Therapeutic drug monitoring and dosage adjustments of immunosuppressive drugs when combined with nirmatrelvir/ritonavir in patients with COVID-19.

ABSTRACT: Nirmatrelvir/ritonavir (Paxlovid®) consists of a peptidomimetic inhibitor (Nirmatrelvir) of the SARS-CoV-2 main protease and a pharmacokinetic enhancer (Ritonavir). It is approved for the treatment of mild-to-moderate COVID-19. This combination of nirmatrelvir and ritonavir can mediate significant and complex drug-drug interactions (DDIs), primarily due to the ritonavir component. Indeed, ritonavir inhibits the metabolism of nirmatrelvir through cytochrome P450 3A (CYP3A) leading to higher plasma concentrations and a longer half-life of nirmatrelvir. Co-administration of nirmatrelvir/ritonavir with immunosuppressant drugs (ISDs) is particularly challenging given the major involvement of CYP3A in the metabolism of most of these drugs and their narrow therapeutic ranges. Exposure of ISDs will be drastically increased through the potent ritonavir-mediated inhibition of CYP3A, resulting in an increased risk of adverse drug reactions. While a decrease in the dosage of ISDs can prevent toxicity, an inappropriate dosage regimen may also result in insufficient exposure and a risk of rejection. Here we provide some general recommendations for therapeutic drug monitoring (TDM) of ISDs and dosing recommendations when co-administered with nirmatrelvir/ritonavir. Particularly, tacrolimus should be discontinued, or patients should be given a microdose on day-1, while cyclosporine dosage should be reduced to 20% of the initial dosage during the antiviral treatment. Dosages of mammalian target of rapamycin inhibitors (m-TORis) should also be adjusted while dosages of mycophenolic acid and corticosteroids are expected to be less impacted.

Quantifying prognosis severity of COVID-19 patients from deep learning based analysis of CT chest images.

The COVID-19 pandemic has affected all the countries in the world with its droplet spread mode. The colossal amount of cases has strained all the healthcare systems due to the serious nature of infections especially for people with comorbidities. A very high specificity Reverse Transcriptase-Polymerase Chain Reaction (RT-PCR) test is the principal technique in use for diagnosing the COVID-19 patients. Also, CT scans have helped medical professionals in patient severity estimation & progression tracking of COVID-19 virus. In study we present our own extensible COVID-19 viral infection tracking prognosis technique. It uses annotated dataset of CT chest scan slice images created with the help of medical professionals. The annotated dataset contains bounding box coordinates of different features for COVID-19 detection like ground glass opacities, crazy paving pattern, consolidations, lesions etc. We qualitatively identify the severity of the patient for later prognosis stages in our study to assist medical staff for patient prioritization. First we detected COVID-19 positive patients with pre-trained Siamese Neural Network (SNN) which obtained 87.6% accuracy, 87.1% F1-Score & 95.1% AUC scores. These metrics were achieved after removal of 40% quantitatively highly similar images from the COVID-CT dataset. This reduced dataset was further medically annotated with COVID-19 features for bounding box detection. After this we assigned severity scores to detected COVID-19 features and calculated the cumulative severity score for COVID-19 patients. For qualitative patient prioritization with prognosis clinical assistance information, we finally converted this score into a multi-classification problem which obtained 47% weighted-average F1-score.