Background Long COVID is defined as the persistence of symptoms beyond 3 months after SARS-CoV-2 infection. To better understand the long-term course and etiology of symptoms we analyzed a cohort of COVID-19 patients prospectively. Methods Patients were included at 5 months after acute COVID-19 in this prospective, non-interventional follow-up study. Patients followed until 12 months after COVID-19 symptom onset (n=96, 32.3% hospitalised, 55.2% females) were included in this analysis of symptoms, quality of life (based on a SF-12 survey), laboratory parameters including antinuclear antibodies (ANA), and SARS-CoV-2 antibody levels. Results At month 12, only 22.9% of patients were completely free of symptoms and the most frequent symptoms were reduced exercise capacity (56.3%), fatigue (53.1%), dyspnoea (37.5%), concentration problems (39.6%), problems finding words (32.3%), and sleeping problems (26.0%). Females showed significantly more neurocognitive symptoms than males. ANA titres were ≥1:160 in 43.6% of patients at 12 months post COVID-19 symptom onset, and neurocognitive symptom frequency was significantly higher in the group with an ANA titre ≥1:160 compared to <1:160. Compared to patients without symptoms, patients with at least one long COVID symptom at 12 months did not differ significantly with respect to their SARS-CoV-2-antibody levels, but had a significantly reduced physical and mental life quality compared to patients without symptoms. Conclusions Neurocognitive long COVID symptoms can persist at least for one year after COVID-19 symptom onset, and reduce life quality significantly. Several neurocognitive symptoms were associated with ANA titre elevations. This may indicate autoimmunity as cofactor in aetiology of long COVID.

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Background COVID-19 pneumonia and subsequent respiratory failure is causing an immense strain on intensive care units globally. Early prediction of severe disease enables clinicians to avoid acute respiratory distress syndrome (ARDS) development and improve management of critically ill patients. The soluble receptor of advanced glycation endproducts (sRAGE) is a biomarker shown to predict ARDS. Although sRAGE level varies depending on the type of disease, there is limited information available on changes in sRAGE levels in COVID-19. Therefore, sRAGE was measured in COVID-19 patients to determine sRAGE level variation in COVID-19 severity and to examine its ability to predict the need for mechanical ventilation (MV) and mortality in COVID-19. Methods In this single-centre observational cohort study in Germany, serum sRAGE during acute COVID-19, 20 weeks after the start of COVID-19 symptoms, as well as in control groups of non-COVID-19 pneumonia patients and healthy controls were measured using ELISA. The primary endpoint was severe disease (high-flow nasal oxygen therapy (HFNO)/MV and need of organ support). The secondary endpoints were respiratory failure with need of MV and 30-day mortality. The area under the curve (AUC), cut-off based on Youden’s index and odds ratio with 95% CI for sRAGE were calculated with regard to prediction of MV need and mortality. Results Serum sRAGE in 164 COVID-19 patients, 101 matched COVID-19 convalescent patients, 23 non-COVID-19 pneumonia patients and 15 healthy volunteers were measured. sRAGE level increased with COVID-19 severity, need for oxygen therapy, HFNO/MV, ARDS severity, need of dialysis and catecholamine support, 30-day mortality, sequential organ failure assessment (SOFA) and quick SOFA (qSOFA) score. sRAGE was found to be a good predictor of MV need in COVID-19 inpatients and mortality with an AUC of 0.871 (0.770–0.973) and 0.903 (0.817–0.990), respectively. When adjusted for male gender, age, comorbidity and SOFA score ≥ 3, sRAGE was independently associated with risk of need for HFNO/MV. When adjusted for SOFA score ≥ 3, sRAGE was independently associated with risk of need for MV. Conclusions Serum sRAGE concentrations are elevated in COVID-19 patients as disease severity increases. sRAGE should be considered as a biomarker for predicting the need for MV and mortality in COVID-19.

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Background The COVID-19 pandemic has caused sudden, severe strain to healthcare systems. Better outpatient management is required to save lives, manage resources effectively and prepare for future pandemics. Methods The Coronataxi digital early warning (CDEW) system deployed in Rhein-Neckar County and Heidelberg, Germany is an outpatient care system consisting of remote digital monitoring via a mobile application, a medical doctor dashboard and medical care delivery to COVID-19 patients in home quarantine when indicated. Patients reported their symptoms, temperature, breathing rate, oxygen saturation, and pulse via the app. This single-center cohort study compared outcomes of the population with and without using the CDEW system. The primary outcome was mortality; the secondary outcomes were hospitalization, duration of hospitalization, intensive care therapy and mechanical ventilation. Results Mortality rate was 3-4-fold lower and hospitalization rate was higher in the CDEW cohort (459 patients) compared to the cohort without CDEW in the same test area and other regions (Mannheim, Karlsruhe town, Karlsruhe district, and Germany), (mortality rate: 0.65% (95%CI: 0.13% - 1.90%) versus 2.16%, 2.32%, 2.48%, 2.82% and 2.76%, respectively, p<.05 for all; hospitalization rate: 14.81% (95%CI: 11.69% - 18.40%) versus 6.89%, 6.93%, 6.59%, 6.15% and 7.22%, respectively, p<.001 for all). The median duration of hospitalization in the CDEW cohort was significantly lower compared to a national sentinel cohort (6(4-9.75) versus 10(5-19) days, Z=-3.156, p=.002). 1.96% patients needed intensive care and 1.09% were mechanically ventilated. Conclusions The CDEW system significantly reduced COVID-19 mortality and duration of hospitalization and can be applied to the management of future pandemics.

Tyrosine kinase inhibitor sunitinib (used in GIST, advanced RCC, and pancreatic neuroendocrine tumors) undergoes CYP3A4 metabolism and is an ABCB1B and ABCG2 efflux transporters substrate. We assessed the pharmacokinetic interaction with ibuprofen (an NSAID used by patients with cancer) in Balb/c male and female mice. Mice (study group) were coadministered (30 min apart) 30 mg/kg of ibuprofen and 60 mg/kg of sunitinib PO and compared with the control groups, which received sunitinib alone (60 mg/kg, PO). Sunitinib concentration in plasma, brain, kidney, and liver was measured by HPLC as scheduled and noncompartmental pharmacokinetic parameters estimated. In female control mice, sunitinib AUC0→∞ decreased in plasma (P < 0.05), was higher in liver and brain (P < 0.001), and lower in kidney (P < 0.001) vs. male control mice. After ibuprofen coadministration, female mice showed lower AUC0→∞ in plasma (P < 0.01), brain, liver, and kidney (all P < 0.001). However, in male mice, AUC0→∞ remained unchanged in plasma, increased in liver and kidney, and decreased in brain (all P < 0.001). The tissue-to-plasma AUC0→∞ ratio was similar between male and female control mice, but changed after ibuprofen coadministration: Male mice showed 1.6-fold higher liver-to-plasma ratio (P < 0.001) while remained unchanged in female mice and in kidney (male and female mice) but decreased 55% in brain (P < 0.05). The tissue-to-plasma partial AUC ratio, the drug tissue targeting index, and the tissue-plasma hysteresis-like plots also showed sex-based ibuprofen-sunitinib drug interaction differences. The results illustrate the relevance of this DDI on sunitinib pharmacokinetics and tissue uptake. These may be due to gender-based P450 and efflux/transporters differences.

Sunitinib is a multitargeted tyrosine kinase inhibitor approved for gastrointestinal stromal tumor (GIST), advanced renal cell carcinoma (RCC) and pancreatic neuroendocrine tumors. It is metabolized via CYP3A4 and has low brain penetration due to efflux transporters ABCB1B and ABCG2. We studied the interaction with ketoconazole (50 mg/kg), antifungal drug which shares metabolic pathways and efflux transporters, in ICR female mice after oral coadministration (30 min apart) of 60 mg/kg sunitinib (study group) versus sunitinib alone (control group). Plasma, liver, kidney and brain sunitinib concentrations were measured by HPLC at 2, 5, 10, 20, 40 min, 1, 2, 4, 6, 12 h post-sunitinib administration, and non-compartmental pharmacokinetic parameters estimated. In plasma, ketoconazole coadministration increased plasma maximum concentration (C MAX) 60 %, delayed time to C MAX (T MAX); 1.6-fold greater area under the curve AUC0→∞ (p < 0.001); lower apparent steady-state volume of distribution (V SS/F) and oral clearance (Cl/F) 40 and 61 %, respectively; and shorter elimination half-life (t 1/2). Sunitinib exhibited extensive tissue distribution which increased after ketoconazole coadministration: total area under the curve (AUC0→∞) increased 1.8-, 2.8- and 1.2-fold in kidney, liver and brain, respectively (all p < 0.001). Sunitinib presented high tissue-to-plasma AUC0→∞ ratio in liver (17.8 ± 1.2), kidney (14.6 ± 1.52) and brain (2.25 ± 0.18) which was modified after coadministration: AUC0→∞ ratio increased in liver (31.4 ± 4.7; p < 0.001), kidney (17.1 ± 2.2; p > 0.05) and decreased in brain (1.70 ± 0.23, p > 0.05). The results showed a significant ketoconazole-sunitinib interaction that affected plasma, tissue pharmacokinetics and tissue uptake mechanisms. The study portrays the risk to increase toxicity and potential clinical translatability to treat tumors in tissues.