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Abstract: Magnetron sputtering from compound targets is widely used for the deposition of compound films since it is easy to scale up and it exhibits a high reproducibility. Controlling film stoichiometry is crucial for obtaining films with desired properties. However, the process is rather complex and sputtering from a compound target frequently results in film compositions that deviate significantly from that of the target. This is due to a number of effects related to the nature of the sputtering process which include preferential re-sputtering due to energetic particle bombardment at the substrate, different take-off angles, scattering in the gas phase, and different sticking coefficients. In this work, we have investigated how sputtering from a WS2 target results in different film compositions as a function of the position in the chamber, for different processing conditions. Hence, the films have not been characterized with respect to structural or morphological properties. A Monte-Carlo based software, accounting for different take-off angles and scattering in the gas phase, was developed to simulate the compositional variations at various positions in the chamber. Further, a number of experiments were performed by varying the target voltage, process pressure, as well as the location of the substrate (on and off axis). Simulations and experiments reveal significant compositional variations for different processing conditions. Experiments show that these variations are only slightly affected by the target voltage, while the most significant variations result from the processing pressure and position on the chamber. From the qualitatively good agreement between experiments and simulations it is clear that gas phase scattering must be taken into account to explain the observed compositional trends, while the other effects are less important and sticking coefficients effects may even be negligible. It is therefore concluded that the major effect responsible for the compositional variation of the film is the different scattering behaviour of S and W in the gas phase. [Copyright &y& Elsevier]

Conventional approaches for establishing bioequivalence (BE) between test and reference formulations using non-compartmental analysis (NCA) may demonstrate low power in pharmacokinetic (PK) studies with sparse sampling. In this case, model-integrated evidence (MIE) approaches for BE assessment have been shown to increase power, but may suffer from selection bias problems if models are built on the same data used for BE assessment. This work presents model averaging methods for BE evaluation and compares the power and type I error of these methods to conventional BE approaches for simulated studies of oral and ophthalmic formulations. Two model averaging methods were examined: bootstrap model selection and weight-based model averaging with parameter uncertainty from three different sources, either from a sandwich covariance matrix, a bootstrap, or from sampling importance resampling (SIR). The proposed approaches increased power compared with conventional NCA-based BE approaches, especially for the ophthalmic formulation scenarios, and were simultaneously able to adequately control type I error. In the rich sampling scenario considered for oral formulation, the weight-based model averaging method with SIR uncertainty provided controlled type I error, that was closest to the target of 5%. In sparse-sampling designs, especially the single sample ophthalmic scenarios, the type I error was best controlled by the bootstrap model selection method., (© 2024 The Author(s). CPT: Pharmacometrics & Systems Pharmacology published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics.)

Background: The use of medications for opioid use disorder such as methadone or buprenorphine is increasing among pregnant women. However, long-term effects of this treatment on the children's health are not well understood. A key challenge is distinguishing the effects of opioid exposure from other confounding factors associated with human opioid use, such as reduced maternal care. In this study, we therefore used a multi-risk factor design to examine anxiety-like behavior in rats prenatally exposed to methadone or buprenorphine, with or without maternal separation the first two weeks after birth., Methods: Female Sprague Dawley rats were exposed to methadone (10mg/kg/day), buprenorphine (1mg/kg/day) or sterile water throughout gestation. Half of the offspring in each litter experienced maternal separation for 3h per day from postnatal day 2 to 12. Male and female offspring (6-9 weeks) were tested in the open field, light-dark transition and elevated plus maze tests to assess anxiety-like behavior., Results: Offspring exposed to buprenorphine and not subjected to maternal separation displayed increased anxiety-like behavior in 3 out of 6 outcomes in the light-dark transition and elevated plus maze tests. Maternal separation did not exacerbate, but rather diminished this behavior. Males and females responded differently to methadone, with a trend towards reduced anxiety for males and increased anxiety for females., Conclusions: Prenatal exposure to methadone or buprenorphine may increase the risk of developing anxiety-like behavior later in life, but the effect depends on specific subgroup characteristics. Further research is required to draw definitive conclusions., Competing Interests: Declaration of Competing Interest No conflict declared., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

The full random-effects model (FREM) is a method for determining covariate effects in mixed-effects models. Covariates are modeled as random variables, described by mean and variance. The method captures the covariate effects in estimated covariances between individual parameters and covariates. This approach is robust against issues that may cause reduced performance in methods based on estimating fixed effects (e.g., correlated covariates where the effects cannot be simultaneously identified in fixed-effects methods). FREM covariate parameterization and transformation of covariate data records can be used to alter the covariate-parameter relation. Four relations (linear, log-linear, exponential, and power) were implemented and shown to provide estimates equivalent to their fixed-effects counterparts. Comparisons between FREM and mathematically equivalent full fixed-effects models (FFEMs) were performed in original and simulated data, in the presence and absence of non-normally distributed and highly correlated covariates. These comparisons show that both FREM and FFEM perform well in the examined cases, with a slightly better estimation accuracy of parameter interindividual variability (IIV) in FREM. In addition, FREM offers the unique advantage of letting a single estimation simultaneously provide covariate effect coefficient estimates and IIV estimates for any subset of the examined covariates, including the effect of each covariate in isolation. Such subsets can be used to apply the model across data sources with different sets of available covariates, or to communicate covariate effects in a way that is not conditional on other covariates., (© 2021 The Authors. CPT: Pharmacometrics & Systems Pharmacology published by Wiley Periodicals LLC on behalf of the American Society for Clinical Pharmacology and Therapeutics.)