基于群体药动学模型的万古霉素个体化给药软件研制及应用

目的:研发基于建立的成人和老年群体药动学(population pharmacokinetics,PPK)模型的万古霉素(vancomycin,VCM)个体化给药软件。方法:根据已建立的成人和老年VCM的PPK模型信息,运用MyEclipse、SQL Server、JRE等工具软件研发VCM给药软件。软件开发方案包括需求分析,概要设计,详细设计,软件编码,软件测试以及软件维护和二次开发。结果:研制的VCM给药软件可实现感染患者信息输入和管理,软件通过接口调用非线性混合效应模型(NONMEM)软件,不仅能预测多种具体VCM给药方案下的血药浓度,供临床医师制定初始用药方案参考,而且能结合已有的血药浓度监测信息和贝叶斯反馈法更精准地预测血药浓度,辅助临床医师进一步优化给药方案。软件应用于VCM血药浓度解读,药师向临床做出剂量调整建议。采纳建议组患者复查的血药浓度均达到目标血药浓度范围。结论:本研究基于VCM的PPK模型研制的给药软件能快速方便地辅助成人和老年感染患者VCM的个体化给药。     

Indications for a ceftriaxone dosing regimen in Japanese paediatric patients using population pharmacokinetic/pharmacodynamic analysis and simulation

Objectives The objective of this study was to build a ceftriaxone population pharmacokinetic model for Japanese paediatric patients and to examine the dosing regimen of ceftriaxone based on pharmacokinetic/pharmacodynamic (PK/PD) analysis. Methods The population pharmacokinetic analysis using NONMEM was based on published serum concentrations of ceftriaxone. A Monte Carlo simulation was examined to evaluate the time above the minimum inhibitory concentration (TAM) in 20 and 60 mg/kg body weight dose regimen using the population pharmacokinetic parameters. Key findings The time course of the serum concentration of ceftriaxone in paediatric patients was fitted to a two‐compartment model and body weight was incorporated to pharmacokinetic parameters as the covariate. Based on the percent TAM estimated from the final population pharmacokinetic model and the minimum inhibitory concentration (MIC) of ceftriaxone in 2004, we have predicted that the once daily administration of 20 mg/kg ceftriaxone would be effective on various infecting organisms. Conclusions A population pharmacokinetic model of ceftriaxone was built for Japanese paediatric patients based on the available data. The estimated PK/PD result confirmed the appropriateness of once daily dose of 20 mg/kg. In some patients for whom no efficacy was observed at 20 mg/kg, an increase to 60 mg/kg may be required.     

Population pharmacokinetic and pharmacodynamic analysis to support treatment optimization of combination chemotherapy with indisulam and carboplatin

AIMS

Indisulam and carboplatin have shown synergistic activity in preclinical studies. In a dose escalation study of the combination, a treatment delay was frequently required in a 3-weekly regimen to allow recovery from myelosuppression from previous cycles. A 4-weekly regimen was better tolerated, but had a decreased dose-intensity which may compromise efficacy. The aims of this study were (i) to develop a pharmacokinetic–pharmacodynamic (PK–PD) model to describe the myelosuppressive effect of the combination, and (ii) to use this model to select a dosing regimen for Phase II evaluation.

METHODS

Sixteen patients were treated at four different dose levels of indisulam (1-h infusion on day 1) and carboplatin (30-min infusion on day 2). Pharmacokinetic data were analysed with nonlinear mixed effects modelling. A semiphysiological model describing chemotherapy-induced myelosuppression characterized the relationship between the pharmacokinetics and the haematological toxicity of indisulam and carboplatin. A simulation study was performed to evaluate the tolerability and dose-intensity for 3-weekly and 4-weekly treatment regimens.

RESULTS

The PK–PD model described the pharmacokinetics and the myelosuppressive effect of indisulam and carboplatin. The risk of a treatment delay at cycle 2 due to myelosuppression was unacceptably high (34–65%) in a 3-weekly regimen for various dose levels (350–600 mg m⁻² indisulam in combination with carboplatin to achieve an AUC of 4–6 mg min⁻¹ ml⁻¹). This risk was acceptable for a 4-weekly regimen (9–24%), which is in line with the clinical study results.

CONCLUSIONS

This PK–PD study supports the selection of indisulam 500 mg m⁻² and a dose of carboplatin to achieve an AUC of 6 mg min⁻¹ ml⁻¹ in a 4-weekly regimen as the recommended dose for future studies.

WHAT IS ALREADY KNOWN ABOUT THIS SUBJECT

• Chemotherapy with indisulam in combination with a standard dose of carboplatin was not well tolerated in a 3-weekly regimen in a Phase I dose escalation study.
• Myelosuppression was the dose limiting toxicity.
• This pharmacokinetic–pharmacodynamic (PK–PD) study was performed to suggest a dosing regimen for the combination therapy indisulam–carboplatin that is well tolerated in patients.

WHAT THIS STUDY ADDS

• This PK–PD study supports the selection of indisulam 500 mg m⁻² and a dose of carboplatin to achieve an AUC of 6 mg min⁻¹ ml⁻¹ in a 4-weekly regimen as the recommended dose for future studies.

     

Designs for population pharmacodynamics: Value of pharmacokinetic data and population analysis

Analyses of simulated data from pharmacokinetic/pharmacodynamic (PK/PD) studies varying with respect to the amount and timing of observations were undertaken to assess the value of these design choices. The simulation models assume mono- or biexponential drug disposition, andE _max-type pharmacodynamics. Data analysis uses a combined PK/PD population analysis or a hybrid, individual-PK/population-PD analysis. Assuming that the goal of the PK/PD studies is to estimate population PD, performance of designs is judged by comparing the precision of estimates of population mean PD parameters and of their interindividual variability. The simulations reveal that (i) PK data, even in small number (2 points per person from as few as 25–50% of persons) are very valuable for estimating population PD; (ii) designs involving more individuals, even if many are sparsely sampled, dominate designs calling for more complete study of fewer persons; (iii) the population analysis is generally superior to the hybrid analysis, especially when the PK model is misspecified (biexponential assumed to be monoexponential for analysis); (iv) varying sampling times and doses among subjects protects against the ill effects of model misspecification. In general, the results are quite encouraging about the usefulness of sparse data designs to estimate population dose response. Work supported in part by U.S. Department of Health, Education and Welfare, Grants GM26676, GM26691.     

Interaction between structural,statistical, and covariate models in population pharmacokinetic analysis

The influence of the choice of pharmacokinetic model on subsequent determination of covariate relationships in population pharmacokinetic analysis was studied using both simulated and real data sets. Simulations and data analysis were both performed with the program NONMEM. Data were simulated using a two-compartment model, but at late sample times, so that preferential selection of the two-compartment model should have been impossible. A simple categorical covariate acting on clearance was included. Initially, on the basis of a difference in the objective function values, the two-compartment model was selected over the one-compartment model. Only when the complexity of the one-compartment model was increased in terms of the covariate and statistical models was the difference in objective function values of the two structural models negligible. For two real data sets, with which the two-compartment model was not selected preferentially, more complex covariate relationships were supported with the one-compartment model than with the two-compartment model. Thus, the choice of structural model can be affected as much by the covariate model as can the choice of covariate model be affected by the structural model; the two choices are interestingly intertwined. A suggestion on how to proceed when building population pharmacokinetic models is given. Supported by National Institutes of Health grants GM 26691 and GM 26676     

Practical experience and issues in designing and performing population pharmacokinetic/pharmacodynamic studies

An expert meeting to discuss issues relating to the design of population pharmacokinetic/pharmacodynamic (PK/PD) studies was held in Brussels in March 1995, under the auspices of the European Co-operation in Science and Technology (COST), Medicine (B1) programme. The purpose of the meeting was to discuss the experts' experience in designing and performing population PK/PD studies. The topics discussed were current practice, logistical issues, ensuring the accuracy of data, covariate assessment, communication, and protocol design.The main conclusions from the meeting were: 1) a population PK/PD analysis should be one of the objectives of a clinical trial and should not compromise the other objectives; 2) it is particularly important to communicate the purpose of the population PK/PD analysis to the investigators and to convince them of the importance of accurately recording dosing and sampling times; 3) some prior knowledge of the PK and PD models and covariate relationships is necessary for the analysis of sparse phase III data; 4) computer simulation and optimal design measures may be useful in defining sampling times; 5) population methods and objectives must be specified as completely as possible in the protocol. Participants: L. Aarons (UK), L. Balant (Switzerland), P. Bechtel (France), R. Bruno (France), P. Burtin (Switzerland), C. Dubruc (France), E. Fuseau (UK), J. Gabrielsson (Sweden), U. Gundert-Remy (Germany), R. Jochemsen (France), M. Karlsson (Sweden), C. Laveille (France), I. Meineke (Germany), F. Mentré (France), P. Morselli (France), G. Paintaud (France), A. Racine-Poon (Switzerland), J. Rodriguez (Spain), F. Rombout (The Netherlands), M. Rowland (UK), J.-L. Steimer (Switzerland), A. Van Peer (Belgium), S. Vozeh (Switzerland), W. Weber (Germany), B. Wittke (Switzerland)The views expressed by the participants do not necessarily reflect those of the organizations they represent.All authors were members of the COST-B1 Working Party on Population Approaches     

Quantitative justification for target concentration intervention--parameter variability and predictive performance using population pharmacokinetic models for aminoglycosides

AIMS: [1] To quantify the random and predictable components of variability for aminoglycoside clearance and volume of distribution [2] To investigate models for predicting aminoglycoside clearance in patients with low serum creatinine concentrations [3] To evaluate the predictive performance of initial dosing strategies for achieving an aminoglycoside target concentration. METHODS: Aminoglycoside demographic, dosing and concentration data were collected from 697 adult patients (> or =20 years old) as part of standard clinical care using a target concentration intervention approach for dose individualization. It was assumed that aminoglycoside clearance had a renal and a nonrenal component, with the renal component being linearly related to predicted creatinine clearance. RESULTS: A two compartment pharmacokinetic model best described the aminoglycoside data. The addition of weight, age, sex and serum creatinine as covariates reduced the random component of between subject variability (BSVR) in clearance (CL) from 94% to 36% of population parameter variability (PPV). The final pharmacokinetic parameter estimates for the model with the best predictive performance were: CL, 4.7 l h(-1) 70 kg(-1); intercompartmental clearance (CLic), 1 l h(-1) 70 kg(-1); volume of central compartment (V1), 19.5 l 70 kg(-1); volume of peripheral compartment (V2) 11.2 l 70 kg(-1). CONCLUSIONS: Using a fixed dose of aminoglycoside will achieve 35% of typical patients within 80-125% of a required dose. Covariate guided predictions increase this up to 61%. However, because we have shown that random within subject variability (WSVR) in clearance is less than safe and effective variability (SEV), target concentration intervention can potentially achieve safe and effective doses in 90% of patients.     

Simulating pharmacokinetic and pharmacodynamic fuzzy-parameterized models: a comparison of numerical methods

Statistical techniques have been traditionally used to deal with parametric variation in pharmacokinetic and pharmacodynamic models, but these require substantial data for estimates of probability distributions. In the presence of limited, inaccurate or imprecise information, simulation with fuzzy numbers represents an alternative tool to handle parametric uncertainty. Existing methods for implementing fuzzy arithmetic may, however, have significant shortcomings in overestimating (e.g., conventional fuzzy arithmetic) and underestimating (e.g., vertex method) the output uncertainty. The purpose of the present study is to apply and compare the applicability of conventional fuzzy arithmetic, vertex method and two recently proposed numerical schemes, namely transformation and optimization methods, for uncertainty modeling in pharmacokinetic and pharmacodynamic fuzzy-parameterized systems. A series of test problems were examined, including empirical pharmacokinetic and pharmacodynamic models, a function non-monotonic in its parameters, and a whole body physiologically based pharmacokinetic model. Our results verified that conventional fuzzy arithmetic can lead to overestimation of response uncertainty and should be avoided. For the monotonic pharmacokinetic and pharmacodynamic models, the vertex method accurately predicted fuzzy-valued output while incurring the least computational cost. It turned out that the choice of a suitable method for fuzzy simulation of the non-monotonic function depended on the required accuracy of the results: the vertex method was capable of eliciting an initial approximate solution with few function evaluations; for more accurate results, the transformation method was the most superior approach in terms of accuracy per unit CPU time.     

Evaluation of population pharmacokinetic models for amikacin dosage individualization in critically ill patients

Objectives The aim of this study was to evaluate the reliability for dosage individualization and Bayesian adaptive control of several literature‐retrieved amikacin population pharmacokinetic models in patients who were critically ill. Methods Four population pharmacokinetic models, three of them customized for critically‐ill patients, were applied using pharmacokinetic software to fifty‐one adult patients on conventional amikacin therapy admitted to the intensive care unit. An estimation of patient‐specific pharmacokinetic parameters for each model was obtained by retrospective analysis of the amikacin serum concentrations measured (n = 162) and different clinical covariates. The model performance for a priori estimation of the area under the serum concentration‐time curve (AUC) and maximum serum drug concentration (C_max) targets was obtained. Key findings Our results provided valuable confirmation of the clinical importance of the choice of population pharmacokinetic models when selecting amikacin dosages for patients who are critically ill. Significant differences in model performance were especially evident when only information concerning clinical covariates was used for dosage individualization and over the two most critical determinants of clinical efficacy of amikacin i.e. the AUC and C_max values. Conclusions Only a single amikacin serum level seemed necessary to diminish the influence of population model on dosage individualization.     

An optimized ibuprofen dosing scheme for preterm neonates with patent ductus arteriosus, based on a population pharmacokinetic and pharmacodynamic study

AIMS

To describe ibuprofen pharmacokinetics in preterm neonates with patent ductus arteriosus (PDA) and to establish relationships between doses, plasma concentrations and ibuprofen efficacy and safety.

METHODS

Sixty-six neonates were treated with median daily doses of 10, 5 and 5 mg kg⁻¹ of ibuprofen-lysine by intravenous infusion on 3 consecutive days. A population pharmacokinetic model was developed with NONMEM. Bayesian individual pharmacokinetic estimates were used to calculate areas under the curve (AUC) and to simulate doses. A logistic regression was performed on PDA closure.

RESULTS

Ibuprofen pharmacokinetics were described by a one-compartment model with linear elimination. Mean population pharmacokinetic estimates with corresponding intersubject variabilities (%) were: elimination clearance CL = 9.49 ml h⁻¹ (62%) and volume of distribution V = 375 ml (72%). Ibuprofen CL significantly increased with postnatal age (PNA): CL = 9.49*(PNA/96.3)^1.49. AUC after the first dose (AUC1D), the sum of AUC after the three doses (AUC3D) and gestational age were significantly higher in 57 neonates with closing PDA than in nine neonates without PDA closure (P = 0.02). PDA closure was observed in 50% of the neonates when AUC1D < 600 mg l⁻¹ h (or AUC3D < 900 mg l⁻¹ h) and in 91% when AUC1D > 600 mg l⁻¹ h (or AUC3D > 900 mg l⁻¹ h) (P = 0.006). No correlation between AUC and side-effects could be demonstrated.

CONCLUSIONS

To achieve these optimal AUCs, irrespective of gestational age, three administrations at 24 h intervals are recommended of 10, 5, 5 mg kg⁻¹ for neonates younger than 70 h, 14, 7, 7 mg kg⁻¹ for neonates between 70 and 108 h and 18, 9, 9 mg kg⁻¹ for neonates between 108 and 180 h.

WHAT IS ALREADY KNOWN ABOUT THIS SUBJECT

• Ibuprofen is a nonsteroidal anti-inflammatory agent that induces closure of the patent ductus arteriosus in neonates.
• Few studies of ibuprofen pharmacokinetics have been performed and were limited to small groups of preterm infants, showing a large intersubject variability and an increase in clearance with either postnatal or gestational age.

WHAT THIS STUDY ADDS

• A population pharmacokinetic study was performed on 66 neonates to characterize the concentration-time courses of ibuprofen.
• Ibuprofen clearance significantly increased from postnatal age day 1 to day 8, but not with gestational age.
• A relationship was shown between ibuprofen area under the curve (AUC) and patent ductus arteriosus closure rate, and an effective threshold AUC was evidenced.
• Dosing schemes were proposed as a function of postnatal age, to achieve this AUC and to improve the efficacy of treatment for patent ductus arteriosus in neonates.

     

A genetic algorithm based global search strategy for population pharmacokinetic/pharmacodynamic model selection

The current algorithm for selecting a population pharmacokinetic/pharmacodynamic model is based on the well-established forward addition/backward elimination method. A central strength of this approach is the opportunity for a modeller to continuously examine the data and postulate new hypotheses to explain observed biases. This algorithm has served the modelling community well, but the model selection process has essentially remained unchanged for the last 30 years. During this time, more robust approaches to model selection have been made feasible by new technology and dramatic increases in computation speed. We review these methods, with emphasis on genetic algorithm approaches and discuss the role these methods may play in population pharmacokinetic/pharmacodynamic model selection.     

重症感染患者多黏菌素B群体药代动力学模型的外部验证

目的:对既往发表的多黏菌素B群体药代动力学(population pharmacokinetics,PPK)模型进行外部验证,评估各模型对重症感染患者血药浓度的预测性能。方法:在PubMed数据库、Web of Science核心合集、中国知网、万方数据库检索建库至2022年3月公开发表的多黏菌素B PPK模型的相关文献,提取模型结构和参数信息。同时收集南京大学医学院附属鼓楼医院接受多黏菌素B治疗重症感染患者的临床数据作为外部数据集。根据患者是否采用连续肾脏替代疗法(continuous renal replacement therapy,CRRT)分为非CRRT组和CRRT组。利用2组数据对已发表模型进行基于模型预测和模型模拟的评价。结果:检索到14个多黏菌素B PPK模型。CRRT组,预测误差检验结果显示,所有已发表模型均不满足选定的标准。非CRRT组,有一个模型具有较好的血药浓度预测性能。结论:可以尝试利用预测结果良好的模型在非CRRT患者进行前瞻性个体化给药。     

Evaluation of methods for estimating population pharmacokinetic parameters. I. Michaelis-menten model: Routine clinical pharmacokinetic data

Individual pharmacokinetic parameters quantify the pharmacokinetics of an individual, while population pharmacokinetic parameters quantify population mean kinetics, interindividual variability, and residual intraindividual variability plus measurement error. Individual pharmacokinetics are estimated by fitting individual data to a pharmacokinetic model. Population pharmacokinetic parameters are estimated either by fitting all individual's data together as though there were no individual kinetic differences (the naive pooled data approach), or by fitting each individual's data separately, and then combining the individual parameter estimates (the two-stage approach). A third approach, NONMEM, takes a middle course between these, and avoids shortcomings of each of them. A data set consisting of 124 steady-state phenytoin concentration-dosage pairs from 49 patients, obtained in the routine course of their therapy, was analyzed by each method. The resulting population parameter estimates differ considerably (population mean Km, for example, is estimated as 1.57, 5.36, and 4.44 g/ml by the naive pooled data, two-stage, and NONMEM approaches, respectively). Simulations of the data were analyzed to investigate these differences. The simulations indicate that the pooled data approach fails to estimate variabilities and produces imprecise estimates of mean kinetics. The two-stage appproach produces good estimates of mean kinetics, but biased and imprecise estimates of interindividual variability. NONMEM produces accurate and precise estimates of all parameters, and also reasonable confidence intervals for them. This performance is exactly what is expected from theoretical considerations and provides empirical support for the use of NONMEM when estimating population pharmacokinetics from routine type patient data.Work supported in part by NIH Grants GM 26676 and GM 26691.     

Do we need full compliance data for population pharmacokinetic analysis?

For population pharmacokinetic analysis of multiple oral doses one of the key issues is knowing as precisely as possible the dose inputs in order to fit a model to the input-output (dose-concentration) relationship. Recently developed electronic monitoring devices, placed on pill containers, permit precise records to be obtained over months, of the time/date opening of the container. Such records are reported to be the most reliable measurement of drug taking behavior for ambulatory patients. To investigate strategies for using and summarizing this new abundant information, a Markov chain process model was developed, that simulates compliance data from real data from electronically monitored patients, and data simulations and analyses were conducted. Results indicate that traditional population pharmacokinetic analysis methods that ignore actual dosing information tend to estimate biased clearance and volume and markedly overestimate random interindividual variability. The best dosing information summarization strategies consist of initially estimating population pharmacokinetic parameters, using no covariates and only a limited number of dose records, the latter chosen based on an a priori estimate of the half-life of the drug in the compartment of interest; then resummarizing the dose records using either population or individual posterior Bayes parameter estimates from the first population fit; and finally reestimating the population parameters using the newly summarized dose records. Such summarization strategies yield the same parameter estimates as using full dosing information records while reducing by at least 75% the CPU time needed for a population pharmacokinetic analysis. Work supported in part by Cooperative agreements AI 27663 and AI 27666 from the National Institute of Allergy and Infectious Diseases, U.S. Department of Health and Human Services. Dr. Girard's salary supported in part by Grant No. 1 F05 TW05185-01 from the Fogarty International Center, National Institutes of Health, U.S. Department of Health and Human Services.     

A comparative population pharmacokinetic analysis for methylprednisolone following multiple dosing of two prodrugs in patients with acute asthma

Aims To conduct a randomized, parallel group comparison of the population pharmacokinetics of the two methylprednisolone (MP) prodrugs Promedrol (MP suleptanate) and Solu-Medrol (MP succinate) in patients hospitalized with acute asthma.
Methods Ninety volunteers were included in the pharmacokinetic analysis. Each volunteer received a dosage regimen of 40  mg (MP equivalents) i.v. 6 hourly for 48  h. The bio-conversion and disposition of a 40  mg (MP equivalent) i.v. dose of either MP suleptanate or MP succinate to MP was modelled as a first order input, and a mono-exponential elimination phase.
Results Population modelling indicated that the only difference in MP pharmacokinetics between MP suleptanate and MP succinate was in the input rate constant (66.0  h⁻¹ vs 5.5  h⁻¹ respectively). Based on individual Bayesian estimates, the exposure of patients to MP was marginally lower for MP suleptanate although the parameter estimates were not significantly different for half-life (2.7  h vs 3.0  h), steady-state AUC (2007.0  ng  ml⁻¹  h vs 2321.0  ng  ml⁻¹  h) and steady-state C _max (698.4  ng  ml⁻¹ vs 647.8  ng  ml⁻¹ ) for MP suleptanate and MP succinate respectively.
Conclusions It was concluded that for the multiple dosage regimen used in patients with acute asthma the systemic exposure to MP following dosing with MP suleptanate is similar to that arising from MP succinate. In addition the differences in the pharmacokinetics for the prodrugs resulted in only a small difference in the relative bioavailability of MP for MP suleptanate (0.94) compared with MP succinate.     

Population pharmacokinetic and pharmacodynamic model of norvancomycin

去甲万古霉素群体药代动力学与药效学研究@张菁$Institute of Antibiotics, Huashan Hospital, Fudan University!Shanghai 200040, China @张婴元$Institute of Antibiotics, Huashan Hospital, Fudan University!Shanghai 200040, China @施耀国$Institute of Antibiotic     

用NONMEM法建立中国癫痫儿童丙戊酸钠的群体药动学/药效学结合模型

目的:建立中国癫痫儿童应用丙戊酸钠(VPA)的群体药动学药效学(PPKPD)结合模型,为设计个体化用药方案奠定基础。方法:回顾性收集246例癫痫患儿应用VPA的临床数据。血药浓度是常规监测的稳态浓度。用246例患儿的数据,通过NONMEM法已经自行成功建立PPK模型。现将246例中单用VPA的69例的数据与已经建立的PPK模型结合,建立PPKPD模型。药效指标用癫痫发作次数减少百分比,分为5级。应用Logistic回归分析,拟合线性药效模型,用NONMEM法建立PPKPD模型,求算血药浓度获得某一级疗效的概率。结果:应用Logistic回归分析,拟合线性药效模型,求算出血药浓度获得某一级疗效的概率:血药浓度超过23μg·ml-1时,5级的概率小于50%,获得4、3、2级的最大概率及浓度为(30μg·ml-1,32.3%)、(50μg·ml-1,26.3%)、(65μg·ml-1,36.5%);血药浓度超过78μg·ml-1时,1级的概率大于50%;浓度为100μg·ml-1时,1级的概率约84.2%。结论:用NONMEM法成功地建立了中国癫痫儿童应用VPA的PPKPD模型,定量地求出某一血药浓度获得不同疗效等级的概率。     

群体遗传学分析软件的应用现状

目的对目前应用比较广泛的6种群体遗传学分析软件进行分析比较,并提供其使用链接。方法通过查阅软件相关的研究和使用文献,从群体遗传学分析软件的功能、操作平台、数据录入和处理等角度进行分析探讨。结果群体遗传学分析软件的应用是进行科学研究的重要武器,分析软件正趋于方便数据录入、多种平台操作、集多项分析功能于一体、傻瓜式操作。结论群体遗传学分析软件正在完善为更加简便、快速、可靠的统计工具。