We conducted a randomized, open-label, parallel-controlled, multicenter trial on the use of Shuanghuanglian (SHL), a traditional Chinese patent medicine, in treating cases of COVID-19. A total of 176 patients received SHL by three doses (56 in low dose, 61 in middle dose, and 59 in high dose) in addition to standard care. The control group was composed of 59 patients who received standard therapy alone. Treatment with SHL was not associated with a difference from standard care in the time to disease recovery. Patients with 14-day SHL treatment had significantly higher rate in negative conversion of SARS-CoV-2 in nucleic acid swab tests than the patients from the control group (93.4% vs. 73.9%, P = 0.006). Analysis of chest computed tomography images showed that treatment with high-dose SHL significantly promoted absorption of inflammatory focus of pneumonia, which was evaluated by density reduction of inflammatory focus from baseline, at day 7 (mean difference (95% CI), -46.39 (-86.83 to -5.94) HU; P = 0.025) and day 14 (mean difference (95% CI), -74.21 (-133.35 to -15.08) HU; P = 0.014). No serious adverse events occurred in the SHL groups. This study illustrated that SHL in combination with standard care was safe and partially effective for the treatment of COVID-19.
COVID-19 ; Humans ; Medicine, Chinese Traditional ; Research ; SARS-CoV-2 ; Treatment Outcome

Model informed precision dosing (MIPD) is a new concept to guide precision dosing for individual patient by modeling and simulation based on the available information about the individual patient, medications and the disease. Compared to the empirical dosing, MIPD could improve the efficacy, safety, economics and adherence of the pharmacotherapy according to the individual's pathophysiology, genotyping and disease progression. This consensus report provides a brief account of the concept, methodology and implementation of MIPD as well as clinical decision supporting systems for MIPD. The status and future advancing of MIPD was also discussed to facilitate the appropriate application and development of MIPD in China.

The clinical trial of OBCA (OCALIVA) in the treatment of primary biliary cirrhosis (PBC) shows its efficacy. As it is difficult to conduct sufficient clinical studies in moderate and severe hepatic impairment population, the applicant and the FDA theorem have different opinions based on the same model, such as population PK, exposure-response and physiologically-based PK (PBPK). The applicant considers that the increase in the exposure of drug in liver tissue is limited, and there is no need for dose adjustment, that is, 5 mg, once a day. FDA believes that the influencing factors of the PBPK model have not been fully taken into account and the validation of the PBPK is not robust with a wide variability, and there is also a risk of high blood drug exposure in patients. It is recommended to significantly reduce the dose, that is, 5 mg, once a week, no more than 10 mg, per week at least 3 days interval, and accordingly written into the medication instructions. After approval many patients with hepatic impairment did not take medicine according to the instructions, therefore overdosed, resulting in death. The results fully prove that the original considerations and decisions of FDA have been verified, and the experience and lessons of this example once again suggest that modeling and simulation need bold assumptions and careful verification.

AIM: Belimumab is a fully humanized IgGl-X monoclonal antibody, which can specifically bind to soluble B cell stimulating factor (BLyS) preventing BLyS from binding to B cells to promote B cell apoptosis. It is the first drug approved by the FDA for the treatment of systemic lupus erythematosus (SLE). Based on data from global clinical literature of the drug, this study evaluated the effect of belimumab in the treatment of SLE through modeling analysis, quantitatively expressed its pharmacodynamic characteristics, and explored its potential influencing factors to establish the efficacy of belimumab in the treatment of SLE, which could provide a reference for the development of drugs for the treatment of SLE. METHODS: Literature retrieval was conducted in Pubmed database. The clinical studies of belimumab in the treatment of SLE were included. Data, including the demography and baseline characteristics, dosage, administration methods, efficacy and safety of belimumab, was extracted to establish an analysis sets. Then a pharmacodynamic model was developed to evaluate the pharmacodynamic characteristics of the drug. The robustness of the model was evaluated via a variety of model evaluation methods. RESULTS: A total of 5 articles containing efficacy data were included in this analysis, involving 11 dosage groups (3 493 subjects). The results of covariate screening showed that race (Asian population or non-Asian population), age, course of disease, positive anti-dsDNA had no significant effect on the therapeutic effect of belimumab in SLE and there were no factors that had significant influence on model parameters. Model evaluation showed that the model established in this study can better describe the dose-effect relationship of belimumab in the treatment of SLE. The final model indicated that the response rate of the efficacy index SRI was close to the peak (approximately 99% of the peak level) at the 52nd week. The SRI response rates of placebo, belimumab 1 mg/kg intravenous injection, 10 mg/kg intravenous injection, and 200 mg subcutaneous injection were 46.1%, 52.9%, 57.9%, and 60.9%, respectively. After deducting the placebo effect, the SRI response rates (drug pure effect) of belimumab 1 mg/kg intravenously, 10 mg/kg intravenously, and 200 mg subcutaneously at 52 weeks were 6.8%, 11.8%, and 14.8%, respectively. CONCLUSION: The efficacy (SRI response rate) of belimumab in the treatment of SLE patients was close to its peak in the 52nd week. The SRI response rates of belimumab 1 mg/kg intravenously, 10 mg/kg intravenously, and 200 mg subcutaneously in the 52nd week were 52.9%, 57.9%, and 60.9%, respectively.

AIM: To study the ways of calculating area under the plasma concentration-time curve (AUC) in non-compartment model pharmacokinetics by comparing the accuracy & precision of every method. METHODS: Three methods were used to calculate the area under 0-T curve (AUC

With the increasing cost of drug development and clinical trials, it is of great value to make full use of all kinds of data to improve the efficiency of drug development and to provide valid information for medication guidelines. Model-based meta-analysis (MBMA) combines mathematical models with meta-analysis to integrate information from multiple sources (preclinical and clinical data, etc.) and multiple dimensions (targets/mechanisms, pharmacokinetics/pharmacodynamics, diseases/indications, populations, regimens, biomarkers/efficacy/safety, etc.), which not only provides decision-making for all key points of drug development, but also provides effective information for rational drug use and cost-effectiveness analysis. The classical meta-analysis requires high homogeneity of the data, while MBMA can combine and analyze the heterogeneous data of different doses, different time courses, and different populations through modeling, so as to quantify the dose-effect relationship, time-effect relationship, and the relevant impact factors, and thus the efficacy or safety features at the level of dose, time and covariable that have not been involved in previous studies. Although the modeling and simulation methods of MBMA are similar to population pharmacokinetics/pharmacodynamics (Pop PK/PD), compared with Pop PK/PD, the advantage of MBMA is that it can make full use of literature data, which not only improves the strength of evidence, but also can answer the questions that have not been proved or can not be answered by a single study. At present, MBMA has become one of the important methods in the strategy of model-informed drug development (MIDD). This paper will focus on the application value, data analysis plan, data acquisition and processing, data analysis and reporting of MBMA, in order to provide reference for the application of MBMA in drug development and clinical practice.

This study was to determine the correlation between endothelial function and neuro-endocrine-immune (NEI) network through observing the changes of NEI network under the different endothelial dysfunction models. Three endothelial dysfunction models were established in male Wistar rats after exposure to homocysteine (Hcy), high fat diet (HFD) and Hcy+HFD. The results showed that there was endothelial dysfunction in all three models with varying degrees. However, the expression of NEI network was totally different. Interestingly, treatment with simvastatin was able to improve vascular endothelial function and restored the imbalance of the NEI network, observed in the Hcy+HFD group. The results indicated that NEI network may have a strong association with endothelial function, and this relationship can be used to distinguish different risk factors and evaluate drug effects.
Animals ; Diet, High-Fat ; Endocrine System ; Homocysteine ; Humans ; Immune System ; Male ; Nervous System ; Rats* ; Rats, Wistar ; Risk Factors ; Simvastatin

Comparative pharmacokinetic (PK) analysis is often carried out throughout the entire period of drug development, the common approach for the assessment of pharmacokinetics between different treatments requires that the individual PK parameters, which employs estimation of 90% confidence intervals for the ratio of average parameters, such as AUC and Cmax, these 90% confidence intervals then need to be compared with the pre-specified equivalent interval, and last we determine whether the two treatments are equivalent. Unfortunately in many clinical circumstances, some or even all of the individuals can only be sparsely sampled, making the individual evaluation difficult by the conventional non-compartmental analysis. In such cases, nonlinear mixed effect model (NONMEM) could be applied to analyze the sparse data. In this article, we simulated a sparsely sampling design trial based on the dense sampling data from a truly comparative PK study. The sparse data were analyzed with NONMEM method, and the original dense data were analyzed with non-compartment analysis. Although the trial design and analysis methods are different, the 90% confidence intervals for the ratio of PK parameters based on 1000 Bootstrap are very similar, indicated that the analysis based on NONMEM is a reliable method to treat with the sparse data in the comparative pharmacokinetic study.

The paper aimed to find the optimal combination and evaluation of the interactions of antitumor effect of the curcumin (Cur) and adriamycin (ADM) in vitro. According to the factorial design and data characteristics, the parameter method combined with the response surface approach were used to analyze the pharmacodynamic interactions of in vitro antitumor effects of the combination of Cur and ADM at different dosages. The results showed that the dose-effect relationship of the combination with the ratio of ADM-Cur 1:3 showed significant differences in comparison with either used alone. The dose-effect curve was shift left in combination. The combination of adriamycin (ADM, 0.693-2.132 micromol L(-1)) and curcumin (Cur, 2.047-6.304 micromol L(-1)) with a fixed ratio (1:3) showed a synergism. With increasing doses of the combination, there is an additive effect. Computer simulation showed a trend of decreasing difference between the observed and expected effects with the dose increasing in Cur from 6.304 to 16.0 micromol L(-1) and ADM from 2.132 to 5.3 micromol L(-1). The response surface analysis showed the optimal combination to be Cur 18.50 micromol L(-1) and ADM 3.89 micromol L(-1) with a ratio of 5:1. This study suggests that the parameter method combined with the response surface analysis provides richer and more reasonable information, and is helpful for quantitative design of drug combination therapy and to describe the nature and degree of drug interaction.

To study the anti-inflammatory effects of the combinations of active components of Painong powder, a compound traditional Chinese herbal medicine, and the quantitative analysis of their interactions.