Pharmacokinetic-pharmacodynamic Model Research Articles

Relative Forgiveness of Different Allopurinol Implementation Patterns in People with Gout and their Impact on Clinical Outcomes: a Simulation Study.

Adherence to urate-lowering therapy among people with gout is poor, so it is important to understand which day-to-day medication-taking ('implementation') patterns are most likely to lead to suboptimal serum urate concentrations and worsen clinical outcomes. This study aimed to (1) determine the relative forgiveness (RF) of allopurinol with hypothetical and real-life implementation patterns in people with gout, (2) explore the use of RF as a means of identifying suboptimal implementation patterns, (3) assess the impact of suboptimal implementation patterns on clinical outcomes. A simulation study was conducted using a pharmacokinetic-pharmacodynamic model for allopurinol and serum urate to determine the RF of allopurinol implementation patterns. With 100% ('perfect') implementation, the probability of adequate urate control (> 90% of days with urate < 0.36 mmol/L over 360 days) for a 300 mg dose of allopurinol was 0.549. Simulations based on real-life individual implementation patterns over a year yielded a median RF of 0.51, indicating that half of the patterns studied were at least 50% less likely to achieve adequate urate control than perfect implementation. Our study provides evidence that missing one or two doses of allopurinol, even repeatedly over a year, does not significantly impact serum urate target achievement or clinical outcomes. However, people who take repeated drug holidays of more than 3 days in a row (followed by less than 15 consecutive days of dosing) are less than 0.3 times as likely (at least 70% less likely) to achieve adequate urate control than those with perfect implementation and may see an increase in the frequency of gout flares.

Translational population target binding model for the anti-FcRn fragment antibody efgartigimod

Efgartigimod is a human IgG1 antibody Fc-fragment that lowers IgG levels through blockade of the neonatal Fc receptor (FcRn) and is being evaluated for the treatment of patients with severe autoimmune diseases mediated by pathogenic IgG autoantibodies. Engineered for increased FcRn affinity at both acidic and physiological pH, efgartigimod can outcompete endogenous IgG binding, preventing FcRn-mediated recycling of IgGs and resulting in increased lysosomal degradation. A population pharmacokinetic-pharmacodynamic (PKPD) model including FcRn binding was developed based on data from two healthy volunteer studies after single and repeated administration of efgartigimod. This model was able to simultaneously describe the serum efgartigimod and total IgG profiles across dose groups, using drug-induced FcRn receptor occupancy as driver of total IgG suppression. The model was expanded to describe the PKPD of efgartigimod in cynomolgus monkeys, rabbits, rats and mice. Most species differences were explainable by including the species-specific in vitro affinity for FcRn binding at pH 7.4 and by allometric scaling of the physiological parameters. In vitro-in vivo scaling proved crucial for translation success: the drug effect was over/underpredicted in rabbits/mice when ignoring the lower/higher binding affinity of efgartigimod for these species versus human, respectively. Given the successful model prediction of the PK and total IgG dynamics across species, it was concluded that the PKPD of efgartigimod can be characterized by target binding. From the model, it is suggested that the initial fast decrease of measurable unbound efgartigimod following dosing is the result of combined clearance of free drug and high affinity target binding, while the relatively slow terminal PK phase reflects release of bound drug from the receptor. High affinity target binding protects the drug from elimination and results in a sustained PD effect characterized by an increase in the IgG degradation rate constant with increasing target receptor occupancy.

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.

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.

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.

Pharmacokinetic-Pharmacodynamic Modeling of the Immune-Enhancing Effect of Shikimic Acid in Growing Pigs.

Shikimic acid (SA), extracted from the fruit of shikimi-no-ki, is used both as a preservative in the food industry and as an intermediate for a variety of active ingredients with a wide range of pharmacological functions. A deeper understanding of the pharmacokinetic process of SA in pigs and its impact on humoral immunity could prove invaluable in facilitating its clinical application in veterinary and human medicine. The pharmacokinetic study employed a two-period, two-sequence, crossover design to animal experiments and developed a novel method of pig plasma preparation using water as an extractant and ionization promoter, followed by purification and enrichment on a MAX solid phase extraction (SPE) column. The results showed that SA is rapidly absorbed after intragastric administration (50 mg/kg BW), reaching a plasma Cmax of 10,823.44 ng/mL at 1.78 h, followed by rapid elimination, with a t1/2 of 1.81 h, consistent with a one-compartment model. The results for intravenous administration (2 mg/kg BW) were consistent with a two-compartment open model with a t1/2 of 3.66 h, with concentrations below the limit of quantification (LOQ) observed beyond 12 h postdose. The absolute bioavailability of SA in pigs was calculated to be 21.68%. Furthermore, the Pearson's correlation analysis demonstrated a strong positive correlation between SA concentration in pig plasma and the changes of C3, C4 and IgG, IgA, and IgM (0.6 < R < 1, P < 0.0001). A more detailed pharmacokinetic-pharmacodynamic (PK-PD) modeling analysis of the intravenous group revealed the EC50/Cmax values of approximately 10%, with all γ values exceeding 3. This study was the inaugural investigation into the pharmacokinetics of SA in growing pigs, and it also revealed that SA has the potential to act as an immunopotentiator.

Ocular Pharmacodynamics of Intravitreal Faricimab in Patients With Neovascular Age-Related Macular Degeneration or Diabetic Macular Edema.

Evaluate the ocular pharmacodynamics (PD) of intravitreal faricimab, a bispecific inhibitor of angiopoietin-2 (Ang-2) and vascular endothelial growth factor-A (VEGF-A), in patients with neovascular age-related macular degeneration (nAMD) or diabetic macular edema (DME). Aqueous humor (AH) samples (1025 free Ang-2 concentrations and 1345 free VEGF-A concentrations) were collected from approximately 300 faricimab-treated patients with nAMD or DME in phase2/3 trials. A population pharmacokinetic pharmacodynamic (popPKPD) model was developed to describe the dynamic effect of faricimab on free AH Ang-2 and VEGF-A. Mean baseline Ang-2 concentrations were 8.1 and 13.4pg/mL in patients with nAMD and DME, respectively. The corresponding mean baseline VEGF-A concentrations were 58 and 135pg/mL, respectively. Overall, approximately 79% of Ang-2 (84% within 8 weeks postdose and 55% beyond 12 weeks postdose) and 7% of VEGF-A postdose observations were below the lower limit of quantification. Model-derived Ang-2 and VEGF-A concentration-time profiles for patients on every 4-week/every 8-week dosing were predicted to maintain greater than 50% suppression of Ang-2 concentrations for the entire dosing period. Patients on every 12-week/16-week dosing were predicted to have greater than 50% Ang-2 suppression for 12 or more weeks, whereas 50% VEGF-A suppression was maintained for 9 to 10 weeks. At 8 weeks postdose, the median Ang-2 concentrations remained suppressed by approximately 80%. At 16 weeks postdose, the median VEGF-A concentrations returned to baseline, but median Ang-2 levels remained below baseline. A popPKPD analysis demonstrated faricimab's rapid and sustained suppression of AH Ang-2 and VEGF-A. A popPKPD analysis suggested that sustained suppression of ocular Ang-2 contributes to faricimab's extended durability, observed in clinical trials.

Interspecies interactions alter the antibiotic sensitivity of Pseudomonas aeruginosa.

Polymicrobial infections are infections that are caused by multiple pathogens and are common in patients with cystic fibrosis (CF). Although polymicrobial infections are associated with poor treatment responses in CF, the effects of the ecological interactions between co-infecting pathogens on antibiotic sensitivity and treatment outcome are poorly characterized. To this end, we systematically quantified the impact of these effects on the antibiotic sensitivity of Pseudomonas aeruginosa for nine antibiotics in medium conditioned by 13 secondary cystic fibrosis-associated bacterial and fungal pathogens through time-kill assays. We fitted pharmacodynamic models to these kill curves for each antibiotic-species combination and found that interspecies interactions changing the antibiotic sensitivity of P. aeruginosa are abundant. Interactions that lower antibiotic sensitivity are more common than those that increase it, with generally more substantial reductions than increases in sensitivity. For a selection of co-infecting species, we performed pharmacokinetic-pharmacodynamic modeling of P. aeruginosa treatment. We predicted that interspecies interactions can either improve or reduce treatment response to the extent that treatment is rendered ineffective from a previously effective antibiotic dosing schedule and vice versa. In summary, we show that quantifying the ecological interaction effects as pharmacodynamic parameters is necessary to determine the abundance and the extent to which these interactions affect antibiotic sensitivity in polymicrobial infections.IMPORTANCEIn cystic fibrosis (CF) patients, chronic respiratory tract infections are often polymicrobial, involving multiple pathogens simultaneously. Polymicrobial infections are difficult to treat as they often respond unexpectedly to antibiotic treatment, which might possibly be explained because co-infecting pathogens can influence each other's antibiotic sensitivity, but it is unknown to what extent such effects occur. To investigate this, we systematically quantified the impact of co-infecting species on antibiotic sensitivity, focusing on P. aeruginosa, a common CF pathogen. We studied for a large set co-infecting species and antibiotics whether changes in antibiotic response occur. Based on these experiments, we used mathematical modeling to simulate P. aeruginosa's response to colistin and tobramycin treatment in the presence of multiple pathogens. This study offers comprehensive data on altered antibiotic sensitivity of P. aeruginosa in polymicrobial infections, serves as a foundation for optimizing treatment of such infections, and consolidates the importance of considering co-infecting pathogens.

Optimizing Lumefantrine Dosing for Young Children in High-Malaria-Burden Countries Using Pharmacokinetic-Pharmacodynamic Simulations.

Artemether-lumefantrine is the most widely used treatment for uncomplicated malaria and it is dosed based on weight bands according to World Health Organization (WHO) guidelines. However, children are vulnerable to underdosing. Inadequate dosing can lead to treatment failure and drug resistance. Nutritional parameters for 372 363 children <5 years old in 25 high-malaria-burden countries were acquired from the Demographic and Health Surveys program. Prevalence of attaining day 7 lumefantrine concentrations ≥200 ng/mL and remaining reinfection free for 42 days were evaluated using a simulation-based approach with a population pharmacokinetic-pharmacodynamic model. Besides the WHO-recommended lumefantrine dosing regimen (twice daily for 3 days), we explored 3 adjusted regimens: extended (2 extra days of dosing), increased (1 extra 120-mg tablet per dose), and intensified (thrice daily for 3 days). We also explored an alternative method dosing malnourished children based on expected weight for age. We estimated that 75% of children reached the 200 ng/mL lumefantrine threshold and 77% were malaria free for 42 days when using WHO treatment guidelines. By switching to the alternative dosing method, 5% more children achieved target lumefantrine levels; 22% more achieved the target using the alternative dosing and the extended regimen. With combined alternative plus extended dosing, 97% of children reached 200 ng/mL lumefantrine and 88% were malaria free for 42 days. This study highlights the inadequacies of weight-based lumefantrine dosing for young and underweight children and supports the need of clinical trials using extended dosing based on expected weight in malnourished children.

Comparison of time-series models for predicting physiological metrics under sedation.

This study presents a comprehensive comparison of multiple time-series models applied to physiological metric predictions. It aims to explore the effectiveness of both statistical prediction models and pharmacokinetic-pharmacodynamic prediction model and modern deep learning approaches. Specifically, the study focuses on predicting the bispectral index (BIS), a vital metric in anesthesia used to assess the depth of sedation during surgery, using datasets collected from real-life surgeries. The goal is to evaluate and compare model performance considering both univariate and multivariate schemes. Accurate BIS prediction is essential for avoiding under- or over-sedation, which can lead to adverse outcomes. The study investigates a range of models: The traditional mathematical models include the pharmacokinetic-pharmacodynamic model and statistical models such as autoregressive integrated moving average (ARIMA) and vector autoregression (VAR). The deep learning models encompass recurrent neural networks (RNNs), specifically Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU), as well as Temporal Convolutional Networks (TCNs) and Transformer models. The analysis focuses on evaluating model performance in predicting the BIS using two distinct datasets of physiological metrics collected from actual surgical procedures. It explores both univariate and multivariate prediction schemes and investigates how different combinations of features and input sequence lengths impact model accuracy. The experimental findings reveal significant performance differences among the models: In univariate prediction scenarios for predicting BIS, the LSTM model demonstrates a 2.88% improvement over the second-best performing model. For multivariate predictions, the LSTM model outperforms others by 6.67% compared to the next best model. Furthermore, the addition of Electromyography (EMG) and Mean Arterial Pressure (MAP) brings significant accuracy improvement when predicting BIS. The study emphasizes the importance of selecting and building appropriate time-series models to achieve accurate predictions in biomedical applications. This research provides insights to guide future efforts in improving vital sign prediction methodologies for clinical and research purposes. Clinically, with improvements in the prediction of physiological parameters, clinicians can be informed of interventions if an anomaly is detected or predicted.

Mechanism-Based Pharmacokinetic/Pharmacodynamic Model of Voriconazole for Predicting the Clinical Outcomes of Adult Patients with Invasive Aspergillosis.

Voriconazole is the first-line therapy for invasive aspergillosis (IA). To determine the minimum inhibitory concentration of Aspergillus, a voriconazole pharmacokinetic-pharmacodynamic (PK-PD) model linked to galactomannan response was developed and evaluated, and its clinical correlation for IA treatment was elucidated. Adult patients with probable or definite IA and at least one serum voriconazole measurement were included. A two-compartment voriconazole PK model was linked to a previously described PD model of galactomannan response. PK and PD parameters were estimated using a nonparametric adaptive grid technique. The relationship between the ratio of voriconazole exposure that induced half-maximum galactomannan response (EC50) and the observed terminal galactomannan concentration was evaluated. The factors associated with the PK-PD parameters and mortality were also determined. Between January 2013 and December 2022, 41 patients were prescribed voriconazole for IA. The 30-day mortality rate was 17%. A high correlation was found for the observed-predicted Bayesian posterior estimates of voriconazole and galactomannan levels. Moreover, a nonlinear relationship was identified between AUC:EC50 and terminal galactomannan. The factors associated with higher AUC:EC50 were intravenous administration and intubation. In the survival analysis, higher EC50 tended to be associated with mortality, higher AUC was significantly associated with increased mortality, and higher AUC:EC50 tended to be associated with higher mortality. After adjusting for the intravenous route, higher AUC and AUC:EC50 were not associated with mortality. Individual EC50 estimation can provide insights into in vivo host and organism responses. Elevated EC50 showed comparable and unfavorable trends to higher minimum inhibitory concentration. Thus, determining EC50 might help guide individualized target serum voriconazole levels.

Pharmacodynamic Exposure-Response Analysis of Fracture Count Data Following Treatment with Burosumab in Patients with XLH.

X-linked hypophosphatemia (XLH) is a rare genetic disorder caused by excessive fibroblast growth factor 23 (FGF23), leading to low serum phosphate levels resulting in increased risk of fractures and pseudofractures. Burosumab is indicated for the treatment of XLH. In this work, we aimed to understand the quantitative relationship between burosumab-treatment-induced improvements in serum phosphate and reduction in fracture and pseudofracture counts in adults with XLH. Burosumab pharmacokinetic pharmacodynamic data from nine clinical studies were first utilized to update a prior population pharmacokinetic pharmacodynamic (PPKPD) model. The updated PPKPD model predictions for serum phosphate exposures along with other factors (i.e., time and treatment) were utilized to evaluate the relationship on fracture counts using Poisson model. The updated PPKPD model suggested that burosumab concentrations required for 50% of maximal effect decreased with increasing baseline serum phosphate levels. A Poisson model with time from baseline, average serum phosphate, and burosumab treatment described the time-varying fracture and pseudofracture count data appropriately. The model suggested a baseline rate of fracture and pseudofracture of 1.87 counts. The model predicted that fracture counts decrease by 1% each week, and by 23% with each unit increase (1.0 mg/dL) in average serum phosphate from lower limit of normal (2.5 mg/dL). An additional 1% decrease in fracture count each week was attributed to burosumab treatment that could not be explained by improvements in serum phosphate. Overall, the model quantified the relationship between burosumab-treatment-induced serum phosphate improvements and reduction in fracture and pseudofracture counts in patients with XLH over time.

Population pharmacokinetic and dose-response models of nepadutant, a selective antagonist of the NK2 receptors, in infants with colic.

The aim of the current analyses was to develop a population pharmacokinetic model for nepadutant in infants with colic, and a pharmacokinetic-pharmacodynamic model based on observations of duration of crying and fussing following oral nepadutant administration in infants (3-25 weeks) with colic. The models were developed based on data obtained at baseline and following treatment with placebo, nepadutant 0.1mg/kg or nepadutant 0.5mg/kg administered for 7days. A continuous response variable, duration of crying and fussing in minutes within 2h interval, was assembled based on records from "baby's day" diary. The pharmacokinetics of nepadutant was described by a one-compartment model with first-order absorption and elimination with body weight as a structural covariate incorporated allometrically. For an infant weighing 5.3kg, the estimated apparent clearance was 68.6L/h (12% relative standard error) and exhibited large variability (78% coefficient of variation). The pharmacokinetic-pharmacodynamic model described (i) a circadian rhythm in the response with lowest and highest observations at 4a.m. and 9p.m., respectively, (ii) a placebo effect increasing and flattening out with time in an exponential manner, and (iii) a statistically significant (P < .01) linearly increasing response with dose. The observed and model predicted relative change in response from baseline was -35% and -28% (95% prediction interval -36%; -19%) following placebo, and -44% and -36% (-46%; -27%) after 0.5mg/kg. Population pharmacokinetic and dose-response models were successfully developed characterizing the available nepadutant pharmacokinetics and duration of crying and fussing data in infants.

Population pharmacokinetic-pharmacodynamic model of elinzanetant based on integrated clinical phase I and II data.

Elinzanetant is a potent and selective dual neurokin-1 (NK-1) and -3 (NK-3) receptor antagonist that is currently developed for the treatment of women with moderate-to-severe vasomotor symptoms (VMS) associated with menopause. Here, we report the development of a population pharmacokinetic (popPK) model for elinzanetant and its principal metabolites based on an integrated dataset from 366 subjects (including 197 women with VMS) collected in 10 phase I or II studies. The pharmacokinetics of elinzanetant and its metabolites could be well described by the popPK model. Within the investigated dose range of 40-160 mg, the oral bioavailability of elinzanetant was dose independent and estimated to be 36.7%. The clearance of elinzanetant was estimated to be 7.26 L/h and the central and peripheral distribution volume were 23.7 and 168 L. No intrinsic or extrinsic influencing factors have been identified in the investigated population other than the effect of a high-fat breakfast on the oral absorption of elinzanetant. The popPK model was then coupled to a pharmacodynamic model to predict occupancies of the NK-1 and NK-3 receptors. After repeated once-daily administration of the anticipated therapeutic dose of 120 mg elinzanetant, the model-predicted median receptor occupancies are >99% for NK-1 and >94.8% for NK-3 during day and night-time, indicating sustained and near-complete inhibition of both target receptors during the dosing interval.

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.

Appropriate use of triazolam in elderly patients considering a quantitative benefit-risk assessment based on the pharmacokinetic-pharmacodynamic modeling and simulation approach supported by real-world data

BackgroundTriazolam is a typical drug commonly used in the elderly; however, there have been concerns about its adverse events resulting from age-related changes in physiological function and drug interactions with concomitant drugs. Thus, updated information contributing to the appropriate use based on the latest pharmacokinetic and post-marketing surveillance methods is needed. In this study, we evaluated the appropriate use of triazolam in the elderly by integrating real-world data with a modeling and simulation approach.MethodsThe occurrence risk of adverse events in the elderly was evaluated using the spontaneous adverse event reporting regulatory databases from Japan and the United States. Information on drug concentrations and reactions was extracted from previous publications to estimate the threshold for plasma triazolam concentrations that cause adverse events. The pharmacokinetic/pharmacodynamic (PK/PD) model was then constructed, and the dose and administration were evaluated in various situations anticipated in medical practice.ResultsAmong all prescriptions, 25.4% were prescribed to individuals aged 80 years or above, and 51.8% were for those aged 70 years or above. A majority of cases involved CYP3A-metabolized drug combinations, accounting for 85.6%. Elderly individuals were at a higher risk of developing delirium and fall-fracture. Based on the constructed PK/PD model, the risk of adverse events increased when the plasma concentration of triazolam exceeded the calculated threshold of 0.44 ng/mL at approximately 6 h after administration. Administering 0.125 mg of triazolam, is half the approved dose for the elderly in Japan was deemed appropriate. Moreover, there was a substantial risk of adverse events even at a dosage of 0.0625 mg in combination with a moderate or strong inhibitor of cytochrome P450 3 A.ConclusionAnalyzing large-scale databases and existing research publications on PK/PD can practically contribute to optimizing triazolam drug therapy for the elderly in the daily clinical setting.

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.

Item Response Theory Quantifies the Relationship Between Improvements in Serum Phosphate and Patient-Reported Outcomes in Adults With X-Linked Hypophosphatemia.

Burosumab is indicated for treatment of a rare bone disease, X-linked hypophosphatemia (XLH). The aim of this analysis was to evaluate the relationship between a treatment response biomarker and patient-reported outcomes (PROs). Longitudinal data for PROs were obtained from adults with XLH from a phase III study. Individual rich time profiles of the biomarker, serum phosphate were simulated using a prior population pharmacokinetic-pharmacodynamic model to calculate serum phosphate exposure metrics for each 28-day treatment cycle, which were then merged with PROs data. Item response theory parameters were first estimated to map a latent variable, ψ, that is, disability score, relative to baseline. Next, the relationships between serum phosphate exposures and ψ were modeled using a nonlinear mixed-effect (NLME) modeling approach. A combined item response theory-NLME model with average serum phosphate as a predictor of ψ described PROs data well. The model estimates suggested 28%, 31%, and 25% reduction in Western Ontario and McMaster Universities Osteoarthritis Index, brief pain inventory, and brief fatigue inventory scores, respectively, with every unit increase in average serum phosphate from the lower limit of normal (2.5 mg/dL). Additionally, a time effect of ~ 0.08% improvements each week was estimated. The analysis suggested that burosumab treatment-induced improvements in serum phosphate levels are associated with improvements in PROs in adults with X-linked hypophosphatemia. The analyses confirmed the importance of prolonged serum phosphate level correction in adult patients with XLH. These results can be useful to guide the design of further studies and to design treatment optimization strategies.

Progress in physiologically based pharmacokinetic-pharmacodynamic models of amino acids in humans.

Amino acids are critical to health, serving both as constituents of proteins and in signaling and metabolism. Amino acids are consumed as nutrients, supplements, and nutraceuticals. Much remains to be learned about amino acid function. Physiologically based pharmacokinetic and pharmacodynamic (PBPK-PD) modeling is an emerging tool for studying their complex biology. This review highlights recent PBPK-PD models developed to study amino acid physiology and metabolism and discusses their potential for addressing unresolved questions in the field. PBPK-PD models provided several insights. They revealed the interplay between the mechanisms by which leucine governs skeletal muscle protein metabolism in healthy adults. The models also identified optimal dosing regimens of amino acid supplementation to treat sickle-cell disease and recurrent hypoglycemia, and to minimize drug side effects in seizure disorders. Additionally, they characterized the effects of novel anticancer drugs that seek to deprive cancer cells of amino acids. Future models may inform treatment strategies for sarcopenia, characterize distinctions between animal- and plant-based nutrition, and inform nutrient-drug interactions in Parkinson's disease. PBPK-PD models are powerful tools for studying amino acid physiology and metabolism, with applications to nutrition, pharmacology, and their interplay.