Maximum Effect Model Research Articles

Causal Relationship Between Physical Activity and Body Weight: A Maximum Likelihood Treatment Effect Model Approach Using Australian Longitudinal Data.

More than two-thirds of Australians are overweight. Existing research based on non-experimental data has primarily established associations, rather than causal inferences, between physical activity (PA) and body mass index (BMI). PA and BMI likely affect each other, a reciprocal interplay most studies overlook. We investigate the causal relationship between PA and BMI using a quasi-experimental approach to overcome reverse causality bias. A maximum likelihood treatment effect model, a quasi-experimental method, was employed. Data was from an observational longitudinal dataset of 130,397 observations with 19,677 unique individuals aged 15-64 (52% are females) from the Household, Income, and Labour Dynamics in Australia survey 2006-2019. We first tested for the reverse relationships (whereby overweight limits PA) before estimating the effect of PA on BMI. The first-stage modelling results showed that overweight and obese adults are less likely to engage in PA, as are those resource constrained (time or socioeconomically). In the second modelling stage, there was a clear and significant effect of PA on BMI. Being physically active more than three times a week led to a 2.55-point reduction in BMI (p < 0.001). For women, this effect was more pronounced, with a 2.92-point reduction (p < 0.001). This study leverages existing longitudinal data to provide causal estimates of PA on BMI-finding that PA reduces BMI, particularly for women. As many individuals face resource constraints, campaigns to promote behavioural change need to be nuanced and shift some of the responsibility for physically activity from individuals to policy and organizational reforms.

Time-Efficacy Relationship of Semaglutide in the Treatment of Type 2 Diabetes Mellitus: A Model-Based Meta-Analysis.

Our objective was to quantify the efficacy of subcutaneous once-weekly semaglutide in treating type 2 diabetes mellitus (T2DM) over time. Based on a literature search of the PubMed, Embase, Cochrane, and Web of Science databases, a modified maximum effect (Emax) model including rebound effects was built using model-based meta-analysis with change from baseline in glycated hemoglobin as the efficacy endpoint. This was combined with the covariate model to form a final model, and then theoretical values of Emax and time to reach 50% of Emax (ET50) were obtained for each dose. Model fit and prediction were assessed using goodness-of-fit plots and visual prediction checking. Emax and ET50 were influenced by the proportion of males and the baseline values, respectively. There was no evidence of a placebo effect with semaglutide. The efficacy of other doses became more significant over time, and a rebound effect was observed after maximum efficacy, at a rate of 0.018. Simulation of the typical efficacy at the different doses yielded a maximum efficacy of -1.58% with 0.5 mg and a maximum efficacy of -1.87% with 1 mg. In addition, the six simulated doses (0, 0.1, 0.2, 0.5, 1, and 2 mg) showed a dose-dependent relationship between dose and efficacy except for 0.4 mg and 0.8 mg. A higher dose would result in greater efficacy and a faster onset of action. The efficacy of semaglutide in glucose control was investigated using the model-based meta-analysis method, which yields new insights into the treatment of T2DM with semaglutide.

Concentration-QTcF analysis of quizartinib in patients with newly diagnosed FLT3-internal-tandem-duplication-positive acute myeloid leukemia.

Quizartinib prolongs QT interval through inhibition of the slow delayed rectifier potassium current (IKs). We used non-linear mixed-effects modeling to explore the relationship between quizartinib and its pharmacologically active metabolite AC886 and the Fridericia-corrected QT interval (QTcF) in newly diagnosed acute myeloid leukemia (AML) patients. We evaluated linear and non-linear drug effect models, using triplicate QTcF measurements with available time-matched pharmacokinetic samples from the Phase 3 QuANTUM-First trial. The effect of intrinsic and extrinsic factors on model parameters was tested using stepwise covariate model building. Simulations were conducted to predict the change from baseline in QTcF (ΔQTcF) at the maximum concentration at steady-state (Cmax,ss) for quizartinib maintenance daily doses of 30 and 60 mg. The concentration-QTcF (C-QTcF) relationship was best described by a sigmoidal maximum effect model. After accounting for the effect of quizartinib, including AC886 concentrations did not further explain changes in QTcF. Circadian variations in QTcF were described using an empirical change from baseline based on clock times. Age and hypokalaemia were identified as statistically significant covariates on baseline QTcF; no covariates were found to impact the C-QTcF relationship. The median model-predicted ΔQTcF at Cmax,ss was 18.4 ms (90% confidence interval (CI): 16.3-20.5) at 30 mg and 24.1 ms (90% CI: 21.4-26.6) at 60 mg. In conclusion, in newly diagnosed AML patients, ΔQTcF increased non-linearly with increasing quizartinib concentrations. The predicted ΔQTcF increase at Cmax,ss supports the proposed dose adaptation based on observed QTcF and the dose reduction in case of strong cytochrome P450 3A (CYP3A) inhibitors coadministration.

Improved Malaria Therapy with Cationic Nanocapsules Demonstrated in Plasmodium berghei-Infected Rodents Using Whole Blood Surrogate Population PK/PD Modeling.

Objectives: Investigating how nanoparticle systems interact in whole blood (WB) is critical to evaluating the effectiveness of malaria therapy. Methods: We decided to establish a pharmacokinetic/pharmacodynamic (PK/PD) model of the quinine population in WB using Plasmodium berghei-infected mice, with a subsequent model comparison for nanocapsules coated with polysorbate (NCP80) or prepared with Eudragit® RS (NCEUD). The WB quinine population pharmacokinetic model in rats was developed using plasma and partition coefficients for rat erythrocytes. Mouse WB quinine population PK/PD modeling was developed using allometrically scaled literature-free mouse quinine pharmacokinetic data and covariate values to obtain a WB population pharmacokinetic model for quinine and nanocapsules in mice. This allowed for PK/PD modeling of the quinine population with the WB concentration and parasitemia data in mice. All models were built in NONMEN. Results: The WB quinine concentration profiles in rats were characterized using a two-compartment model. Nanoencapsulation reduced clearance and central compartment volume and increased peripherical compartimental volume. A maximum effect model described the PK/PD of the quinine WB population in mice, demonstrating that NCEUD enhances the antimalarial effect. Conclusions: Quinine WB is a good surrogate for describing the response to exposure in malaria. NCEUD outperformed NCP80 and free quinine, suggesting that cationic surfaces improve the potential for treating malaria.

Exposure-Response Analysis of Tofacitinib in Active Psoriatic Arthritis: Results from Two Phase 3 Studies.

Tofacitinib is an oral Janus kinase inhibitor for the treatment of psoriatic arthritis (PsA). These post hoc exposure-response (E-R) analyses of pooled data from two Phase 3 studies (NCT01877668 and NCT01882439) characterized the relationships between tofacitinib exposure and efficacy (American College of Rheumatology [ACR] criteria), and changes in hemoglobin (Hgb) in patients with PsA. Efficacy data for the proportion of patients receiving tofacitinib 5 or 10 mg twice daily, or placebo, achieving ACR ≥20%, ≥50%, or ≥70% response criteria (ACR20, ACR50, and ACR70, respectively) at Month 3, were modeled jointly using a four-category ordered categorical exposure-response model (ACR20 non-responder, ACR20 responder but not ACR50 responder, ACR50 responder but not ACR70 responder, and ACR70 responder). A maximum drug effect (Emax) model (using average concentrations of tofacitinib at steady state [Cavg]) adequately described the exposure-ACR response rate relationship. Model-predicted response rates for tofacitinib 5 and 10 mg twice daily were 51% and 58%, respectively, for ACR20; 29% and 36% for ACR50; and 15% and 20% for ACR70. The E-R relationship between tofacitinib exposure and changes in Hgb was assessed using an indirect response model, which generally predicted Hgb concentration-time profiles across treatments well. The proportions of patients experiencing a decrease in Hgb of >2 g/dL were similar with tofacitinib 5 mg twice daily or placebo. These results were generally consistent with previous analyses in rheumatoid arthritis and psoriasis, and support the use of tofacitinib 5 mg twice daily for active PsA.

What can optimized cost distances based on genetic distances offer? A simulation study on the use and misuse of ResistanceGA.

Modelling population connectivity is central to biodiversity conservation and often relies on resistance surfaces reflecting multi-generational gene flow. ResistanceGA (RGA) is a common optimization framework for parameterizing these surfaces by maximizing the fit between genetic distances and cost distances using maximum likelihood population effect models. As the reliability of this framework has rarely been studied, we investigated the conditions maximizing its accuracy for both prediction and interpretation of landscape features' permeability. We ran demo-genetic simulations in contrasted landscapes for species with distinct dispersal capacities and specialization levels, using corresponding reference cost scenarios. We then optimized resistance surfaces from the simulated genetic distances using RGA. First, we evaluated whether RGA identified the drivers of the genetic patterns, that is, distinguished Isolation-by-Resistance (IBR) patterns from either Isolation-by-Distance or patterns unrelated to ecological distances. We then assessed RGA predictive performance using a cross-validation method, and its ability to recover the reference cost scenarios shaping genetic structure in simulations. IBR patterns were well detected and genetic distances were predicted with great accuracy. This performance depended on the strength of the genetic structuring, sampling design and landscape structure. Matching the scale of the genetic pattern by focusing on population pairs connected through gene flow and limiting overfitting through cross-validation further enhanced inference reliability. Yet, the optimized cost values often departed from the reference values, making their interpretation and extrapolation potentially dubious. While demonstrating the value of RGA for predictive modelling, we call for caution and provide additional guidance for its optimal use.

Model-Informed Precision Dosing for Personalized Ustekinumab Treatment in Plaque Psoriasis.

Implementing model-informed precision dosing (MIPD) strategies guided by population pharmacokinetic/pharmacodynamic (PK/PD) models could enhance the management of inflammatory diseases such as psoriasis. However, the extent of individual experimental data gathered during MIPD significantly influences the uncertainty in estimating individual PK/PD parameters, affecting clinical dose selection decisions. This study proposes a methodology to individualize ustekinumab (UTK) dosing strategies for 23 Spanish patients with moderate to severe chronic plaque psoriasis., considering the uncertainty of individual parameters within a population PK/PD model. An indirect response model from previous research was used to describe the PK/PD relationship between UTK serum concentrations and the Psoriasis Area and Severity Index (PASI) score. A maximum inhibition drug effect (Imax) model was selected, and a first-order remission constant rate of psoriatic skin lesion (kout = 0.016 d-1) was estimated. The MIPD approach predicted that 35% and 26% of the patients would need an optimized and intensified dosage regimen, respectively, compared to the regimen typically used in clinical practice. This analysis demonstrated its utility as a tool for selecting personalized UTK dosing regimens in clinical practice in order to optimize the probability of achieving targeted clinical outcomes in patients with psoriasis.

Antibacterial action of penicillin against Mycobacterium avium complex.

β-lactam antibiotics are promising treatments for Mycobacterium avium complex (MAC) lung disease. We hypothesized that benzylpenicillin has efficacy against MAC. Benzylpenicillin lung concentration-time profiles of seven doses in three dosing schedules were administered for 28 days using the hollow fiber system model of intracellular MAC (HFS-MAC). Data were analyzed using the inhibitory sigmoid maximal effect (Emax) model for each sampling day, while two ordinary differential equations (ODEs) were used for the wild-type and penicillin-resistant mutants. Benzylpenicillin killed >2.1 log10 colony-forming unit (CFU)/mL below Day 0, better than azithromycin, ethambutol, and rifabutin. Efficacy was terminated by acquired resistance. Sigmoid Emax parameter estimates significantly differed between sampling days and were a poor fit. However, ODE model parameter estimates vs. exposure were a better fit. The exposure mediating Emax was 84.6% (95% CI 76.91-82.98) of time concentration exceeded the minimum inhibitory concentration (MIC). In Monte Carlo experiments, 24 million international units of benzylpenicillin continuous infusion achieved the target exposure in lungs of >90% of 10,000 subjects until an MIC of 64 mg/L, designated the susceptibility breakpoint. Benzylpenicillin demonstrated a better bactericidal effect against MAC than guideline-recommended drugs before the development of resistance. Its role in combination therapy with other drugs with better efficacy than guideline-recommended drugs should be explored.

In vitro evaluation of using ceftazidime/avibactam against carbapenem-resistant Acinetobacter baumannii

ObjectiveCarbapenem-resistant Acinetobacter baumannii (CRAB) is a global concern as effective treatments are very limited. We previously used a modified susceptibility testing approach to predict growth suppression in carbapenem-resistant Enterobacterales, but there are uncertainties about the generalizability of the model. The objective of this study is to verify if a similar approach can be extended to CRAB. MethodA clinical isolate of CRAB resistant to ceftazidime/avibactam (CAZ/AVI, MIC = 32/4 mg/L) was examined. CAZ susceptibility was determined using increasing concentrations of AVI (0–64 mg/L), and MIC reduction was characterized with a sigmoid inhibitory maximum effect (Emax) model. The effectiveness of CAZ/AVI was validated in a hollow fibre infection model (HFIM) over 72 hours, using simulated unbound serum / epithelial lining fluid (ELF) exposures of 2.5 g over 2 hours every 8 hours. Baseline inocula of approximately 5.5 log CFU/mL were examined. ResultsAn AVI concentration-dependent reduction in CAZ MIC was observed (r2 = 0.99). CAZ MIC was dramatically reduced from 512 mg/L (no AVI) to 32 mg/L (AVI = 4 mg/L), and further to 8 mg/L (AVI = 16 mg/L). Pharmacokinetic simulations were satisfactory in the HFIM (r2 > 0.96). Bacterial suppression was observed >24 hours with the serum exposure, but not that from the ELF. ConclusionUsing multiple AVI concentrations within the clinically relevant range, our susceptibility testing approach could have better insights of treatment outcome for infections caused by CRAB. This could potentially lead to effective intervention(s) overlooked by conventional susceptibility testing method. This case highlights the importance of site-specific drug exposures on determining treatment outcome.

Do we perceive sensations inside and outside of our body differently? Perceptual, emotional, and behavioral differences between visceral and somatic sensation, discomfort, and pain.

Experimental research evaluating differences between the visceral and somatic stimulation is limited to pain and typically uses different induction methods for visceral and somatic stimulation (e.g., rectal balloon distention vs. tactile hand stimulation). Our study aimed to compare differences in response time, intensity, unpleasantness, and threat between identical electrical visceral and somatic stimulations at both painful and non-painful perceptual thresholds. Electrical stimulation was applied to the wrist and distal esophagus in 20 healthy participants. A double pseudorandom staircase determined perceptual thresholds of Sensation, Discomfort, and Pain for the somatic and visceral stimulations, separately. Stimulus reaction time (ms, via button press), and intensity, unpleasantness, and threat ratings were recorded after each stimulus. General linear mixed models compared differences in the four outcomes by stimulation type, threshold, and the stimulation type-by-threshold interaction. Sigmoidal maximum effect models evaluated differences in outcomes across all delivered stimulation intensities. Overall, visceral stimulations were perceived as more intense, threatening, and unpleasant compared to somatic stimulations, but participants responded faster to somatic stimulations. There was no significant interaction effect, but planned contrasts demonstrated differences at individual thresholds. Across all delivered intensities, higher intensity stimulations were needed to reach the half-maximum effect of self-reported intensity, unpleasantness, and threat ratings in the visceral domain. Differences exist between modalities for both non-painful and painful sensations. These findings may have implications for translating paradigms and behavioral treatments from the somatic domain to the visceral domain, though future research in larger clinical samples is needed.

Population Pharmacokinetics of Pediatric Lopinavir/Ritonavir Oral Pellets in Children Living with HIV in Africa.

Antiretroviral therapy for children living with HIV (CLHIV) under 3 years of age commonly includes lopinavir/ritonavir (LPV/r). However, the original liquid LPV/r formulation has taste and cold storage difficulties. To address these challenges, LPV/r oral pellets have been developed. These pellets can be mixed with milk or food for administration and do not require refrigeration. We developed the population pharmacokinetic (PK) model and assessed drug exposure of LPV/r oral pellets administered twice daily to CLHIV per World Health Organization (WHO) weight bands. The PK analysis included Kenyan and Ugandan children participating in the LIVING studies (NCT02346487) receiving LPV/r pellets (40/10 mg) and ABC/3TC (60/30 mg) dispersible tablets. Population PK models were developed for lopinavir (LPV) and ritonavir (RTV) to evaluate the impact of RTV on the oral clearance (CL/F) of LPV. The data obtained from the study were analyzed using nonlinear mixed-effects modeling approach. Data from 514 children, comprising a total of 2,998 plasma concentrations of LPV/r were included in the analysis. The LPV and RTV concentrations were accurately represented by a one-compartment model with first-order absorption (incorporating a lag-time) and elimination. Body weight influenced LPV and RTV PK parameters. The impact of RTV concentrations on the CL/F of LPV was characterized using a maximum effect model. Simulation-predicted target LPV exposures were achieved in children with this pellet formulation across the WHO weight bands. The LPV/r pellets dosed in accordance with WHO weight bands provide adequate LPV exposures in Kenyan and Ugandan children weighing 3.0 to 24.9 kg.

Omadacycline pharmacokinetics/pharmacodynamics and efficacy against multidrug-resistant Mycobacterium tuberculosis in the hollow fiber system model.

Seventy-five years ago, first-generation tetracyclines demonstrated limited efficacy in the treatment of tuberculosis but were more toxic than efficacious. We performed a series of pharmacokinetic/pharmacodynamic (PK/PD) experiments with a potentially safer third-generation tetracycline, omadacycline, for the treatment of multidrug-resistant tuberculosis (MDR-TB). Mycobacterium tuberculosis (Mtb) H37Rv and an MDR-TB clinical strain (16D) were used in the minimum inhibitory concentration (MIC) and static concentration-response studies in test tubes, followed by a PK/PD study using the hollow fiber system model of TB (HFS-TB) that examined six human-like omadacycline doses. The inhibitory sigmoid maximal effect (Emax) model and Monte Carlo experiments (MCEs) were used for data analysis and clinical dose-finding, respectively. The omadacycline MIC for both Mtb H37Rv and MDR-TB clinical strain was 16 mg/L but dropped to 4 mg/L with daily drug supplementation to account for omadacycline degradation. The Mycobacteria Growth Indicator Tube MIC was 2 mg/L. In the test tubes, omadacycline killed 4.39 log10 CFU/mL in 7 days. On Day 28 of the HFS-TB study, the Emax was 4.64 log10 CFU/mL, while exposure mediating 50% of Emax (EC50) was an area under the concentration-time curve to MIC (AUC0-24/MIC) ratio of 22.86. This translates to PK/PD optimal exposure or EC80 as AUC0-24/MIC of 26.93. The target attainment probability of the 300-mg daily oral dose was 90% but fell at MIC ≧4 mg/L. Omadacycline demonstrated efficacy and potency against both drug-susceptible and MDR-TB. Further studies are needed to identify the omadacycline effect in combination therapy for the treatment of both drug-susceptible and MDR-TB.

Model-based meta-analysis using latent variable modeling to set benchmarks for new treatments of systemic lupus erythematosus.

Several investigational agents are under evaluation in systemic lupus erythematosus (SLE) clinical trials but quantitative frameworks to enable comparison of their efficacy to reference benchmark treatments are lacking. To benchmark SLE treatment effects and identify clinically important covariates, we developed a model-based meta-analysis (MBMA) within a latent variable model framework for efficacy end points and SLE composite end point scores (BILAG-based Composite Lupus Assessment and Systemic Lupus Erythematosus Responder Index) using aggregate-level data on approved and investigational therapeutics. SLE trials were searched using PubMed and www.clinicaltrials.gov for treatment name, SLE and clinical trial as search criteria that resulted in four data structures: (1) study and investigational agent, (2) dose and regimen, (3) baseline descriptors, and (4) outcomes. The final dataset consisted of 25 studies and 81 treatment arms evaluating 16 different agents. A previously developed (K Goteti etal. 2022) SLE latent variable model of data from placebo arms (placebo + standard of care treatments) was used to describe aggregate SLE end points over time for the various SLE placebo and treatment arms in a Bayesian MBMA framework. Continuous dose-effect relationships using a maximum effect model were included for anifrolumab, belimumab, CC-220 (iberdomide), epratuzumab, lulizumab pegol, and sifalimumab, whereas the remaining treatments were modeled as discrete dose effects. The final MBMA model was then used to benchmark these compounds with respect to the maximal efficacy on the latent variable compared to the placebo. This MBMA illustrates the application of latent variable models in understanding the trajectories of composite end points in chronic diseases and should enable model-informed development of new investigational agents in SLE.

Sarecycline pharmacokinetics/pharmacodynamics in the hollow-fibre model of Mycobacterium avium complex: so near and yet so far.

Poor sustained sputum culture conversion rates with the standard-of-care therapy highlight the need for better drugs to treat Mycobacterium avium complex pulmonary disease (MAC-PD). To determine the pharmacokinetics/pharmacodynamics (PK/PD)-optimized exposure of sarecycline and its potential role in treating MAC-PD. We performed MIC studies with MAC ATCC 700898 and 19 clinical isolates and test-tube static concentration-response studies. A dynamic hollow-fibre system model of intracellular MAC (HFS-MAC) study was performed mimicking six human-equivalent sarecycline dose concentration-time profiles to identify the PK/PD optimal exposure of sarecycline for MAC kill. The inhibitory sigmoid maximal effect (Emax) model was used for PK/PD analysis. The sarecycline MIC of MAC ATCC 700898 was 1 mg/L, while the MIC for the 19 clinical strains ranged between 32 and >256 mg/L. The concentration mediating 50% of Emax (EC50) was similar between intracellular and extracellular MAC. In the HFS-MAC, all six sarecycline doses killed intracellular MAC, with an Emax of 1.0 log10 cfu/mL below Day 0 burden (stasis). The sarecycline EC80 (optimal) exposure was identified as AUC0-24/MIC = 139.46. Sarecycline demonstrated anti-MAC Emax in the HFS-MAC model better than ethambutol but worse than omadacycline (>5 log10 cfu/mL below stasis) in HFS-MAC. However, since currently approved highest oral sarecycline dose achieves an AUC0-24 of 48.2 mg·h/L and MAC MICs are >32 mg/L, the target AUC0-24/MIC of 139.46 is unlikely to be achieved in patients.

Modeling the protein binding non-linearity in population pharmacokinetic model of valproic acid in children with epilepsy: a systematic evaluation study.

Background: Several studies have investigated the population pharmacokinetics (popPK) of valproic acid (VPA) in children with epilepsy. However, the predictive performance of these models in the extrapolation to other clinical environments has not been studied. Hence, this study evaluated the predictive abilities of pediatric popPK models of VPA and identified the potential effects of protein binding modeling strategies. Methods: A dataset of 255 trough concentrations in 202 children with epilepsy was analyzed to assess the predictive performance of qualified models, following literature review. The evaluation of external predictive ability was conducted by prediction- and simulation-based diagnostics as well as Bayesian forecasting. Furthermore, five popPK models with different protein binding modeling strategies were developed to investigate the discrepancy among the one-binding site model, Langmuir equation, dose-dependent maximum effect model, linear non-saturable binding equation and the simple exponent model on model predictive ability. Results: Ten popPK models were identified in the literature. Co-medication, body weight, daily dose, and age were the four most commonly involved covariates influencing VPA clearance. The model proposed by Serrano et al. showed the best performance with a median prediction error (MDPE) of 1.40%, median absolute prediction error (MAPE) of 17.38%, and percentages of PE within 20% (F20, 55.69%) and 30% (F30, 76.47%). However, all models performed inadequately in terms of the simulation-based normalized prediction distribution error, indicating unsatisfactory normality. Bayesian forecasting enhanced predictive performance, as prior observations were available. More prior observations are needed for model predictability to reach a stable state. The linear non-saturable binding equation had a higher predictive value than other protein binding models. Conclusion: The predictive abilities of most popPK models of VPA in children with epilepsy were unsatisfactory. The linear non-saturable binding equation is more suitable for modeling non-linearity. Moreover, Bayesian forecasting with prior observations improved model fitness.

Modeling time-delayed concentration-QT effects with ACT-1014-6470, a novel oral complement factor 5a receptor 1 (C5a1 receptor) antagonist.

The novel oral complement factor 5a receptor 1 antagonist ACT-1014-6470 was well tolerated in single- and multiple-ascending dose studies, including 24 h Holter electrocardiogram (ECG) recordings evaluating its cardiodynamics based on data from single doses of 30-200 mg and twice-daily (b.i.d.) dosing of 30-120 mg for 4.5 days. By-time point, categorical, and morphological analyses as well as concentration-QT modeling and simulations were performed. No relevant effect of ACT-1014-6470 on ECG parameters was observed in the categorical and morphological analyses. After single-dose administration, the by-time point analysis indicated a delayed dose-dependent increase in placebo-corrected change from baseline in QT interval corrected with Fridericia's formula (ΔΔQTcF) at >6 h postdose. After b.i.d. dosing, ΔΔQTcF remained elevated during the 24-h recording period, suggesting that the effect was not directly related to ACT-1014-6470 plasma concentration. The concentration-QT model described change from baseline in QTcF (ΔQTcF)-time profiles best with a 1-oscillator model of 24 h for circadian rhythm, an effect compartment, and a sigmoidal maximum effect model. Model-predicted ΔΔQTcF was derived for lower doses and less-frequent dosing than assessed clinically. Median and 90% prediction intervals of ΔΔQTcF for once-daily doses of 30 mg and b.i.d. doses of 10 mg did not exceed the regulatory threshold of 10 ms but would achieve ACT-1014-6470 plasma concentrations enabling adequate target engagement. Results from cardiodynamic assessments identified dose levels and dosing regimens that could be considered for future clinical trials, attempting to reduce QT liability.

Mathematical pharmacodynamic modeling for antimicrobial assessment of ceftazidime/colistin versus gentamicin/meropenem combinations against carbapenem-resistant Pseudomonas aeruginosa biofilm

BackgroundCarbapenem-resistant Pseudomonas aeruginosa (CRPA) represents an escalating healthcare hazard with high mortality worldwide, especially in presence of biofilm. The current study aimed to evaluate the anti-biofilm potentials of ceftazidime, colistin, gentamicin, and meropenem alone and in combinations against biofilm-forming CRPA. MethodsBiofilm killing and checkerboard assay were performed to detect the effectiveness of combined antibiotics against biofilms and planktonic cells, respectively. The bacterial bioburden retrieved from the established biofilms following treatment with combined antibiotics was utilized to construct a three-dimensional response surface plot. A sigmoidal maximum effect model was applied to determine the pharmacodynamic parameters (maximal effect, median effective concentration, and Hill factor) of each antibiotic to create a mathematical three-dimensional response surface plot.ResultsData revealed statistically significant (p < 0.05) superior anti-biofilm potential in the case of colistin followed by a lower effect in the case of gentamicin and meropenem, while ceftazidime exhibited the least anti-biofilm activity. The fractional inhibitory concentration index (FICI ≤ 0.5) indicated synergism following treatment with the combined antibiotics. An elevated anti-biofilm activity was recorded in the case of gentamicin/meropenem compared to ceftazidime/colistin. Synergistic anti-biofilm potentials were also detected via the simulated pharmacodynamic modeling, with higher anti-biofilm activity in the case of the in vitro observation compared to the simulated anti-biofilm profile.ConclusionsThe present study highlighted the synergistic potentials of the tested antibiotic combinations against P. aeruginosa biofilms and the importance of the mathematical pharmacodynamic modeling in investigating the efficacy of antibiotics in combination as an effective strategy for successful antibiotic therapy to tackle the extensively growing resistance to the currently available antibiotics.

Modeling Approach with Therapeutic Drug Monitoring Data to Describe Time Course of Clozapine Exposure and Positive and Negative Syndrome Scale Scores

The Positive and Negative Syndrome Scale (PANSS) is commonly used to assess the severity of the clinical symptoms of schizophrenia (SCZ). This study aimed to develop a pharmacokinetic (PK)/pharmacodynamic (PD) model based on therapeutic drug monitoring (TDM) data to characterize the relationship between clozapine exposure and the PANSS scores in patients with SCZ. TDM data for clozapine and PANSS scores from 45 patients with SCZ were included in this modeling analysis using NONMEM. Based on published data, intensive PK sampling data collected up to 12 hours postdose from 23 patients was incorporated into the PK data set to improve the fitting of absorption and disposition. For PD model development, the PANSS score was assessed at baseline, followed by 8 and 18 weeks after the initiation of clozapine dosing. Visual predictive check plots, the precision of parameter estimates, and decreases in the minimum objective function values were used for the model evaluation. A 2-compartment model with an absorption lag and a combined error model adequately described the PK of clozapine. The implementation of disease progression with placebo and drug effects improved the model's ability to describe the time course of the PANSS scores. In the final PK/PD model, Weibull and maximum effect (E max ) models were selected as disease progression models for the placebo and drug effect models, respectively. The model evaluation results supported the adequacy of the final model. A clozapine PK/PD model based on clinical settings adequately described the PANSS time course in patients with SCZ. These findings may aid the development of treatment strategies for patients with SCZ.

Sutimlimab Pharmacokinetics and Pharmacodynamics in Patients with Cold Agglutinin Disease.

Sutimlimab, a humanized monoclonal antibody targeting the classical complement pathway, is approved in the US, Japan, and EU for the treatment of hemolytic anemia in adults with cold agglutinin disease. The objectives of this study were to support dose selection for Phase 3 studies, assess dose recommendations, and establish the relationship between sutimlimab exposure and clinical outcome (hemoglobin [Hb] levels). Clinically meaningful biomarkers were graphically analyzed and the exposure-response relationship was proposed. The pharmacokinetic (PK) characteristics of sutimlimab were best described by a two-compartment model with parallel linear and non-linear clearance terms. Body weight was a significant covariate for the volume of distribution in the central compartment (Vc) and total body clearance of sutimlimab. Ethnicity (Japanese, non-Japanese) was a covariate on Vc and maximal non-linear clearance. There were no PK differences between healthy participants and patients. Following graphical exposure-response analysis for biomarkers, a pharmacokinetic-pharmacodynamic model was developed by integrating an indirect response/turnover model for Hb with a maximum effect (Emax) model, relating the Hb-elevating effect of sutimlimab to plasma exposure. Renal function and occurrence of blood transfusion were identified as covariates on Hb change from baseline. Simulations showed that Emax was attained with the approved dosing (6.5 g in patients <75 kg, and 7.5 g in patients {greater than or equal to}75 kg), independent of covariate characteristics, and provided adequate sutimlimab exposure to maximize effects on Hb, bilirubin, and total complement component C4 levels. A change in Hb from baseline at steady state of 2.2 g/dL was projected, consistent with Phase 3 study observations. Significance Statement The final validated population PK and PK/PD models confirm that the approved dosing regimen for sutimlimab (6.5 g in patients <75 kg, and 7.5 g in patients ≥75 kg) is sufficient, without the need for further dose adjustments in populations of patients with cold agglutinin disease.

Clinical Pharmacokinetics and Pharmacodynamics of Daratumumab.

Daratumumab is a fully human, monoclonal immunoglobulin G1 and a first-in-class CD38-targeting drug approved by the US Food and Drug Administration for the treatment of patients with relapsed/refractory and newly diagnosed multiple myeloma or newly diagnosed light-chain amyloidosis. CD38 is heavily expressed on malignant myeloma cells, and daratumumab exerts anti-myeloma activity via immune-mediated mechanisms, direct induction of apoptosis, and immunomodulation. Daratumumab is used as monotherapy or in combination with standard-of-care myeloma therapies, including proteasome inhibitors, immunomodulatory agents, DNA-alkylating agents, and corticosteroids. Following an intravenous infusion, daratumumab exhibits nonlinear pharmacokinetics (PK), as clearance decreases with higher doses and over time because of target-mediated effects. Dosing schedules vary depending on indications and co-administered drugs, but generally daratumumab is administered weekly for 6-9 weeks followed by a less frequent dosing regimen, once every 2-4 weeks. Daratumumab exposure is strongly correlated with efficacy, and the exposure-efficacy relationship follows a maximal effect model, whereas exposure is not correlated with safety endpoints. The approved dose of 16 mg/kg of daratumumab results in the saturation of 99% of the target at the end of weekly dosing in most patients, and high target saturation is maintained over time during the less frequent dosing schedule. Infusion-related reactions are frequently observed in patients given daratumumab, particularly with the first infusion, thus prompting long durations of infusion (~ 7 h) and splitting of the first dose across 2 days. This led to the development of a subcutaneous delivery formulation for daratumumab (Dara-SC). Dara-SC provides a similar efficacy and safety profile to intravenous daratumumab (Dara-IV) but has a much lower rate of infusion-related reactions and a shorter infusion time. Exposure-response relationships for efficacy and safety endpoints were similar between Dara-SC and Dara-IV, and co-administered drugs with either Dara-IV or Dara-SC do not significantly affect daratumumab PK. Except for baseline myeloma type and albumin level, none of the other investigated disease and patient characteristics (renal/hepatic function, age, sex, race, weight, Eastern Cooperative Oncology Group performance status) was identified to have clinically relevant effects on exposure to daratumumab monotherapy or combination therapy regimens. Dara-IV exposure was significantly lower in patients with immunoglobulin G myeloma compared with patients with non-immunoglobulin G myeloma (p < 0.0001) and in patients with a lower albumin level, whereas the overall response rate was similar regardless of the myeloma type and albumin level. Daratumumab dose adjustment is not currently recommended based on disease and patient characteristics.