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Longitudinal data comprised of neutralizing antibody (NAb) activity measurements from subjects who received COVID-19 vaccinations were collected between November 2020 and April 2022. To detect differences between convalesced and naïve groups with respect to the evolution of NAb activity since the subject’s first COVID-19 vaccine, we initially fit a linear mixed effects model to only the decay section of NAb evolution. We conclude that NAb activity, when restricted to this region, behaves differently between these two groups, with the convalesced group generally having higher neutralizing antibody levels than the naïve group. We then fit a nonlinear mixed effects model over the entire NAb progression using a system of ordinary differential equations described by De Pillis et al. as our structural component to the model. This analysis not only supports the claim that over the entire progression, NAb activity behaves differently for convalesced and naïve groups, but aligns with the linear analysis in confirming that NAb decay is slower in the convalesced group than the naïve group. Finally, we use the estimated parameters from the nonlinear mixed effects model to predict NAb progression for each subject from their last observed measurement to 100 days past this measurement.

This analysis aims to evaluate the population pharmacokinetics (PK) and pharmacodynamics (PD) of denosumab and applied a population PK/PD approach to assess the biosimilarity of SB16 in comparison to reference product, denosumab (DEN). Pooled serum concentrations data for SB16 and DEN from male healthy volunteers (HV) in the Phase I and from postmenopausal women with osteoporosis (PMO) Phase III studies, along with lumbar spine bone mineral density (BMD) data from Phase III study, were analyzed using a nonlinear mixed effects population PK/PD sequential modeling approach. The effects of key patient variables on PK/PD parameters were assessed. Treatment effects on clearance (CL) were retained in the model, regardless of statistical significance, to enable comparative simulation between SB16 and DEN. Modeling and simulation were performed using Monolix Suite™. A two-compartment target-mediated drug disposition (TMDD) model with quasi-steady state (QSS) approximation and first-order absorption adequately characterized the PK profile of denosumab. An indirect response model with maximal inhibitory function captured changes in lumbar spine BMD following treatment. The study population had a minimal effect on drug exposure and on changes in BMD, with <5% difference. Race and body weight accounted for up to 19% and 45% of the variability in drug exposure, respectively, but these differences translated into less than a 2% difference in changes in BMD for each covariate. The treatment group (SB16 vs. DEN) was not identified as a significant covariate. Including this factor on CL in the final PK/PD model, irrespective of its statistical significance, did not affect the PK/PD parameter estimates. Comparative simulations showed similar results for both treatment groups. The developed TMDD-QSS model with indirect response model adequately characterized the PK/PD profile of denosumab. Covariate effects, including study population (HV vs. PMO), age, and race showed no clinically meaningful impact on treatment outcomes. Covariate analysis and simulation results revealed no significant differences in PK/PD parameters between SB16 and DEN. The similarity in the PK profile and change in lumbar spine BMD between SB16 and DEN were demonstrated, supporting the potential for SB16 to be substituted for the reference product in the treatment of osteoporosis.

Cannabidiol (CBD) products gain increasing popularity amongst animal owners and veterinarians as an alternative remedy for treatment of stress, inflammation or pain in horses. Whilst the use of cannabinoids is banned in equine sports, there is limited information available concerning CBD detection times in blood or urine. The aim of this study was to determine the pharmacokinetic properties of CBD following oral administration in the horse to assist doping control laboratories with interpreting CBD analytical results. Part 1: dose escalation study: Single oral administration of three escalating doses of CBD paste (0.2 mg/kg, n  = 3 horses; 1 mg/kg, n  = 3; 3 mg/kg, n  = 5) with >7 days wash-out periods in between. Part 2: multiple dose study: oral administration of CBD paste (3 mg/kg, n  = 6) twice daily for 15 days. Multiple blood and urine samples were collected daily throughout both studies. Following study part 2, blood and urine samples were collected for 2 weeks to observe the elimination phase. Concentrations of CBD, its metabolites and further cannabinoids were evaluated using gas-chromatography/tandem-mass-spectrometry. Pharmacokinetic parameters were assessed via two approaches: population pharmacokinetic analysis using a nonlinear mixed-effects model and non-compartmental analysis. AUC 0–12 h and C max were tested for dose proportionality. During the elimination phase, the CBD steady-state urine to serum concentration ratio (Rss) was calculated. Oral CBD medication was well-tolerated in horses. Based on population pharmacokinetics, a three-compartment model with zero-order absorption most accurately described the pharmacokinetic properties of CBD. High volumes of distribution into peripheral compartments and high concentrations of 7-carboxy-CBD were observed in serum. Non-compartmental analysis identified a C max of 12.17 ± 2.08 ng/mL after single administration of CBD (dose: 3 mg/kg). AUC 0–12 h showed dose proportionality, increase for C max leveled off at higher doses. Following multiple doses, the CBD terminal half-life was 161.29 ± 43.65 h in serum. Rss was 4.45 ± 1.04. CBD is extensively metabolized and shows high volumes of tissue distribution with a resulting extended elimination phase. Further investigation of the potential calming and anti-inflammatory effects of CBD are required to determine cut-off values for medication control using the calculated Rss.

Sirolimus is widely used in transplantation, where its therapeutic drug monitoring (TDM) is well established. Evidence of a crucial role for sirolimus in the PI3K/AkT/mTor pathway has stimulated interest in its involvement in neoplasia, either as monotherapy or in combination with other antineoplastic agents. However, in cancer, there is no consensus on sirolimus TDM. In the RAPIRI phase I trial, the combination sirolimus + irinotecan was evaluated as a new treatment for refractory pediatric cancers. Blood sampling at first sirolimus intake (D1) and at steady state (D8), followed by LC/MS2 analysis, was used to develop a population pharmacokinetic model (Monolix® software). A mono-compartmental model with first-order absorption and elimination best fit the data. The only covariate retained for the final model was “body surface area” (D1 and D8). The model also demonstrated that 1.5 mg/m2 would be the recommended sirolimus dose for further studies and that steady-state TDM is necessary to adjust the dosing regimen in atypical profiles (36.4% of the population). No correlation was found between sirolimus trough concentrations and efficacy and/or observed toxicities. The study reveals the relevance of sirolimus TDM in pediatric oncology as it is needed in organ transplantation.

Background: Amikacin is still an essential antimicrobial to treat life-threatening infections, including multidrug-resistant microorganisms. The effectiveness of treatment has been correlated with the Cmax/MIC ratio, with a ratio of 8 being recommended, which is difficult to reach in some patients. Appropriate antibiotic exposure is important for knowing the disposition of the drug in the population. Objectives: We aimed to integrate therapeutic drug monitoring and a populational pharmacokinetic model to assess an optimal dose regimen and respective plasma exposure. Methods: Plasma levels of amikacin in peaks and troughs were determined by LC-MS/MS. The pharmacokinetic parameter was estimated to use nonlinear mixed effect modeling in Monolix® software. The probability of target attainment was also determined using the Simulx™ software. Results: A total of 39 patients were enrolled. A one-compartment model with proportional error model best described amikacin pharmacokinetic parameters, providing a Cl of 1.49 L/h and Vc of 23.18 L. The model developed could characterize the pharmacokinetic profile in Brazilian patients who underwent therapeutic drug monitoring. Conclusions: Amikacin therapeutic drug monitoring should be associated with population pharmacokinetic analysis in dose optimization and individualization, helping maintain appropriate drug exposure in special populations such as critically ill patients. This strategy may contribute to enhancing clinical outcomes.

Bootstrap methods are used in many disciplines to estimate the uncertainty of parameters, including multi-level or linear mixed-effects models. Residual-based bootstrap methods which resample both random effects and residuals are an alternative approach to case bootstrap, which resamples the individuals. Most PKPD applications use the case bootstrap, for which software is available. In this study, we evaluated the performance of three bootstrap methods (case bootstrap, nonparametric residual bootstrap and parametric bootstrap) by a simulation study and compared them to that of an asymptotic method (Asym) in estimating uncertainty of parameters in nonlinear mixed-effects models (NLMEM) with heteroscedastic error. This simulation was conducted using as an example of the PK model for aflibercept, an anti-angiogenic drug. As expected, we found that the bootstrap methods provided better estimates of uncertainty for parameters in NLMEM with high nonlinearity and having balanced designs compared to the Asym, as implemented in MONOLIX. Overall, the parametric bootstrap performed better than the case bootstrap as the true model and variance distribution were used. However, the case bootstrap is faster and simpler as it makes no assumptions on the model and preserves both between subject and residual variability in one resampling step. The performance of the nonparametric residual bootstrap was found to be limited when applying to NLMEM due to its failure to reflate the variance before resampling in unbalanced designs where the Asym and the parametric bootstrap performed well and better than case bootstrap even with stratification.

Using simulated viral load data for a given maraviroc monotherapy study design, the feasibility of different algorithms to perform parameter estimation for a pharmacokinetic-pharmacodynamic-viral dynamics (PKPD-VD) model was assessed. The assessed algorithms are the first-order conditional estimation method with interaction (FOCEI) implemented in NONMEM VI and the SAEM algorithm implemented in MONOLIX version 2.4. Simulated data were also used to test if an effect compartment and/or a lag time could be distinguished to describe an observed delay in onset of viral inhibition using SAEM. The preferred model was then used to describe the observed maraviroc monotherapy plasma concentration and viral load data using SAEM. In this last step, three modelling approaches were compared; (i) sequential PKPD-VD with fixed individual Empirical Bayesian Estimates (EBE) for PK, (ii) sequential PKPD-VD with fixed population PK parameters and including concentrations, and (iii) simultaneous PKPD-VD. Using FOCEI, many convergence problems (56%) were experienced with fitting the sequential PKPD-VD model to the simulated data. For the sequential modelling approach, SAEM (with default settings) took less time to generate population and individual estimates including diagnostics than with FOCEI without diagnostics. For the given maraviroc monotherapy sampling design, it was difficult to separate the viral dynamics system delay from a pharmacokinetic distributional delay or delay due to receptor binding and subsequent cellular signalling. The preferred model included a viral load lag time without inter-individual variability. Parameter estimates from the SAEM analysis of observed data were comparable among the three modelling approaches. For the sequential methods, computation time is approximately 25% less when fixing individual EBE of PK parameters with omission of the concentration data compared with fixed population PK parameters and retention of concentration data in the PD-VD estimation step. Computation times were similar for the sequential method with fixed population PK parameters and the simultaneous PKPD-VD modelling approach. The current analysis demonstrated that the SAEM algorithm in MONOLIX is useful for fitting complex mechanistic models requiring multiple differential equations. The SAEM algorithm allowed simultaneous estimation of PKPD and viral dynamics parameters, as well as investigation of different model sub-components during the model building process. This was not possible with the FOCEI method (NONMEM version VI or below). SAEM provides a more feasible alternative to FOCEI when facing lengthy computation times and convergence problems with complex models.

Indacaterol/glycopyrronium (IND/GLY) is a fixed-dose combination (FDC) of indacaterol, an inhaled long-acting β2-agonist (LABA), and glycopyrronium, an inhaled long-acting muscarinic antagonist (LAMA), developed as a maintenance bronchodilator treatment for patients with chronic obstructive pulmonary disease (COPD). A population pharmacokinetic (PK) analysis was performed to describe the PK profiles of indacaterol and glycopyrronium following the twice daily (b.i.d.) and once daily (o.d.) inhalation regimens as FDC or as monotherapies and to determine the effect of covariates. PK data in 556 COPD patients were pooled from three phase 3 studies. Two phase 3 studies investigated IND/GLY 27.5/12.5 μg b.i.d. and the third study investigated IND/GLY 110/50 μg o.d. Body weight was included in the model with fixed allometric coefficients for indacaterol and glycopyrronium. Statistically significant effects of smoking, age, and sex on apparent clearance of indacaterol; smoking, and estimated glomerular filtration rate at baseline on apparent clearance and Japanese ethnicity on apparent central volume of distribution of glycopyrronium were identified. Systemic exposure to indacaterol and glycopyrronium was shown to be dose-proportional and time-independent following inhalation either as monotherapies or FDC. None of the identified covariate effects was judged to be clinically relevant. There is no PK drug-drug interaction between indacaterol and glycopyrronium in its FDC.

Recent meta-analyses and publications over the past 15 years have provided evidence showing there are considerable gender differences in the pharmacokinetics of metoprolol. Throughout this time, there have not been any research articles proposing a gender stratified dose-adjustment resulting in an equivalent total drug exposure. Metoprolol pharmacokinetic data was obtained from a previous publication. Data was modeled using nonlinear mixed effect modeling using the MONOLIX software package to quantify metoprolol concentration–time data. Gender-stratified dosing simulations were conducted to identify equivalent total drug exposure based on a 100 mg dose in adults. Based on the pharmacokinetic modeling and simulations, a 50 mg dose in adult women provides an approximately similar metoprolol drug exposure to a 100 mg dose in adult men.

We propose to describe exposure–response relationship of an antiepileptic agent, using mixed hidden Markov modeling methodology, to reveal additional insights in the mode of the drug action which the novel approach offers. Daily seizure frequency data from six clinical studies including patients who received gabapentin were available for the analysis. In the model, seizure frequencies are governed by underlying unobserved disease activity states. Individual neighbouring states are dependent, like in reality and they exhibit their own dynamics with patients transitioning between low and high disease states, according to a set of transition probabilities. Our methodology enables estimation of unobserved disease dynamics and daily seizure frequencies in all disease states. Additional modes of drug action are achievable: gabapentin may influence both daily seizure frequencies and disease state dynamics. Gabapentin significantly reduced seizure frequencies in both disease activity states; however it did not significatively affect disease dynamics. Mixed hidden Markov modeling is able to mimic dynamics of seizure frequencies very well. It offers novel insights into understanding disease dynamics in epilepsy and gabapentin mode of action.