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OBJECTIVE To analyze influential factors for dabigatran exposure in elderly patients with non-valvular atrial fibrillation. METHODS The clinical information of 75 elderly patients diagnosed with non-valvular atrial fibrillation was collected from our hospital in Jan. 2019-Jun. 2020. One or two steady-state blood drug concentration samples were collected from each patient. NONMEM 7.2.0 software was used to establish a population pharmacokinetics model of dabigatran； the effects of different covariates on the apparent clearance of dabigatran were investigated， and the final model was verified by goodness of fit and Bootstrap method； NONMEM 7.2.0 software was used to analyze the drug exposure of ordinary elderly patients and elderly patients after taking dabigatran ester in different disease states. RESULTS Totally 122 blood concentration samples of dabigatran were collected. Advanced age， creatinine clearance and history of chronic heart failure were screened out as three significant covariates that influenced the clearance of dabigatran in elderly patients. The exposure of population with advanced age increased by about 50% compared with the general elderly， the exposure of population with history of chronic heart failure increased by nearly 30% compared with population without， and the exposure of population with moderate and severe renal injury increased by about 30% and 80% compared with mild. CONCLUSIONS Advanced age， renal injury and history of chronic heart failure are influential factors for elevated systemic exposure of dabigatran.
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Linezolid is an antibacterial agent for the treatment of multi-resistant Gram-positive bacterial infections， which is widely used in clinical practice. However， there are large individual differences in the pharmacokinetic characteristics of the drug in patients， and it is difficult to obtain the optimal therapeutic effect when the drug is administered according to the conventional dose in the instructions. Therefore， it is necessary to carry out therapeutic drug monitoring （TDM） for linezolid， and guide and optimize its antibacterial treatment plan by using population pharmacokinetics （PPK） and pharmacodynamics principles. This paper summarizes the PPK changes and the research progress of individualized administration of linezolid in various populations， and recommends that the patient’s steady-state blood concentration is kept at 2-8 mg/mL through TDM when using linezolid clinically. It is recommended to appropriately reduce the dosage for patients with liver and kidney dysfunction， appropriately increase the dosage for obese， burned and children patients， and provide pharmaceutical monitoring during the medication process to promote rational drug use.
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Caspofungin is the firs t echinocandin antifungal drug approved for serious fungal infections caused by Candida or Aspergillus. Currently ，caspofungin has been recommended as the first-line treatment for invasive Candida and the second-line treatment for invasive Aspergillus，for its safety and tolerability. However ，there are still probability of pharmacokinetic variability and the risk of low exposure in different populations. Herein the population pharmacokinetics-pharmacodynamics studies of caspofungin in children and adults were reviewed. The results indicate that the body surface area was the main factor affecting the distribution and clearance of caspofungin in pediatric patients. In adults ，the two-compartment model fits the caspofungin behavior best in vivo with the primary covariates of body weight and albumin level. The efficacy of caspofungin might be related to pharmacokinetics-pharmacodynamics parameters ，such as the ratio of area under blood concentration time curve to minimum inhibitory concentration （AUC/MIC），the ratio of peak concentration to minimum effective concentration （cmax/MEC）.
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The antitumor drug has become one of the focused areas in new drug research and development. Their clinical research generally consumes a long period of time, with high cost and high risk. Model-informed drug development (MIDD) integrates and quantitatively analyzes physiological, pharmacological, and disease progression information through modeling and simulation, which can reduce the cost of drug development and improve the efficiency of clinical research. In this essay, Osimertinib and Pembrolizumab are given as examples to illustrate the specific application of MIDD in different phases of clinical research, aiming to provide references for the application of MIDD to guide the clinical research of antitumor drugs. .
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Humans , Antineoplastic Agents/therapeutic use , Drug Development , Lung NeoplasmsABSTRACT
Imipenem-cilastatin is a broad-spectrum carbapenem antibiotic drug that has been widely used in clinical practice ， but there is a lack of guidelines and expert consensus on the development of individualized regimens for special status populations [e.g. continuous renal replacement therapy （CRRT）patients，extracorporeal membrane oxygenation （ECMO）patients， critically ill burn patients ，neonates and children]. In this paper ，by searching population pharmacokinetics research of imipenem- cilastatin in special status populations ，it is recommended that imipenem-cilastatin is given 1 to 3 g/d for CRRT patients ；500 mg to 1 g，q6 h for burn patients ；750 mg to 1 g，q6 h for ECMO patients ；20 mg/kg or 25 mg/kg，q8 h for neonates ；and 25 mg/kg，q6 h for children.
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BACKGROUND: Previously, a linked pharmacokinetic-pharmacodynamic model (the Kim model) of propofol with concurrent infusion of remifentanil was developed for children aged 2–12 years. There are few options for pharmacokinetic-pharmacodynamic model of propofol for children under two years old. We performed an external validation of the Kim model for children under two years old to evaluate whether the model is applicable to this age group.METHODS: Twenty-four children were enrolled. After routine anesthetic induction, a continuous infusion of 2% propofol and remifentanil was commenced using the Kim model. The target effect-site concentration of propofol was set as 2, 3, 4, and 5 μg/mL, followed by arterial blood sampling after 10 min of each equilibrium. Population estimates of four parameters—pooled bias, inaccuracy, divergence, and wobble—were used to evaluate the performance of the Kim model.RESULTS: A total of 95 plasma concentrations were used for evaluation of the Kim model. The population estimate (95% confidence interval) of bias was −0.96% (−8.45%, 6.54%) and that of inaccuracy was 21.0% (15.0%–27.0%) for the plasma concentration of propofol.CONCLUSION: The pooled bias and inaccuracy of the pharmacokinetic predictions are clinically acceptable. Therefore, our external validation of the Kim model indicated that the model can be applicable to target-controlled infusion of propofol in children younger than 2 years, with the recommended use of actual bispectral index monitoring in clinical settings that remifentanil is present. Trial Registration Clinical Research Information Service Identifier:TRIAL REGISTRATION: Clinical Research Information Service Identifier: KCT0001752
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Model-informed drug development (MIDD) refers to the application of various mathematical models in drug development, in order to facilitate the decision-making process. There have been common and mature applications of MIDD to address drug development and regulatory questions in interactional industries and advanced regulatory agencies, especially the US FDA. However, its application in innovative drug development is relatively rare in China. Representative case studies, clinical pharmacology review ex-periences, and relevant guidelines are reviewed in this article to present a preliminary discussion on the main applications of MIDD. Additionally, several suggestions for the application of MIDD in new drug development as well as general considerations for new drug registration are proposed in this paper, for the discussion or reference of industries and researchers.
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Physiologically based pharmacokinetics (PBPK) is one of the main research fields of pharmacometrics, and it plays an important role at all the stages of drug development and clinical practice. In early drug discovery and development, human pharmacokinetics (PK) could be predicted by PBPK modeling using in silico, in vitro and preclinical in vivo data. During clinical studies, PBPK model could be used to investigate the effects of various physiological and pathological factors on PK, such as age, gender, liver/kidney impairment, and to guide dose adjustment of special population (pregnant women, children, etc.). Furthermore, PBPK modeling is now becoming more appealing with the ability to predict drug-drug interaction (DDI) in the case of co-administration of multiple drugs. In recent years, the application of PBPK modeling in industry has increased widely. Also, regulatory agencies have recognized the potential of PBPK and its impact on labeling recommendations. As the popularity of model-informed drug development, the combination of PBPK modeling with other commonly used modeling methods, such as population pharmacokinetics (PopPK), pharmacokinetic/pharmacodynamic (PK/PD) modeling and model-based meta-analysis (MBMA), has shown attractive advantages. In this paper, the origin and development, as well as the application status of PBPK are introduced briefly, and the application of PBPK modeling merged with PopPK, PK/PD and MBMA is reviewed.
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Population pharmacokinetics (PopPK) is an analytical method that can quantify the variability of drug concentration among individuals. It is widely used in various stages of new drug researches from non-clinic to clinic. With the rapid development of PopPK, more and more sponsors are keen to comprehensively analyze the in vivo processes of new drugs as well as its influencing factors using modeling and simulation methods. Several guidelines have been issued to recommend the use of PopPK in China. However, no explicit requirement of PopPK study report has been issued for regulatory application. This article conducts a preliminary discussion on new drug PopPK study and its reporting format and content, with reference to the requirements in relevant guidelines as well as previous review experiences, for the discussion or reference of industries and researchers.
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AIM: To evaluate the missed and remedial dosage regimens in cancer patients using erlotinib population pharmacokinetics (PPK) model by Monte Carlo simulation (MCS). METHODS: According to erlotinib PPK model (150 mg po qd), 10 000 MCS were estimated for missed doses and remedial dosage regimens (6 h, 12 h, 18 h, and 24 h double doses) by NONMEM. The proportion of people outside the individual treatment window (ITW) and duration outside the ITW (>5%) under the missed and remedial regimens were calculated, and the rationality of the supplemental regimen in each scenario were analyzed. RESULTS:When missed taking erlotinib, the drug concentration continued to drop to the next medication time and affected the next day's concentration. The durations below the ITW were 25.1 h and 6.6 h, respectively. The proportion of people below ITW increased from 6.82% to 14.55%, and the duration time increased from 5.9 h to 23.6 h; the proportion of people above ITW increased from 5.99% to 10.74%, and the duration time increased from 3.7 h to 9.7 h. CONCLUSION: According to MCS results, patients should improve erlotinib medication compliance and avoid missed doses. In case of missed dose, remedial should be given as soon as possible, but it is not recommended to take remedial or double dosage near the next administration time to avoid increased adverse drug reactions.
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The goal of this work was to establish a population pharmacokinetics (PPK) model of tacrolimus in idiopathic membranous nephropathy (IMN) patients and to identify potential covariates that influence pharmacokinetic of tacrolimus. A total of 610 data points on the blood concentration of tacrolimus were collected from 96 IMN patients in routine clinical settings. Nonlinear mixed-effect modeling (NONMEM) was used to investigate the effects of CYP3A5 genotype, age, gender, weight, laboratory tests and co-therapy medications on the pharmacokinetic of tacrolimus. The PPK model was evaluated by the goodness-of-fit (GOT), bootstrap and prediction corrected visual predictive check (pc-VPC). The pharmacokinetic of tacrolimus was described by a one-compartment model. The apparent clearance (CL/F) of CYP3A5*1/*3 and *1/*1 were 1.57 and 1.86 times of that of *3/*3, respectively. The CL/F of tacrolimus was 73.6% in patients undergoing co-therapy with Wuzhi capsules, and 1.2 times than that of the patients undergoing co-therapy with Jinshuibao capsules. The evaluation of the model shows that the model is stable and has satisfactory predictive performance. The clinical trial was approved by the Society of Ethics and conducted in Binzhou Medical University Hospital. The established PPK model can describe the pharmacokinetic characteristics of tacrolimus in Chinese patients with IMN, and can facilitate individualized therapy with tacrolimus.
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OBJECTIVE: To compare all published sodium valproate population pharmacokinetic models of epileptic children in China and assess them by external validation to determine their predictive performance. METHODS: The published population pharmacokinetic model of sodium valproate in children with epilepsy in China was collected by database retrieval. By retrospectively collecting patients' information, we performed an external validation to evaluate the power of prediction. RESULTS: Four sodium valproate models were published before. The external validation of 101 samples showed that the MPE, MAE, RMSE of the four model were similar. All of them showed good adequacy between predicted concentrations and observed concentrations. Comparing with other models, the model established by Ding has better prediction error coincidence rate in most interval, which is more targeted for the prediction of individualized dosage regimen for epileptic children in our hospital. However, the overall prediction accuracy of the four models is not significantly different. CONCLUSION: By the evaluation of four published population pharmacokinetic models of sodium valproate model B performs better than others, while the overall accuracy of the four models did not differ much. Racial difference may be an important factor affecting the accuracy of the model, which needs to be further explored in subsequent studies.
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OBJECTIVE: To establish a population pharmacokinetics(PPK) model of teicoplanin(TEC) in Chinese adult patients and investigate the factors influencing TEC pharmacokinetic parameters. METHODS: A total of 222 blood samples and related information were prospectively collected from 139 inpatients with Gram-positive bacterial infection receiving TEC intravenously. A one-compartment model with first order elimination was used to perform the PPK analysis and the PPK model of TEC was developed via nonlinear mixed effects modeling(NONMEM) approach. The stability and prediction of the final model were evaluated by Bootstrap and normalized predictive distribution error (NPDE). Monte Carlo simulation was used to evaluate the effective of currently recommended dosing regimen. RESULTS: The creatinine clearance(CLcr) and albumin(ALB) were identified as the most significant covariate on the clearance rate of TEC. The established final model was: CL(L•h-1)=1.24×(CLcr/77)0.564×31/ALB;V(L)=69.2. It is verified that the established final model is stable, effective and predictable. For most patients with different serum albumin concentration and CLcr, the initial loading dose of 400 mg/q12h, iv, 3 times, and the maintenance dose of 400-800 mg•d-1 can achieve effective treatment of trough concentration. Severe infections need to adjust the loading dose to 800 mg/q12h, iv, 3 times, and maintain a dose of 400-800 mg•d-1 of the dosing regimens to ensure that the blood concentration reached 15 mg•L-1. CONCLUSION: This study reports that CLcr, ALB has a significant effect on TEC clearance and the model has important value for the individualization of TEC therapy in Chinese adult patients.
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Multiple target tyrosine kinase inhibitors are commonly used in clinical as anti-tumor drugs, which can promote tumor cell apoptosis by inhibiting cell signal transduction, with high selectivity and few side effects. At present, it is widely used in the treatment of non-small cell lung cancer, metastatic renal cancer, thyroid cancer and hematological malignancies. However, the clinical efficacy and pharmacokinetic characteristics of these drugs are affected by many factors, and there are great individual differences. In recent years, the study of population pharmacokinetics is emerging and is widely used in the study of many drugs. In this paper, the pharmacokinetic characteristics and influencing factors of axitinib, imatinib, erlotinib and sunitinib in cancer patients and healthy people were summarized through the retrieval of relevant literature, and then the progress of pharmacokinetic research of tyrosine kinase inhibitors(TKIs) was reviewed. The pharmacokinetic characteristics in different tumor types were analyzed and the related covariates were summarized. The results showed that demographic factors, gene polymorphism, blood biochemical indexes, combined use of drugs and liver and kidney function were important factors affecting metabolism in vivo. Factors such as experimental design and model construction may be the important reasons for the differences in research results. The purpose of this study is to provide a reference for making a reasonable and safe drug therapy plan.
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Ribavirin is a widely used nucleoside antiviral drug. During the epidemic of coronavirus disease 2019 (COVID-19), ribavirin was recommended for empirical treatment in the Clinical Management of Human Infection with COVID-19 (trial guidance v6). However, due to the large inter-individual variations in dose-response relationship, and extremely long terminal half time, it is necessary to perform therapeutic drug monitoring and individualized dose adjustment for ribavirin in special populations. In this article, the pharmacokinetics and therapeutic drug monitoring of ribavirin in different populations are reviewed in order to provide reference for clinical rational use and individualized medication of ribavirin for treatment of COVID-19.
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This study developed a population pharmacokinetic model for sodium tanshinone IIA sulfonate (STS) in healthy volunteers and coronary heart disease (CHD) patients in order to identify significant covariates for the pharmacokinetics of STS. Blood samples were obtained by intense sampling approach from 10 healthy volunteers and sparse sampling from 25 CHD patients, and a population pharmacokinetic analysis was performed by nonlinear mixed-effect modeling. The final model was evaluated by bootstrap and visual predictive check. A total of 230 plasma concentrations were included, 137 from healthy volunteers and 93 from CHD patients. It was a two-compartment model with first-order elimination. The typical value of the apparent clearance (CL) of STS in CHD patients with total bilirubin (TBIL) level of 10 μmol(L was 48.7 L(h with inter individual variability of 27.4%, whereas that in healthy volunteers with the same TBIL level was 63.1 L(h. Residual variability was described by a proportional error model and estimated at 5.2%. The CL of STS in CHD patients was lower than that in healthy volunteers and decreased when TBIL levels increased. The bootstrap and visual predictive check confirmed the stability and validity of the final model. These results suggested that STS dosage adjustment might be considered based on TBIL levels in CHD patients.
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Adult , Aged , Aged, 80 and over , Female , Humans , Male , Middle Aged , Bilirubin , Blood , Coronary Disease , Drug Therapy , Metabolism , Drugs, Chinese Herbal , Pharmacokinetics , Metabolic Clearance Rate , Models, Biological , Phenanthrenes , Blood , PharmacokineticsABSTRACT
Oxcarbazepine (OXC) is a common antiepileptic drugs. In this study, one hundred and eighty four epilepsy patients with 196 observations of oxcarbazepine's active metabolite, 10,11-dihydro-10-monohydroxy carbazepine (MHD) were collected prospectively from routine clinical monitoring. Nonlinear mixed effect modeling was employed to develop a population pharmacokinetic model of oxcarbazepine in Chinese patients with epilepsy to investigate the impact of gender, age, weight, co-medications and genetic polymorphisms of UGT2B7 c.802T>C, ABCC2 c.1249G>A, ABCC 23972C>T on pharmacokinetic characteristics of OXC. The population estimate of apparent clearance (CL/F) and apparent volume of distribution (V/F) was 1.84 L·h−1 and 275 L, respectively. Gender and UGT2B7 c.802T>C affected the clearance rate of MHD significantly. The established model was:CL/F=1.84×0.848UGT2B7×1.17GENDER. Where the genotype of UGT2B7 c.802T>C was CC, UGT2B7=0, otherwise UGT2B7=1. When the patient was male, GENDER=1, otherwise GENDER=0. The final model was evaluated by normalized predictive distribution error (NPDE) and bootstrap method. The model was stable and reliable, which offers a powerful approach for rational use of OXC in epilepsy patients.
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Objective:To establish the population pharmacokinetics model of mitiglinide given by oral route in Chinese healthy volunteers using nonlinear mixed effect model (NONMEM), investigate the pharmacokinetic characteristics of mitiglinide in Chinese healthy people to evaluate the factors that can influence the clinical pharmacokinetics of mitiglinide .Methods: Clinical data from 22 healthy volunteers were collected and the experiment was with single-dose administration.The volunteers were given 10 mg mitiglinide calcium orally and mitiglinide plasma concentration was determined by LC-MS/MS.The data was analyzed by the first order conditional estimation, and the influences of fixed effect factors such as demographic index and biochemical index were quantitatively evaluated . The population pharmacokinetics model of mitiglinide was established , and the result was verified by using the VPC and self-test meth-od.Results:The result showed that mitiglinide pharmacokinetics was fit single-compartment model .The inter-individual variability could be described by an exponential model .The typical values including central volume of distribution , clearance and absorption con-stant was 2.4 L· h-1(24％), 9.82 L(4％) and 6.46 h-1(14％), respectively.The clearance was influenced by creatinine clear-ance rate , and the absorption constant was influenced by ALT .Conclusion:The population pharmacokinetic parameters were mainly influenced by creatinine clearance rate and ALT .The established population pharmacokinetics model can explain the reasons for the in -dividual variation in the plasma concentration of mitiglinide , which can be used to guide the clinical administration of Chinese people .
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Combining classical pharmacokinetic principles with statistical models, population pharmacokinetics (popPK) can effectively utilize sparse data for pharmacokinetic analysis. An optimally designed population pharmacokinetic study will balance the efficiency of a popPK study and the precision with which the parameters are estimated to ensure the unbiased estimation of pharmacokinetic parameters and facilitate the development of clinical and non-clinical trials. Sparse sampling methods have been developed for designing population pharmacokinetic experiments including random sampling method, limited sampling strategy, maximum a posteriori Bayesian method, Fisher information matrix method, and informative block randomized design, which have been widely applied in the uni-response and multi-response popPK sampling optimization. In recent years, population pharmacokinetics has been developed rapidly in Chinese materia medica (CMM), but few studies have been conducted to optimize sampling. By comparing the advantages and disadvantages of each sparse point sampling optimization method and the applicable conditions, this work provides a comparative review of optimal design methodologies and gives its application examples, which provides a reference for pharmacokinetic sampling optimization of CMM.
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OBJECTIVE:To establish population pharmacokinetics(PPK)model of vancomycin so as to evaluate the effects of cystatin C(Cys C)on the pharmacokinetics parameters of vancomycin. METHODS:Totally 333 times therapeutic drug monitoring (TDM)were retrospectively collected from 225 patients who received vancomycin. Using sex,age,body weight(mT),Scr and Cys C as covariates,PPK model was established by using nonlinear mixed effect model method. Bootstrap method and normal prediction distribution error(NPDE)method were adopted for internal validation of model. Forty times of TDM data were collected from other 27 patients for external validation. Predicted accuracy and precision of model were investigated with mean prediction error (MPE) and root mean square error (RMSE). The effects of Cys C change on pharmacokinetic parameters of vancomycin were evaluated with steady state trough concentration and apparent clearance rate (CL/F) of vancomycin in typical patient (65 year-old,64 kg,Scr 66 μmol/L,1 000 mg,q12 h)forecasted with the final model at different levels of Cys C. RESULTS:CL/F of vancomycin was significantly influenced by age,body weight,the levels of Scr and Cys C. The final model was CL/F(L/h)＝3.68×(Scr/66)－0.431×(mT/64)1.1×(Age/65)－0.368×(Cys C/1.04)－0.693,V/F was equal to 82.5 L. The robust rate verified by Bootstrap method was 100%. Except for the interindividual variation of V/F,the relative bias of other pharmacokinetic parameters was less than 5%,and the estimated parameters of the final model were in the 95% confidence intervals of estimated values of Bootstrap. NPDE results showed that the homogeneity of variance was consistent with normal distribution (P＞0.05). In external validation,MPE and RMSE of the simplest model were －1.52 μg/mL and 6.87 μg/mL. MPE and RMSE of the final model were －0.32 μg/mL and 4.27 μg/mL,the accuracy and precision were improved significantly in the final model. When Cys C levle of typical patient was 0.3-4.0 mg/L,the steady state trough concentration predicted by final model were 5.25-29.97 μ g/mL and CL/F were 1.45-8.71 L/h. CONCLUSIONS:Age,body weight,the levels of Scr and Cys C significantly influence the pharmacokinetic parameters of vancomycin;moreover,the level of Cys C can change blood concentration of vancomycin. Established PPK model is of great predictive performance,which can be used to estimate the individual pharmacokinetics parameters of vancomycin.