Drug-drug interactions (DDIs) occur commonly and may lead to severe adverse drug reactions if not handled appropriately. Considerable information to support clinical decision making regarding potential DDIs is available in the literature and through various systems providing electronic decision support for healthcare providers. The challenge for the prescribing physician lies in sorting out the evidence and identifying those drugs for which potential interactions are likely to become clinically manifest. P-glycoprotein (P-gp) is a drug transporting protein that is found in the plasma membranes in cells of barrier and elimination organs, and plays a role in drug absorption and excretion. Increasingly, P-gp has been acknowledged as an important player in potential DDIs and a growing body of information on the role of this transporter in DDIs has become available from research and from the drug approval process. This has led to a clear need for a comprehensive review of P-gp-mediated DDIs with a focus on highlighting the drugs that are likely to lead to clinically relevant DDIs. The objective of this review is to provide information for identifying and interpreting evidence of P-gp-mediated DDIs and to suggest a classification for individual drugs based on both in vitro and in vivo evidence (substrates, inhibitors and inducers). Further, various ways of handling potential DDIs in clinical practice are described and exemplified in relation to drugs interfering with P-gp.

Patients with severe COVID-19 have overwhelmed healthcare systems worldwide. We hypothesized that machine learning (ML) models could be used to predict risks at different stages of management and thereby provide insights into drivers and prognostic markers of disease progression and death. From a cohort of approx. 2.6 million citizens in Denmark, SARS-CoV-2 PCR tests were performed on subjects suspected for COVID-19 disease; 3944 cases had at least one positive test and were subjected to further analysis. SARS-CoV-2 positive cases from the United Kingdom Biobank was used for external validation. The ML models predicted the risk of death (Receiver Operation Characteristics—Area Under the Curve, ROC-AUC) of 0.906 at diagnosis, 0.818, at hospital admission and 0.721 at Intensive Care Unit (ICU) admission. Similar metrics were achieved for predicted risks of hospital and ICU admission and use of mechanical ventilation. Common risk factors, included age, body mass index and hypertension, although the top risk features shifted towards markers of shock and organ dysfunction in ICU patients. The external validation indicated fair predictive performance for mortality prediction, but suboptimal performance for predicting ICU admission. ML may be used to identify drivers of progression to more severe disease and for prognostication patients in patients with COVID-19. We provide access to an online risk calculator based on these findings.

Purpose:The aim of this study was to explore whether general practitioners (GPs) experienced barriers toward medication reviews in polymedicated, multimorbid patients, and how a clinical pharmacologist with a focus on pharmacotherapy can support the GPs in an outpatient clinic.Design:The study was descriptive and exploratory and had a qualitative design with a phenomenological/hermeneutic orientation for the interviews.Participants:The study comprised 14 interviews with 14 different GPs from the Capital Region of Denmark.Results:Three themes emerged from the interviews: (1) The care of patients With polypharmacy is challenged by the lack of professional dialogue and collaboration between GPs and hospital-based clinical pharmacologists, (2) the relationship between the patients with polypharmacy and the GP is characterized by care and individual considerations, and (3) the culture encourages adding medication and inhibits dialogue about medication withdrawal even for patients with polypharmacy.Conclusion and implications for practice:This study found that the primary barriers toward multimorbid patients with polypharmacy were the need for communication and teamwork with specialists (cardiologists, neurologists, endocrinologists, etc). Often, GPs felt that the specialists at the hospitals were more concerned about following standards and guidelines regarding specific diseases instead of a more holistic patient approach. To improve management of polypharmacy patients, the GPs suggest that a joint force is necessary, a partner-like relationship with greater transparency regarding information transfer, feedback, and shared decision-making, but also more education in the pharmacological field is essential.