Model-Informed Precision Dosing of Isoniazid: Parametric Population Pharmacokinetics Model Repository.

Introduction: Isoniazid (INH) is a crucial first-line anti tuberculosis (TB) drug used in adults and children. However, various factors can alter its pharmacokinetics (PK). This article aims to establish a population pharmacokinetic (popPK) models repository of INH to facilitate clinical use. Methods: A literature search was conducted until August 23, 2022, using PubMed, Embase, and Web of Science databases. We excluded published popPK studies that did not provide full model parameters or used a non-parametric method. Monte Carlo simulation works was based on RxODE. The popPK models repository was established using R. Non-compartment analysis was based on IQnca. Results: Fourteen studies included in the repository, with eleven studies conducted in adults, three studies in children, one in pregnant women. Two-compartment with allometric scaling models were commonly used as structural models. NAT2 acetylator phenotype significantly affecting the apparent clearance (CL). Moreover, postmenstrual age (PMA) influenced the CL in pediatric patients. Monte Carlo simulation results showed that the geometric mean ratio (95% Confidence Interval, CI) of PK parameters in most studies were within the acceptable range (50.00-200.00%), pregnant patients showed a lower exposure. After a standard treatment strategy, there was a notable exposure reduction in the patients with the NAT2 RA or nonSA (IA/RA) phenotype, resulting in a 59.5% decrease in AUC0-24 and 83.2% decrease in Cmax (Infants), and a 49.3% reduction in AUC0-24 and 73.5% reduction in Cmax (Adults). Discussion: Body weight and NAT2 acetylator phenotype are the most significant factors affecting the exposure of INH. PMA is a crucial factor in the pediatric population. Clinicians should consider these factors when implementing model-informed precision dosing of INH. The popPK model repository for INH will aid in optimizing treatment and enhancing patient outcomes.

Modeling the complexity of drug-drug interactions: A physiologically-based pharmacokinetic study of Lenvatinib with Schisantherin A/Schisandrin A.

BACKGROUND: Lenvatinib's efficacy as a frontline targeted therapy for radioactive iodine-refractory thyroid carcinoma and advanced hepatocellular carcinoma owes to its inhibition of multiple tyrosine kinases. However, as a CYP3A4 substrate, lenvatinib bears susceptibility to pharmacokinetic modulation by co-administered agents. Schisantherin A (STA) and schisandrin A (SIA) - bioactive lignans abundant in the traditional Chinese medicinal Wuzhi Capsule - act as CYP3A4 inhibitors, engendering the potential for drug-drug interactions (DDIs) with lenvatinib. METHODS: To explore potential DDIs between lenvatinib and STA/SIA, we developed a physiologically-based pharmacokinetic (PBPK) model for lenvatinib and used it to construct a DDI model for lenvatinib and STA/SIA. The model was validated with clinical trial data and used to predict changes in lenvatinib exposure with combined treatment. RESULTS: Following single-dose administration, the predicted area under the plasma concentration-time curve (AUC) and maximum plasma concentrations (Cmax) of lenvatinib increased 1.00- to 1.03-fold and 1.00- to 1.01-fold, respectively, in the presence of STA/SIA. Simulations of multiple-dose regimens revealed slightly greater interactions, with lenvatinib AUC0-t and Cmax increasing up to 1.09-fold and 1.02-fold, respectively. CONCLUSION: Our study developed the first PBPK and DDI models for lenvatinib as a victim drug. STA and SIA slightly increased lenvatinib exposure in simulations, providing clinically valuable information on the safety of concurrent use. Given the minimal pharmacokinetic changes, STA/SIA are unlikely to interact with lenvatinib through pharmacokinetic alterations synergistically but rather may enhance efficacy through inherent anti-cancer efficacy of STA/ SIA.

Escitalopram population pharmacokinetics and remedial strategies based on CYP2C19 phenotype.

BACKGROUND AND PURPOSE: CYP2C19 is a key factor influencing escitalopram (SCIT) exposure. However, different studies reported various results. This study aims to develop a population pharmacokinetic (popPK) model characterizes the disposition of SCIT in the Chinese population. Based on the popPK model, the study simulates non-adherence scenarios and proposes remedial strategies to facilitate SCIT personalized therapy. METHODS: Nonlinear mixed-effects modeling using data from two Chinese bioequivalence studies was employed. Monte-Carlo simulation was used to explore non-adherence scenarios and propose remedial strategies based on the proportion of time within the therapeutic window. RESULTS: Results showed that a one-compartment model with transit absorption and linear elimination described the data well, CYP2C19 phenotypes and weight were identified as significant covariates impacting SCIT exposure. Patients were recommended to take the entire delayed dose immediately if the delay time was no >12 h, followed by the regular regimen at the next scheduled time. When there is one or two doses missed, taking a double dose immediately was recommended to the CYP2C19 intermediate and extensive population, and a 1.5-fold dose was recommended to the CYP2C19 poor metabolizers with the consideration of adverse effects. LIMITATION: All samples were derived from the homogenized Chinese healthy population for model building, which may pose certain constraints on the ability to identify significant covariates, such as age. CONCLUSION: The study highlights the importance of considering patient characteristics for personalized medication and offers a unique perspective on utilizing the popPK repository in precision dosing.

Escitalopram Personalized Dosing: A Population Pharmacokinetics Repository Method.

Escitalopram (SCIT) represents a first-line antidepressant and antianxiety medication. Pharmacokinetic studies of SCIT have demonstrated considerable interindividual variability, emphasizing the need for personalized dosing. Accordingly, we aimed to create a repository of parametric population pharmacokinetic (PPK) models of SCIT to facilitate model-informed precision dosing. In November 2022, we searched PubMed, Embase, and Web of Science for published PPK models and identified eight models. All the structural models reported in the literature were either one- or two-compartment models. In order to investigate the variances in model performance, the parameters of all PPK models were derived from the literature published. A representative virtual population, characterized by an age of 30, a body weight of 70 kg, and a BMI of 23 kg/m2, was generated for the purpose of replicating these models. To accomplish this, the rxode2 package in the R programming language was employed. Subsequently, we compared simulated concentration-time profiles and evaluated the impact of covariates on clearance. The most significant covariates were CYP2C19 phenotype, weight, and age, indicating that dosing regimens should be tailored accordingly. Additionally, among Chinese psychiatric patients, SCIT showed nearly double the exposure compared to other populations, specifically when considering the same CYP2C19 population restriction, which is a knowledge gap that needs further investigation. Furthermore, this repository of parametric PPK models for SCIT has a wide range of potential applications, like design miss or delay dose remedy strategies and external PPK model validation.

Population pharmacokinetics of Amisulpride in Chinese patients with schizophrenia with external validation: the impact of renal function.

Introduction: Amisulpride is primarily eliminated via the kidneys. Given the clear influence of renal clearance on plasma concentration, we aimed to explicitly examine the impact of renal function on amisulpride pharmacokinetics (PK) via population PK modelling and Monte Carlo simulations. Method: Plasma concentrations from 921 patients (776 in development and 145 in validation) were utilized. Results: Amisulpride PK could be described by a one-compartment model with linear elimination where estimated glomerular filtration rate, eGFR, had a significant influence on clearance. All PK parameters (estimate, RSE%) were precisely estimated: apparent volume of distribution (645 L, 18%), apparent clearance (60.5 L/h, 2%), absorption rate constant (0.106 h-1, 12%) and coefficient of renal function on clearance (0.817, 10%). No other significant covariate was found. The predictive performance of the model was externally validated. Covariate analysis showed an inverse relationship between eGFR and exposure, where subjects with eGFR= 30 mL/min/1.73 m2 had more than 2-fold increase in AUC, trough and peak concentration. Simulation results further illustrated that, given a dose of 800 mg, plasma concentrations of all patients with renal impairment would exceed 640 ng/mL. Discussion: Our work demonstrated the importance of renal function in amisulpride dose adjustment and provided a quantitative framework to guide individualized dosing for Chinese patients with schizophrenia.

Prediction of Drug-Drug Interactions with Ensartinib as a Time-Dependent CYP3A Inhibitor Using Physiologically Based Pharmacokinetic Model.

Ensartinib (X-396) is a second-generation anaplastic lymphoma kinase (ALK) tyrosine kinase inhibitor (TKI) indicated for the treatment of ALK-positive patients with locally advanced or metastatic non-small cell lung cancer. Although in vitro experiments and molecular docking suggested its potential as a cytochrome P450 inhibitor, no further investigation or clinical trials have been conducted to assess its drug-drug interaction (DDI) risk. In this study, we conducted a series of in vitro experiments to elucidate the inhibition mechanism of ensartinib. Furthermore, a physiologically-based pharmacokinetic (PBPK) model was developed based on in vitro, in silico, and in vivo parameters, verified using clinical data, and applied to predict the clinical DDI mediated by ensartinib. The in vitro incubation experiments suggested that ensartinib exhibited strong time-dependent inhibition. Simulation results from the PBPK model indicated a significant increase in the exposure of CYP3A substrates in the presence of ensartinib, with the maximal plasma concentration and area under the plasma concentration-time curve increasing up to 12-fold and 29-fold for sensitive substrates. Based on these findings, it is evident that co-administration of ensartinib and CYP3A substrates requires careful regulatory consideration. SIGNIFICANCE STATEMENT: Ensartinib was found to be a strong time-dependent inhibitor of CYP3A for the first time based on in vitro experiments, but there was no research conducted to estimate the risk of drug-drug interaction (DDI) of ensartinib in clinic. Therefore, the first ensartinib physiologically based pharmacokinetic model was developed and applied to predict various untested scenarios. The simulation result indicated that the exposure of CYP3A substrate increased significantly and urged the further clinical DDI study.

Establishment and Evaluation of a Parametric Population Pharmacokinetic Model Repository for Ganciclovir and Valganciclovir.

BACKGROUND: Ganciclovir and valganciclovir are used for prophylaxis and treatment of cytomegalovirus infection. However, there is great interindividual variability in ganciclovir's pharmacokinetics (PK), highlighting the importance of individualized dosing. To facilitate model-informed precision dosing (MIPD), this study aimed to establish a parametric model repository of ganciclovir and valganciclovir by summarizing existing population pharmacokinetic information and analyzing the sources of variability. (2) Methods: A total of four databases were searched for published population PK models. We replicated these models, evaluated the impact of covariates on clearance, calculated the probability of target attainment for each model based on a predetermined dosing regimen, and developed an area under the concentration-time curve (AUC) calculator using maximum a posteriori Bayesian estimation. (3) Results: A total of 16 models, one- or two-compartment models, were included. The most significant covariates were body size (weight and body surface area) and renal function. The results show that 5 mg/kg/12 h of ganciclovir could make the AUC0-24h within 40-80 mg·h/L for 50.03% pediatrics but cause AUC0-24h exceeding the exposure thresholds for toxicity (120 mg·h/L) in 51.24% adults. (4) Conclusions: Dosing regimens of ganciclovir and valganciclovir should be adjusted according to body size and renal function. This model repository has a broad range of potential applications in MIPD.

Population pharmacokinetics of buprenorphine and naloxone sublingual combination in Chinese healthy volunteers and patients with opioid use disorder: Model-based dose optimization.

The sublingual combination of buprenorphine (BUP) and naloxone (NLX) is a new treatment option for opioid use disorder (OUD) and is effective in preventing drug abuse. This study aimed to explore rational dosing regimen for OUD patients in China via a model-based dose optimization approach. BUP, norbuprenorphine (norBUP), and NLX plasma concentrations of 34 healthy volunteers and 12 OUD subjects after single or repeated dosing were included. A parent-metabolite population pharmacokinetics (popPK) model with transit compartments for absorption was implemented to describe the pharmacokinetic profile of BUP-norBUP. In addition, NLX concentrations were well captured by a one-compartment popPK model. Covariate analysis showed that every additional swallow after the administration within the observed range (0-12) resulted in a 3.5% reduction in BUP bioavailability. This provides a possible reason for the less-than-dose proportionality of BUP. There were no differences in the pharmacokinetic characteristics between BUP or NLX in healthy volunteers and OUD subjects. Ethnic sensitivity analysis demonstrated that the dose-normalized peak concentration and area-under-the-curve of BUP in Chinese were about half of Puerto Ricans, which was consistent with a higher clearance observed in Chinese (166 L / h vs. 270 L / h ). Furthermore, Monte Carlo simulations showed that an 8 mg three-times daily dose was the optimized regimen for Chinese OUD subjects. This regimen ensured that opioid receptor occupancy remained at a maximum (70%) in more than 95% of subjects, at the same time, with NLX plasma concentrations below the withdrawal reaction threshold (4.6  n g / m L ).

Evaluating the impact of co-administered drug and disease on ripretinib exposure: A physiologically-based pharmacokinetic modeling approach.

Ripretinib, as an oral kinase inhibitor, has been approved to treat advanced gastrointestinal stromal tumors (GIST) and is often used in combination with other drugs to slow disease progression, thus potential drug-drug Interactions (DDIs) and drug-disease interactions (DDZIs) have received much attention. To guide clinical rational drug use, this study assessed the effect of co-administered drugs and diseases on ripretinib exposure. Simcyp® Simulator was used to develop the physiologically-based pharmacokinetic (PBPK) model of ripretinib, which was validated and refined with clinical data. We then examined the impact of several CYP3A4 inhibitors and inducers as well as different diseases on ripretinib exposure using the validated model. In the DDI simulation, moderate CYP3A4 inhibitors and inducers changed the exposure of ripretinib by 1.25-2 fold. In hepatic impairment (HI), the simulation showed that ripretinib's AUC increased by 32%, 100%, and 152% for Child-Pugh A, B, and C classification while Cmax increased by 2%, 10%, and 15%, respectively. In renal impairment (RI), the model-simulated AUC in moderate and severe RIs increased by 27% and 20%. In conclusion, PBPK models demonstrated quantitative prediction of ripretinib's pharmacokinetic changes under varying conditions that might be useful for its rational use.

Pharmacokinetics of Novel Furoxan/Coumarin Hybrids in Rats Using LC-MS/MS Method and Physiologically Based Pharmacokinetic Model.

Novel furoxan/coumarin hybrids were synthesized, and pharmacologic studies showed that the compounds displayed potent antiproliferation activities via downregulating both the phosphatidylinositide 3-kinase (PI3K) pathway and the mitogen-activated protein kinase (MAPK) pathway. To investigate the preclinical pharmacokinetic (PK) properties of three candidate compounds (CY-14S-4A83, CY-16S-4A43, and CY-16S-4A93), liquid chromatography, in tandem with the mass spectrometry LC-MS/MS method, was developed and validated for the simultaneous determination of these compounds. The absorption, distribution, metabolism, and excretion (ADME) properties were investigated in in vitro studies and in rats. Meanwhile, physiologically based pharmacokinetic (PBPK) models were constructed using only in vitro data to obtain detailed PK information. Good linearity was observed over the concentration range of 0.01−1.0 µg/mL. The free drug fraction (fu) values of the compounds were less than 3%, and the clearance (CL) values were 414.5 ± 145.7 mL/h/kg, 2624.6 ± 648.4 mL/h/kg, and 500.6 ± 195.2 mL/h/kg, respectively. The predicted peak plasma concentration (Cmax) and the area under the concentration-time curve (AUC) were overestimated for the CY-16S-4A43 PBPK model compared with the experimental ones (fold error > 2), suggesting that tissue accumulation and additional elimination pathways may exist. In conclusion, the LC-MS/MS method was successively applied in the preclinical PK studies, and the detailed information from PBPK modeling may improve decision-making in subsequent new drug development.

Foretinib Is Effective against Triple-Negative Breast Cancer Cells MDA-MB-231 In Vitro and In Vivo by Down-Regulating p-MET/HGF Signaling.

This study investigated the antitumor effects of foretinib on triple-negative breast cancer cells MDA-MB-231 xenograft tumors in vivo underlying phosphorylated mesenchymal to epithelial transition (p-MET)/ hepatocyte growth factor (HGF)-related mechanism, as well as its pharmacokinetic characteristics. The MDA-MB-231 human breast cancer cell line was used for in vitro experiments, and the tumor xenograft model was established for in vivo experiments. MDA-MB-231 xenograft mice received oral foretinib (15 or 50 mg/kg/day) or vehicle for 18 days. The xenograft tumors were collected. Protein expressions of p-MET and HGF were examined with Western blotting and immunohistochemical staining. The mRNA expression of MET was examined with real-time PCR. Blood samples were collected from the mice treated with foretinib under different doses of 2, 10, and 50 mg/kg, and the pharmacokinetic profiles of foretinib were evaluated. We found that foretinib treatment caused a significant inhibition in tumor growth in a dose-dependent manner, whereas the continuous administration did not result in weight loss in treated nude mice. In both MDA-MB-231 cells and xenograft tumors, foretinib suppressed the expression of p-MET and HGF. These findings reveal that the decrease of p-MET and HGF may play an important role in the anti-breast cancer properties of foretinib.

Covalent Binding Mechanism of Furmonertinib and Osimertinib With Human Serum Albumin.

As third-generation tyrosine kinase inhibitors, furmonertinib and osimertinib exhibit better efficacy than first- and second-generation tyrosine kinase inhibitors in patients with advanced non-small cell lung cancer. However, radioactive pharmacokinetics studies showed that parent-related components remain in human plasma for at least 21 days after oral administration. Similar pharmacokinetic profiles were found in pyrotinib and neratinib, which have been identified to covalently bind with human serum albumin at Lys-190, leading to low extraction recovery in protein precipitation. However, the binding mechanism of furmonertinib and osimertinib in human plasma has not been confirmed. Comprehensive techniques were used to investigate the mechanism of this binding, including ultra high-performance liquid chromatography coupled with high-resolution mass spectrometry and online/offline radioactivity profiling. SDS-PAGE and further autoradiography were also used to detect drug-protein adducts. We found that most furmonertinib exists in the human plasma following ex vivo incubation in the form of protein-drug adducts. Only lysine-furmonertinb adducts were found in pronase digests. A standard reference of lysine-furmonertinib was synthesized and confirmed by NMR. Through peptide mapping analysis, we confirmed that furmonertinib almost exclusively binds with human serum albumin (HSA) in plasma following ex vivo incubation, via Michael addition at Lys-195 and Lys-199, instead of Lys-190. Two peptides found to bond with furmonertinib were ASSAKQR and LKCASLQK. Osimertinib was also found to bond with Lys-195 and Lys-199 of HSA via peptide mapping analysis. SIGNIFICANCE STATEMENT: Here we report that furmonertinib and osimertinib can covalently bind with human serum albumin at the site of Lys-195 and Lys-199 instead of Lys-190, potentially leading to the long duration of drug-protein adducts in the human body.

Utilization of Physiologically Based Pharmacokinetic Modeling in Pharmacokinetic Study of Natural Medicine: An Overview.

Natural medicine has been widely used for clinical treatment and health care in many countries and regions. Additionally, extracting active ingredients from traditional Chinese medicine and other natural plants, defining their chemical structure and pharmacological effects, and screening potential druggable candidates are also uprising directions in new drug research and development. Physiologically based pharmacokinetic (PBPK) modeling is a mathematical modeling technique that simulates the absorption, distribution, metabolism, and elimination of drugs in various tissues and organs in vivo based on physiological and anatomical characteristics and physicochemical properties. PBPK modeling in drug research and development has gradually been recognized by regulatory authorities in recent years, including the U.S. Food and Drug Administration. This review summarizes the general situation and shortcomings of the current research on the pharmacokinetics of natural medicine and introduces the concept and the advantages of the PBPK model in the study of pharmacokinetics of natural medicine. Finally, the pharmacokinetic studies of natural medicine using the PBPK models are summed up, followed by discussions on the applications of PBPK modeling to the enzyme-mediated pharmacokinetic changes, special populations, new drug research and development, and new indication adding for natural medicine. This paper aims to provide a novel strategy for the preclinical research and clinical use of natural medicine.

Real-world safety of PCSK9 inhibitors: A pharmacovigilance study based on spontaneous reports in FAERS.

Objective: We aimed to evaluate alirocumab- and evolocumab-related adverse events (AEs) in real-world compared with all other drugs, overall and by gender and age subgroups; we also aimed to compare their risks of cognitive impairment, musculoskeletal disorders and diabetes with various statins and ezetimibe. Methods: We retrospectively extracted AE reports from the FDA Adverse Event Reporting System (FAERS) database during July 2015-June 2021. Disproportionality analyses were performed using reporting odds ratios (RORs) to detect AE signals of alirocumab and evolocumab in the overall population and in different age and gender subgroups, respectively. Results: Compared with all other drugs, both alirocumab and evolocumab had a significant signal in "musculoskeletal and connective tissue disorders" (ROR1 = 2.626, 95% CI 2.552-2.702; ROR2 = 2.575, 95% CI 2.538-2.613). The highest ROR value of 2.311 (95% CI 2.272-2.351) was for "injury, poisoning and procedural complications" and was found in patients aged ≥65 years on evolocumab. The most frequent AEs were "general disorders and administration site conditions" and "musculoskeletal and connective tissue disorders" for all subpopulations. At the preferred term level, the most frequent AE signal was myalgia for alirocumab and injection site pain for evolocumab, overall and by subgroups. Compared with statins/ezetimibe, PCSK9 inhibitors exhibited lower ROR values for adverse events associated with SOC "nervous system disorders", "psychiatric disorders" and "metabolism and nutrition disorders" (all RORs < 1), but mixed results for musculoskeletal disorders. Compared with all other drugs, undocumented AEs, such as acute cardiac event (ROR = 30.0, 95% CI 9.4-95.3) and xanthoma (ROR = 9.3, 95% CI 3.4-25.5), were also reported. Conclusion: Real-world evidence showed that PCSK9 inhibitors were associated with an increased risk of musculoskeletal and connective tissue disorders and general disorders and administration site conditions, overall and by subgroups. Muscle toxicity, injection site reactions, and influenza-like illness were significant AE signals. Compared with various statins and ezetimibe, PCSK9 inhibitors have shown a favorable safety profile in muscle-related events, cognitive impairment and diabetes. Some undocumented AE signals were also reported. Due to the limitations of spontaneous reporting databases, further studies are still needed to establish causality and validate our results.

Applications of In Silico Models to Predict Drug-Induced Liver Injury.

Drug-induced liver injury (DILI) is a major cause of the withdrawal of pre-marketed drugs, typically attributed to oxidative stress, mitochondrial damage, disrupted bile acid homeostasis, and innate immune-related inflammation. DILI can be divided into intrinsic and idiosyncratic DILI with cholestatic liver injury as an important manifestation. The diagnosis of DILI remains a challenge today and relies on clinical judgment and knowledge of the insulting agent. Early prediction of hepatotoxicity is an important but still unfulfilled component of drug development. In response, in silico modeling has shown good potential to fill the missing puzzle. Computer algorithms, with machine learning and artificial intelligence as a representative, can be established to initiate a reaction on the given condition to predict DILI. DILIsym is a mechanistic approach that integrates physiologically based pharmacokinetic modeling with the mechanisms of hepatoxicity and has gained increasing popularity for DILI prediction. This article reviews existing in silico approaches utilized to predict DILI risks in clinical medication and provides an overview of the underlying principles and related practical applications.

Research and development of Chinese anti-COVID-19 drugs.

The outbreak and spread of coronavirus disease 2019 (COVID-19) highlighted the importance and urgency of the research and development of therapeutic drugs. Very early into the COVID-19 pandemic, China has begun developing drugs, with some notable progress. Herein, we summarizes the anti-COVID-19 drugs and promising drug candidates originally developed and researched in China. Furthermore, we discussed the developmental prospects, mechanisms of action, and advantages and disadvantages of the anti-COVID-19 drugs in development, with the aim to contribute to the rational use of drugs in COVID-19 treatment and more effective development of new drugs against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and the variants. Neutralizing antibody is an effective approach to overcome COVID-19. However, drug resistance induced by rapid virus mutation will likely to challenge neutralizing antibodies. Taking into account current epidemic trends, small molecule drugs have a crucial role in fighting COVID-19 due to their significant advantage of convenient administration and affordable and broad-spectrum. Traditional Chinese medicines, including natural products and traditional Chinese medicine prescriptions, contribute to the treatment of COVID-19 due to their unique mechanism of action. Currently, the research and development of Chinese anti-COVID-19 drugs have led to some promising achievements, thus prompting us to expect even more rapidly available solutions.

Machine learning techniques for CT imaging diagnosis of novel coronavirus pneumonia: a review.

Since 2020, novel coronavirus pneumonia has been spreading rapidly around the world, bringing tremendous pressure on medical diagnosis and treatment for hospitals. Medical imaging methods, such as computed tomography (CT), play a crucial role in diagnosing and treating COVID-19. A large number of CT images (with large volume) are produced during the CT-based medical diagnosis. In such a situation, the diagnostic judgement by human eyes on the thousands of CT images is inefficient and time-consuming. Recently, in order to improve diagnostic efficiency, the machine learning technology is being widely used in computer-aided diagnosis and treatment systems (i.e., CT Imaging) to help doctors perform accurate analysis and provide them with effective diagnostic decision support. In this paper, we comprehensively review these frequently used machine learning methods applied in the CT Imaging Diagnosis for the COVID-19, discuss the machine learning-based applications from the various kinds of aspects including the image acquisition and pre-processing, image segmentation, quantitative analysis and diagnosis, and disease follow-up and prognosis. Moreover, we also discuss the limitations of the up-to-date machine learning technology in the context of CT imaging computer-aided diagnosis.

Model-Informed Drug Development of New Cefoperazone Sodium and Sulbactam Sodium Combination (3:1): Pharmacokinetic/Pharmacodynamic Analysis and Antibacterial Efficacy Against Enterobacteriaceae.

Objective: Cefoperazone/sulbactam is a commonly used antibiotic combination against the extended-spectrum beta-lactamases (ESBLs)-producing bacteria. The objective of this study was to evaluate the efficacy of a new cefoperazone/sulbactam combination (3:1) for Enterobacteriaceae infection via model-informed drug development (MIDD) approaches. Methods: Sulperazon [cefoperazone/sulbactam (2:1)] was used as a control. Pharmacokinetic (PK) data was collected from a clinical phase I trial. Minimum inhibitory concentrations (MICs) were determined using two-fold broth microdilution method. The percent time that the free drug concentration exceeded the minimum inhibitory concentration (%fT>MIC) was used as the pharmacokinetic/pharmacodynamic indicator correlated with efficacy. Models were developed to characterize the PK profile of cefoperazone and sulbactam. Monte Carlo simulations were employed to determine the investigational regimens of cefoperazone/sulbactam (3:1) for the treatment of infections caused by Enterobacteriaceae based on the probability of target attainment (PTA) against the tested bacteria. Results: Two 2-compartment models were developed to describe the PK profiles of cefoperazone and sulbactam. Simulation results following the single-dose showed that the regimens of cefoperazone/sulbactam combinations in the ratios of 3:1 and 2:1 achieved similar PTA against the tested bacteria. Simulation results from the multiple-dose showed that the dosing regimen of cefoperazone/sulbactam (4 g, TID, 3 g:1 g) showed slightly better antibacterial effect than cefoperazone/sulbactam (6 g, BID, 4 g:2 g) against the Escherichia coli (ESBL-) and Klebsiella pneumoniae (ESBL-). For the other tested bacteria, the above regimens achieved a similar PTA. Conclusions: Cefoperazone/sulbactam (3:1) showed similar bactericidal activity to sulperazon [cefoperazone/sulbactam (2:1)] against the tested bacteria. For the ESBL-producing and cefoperazone-resistant E. coli and K. pneumoniae, Cefoperazone/sulbactam (3:1) did not exhibit advantage as anticipated. Our study indicated that further clinical trials should be carried out cautiously to avoid the potential risks of not achieving the expected target.

Predicting drug-drug interactions with physiologically based pharmacokinetic/pharmacodynamic modelling and optimal dosing of apixaban and rivaroxaban with dronedarone co-administration.

BACKGROUND: The concurrent administration of dronedarone and oral anti-coagulants is common because both are used in managing atrial fibrillation (AF). Dronedarone is a moderate inhibitor of the cytochrome P450 3A4 (CYP3A4) enzyme and P-glycoprotein (P-gp). Apixaban and rivaroxaban are P-gp and CYP3A4 substrates. This study aims to investigate the impact of exposure and bleeding risk of apixaban or rivaroxaban when co-administered with dronedarone using physiologically based pharmacokinetic/pharmacodynamic analysis. METHODS: Modeling and simulation were conducted using Simcyp® Simulator. The parameters required for dronedarone modeling were collected from the literature. The developed dronedarone physiologically based pharmacokinetic (PBPK) model was verified using reported drug-drug interactions (DDIs) between dronedarone and CYP3A4 and P-gp substrates. The model was applied to evaluate the DDI potential of dronedarone on the exposure of apixaban 5 mg every 12 h or rivaroxaban 20 mg every 24 h in geriatric and renally impaired populations. DDIs precipitating major bleeding risks were assessed using exposure-response analyses derived from literature. RESULTS: The model accurately described the pharmacokinetics of orally administered dronedarone in healthy subjects and accurately predicted DDIs between dronedarone and four CYP3A4 and P-gp substrates with fold errors <1.5. Dronedarone co-administration led to a 1.29 (90 % confidence interval (CI): 1.14-1.50) to 1.31 (90 % CI: 1.12-1.46)-fold increase in the area under concentration-time curve for rivaroxaban and 1.33 (90 % CI: 1.15-1.68) to 1.46 (90 % CI: 1.24-1.92)-fold increase for apixaban. The PD model indicated that dronedarone co-administration might potentiate the mean major bleeding risk of apixaban with a 1.45 to 1.95-fold increase. However, the mean major bleeding risk of rivaroxaban was increased by <1.5-fold in patients with normal or impaired renal function. CONCLUSIONS: Dronedarone co-administration increased the exposure of rivaroxaban and apixaban and might potentiate major bleeding risks. Reduced apixaban and rivaroxaban dosing regimens are recommended when dronedarone is co-administered to patients with AF.

The therapeutic effect of Taijiquan combined with acupoint pressing on the treatment of anxiety insomnia in college students: A study protocol for a randomized controlled trial.

Introduction: Sleep health is an important part of health and has become a common concern of society. For anxiety insomnia, the commonly used clinical therapies have limitations. Alternative and complementary therapy is gradually rising and showing remarkable effect in clinical practice. This is the first study to evaluate the therapeutic effect of Taijiquan combined with acupoint pressing in the treatment of anxiety insomnia in college students and to compare the difference in intervention before and after sleep, to choose the best treatment time. Methods and analysis: This is a multicenter, single-blind, randomized controlled trial. A total of 126 eligible subjects who have passed the psychological evaluation and met inclusion criteria by completing a psychometric scale will be randomly divided into treatment group A (treat before sleep), treatment group B (treat after sleep) and control group C (waiting list group) in a ratio of 1:1:1. All the three groups will receive regular psychological counseling during the trial, and the treatment groups will practice 24-style Taijiquan and do meridian acupuncture at Baihui (DU20), Shenting (DU24), Yintang (EX-HN3), Shenmen (HT7) and Sanyinjiao (SP6). This RCT includes a 2-week baseline period, a 12-week intervention period, and a 12-week follow-up period. The main results will be measured by changes in the Pittsburgh sleep quality index (PSQI) and Hamilton anxiety scale (HAMA). The secondary results will be measured by the generalized anxiety scale (GAD-7) and insomnia severity index (ISI). The safety of the intervention will be evaluated at each assessment. The statistical analysis of data will be carried out by SPSSV.26.0 software. Discussion: We expect this trial to explore the effectiveness of Taijiquan combined with acupoint pressing in the treatment of anxiety insomnia in college students and choose the best treatment time by comparison. Clinical trial registration: [www.ClinicalTrials.gov], identifier [ChiCTR2200057003].