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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]

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.

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]

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]

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 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 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]

Dapsone is employed for both non‐dermatological and dermatological indications but with non‐existent population pharmacokinetics (popPK) data in Nigerians. This study was therefore designed to develop a popPK model in Nigerians. Non‐compartmental analysis and nonlinear mixed effects modelling were utilized for data analysis. Eleven participants administered 50 mg dapsone tablet were included in the analysis. Derived pharmacokinetic parameters were: Cmax = 1.16 ± 0.32 μg/mL, Tmax = 3.77 ± 2.40 h, and t1/2z = 30.23 ± 11.76 h. PopPK model parameter estimates with inter‐individual variability were Tlag = 0.40 h (10.0%, fixed); ka = 1.78 h−1 (75.9%); V/F = 89.25 L (21.6%); and Cl/F = 1.32 Lh−1 (27.7%). Sex was significantly associated with Cl/F, and body weight with V/F. Best popPK model was one‐compartment with lag time, and first‐order absorption and elimination. Sex and body weight significantly influenced the clearance and distribution volume of dapsone respectively. [ABSTRACT FROM AUTHOR]

Aims: The aim of this study was to quantify identifiable sources of variability, including key pharmacogenetic variants in oxypurinol pharmacokinetics and their pharmacodynamic effect on serum urate (SU). Methods: Hmong participants (n = 34) received 100 mg allopurinol twice daily for 7 days followed by 150 mg allopurinol twice daily for 7 days. A sequential population pharmacokinetic pharmacodynamics (PKPD) analysis with non‐linear mixed effects modelling was performed. Allopurinol maintenance dose to achieve target SU was simulated based on the final PKPD model. Results: A one‐compartment model with first‐order absorption and elimination best described the oxypurinol concentration–time data. Inhibition of SU by oxypurinol was described with a direct inhibitory Emax model using steady‐state oxypurinol concentrations. Fat‐free body mass, estimated creatinine clearance and SLC22A12 rs505802 genotype (0.32 per T allele, 95% CI 0.13, 0.55) were found to predict differences in oxypurinol clearance. Oxypurinol concentration required to inhibit 50% of xanthine dehydrogenase activity was affected by PDZK1 rs12129861 genotype (−0.27 per A allele, 95% CI −0.38, −0.13). Most individuals with both PDZK1 rs12129861 AA and SLC22A12 rs505802 CC genotypes achieve target SU (with at least 75% success rate) with allopurinol below the maximum dose, regardless of renal function and body mass. In contrast, individuals with both PDZK1 rs12129861 GG and SLC22A12 rs505802 TT genotypes would require more than the maximum dose, thus requiring selection of alternative medications. Conclusions: The proposed allopurinol dosing guide uses individuals' fat‐free mass, renal function and SLC22A12 rs505802 and PDZK1 rs12129861 genotypes to achieve target SU. [ABSTRACT FROM AUTHOR]

Aims: Aripiprazole is one of the most commonly prescribed antipsychotic drugs to children and adolescents worldwide, but it is associated with serious side‐effects, including weight gain. This study assessed the population pharmacokinetics of aripiprazole and its active metabolite and investigated the relationship between pharmacokinetic parameters and body mass index (BMI) in children and adolescents with autism spectrum disorder (ASD) and behavioural problems. Secondary outcomes were metabolic, endocrine, extrapyramidal and cardiac side‐effects and drug effectiveness. Methods: Twenty‐four children and adolescents (15 males, 9 females) aged 6–18 years were included in a 24‐week prospective observational trial. Drug plasma concentrations, side‐effects and drug effectiveness were measured at several time points during follow‐up. Relevant pharmacokinetic covariates, including CYP2D6, CYP3A4, CYP3A5 and P‐glycoprotein (ABCB1) genotypes, were determined. Nonlinear mixed‐effects modelling (NONMEM®) was used for a population pharmacokinetic analysis with 92 aripiprazole and 91 dehydro‐aripiprazole concentrations. Subsequently, model‐based trough concentrations, maximum concentrations and 24‐h area under the curves (AUCs) were analysed to predict outcomes using generalized and linear mixed‐effects models. Results: For both aripiprazole and dehydro‐aripiprazole, one‐compartment models best described the measured concentrations, with albumin and BMI as significant covariates. Of all the pharmacokinetic parameters, higher sum (aripiprazole plus dehydro‐aripiprazole) trough concentrations best predicted higher BMI z‐scores (P <.001) and higher Hb1Ac levels (P =.03) during follow‐up. No significant association was found between sum concentrations and effectiveness. Conclusions: Our results indicate a threshold with regard to safety, which suggests that therapeutic drug monitoring of aripiprazole could potentially increase safety in children and adolescents with ASD and behavioural problems. [ABSTRACT FROM AUTHOR]

Therefore, in 2021, we officially launched the Ugandan Chapter of Pharmacometrics Africa comprising of individuals trained in pharmacometrics but working in Uganda with the aim to Transfer the primary training responsibility to local faculty for sustainability. Keywords: clinical pharmacology; mentorship; pharmacodynamic; pharmacokinetic; pharmacometrics; supervision; training EN clinical pharmacology mentorship pharmacodynamic pharmacokinetic pharmacometrics supervision training 2658 2661 4 08/17/23 20230901 NES 230901 INTRODUCTION Pharmacometrics involves the development and application of mathematical models, informed by knowledge on pharmacology, physiology and the disease process, to characterize the interactions between drugs and patients. A commentary on establishing a local centre of excellence for research and training in pharmacometrics: Lessons from the pharmacometrics Africa-Uganda chapter. [Extracted from the article]

Aims: Fibrinogen is the key substrate for coagulation. Fibrinogen pharmacokinetics (PK) after single doses of fibrinogen concentrate (FC), using modelling approaches, has only been evaluated in congenital afibrinogenaemic patients. The aims of this study are to characterize the fibrinogen PK in patients with acquired—chronic (cirrhosis) or acute—hypofibrinogenaemia (critical haemorrhage), showing endogenous production. Influencing factors of differences on the fibrinogen PK between subpopulations will be identified. Methods: A total of 428 time–concentration values from 132 patients were recorded. Eighty‐two out of 428 values were from 41 cirrhotic patients administered with placebo and 90 out of 428 were from 45 cirrhotic patients that were given FC, 161 out of 428 values were from 14 afibrinogenaemic patients and 95 out of 428 values were from 32 severe acute trauma haemorrhagic patients. A turnover model that accounted for endogenous production and exogenous dose was fitted using NONMEM74. The production rate (Ksyn), distribution volume (V), plasma clearance (CL) and concentration yielding to 50% of maximal fibrinogen production (EC50) were estimated. Results: Fibrinogen disposition was described by a one‐compartment model with CL and V values of 0.0456 L·h−1 and 4.34 L·70 kg−1, respectively. Body weight was statistically significant in V. Three different Ksyn values were identified that increased from 0.00439 g·h−1 (afibrinogenaemia), to 0.0768 g·h−1 (cirrhotics) and 0.1160 g·h−1 (acute severe trauma). EC50 was of 0.460 g·L−1. Conclusions: This model will be key as a support tool for dose calculation to achieve specified target fibrinogen concentrations, in each of the studied populations. [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.

Aims: This study aimed to characterize the population pharmacokinetic parameters of intravenously administered amikacin in newborns and assess the effect of sepsis in amikacin exposure. Methods: Newborns aged ≥3 days who received at least 1 dose of amikacin during their hospitalization period were eligible for the study. Amikacin was administered intravenously during a 60‐min infusion period. Three venous blood samples were taken from each patient during the first 48 h. Population pharmacokinetic parameter estimates were obtained using a population approach with the programme NONMEM. Results: Data from 329 drug assay samples were obtained from 116 newborn patients (postmenstrual age [PMA] 38.3, range 32–42.4 weeks; weight 2.8, range 1.6–3.8 kg). Measured amikacin concentrations ranged from 0.8 to 56.4 mg/L. A 2‐compartment model with linear elimination produced a good fit of the data. Estimated parameters for a typical subject (2.8 kg, 38.3 weeks) were clearance (Cl = 0.16 L/h), intercompartmental clearance (Q = 0.15 L/h), volume of distribution of the central compartment (Vc = 0.98 L) and peripheral volume of distribution (Vp = 1.23 L). Total bodyweight, PMA and the presence of sepsis positively influenced Cl. Plasma creatinine concentration and circulatory instability (shock) negatively influenced Cl. Conclusion: Our main results confirm previous findings showing that weight, PMA and renal function are relevant factors influencing newborn amikacin pharmacokinetics. In addition, current results showed that pathophysiological states of critically ill neonates, such as sepsis and shock, were associated with opposite effects in amikacin clearance and should be considered in dose adjustments. [ABSTRACT FROM AUTHOR]

Background: There are established correlations between risk factors and ischemic stroke (IS) recurrence; however, does the hazard of recurrent IS change over time? What is the predicted baseline hazard of recurrent IS if there is no influence of variable predictors? This study aimed to quantify the hazard of recurrent IS when the variable predictors were set to zero and quantify the secondary prevention influence on the hazard of recurrent ischemic stroke. Methods: In the population cohort involved in this study, data were extracted from 7,697 patients with a history of first IS attack registered with the National Neurology Registry of Malaysia from 2009 to 2016. A time-to-recurrent IS model was developed using NONMEM version 7.5. Three baseline hazard models were fitted into the data. The best model was selected using maximum likelihood estimation, clinical plausibility, and visual predictive checks. Results: Within the maximum 7.37 years of follow-up, 333 (4.32%) patients had at least one incident of recurrent IS. The data were well described by the Gompertz hazard model. Within the first 6 months after the index IS, the hazard of recurrent IS was predicted to be 0.238, and 6 months after the index attack, it reduced to 0.001. The presence of typical risk factors such as hyperlipidemia [HR, 2.22 (95%CI: 1.81-2.72)], hypertension [HR, 2.03 (95%CI: 1.52-2.71)], and ischemic heart disease [HR, 2.10 (95%CI: 1.64-2.69)] accelerated the hazard of recurrent IS, but receiving antiplatelets (APLTs) upon stroke decreased this hazard [HR, 0.59 (95%CI: 0.79-0.44)]. Conclusion: The hazard of recurrent IS magnitude differs during different time intervals based on the concomitant risk factors and secondary prevention. [ABSTRACT FROM AUTHOR]

Background: A critical step in tuberculosis (TB) drug development is the Phase 2a early bactericidal activity (EBA) study which informs if a new drug or treatment has short-term activity in humans. The aim of this work was to present a standardized pharmacometric model-based early bactericidal activity analysis workflow and determine sample sizes needed to detect early bactericidal activity or a difference between treatment arms. Methods: Seven different steps were identified and developed for a standardized pharmacometric model-based early bactericidal activity analysis approach. Nonlinear mixed effects modeling was applied and different scenarios were explored for the sample size calculations. The sample sizes needed to detect early bactericidal activity given different TTP slopes and associated variability was assessed. In addition, the sample sizes needed to detect effect differences between two treatments given the impact of different TTP slopes, variability in TTP slope and effect differences were evaluated. Results: The presented early bactericidal activity analysis approach incorporates estimate of early bactericidal activity with uncertainty through the model-based estimate of TTP slope, variability in TTP slope, impact of covariates and pharmacokinetics on drug efficacy. Further it allows for treatment comparison or dose optimization in Phase 2a. To detect early bactericidal activity with 80% power and at a 5% significance level, 13 and 8 participants/arm were required for a treatment with a TTP-EBA0-14 as low as 11 h when accounting for variability in pharmacokinetics and when variability in TTP slope was 104% [coefficient of variation (CV)] and 22%, respectively. Higher sample sizes are required for smaller early bactericidal activity and when pharmacokinetics is not accounted for. Based on sample size determinations to detect a difference between two groups, TTP slope, variability in TTP slope and effect difference between two treatment arms needs to be considered. Conclusion: In conclusion, a robust standardized pharmacometric model-based EBA analysis approach was established in close collaboration between microbiologists, clinicians and pharmacometricians. The work illustrates the importance of accounting for covariates and drug exposure in EBA analysis in order to increase the power of detecting early bactericidal activity for a single treatment arm as well as differences in EBA between treatments arms in Phase 2a trials of TB drug development. [ABSTRACT FROM AUTHOR]

A common approach to assess the efficacy of piperacillin is to first measure the total concentration and afterwards apply a theoretical unbound fraction of 70% to obtain the unbound concentration. However, hypoalbuminemia is a common phenomenon in critically ill patients, resulting in variations in unbound fraction, therefore we aimed to simulate the impact of piperacillin unbound fraction fluctuations on the predictive performance of a population pharmacokinetic model and on the dosing recommendations of piperacillin. Unbound factors of 70%, 75%, 80% and 85% were applied to total concentrations of piperacillin administered by continuous infusion from an external dataset. A validated model was used for assessment of predictive performance and to estimate patient clearance. Dosing simulations were performed to evaluate target attainment. Variation in unbound fractions caused minimal impact on piperacillin clearance and target attainment but seemed to influence model validity. [ABSTRACT FROM AUTHOR]

Pharmacometrics and the utilization of population pharmacokinetics play an integral role in model-informed drug discovery and development (MIDD). Recently, there has been a growth in the application of deep learning approaches to aid in areas within MIDD. In this study, a deep learning model, LSTM-ANN, was developed to predict olanzapine drug concentrations from the CATIE study. A total of 1527 olanzapine drug concentrations from 523 individuals along with 11 patient-specific covariates were used in model development. The hyperparameters of the LSTM-ANN model were optimized through a Bayesian optimization algorithm. A population pharmacokinetic model using the NONMEM model was constructed as a reference to compare to the performance of the LSTM-ANN model. The RMSE of the LSTM-ANN model was 29.566 in the validation set, while the RMSE of the NONMEM model was 31.129. Permutation importance revealed that age, sex, and smoking were highly influential covariates in the LSTM-ANN model. The LSTM-ANN model showed potential in the application of drug concentration predictions as it was able to capture the relationships within a sparsely sampled pharmacokinetic dataset and perform comparably to the NONMEM model. [ABSTRACT FROM AUTHOR]

The field of pharmacometrics has been responsible for countless advancements within the drug development space. In recent years, we have witnessed the implementation of both new and revived analytical methods to increase clinical trial success and even supplement the need for clinical trials all together. Throughout this article, we will explore the path of pharmacometrics from its inception to the present day. At this point in time, the target of drug development has been the average patient, and population approaches have primarily been utilized to support just that. The challenge we are now facing involves the translation from treating the typical patient to treating the real‐world patient. For this reason, it is our opinion that future development efforts should account more for the individual. With advanced pharmacometric methods and growing technological infrastructure, precision medicine can become a development priority rather than a clinician's burden. [ABSTRACT FROM AUTHOR]

Aims: Pharmacokinetics of tacrolimus after sublingual administration is not characterized in paediatric liver transplant patients. Therefore, we aimed to develop a population pharmacokinetic model of sublingually administered tacrolimus in patients who cannot swallow the capsules due to their age, sedation status and/or mechanical ventilation during the first weeks post‐transplantation. Methods: Demographic, clinical and pharmacological variables, including tacrolimus whole blood concentrations obtained from therapeutic drug monitoring and data from dense‐sampling pharmacokinetic profiles, were recorded in 26 paediatric patients with biliary atresia who underwent liver transplantation between 2016 and 2021. Population pharmacokinetic analysis was performed with NONMEM v7.4. Results: Disposition of tacrolimus was best characterized by a 2‐compartment model with clearance achieving half of the maximum elimination capacity (CLMAX = 4.1 L/h) at 4.6 days post‐transplantation (T50). Compared to sedated patients, nonsedated status showed an increased first‐order absorption rate constant (1.1 vs. 0.1 h−1) and a 24% reduction in bioavailability (FNS) at 14 days post‐transplant. The model was able to explain the oral absorption pattern in nonsedated patients as the result of gut bioavailability (0.9) and hepatic extraction ratio, with the latter being responsible for first‐pass effects. Estimates of interindividual variability remained moderate (25.9% for the gut bioavailability) to high (79.8% for the apparent volume of distribution of the central compartment, and 101% for T50). Conclusion: A population pharmacokinetic model of sublingually administered tacrolimus in paediatric patients was developed to characterize different absorption mechanisms. Once the model is externally validated, the effect of post‐transplant time on clearance and the sedation status may be considered in routine dosing management. [ABSTRACT FROM AUTHOR]

Monitoring cortisol replacement therapy in congenital adrenal hyperplasia (CAH) patients is vital to avoid serious adverse events such as adrenal crises due to cortisol underexposure or metabolic consequences due to cortisol overexposure. The less invasive dried blood spot (DBS) sampling is an advantageous alternative to traditional plasma sampling, especially in pediatric patients. However, target concentrations for important disease biomarkers such as 17α-hydroxyprogesterone (17-OHP) are unknown using DBS. Therefore, a modeling and simulation framework, including a pharmacokinetic/pharmacodynamic model linking plasma cortisol concentrations to DBS 17-OHP concentrations, was used to derive a target morning DBS 17-OHP concentration range of 2–8 nmol/L in pediatric CAH patients. Since either capillary or venous DBS sampling is becoming more common in the clinics, the clinical applicability of this work was shown by demonstrating the comparability of capillary and venous cortisol and 17-OHP concentrations collected by DBS sampling, using a Bland-Altman and Passing-Bablok analysis. The derived target morning DBS 17-OHP concentration range is a first step towards providing improved therapy monitoring using DBS sampling and adjusting hydrocortisone (synthetic cortisol) dosing in children with CAH. In the future, this framework can be used to assess further research questions, e.g., target replacement ranges for the entire day. [ABSTRACT FROM AUTHOR]

PHARMACOM-EPI is a novel framework to predict plasma concentrations of drugs at the time of occurrence of clinical outcomes. In early 2021, the U.S. Food and Drug Administration (FDA) issued a warning on the antiseizure drug lamotrigine claiming that it has the potential to increase the risk of arrhythmias and related sudden cardiac death due to a pharmacological sodium channel-blocking effect. We hypothesized that the risk of arrhythmias and related death is due to toxicity. We used the PHARMACOM-EPI framework to investigate the relationship between lamotrigine’s plasma concentrations and the risk of death in older patients using real-world data. Danish nationwide administrative and healthcare registers were used as data sources and individuals aged 65 years or older during the period 1996 – 2018 were included in the study. According to the PHARMACOM-EPI framework, plasma concentrations of lamotrigine were predicted at the time of death and patients were categorized into non-toxic and toxic groups based on the therapeutic range of lamotrigine (3–15 mg/L). Over 1 year of treatment, the incidence rate ratio (IRR) of all-cause mortality was calculated between the propensities score matched toxic and non-toxic groups. In total, 7286 individuals were diagnosed with epilepsy and were exposed to lamotrigine, 432 of which had at least one plasma concentration measurement The pharmacometric model by Chavez et al. was used to predict lamotrigine’s plasma concentrations considering the lowest absolute percentage error among identified models (14.25 %, 95 % CI: 11.68–16.23). The majority of lamotrigine associated deaths were cardiovascular-related and occurred among individuals with plasma concentrations in the toxic range. The IRR of mortality between the toxic group and non-toxic group was 3.37 [95 % CI: 1.44–8.32] and the cumulative incidence of all-cause mortality exponentially increased in the toxic range. Application of our novel framework PHARMACOM-EPI provided strong evidence to support our hypothesis that the increased risk of all-cause and cardiovascular death was associated with a toxic plasma concentration level of lamotrigine among older lamotrigine users.

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.

One of the most promising trends in clinical pharmacology is pharmacometrics, a combination of pharmacology and statistics that implements quantitative approaches for characterising dose–response relationships and predicting the variability of these relationships attributable to patient-specific characteristics (covariates). The aim of the study was to evaluate the significance of quantitative clinical pharmacology and discuss opportunities for its development in the context of health systems moving towards the value-based care model. The study showed that two key prerequisites for pharmacometrics development were the advancements in mathematical and statistical methodology based upon non-linear mixed effects regression modelling and the emergence of a personalised medicine paradigm aimed at creation of strategies for individualised prescribing of medicinal products. The study demonstrated the necessity for using the dose–response relationship information obtained by exploratory analysis of data stored in existing and newly created bases. Further integration of pharmacostatistical modelling and real-world data processing technologies, as well as their incorporation into clinical and economic evaluation of health technologies, will streamline decision making and, thus, facilitate the transition of health systems to the value-based model.

BackgroundThere are established correlations between risk factors and ischemic stroke (IS) recurrence; however, does the hazard of recurrent IS change over time? What is the predicted baseline hazard of recurrent IS if there is no influence of variable predictors? This study aimed to quantify the hazard of recurrent IS when the variable predictors were set to zero and quantify the secondary prevention influence on the hazard of recurrent ischemic stroke.MethodsIn the population cohort involved in this study, data were extracted from 7,697 patients with a history of first IS attack registered with the National Neurology Registry of Malaysia from 2009 to 2016. A time-to-recurrent IS model was developed using NONMEM version 7.5. Three baseline hazard models were fitted into the data. The best model was selected using maximum likelihood estimation, clinical plausibility, and visual predictive checks.ResultsWithin the maximum 7.37 years of follow-up, 333 (4.32%) patients had at least one incident of recurrent IS. The data were well described by the Gompertz hazard model. Within the first 6 months after the index IS, the hazard of recurrent IS was predicted to be 0.238, and 6 months after the index attack, it reduced to 0.001. The presence of typical risk factors such as hyperlipidemia [HR, 2.22 (95%CI: 1.81–2.72)], hypertension [HR, 2.03 (95%CI: 1.52–2.71)], and ischemic heart disease [HR, 2.10 (95%CI: 1.64–2.69)] accelerated the hazard of recurrent IS, but receiving antiplatelets (APLTs) upon stroke decreased this hazard [HR, 0.59 (95%CI: 0.79–0.44)].ConclusionThe hazard of recurrent IS magnitude differs during different time intervals based on the concomitant risk factors and secondary prevention.

Background: A critical step in tuberculosis (TB) drug development is the Phase 2a early bactericidal activity (EBA) study which informs if a new drug or treatment has short-term activity in humans. The aim of this work was to present a standardized pharmacometric model-based early bactericidal activity analysis workflow and determine sample sizes needed to detect early bactericidal activity or a difference between treatment arms.Methods: Seven different steps were identified and developed for a standardized pharmacometric model-based early bactericidal activity analysis approach. Non-linear mixed effects modeling was applied and different scenarios were explored for the sample size calculations. The sample sizes needed to detect early bactericidal activity given different TTP slopes and associated variability was assessed. In addition, the sample sizes needed to detect effect differences between two treatments given the impact of different TTP slopes, variability in TTP slope and effect differences were evaluated.Results: The presented early bactericidal activity analysis approach incorporates estimate of early bactericidal activity with uncertainty through the model-based estimate of TTP slope, variability in TTP slope, impact of covariates and pharmacokinetics on drug efficacy. Further it allows for treatment comparison or dose optimization in Phase 2a. To detect early bactericidal activity with 80% power and at a 5% significance level, 13 and 8 participants/arm were required for a treatment with a TTP-EBA0-14 as low as 11 h when accounting for variability in pharmacokinetics and when variability in TTP slope was 104% [coefficient of variation (CV)] and 22%, respectively. Higher sample sizes are required for smaller early bactericidal activity and when pharmacokinetics is not accounted for. Based on sample size determinations to detect a difference between two groups, TTP slope, variability in TTP slope and effect difference between two treatment arms needs to be considered.Conclusion: In conclusion, a robust standardized pharmacometric model-based EBA analysis approach was established in close collaboration between microbiologists, clinicians and pharmacometricians. The work illustrates the importance of accounting for covariates and drug exposure in EBA analysis in order to increase the power of detecting early bactericidal activity for a single treatment arm as well as differences in EBA between treatments arms in Phase 2a trials of TB drug development.

Background: Tamoxifen is important in the adjuvant treatment of breast cancer. A plasma concentration of the active metabolite endoxifen of > 16 nM is associated with a lower risk of breast cancer-recurrence. Since inter-individual variability is high and > 20 % of patients do not reach endoxifen levels > 16 nM with the standard dose tamoxifen, therapeutic drug monitoring is advised. However, ideally, the correct tamoxifen dose should be known prior to start of therapy. Our aim is to develop a population pharmacokinetic (POP-PK) model incorporating a continuous CYP2D6 activity scale to support model informed precision dosing (MIPD) of tamoxifen to determine the optimal tamoxifen starting dose. Methods: Data from eight different clinical studies were pooled (539 patients, 3661 samples) and used to develop a POP-PK model. In this model, CYP2D6 activity per allele was estimated on a continuous scale. After inclusion of covariates, the model was subsequently validated using an independent external dataset (378 patients). Thereafter, dosing cut-off values for MIPD were determined. Results: A joint tamoxifen/endoxifen POP-PK model was developed describing the endoxifen formation rate. Using a continuous CYP2D6 activity scale, variability in predicting endoxifen levels was decreased by 37 % compared to using standard CYP2D6 genotype predicted phenotyping. After external validation and determination of dosing cut-off points, MIPD could reduce the proportion of patients with subtherapeutic endoxifen levels at from 22.1 % toward 4.8 %. Conclusion: Implementing MIPD from the start of tamoxifen treatment with this POP-PK model can reduce the proportion of patients with subtherapeutic endoxifen levels at steady-state to less than 5 %.

Aims: To describe the pharmacokinetics (PK) of cefotaxime as pre‐emptive treatment in critically ill adult patients, including covariates and to determine the probability of target attainment (PTA) of different dosage regimens for Enterobacterales and Staphylococcus aureus. Methods: Five samples were drawn during 1 dosage interval in critically ill patients treated with cefotaxime 1 g q6h or q4h. PK parameters were estimated using NONMEM (v7.4.2). The percentage of patients reaching 100% fT>MICECOFF was used to compare different dosage regimens for Enterobacterales and S. aureus. Results: This study included 92 patients (437 samples). The best structural model was a 2‐compartment model with a combined error, interindividual variability on clearance, central volume and intercompartmental clearance. Correlations between interindividual variability were included. Clearance increased with higher estimated glomerular filtration rate (eGFR; creatinine clearance) and albumin concentration. For Enterobacterales, 1 g q8h reached 95% PTA and continuous infusion (CI) of 4 g 24 h−1 100% PTA at the highest eGFR and albumin concentration. For S. aureus the predefined target of 95% PTA was not reached with higher eGFR and/or albumin concentrations. CI of 6 g 24 h−1 for S. aureus resulted in a minimum of 99% PTA. Conclusion: Cefotaxime PK in critically ill patients was best described by a 2‐compartment model with eGFR and albumin concentration as covariates influencing clearance. For Enterobacterales 1 g q8h or CI of 4 g 24 h−1 was adequate for all combinations of eGFR and albumin concentration. For S. aureus CI of 6 g 24 h−1 would be preferred if eGFR and albumin concentration exceed 80 mL min−1 and 40 g L−1 respectively. [ABSTRACT FROM AUTHOR]

Linezolid is an oxazolidinone used to treat multidrug-resistant tuberculosis (MDR-TB), including in the recently-endorsed shorter 6-month treatment regimens. Due to its narrow therapeutic index, linezolid is often either dose-adjusted or discontinued due to intolerance or toxicity during treatment, and the optimal balance between linezolid efficacy and toxicity remains unclear. India carries a significant burden of MDR-TB cases in the world, but limited information on the pharmacokinetics of linezolid and minimum inhibitory concentration (MIC) distribution is available from Indian MDR-TB patients. We enrolled participants from a tertiary care centre in Mumbai, India, treated for MDR-TB and receiving linezolid daily doses of 600 or 300 mg. Pharmacokinetic visits were scheduled between 1 and 15 months after treatment initiation to undergo intensive or sparse blood sampling. Linezolid concentration versus time data were analysed using non-linear mixed-effects modelling, with simulations to evaluate doses for different scenarios. We enrolled 183 participants (121 females), with a median age of 26 years (interquartile range [IQR] 21--35), weight 55.0 kg (IQR 45.6--65.8), and fat-free mass 38.7 kg (IQR 32.7--46.0). Linezolid pharmacokinetics was best described by a one-compartment model with first-order elimination allometrically scaled by fat-free mass and transit compartment absorption. The typical clearance value was 3.81 L/h. Simulations predicted that treatment with 300 mg daily achieves a high probability of target attainment (PTA) when linezolid MIC was ≤0.25 mg/L (61.5% of participant samples tested), while 600 mg daily would be required if MIC were 0.5 mg/L (29% of samples). While linezolid 300 mg daily is predicted to achieve effective targets for the majority of adults with MDR-TB, it failed to achieve the therapeutic target for 21% participants. A dose of 600 mg had a PTA >90% for all susceptible samples, but with a higher likelihood of exceeding toxicity thresholds (31% vs 9.6%). These data suggest potential benefit to individualized dosing taking host and microbial characteristics into account to improve the likelihood of treatment efficacy while minimizing risk of toxicity from linezolid for the treatment of MDR-TB. Further prospective evaluation in different clinical settings is urgently needed to inform safety and efficacy of these lower doses. [ABSTRACT FROM AUTHOR]

Aim: nlmixr offers first‐order conditional estimation (FOCE), FOCE with interaction (FOCEi) and stochastic approximation estimation‐maximisation (SAEM) to fit nonlinear mixed‐effect models (NLMEM). We modelled metformin's pharmacokinetic data using nlmixr and investigated SAEM and FOCEi's performance with respect to bias and precision of parameter estimates, and robustness to initial estimates. Method: Compartmental models were fitted. The final model was determined based on the objective function value and inspection of goodness‐of‐fit plots. The bias and precision of parameter estimates were compared between SAEM and FOCEi using stochastic simulations and estimations. For robustness, parameters were re‐estimated as the initial estimates were perturbed 100 times and resultant changes evaluated. Results: The absorption kinetics of metformin depend significantly on food status. Under the fasted state, the first‐order absorption into the central compartment was preceded by zero‐order infusion into the depot compartment, whereas for the fed state, the absorption into the depot was instantaneous followed by first‐order absorption from depot into the central compartment. The means of relative mean estimation error (rMEE) (MEESAEMMEEFOCEi) and rRMSE (RMSESAEMRMSEFOCEi) were 0.48 and 0.35, respectively. All parameter estimates given by SAEM appeared to be narrowly distributed and were close to the true value used for simulation. In contrast, the distribution of estimates from FOCEi were skewed and more biased. When initial estimates were perturbed, FOCEi estimates were more biased and imprecise. Discussion: nlmixr is reliable for NLMEM. SAEM was superior to FOCEi in terms of bias and precision, and more robust against initial estimate perturbations. [ABSTRACT FROM AUTHOR]