Physiologically-based pharmacokinetic modeling of nafamostat to support dose selection for treatment of pediatric patients with COVID-19

Physiologically Based Pharmacokinetic Modeling in Neonates: Current Status and Future Perspectives

Rational drug use in special populations is a clinical problem that doctors and pharma-cists must consider seriously. Neonates are the most physiologically immature and vulnerable to drug dosing. There is a pronounced difference in the anatomical and physiological profiles be-tween neonates and older people, affecting the absorption, distribution, metabolism, and excretion of drugs in vivo, ultimately leading to changes in drug concentration. Thus, dose adjustments in neonates are necessary to achieve adequate therapeutic concentrations and avoid drug toxicity. Over the past few decades, modeling and simulation techniques, especially physiologically based pharmacokinetic (PBPK) modeling, have been increasingly used in pediatric drug development and clinical therapy. This rigorously designed and verified model can effectively compensate for the deficiencies of clinical trials in neonates, provide a valuable reference for clinical research design, and even replace some clinical trials to predict drug plasma concentrations in newborns. This review introduces previous findings regarding age-dependent physiological changes and pathological factors affecting neonatal pharmacokinetics, along with their research means. The application of PBPK modeling in neonatal pharmacokinetic studies of various medications is also reviewed. Based on this, we propose future perspectives on neonatal PBPK modeling and hope for its broader application.

Multicenter, single-blind, randomized controlled study of the efficacy and safety of favipiravir and nafamostat mesilate in patients with COVID-19 pneumonia

OBJECTIVES

To evaluate the efficacy and safety of nafamostat combined with favipiravir for the treatment of COVID-19.

METHODS

We conducted a multicenter, randomized, single-blind, placebo-controlled, parallel assignment study in hospitalized patients with mild-to-moderate COVID-19 pneumonia. Patients were randomly assigned to receive favipiravir alone (n = 24) or nafamostat with favipiravir (n = 21). The outcomes included changes in the World Health Organization clinical progression scale score, time to improvement in body temperature, and improvement in oxygen saturation (SpO₂).

RESULTS

There was no significant difference in the changes in the clinical progression scale between nafamostat with favipiravir and favipiravir alone groups (median, -0.444 vs -0.150, respectively; least-squares mean difference, -0.294; P = 0.364). The time to improvement in body temperature was significantly shorter in the combination group (5.0 days; 95% confidence interval, 4.0-7.0) than in the favipiravir group (9.0 days; 95% confidence interval, 7.0-18.0; P =0.009). The changes in SpO₂ were greater in the combination group than in the favipiravir group (0.526% vs -1.304%, respectively; least-squares mean difference, 1.831; P = 0.022). No serious adverse events or deaths were reported, but phlebitis occurred in 57.1% of the patients in the combination group.

CONCLUSION

Although our study showed no differences in clinical progression, earlier defervescence, and recovery of SpO₂ were observed in the combination group.

Predicting the correct dose in children: Role of computational Pediatric Physiological-based pharmacokinetics modeling tools

The pharmacokinetics (PKs) and safety of medications in particular groups can be predicted using the physiologically-based pharmacokinetic (PBPK) model. Using the PBPK model may enable safe pediatric clinical trials and speed up the process of new drug research and development, especially for children, a population in which it is relatively difficult to conduct clinical trials. This review summarizes the role of pediatric PBPK (P-PBPK) modeling software in dose prediction over the past 6 years and briefly introduces the process of general P-PBPK modeling. We summarized the theories and applications of this software and discussed the application trends and future perspectives in the area. The modeling software's extensive use will undoubtedly make it easier to predict dose prediction for young patients.