Despite the recent advances in this field, there are limited methods for translating organoid-based study results to clinical response. The goal of this study was to develop a pharmacokinetic/pharmacodynamic (PK/PD) model to facilitate the translation, using oxaliplatin and irinotecan treatments with colorectal cancer (CRC) as examples. The PK models were developed using qualified oxaliplatin and irinotecan PK data from the literature. The PD models were developed based on antitumor efficacy data of SN-38 and oxaliplatin evaluated in vitro using tumor organoids. To predict the clinical response, translational scaling of the models was established by incorporating predicted ultrafiltration platinum in plasma or SN-38 in tumors to PD models as the driver of efficacy. The final PK/PD model can predict PK profiles and responses following treatments with oxaliplatin or irinotecan. After generation of virtual patient cohorts, this model simulated their tumor shrinkages following treatments, which were used in analyzing the efficacies of the two treatments. Consistent with the published clinical trials, the model simulation suggested similar patient responses following the treatments of oxaliplatin and irinotecan with regards to the probabilities of progression-free survival (HR = 1.05, 95%CI [0.97;1.15]) and the objective response rate (OR = 1.15, 95%CI [1.00;1.32]). This proposed translational PK/PD modeling approach provides a significant tool for predicting clinical responses of different agents, which may help decision-making in drug development and guide clinical trial design.

Background: Meropenem (MEM) is frequently prescribed for the empirical management of severe infections in the pediatric intensive care unit (PICU). Critically ill children exhibit substantial pharmacokinetic (PK) variability, and current dosing strategies remain inadequately evaluated, particularly in neonates, infants, and those with altered renal function. Methods: This study employed a dual modeling approach integrating population pharmacokinetic (PopPK) and physiologically based pharmacokinetic (PBPK) methodologies. Clinical data from two PICUs were utilized for PopPK model development and PBPK model evaluation. Both models were rigorously assessed using goodness-of-fit plots and prediction-based metrics. Monte Carlo simulations were subsequently conducted to calculate the probability of target attainment (PTA) for multiple dosing regimens across MICs of 0.25–16 mg/L. The pharmacodynamic target (PDT) was defined as maintaining unbound plasma concentrations above the MIC for 100% of the dosing interval (100% ƒT _{> MIC}), and dosing regimens were considered acceptable if the PTA exceeded 90% for efficacy while avoiding potential toxicity (Css ≥ 50 mg/L). Results: A total of 202 MEM plasma concentrations from 101 pediatric patients were analyzed. Marked inter-individual variability in MEM pharmacokinetics and pharmacodynamics was observed. Augmented renal clearance (ARC) was frequently identified in PICU patients. We simultaneously developed a two-compartment population pharmacokinetic model incorporating body weight and estimated glomerular filtration rate, and a whole-body physiologically based pharmacokinetic model scaled from adults with adjustments for transporter ontogeny and renal function. The PopPK model, by incorporating interindividual variability on clearance and volume of distribution, captured a wider range of drug exposures and demonstrated superior predictive performance, particularly in subgroups with high eGFR. The PBPK model showed higher precision in the low eGFR subgroup but slightly lower overall predictive accuracy. Both models identified ARC as a key driver of subtherapeutic exposure. Standard regimens were insufficient for preterm neonates when the MIC was ≥4 mg/L, and even the maximum label-recommended dose failed to achieve the pharmacodynamic target for infants older than 1 month when the MIC was ≥2 mg/L. Conclusions: Both PBPK and PopPK frameworks reliably predicted MEM pharmacokinetics in critically ill pediatric patients, with complementary strengths across renal function strata. Model-informed simulations highlighted the inadequacy of standard dosing under conditions of ARC or elevated MIC, supporting individualized, precision-guided dosing strategies based on age, eGFR, and pathogen MIC.