bluuebunny/crossref_metadata_2025 huggingface dataset:
Cystatin C complements but does not exceed serum creatinine as a renal function marker: evidence from vancomycin pharmacokinetic model performance in a clinical context
Authors: Tong Dominic , Hughes Jasmine , Keizer Ron
Published: February 2025

Objectives: Cystatin C (CYSC) and serum creatinine (CR) are two biomarkers used in estimating glomerular filtration rate (GFR), a marker of kidney function. CYSC has been shown to be more accurate for older patients and patients with lower muscle mass [1-2], whereas CR is more widely available at a lower cost. For population pharmacokinetic (popPK) models, an accurate estimate of kidney function is critical in determining drug clearance. In vancomycin, popPK models using CR [3-7] or CYSC [1-2] have been developed and used to tailor dosing for individual patients clinically. Here, we compare CR-based and CYSC-based popPK model performance for adult patients treated with vancomycin, and evaluate whether a refit model using one or both biomarkers improves vancomycin prediction.Methods: De-identified retrospective EHR data describing 513 adult patients treated with vancomycin at 16 health systems were used in this analysis. The best performing popPK models describing adult vancomycin PK using CR were identified from previous work, while models using CYSC were identified from literature and evaluated for fit for use in this population [8]. Metrics used to evaluate iterative prediction performance were accuracy (defined as predictions within 20% or 2.5 mg/L of the measured value), mean percent error (MPE), and normalized root mean square error (nRMSE). We then refitted the Hughes model [3] using different GFR equations on 70% of the data and evaluated its performance on the other 30%. GFR was evaluated using the Hoek equation [8] for cystatin C GFR, the CKD-EPI equations for GFR with and without cystatin C [9], and combining Cockcroft-Gault eGFR with a cystatin C term directly for vancomycin clearance. Results: CR-based models generally outperformed CYSC-based models in predicting vancomycin concentrations with the best models for each biomarker being UVM and Liu (accuracy: 44.3%, 43.0% Liu; MPE: -4.9%, 3.3%; nRMSE: 41.1%, 42.1% for UVM and Liu, respectively). The refitted models that use both CYSC and CR, whether via CKD-EPI (Accuracy: 53.2%) or CYSC directly (53.2%) performed better than CR alone (46.1%) or CYSC alone (48.9%) . Conclusions: Clinically proven CR-based popPK models were superior to published CYSC-based models in predicting vancomycin levels. Refitting models with CYSC and/or CR showed CYSC is not a useful substitute for CR. A combined model including both CYSC and CR improved vancomycin prediction performance slightly, though this improvement may not be clinically relevant.Citations: [1] Liu, J Pharm Pharmacol, 2019[2] Tanaka, AAC, 2010[3] Hughes, TDM, 2024[4] Tong, TDM, 2021[5] Colin, Clin Pharmacokinet, 2019[6] Thomson, JAC, 2009[7] Carreno, AAC, 2017[8] Tong, Pharmaceutics, 2022[9] Hoek, Nephrol Dial Transplant, 2003[10] Inker, NEJM, 2021
Evaluation of the population pharmacokinetics of pexmetinib in a simultaneous model of two formulations
Authors: Sun Steven , Wollenberg Lance , Piscitelli Joseph
Published: February 2025

Objectives: Pexmetinib is a novel dual inhibitor of p38 mitogen-activated protein kinase and endothelial tyrosine kinase receptor, Tie2, under prior investigation in low or intermediate-1 risk myelodysplastic syndrome (MDS) (NCT00916227 and NCT01496495). The objectives of this analysis were to describe the population pharmacokinetics (popPK) of pexmetinib and to evaluate potential covariates that may be important predictors of pexmetinib pharmacokinetics (PK). Methods: Data from two Phase 1 healthy volunteer studies and two Phase 1 studies in low or intermediate-1 risk MDS patients were included in this analysis. Two different formulations of pexmetinib have been used in patients in clinical development, a neat powder-in-capsule (PIC) formulation and an enabled liquid-filled capsule (LFC) formulation. Upon selection of a base model accounting for formulation effect via a relative oral bioavailability term (F), a forward selection approach was utilized for the univariate and multivariate screen (p < 0.05) followed by a backward deletion approach (p < 0.01). First order conditional estimation with interaction was used in NONMEM v7.5.0 for pexmetinib popPK model development. Results: 113 participants received the PIC formulation and 65 participants received the LFC formulation. A 2-compartment model with a sequential zero- and first-order absorption (2.13 hr; 0.44 hr-1, respectively) and a relative F term to model formulation effect was used to describe the pooled clinical PK data (3737 concentrations from 178 subjects) from both formulations simultaneously. The relative F of pexmetinib was decreased by approximately 92% when taking the PIC formulation compared to taking the LFC formulation. Subject’s health status (healthy or diagnosed with MDS) was an independent predictor of both apparent clearance (CL/F) and central volume of distribution (Vc/F), whereas weight was an independent predictor only for Vc/F. CL/F decreased by 32% and Vc/F increased by 170% in healthy subjects compared to those diagnosed with MDS. The effect of weight on Vc/F was 1.73, suggesting that the heaviest patients in study had a 2.4 higher fold Vc/F compared to the typical patients in study. All model parameters were estimated with good precision (relative standard error < 45%). Our final popPK model estimates for CL/F, Q/F, Vc/F, and apparent peripheral volume of distribution were 16.4 L/hr, 9 L/hr, 13 L, and 95.6 L, respectively. Conclusion: A population PK model has been developed to describe two different formulations of pexmetinib tested in clinical studies. Although weight was retained as a significant covariate on volume of distribution, the clinical implications of this are thought to be minimal, and no dosing adjustment is needed on the basis of patient weight. In general, the model fit the pooled clinical data well; however, other absorption models may be explored to further characterize the formulation effect.Citations: NA
MBMA Bridging Models as A Tool for Exploration of Clinical Endpoints in Unstudied Indications.
Authors: Javidi Mehrdad , Kosoglou Teddy , Vermeulen An , Valiathan Chandni
Published: February 2025

Objectives: In many instances, drugs used for one indication may be viable for a different indication. However, a randomized controlled trial (RCT) in the new indication of interest is often needed to determine the efficacy or safety of the treatment profile in another disease area. One approach to accelerate clinical development for treatments in a new indication is to use Model-Based Meta-Analysis (MBMA). MBMA is a quantitative approach of utilizing aggregate data from RCTs to develop modeling tools that incorporate properties of clinical data such as drug, dose, time, and other covariates. This approach leverages mathematical models using information-rich datasets to facilitate bridging of clinical endpoints across indications to help determine how novel treatments could behave in a new indication. An example from immunology is used to illustrate the methodology. Methods: Multi-Center, randomized placebo controlled and parallel design studies investigating the treatments of psoriasis (PsO) and psoriatic arthritis (PsA) published between 2000 and 2023 were identified using a systematic literature review from PUBMED, clinicaltrials.gov and other sources (e.g., FDA clinical review). The bridging approach combines multiple datasets to create a joint non-linear model across different indications/endpoints and using data from the first indication the model generates clinical efficacy in new indications. The longitudinal, non-linear mixed effects parametric placebo bridging model with Emax dose response, was developed to bridge between PsO and PsA. PASI75 scores for both indications were used to bridge across indications and additional shift parameters were used to bridge to the ACR20 endpoint commonly used in PsA. A leave-one-out cross-validation (LOOCV) analysis was performed, where a deucravacitinib study in PsA for the ACR20 endpoint was removed from the analysis and compared using the model. Results: The total of 85 studies were identified for PsO and PsA combined, with 49 studies in PsO. This large number of studies and datapoints allowed us to capture drug performance from one endpoint to the next with good precision. In the LOOCV, the models mean [90% CI] ACR20 score of 51.7% [39.1%, 63.8%] for deucravacitinib 6 mg QD at week 24, which closely matched the observed value of 52.9%. Similarly, projections or efficacy in PsA can be estimated using data from PsO.Conclusion: There is often an interest in understanding potential clinical efficacy or safety of treatments in new indications. The combination of datasets across indications facilitated the use of information about the population and therapeutic landscape for cross indication and endpoint analysis using MBMA. The results of the cross-validation showed that efficacy of a new treatment in PsA can be assessed using the MBMA bridging method using data from another indication like PsO. Such assessments can be utilized to explore new treatments for new indications.Citations: N/A
Gompertz Cure Rate Survival Models with Stan and Brms
Authors: Yoder Todd , Tredennick Andrew , Waterhouse Tim
Published: February 2025

Objectives: In drug safety data it is common for a proportion of the population to never experience a specified adverse event. Standard parametric survival models assume the survival curve approaches zero and fail to characterize this aspect of safety data. A Gompertz distribution with a negative scale parameter allows the survival curve to have a non-zero asymptote and may better characterize survival data where a proportion of the population never experiences the event of interest1. The brms package in R enables Bayesian modeling with Stan2,3. The Gompertz distribution is not natively supported in brms or Stan, and a custom probability distribution was implemented in the software to enable cure rate model fitting and inference.Methods: The following Gompertz distribution functions were defined as custom Stan functions: log probability density, log cumulative distribution, log complementary cumulative distribution, and random generation function. The Stan functions and the log likelihood of the survival model were used to define the Gompertz model as a custom family in brms. Data were simulated from a mixture cure rate model with a constant hazard and were used to demonstrate implementation of the custom brms family. With the custom brms family in place, standard brms tooling was used for model fitting and inference.Results: The Gompertz distribution better characterized the simulated survival curve than an exponential, lognormal, or Weibull distribution. The leave-one-out expected log pointwise predictive density (ELPD) model criterion identified the Gompertz model as the most favorable model, and visual predictive checks showed that the asymptotic assumption of the standard survival models led to an overprediction of adverse events in the latter part of the simulated study. The Gompertz model did not suffer from such a misspecification and characterized the data well.Conclusions: The Gompertz distribution characterized the simulated survival data, allowing for a fraction of the patient population to never experience the adverse event. Implementation of custom distributions in brms and Stan can be extended to other models not natively supported in either software.Citations: Citations:[1] Gieser, P. W., M. N. Chang, P. V. Rao, J. J. Shuster, and J. Pullen. 1998. “Modelling Cure Rates Using the Gompertz Model with Covariate Information.” Statistics in Medicine 17 (8): 831–39.[2] Bürkner P (2017). “brms: An R Package for Bayesian Multilevel Models Using Stan.” Journal of Statistical Software, 80(1), 1–28. doi:10.18637/jss.v080.i01.[3] Stan Development Team. 2019. Stan Modeling Language Users Guide and Reference Manual, 2.26. https://mc-stan.org.
Development of a Simeprevir PBPK Model to Describe Changes in Coproporphyrin-I, an Endogenous OATP1B Biomarker, in Subjects with HCV
Authors: Nakayama Shinji , Toshimoto Kota , Snoeys Jan , Yamazaki Shinji , Sugiyama Yuichi
Published: February 2025

[Objectives] Evaluation of drug-drug interaction potency using endogenous substances is useful for efficient drug development.[1] Since OATP1B is involved in the active hepatic uptake of many drugs, the impact of new chemical entities on OATP1B activity should be assessed. Assessing the effects of drugs on blood levels of OATP1B endogenous substrates in subjects with hepatic impairment requires an understanding of not only drug-endogenous substrate interactions but also disease-endogenous substrate interactions. This study aims to quantitatively analyze the effect of anti-HCV drug simeprevir on blood levels of coproporphyrin-I in subjects with hepatic impairment using the PBPK model.[Methods] Impacts of simeprevir dosing on blood coproporphyrin-I concentrations in subjects with HCV were analyzed using the PBPK model. The previously developed PBPK models of simeprevir and coproporphyrin-I were used. A total of 6 virtual clinical studies of the actual clinical trial size were conducted using predefined inhibitory constant of simeprevir against OATP1B. Monte Carlo simulation was performed to consider reduced hepatic OATP1B and CYP3A4 expression levels reported in subjects with HCV and their inter-individual variability.[Results] Blood coproporphyrin-I concentrations were in a similar range around 0.6 nmol/L when the simeprevir blood concentration were below 2000 ng/mL, and then increased up to 6 nmol/L at the simeprevir blood concentration range from 2000 to 30000 ng/mL in the actual clinical trial. All virtual clinical studies adequately predicted this trend. Our simulations showed that blood concentrations of both simeprevir and coproporphyrin-I tended to be higher in subjects with severe hepatic impairment (severe fibrosis and cirrhosis), due to low drug clearances and potent OATP1B inhibition by simeprevir.[2][Conclusions] Our analysis reveals that not only OATP1B inhibition by simeprevir but also decreases in expression levels of hepatic OATP1B and CYP3A4 due to progression of liver fibrosis can affect the blood levels of coproporphyrin-I in subjects with HCV. The PBPK model coupled with Monte Carlo simulation can provide quantitative insights into the complex drug-endogenous biomarker-disease interaction.Citations: [1] Yoshikado T et al. PBPK Modeling of Coproporphyrin I as an Endogenous Biomarker for Drug Interactions Involving Inhibition of Hepatic OATP1B1 and OATP1B3. CPT Pharmacometrics Syst Pharmacol. 2018 Nov;7(11):739-747.[2] Nakayama S et al. Physiologically-based pharmacokinetic modeling for investigating the effect of simeprevir on concomitant drugs and an endogenous biomarker of OATP1B. CPT Pharmacometrics Syst Pharmacol. 2023 Oct;12(10):1461-1472.
A phase I open-label sequential clinical trial and population oral minimal model to study pharmacodynamic interactions of dorzagliatin and empagliflozin in patients with type 2 diabetes and obesity
Authors: Liu Huan , Zhang Yuanhui , Feng Lingge , Zhang Jiayi , Sun Yu , Zhao Yu , Jiang Muhan , Liu Xiang , Fang Zhiyin , Tracey Gregory , Chen Li , Liu Dongyang
Published: February 2025

OBJECTIVE Dorzagliatin, as the first marketed glucokinase activator, was recognized for its good clinical efficacy and safety profile in patients with type 2 diabetes mellitus . Empagliflozin, a sodium-dependent glucose transporters 2 inhibitor , exhibited advantages in lowering glucose and protecting renal and cardiovascular function. The main objective of this study is to evaluate the pharmacokinetic, pharmacodynamic characteristics of the combination of dorzagliatin and empagliflozin, and to assess whether the short-term combination use (4-5 days) of dorzagliatin and empagliflozin has a greater improvement in beta cell function and insulin sensitivity in patients with type 2 diabetes compared to monotherapy by population OMM. METHODS A phase I study (ClinicalTrials.gov, NCT03790787) was conducted in sixteen obese patients with T2DM from US. Empagliflozin, empagliflozin+ dorzagliatin and dorzagliatin were administered over five days sequentially, the PK (AUC (area under curve), Cmax (maximum concentration) for drug concentrations), PD (AUEC (area under effect curve) of blood glucose, C-peptide, and insulin concentrations after oral glucose tolerance test). Mechanistic parameters indicating different phase beta-cell function and overall insulin sensitivity were further estimated by a new method which is called population oral minimal model. RESULTS There were no significant PK DDI and obvious increase in frequency or severity of any adverse events after the combination of dorzagliatin and empagliflozin. Combination therapy significantly enhanced the glucose-lowering effect and C-peptide secretion levels (iAUC0-4h). More importantly, the population OMM method more comprehensively and simply quantitatively evaluated the improvement of beta cell function and insulin sensitivity after short-term drug combination. The combination therapy significantly improved different phase insulin secretion capacity (φd, φs and φtot) and total insulin sensitivity compared to dorzagliatin/empagliflozin in diabetic patients.CONCLUTIONS The promising potential benefit of the combination of dorzagliatin and empagliflozin was found in lowering glucose, enhancing beta-cell function of glucose-stimulated insulin secretion, and improving insulin action with no PK characteristics change and good safety.Citations: [1]Syed Y.Y. Dorzagliatin: First approval. Drugs. 2022, 82: 1745-1750. [2] Scott L.J. Empagliflozin: A review of its use in patients with type 2 diabetes mellitus. Drugs. 2014, 74: 1769-1784. [3] Cobelli C. Dalla Man C. Toffolo G. Basu R. Vella A. Rizza R. The oral minimal model method. Diabetes. 2014, 63: 1203-1213. [4]FDA. Statistical approaches to establishing bioequivalence guidance for industry 2022 https://www.fda.gov/media/163638/downloadl. December 20, 2023.
Development of a Physiologically-Based Pharmacokinetic (PBPK) Model for Caffeine in Pregnancy: Evaluating Neonatal Transfer
Authors: Hajjawi Malek , Quinney Sara , Szyld Edgardo , Thomas Anna , Zaidi Syed
Published: February 2025

Objectives: Caffeine is extensively used in neonatal intensive care units (NICUs) to treat and prevent bronchopulmonary dysplasia (BPD) and neurodevelopmental insufficiency (NDI) in premature infants. It effectively reduces the duration of respiratory assistance needed by stimulating spontaneous breathing through adenosine antagonism. Moderate caffeine consumption (less than 200 mg per day) during pregnancy does not appear to be a major contributing factor in miscarriage or preterm birth [1]. Currently, a loading dose of 20 mg/kg of caffeine citrate is administered within the first 6 hours of life, followed by a maintenance dose of 5 mg/kg every 24 hours until approximately 34 weeks of gestational age. Therapeutic plasma concentrations range from 2-20 mg/L.Concentrations as high as 90 mg/L show only transient adverse effects in preterm infants [2]. We hypothesize that maternal administration of caffeine during labor will provide earlier dosing to the neonate. This study aims to develop a physiologically-based pharmacokinetic (PBPK) model to evaluate the transfer of caffeine from pregnant women to their neonates.Methods: We developed and validated three caffeine PBPK models in Simcyp v23: a non-pregnant model for validation purposes, a pregnant model to predict feto-placental concentrations and a preterm model to predict caffeine plasma concentrations in neonates. The default Simcyp caffeine drug file was modified by adjusting logP and pKa based on data from DrugBank.Results: This led to a more accurate prediction of absorption. We successfully validated and simulated the pharmacokinetic profiles of caffeine across the three models. Our simulations determined that administering caffeine to pregnant individuals in the third trimester at doses of 120-150 mg every 12 hours resulted in feto-placental concentrations of 5.8-8.6 mg/L, with a neonatal half-life of approximately 7 hours. Conclusions: This dosage regimen ensures therapeutic levels in the preterm infant at delivery. This approach has the potential to optimize neonatal outcomes by providing an alternative to direct neonatal caffeine administration.Citations: 1. Glade, M.J., Caffeine—not just a stimulant. Nutrition, 2010. 26(10): p. 932-938. 2. Aranda, J.V. and K.D. Beharry. Pharmacokinetics, pharmacodynamics and metabolism of caffeine in newborns. in Seminars in Fetal and Neonatal Medicine. 2020. Elsevier.
Integrating Physiologically Based Pharmacokinetic Modeling with Machine Learning for the Rational Design of Nanoparticles to Improve Safety in Biomedical Applications
Authors: Cave Joseph , Schiavone Carmine , Wang Zhihui , Cristini Vittorio , Dogra Prashant
Published: February 2025

OBJECTIVES: Inorganic nanoparticles (NPs) as drug delivery systems have shown remarkable advancements, exhibiting high degrees of adaptability, which enables precise tailoring of their physicochemical properties, such as size, shape, charge, surface chemistry, and composition [1]. This has led to the development of NPs with enhanced functionalities for drug delivery. However, concerns about safety due to off-target accumulation have negatively influenced the clinical success of inorganic NPs [2]. To this end, we present a machine learning (ML)-based NP toxicity prediction model, informed by a minimal physiologically based pharmacokinetic (PBPK) model, to predict organ-specific toxicity and guide rational design of NPs for enhanced safety.METHODS: Through rigorous meta-analysis, an extensive in vitro toxicity dataset was curated, standardized, and split 70/30 to train and validate 22 binary classification models (linear, clustering, tree, and boosting-based), where 10-fold nested cross-validation optimized hyperparameters and ensured generalizability. The in vitro dataset (Nf8752) was characterized by ten attributes, including NP physicochemical properties and experimental conditions. The top five performing models were selected for explainability analysis via SHapley Additive exPlanations (SHAP) and Partial Dependence Plots (PDPs) to quantify structure-toxicity relationships. To enable prospective in vivo NP toxicity predictions, we integrated a seven-compartment PBPK model into the ML models to provide inputs of organ-specific NP exposure based on NP design characteristics. RESULTS: • Model validation yielded exceptional performance from our top five models: CatBoost (AUC = 0.9443), XGBoost (AUC = 0.9411), Gradient Boosting Classifier (AUC = 0.93104), Random Forest (AUC = 0.9288), and Extra Trees (AUC = 0.9250)• SHAP ranked concentration, composition, size, exposure time, organ, surface coating, and Zeta potential (ζ) as most influential in determining toxicity. • PDP quantified the probability of NP toxicity as a function of NP features, highlighting the mathematical correlation for concentration, size, and exposure time. • Models were trained against in vivo data, with input of NP exposure obtained from a PBPK model, leading to the high predictive performance of toxicity models.CONCLUSION: We developed a PBPK-integrated ML framework to predict the organ-specific toxicity of inorganic NPs. SHAP and PDPs determined concentration, size, composition, coating, and ζ to heavily influence NP toxicity, providing guidelines for rational NP design.Citations: [1] A.A. Yetisgin, S. Cetinel, M. Zuvin, A. Kosar, O. Kutlu, Molecules, 25 (2020) 2193.[2] D. Bobo, K.J. Robinson, J. Islam, K.J. Thurecht, S.R. Corrie, Pharm Res, 33 (2016) 2373-2387.
A Survey of Model Informed Approaches in Neuroscience Drug Development
Authors: Bewernitz Michael , Sharma Vishnu , Liu Jie , AbuAsal Bilal , Pillai Venkateswaran C , Gottipati Gopichand , Xu Yun , Bhattaram Atul
Published: February 2025

1. Objectives: Model-informed approaches offer key support for drug development and regulatory approval. This research is aimed to assess how the model informed approaches aided regulatory recommendations and approval of neuroscience therapeutics. Key examples are provided. This reflects the views of the authors and should not be construed to represent FDA’s views or policies.2. Methods: Model-informed analyses from Office of Clinical Pharmacology, FDA over the last 10 years were reviewed. The authors screened regulatory reviews of NDAs, BLAs and INDs in neurology, psychiatry, addiction, anesthesia, and analgesia. The authors identified instances where model-informed approaches had a significant impact on the approval or directions of use of a drug or biologic assessment. 3. Results: Multiple submissions applied population pharmacokinetic, population pharmacokinetic pharmacodynamic (PKPD), or exposure-response modeling, and simulation to influence decision making. Applications include: Efficacy Extrapolation: • selecting dosing in pediatric patients based on exposure-matching to older pediatric patients or adults at approved adult dosing, • optimizing dose determination (fixed dose versus mg/kg dosing), • approving a buprenorphine dose regimen not assessed in the Phase 3 program, • updating labeled diazepam dosing for status epilepticus by assessing the labeled dosing versus different dosing that is commonly used in clinical practice, • supporting dose interpolation for valbenazine. Surrogate endpoints: • selection of beta amyloid plaque reduction as a reasonably likely surrogate endpoint for biologics targeting anti-beta-amyloid plaques in Alzheimer’s disease patients, • analyses of a surrogate endpoint and an efficacy endpoint to support efficacy of tofersen. Exposure-Response and Pharmacokinetic Pharmacodynamic (PKPD) Analyses:• informing the duration of driving impairment associated with zuranolone treatment, • informing robustness of single pivotal study to support efficacy of givinostat, • modeling of the reversal of opioid-induced respiratory depression to support efficacy of a naloxone autoinjector. 4. Conclusions: Model-informed analyses informed decision making across the spectrum of neuroscience drug development. These analyses have provided supportive evidence for drug efficacy and safety during NDA or BLA review.Citations: None
Integrated Quantitative Systems Pharmacology and Pharmacometric Model to Evaluate Effective Buprenorphine Induction Treatment Strategies in the Era of Synthetic Opioids
Authors: Purohit Prasad , Saini Anuraag , Chandrasekaran Komalapriya , Rengaswamy Maithreye , Noack Andreas , Wojciechowski Jessica , Pendsey Netravat , Horton Terry , Greenwald Mark
Published: February 2025

Objectives: Buprenorphine treatment induction has been associated with an increased risk of precipitated withdrawal (PW) in chronic fentanyl users diagnosed with opioid use disorder (OUD). Because PW can discourage initiation or continuation of buprenorphine treatment in patients with OUD, we developed an integrated quantitative systems pharmacology (QSP) and non-linear mixed effects model to understand the factors responsible for the increased incidence of PW in chronic fentanyl users and evaluate buprenorphine induction strategies that minimize PW.Methods: A QSP model was developed for buprenorphine and fentanyl, integrating information on opioid pharmacokinetics (PK), mu-opioid receptor (MOR) binding kinetics, MOR tolerance mechanisms and MOR agonist activity. Prior population PK models for intravenous (IV) fentanyl and buprenorphine (IV, sublingual, and subcutaneous extended-release formulations) were incorporated to predict drug plasma exposure. The fentanyl PK model was adapted to include an adipose tissue compartment to investigate the impact of delayed release of lipophilic fentanyl on PW in chronic fentanyl users. The QSP model was calibrated using published data on respiratory depression measured after IV administration of fentanyl and buprenorphine, either alone or in combination. The relationship between opioid agonist activity (predicted by the QSP model) and withdrawal symptoms measured in patients with the Clinical Opiate Withdrawal Scale (COWS) was estimated by non-linear mixed effects modeling, using data from a clinical study of buprenorphine induction in patients with fentanyl-positive urine tests.Results: The integrated modeling approach, leveraging mechanistic understanding from QSP and statistical flexibility of pharmacometric methodologies, successfully described changes in COWS scores in patients with and without PW following initiation of buprenorphine. The model suggests the occurrence of PW is related to an abrupt decrease in MOR agonist activity when buprenorphine is initially administered. Model diagnostics such as visual predictive checks demonstrated that the model adequately predicted the time-course in COWS scores and incidence of PW.Conclusion: An integrated model characterizing the effects of fentanyl and buprenorphine on MORs, respiratory depression, and COWS scores was developed. This model enables the design and evaluation of improved buprenorphine induction protocols to reduce the risk of PW in chronic fentanyl users while providing adequate MOR agonist activity to motivate patient continuation in maintenance treatment.Disclosures: Financial Support provided by Indivior Inc. The results in this abstract have been previously presented in part at CPDD, Montreal, 15-19 June 2024.Citations: NA
A Model-based Pharmacokinetic Simulation Tool to Aid Dose Selection of Antisense Oligonucleotides Administered Intrathecally in Pediatrics
Authors: Abdelmageed Mai , Monine Michael , Schmitz Natalie , Xu Yan , Nestorov Ivan , Wu Shirley
Published: February 2025

Objectives: Intrathecally (IT)-administered antisense oligonucleotides (ASOs) targeting neuromuscular disorders have demonstrated promising clinical effects in pediatrics. However, selection of pediatric doses for IT-administered ASOs is challenging due to limited data available on the central nervous system (CNS) distribution of IT ASOs. A physiologically-based pharmacokinetics (PBPK) model scaled to the effective volumes of the CNS organs and cerebrospinal fluid (CSF) can help with predicting pediatric biodistribution. The current study aims to develop a PBPK and pharmacodynamic (PD) modeling and simulation tool to inform IT-ASO dosing in pediatric subpopulations.Methods: The PBPK model for IT ASOs [1] was implemented using mrgsolve package in an R Shiny application. Virtual pediatric subjects at the different age categories (neonates, infants, children, and adolescents) were created, using literature volume growth relationships for spinal CSF, cranial CSF, spinal cord tissues, and brain tissues [2-3]. The application predicted ASO exposures at user-specified dosing regimens in various compartments across different age ranges. Furthermore, the predicted ASO concentrations were used as an input to the direct or indirect exposure-response models to evaluate mRNA and protein reduction from baseline in target CNS tissues assuming typical values for Emax, IC50 and target turnover rates.Results: Age-dependent changes of CNS volumes may affect ASO CNS distribution and thus response. These changes were most prominent in the younger age groups. An R Shiny application was developed which has the capability to predict ASO exposures in a wide age range of pediatric populations to aid with pediatric dose selection. Conclusions: The ASO PBPK model incorporating CSF and CNS volume growth data provides semi-mechanistic predictions of PK and PD for IT ASO therapeutics in pediatric subjects. This simulation tool is intended to aid in ASO pediatric dose selection particularly for neonates and infants. Disclosures: All authors were employees of Biogen at the time of the study and may own stock of Biogen.Citations: Citations: [1] Monine et al. J Pharmacokinet Pharmacodyn. 2021 Oct; 48(5):639-654. [2] Jang et al. Paediatr Anaesth. 2019 Jan;29(1):92-97. [3] Vannucci and Vannucci. Am J Phys Anthropol. 2019 Feb;168(2):247-261.
App-based in vitro pharmacokinetic driver identification and in silico clinical trial assessment of pharmacokinetic driver models
Authors: Greenwald Eric , Boras Britton , Spilker Mary
Published: February 2025

Objective: Developing a drug requires balancing several factors that define potency, efficacy and pharmacokinetic (PK) properties. Identifying the magnitude and duration of target engagement early in drug development can help focus the chemistry design criteria and accelerate the advancement of therapeutics to patients. To assess this early in the drug development process an in vitro experimental method was developed and coupled with a mathematical model to elucidate which PK metric (Cmax, Cave, or threshold concentration) best correlates with efficacy. This initial effort was streamlined by the development of a MATLAB based app to assist in the model-based analysis of experimental data. Additionally, given the need for robust translation of the in vitro findings, an in silico analysis of the PK driver models was completed to validate their connection to clinical PK driver assessments. Methods: Previously an in vitro washout assay was developed to discriminate between 3 general PK drivers of efficacy: Cave, Cmax and threshold concentrations. By exposing cells to a drug for varying durations and concentrations such that the AUC is consistent across all the samples, the relationship between the duration of exposure and an end-point readout, in this case cell proliferation, the PK driver for a drug mechanism can be determined. 3 different PK driver computational models were defined to analyze the data. With a standardized experimental design, the work was further expanded through the development of a MATLAB app to facilitate fitting these models to the data and assessing the goodness of fit to identify the most likely PK driver. In addition, to assess the compatibility of these computational models with clinical PK driver correlation analysis, in silico clinical trials were carried out where each of the 3 PK driver computational models were assumed to be the ground truth for anti-tumor efficacy and PK variability of a hypothetical compound was applied to these models. The simulated efficacy from each of these clinical trials was then correlated against Cave, Cmax and Cmin using a logistic regression.Results: The analysis methods were successfully deployed in the MATLAB app and applied to several compounds with different PK drivers. Furthermore, the app has sufficient flexibility to address unique effects such as compound instability and incomplete washout, resulting in a robust identification of the correct PK driver. Finally, the in silico exploration of our computational PK driver models confirmed that each model was correctly aligned to correlation outcomes consistent with those commonly performed clinically. This analysis supports the use of the in vitro methodology coupled with the computational models to correctly discern clinically relevant PK drivers early in preclinical drug discovery.Citations: none
Integrated Microphysiological System and PBPK Modeling for Prediction of Human Diclofenac Pharmacokinetics
Authors: Choi Suein , Yim Dong-seok , Han Sungpil , Lee Jeonghyun
Published: February 2025

Objective: The primary objective of this study was to combine an integrated microphysiological system (MPS) with a computational multicompartmental physiologically-based pharmacokinetic (PBPK) model to improve methods for predicting human pharmacokinetics using diclofenac. We also tried to identify transcriptomic biomarkers that respond significantly to diclofenac exposure in MPS chip to determine the similarity of human drug responses to those of the MPS and the potential for using the MPS to improve our understanding of the pharmacodynamics of drugs.Methods: The MPS used advanced microfluidic technology and co-cultures of physiologically relevant human cell types to form a comprehensive model of human organ systems. The system was integrated with a PBPK model to simulate drug absorption, distribution, metabolism, and excretion (ADME) processes and compared to real-world human pharmacokinetic results. The study also performed transcriptomic profiling to validate the predictive accuracy of the MPS against clinical pharmacokinetic data for diclofenac and to correlate drug exposure with gene expression changes.Results: The results from the blank and MPS chips yielded adequate key parameters (CLint,h = 3,342 L/h, Papp, h = 0.000427 cm/s, Papp, g = 0.000968 cm/s) and simulations combining these results with a PBPK model built using only the in vitro results showed relatively good agreement with clinical data for the pharmacokinetics of diclofenac without additional optimization (AUCR(predicted/observed) = 0.89). In addition, transcriptomic analyses showed a high correlation in gene expression associated with diclofenac exposure, particularly those involved in gastrointestinal toxicity and other systemic effects.Conclusion: The findings support the potential of MPS as a reliable alternative to traditional animal testing for predicting drug response in humans. It has shown the potential to improve the drug development process by more accurately predicting pharmacokinetics and pharmacodynamics in humans at an early stage, and identifying novel transcriptomic markers which might help us understand drug mechanisms and safety profiles.Citations: [1] Prantil-Baun, R., et al., Physiologically Based Pharmacokinetic and Pharmacodynamic Analysis Enabled by Microfluidically Linked Organs-on-Chips. Annu Rev Pharmacol Toxicol, 2018. 58: p. 37-64[2] Herland, A., et al., Quantitative prediction of human pharmacokinetic responses to drugs via fluidically coupled vascularized organ chips. Nature biomedical engineering, 2020. 4(4): p. 421-436.
Dimensional and spectral characteristics of daunomycin/erythromycin complexes with dexrtan-stabilized silver nanoparticles
Authors: Kipper Al'bert Ivanovich , Plushenko Anna V , Kutin Aleksandr Aleksandrovich , Garkushina Irina S
Published: February 2025

The conditions for the production and stabilization of silver nanoparticles with dextran with the formation of complexes with daunomycin and erythromycin antibiotics were determined. Using UV-visible spectroscopy and dynamic light scattering methods, the spectral and dimensional characteristics of the synthesized systems were established.
Eluotropic strength of solvents for parallel-displaced π-π interaction chromatography
Authors: Upyrenko Evgeny Vital'evich , Kurgachev Dmitriy A , Bakibaev Abdigali Abdimanapovich
Published: February 2025

A solvent eluotropic strength series for liquid chromatography in the parallel-displaced π-π interaction mode was established. Experiments were carried out on a pyrenylethyl group bonded stationary phase with 1,3-initrobenzene as a marker compound and a 40 : 60 isopropanol-n-hexane mixture as the initial mobile phase, in which 1, 3, 5, 10, 15 and 20
General AMBER force field parameters for modeling polyalkylsiloxane chains
Authors: Makarov Gennady Ivanovich , Makarova Tatiana M
Published: February 2025

As a supplement to the widely used general AMBER force field, a set of parameters is proposed for molecular dynamics modeling of the structure and properties of polymer chains in polyalkylsiloxane matrices of materials capable of selfhealing after stress loads.
Migration of cationic polymer between anionic polymer microspheres
Authors: Yuzhanin Kirill Igorevich , Panova Irina Gennadievna , Shevchenko Natalia N. , Yaroslavov Aleksandr Anatol'evich
Published: February 2025

In aqueous solution, the cationic polymers poly(diallyldimethylammonium chloride) and poly-L-lysine hydrobromide are electrostatically adsorbed onto anionic polystyrene microspheres, which are used as a model of microplastics. The adsorbed polymers are able to migrate between the particles, resulting in a uniform distribution of macromolecules over all particles in the system. Interparticle migration should be taken into account when discussing the spread of toxicants associated with micron-sized vectors.
Polycaprolactone degradation under the action of iodine vapor
Authors: Trofimchuk Elena Sergeevna , Balobanova Sofia Igorevna , Puchkov Alexander A , Piryazev Alexey Andreevich , Sedush Nikita Gennad'evich , Nikonorova Nina Ivanovna , Kovtun Ivan D , Chvalun Sergei Nikolaevich
Published: February 2025

Upon the solid-state degradation of poly(ε-caprolactone) under the action of iodine vapor, the number-average molecular weight of the polymer decreased by almost an order of magnitude 48 h after the incorporation of iodine ( 6 wt
Molecular structure of gaseous 1,1',2,2'-tetramethyl-3,3'-bidiaziridine, established by electron diffraction and quantum chemical calculations
Authors: Belyakov Alexander Vasil'evich , Kuznetsov Vladimir Vladimirovich , Shimanskaya Galina S , Rykov Anatoly Nikolaevich , Dmitrenok Andrey Sergeevich , Khakimov Dmitry V , Tokarev Konstantin L'vovich , Shishkov Igor Fedorovich
Published: February 2025

The equilibrium geometry of the 1,1',2,2'-tetramethyl-3,3'‑bidiaziridine (TMBDA) molecule in the gas phase was determined using the gas-phase electron diffraction method in combination with quantum chemical calculations up to the allelectron RI-MP2/def2-QZVPP level of theory. It was confirmed that the TMBDA conformer with C2 symmetry dominates in the gas phase. Quantum chemical simulations of the crystal packing of TMBDA showed that the racemic form has lower lattice energy than the meso-form, which is consistent with the experimental ratio in the synthesized product.
Two-dimensional C6B4 and C8B4 monolayers: a new family of non-classical systems with planar tetracoordinate carbon atoms and highly anisotropic mechanical properties
Authors: Zaitsev Stanislav Alexandrovich , Getmanskii Iliya Vladimirovich , Gribanova Tatyana N , Minyaev Ruslan Mikhailovich
Published: February 2025

Crystalline forms of two-dimensional C6B4 and C8B4 monolayers designed on the basis of diboraspiropentadiene C3B2H4 structural blocks with planar tetracoordinate carbon atoms were studied using DFT calculations. The calculations predict the structural and dynamic stability of isomeric forms of C6B4 and C8B4 monolayers, which exhibit pronounced in-plane anisotropy of mechanical properties with high values of the mechanical anisotropy coefficient (up to 6.6) and maximum values of the Poisson’s ratio (up to 1.5). Isomers containing a continuous six-membered carbon ring within the structural block have negative Poisson’s ratios for some directions and can be characterized as anisotropic auxetics.
Two-photonic photochemistry of NaBr in methanol solutions
Authors: Malakhova Iuliya A , Grivin Vjacheslav P , Glebov Evgeni Mikhailovich
Published: February 2025

Two-photonic ionization of NaBr in methanol solutions at 266 nm was studied by nanosecond laser flash photolysis. The mechanism of photolysis includes reactions of a solvated electron and a dibromide radical anion. For quantitative description, two-photonic ionization of methanol should be taken into account.
Adaptation, Hope(lessness) and Deterioration; Whole Life Imprisonment within England and Wales
Authors:   , Gilman Hannah
Published: February 2025

This study considers some of the key issues associated with England and Wales’ most retributory form of life imprisonment, whole life orders; namely (1) their contemporary proliferation, and (2) their effect(s) on those who serve them. The study commences by considering the contemporary proliferation in the use of whole life orders; where existing research has considered the history and legitimacy of whole life orders, I used secondary data analysis to understand how (and why) the use of whole life orders has changed so considerably, particularly since the enactment of the Criminal Justice Act 2003. I also conducted qualitative interviews with a small sample of eight prison staff members from across two English and Welsh high-security prisons, to consider the ways in which male whole life order prisoners are observed (and/or perceived) by prison staff members to navigate and experience their whole life sentences, by means of pre-existing theoretical lenses. The study draws upon the pains of imprisonment framework (see Sykes, 1958; Crewe, 2011; Jewkes, Bennett and Crewe, 2016) and Snyder’s (1989; 1994a; 2002) theory of hope, to further existing (international) research and knowledge surrounding whole life sentence prisoners and whole life imprisonment more broadly, by considering whether (and/or how) whole life order prisoners are able to adapt and adjust to their sentence, and whether sentencing a person to whole life imprisonment also condemns a person to a life devoid of hope. The study identifies both individually experienced harms (e.g., physical and psychological) and human rights based harms which are specific to whole life order prisoners, and - in so doing - questions the extent to which whole life orders meet international human rights standards. This study ultimately provides a unique insight into our whole life order prisoner population, as well as the challenges that they face, and concludes by providing policy recommendations which aim to ensure that whole life orders meet international human rights standards, safeguard the rights of whole life order prisoners, and (most significantly) minimise the harms that whole life orders cause for those who serve them, thereby making an original contribution to existing research, knowledge and literature concerning whole life order prisoners and whole life imprisonment both within England and Wales, and internationally.
Estrategias del Ministerio de Sanidad de España en Instagram: Un estudio comparativo antes, durante y después de la pandemia COVID-19
Authors: Cordeiro Douglas Farias , Lopezosa Carlos , Vállez Mari , Guallar Javier
Published: February 2025

Este estudio analiza el uso de Instagram por el Ministerio de Sanidad de España con el objetivo de comprender cómo se ha utilizado esta plataforma para difundir contenido y comprometer al público en temas sanitarios. Se consideran las publicaciones realizadas entre 2018 y 2023, segmentadas en tres períodos: antes de la pandemia de COVID-19, durante la pandemia y después de la pandemia. Se realiza un análisis cuantitativo sobre los datos para comprender el volumen, tipo de publicaciones, métricas de participación, y el uso de hashtags y menciones. Se utiliza un modelo de inteligencia artificial para la identificación de nombres de personas mencionados en los textos de las publicaciones. Los resultados obtenidos indicaron que durante la pandemia, el aumento de publicaciones y el uso de vídeos incrementaron la interacción, mientras que las menciones y nombres de personas redujeron la participación. Además, en todos los períodos, los textos más cortos y el uso de carruseles y hashtags específicos se asociaron con un mayor engagement, destacando la importancia de estas tácticas en la comunicación institucional en redes sociales.
(Re)construire la maison de Swift : la scène emprisonnée comme dispositif de recherche
Authors: Ali Waël , Dubois Simon
Published: February 2025

Dans cet article écrit à deux mains, Simon Dubois et Waël Ali reviennent sur un pan peu connu de l’histoire du théâtre syrien contemporain. En effet, leur démarche d’archivage d’une histoire sensible de l’engagement en Syrie durant les années 1980 les a conduits à reconstituer l’expérience d’une troupe de théâtre clandestine en prison. Ils réalisent ici un retour historique et analytique sur ces pièces en cellule qui ont été mises en scène entre 1987 et 1991 dans la prison de Saidnaya, où plusieurs militants de l’opposition étaient emprisonnés.
Théâtre afghan post-2021
Authors: Hermann Sarah , Khajehi Yassaman
Published: February 2025

Cet article examine la renaissance du théâtre afghan contemporain, particulièrement depuis 2021, à travers des créations d’artistes afghan·es exilé·es en France. Ces oeuvres explorent les thèmes de l’exil, de l’identité et de la résistance culturelle face aux crises récentes, comme la reprise du pouvoir par les talibans. En mettant en scène leurs parcours personnels, ces artistes transforment le théâtre en un espace de guérison et de revendication. Les pièces abordent des sujets sensibles, comme la condition féminine et le patriarcat, tout en célébrant la mémoire culturelle afghane. Ce théâtre en exil incarne une quête identitaire et un besoin de témoignage.