Physiologically-based pharmacokinetic modeling to predict the exposure and provide dosage regimens of Ustekinumab in pediatric patients with inflammatory bowel disease.

Ustekinumab (UST), a fully human immunoglobulin G1 κ monoclonal antibody, exhibiting high affinity for the p40 subunit shared by IL-12 and IL-23, which play key roles in the pathogenesis of inflammatory bowel disease (IBD). By scaling the physiologically-based pharmacokinetic modeling (PBPK) model of UST in adult patients with IBD, we aim to predict effective dosages for UST in pediatric patients, thereby offering a more practical dosing regimen for real-world applications. In this work, a PBPK model for UST in adult patients with IBD has been developed using PK-Sim and Mobi. Advanced ontogeny model has been incorporated to extrapolate the model to pediatric patients. The simulation results showed that the fold errors of the predicted and observed values of the area under the curve (AUC) and peak plasma concentration (Cmax) were between 0.79 and 1.73. For children aged 6-18, it is recommended to administer the drug per kilogram of body weight, at the model-recommended dose, to achieve a median AUC similar to that of the adult reference population post-administration. This comprehensive model construction enables us to comprehensively and extensively explore the pharmacokinetic characteristics of UST in pediatric patients of different age groups, providing robust support for clinical applications and personalized drug therapy.

Physiologically-Based Pharmacokinetic Modeling of Anti-Tumor Necrosis Factor Agents for Inflammatory Bowel Disease Patients to Predict the Withdrawal Time in Pregnancy and Vaccine Time in Infants.

Anti-tumor necrosis factor (anti-TNF) agents are widely applied for patients with inflammatory bowel disease (IBD); however, the timing of the last dosing for IBD pregnancy and time to elimination in anti-TNF agent-exposed infants is controversial. This study aimed to determine the optimal timing for the last dosing of anti-TNF agents (infliximab, adalimumab, and golimumab) in pregnant women with IBD, as well as to investigate the recommended vaccine schedules for infants exposed to these drugs. A physiologically-based pharmacokinetic (PBPK) model of anti-TNF agents was built for adults and extrapolated to pregnant patients, fetuses, and infants. The PBPK models successfully predicted and verified the pharmacokinetics (PKs) of infliximab, adalimumab, and golimumab in pregnancy, fetuses, and infants. The predicted PK data were within two-fold of the observed data. The simulated results were used as timing advice. According to the dose of administration, the suggested timing of the last dosing for infliximab, adalimumab, and golimumab is successfully provided based on PBPK predictions. PBPK models indicated that, for infants, the advocated timing of vaccination is 12, 8, and 5 months after birth for infliximab, adalimumab, and golimumab, respectively. Our study illustrated that PBPK models can provide a valuable tool to predict the PKs of large macromolecules in pregnant women, fetuses, and infants, ultimately informing drug-treatment decisions for pregnancy and vaccination regimens for infants.

Physiologically-Based Pharmacokinetic Modeling of Omalizumab to Predict the Pharmacokinetics and Pharmacodynamics in Pediatric Patients.

Omalizumab is widely used in clinical practice; however, knowledge gaps in the dosage of omalizumab for children aged 2-6 years with moderate-to-severe persistent allergic asthma have been identified. The aim of this study was to explore dosing regimens for moderately-to-severely allergic pediatric patients aged 2-6 years. The physiologically-based pharmacokinetic (PBPK) model of omalizumab was developed and verified in adult patients, extrapolated to pediatric patients, and simulated for omalizumab by adding two observation chambers (free IgE and total IgE). The simulation results showed that the fold errors of the predicted and observed values of the area under the curve (AUC) and peak plasma concentration (Cmax ) were between 0.5 and 2.0, and the average folding error and the absolute average folding error values for all concentration-time data points were 1.09 and 1.48, respectively. The PBPK model combined with pharmacokinetic/pharmacodynamic analysis of omalizumab demonstrated that both the model-derived dose and the original dose could control the average free IgE of 2-6-year-old children with moderate-to-severe allergic asthma below 25 ng/mL, and some of the model-derived doses were lower. This conclusion provides a basis for the selection of dosage in clinical practice reference.

Application of PBPK modeling in predicting maternal and fetal pharmacokinetics of levetiracetam during pregnancy.

Levetiracetam is currently being used to treat epilepsy in pregnant women. The plasma concentration of levetiracetam drops sharply during pregnancy, and the inability of pregnant women to maintain therapeutic concentrations can lead to seizures. This study aimed to predict the changes in fetal and maternal plasma exposure to levetiracetam during pregnancy and provide advice on dose adjustment. The physiology-based pharmacokinetics (PBPK) model was developed using PK-Sim and Mobi software, and validated following comparison of the observed plasma concentration and pharmacokinetic parameters. The levetiracetam PBPK model for mother and the fetus at various stages of pregnancy was successfully established and verified. Predictions indicated that the area under the steady-state concentration-time curve for levetiracetam decreased to 83, 62, and 67% of baseline values in the first, second, and third trimesters, respectively. Based on PBPK predictions, the recommended dose of levetiracetam is 1.2, 1.6, and 1.5 times the baseline dose in the first, second, and third trimesters, respectively, not exceeding 4000 mg/day in the third trimester due to fetal safety. The levetiracetam PBPK model for pregnancy was successfully developed and validated, and could provide alternative levetiracetam dosing regimens across the stages of pregnancy.

Application of Physiologically Based Pharmacokinetic Modeling to Predict Maternal Pharmacokinetics and Fetal Exposure to Oxcarbazepine.

Pregnancy is associated with physiological changes that may affect drug pharmacokinetics (PKs). The aim of this study was to establish a maternal-fetal physiologically based pharmacokinetic (PBPK) model of oxcarbazepine (OXC) and its active metabolite, 10,11-dihydro-10-hydroxy-carbazepine (MHD), to (1) assess differences in pregnancy, (2) predict changes in PK target parameters of these molecules following the current dosing regimen, (3) assess predicted concentrations of these molecules in the umbilical vein at delivery, and (4) compare different methods for estimating drug placental penetration. Predictions using the pregnancy PBPK model of OXC resulted in maternal concentrations within a 2-fold error, and extrapolation of the model to early-stage pregnancies indicated that changes in median PK parameters remained above target thresholds, requiring increased frequency of monitoring. The dosing simulation results suggested dose adjustment in the last two trimesters. We generally recommend that women administer ≥ 1.5× their baseline dose of OXC during their second and third trimesters. Test methods for predicting placental transfer showed varying performance, with the in vitro method showing the highest predictive accuracy. Exposure to MHD in maternal and fetal venous blood was similar. Overall, the above-mentioned models can enhance understanding of the maternal-fetal PK behavior of drugs, ultimately informing drug-treatment decisions for pregnant women and their fetuses.

Development of Physiologically Based Pharmacokinetic Model for Pregabalin to Predict the Pharmacokinetics in Pediatric Patients with Renal Impairment and Adjust Dosage Regimens: PBPK Model of Pregabalin in Pediatric Patients with Renal Impairment.

Pregabalin (PGB) is widely used clinically; however, its pharmacokinetics (PK) has not been studied in pediatric patients with renal impairment (RI). To design optimized PGB regimens for pediatric patients with varying degrees of RI and predict exposure to PGB, physiologically based pharmacokinetic (PBPK) models of PGB were developed and verified, and its disposition was simulated in the healthy population and adults with RI. The simulated results from the PBPK models after single-dose and multi-dose administrations of PGB were consistent with the corresponding observed data based on the fold error values of less than 2. The area under curve ratios were 1.23 ± 0.06, 2.02 ± 0.10, 3.86 ± 0.21, and 9.92 ± 0.79 in pediatric patients with mild, moderate, severe, and end-stage RI, respectively. Based on the predictions for pediatric patients with moderate, severe, and end-stage RI, the maximum dose should not exceed 7, 3.5, and 1.4 mg/kg/day, respectively, among those weighing < 30 kg, and it should not exceed 5, 2.5, and 1 mg/kg/day, respectively, among those weighing > 30 kg. In conclusion, the developed PBPK model is a valuable tool for predicting PGB dosage for pediatric patients with RI.

Biological Network Mining.

In this book chapter, we introduce a pipeline to mine significant biomedical entities (or bioentities) in biological networks. Our focus is on prioritizing both bioentities themselves and the associations between bioentities in order to reveal their biological functions. We will introduce three tools BEERE, WIPER, and PAGER 2.0 that can be used together for network analysis and function interpretation: (1) BEERE is a network analysis tool for "Biomedical Entity Expansion, Ranking and Explorations," (2) WIPER is an entity-to-entity association ranking tool, and (3) PAGER 2.0 is a service for gene enrichment analysis.

Ceftaroline Dosage Optimized for Pediatric Patients With Renal Impairment Using Physiologically Based Pharmacokinetic Modeling.

Ceftaroline fosamil is a fifth-generation cephalosporin approved as a treatment for adults and children with community-acquired bacterial pneumonia and acute bacterial skin and skin structure infections. However, its pharmacokinetics have not been fully evaluated in children with renal impairment. This study aimed to propose proper ceftaroline dosages optimized for the renally impaired pediatric population using physiologically based pharmacokinetic (PBPK) modeling. A PBPK model of ceftaroline was established and verified to simulate its disposition in the healthy population and renally impaired adults and to predict the exposure in renally impaired pediatric patients. Consistency was confirmed between simulated and observed data after intravenous administration of various ceftaroline regimens; fold errors were within the 2-fold error range. Among 6-year-old children, healthy subjects had 1.5-fold, 2-fold, and 2.6-fold lower areas under the plasma concentration-time curve (AUCs) than the moderate, severe, and end-stage renally impaired patient groups, respectively; among 1-year-old children, healthy subjects had 1.5-fold, 2.1-fold, and 2.5-fold lower AUCs than the respective renally impaired patient groups; among 1-month-old children, healthy subjects had 1.5-fold, 1.8-fold, and 2.2-fold lower AUCs than the respective renally impaired patient groups. The proposed dosage should be adjusted to 8, 6, and 5 mg/kg every 8 hours for patients aged ≥2 years to <18 years (≤33 kg) with moderate, severe, and end-stage renal impairment, respectively; 5, 4, and 3 mg/kg every 8 hours for patients aged 2 months to <2 years with moderate, severe, and end-stage renal impairment, respectively; 4, 3.5, and 2.5 mg/kg every 8 hours for patients 0 to <2 months of age with moderate, severe, and end-stage renal impairment, respectively. Furthermore, pharmacodynamic investigations demonstrated that adequate antimicrobial effects were attained at the proposed doses in 3 age groups. Hence, our PBPK model can be an effective tool to support ceftaroline dosage proposals for renally impaired pediatric patients.

Scalable De Novo Genome Assembly Using a Pregel-Like Graph-Parallel System.

De novo genome assembly is the process of stitching short DNA sequences to generate longer DNA sequences, without using any reference sequence for alignment. It enables high-throughput genome sequencing and thus accelerates the discovery of new genomes. In this paper, we present a toolkit, called PPA-assembler, for de novo genome assembly in a distributed setting. The operations in our toolkit provide strong performance guarantees, and can be assembled to implement various sequencing strategies. PPA-assembler adopts the popular de Bruijn graph based approach for sequencing, and each operation is implemented as a program in Google's Pregel framework which can be easily deployed in a generic cluster. Experiments on large real and simulated datasets demonstrate that PPA-assembler is much more efficient than the state-of-the-arts while providing comparable sequencing quality. PPA-assembler has been open-sourced at https://github.com/yaobaiwei/PPA-Assembler.