detected by Western blotting and immunocytochemistry. Plasma concentrations of MTX were determined using high-performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS). RESULTS: Plasma IL-6 levels in patients with newly diagnosed ALL were increased compared to children with pneumonia. Similarly, plasma IL-6 levels in ALL, ALL-tocilizumab (TCZ, an IL-6 receptor inhibitor) and ALL-S3I-201 (a selective inhibitor of STAT3) mice were increased compared to the control group. The MRP2, MRP4, and PXR expression in HK-2 cells treated with IL-6 were decreased, whereas the p-STAT3 expression was significantly increased compared to the control group results. These results are consistent with clearance of MRPs-mediated MTX in the ALL group. These effects were attenuated by blocking IL-6/STAT3/PXR signaling pathway. CONCLUSION: Inflammation-mediated changes in pharmacokinetics are thought to be executed through pathways IL-6-activated pathways, which can facilitate a better understanding of the potential for the use of IL-6 to predict the severity of adverse outcomes and the major implications on potential ALL treatments.