Effect of vaccinations and school restrictions on the spread of COVID-19 in different age groups in Germany.

With the emergence of SARS-CoV-2, various non-pharmaceutical interventions were adopted to control virus transmission, including school closures. Subsequently, the introduction of vaccines mitigated not only disease severity but also the spread of SARS-CoV-2. This study leveraged an adapted SIR model and non-linear mixed-effects modeling to quantify the impact of remote learning, school holidays, the emergence of Variants of Concern (VOCs), and the role of vaccinations in controlling SARS-CoV-2 spread across 16 German federal states with an age-stratified approach. Findings highlight a significant inverse correlation (Spearman's ρ = -0.92, p < 0.001) between vaccination rates and peak incidence rates across all age groups. Model-parameter estimation using the observed number of cases stratified by federal state and age allowed to assess the effects of school closure and holidays, considering adjustments for vaccinations and spread of VOCs over time. Here, modeling revealed significant (p < 0.001) differences in the virus's spread among pre-school children (0-4), children (5-11), adolescents (12-17), adults (18-59), and the elderly (60+). The transition to remote learning emerged as a critical measure in significantly reducing infection rates among children and adolescents (p < 0.001), whereas an increased infection risk was noted among the elderly during these periods, suggesting a shift in infection networks due to altered caregiving roles. Conversely, during school holiday periods, infection rates among adolescents mirrored those observed when schools were open. Simulation exercises based on the model provided evidence that COVID-19 vaccinations might serve a dual purpose: they protect the vaccinated individuals and contribute to the broader community's safety.

Population kinetic/pharmacodynamic modelling of the haemodynamic effects of cafedrine/theodrenaline (Akrinor) under general anaesthesia.

AIMS: The 20:1 combination of cafedrine and theodrenaline (C/T) is widely used in Germany for the treatment of arterial hypotension. Since there is little knowledge about the impact of covariates on the effect, the aim was to develop a kinetic/pharmacodynamic covariate model describing mean arterial pressure (MAP), systolic (SBP) and diastolic blood pressure (DBP), and heart rate (HR) for 30 min after the administration of C/T. METHODS: Data of patients receiving C/T from the HYPOTENS study (NCT02893241, DRKS00010740) were analysed using nonlinear mixed-effects modelling techniques. RESULTS: Overall, 16 579 measurements from 315 patients were analysed. The combination of two kinetic compartments and a delayed effect model, coupled with distinct Emax models for HR, SBP and DBP, described the data best. The model included age, sex, body mass index (BMI), antihypertensive medication, American Society of Anaesthesiologists (ASA) physical status classification grade, baseline SBP at the time of hypotension and pre-surgery HR as covariates (all P < .001). A higher baseline SBP led to a lower absolute increase in MAP. Patients with higher age, higher BMI and lower ASA grade showed smaller increases in MAP. The initial increase was similar for male and female patients. The long-term effect was higher in women. Concomitant antihypertensive medication caused a delayed effect and a lower maximum MAP. The HR increased only slightly (median increase 2.6 bpm, P < .001). CONCLUSIONS: Seven covariates with an impact on the effect of C/T could be identified. The results will enable physicians to optimize the dose with respect to individual patients.

A physiologically-based pharmacokinetic precision dosing approach to manage dasatinib drug-drug interactions.

Dasatinib, a second-generation tyrosine kinase inhibitor, is approved for treating chronic myeloid and acute lymphoblastic leukemia. As a sensitive cytochrome P450 (CYP) 3A4 substrate and weak base with strong pH-sensitive solubility, dasatinib is susceptible to enzyme-mediated drug-drug interactions (DDIs) with CYP3A4 perpetrators and pH-dependent DDIs with acid-reducing agents. This work aimed to develop a whole-body physiologically-based pharmacokinetic (PBPK) model of dasatinib to describe and predict enzyme-mediated and pH-dependent DDIs, to evaluate the impact of strong and moderate CYP3A4 inhibitors and inducers on dasatinib exposure and to support optimized dasatinib dosing. Overall, 63 plasma profiles from perorally administered dasatinib in healthy volunteers and cancer patients were used for model development. The model accurately described and predicted plasma profiles with geometric mean fold errors (GMFEs) for area under the concentration-time curve from the first to the last timepoint of measurement (AUClast) and maximum plasma concentration (Cmax) of 1.27 and 1.29, respectively. Regarding the DDI studies used for model development, all (8/8) predicted AUClast and Cmax ratios were within twofold of observed ratios. Application of the PBPK model for dose adaptations within various DDIs revealed dasatinib dose reductions of 50%-80% for strong and 0%-70% for moderate CYP3A4 inhibitors and a 2.3-3.1-fold increase of the daily dasatinib dose for CYP3A4 inducers to match the exposure of dasatinib administered alone. The developed model can be further employed to personalize dasatinib therapy, thereby help coping with clinical challenges resulting from DDIs and patient-related factors, such as elevated gastric pH.

Physiologically based pharmacokinetic modeling of imatinib and N-desmethyl imatinib for drug-drug interaction predictions.

The first-generation tyrosine kinase inhibitor imatinib has revolutionized the development of targeted cancer therapy and remains among the frontline treatments, for example, against chronic myeloid leukemia. As a substrate of cytochrome P450 (CYP) 2C8, CYP3A4, and various transporters, imatinib is highly susceptible to drug-drug interactions (DDIs) when co-administered with corresponding perpetrator drugs. Additionally, imatinib and its main metabolite N-desmethyl imatinib (NDMI) act as inhibitors of CYP2C8, CYP2D6, and CYP3A4 affecting their own metabolism as well as the exposure of co-medications. This work presents the development of a parent-metabolite whole-body physiologically based pharmacokinetic (PBPK) model for imatinib and NDMI used for the investigation and prediction of different DDI scenarios centered around imatinib as both a victim and perpetrator drug. Model development was performed in PK-Sim® using a total of 60 plasma concentration-time profiles of imatinib and NDMI in healthy subjects and cancer patients. Metabolism of both compounds was integrated via CYP2C8 and CYP3A4, with imatinib additionally transported via P-glycoprotein. The subsequently developed DDI network demonstrated good predictive performance. DDIs involving imatinib and NDMI were simulated with perpetrator drugs rifampicin, ketoconazole, and gemfibrozil as well as victim drugs simvastatin and metoprolol. Overall, 12/12 predicted DDI area under the curve determined between first and last plasma concentration measurements (AUClast) ratios and 12/12 predicted DDI maximum plasma concentration (Cmax) ratios were within twofold of the respective observed ratios. Potential applications of the final model include model-informed drug development or the support of model-informed precision dosing.

Redefining pandemic preparedness: Multidisciplinary insights from the CERP modelling workshop in infectious diseases, workshop report.

In July 2023, the Center of Excellence in Respiratory Pathogens organized a two-day workshop on infectious diseases modelling and the lessons learnt from the Covid-19 pandemic. This report summarizes the rich discussions that occurred during the workshop. The workshop participants discussed multisource data integration and highlighted the benefits of combining traditional surveillance with more novel data sources like mobility data, social media, and wastewater monitoring. Significant advancements were noted in the development of predictive models, with examples from various countries showcasing the use of machine learning and artificial intelligence in detecting and monitoring disease trends. The role of open collaboration between various stakeholders in modelling was stressed, advocating for the continuation of such partnerships beyond the pandemic. A major gap identified was the absence of a common international framework for data sharing, which is crucial for global pandemic preparedness. Overall, the workshop underscored the need for robust, adaptable modelling frameworks and the integration of different data sources and collaboration across sectors, as key elements in enhancing future pandemic response and preparedness.

Personalized Chronomodulated 5-Fluorouracil Treatment: A Physiologically-Based Pharmacokinetic Precision Dosing Approach for Optimizing Cancer Therapy.

The discovery of circadian clock genes greatly amplified the study of diurnal variations impacting cancer therapy, transforming it into a rapidly growing field of research. Especially, use of chronomodulated treatment with 5-fluorouracil (5-FU) has gained significance. Studies indicate high interindividual variability (IIV) in diurnal variations in dihydropyrimidine dehydrogenase (DPD) activity - a key enzyme for 5-FU metabolism. However, the influence of individual DPD chronotypes on chronomodulated therapy remains unclear and warrants further investigation. To optimize precision dosing of chronomodulated 5-FU, this study aims to: (i) build physiologically-based pharmacokinetic (PBPK) models for 5-FU, uracil, and their metabolites, (ii) assess the impact of diurnal variation on DPD activity, (iii) estimate individual DPD chronotypes, and (iv) personalize chronomodulated 5-FU infusion rates based on a patient's DPD chronotype. Whole-body PBPK models were developed with PK-Sim(R) and MoBi(R). Sinusoidal functions were used to incorporate variations in enzyme activity and chronomodulated infusion rates as well as to estimate individual DPD chronotypes from DPYD mRNA expression or DPD enzymatic activity. Four whole-body PBPK models for 5-FU, uracil, and their metabolites were established utilizing data from 41 5-FU and 10 publicly available uracil studies. IIV in DPD chronotypes was assessed and personalized chronomodulated administrations were developed to achieve (i) comparable 5-FU peak plasma concentrations, (ii) comparable 5-FU exposure, and (iii) constant 5-FU plasma levels via "noise cancellation" chronomodulated infusion. The developed PBPK models capture the extent of diurnal variations in DPD activity and can help investigate individualized chronomodulated 5-FU therapy through testing alternative personalized dosing strategies.

A dynamic time-to-event model for prediction of acute graft-versus-host disease in patients after allogeneic hematopoietic stem cell transplantation.

BACKGROUND: Acute graft-versus-host disease (aGvHD) is a major cause of death for patients following allogeneic hematopoietic stem cell transplantation (HSCT). Effective management of moderate to severe aGvHD remains challenging despite recent advances in HSCT, emphasizing the importance of prophylaxis and risk factor identification. METHODS: In this study, we analyzed data from 1479 adults who underwent HSCT between 2005 and 2017 to investigate the effects of aGvHD prophylaxis and time-dependent risk factors on the development of grades II-IV aGvHD within 100 days post-HSCT. RESULTS: Using a dynamic longitudinal time-to-event model, we observed a non-monotonic baseline hazard overtime with a low hazard during the first few days and a maximum hazard at day 17, described by Bateman function with a mean transit time of approximately 11 days. Multivariable analysis revealed significant time-dependent effects of white blood cell counts and cyclosporine A exposure as well as static effects of female donors for male recipients, patients with matched related donors, conditioning regimen consisting of fludarabine plus total body irradiation, and patient age in recipients of grafts from related donors on the risk to develop grades II-IV aGvHD. Additionally, we found that higher cumulative hazard on day 7 after allo-HSCT are associated with an increased incidence of grades II-IV aGvHD within 100 days indicating that an individual assessment of the cumulative hazard on day 7 could potentially serve as valuable predictor for later grades II-IV aGvHD development. Using the final model, stochastic simulations were performed to explore covariate effects on the cumulative incidence over time and to estimate risk ratios. CONCLUSION: Overall, the presented model showed good descriptive and predictive performance and provides valuable insights into the interplay of multiple static and time-dependent risk factors for the prediction of aGvHD.

Efficacy and safety of MP1032 plus standard-of-care compared to standard-of-care in hospitalised patients with COVID-19: a multicentre, randomised double-blind, placebo-controlled phase 2a trial.

Background: SARS-CoV-2 infections still have a significant impact on the global population. The existing vaccinations have contributed to reducing the severe disease courses, decreasing hospitalisations, and lowering the mortality rate. However, due to the variability of COVID-19 symptoms, the emergence of new variants and the uneven global distribution of vaccines there is still a great need for new therapy options. One promising approach is provided by host-directed therapies. We assessed here the efficacy and safety of MP1032, a host-directed anti-viral/anti-inflammatory drug in hospitalised patients with moderate to severe COVID-19. Methods: In a randomised, double-blind, placebo-controlled, Phase IIa study, patients were randomised 2:1 to receive either 300 mg MP032 bid + Standard-of-Care (SoC) or placebo bid + SoC for 28 days. Eligible patients were ≥18 years old, tested positive for SARS-CoV-2, and had moderate to severe COVID-19 symptoms. The study spanned 20 sites in six countries (Bulgaria, France, Hungary, Italy, Romania, Spain), assessing disease progression according the NIAID scale as the primary outcome on day 14. Secondary objectives included disease progression (day 28), disease resolution (days 14 and 28), mortality rate, COVID-19 related parameters and safety. Exposure-response analyses were performed, linking MP1032 to COVID-19 biomarkers (eGFR, D-dimer). Findings: 132 patients were enrolled to receive MP1032 + SoC (n = 87) or placebo + SoC (n = 45). The patients were all white or Caucasian with a mean (median) age of 60.5 (63) years. Overall, only 10 patients were vaccinated, 5 in each group. No significant risk difference of disease progression could be detected between groups on both day 14 (9.8% MP1032 vs. 11.6% placebo) and day 28 with MH common risk differences of -0.276% (95% CI, -11.634 to 11.081; p = 0.962) and 1.722% (95% CI, -4.576 to 8.019; p = 0.592), respectively.The treatment with MP1032 + SoC was safe and well-tolerated. Overall, 182 TEAEs including 10 SAEs were reported in 53.5% (46/86) of patients of the verum group and in 57.8% (26/45) of patients of the placebo group; the SAEs occurred in 5.8% (5/86) and 6.7% (3/45) of verum and placebo patients, respectively. None of the SAEs was considered as related. Interpretation: Despite the study's limitation in size and the variation in concurrent SoCs, these findings warrant further investigation of MP1032 as a host-directed anti-viral drug candidate. Funding: The study was funded by the COVID-19 Horizon Europe work programme and MetrioPharm AG.

Motivations for Adolescent COVID-19 Vaccination: A Comparative Study of Adolescent and Caregiver Perspectives in Germany.

Given the crucial role of vaccination in halting the COVID-19 pandemic, it is imperative to understand the factors that motivate adolescents to get vaccinated. We surveyed adolescents and their accompanying guardians scheduled to receive a COVID-19 vaccination (Comirnaty) in an urban region in Germany in mid-2021 regarding their motivation for getting vaccinated and collected data on their sociodemographic characteristics, medical history, vaccination status, and any history of COVID-19 infection in the family. We also queried information strategies related to the SARS-CoV-2 pandemic. Motivations for getting vaccinated were similar among adolescents and their parents. The primary reasons for vaccination were protection against SARS-CoV-2-related illness and gaining access to leisure facilities. This was not influenced by gender, health status, migration background, or the presence of chronic or acute diseases. The percentage of parents who had received SARS-CoV-2 immunization and the proportion of parents with a high level of education were higher among study participants than in the general population. Adolescents were especially willing to be vaccinated if they came from a better educational environment and had a high vaccination rate in the family. Emphasizing the importance of vaccination among all segments of the population and removing barriers to vaccines may lead to an ameliorated acceptance of COVID-19 vaccines.

Population pharmacokinetic modeling of multiple-dose intravenous fosfomycin in critically ill patients during continuous venovenous hemodialysis.

The aim of this study was to investigate the pharmacokinetics of multiple-dose intravenous (i.v.) fosfomycin in critically ill patients during continuous venovenous hemodialysis (CVVHD). Non-compartmental analysis and population pharmacokinetic modeling were used to simulate different dosing regimens. We evaluated 15 critically ill patients with renal insufficiency and CVVHD undergoing anti-infective treatment with fosfomycin in our ICU. Five grams of fosfomycin were administered for 120 min every 6 h. Plasma concentrations were determined with and without CVVHD. Pharmacokinetic analysis and simulations were performed using non-linear mixed effects modelling (NONMEM). A two-compartment model with renal and dialysis clearance was most accurate in describing the pharmacokinetics of i.v. fosfomycin during CVVHD. Population parameter estimates were 18.20 L and 20.80 L for the central and peripheral compartment volumes, and 0.26 L/h and 5.08 L/h for renal and intercompartmental clearance, respectively. Urinary creatinine clearance (CLCR) represented a considerable component of renal clearance. Central compartment volume increased over time after the first dose. For patients with CLCR > 50 (90) mL/min and CVVHD, dosage should be increased to ≥ 15 (16) grams of i.v. fosfomycin across three (four) daily doses. Individual CLCR must be considered when dosing i.v. fosfomycin in critically ill patients during CVVHD.

Physiologically Based Pharmacokinetic Modeling of Bergamottin and 6,7-Dihydroxybergamottin to Describe CYP3A4 Mediated Grapefruit-Drug Interactions.

Grapefruit is a moderate to strong inactivator of CYP3A4, which metabolizes up to 50% of marketed drugs. The inhibitory effect is mainly attributed to furanocoumarins present in the fruit, irreversibly inhibiting preferably intestinal CYP3A4 as suicide inhibitors. Effects on CYP3A4 victim drugs can still be measured up to 24 hours after grapefruit juice (GFJ) consumption. The current study aimed to establish a physiologically-based pharmacokinetic (PBPK) grapefruit-drug interaction model by modeling the relevant CYP3A4 inhibiting ingredients of the fruit to simulate and predict the effect of GFJ consumption on plasma concentration-time profiles of various CYP3A4 victim drugs. The grapefruit model was developed in PK-Sim and coupled with previously developed PBPK models of CYP3A4 substrates that were publicly available and already evaluated for CYP3A4-mediated drug-drug interactions. Overall, 43 clinical studies were used for model development. Models of bergamottin (BGT) and 6,7-dihydroxybergamottin (DHB) as relevant active ingredients in GFJ were established. Both models include: (i) CYP3A4 inactivation informed by in vitro parameters, (ii) a CYP3A4 mediated clearance estimated during model development, as well as (iii) passive glomerular filtration. The final model successfully describes interactions of GFJ ingredients with 10 different CYP3A4 victim drugs, simulating the effect of the CYP3A4 inactivation on the victims' pharmacokinetics as well as their main metabolites. Furthermore, the model sufficiently captures the time-dependent effect of CYP3A4 inactivation as well as the effect of grapefruit ingestion on intestinal and hepatic CYP3A4 concentrations.

Physiologically-based pharmacokinetic modeling of quinidine to establish a CYP3A4, P-gp, and CYP2D6 drug-drug-gene interaction network.

The antiarrhythmic agent quinidine is a potent inhibitor of cytochrome P450 (CYP) 2D6 and P-glycoprotein (P-gp) and is therefore recommended for use in clinical drug-drug interaction (DDI) studies. However, as quinidine is also a substrate of CYP3A4 and P-gp, it is susceptible to DDIs involving these proteins. Physiologically-based pharmacokinetic (PBPK) modeling can help to mechanistically assess the absorption, distribution, metabolism, and excretion processes of a drug and has proven its usefulness in predicting even complex interaction scenarios. The objectives of the presented work were to develop a PBPK model of quinidine and to integrate the model into a comprehensive drug-drug(-gene) interaction (DD(G)I) network with a diverse set of CYP3A4 and P-gp perpetrators as well as CYP2D6 and P-gp victims. The quinidine parent-metabolite model including 3-hydroxyquinidine was developed using pharmacokinetic profiles from clinical studies after intravenous and oral administration covering a broad dosing range (0.1-600 mg). The model covers efflux transport via P-gp and metabolic transformation to either 3-hydroxyquinidine or unspecified metabolites via CYP3A4. The 3-hydroxyquinidine model includes further metabolism by CYP3A4 as well as an unspecific hepatic clearance. Model performance was assessed graphically and quantitatively with greater than 90% of predicted pharmacokinetic parameters within two-fold of corresponding observed values. The model was successfully used to simulate various DD(G)I scenarios with greater than 90% of predicted DD(G)I pharmacokinetic parameter ratios within two-fold prediction success limits. The presented network will be provided to the research community and can be extended to include further perpetrators, victims, and targets, to support investigations of DD(G)Is.

Significant impact of time-of-day variation on metformin pharmacokinetics.

AIMS/HYPOTHESIS: The objective was to investigate if metformin pharmacokinetics is modulated by time-of-day in humans using empirical and mechanistic pharmacokinetic modelling techniques on a large clinical dataset. This study also aimed to generate and test hypotheses on the underlying mechanisms, including evidence for chronotype-dependent interindividual differences in metformin plasma and efficacy-related tissue concentrations. METHODS: A large clinical dataset consisting of individual metformin plasma and urine measurements was analysed using a newly developed empirical pharmacokinetic model. Causes of daily variation of metformin pharmacokinetics and interindividual variability were further investigated by a literature-informed mechanistic modelling analysis. RESULTS: A significant effect of time-of-day on metformin pharmacokinetics was found. Daily rhythms of gastrointestinal, hepatic and renal processes are described in the literature, possibly affecting drug pharmacokinetics. Observed metformin plasma levels were best described by a combination of a rhythm in GFR, renal plasma flow (RPF) and organic cation transporter (OCT) 2 activity. Furthermore, the large interindividual differences in measured metformin concentrations were best explained by individual chronotypes affecting metformin clearance, with impact on plasma and tissue concentrations that may have implications for metformin efficacy. CONCLUSIONS/INTERPRETATION: Metformin's pharmacology significantly depends on time-of-day in humans, determined with the help of empirical and mechanistic pharmacokinetic modelling, and rhythmic GFR, RPF and OCT2 were found to govern intraday variation. Interindividual variation was found to be partly dependent on individual chronotype, suggesting diurnal preference as an interesting, but so-far underappreciated, topic with regard to future personalised chronomodulated therapy in people with type 2 diabetes.

A Physiologically Based Pharmacokinetic Model of Ketoconazole and Its Metabolites as Drug-Drug Interaction Perpetrators.

The antifungal ketoconazole, which is mainly used for dermal infections and treatment of Cushing's syndrome, is prone to drug-food interactions (DFIs) and is well known for its strong drug-drug interaction (DDI) potential. Some of ketoconazole's potent inhibitory activity can be attributed to its metabolites that predominantly accumulate in the liver. This work aimed to develop a whole-body physiologically based pharmacokinetic (PBPK) model of ketoconazole and its metabolites for fasted and fed states and to investigate the impact of ketoconazole's metabolites on its DDI potential. The parent-metabolites model was developed with PK-Sim® and MoBi® using 53 plasma concentration-time profiles. With 7 out of 7 (7/7) DFI AUClast and DFI Cmax ratios within two-fold of observed ratios, the developed model demonstrated good predictive performance under fasted and fed conditions. DDI scenarios that included either the parent alone or with its metabolites were simulated and evaluated for the victim drugs alfentanil, alprazolam, midazolam, triazolam, and digoxin. DDI scenarios that included all metabolites as reversible inhibitors of CYP3A4 and P-gp performed best: 26/27 of DDI AUClast and 21/21 DDI Cmax ratios were within two-fold of observed ratios, while DDI models that simulated only ketoconazole as the perpetrator underperformed: 12/27 DDI AUClast and 18/21 DDI Cmax ratios were within the success limits.

Physiologically based pharmacokinetic modeling of tacrolimus for food-drug and CYP3A drug-drug-gene interaction predictions.

The immunosuppressant and narrow therapeutic index drug tacrolimus is metabolized mainly via cytochrome P450 (CYP) 3A4 and CYP3A5. For its pharmacokinetics (PK), high inter- and intra-individual variability can be observed. Underlying causes include the effect of food intake on tacrolimus absorption as well as genetic polymorphism in the CYP3A5 gene. Furthermore, tacrolimus is highly susceptible to drug-drug interactions, acting as a victim drug when coadministered with CYP3A perpetrators. This work describes the development of a whole-body physiologically based pharmacokinetic model for tacrolimus as well as its application for investigation and prediction of (i) the impact of food intake on tacrolimus PK (food-drug interactions [FDIs]) and (ii) drug-drug(-gene) interactions (DD[G]Is) involving the CYP3A perpetrator drugs voriconazole, itraconazole, and rifampicin. The model was built in PK-Sim® Version 10 using a total of 37 whole blood concentration-time profiles of tacrolimus (training and test) compiled from 911 healthy individuals covering the administration of tacrolimus as intravenous infusions as well as immediate-release and extended-release capsules. Metabolism was incorporated via CYP3A4 and CYP3A5, with varying activities implemented for different CYP3A5 genotypes and study populations. The good predictive model performance is demonstrated for the examined food effect studies with 6/6 predicted FDI area under the curve determined between first and last concentration measurements (AUClast ) and 6/6 predicted FDI maximum whole blood concentration (Cmax ) ratios within twofold of the respective observed ratios. In addition, 7/7 predicted DD(G)I AUClast and 6/7 predicted DD(G)I Cmax ratios were within twofold of their observed values. Potential applications of the final model include model-informed drug discovery and development or the support of model-informed precision dosing.

A 12-gene pharmacogenetic panel to prevent adverse drug reactions: an open-label, multicentre, controlled, cluster-randomised crossover implementation study.

BACKGROUND: The benefit of pharmacogenetic testing before starting drug therapy has been well documented for several single gene-drug combinations. However, the clinical utility of a pre-emptive genotyping strategy using a pharmacogenetic panel has not been rigorously assessed. METHODS: We conducted an open-label, multicentre, controlled, cluster-randomised, crossover implementation study of a 12-gene pharmacogenetic panel in 18 hospitals, nine community health centres, and 28 community pharmacies in seven European countries (Austria, Greece, Italy, the Netherlands, Slovenia, Spain, and the UK). Patients aged 18 years or older receiving a first prescription for a drug clinically recommended in the guidelines of the Dutch Pharmacogenetics Working Group (ie, the index drug) as part of routine care were eligible for inclusion. Exclusion criteria included previous genetic testing for a gene relevant to the index drug, a planned duration of treatment of less than 7 consecutive days, and severe renal or liver insufficiency. All patients gave written informed consent before taking part in the study. Participants were genotyped for 50 germline variants in 12 genes, and those with an actionable variant (ie, a drug-gene interaction test result for which the Dutch Pharmacogenetics Working Group [DPWG] recommended a change to standard-of-care drug treatment) were treated according to DPWG recommendations. Patients in the control group received standard treatment. To prepare clinicians for pre-emptive pharmacogenetic testing, local teams were educated during a site-initiation visit and online educational material was made available. The primary outcome was the occurrence of clinically relevant adverse drug reactions within the 12-week follow-up period. Analyses were irrespective of patient adherence to the DPWG guidelines. The primary analysis was done using a gatekeeping analysis, in which outcomes in people with an actionable drug-gene interaction in the study group versus the control group were compared, and only if the difference was statistically significant was an analysis done that included all of the patients in the study. Outcomes were compared between the study and control groups, both for patients with an actionable drug-gene interaction test result (ie, a result for which the DPWG recommended a change to standard-of-care drug treatment) and for all patients who received at least one dose of index drug. The safety analysis included all participants who received at least one dose of a study drug. This study is registered with ClinicalTrials.gov, NCT03093818 and is closed to new participants. FINDINGS: Between March 7, 2017, and June 30, 2020, 41 696 patients were assessed for eligibility and 6944 (51·4 % female, 48·6% male; 97·7% self-reported European, Mediterranean, or Middle Eastern ethnicity) were enrolled and assigned to receive genotype-guided drug treatment (n=3342) or standard care (n=3602). 99 patients (52 [1·6%] of the study group and 47 [1·3%] of the control group) withdrew consent after group assignment. 652 participants (367 [11·0%] in the study group and 285 [7·9%] in the control group) were lost to follow-up. In patients with an actionable test result for the index drug (n=1558), a clinically relevant adverse drug reaction occurred in 152 (21·0%) of 725 patients in the study group and 231 (27·7%) of 833 patients in the control group (odds ratio [OR] 0·70 [95% CI 0·54-0·91]; p=0·0075), whereas for all patients, the incidence was 628 (21·5%) of 2923 patients in the study group and 934 (28·6%) of 3270 patients in the control group (OR 0·70 [95% CI 0·61-0·79]; p <0·0001). INTERPRETATION: Genotype-guided treatment using a 12-gene pharmacogenetic panel significantly reduced the incidence of clinically relevant adverse drug reactions and was feasible across diverse European health-care system organisations and settings. Large-scale implementation could help to make drug therapy increasingly safe. FUNDING: European Union Horizon 2020.

Prediction of Drug-Drug-Gene Interaction Scenarios of (E)-Clomiphene and Its Metabolites Using Physiologically Based Pharmacokinetic Modeling.

Clomiphene, a selective estrogen receptor modulator (SERM), has been used for the treatment of anovulation for more than 50 years. However, since (E)-clomiphene ((E)-Clom) and its metabolites are eliminated primarily via Cytochrome P450 (CYP) 2D6 and CYP3A4, exposure can be affected by CYP2D6 polymorphisms and concomitant use with CYP inhibitors. Thus, clomiphene therapy may be susceptible to drug-gene interactions (DGIs), drug-drug interactions (DDIs) and drug-drug-gene interactions (DDGIs). Physiologically based pharmacokinetic (PBPK) modeling is a tool to quantify such DGI and DD(G)I scenarios. This study aimed to develop a whole-body PBPK model of (E)-Clom including three important metabolites to describe and predict DGI and DD(G)I effects. Model performance was evaluated both graphically and by calculating quantitative measures. Here, 90% of predicted Cmax and 80% of AUClast values were within two-fold of the corresponding observed value for DGIs and DD(G)Is with clarithromycin and paroxetine. The model also revealed quantitative contributions of different CYP enzymes to the involved metabolic pathways of (E)-Clom and its metabolites. The developed PBPK model can be employed to assess the exposure of (E)-Clom and its active metabolites in as-yet unexplored DD(G)I scenarios in future studies.

Problematic Internet Use among Adolescents 18 Months after the Onset of the COVID-19 Pandemic.

Studies in recent years and especially since the beginning of the COVID-19 pandemic have shown a significant increase in the problematic use of computer games and social media. Adolescents having difficulties in regulating their unpleasant emotions are especially prone to Problematic Internet Use (PIU), which is why emotion dysregulation has been considered a risk factor for PIU. The aim of the present study was to assess problematic internet use (PIU) in adolescents after the third wave (nearly 1.5 years after the onset in Europe) of the COVID-19 pandemic. In the German region of Siegen-Wittgenstein, all students 12 years and older from secondary-level schools, vocational schools and universities were offered a prioritized vaccination in August 2021 with an approved vaccine against COVID-19. In this context, the participants filled out the Short Compulsive Internet Use Scale (SCIUS) and two additional items to capture a possible change in digital media usage time and regulation of negative affect due to the COVID-19 pandemic. A multiple regression analysis was performed to identify predictors of PIU. The original sample consisted of 1477 participants, and after excluding invalid cases the final sample size amounted to 1268 adolescents aged 12-17 (x = 14.37 years, SD = 1.64). The average prevalence of PIU was 43.69%. Gender, age, digital media usage time and the intensity of negative emotions during the COVID-19 pandemic were all found to be significant predictors of PIU: female gender, increasing age, longer digital media usage time and higher intensity of negative emotions during the COVID-19 pandemic were associated with higher SCIUS total scores. This study found a very high prevalence of PIU among 12- to 17-year-olds for the period after the third wave of the COVID-19 pandemic, which has increased significantly compared to pre-pandemic prevalence rates. PIU is emerging as a serious problem among young people in the pandemic. Besides gender and age, pandemic-associated time of digital media use and emotion regulation have an impact on PIU, which provides starting points for preventive interventions.

Pharmacometric Modeling of the Impact of Azelastine Nasal Spray on SARS-CoV-2 Viral Load and Related Symptoms in COVID-19 Patients.

The histamine-1 receptor antagonist azelastine was recently found to impact SARS-CoV-2 viral kinetics in a Phase 2 clinical trial (CARVIN). Thus, we investigated the relationship between intranasal azelastine administrations and viral load, as well as symptom severity in COVID-19 patients and analyzed the impact of covariates using non-linear mixed-effects modeling. For this, we developed a pharmacokinetic (PK) model for the oral and intranasal administration of azelastine. A one-compartment model with parallel absorption after intranasal administration described the PK best, covering both the intranasal and the gastro-intestinal absorption pathways. For virus kinetic and symptoms modeling, viral load and symptom records were gathered from the CARVIN study that included data of 82 COVID-19 patients receiving placebo or intranasal azelastine. The effect of azelastine on viral load was described by a dose-effect model targeting the virus elimination rate. An extension of the model revealed a relationship between COVID-19 symptoms severity and the number of infected cells. The analysis revealed that the intranasal administration of azelastine led to a faster decline in viral load and symptoms severity compared to placebo. Moreover, older patients showed a slower decline in viral load compared to younger patients and male patients experienced higher peak viral loads than females.

Model-Based Analysis of SARS-CoV-2 Infections, Hospitalization and Outcome in Germany, the Federal States and Districts.

The coronavirus disease 2019 (COVID-19) pandemic challenged many national health care systems, with hospitals reaching capacity limits of intensive care units (ICU). Thus, the estimation of acute local burden of ICUs is critical for appropriate management of health care resources. In this work, we applied non-linear mixed effects modeling to develop an epidemiological SARS-CoV-2 infection model for Germany, with its 16 federal states and 400 districts, that describes infections as well as COVID-19 inpatients, ICU patients with and without mechanical ventilation, recoveries, and fatalities during the first two waves of the pandemic until April 2021. Based on model analyses, covariates influencing the relation between infections and outcomes were explored. Non-pharmaceutical interventions imposed by governments were found to have a major impact on the spreading of SARS-CoV-2. Patient age and sex, the spread of variant B.1.1.7, and the testing strategy (number of tests performed weekly, rate of positive tests) affected the severity and outcome of recorded cases and could reduce the observed unexplained variability between the states. Modeling could reasonably link the discrepancies between fine-grained model simulations of the 400 German districts and the reported number of available ICU beds to coarse-grained COVID-19 patient distribution patterns within German regions.