Comparative assessment of viral dynamic models for SARS-CoV-2 for pharmacodynamic assessment in early treatment trials.

Pharmacometric analyses of time series viral load data may detect drug effects with greater power than approaches using single time points. Because SARS-CoV-2 viral load rapidly rises and then falls, viral dynamic models have been used. We compared different modelling approaches when analysing Phase II-type viral dynamic data. Using two SARS-CoV-2 datasets of viral load starting within 7 days of symptoms, we fitted the slope-intercept exponential decay (SI), reduced target cell limited (rTCL), target cell limited (TCL) and TCL with eclipse phase (TCLE) models using nlmixr. Model performance was assessed via Bayesian information criterion (BIC), visual predictive checks (VPCs), goodness-of-fit plots, and parameter precision. The most complex (TCLE) model had the highest BIC for both datasets. The estimated viral decline rate was similar for all models except the TCL model for dataset A with a higher rate (median [range] day-1 : dataset A; 0.63 [0.56-1.84]; dataset B: 0.81 [0.74-0.85]). Our findings suggest simple models should be considered during pharmacodynamic model development.

Personalise vitamin D3 using physiologically based pharmacokinetic modelling.

Plasma concentration of vitamin D3 metabolite 25-hydroxyvitamin D3 (25(OH)D3 ) is variable among individuals. The objective of this study is to establish an accurate model for 25(OH)D3 pharmacokinetics (PKs) to support selection of a suitable dose regimen for an individual. We collated vitamin D3 and 25(OH)D3 plasma PK data from reported clinical trials and developed a physiologically-based pharmacokinetic (PBPK) model to appropriately recapitulate training data. Model predictions were then qualified with 25(OH)D3 plasma PKs under vitamin D3 and 25(OH)D3 dose regimens distinct from training data. From data exploration, we observed the increase in plasma 25(OH)D3 after repeated dosing was negatively correlated with 25(OH)D3 baseline levels. Our final model included a first-order vitamin D3 absorption, a first-order vitamin D3 metabolism, and a nonlinear 25(OH)D3 elimination function. This structure explained the apparent paradox. Remarkably, the model accurately predicted plasma 25(OH)D3 following repeated dosing up to 1250 µg/d in the test set. It also made sensible predictions for large single vitamin D3 doses up to 50,000 µg in the test set. Model predicts 10 µg/d regimen may be ineffective for achieving sufficiency (plasma 25(OH)D3 ≥ 75 nmol/L) for a severely deficient individual (baseline 25(OH)D3 = 10 nmol/L), and it might take the same person over 200 days to reach sufficiency at 20 µg/d dose. We propose to personalize vitamin D3 supplementation protocol with this PBPK model. It would require measuring 25(OH)D3 baseline levels, which is not routinely performed under the current UK public health advice. STUDY HIGHLIGHTS: WHAT IS THE CURRENT KNOWLEDGE ON THE TOPIC? Vitamin D PK exhibits substantial inter-individual variability. Different officially recommended daily doses are confusing. ​ WHAT QUESTION DID THIS STUDY ADDRESS? Is the UK's recommended 10 µg daily dose sufficient? Should everyone be given same dose? ​ WHAT DOES THIS STUDY ADD TO OUR KNOWLEDGE? Our model accurately predicts plasma 25(OH)D under daily oral administration of vitamin D3 . The 10 µg daily vitamin D3 dose is insufficient for prophylaxis (plasma 25(OH)D at 75 nmol/L). ​ HOW MIGHT THIS CHANGE DRUG DISCOVERY, DEVELOPMENT, AND/OR THERAPEUTICS? Combining blood test to measure 25(OH)D baseline with this PBPK model will help inform dosage selection and select follow-up date to improve effectiveness of Hypovitaminosis D treatment.

The clinical classification of patients with COVID-19 pneumonia was predicted by Radiomics using chest CT.

ABSTRACT: In 2020, the new type of coronal pneumonitis became a pandemic in the world, and has firstly been reported in Wuhan, China. Chest CT is a vital component in the diagnostic algorithm for patients with suspected or confirmed COVID-19 infection. Therefore, it is necessary to conduct automatic and accurate detection of COVID-19 by chest CT.The clinical classification of patients with COVID-19 pneumonia was predicted by Radiomics using chest CT.From the COVID-19 cases in our institution, 136 moderate patients and 83 severe patients were screened, and their clinical and laboratory data on admission were collected for statistical analysis. Initial CT Radiomics were modeled by automatic machine learning, and diagnostic performance was evaluated according to AUC, TPR, TNR, PPV and NPV of the subjects. At the same time, the initial CT main features of the two groups were analyzed semi-quantitatively, and the results were statistically analyzed.There was a statistical difference in age between the moderate group and the severe group. The model cohort showed TPR 96.9%, TNR 99.1%, PPV98.4%, NPV98.2%, and AUC 0.98. The test cohort showed TPR 94.4%, TNR100%, PPV100%, NPV96.2%, and AUC 0.97. There was statistical difference between the two groups with grade 1 score (P = .001), the AUC of grade 1 score, grade 2 score, grade 3 score and CT score were 0.619, 0.519, 0.478 and 0.548, respectively.Radiomics' Auto ML model was built by CT image of initial COVID -19 pneumonia, and it proved to be effectively used to predict the clinical classification of COVID-19 pneumonia. CT features have limited ability to predict the clinical typing of Covid-19 pneumonia.

The study of automatic machine learning base on radiomics of non-focus area in the first chest CT of different clinical types of COVID-19 pneumonia.

To explore the possibility of predicting the clinical types of Corona-Virus-Disease-2019 (COVID-19) pneumonia by analyzing the non-focus area of the lung in the first chest CT image of patients with COVID-19 by using automatic machine learning (Auto-ML). 136 moderate and 83 severe patients were selected from the patients with COVID-19 pneumonia. The clinical and laboratory data were collected for statistical analysis. The texture features of the Non-focus area of the first chest CT of patients with COVID-19 pneumonia were extracted, and then the classification model of the first chest CT of COVID-19 pneumonia was constructed by using these texture features based on the Auto-ML method of radiomics, The area under curve(AUC), true positive rate(TPR), true negative rate (TNR), positive predictive value(PPV) and negative predictive value (NPV) of the operating characteristic curve (ROC) were used to evaluate the accuracy of the first chest CT image classification model in patients with COVID-19 pneumonia. The TPR, TNR, PPV, NPV and AUC of the training cohort and test cohort of the moderate group and the control group, the severe group and the control group, the moderate group and the severe group were all greater than 95% and 0.95 respectively. The non-focus area of the first CT image of COVID-19 pneumonia has obvious difference in different clinical types. The AUTO-ML classification model of Radiomics based on this difference can be used to predict the clinical types of COVID-19 pneumonia.