Predictive Checks Research Articles

Selection of Single-Analyte Delta Check Rules with Logistic Regression for Detection of Intravenous Fluid Contamination in a Clinical Chemistry Laboratory.

The conventional single-analyte delta check, utilized for identifying intravenous fluid contamination and other preanalytical errors, is known to flag many specimens reflecting true patient status changes. This study aimed to derive delta check rules that more accurately identify contamination. Results for calcium, creatinine, glucose, sodium, and potassium were retrieved from 326 103 basic or comprehensive metabolic panels tested between February 2021 and January 2022. In total, 7934 specimens showed substantial result changes, of which 1489 were labeled as either contaminated or non-contaminated based on chart review. These labeled specimens were used to derive logistic regression models and to select the most predictive single-analyte delta checks for 4 common contaminants. Their collective performance was evaluated using a test data set from October 2023 comprising 14 717 specimens. The most predictive single-analyte delta checks included a calcium change by ≤-24% for both saline and Plasma-Lyte A contamination, a potassium increase by ≥3.0 mmol/L for potassium contamination, and a glucose increase by ≥400 mg/dL (22.2 mmol/L) for dextrose contamination. In the training data sets, multi-analyte logistic regression models performed better than single-analyte delta checks. In the test data set, logistic regression models and single-analyte delta checks demonstrated collective alert rates of 0.58% (95% CI, 0.46%-0.71%) and 0.60% (95% CI, 0.49%-0.74%), respectively, along with collective positive predictive values of 79% (95% CI, 70%-89%) and 77% (95% CI, 68%-87%). Single-analyte delta checks selected by logistic regression demonstrated a low false alert rate.

A joint population pharmacokinetic model to assess the high variability of whole-blood and intracellular tacrolimus in early adult renal transplant recipients

Tacrolimus (TAC) has high pharmacokinetic (PK) variability during the early transplantation period. The relationships between whole-blood and intracellular TAC concentrations and clinical outcomes remain controversial. This study identifies the factors affecting the PK variability of TAC and characterizes the relationships between whole-blood and intracellular TAC concentrations. Data regarding whole-blood TAC concentrations of 1,787 samples from 215 renal transplant recipients (<90 days postoperative) across two centers and intracellular TAC concentrations (648 samples) digitized from previous studies were analyzed using nonlinear mixed-effects modeling. The effects of potential covariates were screened, and the distribution of whole-blood to intracellular TAC concentration ratios (RWB:IC) was estimated. The final model was evaluated using bootstrap, goodness of fit, and prediction-corrected visual predictive checks. The optimal dosing regimens and target ranges for each type of immune cell subsets were determined using Monte Carlo simulations. A two-compartment model adequately described the data, and the estimated mean TAC CL/F was 23.6 L·h−1 (relative standard error: 11.5 %). The hematocrit level, CYP3A5*3 carrier status, co-administration with Wuzhi capsules, and tapering prednisolone dose may contribute to the high variability of TAC PK variability during the early post-transplant period. The estimated RWB:IC of all TAC concentrations in peripheral blood mononuclear cells (PBMCs) was 4940, and inter-center variability of PBMCs was observed. The simulated TAC target range in PBMCs was 20.2–85.9 pg·million cells−1. Inter-center variability in intracellular concentrations should be taken into account in further analyses. TAC dosage adjustments can be guided based on PK/PD variability and simulated intracellular concentrations.

Development of a population pharmacokinetic model for the novel long-acting injectable antipsychotic risperidone ISM®.

The aims of this study were to develop a population pharmacokinetic (PK) model for risperidone ISM® and to investigate the relationships between active moiety exposure, as described by apparent clearance (CL40), and several covariates using all data from five clinical studies. A population PK model was developed using active moiety concentrations from a study in healthy volunteers and two studies in patients with schizophrenia. Data from a comparative bioavailability study in medically stable patients and a Phase III study in patients with acute exacerbation of schizophrenia were then incorporated, using empirical Bayesian feedback and model refinement in NONMEM. Finally, covariate analysis was performed on CL40. The final model adequately described the pharmacokinetics of 6288 active moiety concentrations in 17 healthy volunteers and 430 patients with schizophrenia. This one-compartment disposition model had a complex absorption process, combining a small amount immediately entering the central active moiety compartment, two first-order absorption processes and a combined zero-order and first order process, with first-order elimination from the central compartment. Significant covariates on CL40 were BMI and sex. Goodness-of-fit (GOF) plots and visual predictive checks (VPC) confirmed acceptable description of the data. The population PK model adequately described active moiety concentrations from five clinical studies after risperidone ISM® administration. Relationships between active moiety exposure and covariates were defined in order to facilitate simulations for future studies. The model showed that risperidone ISM® rapidly achieves therapeutic plasma levels within the first hours after the first injection that are maintained sustainedly throughout the whole dosing interval following once-monthly gluteal injections of 100 mg and 75 mg.

Investigating clinical pharmacokinetics of brivaracetam by using a pharmacokinetic modeling approach

The development of technology and the processing speed of computing machines have facilitated the evaluation of advanced pharmacokinetic (PK) models, making modeling processes simple and faster. The present model aims to analyze the PK of brivaracetam (BRV) in healthy and diseased populations. A comprehensive literature review was conducted to incorporate the BRV plasma concentration data and its input parameters into PK-Sim software, leading to the creation of intravenous (IV) and oral models for both populations. The developed physiologically based pharmacokinetic (PBPK) model of BRV was then assessed using the visual predictive checks, mean observed/predicted ratios (Robs/pre), and average fold error for PK parameters including the maximum systemic concentration (Cmax), the area under the curve at time 0 to t (AUC0–∞), and drug clearance (CL). The PBPK model of BRV demonstrated that mean Robs/pre ratios of the PK parameters remained within the acceptable limits when assessed against a twofold error margin. Furthermore, model predictions were carried out to assess how AUC0–∞ is affected following the administration of BRV in individuals with varying degrees of liver cirrhosis, ranging from different child–pugh (CP) scores like A, B, and C. Moreover, dose adjustments were recommended by considering the variations in Cmax and CL in various kidney disease stages (mild to severe).

Comparing the efficacy of two generations of EGFR-TKIs: An integrated drug-disease mechanistic model approach in EGFR-mutant lung adenocarcinoma.

e20578 Background: The epidermal growth factor receptor (EGFR) is frequently mutated in patients with lung adenocarcinoma (LUAD), leading to its constitutive activation and subsequent uncontrolled cell proliferation. To counteract this, tyrosine kinase inhibitors (TKIs) targeting EGFR have been developed. However, the emergence of metastases and resistance mutations often undermines the durability of the treatment response. Knowledge-based mechanistic models that replicate existing clinical trial outcomes, tailored to specific population characteristics, provide invaluable assistance in designing future clinical trials. Methods: We have developed physiologically based pharmacokinetic (PBPK) models for gefitinib and osimertinib, two EGFR TKIs, to accurately simulate the drugs' distribution within the primary tumor and metastatic sites following administration. These models were integrated with a mechanistic model of EGFR-mutant LUAD, enabling the representation of the mechanism of action of gefitinib and osimertinib. This model outputs the evolutions of the primary tumor and each metastasis, facilitating the evaluation of patient progression following RECIST criteria. Notably, the model encapsulates the heterogeneity within the tumor, representing various subclones defined by unique mutation profiles, thereby reflecting differential responses to treatment. Calibration of the model was achieved using publicly available data from the NEJ002, FLAURA and AURA3 clinical trials. Rigorous visual predictive checks and statistical tests were employed to ensure the proper behavior of the model. Results: The model adeptly replicated the time to progression in EGFR-mutant LUAD patients receiving gefitinib or osimertinib, whether as first-line or second-line therapy. Furthermore, it accurately mirrored the progression causes as per the RECIST criteria, including the emergence of new metastases in the lung, brain, liver, and bone. With a consistent virtual population as a reference, the combined model facilitated a comparative analysis of the efficacy of both treatments, thereby underscoring its utility in evaluating therapeutic strategies. Conclusions: The faithful replication of real-world data significantly enhances the credibility of our model, rendering it a promising tool for deriving pertinent insights and informing treatment strategies. Following its consecutive prospective validations in the FLAURA2 and MARIPOSA trials, the model is poised to facilitate the generation of synthetic control arms in future clinical trials. This advancement promises a more nuanced analysis of covariate relationships, especially when comparing investigational treatments to established standards of care. Additionally, it potentially reduces the requisite patient cohort size in such trials, optimizing resource utilization.

Population pharmacokinetics and dosage optimization of linezolid in Chinese older patients.

To assess the pharmacokinetics and pharmacodynamics of linezolid in a retrospective cohort of hospitalized Chinese older patients. Patients > 60years of age, who received intravenous linezolid (600mg), were included. A population pharmacokinetics (PPK) model was established using nonlinear mixed-effects modeling. The predictive performance of the final model was assessed using goodness-of-fit plots, bootstrap analyses, and visual predictive checks. Monte Carlo simulations were used to evaluate the achievement of a pharmacodynamics target for the area under the serum concentration-time curve/minimum inhibitory concentration (AUC0-24/MIC). A total of 210 samples were collected from 120 patients. A one-compartment PPK model with linear elimination best predicted the linezolid plasma concentrations. Linezolid clearance (CL) was 4.22 L h-1 and volume of distribution (Vd) was 45.80 L; serum uric acid (SUA) was a significant covariate of CL. The results of this study indicated that the standard dose was associated with a risk of overexposure in older patients, particularly those with high SUA values; these patients would benefit from a lower dose (300mg every 12h).

Population pharmacokinetics of imetelstat, a first-in-class oligonucleotide telomerase inhibitor.

Imetelstat is a novel, first-in-class, oligonucleotide telomerase inhibitor in development for the treatment of hematologic malignancies including lower-risk myelodysplastic syndromes and myelofibrosis. A nonlinear mixed-effects model was developed to characterize the population pharmacokinetics of imetelstat and identify and quantify covariates that contribute to its pharmacokinetic variability. The model was developed using plasma concentrations from 7 clinical studies including 424 patients with solid tumors or hematologic malignancies who received single-agent imetelstat via intravenous infusion at various dose levels (0.4-11.7 mg/kg) and schedules (every week to every 4 weeks). Covariate analysis included factors related to demographics, disease, laboratory results, renal and hepatic function, and antidrug antibodies. Imetelstat was described by a two-compartment, nonlinear disposition model with saturable binding/distribution and dose- and time-dependent elimination from the central compartment. Theory-based allometric scaling for body weight was included in disposition parameters. The final covariates included sex, time, malignancy, and dose on clearance; malignancy and sex on volume of the central compartment; and malignancy and spleen volume on concentration of target. Clearance in females was modestly lower, resulting in nonclinically relevant increases in predicted exposure relative to males. No effects on imetelstat pharmacokinetics were identified for mild-to-moderate hepatic or renal impairment, age, race, and antidrug antibody status. All model parameters were estimated with adequate precision (relative standard error < 29%). Visual predictive checks confirmed the capacity of the model to describe the data. The analysis supports the imetelstat body-weight-based dosing approach and lack of need for dose individualizations for imetelstat-treated patients.

Population Pharmacokinetics and Limited Sampling Strategy of Mycophenolate Mofetil for Indian Patients With Lupus Nephritis.

Mycophenolic acid is widely used to treat lupus nephritis (LN). However, it exhibits complex pharmacokinetics with large interindividual variability. This study aimed to develop a population pharmacokinetic (popPK) model and a 3-sample limited sampling strategy (LSS) to optimize therapeutic drug monitoring in Indian patients with LN. Five blood samples from each LN patient treated with mycophenolic acid were collected at steady-state predose and 1, 2, 4, and 6 hours postdose. Demographic parameters were tested as covariates to explain interindividual variability. PopPK analysis was performed using Monolix and the stochastic approximation expectation-maximization algorithm. An LSS was derived from 500 simulated pharmacokinetic (PK) profiles using maximum a posteriori Bayesian estimation to estimate individual PK parameters and area under the curve (AUC). The LSS-calculated AUC was compared with the AUC calculated using the trapezoidal rule and all the simulated samples. A total of 51 patients were included in this study. Based on the 245 mycophenolic acid concentrations, a 1-compartmental model with double absorption using gamma distributions best fitted the data. None of the covariates improved the model significantly. The model was internally validated using diagnostic plots, prediction-corrected visual predictive checks, and bootstrapping. The best LSS included samples at 1, 2, and 4 hours postdose and exhibited good performances in an external dataset (root mean squared error, 21.9%; mean bias, -4.20%). The popPK model developed in this study adequately estimated the PK of mycophenolic acid in adult Indian patients with LN. This simple LSS can optimize TDM based on the AUC in routine practice.

Random-Effects Substitution Models for Phylogenetics via Scalable Gradient Approximations.

Phylogenetic and discrete-trait evolutionary inference depend heavily on an appropriate characterization of the underlying character substitution process. In this paper, we present random-effects substitution models that extend common continuous-time Markov chain models into a richer class of processes capable of capturing a wider variety of substitution dynamics. As these random-effects substitution models often require many more parameters than their usual counterparts, inference can be both statistically and computationally challenging. Thus, we also propose an efficient approach to compute an approximation to the gradient of the data likelihood with respect to all unknown substitution model parameters. We demonstrate that this approximate gradient enables scaling of sampling-based inference, namely Bayesian inference via Hamiltonian Monte Carlo, under random-effects substitution models across large trees and state-spaces. Applied to a dataset of 583 SARS-CoV-2 sequences, an HKY model with random-effects shows strong signals of nonreversibility in the substitution process, and posterior predictive model checks clearly show that it is a more adequate model than a reversible model. When analyzing the pattern of phylogeographic spread of 1441 influenza A virus (H3N2) sequences between 14 regions, a random-effects phylogeographic substitution model infers that air travel volume adequately predicts almost all dispersal rates. A random-effects state-dependent substitution model reveals no evidence for an effect of arboreality on the swimming mode in the tree frog subfamily Hylinae. Simulations reveal that random-effects substitution models can accommodate both negligible and radical departures from the underlying base substitution model. We show that our gradient-based inference approach is over an order of magnitude more time efficient than conventional approaches.

Predicting lung exposure of intramuscular niclosamide as an antiviral agent: Power-law based pharmacokinetic modeling.

Niclosamide, a potent anthelmintic agent, has emerged as a candidate against COVID-19 in recent studies. Its formulation has been investigated extensively to address challenges related to systemic exposure. In this study, niclosamide was formulated as a long-acting intramuscular injection to achieve systemic exposure in the lungs for combating the virus. To establish the dose-exposure relationship, a hamster model was selected, given its utility in previous COVID-19 infection studies. Pharmacokinetic (PK) analysis was performed using NONMEM and PsN. Hamsters were administered doses of 55, 96, 128, and 240 mg/kg with each group comprising five animals. Two types of PK models were developed, linear models incorporating partition coefficients and power-law distributed models, to characterize the relationship between drug concentrations in the plasma and lungs of the hamsters. Numerical and visual diagnostics, including basic goodness-of-fit and visual predictive checks, were employed to assess the models. The power-law-based PK model not only demonstrated superior numerical performance compared with the linear model but also exhibited better agreement in visual diagnostic evaluations. This phenomenon was attributed to the nonlinear relationship between drug concentrations in the plasma and lungs, reflecting kinetic heterogeneity. Dose optimization, based on predicting lung exposure, was conducted iteratively across different drug doses, with the minimum effective dose estimated to be ~1115 mg/kg. The development of a power-law-based PK model proved successful and effectively captured the nonlinearities observed in this study. This method is expected to be applicable for investigating the drug disposition of specific formulations in the lungs.

Population Pharmacokinetics of Lamotrigine and Its N2-Glucuronide Metabolite in Chinese Patients With Epilepsy.

Lamotrigine is a new antiepileptic drug with substantial interindividual variability in its pharmacokinetics and therapeutic responses. This study aimed to develop population pharmacokinetic (PPK) models of lamotrigine and its N2-glucuronide metabolites for model-informed individualized therapy. A total of 353 plasma concentrations from Chinese patients with epilepsy receiving oral lamotrigine were used to develop a population PPK model using a nonlinear mixed effects modeling method. One- and two-compartment models were applied to the nonmetabolite and metabolite model, respectively. Forward addition and backward elimination were used to establish the final model. Model validation was performed using standard goodness-of-fit, bootstrap, visual predictive checks, and normalized prediction distribution errors. Finally, simulations were performed to propose lamotrigine dosages in different situations to achieve trough concentrations within the reference interval (2.5-15 mg/L). For both final population PPK models, coadministration with valproic acid (VPA) or enzyme inducer, and body weight significantly affected lamotrigine clearance. The final models for lamotrigine clearance were and for nonmetabolite and metabolite models, respectively. The precision of the PPK parameters was acceptable, and the models exhibited good predictability. Monte Carlo simulations revealed that the lamotrigine dosage administered to patients combined with an enzyme inducer must be tripled that administered with VPA to reach the target trough concentration. Variability in the pharmacokinetics of lamotrigine is large. Coadministration of VPA or an enzyme inducer and body weight are the most important factors in lamotrigine clearance in Chinese patients with epilepsy. The developed population PPK models might support further optimization of lamotrigine dosing regimens.

A Population Pharmacokinetic Study to Compare a Novel Empagliflozin L-Proline Formulation with Its Conventional Formulation in Healthy Subjects.

Empagliflozin is a sodium-glucose cotransporter 2 (SGLT2) inhibitor that is commonly used for the treatment of type 2 diabetes mellitus (T2DM). CKD-370 was newly developed as a cocrystal formulation of empagliflozin with co-former L-proline, which has been confirmed to be bioequivalent in South Korea. This study aimed to quantify the differences in the absorption phase and pharmacokinetic (PK) parameters of two empagliflozin formulations in healthy subjects by using population PK analysis. The plasma concentration data of empagliflozin were obtained from two randomized, open-label, crossover, phase 1 clinical studies in healthy Korean subjects after a single-dose administration. A population PK model was constructed by using a nonlinear mixed-effects (NLME) approach (Monolix Suite 2021R1). Interindividual variability (IIV) and interoccasion variability (IOV) were investigated. The final model was evaluated by goodness-of-fit (GOF) diagnostic plots, visual predictive checks (VPCs), prediction errors, and bootstrapping. The PK of empagliflozin was adequately described with a two-compartment combined transit compartment model with first-order absorption and elimination. Log-transformed body weight significantly influenced systemic clearance (CL) and the volume of distribution in the peripheral compartment (V2) of empagliflozin. GOF plots, VPCs, prediction errors, and the bootstrapping of the final model suggested that the proposed model was adequate and robust, with good precision at different dose strengths. The cocrystal form did not affect the absorption phase of the drug, and the PK parameters were not affected by the different treatments.

Rapid identification of potential nonsteroidal anti-inflammatory drug overdose-induced liver toxicity and prediction of follow-up exposure: Integrating bioanalytical and population pharmacokinetic assay.

Nonsteroidal anti-inflammatory drugs (NSAIDs) are among the most frequently used drugs that can cause liver toxicity. The aim of this study was to integrate bioanalytical and population pharmacokinetic (PopPK) assay to rapidly screen and quantify the concentrations of NSAIDs in plasma and monitor clinical safety. A liquid chromatography-tandem mass spectrometry (LC-MS/MS) method was developed for the simultaneous quantification of acetaminophen (APAP), flurbiprofen (FLB), aspirin (ASP), and ibuprofen (IBP), four commonly used NSAIDs. The PopPK model of the signature toxicant was analyzed based on the published literature. The LC-MS/MS method was successfully validated and applied to determine NSAID concentrations in patient plasma samples. APAP, ASP, and IBP data were best fitted using a one-compartment model, and FLB data were best fitted using a two-compartment model. Bootstrapping and visual predictive checks suggested that a robust and reliable pharmacokinetic model was developed. A fast, simple, and sensitive LC-MS/MS method was developed and validated for determining APAP, FLB, ASP, and IBP in human plasma. Combined with the PopPK model, this method was applied to rapidly analyze the concentrations of NSAIDs in clinical samples from patients presenting to the emergency department with acute liver dysfunction and monitored NSAIDs clinical safety.

New insights into estimating the cementation exponent of the tight and deep carbonate pore systems via rigorous numerical strategies

One of the main constituents of any reservoir characterization is an accurate forecast of water saturation, which is highly dependent upon the cementation exponent. Even though there have been a lot of studies, the most common correlations depend on total porosity. This means that they do not work as well in heterogeneous carbonate reservoirs, especially tight formations with total porosities less than 10%. This study aims to develop accurate and universal models for estimating the cementation exponent in deep and tight carbonate pore systems located in West Asia. Two heuristic algorithms, including the radial basis function neural network optimized by ant colony optimization (RBFNN-ACO) and gene expression programming (GEP), were employed to calculate the cementation exponent. To do this, we prepared a databank incorporating cementation exponents, total porosity, and various pore types. Then, the databank is classified into the test subset (for model prediction checking) and the train subset (for model construction). The reliability of the new recommended models is inspected by applying several statistical quality measures associated with graphical analyses. So, the consequences of the modeling disclose the large precision of the above-mentioned RBFNN-ACO, GEP Model-I, and GEP Model-II by average absolute percentage relative deviations (AAPRD%) of 6.28%, 6.39%, and 7.45%, respectively. Based on the outliers analysis, nearly 95% of the databank and model estimations are, respectively, valid and reliable. Additionally, the three input variables, including moldic porosity (with a + 70% impact value), non-fabric-selective dissolution (connected) porosity (with a -30% impact value), and interparticle porosity (with a -23% impact value), exhibit the main affecting parameters on the cementation exponent. Comparing current results with traditional literature correlations demonstrates the supremacy of the RBFNN-ACO model (AAPRD = 6.28% and root mean squared error (RMSE) = 0.17) over the examined literature correlations such as Borai’s equation (AAPRD = 12.30% and RMSE = 0.41). In addition, RBFNN-ACO can give better results than Borai’s Eqn. for tight (porosity less than 10%) and deep carbonate samples.

An Improved Machine Learning Model for Pure Component Property Estimation

Information on the physicochemical properties of chemical species is an important prerequisite when performing tasks such as process design and product design. However, the lack of extensive data and high experimental costs hinder the development of prediction techniques for these properties. Moreover, accuracy and predictive capabilities still limit the scope and applicability of most property estimation methods. This paper proposes a new Gaussian process-based modeling framework that aims to manage a discrete and high-dimensional input space related to molecular structure representation with the group-contribution approach. A warping function is used to map discrete input into a continuous domain in order to adjust the correlation between different compounds. Prior selection techniques, including prior elicitation and prior predictive checking, are also applied during the building procedure to provide the model with more information from previous research findings. The framework is assessed using datasets of varying sizes for 20 pure component properties. For 18 out of the 20 pure component properties, the new models are found to give improved accuracy and predictive power in comparison with other published models, with and without machine learning.

Bayesian Forecasting of Mortality Rates for Small Areas Using Spatiotemporal Models

Estimation and prediction of subnational mortality rates for small areas are essential planning tools for studying health inequalities. Standard methods do not perform well when data are noisy, a typical behavior of subnational datasets. Thus, reliable estimates are difficult to obtain. I present a Bayesian hierarchical model framework for prediction of mortality rates at a small or subnational level. By combining ideas from demography and epidemiology, the classical mortality modeling framework is extended to include an additional spatial component capturing regional heterogeneity. Information is pooled across neighboring regions and smoothed over time and age. To make predictions more robust and address the issue of model selection, a Bayesian version of stacking is considered using leave-future-out validation. I apply this method to forecast mortality rates for 96 regions in Bavaria, Germany, disaggregated by age and sex. Uncertainty surrounding the forecasts is provided in terms of prediction intervals. Using posterior predictive checks, I show that the models capture the essential features and are suitable to forecast the data at hand. On held-out data, my predictions outperform those of standard models lacking a regional component.

EDITH: An R‐package to spatially project eDNA‐based biodiversity across river networks with minimal prior information

Abstract Ecological and ecosystem monitoring is rapidly shifting towards using environmental DNA (eDNA) data, particularly in aquatic systems. This approach enables a combined coverage of biodiversity across all major organismal groups and the assessment of ecological indices. Yet, most current approaches are not exploiting the full potential of eDNA data, largely interpreting results in a localized perspective. In riverine networks, by explicitly modelling hydrological transport and associated DNA decay, hydrology‐based models enable upscaling eDNA‐based diversity information, providing spatially integrated inference. To capitalize on these unprecedented biodiversity data and translate it into space‐filling biodiversity projections, a streamlined implementation is needed. Here, we introduce the eDITH R‐package, implementing the eDITH model to project biodiversity across riverine networks with minimal prior information. eDITH couples a species distribution model relating a local taxon's eDNA shedding rate in streamwater to environmental covariates, a mass balance expressing the eDNA concentration at a river's cross‐section as a weighted sum of upstream contributions, and an observational model accounting for uncertainties in eDNA measurements. By leveraging on spatially replicated eDNA measurements and minimal hydro‐morphological data, eDITH enables disentangling the various upstream eDNA sources, and produces space‐filling maps of a taxon's spatial distribution at any chosen resolution. eDITH is applicable to both eDNA concentration and metabarcoding data, and to any taxon whose DNA can be retrieved in streamwater. The eDITH package provides user‐friendly functions for single‐run execution and fitting of eDITH to eDNA data with both Bayesian methods (via the BayesianTools package) and non‐linear optimization. An interface to the DHARMa package allows model validation via posterior predictive checks. Necessary preliminary steps such as watershed delineation and hydrological characterization are implemented via the rivnet package. We illustrate eDITH's workflow and functionalities with two case studies from published fish eDNA data. The eDITH package provides a user‐friendly implementation of eDITH, specifically intended for ecologists and conservation biologists. It can be used without previous modelling knowledge but also allows customization for experienced users. Ultimately, eDITH allows upscaling eDNA biodiversity data for any river globally, transforming how state and change in biodiversity in riverine systems can be tracked at high resolution in a highly versatile manner.

Dosing optimization of rituximab for primary membranous nephropathy by population pharmacokinetic and pharmacodynamic study.

Primary membranous nephropathy (PMN) is the most common cause for adult nephrotic syndrome. Rituximab has demonstrated promising clinical efficacy by random controlled trials and the off-label use is widely adopted in PMN. However, the standard dosage is borrowed from B cell lymphoma treatment with far more antigens and is oversaturated for PMN treatment, accompanied with additional safety risk and unnecessary medical cost. More than 15% serious adverse events were observed under standard dosage and low dose therapies were explored recently. Dose optimization by clinical trials is extremely time- and cost-consuming and can be significantly accelerated with the aid of model-informed drug development. Here, we aim to establish the first population pharmacokinetic and pharmacodynamic (PPK/PD) model for rituximab in PMN to guide its dosage optimization. Rituximab pharmacokinetic and pharmacodynamic data from 41 PMN patients in a retrospective study under a newly proposed monthly mini-dose were used to construct quantitative dose-exposure-response relationship via mechanistic target-mediated drug disposition (TMDD) model followed by regression between the reduction of anti-PLA2R titer and time after the treatment. The final model, validated by goodness-of-fit plots, visual predictive checks and bootstrap, was used to recommend the optimized dosing regimen by simulations. The model was well validated for PK/PD prediction. The systemic clearance and half-life are 0.54L/h and 14.7 days, respectively. Simulation of a novel regimen (6 monthly doses of 100mg) indicated the comparable ability and superior duration time of CD20+ B cell depletion compared with standard dosage, while the cumulative dosage and safety risk was significantly decreased. We established the first PPK/PD model and provide evidence to support the dosage optimization based on monthly mini-dose. Our study can also efficiently accelerate dosage optimization of novel anti-CD20 antibodies in PMN and other indications.

Comparing the Efficacy of Two Generations of EGFR-TKIs: An Integrated Drug-Disease Mechanistic Model Approach in EGFR-Mutated Lung Adenocarcinoma.

Mutationsin epidermal growth factor receptor (EGFR) are found in approximately 48% of Asian and 19% of Western patients with lung adenocarcinoma (LUAD), leading to aggressive tumor growth. While tyrosine kinase inhibitors (TKIs) like gefitinib and osimertinib target this mutation, treatments often face challenges such as metastasis and resistance. To address this, we developed physiologically based pharmacokinetic (PBPK) models for both drugs, simulating their distribution within the primary tumor and metastases following oral administration. These models, combined with a mechanistic knowledge-based disease model of EGFR-mutated LUAD, allow us to predict the tumor's behavior under treatment considering the diversity within the tumor cells due to different mutations. The combined model reproduces the drugs' distribution within the body, as well as the effects of both gefitinib and osimertinib on EGFR-activation-induced signaling pathways. In addition, the disease model encapsulates the heterogeneity within the tumor through the representation of various subclones. Each subclone is characterized by unique mutation profiles, allowing the model to accurately reproduce clinical outcomes, including patients' progression, aligning with RECIST criteria guidelines (version 1.1). Datasets used for calibration came from NEJ002 and FLAURA clinical trials. The quality of the fit was ensured with rigorous visual predictive checks and statistical tests (comparison metrics computed from bootstrapped, weighted log-rank tests: 98.4% (NEJ002) and 99.9% (FLAURA) similarity). In addition, the model was able to predict outcomes from an independent retrospective study comparing gefitinib and osimertinib which had not been used within the model development phase. This output validation underscores mechanistic models' potential in guiding future clinical trials by comparing treatment efficacies and identifying patients who would benefit most from specific TKIs. Our work is a step towards the design of a powerful tool enhancing personalized treatment in LUAD. It could support treatment strategy evaluations and potentially reduce trial sizes, promising more efficient and targeted therapeutic approaches. Following its consecutive prospective validations with the FLAURA2 and MARIPOSA trials (validation metrics computed from bootstrapped, weighted log-rank tests: 94.0% and 98.1%, respectively), the model could be used to generate a synthetic control arm.

Decomposition of Reinforcement Learning Deficits in Disordered Gambling via Drift Diffusion Modeling and Functional Magnetic Resonance Imaging.

Gambling disorder is associated with deficits in reward-based learning, but the underlying computational mechanisms are still poorly understood. Here, we examined this issue using a stationary reinforcement learning task in combination with computational modeling and functional resonance imaging (fMRI) in individuals that regular participate in gambling (n = 23, seven fulfilled one to three DSM 5 criteria for gambling disorder, sixteen fulfilled four or more) and matched controls (n = 23). As predicted, the gambling group exhibited substantially reduced accuracy, whereas overall response times (RTs) were not reliably different between groups. We then used comprehensive modeling using reinforcement learning drift diffusion models (RLDDMs) in combination with hierarchical Bayesian parameter estimation to shed light on the computational underpinnings of this performance deficit. In both groups, an RLDDM in which both non-decision time and decision threshold (boundary separation) changed over the course of the experiment accounted for the data best. The model showed good parameter and model recovery, and posterior predictive checks revealed that, in both groups, the model accurately reproduced the evolution of accuracies and RTs over time. Modeling revealed that, compared to controls, the learning impairment in the gambling group was linked to a more rapid reduction in decision thresholds over time, and a reduced impact of value-differences on the drift rate. The gambling group also showed shorter non-decision times. FMRI analyses replicated effects of prediction error coding in the ventral striatum and value coding in the ventro-medial prefrontal cortex, but there was no credible evidence for group differences in these effects. Taken together, our findings show that reinforcement learning impairments in disordered gambling are linked to both maladaptive decision threshold adjustments and a reduced consideration of option values in the choice process.