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Objective To evaluate the predictive ability and influencing factors of individualized drug administration adjuvant decision-making system Java PK® for Desktop (JPKD) for tacrolimus blood concentration in kidney transplant recipients. Methods The monitoring data of tacrolimus blood concentration from 149 recipients early after kidney transplantation were collected. The trough blood concentration of tacrolimus was predicted by JPKD. The absolute weighted deviation and relative prediction deviation between the actual and predicted concentration were calculated. The influencing factors of the absolute weighted deviation were analyzed by univariate and multivariate logistic regression analyses, and the predictive values of these influencing factors on the accuracy of software prediction were assessed by delineating the receiver operating characteristic (ROC) curve. Results Two hundred and sixty-six samples of tacrolimus blood concentration data were collected from 149 patients. The measured blood concentration of tacrolimus was (6.5±3.0) ng/mL (1.1-16.6 ng/mL), and the predicted value calculated by JPKD was (5.6±2.5) ng/mL (1.4-14.4 ng/mL). The absolute weighted deviation of the calculated data was 28.38%, and the relative prediction deviation was −13.55%. Univariate analysis showed that gender, albumin, changes in hematocrit, cytochrome P450 (CYP)3A5*3 genotype and C3435T genotype were associated with the inaccurate prediction results. Multivariate logistic regression analysis found that CYP3A5*3 genotype of AA and the changes in hematocrit were the independent risk factors affecting the accuracy of tacrolimus blood concentration predicted by JPKD. ROC curve analysis showed that when the changes in hematocrit exceeded 2.25%, the risk of inaccurate software prediction was increased. Conclusions JPKD possesses certain accuracy in predicting the blood concentration of tacrolimus in kidney transplant recipients, which may improve the qualified rate of tacrolimus blood concentration. Nevertheless, CYP3A5*3 genotype and the changes of hematocrit may affect the accuracy of predictions.
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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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AIM: To compare the results calculated by population pharmacokinetic analysis tools Phoenix NLME, Monolix, R nlmixr package and CPhaMAS cloud platform with the gold standard sofeware NONMEM. METHODS: Fifty sparse sampling data sets based on a one-compartment model and fifty dense sampling data sets based on a two-compartment model were simulated, and the above five analysis tools were used to calculate the population typical value, individual variability and individual pharmacokinetic parameters. RESULTS: The population typical value and individual variability calculated by CPhaMAS and Phoenix NLME had the highest matching degree with NONMEM, followed by nlmixr. Monolix had the lowest matching degree, but Monolix and nlmixr might be more robust. The correspondence between clearance and distribution volume was better than the absorption rate constant. Except the absorption rate constant calculated by Monolix and intercompartmental clearance calculated by nlmixr, the correlation coefficients of individual pharmacokinetic parameters calculated by all analytical tools were greater than 0.99. CONCLUSION: The results calculated by the above four population pharmacokinetic analysis tools are highly correlated with that of NONMEM.
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Rituximab, a chimeric human-mouse monoclonal antibody, has been used as a first-line treatment for CD20
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Polymorphism refers to the simultaneous and frequent existence of two or more discontinuous variants or genotypes or alleles in a biological population, also known as genetic polymorphisms or genes Polymorphism. This gene polymorphism may have a certain degree of influence on the pharmacokinetics and pharmacodynamics of the drug. The study of genomics plays an important role in realizing personalized, patient-oriented precision medicine treatment. Population model analysis is to use a modeling method to quantitatively describe the correlation and variability between pharmacokinetic and pharmacodynamic parameters and individual characteristics and to quantify the impact of covariates. At present, this method has been widely used. This paper systematically introduces the application examples of using the population model approach to assess the effects of genetic polymorphisms on the drug PK/PD.
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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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In recent years, modeling and simulation technology based on pharmacometrics has received increasing attention in the development of innovation drugs. In August of 2021, FDA issued a guidance named Pharmacokinetic-Based Criteria for Supporting Alternative Dosing Regimens of Programmed Cell Death Receptor-1 (PD-1) or Programmed Cell Death-Ligand 1 (PD-L1) Blocking Antibodies for Treatment of Patients with Cancer Guidance for Industry, claiming the necessity of using population PK-based simulation method for the optimization of dosing regimens, and the corresponding implementation standards. This article first summarized the existing therapeutic regimens of PD-1/PD-L1 blocking antibodies in clinic as well as the main content of the guidance, and then cited some actual examples where population PK-based simulation method did contribute to the approval of the alternative dosing regimens. Besides, some critical considerations for the dosing regimen optimization of PD-1/PD-L1 blocking antibodies were also analyzed. In our view, this guidance would have positive impacts on the development of PD-1/PD-L1 blocking antibodies in the future. We hope that this article may provide some references for the colleagues in China.
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AIM: To provide reference for the clinical application of tigecycline and subsequent population pharmacokinetic-pharmacodynamics study in the future. METHODS: The Chinese and English keywords of "Tigecycline", "population pharmacokinetics", "population pharmacokinetic model", "pharmacodynamics" or "Tigecycline" pharmacokinetics "were used to search the relevant references published from the time of self-establishment to June 1, 2021 in PubMed, China Knowledge Infrastructure, Wanfang and other databases. The research progress of population pharmacokinetics and pharmacodynamics of tigecycline was reviewed. RESULTS & CONCLUSION: A total of 73 relevant references were retrieved, including 8 tigecycline PPK studies and 7 tigecycline PK/PD studies. At present, tigecycline PPK models had been established in patients with complex intra-abdominal infections, skin and skin and soft tissue infections, community-acquired pneumonia, nosocomial pneumonia, septic shock and other severe infections, including 8 two-compartment models. The main covariates affecting tigecycline plasma clearance were weight-related, liver function and renal function-related parameters. Body weight was also an important factor influencing the apparent volume of distribution. The effect of different disease types on the pharmacokinetics of tigecycline was different, and it needed to be considered and selected in combination with the specific circumstances of patients when formulating clinical dosing regimens. Pharmacodynamics studies should consider not only the type of disease, pathogens and patient factors themselves, but also the characteristics of atypical nonlinear plasma protein binding of tigecycline. In order to accurately understand the efficacy of different dose regimens, it was necessary to monitor the therapeutic drugs of tigecycline.
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AIM: To establish a population pharmacokinetics (PPK) model of vancomycin in patients with hematological diseases who developed neutropenia. METHODS: Patients from department of hematology with neutropenia in our hospital were taken into oue study.The patients (n=77) were performed trough and peak serum concentration of vancomycin, and their clinical data and medication information were collected. The Nonlinear mixed effect modeling approach (NONMEM) was used to establish the PPK model of those patients and model assessment and validation was carried out. Goodneess of fit plots and visual predictive check plus Bootstrap approach were used to assess validate our model. RESULTS: The model was a two compartment model, the final formulas were: clearance rate CL=6.84×(BW/70)
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AIM: To investigate the population pharmacokinetic characteristics of capecitabine and its possible influencing factors in Chinese patients of breast cancer. METHODS: 78 cases of Chinese patients with breast cancer were chosen as the objects in this study. Following treatment with capecitabine (0.6 g, 0.15 g/piece, 4 pieces, orally), blood samples were collected and concentrations of capecitabine in plasma were analyzed by high performance liquid chromatography-mass spectrometry (HPLC-MS/MS) method. The nonlinear mixed-effects software (NONMEM) was used to analyze the data and the population pharmacokinetic model was constructed accordingly. RESULTS: The final established model of absorption and elimination is one-compartment model. The clearance (CL/F) in pharmacokinetic formula of the model is as follows: CL/F=291×e
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AIM: To develop software for individualizing dosage regimens of vancomycin (VCM) according to the established population pharmacokinetics (PPK) models. METHODS: VCM dosing software was developed using MyEclipse, SQL Server, and JRE. The software developing schemes included requirement analysis, general design, detailed design, software coding, software test, software maintenance and software redevelopment. RESULTS: The developed software achieved the functions such as input and management of patient information, prediction of trough concentrations under various dosing regimens which could help initial dosage design, and prediction of trough concentrations more accurately based on therapeutic drug monitoring results and Bayesian method which could help dosage adjustment. The software was utilized in the interpretation of VCM serum concentration, pharmacists proposed the suggestions for adjusting dosage regimens. The rechecked serum concentrations all reached the expected target blood concentration range in the group of adopting advice. CONCLUSION: The new developed software based on our established PPK models can provide a useful tool in the clinical setting to facilitate the individualized therapy for the adult and elderly infected patients.
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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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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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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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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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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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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.