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In March 2024, ponatinib received accelerated FDA approval for the treatment of newly diagnosed Philadelphia chromosome‐positive acute lymphoblastic leukemia (Ph + ALL) in combination with chemotherapy based on the Phase 3 PhALLCON study (NCT03589326), which demonstrated a higher rate of minimal residual disease (MRD)‐negative complete remission (CR) at the end of induction (EOI) with ponatinib (34.4%) versus imatinib (16.7%; p = 0.002). Patients received ponatinib (30 mg QD with reduction to 15 mg QD upon achievement of MRD‐negative CR at EOI) or imatinib (600 mg QD) combined with 20 cycles of reduced‐intensity chemotherapy (induction: 3 cycles; consolidation: 6 cycles; and maintenance: 11 cycles). Ponatinib pharmacokinetics (PK) were similar in patients in PhALLCON and patients in a previous population PK analysis. Bayesian re‐estimation of the previously developed population PK model adequately described PhALLCON PK data. Exposure–efficacy analyses did not identify a significant relationship between ponatinib exposure and the probability of MRD‐negative CR at EOI (p = 0.619), suggesting a consistent efficacy benefit across exposures. Ponatinib exposure was not a significant predictor of arterial occlusive events, venous thromboembolic events, thrombocytopenia, or lipase increase (p > 0.05). However, higher exposures were associated with a higher probability of hypertension (p = 0.0340) and alanine aminotransferase (ALT) increase (p = 0.0034). Dose reduction from 30 to 15 mg was predicted to decrease the odds of experiencing hypertension by 37.7% and ALT increase by 44.2%. Collectively, exposure–response analyses support a favorable benefit–risk profile of the approved ponatinib dosage (30 mg QD reduced to 15 mg QD upon achievement of MRD‐negative CR at EOI), combined with chemotherapy, for frontline treatment of Ph + ALL.

CKD-519 is a selective and potent cholesteryl ester transfer protein (CETP) inhibitor that is being developed for dyslipidemia. Even though CKD-519 has shown potent CETP inhibition, the exposure of CKD-519 was highly varied, depending on food and dose. For highly variable exposure drugs, it is crucial to use modeling and simulation to plan proper dose selection. This study aimed to develop population pharmacokinetic (PK) and pharmacodynamics (PD) models of CKD-519 and to predict the proper dose of CKD-519 to achieve target levels for HDL-C and LDL-C using results from multiple dosing study of CKD-519 with a standard meal for two weeks in healthy subjects. The results showed that a 3-compartment with Erlang’s distribution, followed by the first-order absorption, adequately described CKD-519 PK, and the bioavailability, which decreased by dose and time was incorporated into the model (NONMEM version 7.3). After the PK model development, the CETP activity and cholesterol (HDL-C and LDL-C) levels were sequentially modeled using the turnover model, including the placebo effect. According to PK-PD simulation results, 200 to 400 mg of CKD-519 showing a 40% change in HDL-C and LDL-C from baselines was recommended for proof of concept studies in patients with dyslipidemia.

Background. Recent reports indicated high miltefosine treatment failure rates for visceral leishmaniasis (VL) on the Indian subcontinent. To further explore the pharmacological factors associated with these treatment failures, a population pharmacokinetic-pharmacodynamic study was performed to examine the relationship between miltefosine drug exposure and treatment failure in a cohort of Nepalese patients with VL. Methods. Miltefosine steady-state blood concentrations at the end of treatment were analyzed using liquid chromatography tandem mass spectrometry. A population pharmacokinetic-pharmacodynamic analysis was performed using nonlinear mixed-effects modeling and a logistic regression model. Individual estimates of miltefosine exposure were explored for their relationship with treatment failure. Results. The overall probability of treatment failure was 21%. The time that the blood concentration was > 10 times the half maximal effective concentration of miltefosine (median, 30.2 days) was significantly associated with treatment failure: each 1-day decrease in miltefosine exposure was associated with a 1.08-fold (95% confidence interval, 1.01-1.17) increased odds of treatment failure. Conclusions. Achieving a sufficient exposure to miltefosine is a significant and critical factor for VL treatment success, suggesting an urgent need to evaluate the recently proposed optimal allometric miltefosine dosing regimen. This study establishes the first evidence for a drug exposure-effect relationship for miltefosine in the treatment of VL.

A fit-for-purpose structural and statistical model is the first major requirement in population pharmacometric model development. In this manuscript we discuss how this complex and computationally intensive task could benefit from supervised machine learning algorithms. We compared the classical pharmacometric approach with two machine learning methods, genetic algorithm and neural networks, in different scenarios based on simulated pharmacokinetic data. Genetic algorithm performance was assessed using a fitness function based on log-likelihood, whilst neural networks were trained using mean square error or binary cross-entropy loss. Machine learning provided a selection based only on statistical rules and achieved accurate selection. The minimization process of genetic algorithm was successful at allowing the algorithm to select plausible models. Neural network classification tasks achieved the most accurate results. Neural network regression tasks were less precise than neural network classification and genetic algorithm methods. The computational gain obtained by using machine learning was substantial, especially in the case of neural networks. We demonstrated that machine learning methods can greatly increase the efficiency of pharmacokinetic population model selection in case of large datasets or complex models requiring long run-times. Our results suggest that machine learning approaches can achieve a first fast selection of models which can be followed by more conventional pharmacometric approaches.

The purpose of this paper is to study the genetic polymorphisms of related gene loci (CYP2C9*3, VKORC1-1639G > A) based on demographic and clinical factors, and use the maximum a posterior Bayesian method to construct a warfarin individualized dose prediction model in line with the Chinese Han population. Finally, the built model is compared and analyzed with the widely used models at home and abroad. In this study, a total of 5467 INR measurements are collected from 646 eligible subjects in our hospital, and the maximum a posterior Bayesian method is used to construct a warfarin dose prediction that conforms to the Chinese Han population on the basis of the Hamberg model. The model is verified and compared with foreign models. This study finds that body weight and concomitant use of amiodarone have a significant effect on the anticoagulant effect of warfarin. The model can provide an effective basis for individualized and rational dosing of warfarin in Han population more accurately. In the performance of comparison with different warfarin dose prediction models, the new model has the highest prediction accuracy, and the prediction percentage is as high as 72.56%. The dose predicted by the Huang model is the closest to the actual dose of warfarin. The population pharmacokinetics and pharmacodynamics model established in this study can better reflect the distribution characteristics of INR values after warfarin administration in the Han population, and performs better than the models reported in the literature.

Covariate identification is an important step in the development of a population pharmacokinetic/pharmacodynamic model. Among the different available approaches, the stepwise covariate model (SCM) is the most used. However, SCM is based on a local search strategy, in which the model-building process iteratively tests the addition or elimination of a single covariate at a time given all the others. This introduces a heuristic to limit the searching space and then the computational complexity, but, at the same time, can lead to a suboptimal solution. The application of genetic algorithms (GAs) for covariate selection has been proposed as a possible solution to overcome these limitations. However, their actual use during model building is limited by the extremely high computational costs and convergence issues, both related to the number of models being tested. In this paper, we proposed a new GA for covariate selection to address these challenges. The GA was first developed on a simulated case study where the heuristics introduced to overcome the limitations affecting currently available GA approaches resulted able to limit the selection of redundant covariates, increase replicability of results and reduce convergence times. Then, we tested the proposed GA on a real-world problem related to remifentanil. It obtained good results both in terms of selected covariates and fitness optimization, outperforming the SCM.

Covariate identification in population pharmacokinetic/pharmacodynamic (popPK/PD) modeling is a key component in model development that is often prone to bias, time‐consuming, and even intractable when too many covariates or complicated models are being considered. Early work leveraging machine learning (ML) for covariate screening has shown promising results over traditional methods. In this work, we expand this effort by integrating explainable machine learning facilitated by Shapley Additive Explanations (SHAP) analysis and covariate uncertainty quantification as well as a formal framework for establishing statistical significance of covariate relationships. Finally, we have packaged the proposed methodology into a flexible set of functions (shap‐cov) to support popPK/PD modeling covariate identification.

Midazolam (MDZ) is used for sedation in surgical procedures; its clinical effect is related to its receptor affinity and the dose administered. Therefore, a pharmacokinetic–pharmacodynamic (PK-PD) population model of MDZ in pediatric patients undergoing minor surgery is proposed. A descriptive, observational, prospective, and longitudinal, study that included patients of both sexes, aged 2–17 years, ASA I/II, who received MDZ in IV doses (0.05 mg/kg) before surgery. Three blood samples were randomly taken between 5–120 min; both sedation by the Bispectral Index Scale (BIS) and its adverse effects were recorded. The PK-PD relationship was determined using a nonlinear mixed-effects, bicompartmental first-order elimination model using Monolix Suite™. Concentrations and the BIS were fitted to the sigmoid Emax PK-PD population and sigmoid Emax PK/PD indirect binding models, obtaining drug concentrations at the effect site (biophase). The relationship of concentrations and BIS showed a clockwise hysteresis loop, probably indicating time-dependent protein binding. Of note, at half the dose used in pediatric patients, adequate sedation without adverse effects was demonstrated. Further PK-PD studies are needed to optimize dosing schedules and avoid overdosing or possible adverse effects.

A population pharmacokinetic/pharmacodynamic (popPK/PD) model for BIIB059 (anti-blood dendritic cell antigen 2 [anti-BDCA2]), a humanized immunoglobulin G1 monoclonal antibody currently under development for the treatment of SLE and CLE, is presented. BIIB059 binds BDCA2, a plasmacytoid dendritic cell (pDC)-specific receptor that inhibits the production of IFN-I and other inflammatory mediators when ligated. Phase 1 PK and PD data of healthy adult volunteers (HV, n = 87) and SLE subjects (n = 22) were utilized for the development of the popPK/PD model. The data included single and multiple dosing of intravenous and subcutaneous BIIB059. BDCA2 internalization (PD marker) was measured for all subjects by monitoring reduction of BDCA2 on pDC cell surface and used for development of the popPD model. A two-compartment popPK model with linear plus non-linear elimination was found to best describe BIIB059 PK. BDCA2 levels were best captured using an indirect response model with stimulation of the elimination of BDCA2. Clearance in SLE subjects was 25% higher compared to HV (6.87 vs 5.52 mL/h). Bodyweight was identified as only other covariate on clearance and central volume. The estimates of EC50 and Emax were 0.35 μg/mL and 8.92, respectively. No difference in EC50 and Emax was observed between SLE and HV. The popPK/PD model described the data accurately, as evaluated by pcVPCs and bootstrap. The presented popPK/PD model for BIIB059 provides valuable insight into the dynamics and dose–response relationship of BIIB059 for the treatment of SLE and CLE and was used to guide dose selection for the Phase 2 clinical study (NCT02847598).

Background/Objectives: Hypertensive disorders of pregnancy are a leading cause of pregnancy-related deaths in the United States, accounting for 7% of maternal mortality. Labetalol and nifedipine are the first-line drugs for the management of hypertension in pregnancy, but there are little data guiding the choice of one drug over the other. The current pilot longitudinal study aims to characterize the pharmacokinetics (PK) and pharmacodynamics (PD) of labetalol stereoisomers throughout pregnancy and postpartum. Methods: This is a single-center clinical study recruiting up to 40 pregnant individuals ≥ 18 years of age at the time of enrollment, taking labetalol as per the standard of care. The exclusion criteria include any pathophysiology impacting the PK of labetalol, e.g., liver failure. Maternal plasma, urine, amniotic fluid, cord blood, and breast milk will be collected, and labetalol stereoisomers will be measured using a validated LC-MS/MS assay. Heart rate and blood pressure will be measured as the PD endpoints. These may be assessed throughout a participant’s dosing interval at scheduled PK study visits, which will occur every 6–10 weeks during pregnancy, at delivery, during the 1st week postpartum, and up to 20 weeks postpartum. The primary aim is to characterize the PK and PD of labetalol during pregnancy and in the postpartum period. The secondary aim is to determine the extent of breast milk excretion of and infant exposure to labetalol from breast milk. The data will be analyzed using population PK modeling to evaluate the PK/PD relationship and ultimately develop trimester-specific dosing recommendations. Results/Conclusions: To our knowledge, this is the first study aiming to characterize the PK of labetalol stereoisomers across pregnancy and postpartum, utilizing individual stereoisomer data to evaluate the PK/PD relationship, and collecting postpartum samples, including breast milk, to model infant exposure to labetalol through breast milk. This study will provide important PK/PD data and knowledge which will be combined with large multi-center clinical trial data to develop trimester-specific dosing regimens for anti-hypertensive agents.