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Aims Methods Results Conclusions 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 0.1 mg/kg or nepadutant 0.5 mg/kg administered for 7 days. A continuous response variable, duration of crying and fussing in minutes within 2 h 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 5.3 kg, the estimated apparent clearance was 68.6 L/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 4 a.m. and 9 p.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 0.5 mg/kg.Population pharmacokinetic and dose–response models were successfully developed characterizing the available nepadutant pharmacokinetics and duration of crying and fussing data in infants. [ABSTRACT FROM AUTHOR]

The antiparasitic drug ivermectin is approved for persons > 15 kg in the US and EU. A pharmacometric (PMX) population model with clinical PK data was developed (i) to characterize the effect of the patient-friendly ivermectin formulation CHILD-IVITAB on the absorption process and (ii) to evaluate dosing for studies in children < 15 kg. Simulations were performed to identify dosing with CHILD-IVITAB associated with similar exposure coverage in children ≥ 15 kg and < 15 kg as observed in adults receiving the reference formulation STROMECTOL®. A total of 448 ivermectin concentrations were available from 16 healthy adults. The absorption rate constant was 2.41 h−1 (CV 19%) for CHILD-IVITAB vs. 1.56 h−1 (CV 43%) for STROMECTOL®. Simulations indicated that 250 µg/kg of CHILD-IVITAB is associated with exposure coverage in children < 15 kg consistent with that observed in children ≥ 15 kg and adults receiving 200 µg/kg of STROMECTOL®. Performed analysis confirmed that CHILD-IVITAB is associated with faster and more controlled absorption than STROMECTOL®. Simulations indicate that 250 µg/kg of CHILD-IVITAB achieves equivalent ivermectin exposure coverage in children < 15 kg as seen in children ≥ 15 kg and adults. [ABSTRACT FROM AUTHOR]

Study objectives: TLD-1 is a novel pegylated liposomal doxorubicin (PLD) formulation aiming to optimise the PLD efficacy-toxicity ratio. We aimed to characterise TLD-1's population pharmacokinetics using non-compartmental analysis and nonlinear mixed-effects modelling. Methods: The PK of TLD-1 was analysed by performing a non-compartmental analysis of longitudinal doxorubicin plasma concentration measurements obtained from a clinical trial in 30 patients with advanced solid tumours across a 4.5-fold dose range. Furthermore, a joint parent-metabolite PK model of doxorubicinentrapped, doxorubicinfree, and metabolite doxorubicinol was developed. Interindividual and interoccasion variability around the typical PK parameters and potential covariates to explain parts of this variability were explored. Results: Medians ± standard deviations of dose-normalised doxorubicinentrapped+free Cmax and AUC0−∞ were 0.342 ± 0.134 mg/L and 40.1 ± 18.9 mg·h/L, respectively. The median half-life (95 h) was 23.5 h longer than the half-life of currently marketed PLD. The novel joint parent-metabolite model comprised a one-compartment model with linear release (doxorubicinentrapped), a two-compartment model with linear elimination (doxorubicinfree), and a one-compartment model with linear elimination for doxorubicinol. Body surface area on the volumes of distribution for free doxorubicin was the only significant covariate. Conclusion: The population PK of TLD-1, including its release and main metabolite, were successfully characterised using non-compartmental and compartmental analyses. Based on its long half-life, TLD-1 presents a promising candidate for further clinical development. The PK characteristics form the basis to investigate TLD-1 exposure-response (i.e., clinical efficacy) and exposure-toxicity relationships in the future. Once such relationships have been established, the developed population PK model can be further used in model-informed precision dosing strategies. Clinical trial registration: ClinicalTrials.gov–NCT03387917–January 2, 2018 [ABSTRACT FROM AUTHOR]

Obicetrapib is a selective inhibitor of cholesteryl ester transfer protein that is currently in phase 3 of development for the treatment of dyslipidemia as adjunct therapy. The purpose of this study was to comprehensively characterize the pharmacokinetic (PK) and pharmacodynamic (PD) disposition of obicetrapib. Data from 7 clinical trials conducted in healthy adults and those with varying degrees of dyslipidemia were included for model development. The structural model that best described obicetrapib PK was a 3‐compartment model with 4‐compartment transit absorption and first‐order elimination. Body weight was the only covariate found to significantly explain observed variability and was therefore included using allometric scaling on all disposition parameters. For a typical patient weighing 75 kg, the estimated apparent total body clearance and apparent volume of distribution of the central compartment was 0.81 L/h and 36.1 L, respectively. The final PK model parameters were estimated with good precision and were ultimately leveraged to sequentially inform 2 turnover models that describe obicetrapib's effect on low‐density lipoprotein cholesterol (LDL‐C) and high‐density lipoprotein cholesterol (HDL‐C) concentrations. The maximum stimulatory effect of obicetrapib on LDL‐C loss was estimated to be 1.046, while the maximum inhibitory effect of obicetrapib on HDL‐C loss was 0.691. This corresponds to a predicted typical maximum percent change from baseline LDL‐C and HDL‐C of 51.1% and 224%, respectively. The final sequential model described obicetrapib PKPD well and was ultimately able to both demonstrate evidence of internal consistency and support decision‐making throughout the development lifecycle. [ABSTRACT FROM AUTHOR]

Interspecies scaling of the pharmacokinetics (PK) of CB 4332, a 150 kDa recombinant complement factor I protein, was performed using traditional and model-based approaches to inform first-in-human dose selection. Plasma concentration versus time data from four preclinical PK studies of single intravenous and subcutaneous (SC) CB 4332 dosing in mice, rats and nonhuman primates (NHPs) were modeled simultaneously using naive pooling including allometric scaling. The human-equivalent dose was calculated using the preclinical no observed adverse effect level (NOAEL) as part of the dose-by-factor approach. Pharmacokinetic modeling of CB 4332 revealed species-specific differences in the elimination, which was accounted for by including an additional rat-specific clearance. Signs of anti-drug antibodies (ADA) formation in all rats and some NHPs were observed. Consequently, an additional ADA-induced clearance parameter was estimated including the time of onset. The traditional dose-by-factor approach calculated a maximum recommended starting SC dose of 0.9 mg/kg once weekly, which was predicted it to result in a trough steady-state concentration lower than the determined efficacy target range for CB 4332 in humans. Model simulations predicted the efficacy target range to be reached using 5 mg/kg once weekly SC dosing. [Display omitted] • Three-species model-based scaling accounts for species-specific differences in protein elimination. • Model-based scaling can characterize the preclinical immunogenicity against proteins. • Model-based scaling is not sensitive to potential uncertainties in the exponents. [ABSTRACT FROM AUTHOR]

Abstract This study was aimed to evaluate whether the dose–response relationship of the sodium glucose co-transporter-2 inhibitors (SGLT2is) in patients with type 2 diabetes mellitus (T2DM)—canagliflozin, dapagliflozin, empagliflozin, ipragliflozin, luseogliflozin, and tofogliflozin—can be explained in a unified manner based on their ability to promote urinary glucose excretion (UGE). Information on HbA1c reduction at various doses of each SGLT2i was collected from literatures on randomized controlled trials and was normalized based on the daily UGE data from phase I studies. After normalizing doses, the dose–response relationship of HbA1c reduction of most of SGLT2is was represented by a unified nonlinear mixed-effect model, with the estimated maximum HbA1c (%) reduction (Emax) of 0.796 points, whereas covariate analysis showed that canagliflozin had a 1.33-fold higher Emax than those of the other drugs. Other covariates included baseline HbA1c levels, body weight, disease duration, prior treatment, and renal function. Findings from this study would influence drug selection and adjustment in clinical practice. As with SGLT2is, in cases where the efficacy cannot be easily evaluated but an appropriate pharmacodynamic marker was assessed in early clinical trials, similar approaches for other drug classes can guide strategic and evidence-based dose selection in phase III trials.

ABSTRACTBackground Next generation, higher valency pneumococcal conjugate vaccines (PCVs) are assessed and licensed by comparing the immune response across serotypes shared with the PCVs that are standard of care for prevention of pneumococcal disease.Methods Using a previously qualified method we predicted the serotype-specific vaccine effectiveness (VE) against invasive pneumococcal disease of V114 and PCV20 for the serotypes shared with PCV13 in an EU, Russian, and Australian pediatric population that is recommended to receive a 2 + 1 dosing regimen.Results The estimated protective antibody concentrations ranged from 0.03 (serotype 23F) to 1.49 µg/mL (serotype 19F). Predicted VE values for V114 ranged from 79% (serotype 5) to 100% (serotype 23F). V114 had comparable effectiveness to PCV13 for all but one of shared serotypes, with predicted higher effectiveness (in V114) against serotype 3 (93% vs. 65%). Predicted VE values for PCV20 ranged from 47% (serotype 3) to 91% (serotype 14). PCV20 predicted VE was lower than PCV13’s for serotypes 4, 19F, 23F, 1, 3, 5, 6A, 7F, and 19A.Conclusions Predicted serotype-specific VE values suggest that, with a 2 + 1 dosing regimen, V114 will have greater effectiveness than PCV20 against PCV13 serotypes, particularly for the still-prevalent serotype 3. Real-world VE studies will ultimately provide clarity on the effectiveness of novel PCVs and support further confidence in and/or improvements to modeling efforts.

Stepwise covariate modeling (SCM) has a high computational burden and can select the wrong covariates. Machine learning (ML) has been proposed as a screening tool to improve the efficiency of covariate selection, but little is known about how to apply ML on actual clinical data. First, we simulated datasets based on clinical data to compare the performance of various ML and traditional pharmacometrics (PMX) techniques with and without accounting for highly-correlated covariates. This simulation step identified the ML algorithm and the number of top covariates to select when using the actual clinical data. A previously developed desipramine population-pharmacokinetic model was used to simulate virtual subjects. Fifteen covariates were considered with four having an effect included. Based on the F1 score (an accuracy measure), ridge regression was the most accurate ML technique on 200 simulated datasets (F1 score = 0.475 ± 0.231), a performance which almost doubled when highly-correlated covariates were accounted for (F1 score = 0.860 ± 0.158). These performances were better than forwards selection with SCM (F1 score = 0.251 ± 0.274 and 0.499 ± 0.381 without/with correlations respectively). In terms of computational cost, ridge regression (0.42 ± 0.07 seconds/simulated dataset, 1 thread) was ~20,000 times faster than SCM (2.30 ± 2.29 hours, 15 threads). On the clinical dataset, prescreening with the selected ML algorithm reduced SCM runtime by 42.86% (from 1.75 to 1.00 days) and produced the same final model as SCM only. In conclusion, we have demonstrated that accounting for highly-correlated covariates improves ML prescreening accuracy. The choice of ML method and the proportion of important covariates (unknown a priori) can be guided by simulations. [ABSTRACT FROM AUTHOR]

The recruitment of a parallel, healthy participants (HPs) arm in renal and hepatic impairment (RI and HI) studies is a common strategy to assess differences in pharmacokinetics. Limitations in this approach include the underpowered estimate of exposure differences and the use of the drug in a population for which there is no benefit. Recently, a method was published by Purohit et. al. (2023) that leveraged prior population pharmacokinetic (PopPK) modeling-based simulation to infer the distribution of exposure ratios between the RI/HI arms and HPs. The approach was successful, but it was a single example with a robust model having several iterations of development and fitting to extensive HP data. To test in more studies and models at different stages of development, our catalogue of RI/HI studies was searched, and those with suitable properties and from programs with available models were analyzed with the simulation approach. There were 9 studies included in the analysis. Most studies were associated with models that would have been available at the time (ATT) of the study, and all had a current, final model. For 3 studies, the HP PK was not predicted well by the ATT (2) or final (1) models. In comparison to conventional analysis of variance (ANOVA), the simulation approach provided similar point estimates and confidence intervals of exposure ratios. This PopPK based approach can be considered as a method of choice in situations where the simulation of HP data would not be an extrapolation, and when no other complicating factors are present. [ABSTRACT FROM AUTHOR]

Background Methods Results Conclusions The pharmacodynamics of propofol in children have previously been described with the proprietary bispectral index (BIS) as an effect‐site marker, and it has been suggested that the rate of onset of propofol might be age dependent, that is, a shorter time to peak effect in younger children. However, these analyses were potentially confounded by co‐administered drugs, in particular opioids and benzodiazepines. Thus, the goal of this prospective study was to characterize the influence of age and weight on the onset of hypnotic effects from propofol, reflected by the time to peak of propofol effect‐site concentration in the absence of additional drugs.A total of 46 healthy children aged 2–12 years presenting for elective surgery were included in our observational cohort study. Solely propofol was administered via a target‐controlled infusion pump programmed with the Paedfusor pharmacokinetic model. The BIS and infusion pump data were recorded. The effect of an induction “bolus” was recorded having stopped the pump once a propofol plasma target concentration of 7 μg.mL−1 was achieved. A direct‐response and an indirect‐response model in the context of nonlinear mixed‐effects modeling was used to characterize and compare BIS data in children aged 2–6 years and older children aged 8–12 years.Time to peak of propofol effect‐site concentration had a difference (p‐value <.01) for age and weight, that is 84 [74, 96] (median [IQR] secs for children aged 2–6 years vs. 99 [91, 113] secs for children aged 8–12 years and 82 [71, 95] secs for weight 11–25 kg vs. 99 [91, 114] secs for weight 30–63 kg). The plasma effect‐site equilibration rate constant for propofol had a heterogeneous distribution with a median of 2.36 (IQR: 2.05–2.93; range: 0.83–7.31) per minute but showed a weight‐dependent effect in patients with weight below 45 kg.In children, the age and weight have an influence on time to peak effect of propofol. In the absence of opioids and benzodiazepines, time to peak effect was approximately 20% longer in children aged 8–12 years as compared to younger children. Such clinically relevant age and weight effects are an important consideration in the individualized titration of propofol dosing. [ABSTRACT FROM AUTHOR]

The article emphasizes the significance of including life scientists in quantitative systems pharmacology (QSP) modeling teams. QSP models integrate data on physiology, pathophysiology, and pharmacology to enhance drug development. However, many QSP modelers lack a background in biological sciences, which can result in limitations in model quality and impact. Life scientists contribute essential expertise in model design, data curation, and result interpretation, ensuring the model's relevance and biological accuracy. Without life scientists, modelers may overlook crucial connections and construct models with unrealistic parameters. Including life scientists in modeling teams enhances model development and outcomes. The article suggests providing educational programs and workshops to train life scientists in modeling concepts and biology. Including life scientists in the core modeling team leads to more efficient model building and improved result interpretation. [Extracted from the article]

Understanding pharmacokinetics (PK) in children is a prerequisite to determine optimal pediatric dosing. As plasma sampling in children is challenging, alternative PK sampling strategies are needed. In this case study we evaluated the suitability of saliva as alternative PK matrix to simplify studies in infants, investigating metamizole, an analgesic used off‐label in infants. Six plasma and 6 saliva PK sample collections were scheduled after a single intravenous dose of 10 mg/kg metamizole. Plasma/saliva pharmacometric (PMX) modeling of the active metabolites 4‐methylaminoantipyrine (4‐MAA) and 4‐aminoantipyrine (4‐AA) was performed. Various reduced plasma sampling scenarios were evaluated by PMX simulations. Saliva and plasma samples from 25 children were included (age range, 5–70 months; weight range, 8.7–24.8 kg). Distribution of metamizole metabolites between plasma and saliva was without delay. Estimated mean (individual range) saliva/plasma fractions of 4‐MAA and 4‐AA were 0.32 (0.05–0.57) and 0.57 (0.25–0.70), respectively. Residual variability of 4‐MAA (4‐AA) in saliva was 47% (28%) versus 17% (11%) in plasma. A simplified sampling scenario with up to 6 saliva samples combined with 1 plasma sample was associated with similar PK parameter estimates as the full plasma sampling scenario. This case study with metamizole shows increased PK variability in saliva compared to plasma, compromising its suitability as single matrix for PK studies in infants. Nonetheless, rich saliva sampling can reduce the number of plasma samples required for PK characterization, thereby facilitating the conduct of PK studies to optimize dosing in pediatric patients. [ABSTRACT FROM AUTHOR]

The objective of this project is to compare the results of the same study carried out on NONMEM and nlmixr2. This analysis consists of evaluating previously published population pharmacokinetic models of gentamicin and tobramycin in our population of interest with sparse concentrations. A literature review was performed to determine the gentamicin and tobramycin models in critically ill adult patients. In parallel, gentamicin and tobramycin dosing data, information on the treatment, the patient, and the bacteria were collected retrospectively in 2 Quebec establishments. The external evaluations were previously performed using NONMEM Version 7.5. Model equations were rewritten with R, and external evaluations were performed using nlmixr2. Predictive performance was assessed based on the estimation of bias and imprecision of the prediction error for maximum a posteriori (MAP) Bayesian PK parameter and observed concentrations. Comparison between nlmixr2 and NONMEM was performed on 4 gentamicin and 3 tobramycin population pharmacokinetic models. Compared to NONMEM, for gentamicin and tobramycin clearance and central volume of distribution, nlmixr2 produced individual pharmacokinetic parameters with bias values ranging from −32.5% to 5.67% and imprecision values ranging from 6.33% to 32.5%. Despite these differences, population bias and imprecision for sparse concentrations were low and ranged from 0% to 5.3% and 0.2% to 6.5%, respectively. The external evaluations performed with both software packages resulted in the same interpretation in terms of population predictive performance for all 7 models. Nlmxir2 showed comparable predictive performance with NONMEM with sparse concentrations that are, at most, sampled twice within a single dose administration (peak and trough). [ABSTRACT FROM AUTHOR]

Sophisticated mathematical and computational tools have become widespread and important in veterinary pharmacology. Although the theoretical basis and practical applications of these have been widely explored in the literature, statistical inference in the context of these models has received less attention. Optimization methods, often with frequentist statistical inference, have been predominant. In contrast, Bayesian statistics have not been widely applied, but offer both practical utility and arguably greater interpretability. Veterinary pharmacology applications are generally well supported by relevant prior information, from either existing substantive knowledge, or an understanding of study and model design. This facilitates practical implementation of Bayesian analyses that can take advantage of this knowledge. This essay will explore the specification of Bayesian models relevant to veterinary pharmacology, including demonstration of prior selection, and illustrate the capability of these models to generate practically useful statistics, including uncertainty statements, that are difficult or impossible to obtain otherwise. Case studies using simulated data will describe applications in clinical trials, pharmacodynamics, and pharmacokinetics, all including multilevel modeling. This content may serve as a suitable starting point for researchers in veterinary pharmacology and related disciplines considering Bayesian estimation for their applied work. [ABSTRACT FROM AUTHOR]

Background and aim: Chronic myeloid leukemia is a myeloproliferative neoplasm associated with the specific chromosomal translocation known as the Philadelphia chromosome. Imatinib is a potent BCR-ABL tyrosine kinase inhibitor, which is approved as the first line therapy for CML patients. There are various population pharmacokinetic studies available in the literature for this population. However, their use in other populations outside of their cohort for the model development has not been evaluated. This study was aimed to perform the predictive performance of the published population pharmacokinetic models for imatinib in CML population and propose a dosing nomogram. Methods: A systematic review was conducted through PubMed, and WoS databases to identify PopPK models. Clinical data collected in adult CML patients treated with imatinib was used for evaluation of these models. Various prediction-based metrics were used for assessing the bias and precision of PopPK models using individual predictions. Results: Eight imatinib PopPK model were selected for evaluating the model performance. A total of 145 plasma imatinib samples were collected from 43 adult patients diagnosed with CML and treated with imatinib. The PopPK model reported by Menon et al. had better performance than all other PopPK models. Conclusion: Menon et al. model was able to predict well for our clinical data where it had the relative mean prediction error percentage ≤ 20%, relative median absolute prediction error ≤ 30% and relative root mean square error close to zero. Based on this final model, we proposed a dosing nomogram for various weight groups, which could potentially help to maintain the trough concentrations in the therapeutic range. [ABSTRACT FROM AUTHOR]

To assess ChatGPT 4.0 (ChatGPT) and Gemini Ultra 1.0 (Gemini) large language models on NONMEM coding tasks relevant to pharmacometrics and clinical pharmacology. ChatGPT and Gemini were assessed on tasks mimicking real-world applications of NONMEM. The tasks ranged from providing a curriculum for learning NONMEM, an overview of NONMEM code structure to generating code. Prompts in lay language to elicit NONMEM code for a linear pharmacokinetic (PK) model with oral administration and a more complex model with two parallel first-order absorption mechanisms were investigated. Reproducibility and the impact of "temperature" hyperparameter settings were assessed. The code was reviewed by two NONMEM experts. ChatGPT and Gemini provided NONMEM curriculum structures combining foundational knowledge with advanced concepts (e.g., covariate modeling and Bayesian approaches) and practical skills including NONMEM code structure and syntax. ChatGPT provided an informative summary of the NONMEM control stream structure and outlined the key NONMEM Translator (NM-TRAN) records needed. ChatGPT and Gemini were able to generate code blocks for the NONMEM control stream from the lay language prompts for the two coding tasks. The control streams contained focal structural and syntax errors that required revision before they could be executed without errors and warnings. The code output from ChatGPT and Gemini was not reproducible, and varying the temperature hyperparameter did not reduce the errors and omissions substantively. Large language models may be useful in pharmacometrics for efficiently generating an initial coding template for modeling projects. However, the output can contain errors and omissions that require correction. [ABSTRACT FROM AUTHOR]

Belimumab was approved for active lupus nephritis (LN) in adults in the European Union and patients ≥ 5 years of age in the USA based on a Phase 3, double-blind, placebo-controlled, 104-week study. The study evaluated the efficacy of belimumab plus background standard therapy in adults with active LN using an intravenous (IV) dose of 10 mg/kg. A longitudinal analysis of Primary Efficacy Renal Response (PERR) and Complete Renal Response (CRR) was performed to assess whether patients with high proteinuria at the start of belimumab treatment would benefit from a higher dose. Responder probability was modeled as a logistic regression with probability a function of time and treatment (belimumab or placebo). Dropout risk at each visit was incorporated into a joint model of efficacy response; only efficacy data prior to dropout events (belimumab discontinuation, treatment failure, or withdrawal) were included. Average belimumab concentration over the first 4 and 12 weeks and baseline proteinuria were considered as continuous covariates. In general, renal response (PERR and CRR) over time was higher in patients receiving belimumab than in those receiving placebo. Baseline proteinuria was considered the most relevant predictor of renal response, with reduced efficacy in patients with increased proteinuria for both belimumab or placebo treatment. For belimumab-treated patients, belimumab exposure was not found to be an important predictor of renal response. In conclusion, the 10 mg/kg IV dose was considered appropriate in all patients and there was no evidence to suggest a higher response would be achieved by increasing the dose. [ABSTRACT FROM AUTHOR]

A well-documented pharmacometric (PMx) analysis dataset specification ensures consistency in derivations of the variables, naming conventions, traceability to the source data, and reproducibility of the analysis dataset. Lack of standards in creating the dataset specification can lead to poor quality analysis datasets, negatively impacting the quality of the PMx analysis. Standardization of the dataset specification within an individual organization helps address some of these inconsistencies. The recent introduction of the Clinical Data Interchange Standards Consortium (CDISC) Analysis Data Model (ADaM) Population Pharmacokinetic (popPK) Implementation Guide (IG) further promotes industry-wide standards by providing guidelines for the basic data structure of popPK analysis datasets. However, manual implementation of the standards can be labor intensive and error-prone. Hence, there is still a need to automate the implementation of these standards. In this paper, we present PmWebSpec, an easily deployable web-based application to facilitate the creation and management of CDISC-compliant PMx analysis dataset specifications. We describe the application of this tool through examples and highlight its key features including pre-populated dataset specifications, built-in checks to enforce standards, and generation of an electronic Common Technical Document (eCTD)-compliant data definition file. The application increases efficiency, quality and semi-automates PMx analysis dataset, and specification creation and has been well accepted by pharmacometricians and programmers internally. The success of this application suggests its potential for broader usage across the PMx community. [ABSTRACT FROM AUTHOR]

Purpose: To demonstrate the effective integration of pharmacometrics and pharmacovigilance in managing medication errors, highlighted by a case involving secukinumab in a patient with hidradenitis suppurativa. Methods: We present the case of a 41-year-old male with progressive hidradenitis suppurativa, unresponsive to multiple antibiotic regimens and infliximab treatment. Due to a medication error, the patient received 300 mg of secukinumab daily for 4 days instead of weekly, totaling 1200 mg. The regional pharmacovigilance center assessed potential toxicity, and a pharmacometric analysis using a population pharmacokinetic model was performed to inform dosing adjustments. Results: Clinical data indicated that the received doses were within a non-toxic range. No adverse effects were observed. Pharmacometric simulations revealed a risk of underexposure due to the dosing error. Based on these simulations, it was recommended to restart monthly secukinumab injections on day 35 after the initial dose. Measured plasma concentrations before re-administration confirmed the model's accuracy. Conclusion: This case highlights the crucial collaboration between clinical services, pharmacovigilance, and pharmacometrics in managing medication errors. Such interdisciplinary efforts ensure therapeutic efficacy and patient safety by maintaining appropriate drug exposure levels. [ABSTRACT FROM AUTHOR]

Aims: The pharmacokinetics of doravirine has been studied in clinical trials but not in real‐world settings. Our study aims to characterize and identify factors influencing doravirine (a CYP3A4 substrate) pharmacokinetics in real‐world people with HIV (PWH). Methods: A total of 174 doravirine concentrations measured in 146 PWH followed up in the therapeutic drug monitoring (TDM) program at the University Hospital of Lausanne (Switzerland) between 2019 and 2023 were included in the analysis. Demographic data, clinical information and comedications were recorded during the routine SHCS visits (every 3–6 months). Population pharmacokinetic analysis and Monte Carlo simulations to investigate the clinical significance of the covariates retained in the final model were performed using NONMEM. Results: A one‐compartment model with first‐order absorption and linear elimination best described doravirine pharmacokinetics. Potent CYP3A4 inhibitors and, to a lesser extent age, were the only tested covariates to significantly impact doravirine clearance (CL). Potent CYP3A4 inhibitors reduced CL by 50%, and a 30% decrease in CL was observed in an 80‐year‐old compared with a 55‐year‐old PWH. The effect of potent CYP3A4 inhibitors was prominent, explaining 59% of between‐subject variability in CL. Model‐based simulations predicted 2.8‐fold and 1.6‐fold increases in median steady‐state trough and maximum doravirine concentrations, respectively, when a potent CYP3A4 inhibitor was co‐administered. Conclusions: Our findings show that potent CYP3A4 inhibitors and age influence doravirine pharmacokinetics. However, given the good tolerability of doravirine, dosing adjustment of doravirine is probably not mandatory in those situations. TDM remains useful essentially in specific clinical situations, such as hepatic impairment, suspected nonadherence or pregnancy. [ABSTRACT FROM AUTHOR]

Aims: We propose using glomerular filtration rate (GFR) as the physiological basis for distinguishing components of renal clearance. Methods: Gentamicin, amikacin and vancomycin are thought to be predominantly excreted by the kidneys. A mixed‐effects joint model of the pharmacokinetics of these drugs was developed, with a wide dispersion of weight, age and serum creatinine. A dataset created from 18 sources resulted in 27,338 drug concentrations from 9,901 patients. Body size and composition, maturation and renal function were used to describe differences in drug clearance and volume of distribution. Results: This study demonstrates that GFR is a predictor of two distinct components of renal elimination clearance: (1) GFR clearance associated with normal GFR and (2) non‐GFR clearance not associated with normal GFR. All three drugs had GFR clearance estimated as a drug‐specific percentage of normal GFR (gentamicin 39%, amikacin 90% and vancomycin 57%). The total clearance (sum of GFR and non‐GFR clearance), standardized to 70 kg total body mass, 176 cm, male, renal function 1, was 5.58 L/h (95% confidence interval [CI] 5.50‐5.69) (gentamicin), 7.77 L/h (95% CI 7.26‐8.19) (amikacin) and 4.70 L/h (95% CI 4.61‐4.80) (vancomycin). Conclusions: GFR provides a physiological basis for renal drug elimination. It has been used to distinguish two elimination components. This physiological approach has been applied to describe clearance and volume of distribution from premature neonates to elderly adults with a wide dispersion of size, body composition and renal function. Dose individualization has been implemented using target concentration intervention. [ABSTRACT FROM AUTHOR]

Purpose: Gemcitabine and nab-paclitaxel (GnP) treatment, the standard first-line chemotherapy for unresectable pancreatic cancer, often causes peripheral neuropathy (PN). To develop alternative dosing strategies to avoid severe PN, understanding the relationship between pharmacokinetics (PK) and pharmacodynamics/toxicodynamics (PD/TD) is necessary. We established a PK-PD/TD model of GnP treatment to develop an optimal dose schedule. Methods: A mouse xenograft model of human pancreatic cancer was generated to measure drug concentrations in the plasma and tumor, antitumor effects, and PN after GnP treatment. The Simeoni tumor growth inhibition model with tumor concentrations and empirical indirect response models were used for the PD and TD models, respectively. Clinical outcomes were predicted with reported population estimates of PK parameters in cancer patients. Results: The PK-PD/TD model simultaneously described the observed tumor volume and paw withdrawal frequency in the von Frey test. For the standard GnP regimen, the model predicted clinical overall response (75.1%), which was overestimated compared to that in a recent phase II study (42.1%) but lower than the observed disease control rate (96.5%). Model simulation showed that dose reduction to less than 40% GnP dose was not effective; a change of dose schedule from every week for 3 weeks to every 2 weeks was a more favorable approach than dose reduction to 60% every week. Conclusion: The PK-PD/TD model-based translational approach provides a guide for optimal dose determination to avoid severe PN while maintaining antitumor effects during GnP chemotherapy. Further research is needed to enhance its applicability and potential for combination chemotherapy regimens. [ABSTRACT FROM AUTHOR]

Machine Learning (ML) is a fast-evolving field, integrated in many of today's scientific disciplines. With the recent development of neural ordinary differential equations (NODEs), ML provides a new tool to model dynamical systems in the field of pharmacology and pharmacometrics, such as pharmacokinetics (PK) or pharmacodynamics. The novel and conceptionally different approach of NODEs compared to classical PK modeling creates challenges but also provides opportunities for its application. In this manuscript, we introduce the functionality of NODEs and develop specific low-dimensional NODE structures based on PK principles. We discuss two challenges of NODEs, overfitting and extrapolation to unseen data, and provide practical solutions to these problems. We illustrate concept and application of our proposed low-dimensional NODE approach with several PK modeling examples, including multi-compartmental, target-mediated drug disposition, and delayed absorption behavior. In all investigated scenarios, the NODEs were able to describe the data well and simulate data for new subjects within the observed dosing range. Finally, we briefly demonstrate how NODEs can be combined with mechanistic models. This research work enhances understanding of how NODEs can be applied in PK analyses and illustrates the potential for NODEs in the field of pharmacology and pharmacometrics. [ABSTRACT FROM AUTHOR]

Efficiently finding covariate model structures that minimize the need for random effects to describe pharmacological data is challenging. The standard approach focuses on identification of relevant covariates, and present methodology lacks tools for automatic identification of covariate model structures. Although neural networks could potentially be used to approximate covariate-parameter relationships, such approximations are not human-readable and come at the risk of poor generalizability due to high model complexity.In the present study, a novel methodology for the simultaneous selection of covariate model structure and optimization of its parameters is proposed. It is based on symbolic regression, posed as an optimization problem with a smooth loss function. This enables training of the model through back-propagation using efficient gradient computations.Feasibility and effectiveness are demonstrated by application to a clinical pharmacokinetic data set for propofol, containing infusion and blood sample time series from 1031 individuals. The resulting model is compared to a published state-of-the-art model for the same data set. Our methodology finds a covariate model structure and corresponding parameter values with a slightly better fit, while relying on notably fewer covariates than the state-of-the-art model. Unlike contemporary practice, finding the covariate model structure is achieved without an iterative procedure involving manual interactions. [ABSTRACT FROM AUTHOR]

Aims: To investigate an innovative pharmacometrics approach that addresses the challenges of using real‐world evidence to model the progression of illicit substance use. Methods: The modelling strategy analysed real‐world data from the National Longitudinal Study of Adolescent to Adult Health (AddHealth) survey using survival analyses and differential equations. Respondents were categorized into drug‐naïve, active users and nonusers. The transitions between categories were modelled using interval‐censored parametric survival analysis. The resulting hazard rate functions were used as time‐dependent rate constants in a differential equation system. Covariate models for sex and depression status were assessed. Results: AddHealth enrolled 6504 American teenagers (median age 16 years, range 11–21 years); this cohort was followed with five interviews over a 22‐year period; the median age at the last interview was 38 years (range 34–45 years). The percentages of illicit drug users at Interviews 1–5 were 7.7%, 5.9%, 15.8%, 21.4% and 0.98%, respectively. The generalized gamma distribution emerged as the preferred model for the survival functions for transitions between categories. Age‐dependent prevalence was obtained from the differential equation system. Active drug use was more prevalent in males, increased in adolescence and college years, peaked at 24 years, and decreased to low levels by 35 years. Depression, which was more frequent in females, increased the drug‐naïve‐active user transition rates but not the active user‐nonuser and nonuser‐active user transition rates. The evidence did not support an interaction between sex and depression. Conclusions: The model provided a satisfactory approximation for the age‐dependent progression of illicit substance use from preadolescence to early middle age. [ABSTRACT FROM AUTHOR]

Aims: Genotype‐guided dosing algorithms can explain about half of the interindividual variability in prothrombin time–international normalized ratio (PT‐INR) under warfarin treatment. This study aimed to refine a published kinetic–pharmacodynamic model and guide warfarin dosage for an optimal PT‐INR based on renal function. Methods: Using a retrospective cohort of adult patients (>20 years) who were administered warfarin and underwent PT‐INR measurements, we refined the kinetic–pharmacodynamic model with age and the genotypes of cytochrome P450 2C9 and vitamin K epoxide reductase complex subunit 1 using the PRIOR subroutine in the nonlinear‐mixed‐effect modelling programme. We searched the significant covariates for parameters, such as the dose rate for 50% inhibition of coagulation (EDR50), using a stepwise forward and backward method. Monte Carlo simulation determined a required daily dose of warfarin with a target range of PT‐INR (2.0–3.0 or 1.6–2.6) based on the significant covariates. Results: A total of 350 patients with 2762 PT‐INR measurements were enrolled (estimated glomerular filtration rate [eGFR]: 47.5 [range: 2.6–199.0] mL/min/1.73 m2). The final kinetic–pharmacodynamic model showed that the EDR50 changed power functionally with body surface area, serum albumin level and eGFR. Monte Carlo simulation revealed that a lower daily dose of warfarin was required to attain the target PT‐INR range as eGFR decreased. Conclusions: Model‐informed precision dosing of warfarin is a valuable approach for estimating its dosage in patients with renal impairment. [ABSTRACT FROM AUTHOR]

Bepotastine, a second-generation antihistamine for allergic rhinitis and urticaria, is widely used in all age groups but lacks appropriate dosing guidelines for pediatric patients, leading to off-label prescriptions. We conducted this study to propose an optimal dosing regimen for pediatric patients based on population pharmacokinetic (popPK) and physiologically based pharmacokinetic (PBPK) models using data from two previous trials. A popPK model was built using NONMEM software. A one-compartment model with first-order absorption and absorption lag time described our data well, with body weight incorporated as the only covariate. A PBPK model was developed using PK-Sim software version 10, and the model well predicted the drug concentrations obtained from pediatric patients. Furthermore, the final PBPK model showed good concordance with the known properties of bepotastine. Appropriate pediatric doses for different weight and age groups were proposed based on the simulations. Discrepancies in recommended doses from the two models were likely due to the incorporation of age-dependent physiological factors in the PBPK model. In conclusion, our study is the first to suggest an optimal oral dosing regimen of bepotastine in pediatric patients using both approaches. This is expected to foster safer and more productive use of the drug. [ABSTRACT FROM AUTHOR]

(1) Background: Some individuals are more susceptible to developing respiratory tract infections (RTIs) or coronavirus disease (COVID-19) than others. The aim of this work was to identify risk factors for symptomatic RTIs including COVID-19 and symptomatic COVID-19 during the coronavirus pandemic by using infection incidence, participant baseline, and regional COVID-19 burden data. (2) Methods: Data from a prospective study of 1000 frontline healthcare workers randomized to Bacillus Calmette–Guérin vaccination or placebo, and followed for one year, was analyzed. Parametric time-to-event analysis was performed to identify the risk factors associated with (a) non-specific symptomatic respiratory tract infections including COVID-19 (RTIs+COVID-19) and (b) symptomatic RTIs confirmed as COVID-19 using a polymerase chain reaction or antigen test (COVID-19). (3) Results: Job description of doctor or nurse (median hazard ratio [HR] 1.541 and 95% confidence interval [CI] 1.299–1.822), the reported COVID-19 burden (median HR 1.361 and 95% CI 1.260–1.469 for 1.4 COVID-19 cases per 10,000 capita), or a BMI > 30 kg/m2 (median HR 1.238 and 95% CI 1.132–1.336 for BMI of 35.4 kg/m2) increased the probability of RTIs+COVID-19, while positive SARS-CoV-2 serology at enrollment (median HR 0.583 and 95% CI 0.449–0.764) had the opposite effect. The reported COVID-19 burden (median HR 2.372 and 95% CI 2.116–2.662 for 1.4 COVID-19 cases per 10,000 capita) and a job description of doctor or nurse (median HR 1.679 and 95% CI 1.253–2.256) increased the probability of developing COVID-19, while smoking (median HR 0.428 and 95% CI 0.284–0.648) and positive SARS-CoV-2 serology at enrollment (median HR 0.076 and 95% CI 0.026–0.212) decreased it. (4) Conclusions: Nurses and doctors with obesity had the highest probability of developing RTIs including COVID-19. Non-smoking nurses and doctors had the highest probability of developing COVID-19 specifically. The reported COVID-19 burden increased the event probability, while positive SARS-CoV-2 IgG serology at enrollment decreased the probability of RTIs including COVID-19, and COVID-19 specifically. [ABSTRACT FROM AUTHOR]

In oncology drug development, tumor dynamics modeling is widely applied to predict patients' overall survival (OS) via parametric models. However, the current modeling paradigm, which assumes a disease-specific link between tumor dynamics and survival, has its limitations. This is particularly evident in drug development scenarios where the clinical trial under consideration contains patients with tumor types for which there is little to no prior institutional data. In this work, we propose the use of a pan-indication solid tumor machine learning (ML) approach whereby all three tumor metrics (tumor shrinkage rate, tumor regrowth rate and time to tumor growth) are simultaneously used to predict patients' OS in a tumor type independent manner. We demonstrate the utility of this approach in a clinical trial of cancer patients treated with the tyrosine kinase inhibitor, pralsetinib. We compared the parametric and ML models and the results showed that the proposed ML approach is able to adequately predict patient OS across RET-altered solid tumors, including non-small cell lung cancer, medullary thyroid cancer as well as other solid tumors. While the findings of this study are promising, further research is needed for evaluating the generalizability of the ML model to other solid tumor types.

AimsCabotegravir delivered as a long-acting intramuscular injection has shown superior efficacy to oral tenofovir-emtricitabine as pre-exposure prophylaxis (PrEP) for HIV. Cabotegravir pharmacokinetics (PK), like those of other long-acting depot preparations, exhibit variability between individuals and between injection occasions. The aim of this study is to describe the population pharmacokinetics of long-acting cabotegravir (CAB-LA).MethodsUsing available PK measurements from 133 participants in the HIV Prevention Trials Network (HPTN) 077 trial, we analysed CAB-LA PK data using nonlinear mixed-effects modelling to develop a population PK model.ResultsA two-compartment model with first order absorption best described the CAB-LA PK. The analysis identified between-occasion variability (BOV, i.e., differences in PK within one individual from one injection to the next) as a significant covariate affecting the absorption rate, with an estimated contribution of BOV to PK variability on the absorption rate (ka ) of 38.5%. Sex and body weight were identified as significant covariates influencing the absorption rate and apparent clearance of CAB-LA after intramuscular injection at various doses and frequencies. Male participants had 67% higher ka than female participants. Serially adding to the model body weight on clearance, sex on ka , and BOV on ka led to a decrease in the objective function value (OFV) of 24.4, 36 and 321.4, respectively.ConclusionThe public availability of this model will facilitate and enable a wide variety of future clinically relevant simulations to inform the optimal use of CAB-LA.

Abstract Brief summary In early symptomatic COVID-19 treatment, high dose oral favipiravir did not accelerate viral clearance. Background Favipiravir, an anti-influenza drug, has in vitro antiviral activity against SARS-CoV-2. Clinical trial evidence to date is inconclusive. Favipiravir has been recommended for the treatment of COVID-19 in some countries. Methods In a multicentre open-label, randomised, controlled, adaptive platform trial, low-risk adult patients with early symptomatic COVID-19 were randomised to one of ten treatment arms including high dose oral favipiravir (3.6g on day 0 followed by 1.6g daily to complete 7 days treatment) or no study drug. The primary outcome was the rate of viral clearance (derived under a linear mixed-effects model from the daily log10 viral densities in standardised duplicate oropharyngeal swab eluates taken daily over 8 days [18 swabs per patient]), assessed in a modified intention-to-treat population (mITT). The safety population included all patients who received at least one dose of the allocated intervention. This ongoing adaptive platform trial was registered at ClinicalTrials.gov (NCT05041907) on 13/09/2021. Results In the final analysis, the mITT population contained data from 114 patients randomised to favipiravir and 126 patients randomised concurrently to no study drug. Under the linear mixed-effects model fitted to all oropharyngeal viral density estimates in the first 8 days from randomisation (4,318 swabs), there was no difference in the rate of viral clearance between patients given favipiravir and patients receiving no study drug; a -1% (95% credible interval: -14 to 14%) difference. High dose favipiravir was well-tolerated. Interpretation Favipiravir does not accelerate viral clearance in early symptomatic COVID-19. The viral clearance rate estimated from quantitative measurements of oropharyngeal eluate viral densities assesses the antiviral efficacy of drugs in vivo with comparatively few studied patients.

Khalid Altigani Ahmed,1 Alnada Ibrahim,2 Daniel Gonzalez,3 Abubakr O Nur4 1Department of Clinical Pharmacy, College of Pharmacy, Najran University, Najran, Saudi Arabia; 2Department of Pharmacy Practice, College of Pharmacy, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia; 3Division of Clinical Pharmacology, Department of Medicine, Duke University School of Medicine, Durham, NC, USA; 4Department of Pharmaceutics, Faculty of Pharmacy, University of Khartoum, Khartoum, SudanCorrespondence: Alnada Ibrahim, Department of Pharmacy Practice, College of Pharmacy, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia, Email alaibrahim@pnu.edu.saPurpose: The study aimed to perform a population pharmacokinetic (PK) analysis to obtain vancomycin PK parameter estimates in Sudanese adult patients. The population PK model is then applied to perform model-based dose optimization.Patients and Methods: Data were collected through a retrospective, single-center, observational cohort study performed in Aliaa Specialist Hospital, Khartoum, Sudan. A population PK model was developed using the MonolixSuite 2020R1 to explore the potential effects of demographics and laboratory covariates on vancomycin PK. Monte Carlo simulations were performed to optimize dosage regimens as a function of creatinine clearance (CLcr) and virtual patients were partitioned into five CLcr groups.Results: We retrospectively collected 194 vancomycin plasma concentrations from 99 adults. The median (interquartile range) for age (years) and CLcr (mL/min) were 65 (50– 75) and 12.7 (5.52– 25.78), respectively. Vancomycin PK data were best fitted using a one-compartment model with linear elimination. The estimates of clearance and volume of distribution were 2.02 L/h and 65 L, respectively. CLcr was identified as the main covariate explaining the PK variability in vancomycin CL. CL significantly decreased with decreasing CLcr. For the five CLcr groups evaluated, a tailored vancomycin daily maintenance dose (using patients’ CLcr) ranged from 200 to 1650 mg. Overall, simulations showed that 45% (CI; 41.11– 47.36%) of patients would achieve a target AUC with the suggested dosages.Conclusion: A population PK model of vancomycin was developed using data obtained from adult Sudanese patients. Model-based dose optimization can aid clinicians in selecting initial vancomycin doses that will maximize the likelihood of a favorable treatment response.Keywords: vancomycin, dose individualization, monitoring, pharmacokinetics dosing, pharmacometrics

The article discusses the concept of physiologic time in the context of anesthesia in children. It emphasizes the importance of understanding the effects of time on pharmacokinetic-pharmacodynamic parameters to improve patient care. The study examines the time to peak effect-site concentration of propofol in children aged 2-12 years and highlights the influence of age and weight on this parameter. The article also discusses the application of allometric theory in pharmacology modeling and the potential impact of general anesthesia on the circadian clock. Overall, the article provides valuable insights into the relationship between time and anesthesia in pediatric patients. [Extracted from the article]

The antiparasitic drug ivermectin is approved for persons > 15 kg in the US and EU. A pharmacometric (PMX) population model with clinical PK data was developed (i) to characterize the effect of the patient-friendly ivermectin formulation CHILD-IVITAB on the absorption process and (ii) to evaluate dosing for studies in children < 15 kg. Simulations were performed to identify dosing with CHILD-IVITAB associated with similar exposure coverage in children ≥ 15 kg and < 15 kg as observed in adults receiving the reference formulation STROMECTOL®. A total of 448 ivermectin concentrations were available from 16 healthy adults. The absorption rate constant was 2.41 h−1 (CV 19%) for CHILD-IVITAB vs. 1.56 h−1 (CV 43%) for STROMECTOL®. Simulations indicated that 250 µg/kg of CHILD-IVITAB is associated with exposure coverage in children < 15 kg consistent with that observed in children ≥ 15 kg and adults receiving 200 µg/kg of STROMECTOL®. Performed analysis confirmed that CHILD-IVITAB is associated with faster and more controlled absorption than STROMECTOL®. Simulations indicate that 250 µg/kg of CHILD-IVITAB achieves equivalent ivermectin exposure coverage in children < 15 kg as seen in children ≥ 15 kg and adults.

The current demand for pharmacometricians outmatches the supply provided by academic institutions and considerable investments are made to develop the competencies of these scientists on-the-job. Even with the observed increase in academic programs related to pharmacometrics, this need is unlikely to change in the foreseeable future, as the demand and scope of pharmacometrics applications keep expanding. Further, the field of pharmacometrics is changing. The field largely started when Lewis Sheiner and Stuart Beal published their seminal papers on population pharmacokinetics in the late 1970's and early 1980's and has continued to grow in impact and use since its inception. Physiological-based pharmacokinetics and systems pharmacology have grown rapidly in scope and impact in the last decade and machine learning is just on the horizon. While all these methodologies are categorized as pharmacometrics, no one person can be an expert in everything. So how do you train future pharmacometricians? Leading experts in academia, industry, contract research organizations, clinical medicine, and regulatory gave their opinions on how to best train future pharmacometricians. Their opinions were collected and synthesized to create some general recommendations. [ABSTRACT FROM AUTHOR]

There are examples in the literature demonstrating different approaches to defining the item characteristic functions (ICF) and characterizing the latent variable time-course within a pharmacometrics item response theory (IRT) framework. One such method estimates both the ICF and latent variable time-course simultaneously, and another method establishes the ICF first then models the latent variable directly. To date, a direct comparison of the “simultaneous” and “sequential” methodologies described in this work has not yet been systematically investigated. Item parameters from a graded response IRT model developed from Parkinson’s Progression Marker Initiative (PPMI) study data were used as simulation parameters. Each method was evaluated under the following conditions: (i) with and without drug effect and (ii) slow progression rate with smaller sample size and rapid progression rate with larger sample size. Overall, the methods performed similarly, with low bias and good precision for key parameters and hypothesis testing for drug effect. The ICF parameters were well determined when the model was correctly specified, with an increase in precision in the scenario with rapid progression. In terms of drug effect, both methods had large estimation bias for the slow progression rate; however, this bias can be considered small relative to overall progression rate. Both methods demonstrated type 1 error control and similar discrimination between model with and without drug effect. The simultaneous method was slightly more precise than the sequential method while the sequential method was more robust towards longitudinal model misspecification and offers practical advantages in model building. [ABSTRACT FROM AUTHOR]

The potential of using ChatGPT in pharmacometrics was explored in this study, with a focus on developing a population pharmacokinetic (PK) model for standard half‐life factor VIII. Our results demonstrated that ChatGPT can be utilized to accurately obtain typical PK parameters from literature, generate a population PK model in R and develop an interactive Shiny application to visualize the results. ChatGPT's language generation capabilities enabled the development of R codes with minimal programming knowledge and helped to identify as well fix errors in the code. While ChatGPT presents several advantages, such as its ability to streamline the development process, its use in pharmacometrics also has limitations and challenges, including the accuracy and reliability of AI‐generated data, the lack of transparency and reproducibility regarding codes generated by ChatGPT. Overall, our study demonstrates the potential of using ChatGPT in pharmacometrics, but researchers must carefully evaluate its use for their specific needs. [ABSTRACT FROM AUTHOR]

ABSTRACT This study aims to determine therapeutic protocols of intramuscular sodium cloxacillin (IM) in goats with potential antibacterial effects against Staphylococcus spp. We constructed a pharmacokinetic (PK) model of IM, followed by a pharmacokinetic/pharmacodynamic integration (PK/PD). Simulations of different therapeutic protocols were then performed, with the doses ranging from 30 to100 mg/kg every 8, 12, or 24 hours. We calculated the probability to target attainment (PTA) of reach protocol's therapeutic according to the minimum inhibitory concentration (MIC) range of 0.06 to 4 μg/mL. The PK/PD index (PDT) used was "time above the MIC for 40% of the time" (T>MIC ≥40%). Protocols with single administration per day were incapable of achieving PTA ≥ 90% for any of the estimated MICs. However, by decreasing the administration interval, the PTA was increased. Thus, from the dose of 50 mg/kg every 12 hours, a PTA≥ 90% for MICs ≤ 0.5 μg/mL was achieved, while the 30 mg/kg dose every 8 hours was able to achieve a PTA≥ 90% for MICs of 2 μg/mL. The results suggest using 30 mg/kg dose every 8 hours in clinical studies of agents with MICs ≤ 2μg/mL; Nevertheless, the practitioner should adjust the dose in severe patients.

Introduction: Forty-four percent of lactating women in the United States consume beverages containing low calorie sweeteners (LCS), and the presence of LCS in the food supply has continued to increase in recent years. While LCS are approved by the United States Food and Drug Administration (FDA) and are believed to be safe for human consumption, intergenerational LCS transmission and the health impacts of early life LCS exposure are severely understudied. Methods and analysis: In a tightly controlled, single site, prospective interventional study, mothers' plasma and breast milk, and infants’ plasma will be collected from 40 mother-infant dyads over the course of 72 h, with rich sampling following maternal ingestion of a LCS sweetened beverage containing sucralose and acesulfame potassium (ace-K). Concentration-time data will be used to build maternal and infant pharmacokinetic models for future simulations and analysis. Conclusion: This study aims to measure LCS concentrations in breast milk, maternal plasma, and infant plasma, to gain insight into infant exposure and inform recommendations for LCS consumption during breastfeeding.

Background: Lopinavir/ritonavir (LPV/r) is a drug traditionally used for the treatment of HIV that has been repurposed as a potential post-exposure prophylaxis agent against COVID-19 in the COronavirus Post-Exposure Prophylaxis (COPEP) study. The present analysis aims to evaluate LPV levels in individuals exposed to SARS-CoV-2 versus people living with HIV (PLWH) by developing a population pharmacokinetic (popPK) model, while characterizing external and patient-related factors that might affect LPV exposure along with dose–response association. Methods: We built a popPK model on 105 LPV concentrations measured in 105 HIV-negative COPEP individuals exposed to SARS-CoV-2, complemented with 170 LPV concentrations from 119 PLWH followed in our routine therapeutic drug-monitoring programme. Published LPV popPK models developed in PLWH and in COVID-19 patients were retrieved and validated in our study population by mean prediction error (MPE) and root mean square error (RMSE). The association between LPV model-predicted residual concentrations (Cmin) and the appearance of the COVID-19 infection in the COPEP participants was investigated. Results: A one-compartment model with linear absorption and elimination best described LPV concentrations in both our analysis and in the majority of the identified studies. Globally, similar PK parameters were found in all PK models, and provided close MPEs (from -19.4% to 8.0%, with a RMSE of 3.4% to 49.5%). No statistically significant association between Cmin and the occurrence of a COVID-19 infection could be detected. Conclusion: Our analysis indicated that LPV circulating concentrations were similar between COPEP participants and PLWH, and that published popPK models described our data in a comparable way. [ABSTRACT FROM AUTHOR]