Transporter-mediated drug-drug interactions: regulatory guidelines, in vitro and in vivo methodologies and translation, special populations, and the blood-brain barrier.

This review, part of a special issue on drug-drug interactions (DDIs) spearheaded by the International Society for the Study of Xenobiotics (ISSX) New Investigators, explores the critical role of drug transporters in absorption, disposition, and clearance in the context of DDIs. Over the past two decades, significant advances have been made in understanding the clinical relevance of these transporters. Current knowledge on key uptake and efflux transporters that affect drug disposition and development is summarized. Regulatory guidelines from the FDA, EMA, and PMDA that inform the evaluation of potential transporter-mediated DDIs are discussed in detail. Methodologies for preclinical and clinical testing to assess potential DDIs are reviewed, with an emphasis on the utility of physiologically based pharmacokinetic (PBPK) modeling. This includes the application of relative abundance and expression factors to predict human pharmacokinetics (PK) using preclinical data, integrating the latest regulatory guidelines. Considerations for assessing transporter-mediated DDIs in special populations, including pediatric, hepatic, and renal impairment groups, are provided. Additionally, the impact of transporters at the blood-brain barrier (BBB) on the disposition of CNS-related drugs is explored. Enhancing the understanding of drug transporters and their role in drug disposition and toxicity can improve efficacy and reduce adverse effects. Continued research is essential to bridge remaining gaps in knowledge, particularly in comparison with cytochrome P450 (CYP) enzymes.

Assessing climate niche similarity between persian fallow deer (Dama mesopotamica) areas in Iran.

The Persian fallow deer or Mesopotamian fallow Deer (Dama mesopotamica, Brook 1875), a species of significant ecological importance, had faced the threat of extinction in Iran. One conservation strategy involved the translocation of Persian deer to enclosed areas across Iran, where they were afforded protection from external threats and provided with essential care by human caretakers. While human caretakers diligently attend to their needs and mitigate external threats, climate variables may now become critical factors affecting population dynamics in enclosed areas. This study aims to assess the similarity in climate niches between the original area (Dez and Karkheh) of the Persian deer species and 11 newly enclosed areas. To achieve this, we employed climate data and ecological niche modeling (ENM) techniques to assess the variations in climate among 12 areas. We utilized the environmental equivalency test to determine whether the environmental spaces of area pairs exhibit significant differences and whether these spaces are interchangeable. Extrapolation analyses were also constructed in the next steps to explore climatic conditions in original fallow deer habitats that are non-analogous to those in other parts of Iran. Our results reveal significant disparities in climate conditions between the original and all translocated areas. Based on observations of population growth in specific enclosed areas where translocated deer populations have thrived, we hypothesize that the species may demonstrate a non-equilibrium distribution in Iran. Consequently, these new areas could potentially be regarded as part of the species' potential climate niche. Extrapolation analysis showed that for a significant portion of Iran, extrapolation predictions are highly uncertain and potentially unreliable for the translocation of Persian fallow deer. However, the primary objective of translocation efforts remains the establishment of self-sustaining populations of Persian deer capable of thriving in natural areas beyond enclosed areas, thus ensuring their long-term survival and contributing to preservation efforts. Evaluating the success of newly translocated species requires additional time, with varying levels of success observed. In cases where the growth rate of the species in certain enclosed areas falls below expectations, it is prudent to consider climate variables that may contribute to population declines. Furthermore, for future translocations, we recommend selecting areas with climate similarities to regions where the species has demonstrated growth rates.

Bridging the Gap Between Human Toxicology and Ecotoxicology Under One Health Perspective by a Cross-Species Adverse Outcome Pathway Network for Reproductive Toxicity.

Although ecotoxicological and toxicological risk assessments are performed separately from each other, recent efforts have been made in both disciplines to reduce animal testing and develop predictive approaches instead, for example, via conserved molecular markers, and in vitro and in silico approaches. Among them, adverse outcome pathways (AOPs) have been proposed to facilitate the prediction of molecular toxic effects at larger biological scales. Thus, more toxicological data are used to inform on ecotoxicological risks and vice versa. An AOP has been previously developed to predict reproductive toxicity of silver nanoparticles via oxidative stress on the nematode Caenorhabditis elegans (AOPwiki ID 207). Following this previous study, our present study aims to extend the biologically plausible taxonomic domain of applicability (tDOA) of AOP 207. Various types of data, including in vitro human cells, in vivo, and molecular to individual, from previous studies have been collected and structured into a cross-species AOP network that can inform both human toxicology and ecotoxicology risk assessments. The first step was the collection and analysis of literature data to fit the AOP criteria and build a first AOP network. Then, key event relationships were assessed using a Bayesian network modeling approach, which gave more confidence in our overall AOP network. Finally, the biologically plausible tDOA was extended using in silico approaches (Genes-to-Pathways Species Conservation Analysis and Sequence Alignment to Predict Across Species Susceptibility), which led to the extrapolation of our AOP network across over 100 taxonomic groups. Our approach shows that various types of data can be integrated into an AOP framework, and thus facilitates access to knowledge and prediction of toxic mechanisms without the need for further animal testing. Environ Toxicol Chem 2024;00:1-14. © 2024 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

Physiologically based Kinetic Modeling-Facilitated Quantitative In Vitro to In Vivo Extrapolation to Predict the Effects of Aloe-Emodin in Rats and Humans.

Aloe-emodin, a natural hydroxyanthraquinone, exerts both adverse and protective effects. This study aimed at investigating these potential effects of aloe-emodin in humans upon the use of food supplements and herbal medicines using a physiologically based kinetic (PBK) modeling-facilitated quantitative in vitro to in vivo extrapolation (QIVIVE) approach. For this, PBK models in rats and humans were established for aloe-emodin including its active metabolite rhein and used to convert in vitro data on hepatotoxicity, nephrotoxicity, reactive oxidative species (ROS) generation, and Nrf2 induction to corresponding in vivo dose-response curves, from which points of departure (PODs) were derived by BMD analysis. The derived PODs were subsequently compared to the estimated daily intakes (EDIs) resulting from the use of food supplements or herbal medicines. It is concluded that the dose levels of aloe-emodin from food supplements or herbal medicines are unlikely to induce toxicity, ROS generation, or Nrf2 activation in liver and kidney.

PLEASING: Exploring the historical and potential events for temporal knowledge graph reasoning.

Temporal Knowledge Graphs (TKGs) enable effective modeling of knowledge dynamics and event evolution, facilitating deeper insights and analysis into temporal information. Recently, extrapolation of TKG reasoning has attracted great significance due to its remarkable ability to capture historical correlations and predict future events. Existing studies of extrapolation aim mainly at encoding the structural and temporal semantics based on snapshot sequences, which contain graph aggregators for the association within snapshots and recurrent units for the evolution. However, these methods are limited to modeling long-distance history, as they primarily focus on capturing temporal correlations over shorter periods. Besides, a few approaches rely on compiling historical repetitive statistics of TKGs for predicting future facts. But they often overlook explicit interactions in the graph structure among concurrent events. To address these issues, we propose a PotentiaL concurrEnt Aggregation and contraStive learnING (PLEASING) method for TKG extrapolation. PLEASING is a two-step reasoning framework that effectively leverages the historical and potential features of TKGs. It includes two encoders for historical and global events with an adaptive gated mechanism, acquiring predictions with appropriate weight of the two aspects. Specifically, PLEASING constructs two auxiliary graphs to capture temporal interaction among timestamps and correlations among potential concurrent events, respectively, enabling a holistic investigation of temporal characteristics and future potential possibilities in TKGs. Furthermore, PLEASING incorporates contrastive learning to strengthen its capacity to identify whether queries are related to history. Extensive experiments on seven benchmark datasets demonstrate the state-of-the-art performances of PLEASING and its comprehensive ability to model TKG semantics.

Preclinical pharmacokinetic characterization of amdizalisib, a novel PI3Kδ inhibitor for the treatment of hematological malignancies.

Amdizalisib, also named HMPL-689, a novel selective and potent PI3Kδ inhibitor, is currently under Phase II clinical development in China for treating hematological malignancies. The preclinical pharmacokinetics (PK) of amdizalisib were extensively characterized in vitro and in vivo to support the further development of amdizalisib. We characterized the plasma protein binding, blood-to-plasma partition ratio, cell permeability, hepatic microsomal metabolic stability, and drug-drug interaction potential of amdizalisib using in vitro experiments. In vivo PK assessment was undertaken in mice, rats, dogs, and monkeys following a single intravenous or oral administration of amdizalisib. The tissue distribution and excretion of amdizalisib were evaluated in rats. The PK parameters (CL and Vss) of amdizalisib in preclinical species (mice, rats, dogs, and monkeys) were utilized for the human PK projection using the allometric scaling (AS) approach. Amdizalisib was well absorbed and showed low-to-moderate clearance in mice, rats, dogs, and monkeys. It had high cell permeability without P-glycoprotein (P-gp) or breast cancer resistance protein (BCRP) substrate liability. Plasma protein binding of amdizalisib was high (approximately 90%). It was extensively distributed but with a low brain-to-plasma exposure ratio in rats. Amdizalisib was extensively metabolized in vivo, and the recovery rate of the prototype drug was low in the excreta. Amdizalisib and/or its metabolites were primarily excreted via the bile and urine in rats. Amdizalisib showed inhibition potential on P-gp but not on BCRP and was observed to inhibit CYP2C8 and CYP2C9 with IC50 values of 30.4 and 10.7 µM, respectively. It exhibited induction potential on CYP1A2, CYP2B6, CYP3A4, and CYP2C9. The preclinical data from these ADME studies demonstrate a favorable pharmacokinetic profile for amdizalisib, which is expected to support the future clinical development of amdizalisib as a promising anti-cancer agent.

Impact of Healthy Lifestyle Factors on Life Expectancy and Lifetime Health Care Expenditure: Nationwide Cohort Study.

BACKGROUND: The association between lifestyle risk factors and the risk of mortality and chronic diseases has been established, while limited research has explored the impact of healthy lifestyle factors on lifetime health care expenditure using longitudinal individual data. OBJECTIVE: We aimed to determine the individual and combined effects of 5 healthy lifestyle factors on life expectancy and lifetime health care expenditure in Taiwan. METHODS: Using data from the National Health Interview Survey cohort, 5 healthy lifestyle behaviors were defined and analyzed: nonsmoking, avoiding excessive alcohol consumption, engaging in sufficient physical activity, ensuring sufficient fruit and vegetable intake, and maintaining a normal weight. We used a rolling extrapolation algorithm that incorporated inverse probability of treatment weighting to estimate the life expectancy and lifetime health care expenditure of the study populations with and without healthy lifestyle factors. RESULTS: A total of 19,893 participants aged ≥30 (mean age 48.8, SD 13.4) years were included, with 3815 deaths recorded during a median follow-up period of 15.6 years. The life expectancy and per capita estimated lifetime health care expenditures for the overall study population were 35.32 years and US $58,560, respectively. Multivariable-adjusted hazard ratios for all-cause mortality in participants adhering to all 5 healthy lifestyle factors, compared with those adhering to none, were 0.37 (95% CI 0.27-0.49). We found significant increases in life expectancy for nonsmokers (2.31 years; 95% CI 0.04-5.13; P=.03), those with sufficient physical activity (1.85 years; 95% CI 0.25-4.34; P=.02), and those with adequate fruit and vegetable intake (3.25 years; 95% CI 1.29-6.81; P=.01). In addition, nonsmokers experienced a significant reduction in annual health care expenditure (-9.78%; 95% CI -46.53% to -1.45%; P=.03), as did individuals maintaining optimal body weight (-18.36%; 95% CI -29.66% to -8.57%; P=.01). Overall, participants adhering to all 5 healthy lifestyle behaviors exhibited a life gain of 7.13 years (95% CI 1.33-11.11; P=.02) compared with those adhering to one or none, with a life expectancy of 29.19 years (95% CI 25.45-33.62). Furthermore, individuals adopting all 5 healthy lifestyle factors experienced an average annual health care expenditure reduction of 28.12% (95% CI 4.43%-57.61%; P=.02) compared with those adopting one or none. CONCLUSIONS: Adopting a healthy lifestyle is associated with a longer life expectancy and a reduction of health care expenditure in Taiwanese adults. This contributes to a more comprehensive understanding of the impact of healthy lifestyle factors on the overall health and economic burden.

Efficient Guided Wave Modelling for Tomographic Corrosion Mapping via One-Way Wavefield Extrapolation.

Mapping corrosion depths along pipeline sections using guided-wave-based tomographic methods is a challenging task. Accurate defect sizing depends heavily on the precision of the forward model in guided wave tomography. This model is fitted to measured data using inversion techniques. This study evaluates the effectiveness of a recursive extrapolation scheme for tomography applications and full waveform inversion. It employs a table-driven approach, with precomputed extrapolation operators stored across a spectrum of wavenumbers. This enables fast modelling for extensive pipe sections, approaching the speed of ray tracing while accurately handling complex velocity models within the full frequency band. This ensures an accurate representation of diffraction phenomena. The study examines the assumptions underlying the extrapolation approach, namely, the negligible reflection and conversion of modes at defects. In our tomography approach, we intend to use multiple wave modes-A0, S0, and SH1-and helical paths. The acoustic extrapolation method is validated through numerical studies for different wave modes, solving the 3D elastodynamic wave equation. Comparison with an experimentally measured single-mode wavefield from an aluminium plate with an artificial defect reveals good agreement.

An efficient numerical approach for singularly perturbed time delayed parabolic problems with two-parameters.

OBJECTIVES: The main objective of this work is to design an efficient numerical scheme is proposed for solving singularly perturbed time delayed parabolic problems with two parameters. RESULTS: The scheme is constructed via the implicit Euler and non-standard finite difference method to approximate the time and space derivatives, respectively. Besides, to enhance the accuracy and order of convergence of the method Richardson extrapolation technique is employed. Intensive numerical experimentation has been done on some model examples. Further, the layer behavior of the solutions is presented using graphs and observed to agree with the existing theories. Finally, error analysis of the scheme is done and observed that the proposed method is parameter uniform convergent with the order of convergence ( Δ t ) 2 + h 2 .

Recent advances in cell-based in vitro models for predicting drug permeability across brain, intestinal, and pulmonary barriers.

INTRODUCTION: Recent years have witnessed remarkable progress in the development of cell-based in vitro models aimed at predicting drug permeability, particularly focusing on replicating the barrier properties of the blood-brain barrier (BBB), intestinal epithelium, and lung epithelium. AREA COVERED: This review provides an overview of 2D in vitro platforms, including monocultures and co-culture systems, highlighting their respective advantages and limitations. Additionally, it discusses tools and techniques utilized to overcome these limitations, paving the way for more accurate predictions of drug permeability. Furthermore, this review delves into emerging technologies, particularly microphysiological systems (MPS), encompassing static platforms such as organoids and dynamic platforms like microfluidic devices. Literature searches were performed using PubMed and Google Scholar. We focus on key terms such as in vitro permeability models, MPS, organoids, intestine, BBB, and lungs. EXPERT OPINION: The potential of these MPS to mimic physiological conditions more closely offers promising avenues for drug permeability assessment. However, transitioning these advanced models from bench to industry requires rigorous validation against regulatory standards. Thus, there is a pressing need to validate MPS to industry and regulatory agency standards to exploit their potential in drug permeability prediction fully. This review underscores the importance of such validation processes to facilitate the translation of these innovative technologies into routine pharmaceutical practice.

Data derived extrapolation factors (DDEFs) for rat to human interspecies extrapolation for the HPPD inhibitor mesotrione.

In the risk assessment of agrochemicals, there has been a historical paucity of using data to refine the default adjustment factors, even though large datasets are available to support this. The current state of the science for addressing uncertainty regarding animal to human extrapolation (AFA) is to develop a "data-derived" adjustment factor (DDEF) to quantify such differences, if data are available. Toxicokinetic (TK) and toxicodynamic (TD) differences between species can be utilized for the DDEF, with human datasets being ideal yet rare. We identified a case for a currently registered herbicide, mesotrione, in which human TK and TD are available. This case study outlines an approach for the development of DDEFs using comparative human and animal data and based on an adverse outcome pathway (AOP) for inhibition of 4-hydroxyphenol pyruvate dioxygenase (HHPD). The calculated DDEF for rat to human extrapolation (AFA) for kinetics (AFAK = 2.5) was multiplied by the AFA for dynamics (AFAD = 0.3) resulting in a composite DDEF of ∼1 (AFA = 0.75). This reflects the AOP and available scientific evidence that humans are less sensitive than rats to the effects of HPPD inhibitors. Further analyses were conducted utilizing in vitro datasets from hepatocytes and liver cytosols and extrapolated to whole animal using in vitro to in vivo extrapolation (IVIVE) to support toxicodynamic extrapolation. The in vitro datasets resulted in the same AFAD as derived for in vivo data (AFAD = 0.3). These analyses demonstrate that a majority of the species differences are related to toxicodynamics. Future work with additional in vitro/in vivo datasets for other HPPD inhibitors and cell types will further support this result. This work demonstrates utilization of all available toxicokinetic and toxicodynamic data to replace default uncertainty factors for agrochemical human health risk assessment.

Determining tissue distribution of the oral antileishmanial agent miltefosine: a physiologically-based pharmacokinetic modeling approach.

Miltefosine (MTS) is the only approved oral drug for treating leishmaniasis caused by intracellular Leishmania parasites that localize in macrophages of the liver, spleen, skin, bone marrow, and lymph nodes. MTS is extensively distributed in tissues and has prolonged elimination half-lives due to its high plasma protein binding, slow metabolic clearance, and minimal urinary excretion. Thus, understanding and predicting the tissue distribution of MTS help assess therapeutic and toxicologic outcomes of MTS, especially in special populations, e.g., pediatrics. In this study, a whole-body physiologically-based pharmacokinetic (PBPK) model of MTS was built on mice and extrapolated to rats and humans. MTS plasma and tissue concentration data obtained by intravenous and oral administration to mice were fitted simultaneously to estimate model parameters. The resulting high tissue-to-plasma partition coefficient values corroborate extensive distribution in all major organs except the bone marrow. Sensitivity analysis suggests that plasma exposure is most susceptible to changes in fraction unbound in plasma. The murine oral-PBPK model was further validated by assessing overlay of simulations with plasma and tissue profiles obtained from an independent study. Subsequently, the murine PBPK model was extrapolated to rats and humans based on species-specific physiological and drug-related parameters, as well as allometrically scaled parameters. Fold errors for pharmacokinetic parameters were within acceptable range in both extrapolated models, except for a slight underprediction in the human plasma exposure. These animal and human PBPK models are expected to provide reliable estimates of MTS tissue distribution and assist dose regimen optimization in special populations.

Occupational exposure limits for reproductive toxicants - A comparative analysis.

We investigated the level of protection of reproductive and developmental toxicity offered through occupational exposure limits (OELs) and Derived No-Effect Levels for workers' inhalation exposure (wDNELs). We compared coverage of substances that have a harmonised classification as reproductive toxicant 1 A or 1B (Repr.1 A/B), numerical values and scientific basis of 12 lists of OELs and wDNELs from REACH Registrants' and the Committee for Risk Assessment. Across the 14 sources of OELs and wDNELs, 53 % of the Repr1A/B-substances had at least one exposure limit (counting groups of metals as one entry). Registrants' wDNELs covered the largest share, 40 %. The numerical values could be highly variable for the same substance across the lists. How often reproductive toxicity is identified as the critical effect varies between the examined lists, both due to different assessments of the same substance and different substance coverage. Reviewing the margin of safety to reproductive toxicity cited in the documents, we found that 15 % of safety margins were lower to reproductive toxicity than the critical effect. To conclude, neither the REACH nor work environment legislation supply wDNELs or OELs for a substantial share of known reproductive toxicants. EU OELs cover among the fewest substances in the range, and in many cases national OELs or wDNELs are set at more conservative levels.

Short-Term and Imminent Rainfall Prediction Model Based on ConvLSTM and SmaAT-UNet.

Short-term precipitation forecasting methods are mainly divided into statistical forecasting, numerical model-based forecasting, and radar image extrapolation techniques. The two methods based on statistical prediction and numerical model have the disadvantages of being unstable and generating large errors. Therefore, this study proposes the use of deep learning for radar image extrapolation for precipitation forecasting, in particular by developing algorithms for ConvLSTM and SmaAT-UNet. The ConvLSTM model is a fusion of a CNN (Convolutional Neural Network) and LSTM (Long Short-Term Memory network), which solves the challenge of processing spatial sequence data, which is a task that traditional LSTM models cannot accomplish. At the same time, SmaAT-UNet enhances the traditional UNet structure by incorporating the CBAM (Convolutional Block Attention Module) attention mechanism and replacing the standard convolutional layer with depthwise separable convolution. This innovative approach aims to improve the efficiency and accuracy of short-term precipitation forecasting by improving feature extraction and data processing techniques. Evaluation and analysis of experimental data show that both models exhibit good predictive ability, with the SmaAT-UNet model outperforming ConvLSTM in terms of accuracy. The results show that the performance indicators of precipitation prediction, especially detection probability (POD) and the Critical Success index (CSI), show a downward trend with the extension of the prediction time. This trend highlights the inherent challenges of maintaining predictive accuracy over longer periods of time and highlights the superior performance and resilience of the SmaAT-UNet model under these conditions. Compared with the statistical forecasting method and numerical model forecasting method, its accuracy in short-term rainfall forecasting is improved.

In vitro to in vivo extrapolation from three-dimensional hiPSC-derived cardiac microtissues and physiologically based pharmacokinetic modeling to inform next-generation arrhythmia risk assessment.

Proarrhythmic cardiotoxicity remains a substantial barrier to drug development as well as a major global health challenge. In vitro human pluripotent stem cell-based new approach methodologies have been increasingly proposed and employed as alternatives to existing in vitro and in vivo models that do not accurately recapitulate human cardiac electrophysiology or cardiotoxicity risk. In this study, we expanded the capacity of our previously established three-dimensional human cardiac microtissue model to perform quantitative risk assessment by combining it with a physiologically based pharmacokinetic model, allowing a direct comparison of potentially harmful concentrations predicted in vitro to in vivo therapeutic levels. This approach enabled the measurement of concentration responses and margins of exposure for two physiologically relevant metrics of proarrhythmic risk (ie, action potential duration and triangulation assessed by optical mapping) across concentrations spanning three orders of magnitude. The combination of both metrics enabled accurate proarrhythmic risk assessment of four compounds with a range of known proarrhythmic risk profiles (ie, quinidine, cisapride, ranolazine, and verapamil) and demonstrated close agreement with their known clinical effects. Action potential triangulation was found to be a more sensitive metric for predicting proarrhythmic risk associated with the primary mechanism of concern for pharmaceutical-induced fatal ventricular arrhythmias, delayed cardiac repolarization due to inhibition of the rapid delayed rectifier potassium channel, or hERG channel. This study advances human induced pluripotent stem cell-based three-dimensional cardiac tissue models as new approach methodologies that enable in vitro proarrhythmic risk assessment with high precision of quantitative metrics for understanding clinically relevant cardiotoxicity.

Population pharmacokinetics and dose rationale for aciclovir in term and pre-term neonates with herpes.

Aciclovir is considered the first-line treatment against Herpes simplex virus (HSV) infections in new-borns and infants. As renal excretion is the major route of elimination, in renally-impaired patients, aciclovir doses are adjusted according to the degree of impairment. However, limited attention has been given to the implications of immature renal function or dysfunction due to the viral disease itself. The aim of this investigation was to characterize the pharmacokinetics of aciclovir taking into account maturation and disease processes in the neonatal population. Pharmacokinetic data obtained from 2 previously published clinical trials (n = 28) were analyzed using a nonlinear mixed effects modeling approach. Post-menstrual age (PMA) and creatinine clearance (CLCR) were assessed as descriptors of maturation and renal function. Simulation scenarios were also implemented to illustrate the use of pharmacokinetic data to extrapolate efficacy from adults. Aciclovir pharmacokinetics was described by a one-compartment model with first-order elimination. Body weight and diagnosis (systemic infection) were statistically significant covariates on the volume of distribution, whereas body weight, CLCR and PMA had a significant effect on clearance. Median clearance varied from 0.2 to 1.0 L/h in subjects with PMA <34 or ≥34 weeks, respectively. Population estimate for volume of distribution was 1.93 L with systemic infection increasing this value by almost 3-fold (2.67 times higher). A suitable model parameterization was identified, which discriminates the effects of developmental growth, maturation, and organ function. Exposure to aciclovir was found to increase with decreasing PMA and renal function (CLCR), suggesting different dosing requirement for pre-term neonates.

Sample size determination for prediction models via learning-type curves.

This article is concerned with sample size determination methodology for prediction models. We propose to combine the individual calculations via learning-type curves. We suggest two distinct ways of doing so, a deterministic skeleton of a learning curve and a Gaussian process centered upon its deterministic counterpart. We employ several learning algorithms for modeling the primary endpoint and distinct measures for trial efficacy. We find that the performance may vary with the sample size, but borrowing information across sample size universally improves the performance of such calculations. The Gaussian process-based learning curve appears more robust and statistically efficient, while computational efficiency is comparable. We suggest that anchoring against historical evidence when extrapolating sample sizes should be adopted when such data are available. The methods are illustrated on binary and survival endpoints.

Responses to the correspondence from McDowell et al.'s on CAM integrative review of health care professionals in New Zealand.

The authors of the manuscript 'Complementary and alternative medicine - practice, attitudes, and knowledge among healthcare professionals in New Zealand: an integrative review' [1] disagree with the assertion by McDowell et al. that our manuscript has extrapolation errors.

Extrapolation errors in Liu et al.'s CAM integrative review of health care professionals in New Zealand.

This letter is to highlight errors made by Liu et al. in their 2020 paper in BMC Complementary Medicine and Therapies, "Complementary and alternative medicine-practice, attitudes, and knowledge among healthcare professionals in New Zealand: an integrative review". Substantial errors in their citation of the recent research and methodology by McDowell, Kohut & Betts (2019) pertaining to the practice of acupuncture in New Zealand by physiotherapists are presented. The actual results of McDowell et al.'s work and the true state of acupuncture use by their sample group is reported.

Drug dissolution and transit in a heterogenous gastric chyme after fed administration: Semi-mechanistic modeling and simulations for an immediate-release and orodispersible tablets containing a poorly soluble drug.

Mathematical models that treat the fed stomach content as a uniform entity emptied with a constant rate may not suffice to explain pharmacokinetic profiles recorded in clinical trials. In reality, phenomena such as the Magenstrasse or chyme areas of different pH and viscosity, play an important role in the intragastric drug dissolution and its transfer to the intestine. In this study, we investigated the data gathered in the bioequivalence trial between an immediate-release tablet (Reference) and an orally dispersible tablet (Test) with a poorly soluble weak base drug administered with or without water after a high-fat high-calorie breakfast. Maximum concentrations (Cmax) were significantly greater after administering the Reference product than the Test tablets, despite similar in vitro dissolution profiles. To explain this difference, we constructed a novel semi-mechanistic IVIVP model including a heterogeneous gastric chyme. The drug dissolution in vivo was modeled from the in vitro experiments in biorelevant media simulating gastric and intestinal fluids in the fed state (FEDGAS and FeSSIF). The key novelty of the model was separating the stomach contents into two compartments: isolated chyme (the viscous food content) that carries the drug slowly, and aq_chyme open for rapid Magenstrasse-like routes of drug transit. Drug distribution between these two compartments was both formulation- and administration-dependent, and recognized the respective drug fractions from the clinical pharmacokinetic data. The model's assumption about the nonuniform mixing of the API with the chyme, influencing differential drug dissolution and transit kinetics, led to simulating plasma concentration profiles that reflected well the variability observed in the clinical trial. The model indicated that, after administration, the Reference product mixes to a greater extent with aq_chyme, where the released drug dissolves better and transfers faster to the intestine. In conclusion, this novel approach underlines that diverse gastric emptying of different oral dosage forms may significantly impact pharmacokinetics and affect the outcomes of bioequivalence trials.