PK-PD Model Research Articles

Integration of tissue distribution, PK-PD modeling and metabolomics reveals inflammatory-immune response alterations in Gaultheria leucocarpa var. yunnanensis alleviating rheumatoid arthritis.

Gaultheria leucocarpa var. yunnanensis, a distinguished member of the Gaultheria Kalm ex L. in the Ericaceae family, has been traditionally employed in the southwestern regions of China for the efficacious treatment of rheumatoid arthritis (RA). The anti-RA fraction (ARF) derived from Gaultheria leucocarpa var. yunnanensis has been previously demonstrated to effectively alleviate RA in vivo and in vitro. This research endeavor is dedicated to surveying the pharmacokinetic (PK) processes of ARF within plasma and tissues, profiling its metabolites in vivo, discerning the material foundation of its therapeutic efficacy, and delineating its anti-RA mechanisms. The prototype components and metabolites of ARF in plasma and seven tissues of RA rats were analyzed by LC-MSn. Advanced LC-MS/MS and HPLC-DAD methodologies were developed to investigate the plasma PK profiles and tissue distribution characteristics of MSTG-A, MSTG-B, and Gaultherin in both RA model rats and healthy controls. A panel of four cytokines (TNF-α, IL-1, IL-6, and IL-2) was selected as pharmacodynamic (PD) biomarkers and quantified using ELISA. The PK, PD, and PK-PD modeling of ARF were skillfully constructed by combining WinNonlin with Matlab software, enabling a comprehensive analysis of the interrelationships between components and effect markers. A non-targeted plasma metabolomics approach employing LC-QE-MS was utilized to insight into the underlying mechanisms of ARF alleviating RA. The quantity and diversity of identified prototypical components and metabolites of ARF in model rat plasma increased over time. The spleen exhibited the highest number of metabolites and prototypical compounds of ARF. The UPLC-QQQ-MS/MS and HPLC-DAD method were developed and validated for the quantification of three chemical markers in rat plasma and tissues, respectively. Three effective components (MSTG-B, MSTG-A, and Gautherin) demonstrated linear dynamics in plasma and tissues at an oral dosage of 3 g/kg ARF. The PK-PD models involving three components and four inflammatory cytokines aligned with the one company model, demonstrating a linear correlation through compartmental modeling and curve fitting analysis. Significant variations were identified in the concentrations of various amino acids and lipid metabolites among the CON, ARF, and MTX groups in comparison to the MOD group, which are intricately linked to the inflammation-immunity response. The three components displayed favorable bioavailability and were rapidly eliminated in RA rats, collectively exerting an anti-RA effect. The mechanism by which ARF mitigates RA is associated with the modulation of inflammation-immunity related metabolic pathways. The spleen may serve as the target tissue for ARF attenuating RA. These findings provide a robust foundation for rationalizing intervention strategies, elucidating biological mechanisms, and advancing the clinical application of ARF in the amelioration of RA.

An integrative and translational PKPD modelling approach to explore the combined effect of polymyxin B and minocycline against Klebsiella pneumoniae.

To expand a translational pharmacokinetic-pharmacodynamic (PKPD) modelling approach for assessing the combined effect of polymyxin B and minocycline against Klebsiella pneumoniae. A PKPD model developed based on in vitro static time-kill experiments of one strain (ARU613) was first translated to characterize that of a more susceptible strain (ARU705), and thereafter to dynamic time-kill experiments (both strains) and to a murine thigh infection model (ARU705 only). The PKPD model was updated stepwise using accumulated data. Predictions of bacterial killing in humans were performed. The same model structure could be used in each translational step, with parameters being re-estimated. Dynamic data were well predicted by static-data-based models. The in vitro/in vivo differences were primarily quantified as a change in polymyxin B effect: a lower killing rate constant in vivo compared with in vitro (concentration of 3 mg/L corresponds to 0.05/h and 57/h, respectively), and a slower adaptive resistance rate (the constant in vivo was 2.5% of that in vitro). There was no significant difference in polymyxin B-minocycline interaction functions. Predictions based on both in vitro and in vivo parameters indicated that the combination has a greater-than-monotherapy antibacterial effect in humans, forecasting a reduction of approximately 5 and 2 log10 colony-forming units/mL at 24 h, respectively, under combined therapy, while the maximum bacterial load was reached in monotherapy. This study demonstrated the utility of the PKPD modelling approach to understand translation of antibiotic effects across experimental systems, and showed a promising antibacterial effect of polymyxin B and minocycline in combination against K. pneumoniae.

Personalized parathyroid hormone therapy for hypoparathyroidism: Insights from pharmacokinetic-pharmacodynamic modelling.

A 42-year-old male developed chronic primary hypoparathyroidism after total thyroidectomy. Conventional therapy led to recurrent nephrolithiasis and therefore rhPTH(1-84) (parathyroid hormone [PTH]) treatment was considered. According to the dosing guideline for PTH, calcium plasma levels are adequately controlled with once-daily administration. However, the effect on urinary calcium excretion is only transient and hence does not lower the risk of nephrolithiasis. This raises the question of whether multiple-daily or continuous administration of PTH is more effective in lowering urinary calcium excretion. We aimed to construct a pharmacokinetic-pharmacodynamic (PKPD) model to answer this question. A single patient was treated with intermittent PTH followed by off-label continuous infusion of PTH. PTH was measured in plasma, calcium and phosphate in plasma and urine. A one-compartment PKPD model for PTH was developed with Edsim++. The effect of PTH was described by the relative clearance of calcium and phosphate. The PKPD model for PTH showed visually a marked effect on phosphate clearance, but less on calcium clearance. During the study, the patient also received medication that influenced calcium homeostasis but to a lesser extent phosphate homeostasis. Therefore, phosphate was chosen as the effect parameter, resulting in an EC50 of 6.3pmol/L PTH. The PKPD model for PTH was completed with the unique data of a single patient who received PTH according to various dosing regimens, including continuous infusion. Continuous administration of PTH is favoured because it permanently increases the phosphate clearance and therefore needs to be further investigated.

Multiorgan-on-a-chip for cancer drug pharmacokinetics-pharmacodynamics (PK-PD) modeling and simulations.

Cancer is one of the most common and fatal diseases worldwide and kills millions of people every year. Cancer drug resistance, lack of efficacy, and safety are significant problems in cancer patients. A multiorgan-on-a-chip (MOC) device consisting of breast and liver compartments was designed with AutoCAD software. The MOC molds were printed by a Formlabs Form 2 3D printer. MDA-MB-231, HepG2, and MCF-10A cells were used for the MOC experiments. The cell lines were cultured at 37°C with 5% CO2, and cell viability was assessed via Alamar blue dye to generate pharmacodynamics (PD) data. Drug concentrations from the cell culture media were analyzed via Agilent 1260 Infinity II HPLC with a Waters Symmetry C18 column and used to generate pharmacokinetics (PK) data. The PK and PD data were modeled and simulated by Monolix and Simulix software, respectively. The safety and efficacy of drug dosing regimens were compared, and the best dosing regimens were selected. This research designed and fabricated a unique MOC consisting of liver and breast compartments that overcomes the need for sealing or assembling. It was used for PK-PD modeling and simulations, and its functionality was proven experimentally. The new MOC will be helpful in preclinical trials to evaluate the efficacy and safety of drugs.

PKPD modelling and simulation of longitudinal meropenem in vivo effects against Escherichia coli and Klebsiella pneumoniae strains with high MIC

Carbapenem-resistant bacteria pose a threat to public health. Characterising the pharmacokinetics-pharmacodynamics (PKPD) of meropenem longitudinally in vivo against resistant bacteria could provide valuable information for development and translation of carbapenem-based therapies. To assess the time course of meropenem effects in vivo against strains with high MIC to predict PK/PD indices and expected efficacy in patients using a modelling approach. A PKPD model was built on longitudinal bacterial count data to describe meropenem effects against six Escherichia coli and Klebsiella pneumoniae strains (MIC values 32-128 mg/L) in a 24 h mouse thigh infection model. The model was used to derive PK/PD indices from simulated studies in mice and to predict the efficacy of different infusion durations with high-dose meropenem (2 g q8 h/q12 h for normal/reduced kidney function) in patients. Data from 592 mice were available for model development. The estimated meropenem concentration-dependent killing rate was not associated with differences in MIC. The fraction of time that unbound concentrations exceeded EC50 (fT>EC50, EC50 = 1.01 mg/L) showed higher correlations than fT>MIC. For all investigated strains, bacteriostasis at 24 h was predicted for prolonged infusions of high-dose meropenem monotherapy in >90% of patients. The developed PKPD model successfully described bacterial growth and meropenem killing over time in the thigh infection model. For the investigated strains, the MIC, determined in vitro, or MIC-based PK/PD indices, did not predict in vivo response. Simulations suggested prolonged infusions of high-dose meropenem to be efficacious in patients infected by the studied strains.

Translational PK-PD model for invivo CAR-T-cell therapy delivered using CAR mRNA-loaded polymeric nanoparticle vector.

Autologous chimeric antigen receptor (CAR) T-cell therapy has demonstrated remarkable response rates, yet its widespread implementation is hindered by logistical, financial, and physical constraints. Additionally, challenges such as poor persistence and allorejection are associated with allogeneic cell therapies. An innovative approach involves invivo transduction of endogenous T-cells through the administration of CAR mRNA encapsulated in polymeric nanoparticles (NPs), resulting in transient CAR surface expression on circulating T-cells. This method presents a promising alternative, although the dose-exposure-response relationship of invivo CAR-Ts remains poorly elucidated. The transient nature of CAR expression may necessitate repeated dosing, potentially introducing additional hurdles like cost and patient compliance. To address this issue, we have devised a translational pharmacokinetic-pharmacodynamic (PK-PD) model that characterizes the transient surface CAR expression following mRNA-encapsulated NP administration, leveraging invitro and invivo data alongside critical binding kinetic parameters sourced from literature. Our model adequately captures the transient surface CAR expression in both settings, while incorporating known physiological parameter values and exhibiting precise estimation of unknown parameters (coefficient of variation < 30%). Global sensitivity analyses underscore the significance of intracellular mRNA stability, highlighting the sensitivity of parameters linked to free intracellular mRNA concentration. Model-based simulations indicate that optimizing dose and dosing frequency can achieve sustained CAR expression, despite the transient protein expression characteristic of mRNA-based therapies. This mechanistic PK-PD model holds potential for integration into physiologically-based pharmacokinetic models, facilitating the translation of invivo CAR-T-cell therapies from preclinical studies to human applications.

CMINNs: Compartment model informed neural networks — Unlocking drug dynamics

In the field of pharmacokinetics and pharmacodynamics (PKPD) modeling, which plays a pivotal role in the drug development process, traditional models frequently encounter difficulties in fully encapsulating the complexities of drug absorption, distribution, and their impact on targets. Although multi-compartment models are frequently utilized to elucidate intricate drug dynamics, they can also be overly complex. To generalize modeling while maintaining simplicity, we propose an innovative approach that enhances PK and integrated PK-PD modeling by incorporating fractional calculus or time-varying parameter(s), combined with constant or piecewise constant parameters. These approaches effectively model anomalous diffusion, thereby capturing drug trapping and escape rates in heterogeneous tissues, which is a prevalent phenomenon in drug dynamics. Furthermore, this method provides insight into the dynamics of drug in cancer in multi-dose administrations. Our methodology employs a Physics-Informed Neural Network (PINN) and fractional Physics-Informed Neural Networks (fPINNs), integrating ordinary differential equations (ODEs) with integer/fractional derivative order from compartmental modeling with neural networks. This integration optimizes parameter estimation for variables that are time-variant, constant, piecewise constant, or related to the fractional derivative order. The results demonstrate that this methodology offers a robust framework that not only markedly enhances the model's depiction of drug absorption rates and distributed delayed responses but also unlocks different drug-effect dynamics, providing new insights into absorption rates, anomalous diffusion, drug resistance, persistence, and pharmacokinetic tolerance, all within a system of just two (fractional) ODEs with explainable results. These findings have the potential to streamline drug development by improving the prediction of drug behavior in complex biological systems and shedding light on cancer cell death mechanisms, ultimately aiding in the design of more effective therapeutic strategies.

Population pharmacokinetics of unfractionated heparin and multivariable analysis of activated clotting time in patients undergoing radiofrequency ablation of atrial fibrillation

Atrial fibrillation (AF) increases cardiovascular morbidity and mortality. To reduce thrombosis and bleeding risks, and due to high variability of unfractionated heparin (UFH) effect, activated clotting time (ACT) is used during radiofrequency catheter ablation (RFCA) of AF to guide UFH dose. This study aimed to develop a population PK-PD model and perform multivariable analysis in order to identify the most significant covariates associated with interindividual variability of UFH. Electronic medical records from 668 patients undergoing RFCA were analyzed, including relevant covariates. The relationship between UFH dose and ACT and the impact of the main covariates were characterized using a mixed-effect PK-PD model. Multivariable analysis was then used to identify predictors of ACT 15 minutes after UFH administration (ACT15). A two-compartment PK model with linear elimination and a direct Emax PD model with a baseline and sigmoidicity best described the observed ACT values. Pretreatment with dabigatran, warfarin, or fluindione significantly influenced baseline ACT. Pretreatment with vitamin K antagonists or low molecular weight heparin explained Emax variability. The multivariable model identified baseline ACT, initial UFH dose, and previous anticoagulant as the main predictors of ACT15. Model evaluation through resampling and external validation showed accurate ACT15 predictions. This study presents the first population PK-PD model characterizing the relationship between UFH doses and ACT during RFCA, along with multivariable analysis. Additionally, predictive calculators for ACT15 and UFH dose based on patient and procedural characteristics were developed, enhancing personalized anticoagulation management during RFCA.

Model-informed drug development for antimicrobials: translational pharmacokinetic-pharmacodynamic modelling of apramycin to facilitate prediction of efficacious dose in complicated urinary tract infections.

The use of mouse models of complicated urinary tract infection (cUTI) has usually been limited to a single timepoint assessment of bacterial burden. Based on longitudinal in vitro and in vivo data, we developed a pharmacokinetic-pharmacodynamic (PKPD) model to assess the efficacy of apramycin, a broad-spectrum aminoglycoside antibiotic, in mouse models of cUTI. Two Escherichia coli strains were studied (EN591 and ATCC 700336). Apramycin exposure-effect relationships were established with in vitro time-kill data at pH 6 and pH 7.4 and in mice with cUTI. Immunocompetent mice were treated with apramycin (1.5-30 mg/kg) starting 24 h post-infection. Kidney and bladder tissue were collected 6-96 h post-infection for cfu determination. A PKPD model integrating all data was developed and simulations were performed to predict bacterial burden in humans. Treatment with apramycin reduced the bacterial load in kidneys and bladder tissue up to 4.3-log compared with vehicle control. In vitro and in vivo tissue time-course efficacy data were integrated into the PKPD model, showing 76%-98% reduction of bacterial net growth and 3- to 145-fold increase in apramycin potency in vivo compared with in vitro. Simulations suggested that an 11 mg/kg daily dose would be sufficient to achieve bacterial stasis in kidneys and bladder in humans. PKPD modelling with in vitro and in vivo PK and PD data enabled simultaneous evaluation of the different components that influence drug effect, an approach that had not yet been evaluated for antibiotics in the cUTI model and that has potential to enhance model-informed drug development of antibiotics.

Characterization of the temporal profile of the antinociceptive effects of an intravenous bolus of ketamine using the analgesia nociception index in no-anesthetized adult patients.

An effect-site target-controlled infusion (TCI) would allow a more precise titration of intravenous analgesics effect. The analgesia nociception index (ANI) continuously monitors the analgesia/nociception balance during general anesthesia. This study aims to derive a PKPD model of ketamine antinociceptive effect using the Domino PK parameter set and the ANI response data in awake patients without other drugs affecting the ANI response. Twenty awake adult patients were prospectively studied before general anesthesia. Patients received a single intravenous bolus of ketamine 0.1mg·kg- 1, and the subsequent ANI values were recorded. An effect compartment model incorporating the Domino PK parameter set was used to characterize the time lag between ketamine plasma concentrations and the ANI response. The model was parameterized with a single parameter Ke0. An Emax pharmacodynamic model was used to fit the ANI response data. Model parameters were estimated with NONMEM® 7.5. The minimum objective function value guided the model construction. After the ketamine administration, basal ANI values increased from 38.5 ± 4.95 to a maximum of 53.5 ± 4.95 with an observed time-to-peak effect of 1.83 ± 0.74min. Modeling analysis revealed hysteresis between predicted plasma concentrations from the Domino model and observed ANI data. Hysteresis was characterized, incorporating an estimated Keo of 0.238 (CI95% 0.20-0.28) min-1 to the described PK parameters set. The developed PKPD model, using Domino's PK parameters and the ANI response data, adequately characterized the temporal profile of ketamine's antinociceptive effect. The current estimated model parameters can be used to perform an effect-site TCI of ketamine for analgesic purposes.

First-in-human study to evaluate the safety, pharmacokinetics (PK), and pharmacodynamics (PD) of RBD4059, a GalNAc-siRNA drug targeting coagulation factor XI (FXI), in healthy subjects

Abstract Background Patients at risk of thrombosis/thromboembolism, require effective and safe anti-coagulation with high compliance. Inhibiting FXI has emerged as a promising strategy with potential for lower bleeding risk compared to currently approved anti-coagulants. Here we report on the safety, PK and PD effects of the GalNAc-conjugated siRNA RBD4059, specifically targeting and repressing FXI protein synthesis in the liver. Purpose Investigate safety, PK, and PD of first-in-human dosing of RBD4059 in healthy subjects. Methods A randomized, single-blind, placebo-controlled, single ascending dose (SAD) phase I study in healthy subjects (NCT05653037), in cohorts of eight randomized 3:1 to receive escalating doses of RBD4059 (50, 150, and 400 mg) or placebo administered subcutaneously. Results As of January 25, 2024, 24 subjects (with follow-up to week 24) with demographics and baseline clinical characteristics shown in Table 1 were enrolled. RBD4059 was well tolerated (Table 2), all related AEs were grade 1. There was no evidence of any clinically relevant bleeding events. Plasma exposure of RBD4059 tended to increase in proportion to the dose. Maximum concentration in plasma (Cmax) was reached in 2 to 4 h and then declined in line with one-compartment kinetics. The mean plasma half-life (t1/2) ranged from 3.2 to 7.0 h. RBD4059 treatment resulted in dose dependent and sustained reductions in FXI activity and FXI antigen (Figure 1A, B, D). Observed mean maximum percentage change from baseline in FXI activity from the 50 mg, 150 mg, and 400 mg SAD cohorts were 67.5%, 81.0%, and 85.8%, respectively, recovering with a maintained effect observed on D141 (50mg: 50.6% inhibition; 150mg: 68.2% inhibition). The reduction in FXI antigen showed a very similar pattern as the reduction in FXI activity, and reductions were accompanied by a concomitant increase in APTT. (Figure 1C, D). PKPD modelling supports that RBD4059 may be dosed every 3 months or less frequent with a &amp;gt;90% FXI reduction throughout the dosing interval, and will be tested in the upcoming phase 2 study. Conclusion(s) RBD4059 demonstrated favorable safety, predictable PK, and pronounced durable PD effect in the dose range of 50 mg - 400 mg. This first-in-human SAD study supports the further development of RBD4059 as an anticoagulant that can maintain a high level of PD effect with a once every 3 month or less frequent dosing regimen, with potential for enhanced compliance in chronic anti-coagulant therapy.Table 1 &amp; Table 2Figure 1.Panels A-D

Tumoral Pharmacokinetic-Pharmacodynamic Simulation of Doxorubicin-Encapsulated Polymeric Micelles Using a Tissue-Isolated Tumor Perfusion System.

Pharmacokinetic (PK) elucidation of polymeric micelles delivering anticancer drugs is crucial for accurate antitumor PK-pharmacodynamic (PK-PD) simulations. Particularly, establishing a methodology to quantify the tumor inflow and outflow of anticancer drugs encapsulated in polymeric micelles is an essential challenge. General tumor biodistribution experiments are disadvantageous in that inflow quantification is easy, but outflow quantification is challenging. We addressed this issue by proposing a quantification method that combines a tissue-isolated tumor perfusion system with microdialysis. This method aims to determine tumoral drug inflow and outflow by quantifying the drugs released from the polymeric micelles via a tumor-embedded microdialysis probe and perfusate, respectively. Furthermore, we evaluated the feasibility of this method by perfusing pH-sensitive polyethylene glycol-poly(aspartate-hydrazone-doxorubicin/phenylalanine)n (PPDF-Hyd-DOX) in a tissue-isolated tumor perfusion system, and we quantified tumor inflow and outflow released DOX. Based on the quantitative results, we performed compartmental analyses by incorporating the gamma-distributed delay function and calculated the PK rate constants. These parameters were input into a tumor-bearing rat compartment model for ex vivo-in vivo extrapolation (EVIVE) of the rat plasma PPDF-Hyd-DOX concentrations and simulated intratumorally released DOX concentrations. The simulation profiles demonstrated a good fit with the Walker 256 intratumoral released DOX concentration profiles previously reported. This EVIVE-PK model was coupled with the threshold natural-growth tumor PD model, and PK-PD analysis was performed. This model exhibited a better fit to the tumor weight profile of Walker 256-bearing rats treated with PPDF-Hyd-DOX than that of our previously reported PK-PD model. Thus, EVIVE, based on a tissue-isolated tumor perfusion system with microdialysis, is a promising approach for the PK-PD simulation of polymeric micelle anticancer therapy.

Prospectively predicting BPaMZ phase IIb/III trial outcomes using a translational mouse-to-human platform.

Despite known treatments, tuberculosis (TB) remains the world's top infectious killer, highlighting the pressing need for new drug regimens. To prioritize the most efficacious drugs for clinical testing, we previously developed a PK-PD translational platform with bacterial dynamics that reliably predicted short-term monotherapy outcomes in Phase IIa trials from preclinical mouse studies. In this study, we extended our platform to include PK-PD models that account for drug-drug interactions in combination regimens and bacterial regrowth in our bacterial dynamics model to predict cure at the end of treatment and relapse 6 months post-treatment. The Phase III STAND trial testing a new regimen comprised of pretomanid (Pa), moxifloxacin (M), and pyrazinamide (Z) (PaMZ) was suspended after a separate ongoing trial (NC-005) suggested that adding bedaquiline (B) to the PaMZ regimen would improve efficacy. To forecast if the addition of B would, indeed, benefit the PaMZ regimen, we applied an extended translational platform to both regimens. We predicted currently available short- and long-term clinical data well for drug combinations related to BPaMZ. We predicted the addition of B to PaMZ to shorten treatment duration by 2 months and to have similar bacteriological success to standard HRZE treatment (considering only treatment success but not withdrawal from side effects and other adverse events), both at the end of treatment for treatment efficacy and 6 months after treatment has ended in relapse prevention. Using BPaMZ as a case study, we have demonstrated our translational platform can predict Phase II and III outcomes prior to actual trials, allowing us to better prioritize the regimens most likely to succeed.

Evaluation of a Bayesian hierarchical pharmacokinetic-pharmacodynamic model for predicting parasitological outcomes in Phase 2 studies of new antimalarial drugs.

The rise of multidrug-resistant malaria requires accelerated development of novel antimalarial drugs. Pharmacokinetic-pharmacodynamic (PK-PD) models relate blood antimalarial drug concentrations with the parasite-time profile to inform dosing regimens. We performed a simulation study to assess the utility of a Bayesian hierarchical mechanistic PK-PD model for predicting parasite-time profiles for a Phase 2 study of a new antimalarial drug, cipargamin. We simulated cipargamin concentration- and malaria parasite-profiles based on a Phase 2 study of eight volunteers who received cipargamin 7 days after inoculation with malaria parasites. The cipargamin profiles were generated from a two-compartment PK model and parasite profiles from a previously published biologically informed PD model. One thousand PK-PD data sets of eight patients were simulated, following the sampling intervals of the Phase 2 study. The mechanistic PK-PD model was incorporated in a Bayesian hierarchical framework, and the parameters were estimated. Population PK model parameters describing absorption, distribution, and clearance were estimated with minimal bias (mean relative bias ranged from 1.7% to 8.4%). The PD model was fitted to the parasitaemia profiles in each simulated data set using the estimated PK parameters. Posterior predictive checks demonstrate that our PK-PD model adequately captures the simulated PD profiles. The bias of the estimated population average PD parameters was low-moderate in magnitude. This simulation study demonstrates the viability of our PK-PD model to predict parasitological outcomes in Phase 2 volunteer infection studies. This work will inform the dose-effect relationship of cipargamin, guiding decisions on dosing regimens to be evaluated in Phase 3 trials.

Using PKPD Modeling to Optimize Gentamicin Dosing in Saudi Neonates

The intricate interplay between pharmacokinetics and pharmacodynamics is pivotal in optimizing gentamicin therapy for neonates, a population characterized by unique physiological developmental considerations and pathophysiological changes in critically ill neonates. Rapid growth, Altered body composition, the immature renal function and premature immune system, significantly influence pharmacokinetic parameters Cl and Vd which aggravate complexities to achieve targets attainment and therapeutic drug monitoring compared to adults, necessitating individualized dosing strategies. The primary aim of this review, highlights the importance of PKPD modeling to optimizing gentamicin dosing in neonates Emerging trends in pediatric pharmacotherapy underscore the urgency for advancing pharmacokinetic-pharmacodynamic (PKPD) modeling to enhance therapeutic efficacy. Future research should focus on integrating real-world data from diverse neonatal populations. Genetic variations may influence the outcomes of PKPD studies, and subsequent dosing recommendations. Conducting PKPD studies in diverse populations is crucial for obtaining more robust and reliable results, which would allow for more representative models that account for physiological variations. Incorporating advanced computational techniques such as machine learning may further refine predictive accuracy, enabling personalized dosing strategies that minimize toxicity while maximizing therapeutic benefits As research evolves, the role of pediatric-specific biomarkers in PKPD modeling warrants closer investigation, potentially leading to breakthroughs that redefine dosing paradigms. By addressing these critical areas, the field can advance toward more effective and individualized pharmacotherapy, reducing adverse outcomes and improving clinical care for vulnerable neonates.

Population Pharmacokinetic-Pharmacodynamic Analysis of a Reserpine-Induced Myalgia Model in Rats.

(1) Background: Fibromyalgia syndrome (FMS) is a chronic pain condition with widespread pain and multiple comorbidities, for which conventional therapies offer limited benefits. The reserpine-induced myalgia (RIM) model is an efficient animal model of FMS in rodents. This study aimed to develop a pharmacokinetic-pharmacodynamic (PK-PD) model of reserpine in rats, linking to its impact on monoamines (MAs). (2) Methods: Reserpine was administered daily for three consecutive days at dose levels of 0.1, 0.5, and 1 mg/kg. A total of 120 rats were included, and 120 PK and 828 PD observations were collected from 48 to 96 h after the first dose of reserpine. Non-linear mixed-effect data analysis was applied for structural PK-PD model definition, variability characterization, and covariate analysis. (3) Results: A one-compartment model best described reserpine in rats (V = 1.3 mL/kg and CL = 4.5 × 10-1 mL/h/kg). A precursor-pool PK-PD model (kin = 6.1 × 10-3 mg/h, kp = 8.6 × 10-4 h-1 and kout = 2.7 × 10-2 h-1) with a parallel transit chain (k0 = 1.9 × 10-1 h-1) characterized the longitudinal levels of MA in the prefrontal cortex, spinal cord, and amygdala in rats. Reserpine stimulates the degradation of MA from the pool compartment (Slope1 = 1.1 × 10-1 h) and the elimination of MA (Slope2 = 1.25 h) through the transit chain. Regarding the reference dose (1 mg/kg) of the RIM model, the administration of 4 mg/kg would lead to a mean reduction of 65% (Cmax), 80% (Cmin), and 70% (AUC) of MA across the brain regions tested. (4) Conclusions: Regional brain variations in neurotransmitter depletion were identified, particularly in the amygdala, offering insights for therapeutic strategies and biomarker identification in FMS research.

Physiologically-based pharmacokinetic/pharmacodynamic modeling of meropenem in critically ill patients

This study aimed to develop a physiologically based pharmacokinetic/pharmacodynamic model (PBPK/PD) of meropenem for critically ill patients. A PBPK model of meropenem in healthy adults was established using PK-Sim software and subsequently extrapolated to critically ill patients based on anatomic and physiological parameters. The mean fold error (MFE) and geometric mean fold error (GMFE) methods were used to compare the differences between predicted and observed values of pharmacokinetic parameters Cmax, AUC0-∞, and CL to evaluate the accuracy of the PBPK model. The model was verified using meropenem plasma samples obtained from Intensive Care Unit (ICU) patients, which were determined by HPLC-MS/MS. After that, the PBPK model was combined with a PKPD model, which was developed based on f%T > MIC. Monte Carlo simulation was utilized to calculate the probability of target attainment (PTA) in patients. The developed PBPK model successfully predicted the meropenem disposition in critically ill patients, wherein the MFE average and GMFE of all predicted PK parameters were within the 1.25-fold error range. The therapeutic drug monitoring (TDM) of meropenem was conducted with 92 blood samples from 31 ICU patients, of which 71 (77.17%) blood samples were consistent with the simulated value. The TDM results showed that meropenem PBPK modeling is well simulated in critically ill patients. Monte Carlo simulations showed that extended infusion and frequent administration were necessary to achieve curative effect for critically ill patients, whereas excessive infusion time (> 4 h) was unnecessary. The PBPK/PD modeling incorporating literature and prospective study data can predict meropenem pharmacokinetics in critically ill patients correctly. Our study provides a reference for dose adjustment in critically ill patients.

56 Clinical Pharmacokinetic/Pharmacodynamic (PK/PD) Relationship Confirms Best-in-class Potential of Casdatifan (AB521), a Small Molecule Inhibitor of HIF-2α Being Developed in Renal Cancer

Abstract Background Casdatifan, an orally bioavailable small molecule inhibitor of HIF-2α, potently inhibits transcription of HIF-2α-dependent genes in cell lines and preclinical species. The objective of this analysis was to develop an understanding of the relationship between clinical dose, casdatifan PK, erythropoietin (EPO), a PD biomarker for peripheral (non-tumor) HIF-2α inhibition, and hemoglobin and to use this information to guide dose selection in future clinical trials. Methods Casdatifan plasma concentrations, serum EPO concentration, and hemoglobin data were obtained from 79 healthy participants in two Phase 1 studies, ARC-14 (NCT05117554) and ARC-28 (NCT05999513), and from 71 patients with clear cell renal cell carcinoma (ccRCC) and other solid tumors in an ongoing Phase 1 study, ARC-20 (NCT05536141). The available PK and PD data were collected following single oral doses of casdatifan ranging from 3 mg to 100 mg in healthy participants, and multiple oral doses of casdatifan ranging from 15 mg to 150 mg once daily (QD) in healthy participants and cancer patients. Serial PK, EPO, and Hb data were gathered in all study participants pre-dose till end of treatment. The population PKPD model was developed using mixed effects methodology with NONMEM software to relate dose, PK, and PD (EPO and Hb) data. Results Casdatifan showed dose-proportional increases in plasma exposure over the 3-150 mg dose range after single and multiple doses. Casdatifan PK was also invariant over time in patients with an approximately 2.0-fold accumulation at steady-state compared to first dose. The mean terminal half-life of casdatifan was approximately 24 h. The PK was similar in healthy participants and cancer patients. Dose-dependent reduction in EPO was observed after single and multiple doses in healthy participants and patients. A two-compartment model with first-order absorption adequately described the casdatifan plasma PK across the dose range tested. The effect of casdatifan plasma concentrations on EPO production rate was modeled using an inhibitory function. Analysis of the casdatifan dose-PD (EPO suppression) relationship indicated that casdatifan 20 mg once daily provided a similar level of EPO suppression in patients as belzutifan 120 mg daily (benchmark peripheral PD). Furthermore, due to the dose-proportional PK of casdatifan, the selected dose (100 mg daily) for further development results in plasma levels approximately 5 times higher than those associated with the benchmark peripheral PD. Conclusions Casdatifan exhibits dose-linear and time-invariant PK in the dose range 15-150 mg. Dose-dependent reduction in EPO levels, consistent with the mechanism of action of HIF-2α inhibition, was seen after casdatifan administration. A PKPD analysis of available data indicated that 20 mg daily dose of casdatifan would result in similar EPO effect as the registered dose of belzutifan. Available PKPD data and analyses indicated best-in-class properties of casdatifan.

Model-based interspecies interpretation of botulinum neurotoxin type A on muscle-contraction inhibition.

Botulinum neurotoxins (BoNTs) are commonly used in therapeutic and cosmetic applications. One such neurotoxin, BoNT type A (BoNT/A), has been studied widely for its effects on muscle function and contraction. Despite the importance of BoNT/A products, determining the blood concentrations of these toxins can be challenging. To address this, researchers have focused on pharmacodynamic (PD) markers, including compound muscle action potential (CMAP) and digit abduction scoring (DAS). In this study, we aimed to develop a probabilistic kinetic-pharmacodynamic (K-PD) model to interpret CMAP and DAS data obtained from mice and rats during the development of BoNT/A products. The researchers also wanted to gain a better understanding of how the estimated parameters from the model relate to the bridging of animal models to human responses. We used female Institute of Cancer Research mice and Sprague-Dawley (SD) rats to measure CMAP and DAS levels over 32weeks after administering BoNT/A. We developed a muscle-contraction inhibition model using a virtual pharmacokinetic (PK) compartment combined with an indirect response model and performed model diagnostics using goodness-of-fit analysis, visual predictive checks (VPC), and bootstrap analysis. The CMAP and DAS profiles were dose-dependent, with recovery times varying depending on the administered dose. The final K-PD model effectively characterized the data and provided insights into species-specific differences in the PK and PD parameters. Overall, this study demonstrated the utility of PK-PD modeling in understanding the effects of BoNT/A and provides a foundation for future research on other BoNT/A products.

Integration of individual preclinical and clinical anti-infective PKPD data to predict clinical study outcomes.

The AIDA randomized clinical trial found no significant difference in clinical failure or survival between colistin monotherapy and colistin-meropenem combination therapy in carbapenem-resistant Gram-negative infections. The aim of this reverse translational study was to integrate all individual preclinical and clinical pharmacokinetic-pharmacodynamic (PKPD) data from the AIDA trial in a pharmacometric framework to explore whether individualized predictions of bacterial burden were associated with the trial outcomes. The compiled dataset included for each of the 207 patients was (i) information on the infecting Acinetobacter baumannii isolate (minimum inhibitory concentration, checkerboard assay data, and fitness in a murine model), (ii) colistin plasma concentrations and colistin and meropenem dosing history, and (iii) disease scores and demographics. The individual information was integrated into PKPD models, and the predicted change in bacterial count at 24 h for each patient, as well as patient characteristics, was correlated with clinical outcomes using logistic regression. The invivo fitness was the most important factor for change in bacterial count. A model-predicted growth at 24 h of ≥2-log10 (164/207) correlated positively with clinical failure (adjusted odds ratio, aOR = 2.01). The aOR for one unit increase of other significant predictors were 1.24 for SOFA score, 1.19 for Charlson comorbidity index, and 1.01 for age. This study exemplifies how preclinical and clinical anti-infective PKPD data can be integrated through pharmacodynamic modeling and identify patient- and pathogen-specific factors related to clinical outcomes - an approach that may improve understanding of study outcomes.