As global health services respond to the coronavirus pandemic, many prescribers are turning to experimental drugs. This review aims to assess the risk of drug-drug interactions in the severely ill COVID-19 patient. Experimental therapies were identified by searching ClinicalTrials.gov for 'COVID-19', '2019-nCoV', '2019 novel coronavirus' and 'SARS-CoV-2'. The last search was performed on 30 June 2020. Herbal medicines, blood-derived products and in vitro studies were excluded. We identified comorbidities by searching PubMed for the MeSH terms 'COVID-19', 'Comorbidity' and 'Epidemiological Factors'. Potential drug-drug interactions were evaluated according to known pharmacokinetics, overlapping toxicities and QT risk. Drug-drug interactions were graded GREEN and YELLOW: no clinically significant interaction; AMBER: caution; RED: serious risk. A total of 2378 records were retrieved from ClinicalTrials.gov, which yielded 249 drugs that met inclusion criteria. Thirteen primary compounds were screened against 512 comedications. A full database of these interactions is available at www.covid19-druginteractions.org. Experimental therapies for COVID-19 present a risk of drug-drug interactions, with lopinavir/ritonavir (10% RED, 41% AMBER; mainly a perpetrator of pharmacokinetic interactions but also risk of QT prolongation particularly when given with concomitant drugs that can prolong QT), chloroquine and hydroxychloroquine (both 7% RED and 27% AMBER, victims of some interactions due to metabolic profile but also perpetrators of QT prolongation) posing the greatest risk. With management, these risks can be mitigated. We have published a drug-drug interaction resource to facilitate medication review for the critically ill patient.

With the introduction of direct-acting antivirals (DAAs) for hepatitis C virus (HCV) infection, drug-drug interactions (DDIs) emerged as significant challenge. Since then, HCV therapy and the infected population have rapidly changed. So far, very limited data are available regarding the clinical relevance of DDIs when using most modern DAA regimens. We aimed to assess how the importance of DDIs has evolved over time.

Nosocomial infections are a particular threat for patients with liver cirrhosis. It is not uncommon that individuals develop even several consecutive infections during a single hospital stay. We aimed to investigate the impact and characteristics of multiple, consecutive nosocomial infections.

Direct-acting antiviral agents (DAAs) for the treatment of hepatitis C (HCV) can be associated with drug-drug interactions (DDIs) with concomitant medications. The practical clinical implications of such DDIs are poorly understood. We assessed the clinical impact of possible pharmacokinetic (PK) interactions between simeprevir and frequently prescribed concomitant medications.

Treatment with nucleos(t)ide analogues (NAs) may be stopped after 1-3 years of hepatitis B virus DNA suppression in hepatitis B e antigen (HBeAg)-negative patients according to Asian Pacific Association for the Study of Liver and European Association for the Study of Liver guidelines. However, virological relapse (VR) occurs in most patients. We aimed to analyze soluble immune markers (SIMs) and use machine learning to identify SIM combinations as predictor for early VR after NA discontinuation. A validation cohort was used to verify the predictive power of the SIM combination. In a post hoc analysis of a prospective, multicenter therapeutic vaccination trial (ABX-203, NCT02249988), hepatitis B surface antigen, hepatitis B core antigen, and 47 SIMs were repeatedly determined before NA was stopped. Forty-three HBeAg-negative patients were included. To detect the highest predictive constellation of host and viral markers, a supervised machine learning approach was used. Data were validated in a different cohort of 49 patients treated with entecavir. VR (hepatitis B virus DNA ≥ 2,000 IU/mL) occurred in 27 patients. The predictive value for VR of single SIMs at the time of NA stop was best for interleukin (IL)-2, IL-17, and regulated on activation, normal T cell expressed and secreted (RANTES/CCL5) with a maximum area under the curve of 0.65. Hepatitis B core antigen had a higher predictive power than hepatitis B surface antigen but lower than the SIMs. A supervised machine-learning algorithm allowed a remarkable improvement of early relapse prediction in patients treated with entecavir. The combination of IL-2, monokine induced by interferon γ (MIG)/chemokine (C-C motif) ligand 9 (CCL9), RANTES/CCL5, stem cell factor (SCF), and TNF-related apoptosis-inducing ligand (TRAIL) was reliable in predicting VR (0.89; 95% confidence interval: 0.5-1.0) and showed viable results in the validation cohort (0.63; 0.1-0.99). Host immune markers such as SIMs appear to be underestimated in guiding treatment cessation in HBeAg-negative patients. Machine learning can help find predictive SIM patterns that allow a precise identification of patients particularly suitable for NA cessation.

More than 70 million people are estimated to be infected with hepatitis C virus globally. Glecaprevir/pibrentasvir is a widely used treatment and has recently been approved for an 8-week regimen for treatment-naïve patients with compensated cirrhosis in Europe and the USA, who would previously have received glecaprevir/pibrentasvir for 12 weeks. This label update was based on the EXPEDITION-8 study, which included 343 treatment-naïve patients with compensated cirrhosis. However, there is currently a lack of similarly large-scale real-world studies of the 8-week glecaprevir/pibrentasvir regimen in this population.

Ombitasvir/paritaprevir/ritonavir ± dasabuvir ± ribavirin (OBV/PTV/r ± DSV ± RBV) regimens show high efficacy and good tolerability in clinical trials for chronic hepatitis C virus (HCV) genotypes (GT) 1 or 4. To evaluate whether these results translate to clinical practice, data were pooled from observational studies across 13 countries. Treatment-naïve or -experienced patients, with or without cirrhosis, received OBV/PTV/r ± DSV ± RBV according to approved local labels and clinical practice. Sustained virologic response at post-treatment Week 12 (SVR12), adverse events (AEs) and comedication management were assessed for patients initiating treatment before 1 June 2017. The safety population included 3850 patients who received ≥1 dose of study drug. The core population (N = 3808) further excluded patients with unknown GT or cirrhosis status, or who received off-label treatment. Patients had HCV GT1a (n = 732; 19%), GT1b (n = 2619; 69%) or GT4 (n = 457; 12%). In 3546 patients with sufficient follow-up data at post-treatment Week 12, the SVR12 rate was 96% (n/N = 3401/3546 [95% CI 95.2-96.5]). In patients with or without cirrhosis, SVR12 was comparable (96%). In patients with HCV GT1a, GT1b or GT4, SVR12 rates were 93%, 97% and 94%. In GT1b-infected patients with planned treatment for 8 weeks, SVR12 was 96%. In patients with ≥1 comorbidity (67%), SVR12 was 95%. 58% of patients received ≥1 comedication, and there was minimal impact on SVR12 rates using comedications for peptic ulcers and gastro-esophageal reflux disease, statins, antipsychotics or antiepileptics. Most comedications were maintained during treatment although 58% of patients changed their statin medication. AEs and serious AEs occurred in 26% and 3% of patients. Post-baseline Grade 3-4 laboratory abnormalities were rare (<3%), and discontinuation rates were low (<4%). Real-world evidence confirms the effectiveness of OBV/PTV/r ± DSV ± RBV in patients with HCV GT1 or GT4, regardless of common comorbidities or comedications, and is consistent with clinical trial results. Adverse safety outcomes may be limited by underreporting in the real-world setting.

In December 2019, an outbreak of a novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) began, resulting in a number of antivirals and immune modulators being repurposed to treat the associated coronavirus disease 2019 (COVID-19). Many patients requiring treatment for COVID-19 may have either pre-existing renal or hepatic disease or experience acute renal/hepatic injury as a result of the acute infection. Altered renal or hepatic function can significantly affect drug concentrations of medications due to impaired drug metabolism and excretion, resulting in toxicity or reduced efficacy. The aim of this paper is to review the pharmacokinetics and available study data for the experimental COVID-19 therapies in patients with any degree of renal or hepatic impairment to make recommendations for dosing.