The EPINetz Twitter Politicians Dataset 2021. A New Resource for the Study of the German Twittersphere and Its Application for the 2021 Federal Elections.

This research note introduces the EPINetz Twitter Politicians Dataset, a comprehensive dataset of 2449 Twitter accounts of German parliamentarians, minsters, state secretaries, parties, and ministries on a state, federal, and European Union level for the year 2021. This hand-curated dataset not only provides up-to-date information on elected officials, but it also includes additional variables such as their party affiliation, age, and gender. Furthermore, it provides linkages to additional data sources by providing the accounts' Wikidata and Abgeordnetenwatch (Parliamentwatch) IDs. While it does not provide actual tweet data, the dataset will be a valuable resource for researchers by providing easy access to elected German politicians. We demonstrate some of the dataset's uses with an analysis of the 2021 German Federal Elections. The full dataset can be accessed via 10.7802/2415. Supplementary Information: The online version of this article (10.1007/s11615-022-00405-7) contains supplementary material, which is available to authorized users.

In Vitro and in Silico Tools To Assess Extent of Cellular Uptake and Lysosomal Sequestration of Respiratory Drugs in Human Alveolar Macrophages.

Accumulation of respiratory drugs in human alveolar macrophages (AMs) has not been extensively studied in vitro and in silico despite its potential impact on therapeutic efficacy and/or occurrence of phospholipidosis. The current study aims to characterize the accumulation and subcellular distribution of drugs with respiratory indication in human AMs and to develop an in silico mechanistic AM model to predict lysosomal accumulation of investigated drugs. The data set included 9 drugs previously investigated in rat AM cell line NR8383. Cell-to-unbound medium concentration ratio (Kp,cell) of all drugs (5 µM) was determined to assess the magnitude of intracellular accumulation. The extent of lysosomal sequestration in freshly isolated human AMs from multiple donors (n = 5) was investigated for clarithromycin and imipramine (positive control) using an indirect in vitro method (±20 mM ammonium chloride, NH4Cl). The AM cell parameters and drug physicochemical data were collated to develop an in silico mechanistic AM model. Three in silico models differing in their description of drug membrane partitioning were evaluated; model (1) relied on octanol-water partitioning of drugs, model (2) used in vitro data to account for this process, and model (3) predicted membrane partitioning by incorporating AM phospholipid fractions. In vitro Kp,cell ranged >200-fold for respiratory drugs, with the highest accumulation seen for clarithromycin. A good agreement in Kp,cell was observed between human AMs and NR8383 (2.45-fold bias), highlighting NR8383 as a potentially useful in vitro surrogate tool to characterize drug accumulation in AMs. The mean Kp,cell of clarithromycin (81, CV = 51%) and imipramine (963, CV = 54%) were reduced in the presence of NH4Cl by up to 67% and 81%, respectively, suggesting substantial contribution of lysosomal sequestration and intracellular binding in the accumulation of these drugs in human AMs. The in vitro data showed variability in drug accumulation between individual human AM donors due to possible differences in lysosomal abundance, volume, and phospholipid content, which may have important clinical implications. Consideration of drug-acidic phospholipid interactions significantly improved the performance of the in silico models; use of in vitro Kp,cell obtained in the presence of NH4Cl as a surrogate for membrane partitioning (model (2)) captured the variability in clarithromycin and imipramine Kp,cell observed in vitro and showed the best ability to predict correctly positive and negative lysosomotropic properties. The developed mechanistic AM model represents a useful in silico tool to predict lysosomal and cellular drug concentrations based on drug physicochemical data and system specific properties, with potential application to other cell types.

Injury Severity Score Inflation Resulting From Pan-Computed Tomography in Patients With Blunt Trauma.

STUDY OBJECTIVE: All articles that demonstrate a mortality benefit from liberal pan-computed tomography (CT) use in patients with blunt trauma have relied on Injury Severity Score (ISS) to control for morbidity. This mortality benefit may be artifact, the result of an increased use of a sensitive diagnostic modality rather than a true benefit. We quantify the magnitude of ISS inflation in patients with blunt trauma who are undergoing routine pan-CT compared with patients who receive more selective scanning. METHODS: This study re-analyzes data collected from a previous study of pan-CT use in patients with blunt trauma in which surveyed emergency physicians prospectively indicated which portion of a pan-CT they wished to obtain. The trauma surgeons who jointly managed all patients in this study ultimately decided which CTs to obtain. We recalculated the ISS excluding injuries found on the undesired CT scans that did not lead to a predefined set of critical actions and compared original and recalculated ISS. RESULTS: There were 701 study subjects who received a total of 2,615 scans. Of these, there were 992 undesired scans. Ninety-nine of the obtained undesired scans, performed in 92 patients, had noncritical abnormalities. The original ISS for these 92 patients was 10 (IQR 5, 18); the recalculated ISS was 5 (interquartile range 1, 10), a 50% decrease. CONCLUSION: Although the median ISS for our study was lower than that of previous studies claiming a mortality benefit, ISS inflation appears to be a real phenomenon and may confound studies that use ISS to control for morbidity.

The extended clinical phenotype of 64 patients with dedicator of cytokinesis 8 deficiency.

BACKGROUND: Mutations in dedicator of cytokinesis 8 (DOCK8) cause a combined immunodeficiency (CID) also classified as autosomal recessive (AR) hyper-IgE syndrome (HIES). Recognizing patients with CID/HIES is of clinical importance because of the difference in prognosis and management. OBJECTIVES: We sought to define the clinical features that distinguish DOCK8 deficiency from other forms of HIES and CIDs, study the mutational spectrum of DOCK8 deficiency, and report on the frequency of specific clinical findings. METHODS: Eighty-two patients from 60 families with CID and the phenotype of AR-HIES with (64 patients) and without (18 patients) DOCK8 mutations were studied. Support vector machines were used to compare clinical data from 35 patients with DOCK8 deficiency with those from 10 patients with AR-HIES without a DOCK8 mutation and 64 patients with signal transducer and activator of transcription 3 (STAT3) mutations. RESULTS: DOCK8-deficient patients had median IgE levels of 5201 IU, high eosinophil levels of usually at least 800/µL (92% of patients), and low IgM levels (62%). About 20% of patients were lymphopenic, mainly because of low CD4(+) and CD8(+) T-cell counts. Fewer than half of the patients tested produced normal specific antibody responses to recall antigens. Bacterial (84%), viral (78%), and fungal (70%) infections were frequently observed. Skin abscesses (60%) and allergies (73%) were common clinical problems. In contrast to STAT3 deficiency, there were few pneumatoceles, bone fractures, and teething problems. Mortality was high (34%). A combination of 5 clinical features was helpful in distinguishing patients with DOCK8 mutations from those with STAT3 mutations. CONCLUSIONS: DOCK8 deficiency is likely in patients with severe viral infections, allergies, and/or low IgM levels who have a diagnosis of HIES plus hypereosinophilia and upper respiratory tract infections in the absence of parenchymal lung abnormalities, retained primary teeth, and minimal trauma fractures.

Reduced physiologically-based pharmacokinetic model of repaglinide: impact of OATP1B1 and CYP2C8 genotype and source of in vitro data on the prediction of drug-drug interaction risk.

PURPOSE: To investigate the effect of OATP1B1 genotype as a covariate on repaglinide pharmacokinetics and drug-drug interaction (DDIs) risk using a reduced physiologically-based pharmacokinetic (PBPK) model. METHODS: Twenty nine mean plasma concentration-time profiles for SLCO1B1 c.521T>C were used to estimate hepatic uptake clearance (CLuptake) in different genotype groups applying a population approach in NONMEM v.7.2. RESULTS: Estimated repaglinide CLuptake corresponded to 217 and 113 µL/min/10(6) cells for SLCO1B1 c.521TT/TC and CC, respectively. A significant effect of OATP1B1 genotype was seen on CLuptake (48% reduction for CC relative to wild type). Sensitivity analysis highlighted the impact of CLmet and CLdiff uncertainty on the CLuptake optimization using plasma data. Propagation of this uncertainty had a marginal effect on the prediction of repaglinide OATP1B1-mediated DDI with cyclosporine; however, sensitivity of the predicted magnitude of repaglinide metabolic DDI was high. In addition, the reduced PBPK model was used to assess the effect of both CYP2C8*3 and SLCO1B1 c.521T>C on repaglinide exposure by simulations; power calculations were performed to guide prospective DDI and pharmacogenetic studies. CONCLUSIONS: The application of reduced PBPK model for parameter optimization and limitations of this process associated with the use of plasma rather than tissue profiles are illustrated.

In silico modeling and simulation of organ-on-a-chip systems to support data analysis and a priori experimental design.

Organ-on-a-chip (OoC) systems are a promising new class of in vitro devices that can combine various tissues, cultured in different compartments, linked by media flow. The properties of these novel in vitro systems linked to increased physiological relevance of culture conditions may lead to more in vivo-relevant cell phenotypes, enabling better in vitro pharmacology and toxicology assessment. Improved cell activities combined with longer lasting cultures offer opportunities to improve the characterization of absorption, distribution, metabolism, and excretion (ADME) processes, potentially leading to more accurate prediction of human pharmacokinetics (PKs). The inclusion of barrier tissue elements and metabolically competent tissue types results in complex concentration-time profiles (in vitro PK) for test drugs and their metabolites that require appropriate mathematical modeling of in vitro data for parameter estimation. In particular, modeling is critical to estimate in vitro ADME parameters when multiple different tissues are combined in a single device. Therefore, sophisticated in silico data analysis and a priori experimental design are highly recommended for OoC experiments in a manner not needed with standard ADME screening. The design of the experiment should be optimized based on an investigation of the structural characteristics of the in vitro system, the ADME features of the test compound and any available knowledge of cell phenotypes. This tutorial aims to provide such a modeling framework to inform experimental design and refine parameter estimation in a Gut-Liver OoC (the most studied multi-organ systems to predict the oral drug PKs) to improve translatability of data generated in such complex cellular systems.

Application of a physiologically based pharmacokinetic model to assess propofol hepatic and renal glucuronidation in isolation: utility of in vitro and in vivo data.

A physiologically based pharmacokinetic (PBPK) modeling approach was used to assess the prediction accuracy of propofol hepatic and extrahepatic metabolic clearance and to address previously reported underprediction of in vivo clearance based on static in vitro-in vivo extrapolation methods. The predictive capacity of propofol intrinsic clearance data (CLint) obtained in human hepatocytes and liver and kidney microsomes was assessed using the PBPK model developed in MATLAB software. Microsomal data obtained by both substrate depletion and metabolite formation methods and in the presence of 2% bovine serum albumin were considered in the analysis. Incorporation of hepatic and renal in vitro metabolic clearance in the PBPK model resulted in underprediction of propofol clearance regardless of the source of in vitro data; the predicted value did not exceed 35% of the observed clearance. Subsequently, propofol clinical data from three dose levels in intact patients and anhepatic subjects were used for the optimization of hepatic and renal CLint in a simultaneous fitting routine. Optimization process highlighted that renal glucuronidation clearance was underpredicted to a greater extent than liver clearance, requiring empirical scaling factors of 17 and 9, respectively. The use of optimized clearance parameters predicted hepatic and renal extraction ratios within 20% of the observed values, reported in an additional independent clinical study. This study highlights the complexity involved in assessing the contribution of extrahepatic clearance mechanisms and illustrates the application of PBPK modeling, in conjunction with clinical data, to assess prediction of clearance from in vitro data for each tissue individually.

Cyclosporine inhibition of hepatic and intestinal CYP3A4, uptake and efflux transporters: application of PBPK modeling in the assessment of drug-drug interaction potential.

PURPOSE: To apply physiologically-based pharmacokinetic (PBPK) modeling to investigate the consequences of reduction in activity of hepatic and intestinal uptake and efflux transporters by cyclosporine and its metabolite AM1. METHODS: Inhibitory potencies of cyclosporine and AM1 against OATP1B1, OATP1B3 and OATP2B1 were investigated in HEK293 cells +/- pre-incubation. Cyclosporine PBPK model implemented in Matlab was used to assess interaction potential (+/- metabolite) against different processes (uptake, efflux and metabolism) in liver and intestine and to predict quantitatively drug-drug interaction with repaglinide. RESULTS: Cyclosporine and AM1 were potent inhibitors of OATP1B1 and OATP1B3, IC(50) ranging from 0.019-0.093 µM following pre-incubation. Cyclosporine PBPK model predicted the highest interaction potential against liver uptake transporters, with a maximal reduction of >70% in OATP1B1 activity; the effect on hepatic efflux and metabolism was minimal. In contrast, 80-97% of intestinal P-gp and CYP3A4 activity was reduced due to the 50-fold higher cyclosporine enterocytic concentrations relative to unbound hepatic inlet. The inclusion of AM1 resulted in a minor increase in the predicted maximal reduction of OATP1B1/1B3 activity. Good predictability of cyclosporine-repaglinide DDI and the impact of dose staggering are illustrated. CONCLUSIONS: This study highlights the application of PBPK modeling for quantitative prediction of transporter-mediated DDIs with concomitant consideration of P450 inhibition.

Investigation of simplified physiologically-based pharmacokinetic models in rat and human.

Whole-body physiologically-based pharmacokinetic (PBPK) models have many applications in drug research and development. It is often necessary to inform these models with animal or clinical data, updating model parameters, and making the model more predictive for future applications. This provides an opportunity and a challenge given the large number of parameters of such models. The aim of this work was to propose new mechanistic model structures with reduced complexity allowing for parameter optimization. These models were evaluated for their ability to estimate realistic values for unbound tissue to plasma partition coefficients (Kpu) and simulate observed pharmacokinetic (PK) data. Two approaches are presented: using either established kinetic lumping methods based on tissue time constants (drug-dependent) or a novel clustering analysis to identify tissues sharing common Kpu values or Kpu scalars based on similarities of tissue composition (drug-independent). PBPK models derived from these approaches were assessed using PK data of diazepam in rats and humans. Although the clustering analysis produced minor differences in tissue grouping depending on the method used, two larger groups of tissues emerged. One including the kidneys, liver, spleen, gut, heart, and lungs, and another including bone, brain, muscle, and pancreas whereas adipose and skin were generally considered distinct. Overall, a subdivision into four tissue groups appeared most physiologically relevant in terms of tissue composition. Several models were found to have similar abilities to describe diazepam i.v. data as empirical models. Comparability of estimated Kpus to experimental Kpu values for diazepam was one criterion for selecting the appropriate PK model structure.

A "middle-out approach" for the prediction of human drug disposition from preclinical data using simplified physiologically based pharmacokinetic (PBPK) models.

Simplified physiologically based pharmacokinetic (PBPK) models using estimated tissue-to-unbound plasma partition coefficients (Kpus) were previously investigated by fitting them to in vivo pharmacokinetic (PK) data. After optimization with preclinical data, the performance of these models for extrapolation of distribution kinetics to human were evaluated to determine the best approach for the prediction of human drug disposition and volume of distribution (Vss) using PBPK modeling. Three lipophilic bases were tested (diazepam, midazolam, and basmisanil) for which intravenous PK data were available in rat, monkey, and human. The models with Kpu scalars using k-means clustering were generally the best for fitting data in the preclinical species and gave plausible Kpu values. Extrapolations of plasma concentrations for diazepam and midazolam using these models and parameters obtained were consistent with the observed clinical data. For diazepam and midazolam, the human predictions of Vss after optimization in rats and monkeys were better compared with the Vss estimated from the traditional PBPK modeling approach (varying from 1.1 to 3.1 vs. 3.7-fold error). For basmisanil, the sparse preclinical data available could have affected the model performance for fitting and the subsequent extrapolation to human. Overall, this work provides a rational strategy to predict human drug distribution using preclinical PK data within the PBPK modeling strategy.

Estimation of FMO3 Ontogeny by Mechanistic Population Pharmacokinetic Modelling of Risdiplam and Its Impact on Drug-Drug Interactions in Children.

BACKGROUND AND OBJECTIVE: Spinal muscular atrophy (SMA) is a progressive neuromuscular disease caused by insufficient levels of survival motor neuron (SMN) protein. Risdiplam (EvrysdiTM) increases SMN protein and is approved for the treatment of SMA. Risdiplam has high oral bioavailability and is primarily eliminated through hepatic metabolism by flavin-containing monooxygenase3 (FMO3) and cytochrome P450 (CYP) 3A, by 75% and 20%, respectively. While the FMO3 ontogeny is critical input data for the prediction of risdiplam pharmacokinetics (PK) in children, it was mostly studied in vitro, and robust in vivo FMO3 ontogeny is currently lacking. We derived in vivo FMO3 ontogeny by mechanistic population PK modelling of risdiplam and investigated its impact on drug-drug interactions in children. METHODS: Population and physiologically based PK (PPK and PBPK) modelling conducted during the development of risdiplam were integrated into a mechanistic PPK (Mech-PPK) model to estimate in vivo FMO3 ontogeny. A total of 10,205 risdiplam plasma concentration-time data from 525 subjects aged 2 months-61 years were included. Six different structural models were examined to describe the in vivo FMO3 ontogeny. Impact of the newly estimated FMO3 ontogeny on predictions of drug-drug interaction (DDI) in children was investigated by simulations for dual CYP3A-FMO3 substrates including risdiplam and theoretical substrates covering a range of metabolic fractions (fm) of CYP3A and FMO3 (fmCYP3A:fmFMO3 = 10%:90%, 50%:50%, 90%:10%). RESULTS: All six models consistently predicted higher FMO3 expression/activity in children, reaching a maximum at the age of 2 years with an approximately threefold difference compared with adults. Different trajectories of FMO3 ontogeny in infants < 4 months of age were predicted by the six models, likely due to limited observations for this age range. Use of this  in vivo FMO3 ontogeny function improved prediction of risdiplam PK in children compared to in vitro FMO3 ontogeny functions. The simulations of theoretical dual CYP3A-FMO3 substrates predicted comparable or decreased CYP3A-victim DDI propensity in children compared to adults across the range of fm values. Refinement of FMO3 ontogeny in the risdiplam model had no impact on the previously predicted low CYP3A-victim or -perpetrator DDI risk of risdiplam in children. CONCLUSION: Mech-PPK modelling successfully estimated in vivo FMO3 ontogeny from risdiplam data collected from 525 subjects aged 2 months-61 years. To our knowledge, this is the first investigation of in vivo FMO3 ontogeny by population approach using comprehensive data covering a wide age range. Derivation of a robust in vivo FMO3 ontogeny function has significant implications on the prospective prediction of PK and DDI in children for other FMO3 substrates in the future, as illustrated in the current study for FMO3 and/or dual CYP3A-FMO3 substrates. CLINICAL TRIAL REGISTRY NUMBERS: NCT02633709, NCT03032172, NCT02908685, NCT02913482, NCT03988907.

[Formula: see text]: Deep Generative Network Completion.

Most network data are collected from partially observable networks with both missing nodes and missing edges, for example, due to limited resources and privacy settings specified by users on social media. Thus, it stands to reason that inferring the missing parts of the networks by performing network completion should precede downstream applications. However, despite this need, the recovery of missing nodes and edges in such incomplete networks is an insufficiently explored problem due to the modeling difficulty, which is much more challenging than link prediction that only infers missing edges. In this paper, we present DeepNC, a novel method for inferring the missing parts of a network based on a deep generative model of graphs. Specifically, our method first learns a likelihood over edges via an autoregressive generative model, and then identifies the graph that maximizes the learned likelihood conditioned on the observable graph topology. Moreover, we propose a computationally efficient [Formula: see text] algorithm that consecutively finds individual nodes that maximize the probability in each node generation step, as well as an enhanced version using the expectation-maximization algorithm. The runtime complexities of both algorithms are shown to be almost linear in the number of nodes in the network. We empirically demonstrate the superiority of DeepNC over state-of-the-art network completion approaches.

Application of a gut-liver-on-a-chip device and mechanistic modelling to the quantitative in vitro pharmacokinetic study of mycophenolate mofetil.

Microphysiological systems (MPS) consisting of multiple linked organ-on-a-chip (OoC) components are highly promising tools with potential to provide more relevant in vitro to in vivo translation of drug disposition, efficacy and toxicity. A gut-liver OoC system was employed with Caco2 cells in co-culture with HT29 cells in the intestinal compartment and single donor primary hepatocytes in the hepatic compartment for the investigation of intestinal permeability, metabolism (intestinal and hepatic) and potential interplay of those processes. The prodrug mycophenolate mofetil was tested for quantitative evaluation of the gut-liver OoC due to the contribution of both gut and liver in its metabolism. Conversion of mycophenolate mofetil to active drug mycophenolic acid and further metabolism to a glucuronide metabolite was assessed over time in the gut apical, gut basolateral and liver compartments. Mechanistic modelling of experimental data was performed to estimate clearance and permeability parameters for the prodrug, active drug and glucuronide metabolite. Integration of gut-liver OoC data with in silico modelling allowed investigation of the complex combination of intestinal and hepatic processes, which is not possible with standard single tissue in vitro systems. A comprehensive evaluation of the mechanistic model, including structural model and parameter identifiability and global sensitivity analysis, enabled a robust experimental design and estimation of in vitro pharmacokinetic parameters. We propose that similar methodologies may be applied to other multi-organ microphysiological systems used for drug metabolism studies or wherever quantitative knowledge of changing drug concentration with time enables better understanding of biological effect.

Exploration and application of a liver-on-a-chip device in combination with modelling and simulation for quantitative drug metabolism studies.

Microphysiological systems (MPS) are complex and more physiologically realistic cellular in vitro tools that aim to provide more relevant human in vitro data for quantitative prediction of clinical pharmacokinetics while also reducing the need for animal testing. The PhysioMimix liver-on-a-chip integrates medium flow with hepatocyte culture and has the potential to be adopted for in vitro studies investigating the hepatic disposition characteristics of drug candidates. The current study focusses on liver-on-a-chip system exploration for multiple drug metabolism applications. Characterization of cytochrome P450 (CYP), UDP-glucuronosyl transferase (UGT) and aldehyde oxidase (AO) activities was performed using 15 drugs and in vitro to in vivo extrapolation (IVIVE) was assessed for 12 of them. Next, the utility of the liver-on-a-chip for estimation of the fraction metabolized (fm) via specific biotransformation pathways of quinidine and diclofenac was established. Finally, the metabolite identification opportunities were also explored using efavirenz as an example drug with complex primary and secondary metabolism involving a combination of CYP, UGT and sulfotransferase enzymes. A key aspect of these investigations was the application of mathematical modelling for improved parameter calculation. Such approaches will be required for quantitative assessment of metabolism and/or transporter processes in systems where medium flow and system compartments result in non-homogeneous drug concentrations. In particular, modelling was used to explore the effect of evaporation from the medium and it was found that the intrinsic clearance (CLint) might be underestimated by up to 40% for low clearance compounds if evaporation is not accounted for. Modelling of liver-on-a-chip in vitro data also enhanced the approach to fm estimation allowing objective assessment of metabolism models of different complexity. The resultant diclofenac fm,UGT of 0.64 was highly comparable with values reported previously in the literature. The current study demonstrates the integration of mathematical modelling with experimental liver-on-a-chip studies and illustrates how this approach supports generation of high quality of data from complex in vitro cellular systems.

Physiologically based pharmacokinetic modeling of intestinal first-pass metabolism of CYP3A substrates with high intestinal extraction.

Prediction of intestinal availability (F(G)), in conjunction with hepatic metabolism, is of considerable importance in drug disposition to assess oral clearance and liability to drug-drug interactions. In the current study, F(G) predictions were performed within a physiologically based pharmacokinetic (PBPK) model using in vitro permeability and clearance data. The prediction success was assessed in comparison with the Q(Gut) model. In addition, apparent oral clearance values, predicted using the PBPK model, were compared with in vivo observations from meta-analyses. Finally, unbound intrinsic clearance values (CLu(int)) were determined for 12 CYP3A substrates in eight individual human jejunal microsome (HJM) samples to assess interindividual variability in intestinal intrinsic clearance and subsequent F(G) predictions. Overall, the PBPK model improved F(G) predictions in comparison with the Q(Gut) model; this was apparent by a reduced bias and increased precision. In particular, F(G) predictions of indinavir, saquinavir, and terfenadine were model-dependent. The predicted oral clearance values of the drugs investigated ranged from 8.79 to 6320 l/h for tacrolimus and simvastatin, respectively, and were overall within 3-fold of the observed data with the exception of indinavir, atorvastatin, and buspirone. The individual HJM CLu(int) values ranged from 17 to 14,000 µl · min(-1) · mg(-1) for atorvastatin and saquinavir, respectively, and corresponding interindividual variability in CLu(int) estimates ranged from 41 to 67%. These in vitro data resulted in predicted F(G) values ranging from 0.03 to 0.94 for simvastatin and indinavir, respectively. The largest interindividual variability of F(G) was predicted for terfenadine (65%) in contrast with the low variability in the case of indinavir (3%).

California ACEP Firearm Injury Prevention Policy.

Firearm-related deaths and injuries are a serious public health problem in California and the United States. The rate of firearm-related deaths is many times higher in the US than other democratic, industrialized nations, yet many of the deaths and injuries are preventable. The California American College of Emergency Physicians Firearm Injury Prevention Policy was approved and adopted in 2013 as an evidence-based, apolitical statement to promote harm reduction. It recognizes and frames firearm injuries as a public health epidemic requiring allocation of robust resources, including increased governmental funding of high-quality research and the development of a national database system. The policy further calls for relevant legislation to be informed by best evidence and expert consensus, and advocates for legislation regarding the following: mandatory universal background checks; mandatory reporting of firearm loss/theft; restrictions against law-enforcement or military-style assault weapons and high capacity magazines; child-protective safety and storage systems; and prohibitions for high-risk individuals. It also strongly defends the right of physicians to screen and counsel patients about firearm-related risk factors and safety. Based upon best-available evidenced, the policy was recently updated to include extreme risk protection orders, which are also known as gun violence restraining orders.

Model-Based Drug-Drug Interaction Extrapolation Strategy From Adults to Children: Risdiplam in Pediatric Patients With Spinal Muscular Atrophy.

Risdiplam (Evrysdi) improves motor neuron function in patients with spinal muscular atrophy (SMA) and has been approved for the treatment of patients ≥2 months old. Risdiplam exhibits time-dependent inhibition of cytochrome P450 (CYP) 3A in vitro. While many pediatric patients receive risdiplam, a drug-drug interaction (DDI) study in pediatric patients with SMA was not feasible. Therefore, a novel physiologically-based pharmacokinetic (PBPK) model-based strategy was proposed to extrapolate DDI risk from healthy adults to children with SMA in an iterative manner. A clinical DDI study was performed in healthy adults at relevant risdiplam exposures observed in children. Risdiplam caused an 1.11-fold increase in the ratio of midazolam area under the curve with and without risdiplam (AUCR)), suggesting an 18-fold lower in vivo CYP3A inactivation constant compared with the in vitro value. A pediatric PBPK model for risdiplam was validated with independent data and combined with a validated midazolam pediatric PBPK model to extrapolate DDI from adults to pediatric patients with SMA. The impact of selected intestinal and hepatic CYP3A ontogenies on the DDI susceptibility in children relative to adults was investigated. The PBPK analysis suggests that primary CYP3A inhibition by risdiplam occurs in the intestine rather than the liver. The PBPK-predicted risdiplam CYP3A inhibition risk in pediatric patients with SMA aged 2 months-18 years was negligible (midazolam AUCR of 1.09-1.18) and included in the US prescribing information of risdiplam. Comprehensive evaluation of the sensitivity of predicted CYP3A DDI on selected intestinal and hepatic CYP3A ontogeny functions, together with PBPK model-based strategy proposed here, aim to guide and facilitate DDI extrapolations in pediatric populations.

Pharmacokinetics and Determination of Tumor Interstitial Distribution of a Therapeutic Monoclonal Antibody Using Large-Pore Microdialysis.

R7072 is a fully human monoclonal antibody (mAb) exerting anti-tumor activity via blockade of insulin like growth factor 1 receptor. The tumoral interstitial concentrations are anticipated to be better surrogates of active site concentrations than commonly used serum concentrations for pharmacokinetic-pharmacodynamic correlation of anti-tumor mAbs. Previously, a large-pore microdialysis technique for measuring tissue interstitial concentrations of R7072 in non-tumor bearing mice was established. In the current studies, the serum pharmacokinetics of R7072 were assessed and tissue interstitial concentrations were measured by large-pore microdialysis following intravenous and intraperitoneal administration of R7072 in tumor bearing mice. R7072 exhibited nonlinear pharmacokinetics in the studied dose range. Tumor and subcutaneous interstitial concentration data suggested some delay in tissue distribution after dosing. A dose-dependent increase in the ratio of tumor interstitial to serum concentration was observed indicating target-mediated drug disposition in tumor tissue. However, subcutaneous interstitial to serum concentration ratios were similar across the doses as observed previously in non-tumor bearing mice. A two-compartment population pharmacokinetic model with subcutaneous and tumor as open-loop compartments comprising of parallel linear and non-linear elimination from serum, linear disposition from subcutaneous interstitium and non-linear disposition from tumor interstitium was developed to simultaneously describe the pharmacokinetic data from all matrices.

Prediction of human intestinal first-pass metabolism of 25 CYP3A substrates from in vitro clearance and permeability data.

Intestinal first-pass metabolism may contribute to low oral drug bioavailability and drug-drug interactions, particularly for CYP3A substrates. The current analysis predicted intestinal availability (F(G)) from in vitro metabolic clearance and permeability data of 25 drugs using the Q(Gut) model. The drug selection included a wide range of physicochemical properties and in vivo F(G) values (0.07-0.94). In vitro clearance data (CLu(int)) were determined in human intestinal (HIM) and three liver (HLM) microsomal pools (n = 105 donors) using the substrate depletion method. Apparent drug permeability (P(app)) was determined in Caco-2 and Madin-Darby canine kidney cells transfected with human MDR1 gene (MDCK-MDR1 cells) under isotonic conditions (pH = 7.4). In addition, effective permeability (P(eff)) data, estimated from regression analyses to P(app) or physicochemical properties were used in the F(G) predictions. Determined CLu(int) values ranged from 0.022 to 76.7 microl/min/pmol of CYP3A (zolpidem and nisoldipine, respectively). Differences in CLu(int) values obtained in HIM and HLM were not significant after normalization for tissue-specific CYP3A abundance, supporting their interchangeable usability. The F(G) predictions were most successful when P(app) data from Caco-2/MDCK-MDR1 cells were used directly; in contrast, the use of physicochemical parameters resulted in significant F(G) underpredictions. Good agreement between predicted and in vivo F(G) was noted for drugs with low to medium intestinal extraction (e.g., midazolam predicted F(G) value 0.54 and in vivo value 0.51). In contrast, low prediction accuracy was observed for drugs with in vivo F(G) <0.5, resulting in considerable underprediction in some instances, as for saquinavir (predicted F(G) is 6% of the observed value). Implications of the findings are discussed.