Population Fisher information matrix and optimal design of discrete data responses in population pharmacodynamic experiments

In the recent years, interest in the application of experimental design theory to population pharmacokinetic (PK) and pharmacodynamic (PD) experiments has increased. The aim is to improve the efficiency and the precision with which parameters are estimated during data analysis and sometimes to increase the power and reduce the sample size required for hypothesis testing. The population Fisher information matrix (PFIM) has been described for uniresponse and multiresponse population PK experiments for design evaluation and optimisation. Despite these developments and availability of tools for optimal design of population PK and PD experiments much of the effort has been focused on repeated continuous variable measurements with less work being done on repeated discrete type measurements. Discrete data arise mainly in PDs e.g. ordinal, nominal, dichotomous or count measurements. This paper implements expressions for the PFIM for repeated ordinal, dichotomous and count measurements based on analysis by a mixed-effects modelling technique. Three simulation studies were used to investigate the performance of the expressions. Example 1 is based on repeated dichotomous measurements, Example 2 is based on repeated count measurements and Example 3 is based on repeated ordinal measurements. Data simulated in MATLAB were analysed using NONMEM (Laplace method) and the glmmML package in R (Laplace and adaptive Gauss-Hermite quadrature methods). The results obtained for Examples 1 and 2 showed good agreement between the relative standard errors obtained using the PFIM and simulations. The results obtained for Example 3 showed the importance of sampling at the most informative time points. Implementation of these expressions will provide the opportunity for efficient design of population PD experiments that involve discrete type data through design evaluation and optimisation.     

Further developments of the Fisher information matrix in nonlinear mixed effects models with evaluation in population pharmacokinetics

We extend the development of the expression of the Fisher information matrix in nonlinear mixed effects models for designs evaluation. We consider the dependence of the marginal variance of the observations with the mean parameters and assume an heteroscedastic variance error model. Complex models with interoccasions variability and parameters quantifying the influence of covariates are introduced. Two methods using a Taylor expansion of the model around the expectation of the random effects or a simulated value, using then Monte Carlo integration, are proposed and compared. Relevance of the resulting standard errors is investigated in a simulation study with NONMEM.     

Simultaneous population optimal design for pharmacokinetic-pharmacodynamic experiments

Multiple outputs or measurement types are commonly gathered in biological experiments. Often, these experiments are expensive (such as clinical drug trials) or require careful design to achieve the desired information content. Optimal experimental design protocols could help alleviate the cost and increase the accuracy of these experiments. In general, optimal design techniques ignore between-individual variability, but even work that incorporates it (population optimal design) has treated simultaneous multiple output experiments separately by computing the optimal design sequentially, first finding the optimal design for one output (eg, a pharmacokinetic [PK] measurement) and then determining the design for the second output (eg, a pharmacodynamic [PD] measurement). Theoretically, this procedure can lead to biased and imprecise results when the second model parameters are also included in the first model (as in PK-PD models). We present methods and tools for simultaneous population D-optimal experimental designs, which simultaneously compute the design of multiple output experiments, allowing for correlation between model parameters. We then apply these methods to simulated PK-PD experiments. We compare the new simultaneous designs to sequential designs that first compute the PK design, fix the PK parameters, and then compute the PD design in an experiment. We find that both population designs yield similar results in designs for low sample number experiments, with simultaneous designs being possibly superior in situations in which the number of samples is unevenly distributed between outputs. Simultaneous population D-optimality is a potentially useful tool in the emerging field of experimental design.     

Robust optimal design for the estimation of hyperparameters in population pharmacokinetics.

The expectation of the determinant of the inverse of the population Fisher information matrix is proposed as a criterion to evaluate and optimize designs for the estimation of population pharmacokinetic (PK) parameters. Given a PK model, a measurement error model, a parametric distribution of the parameters and a prior distribution representing the belief about the hyperparameters to be estimated, the EID criterion is minimized in order to find the optimal population design. In this approach, a group is defined as a number of subjects to whom the same sampling schedule (i.e., the number of samples and their timing) is applied. The constraints, which are defined a priori, are the number of groups, the size of each group and the number of samples per subject in each group. The goal of the optimization is to determine the optimal sampling times in each group. This criterion is applied to a one-compartment open model with first-order absorption. The error model is either homoscedastic or heteroscedastic with constant coefficient of variation. Individual parameters are assumed to arise from a lognormal distribution with mean vector M and covariance matrix C. Uncertainties about the M and C are accounted for by a prior distribution which is normal for M and Wishart for C. Sampling times are optimized by using a stochastic gradient algorithm. Influence of the number of different sampling schemes, the number of subjects per sampling schedule, the number of samples per subject in each sampling scheme, the uncertainties on M and C and the assumption about the error model and the dose have been investigated.     

The effect of workstation technology on methods in drug design and discovery

Summary The last two decades have seen the birth, emergence and acceptance of the field of computational drug design and discovery. In the early days of this period, computer-aided drug design was performed on mainframe or supercomputers by specialists. Modern-day workstations provide access to a large palette of powerful software tools to a wide audience of computational, medicinal and bioorganic chemists. In this paper we review the trends in computer hardware that have led to powerful computer systems, including the evolution of workstations from the microprocessors used in personal computers and the gradual development of workstation networks. We predict how advances in workstation technology will affect computational drug design and discovery in the future. We also outline some challenges that need to be faced to make workstation-based computational chemistry even more useful.     

LC-MS~n应用于生物样品检测中基质效应的评价

液质联用技术具有灵敏度高,特异性强的特点,已被广泛运用于生物样品的分析和检测中。然而基质效应问题往往被忽略,事实上基质效应的存在可能极大影响液质联用技术的准确度和精确度,降低实验数据的可靠性。文中通过查阅和比较美国FDA和欧洲EMEA相关指导原则,探讨基质效应存在的原因和消除基质效应的方法,提出基质效应的评价方法和评价指标,供生物样品分析工作者参考。     

基于Taguchi和ANOVA方法的HPLC中药成分检测实验优化设计

目的 HPLC中药成分检测实验的系统优化设计.方法 结合Taguchi方法和ANOVA方法,兼顾色谱信号的最小变异和最优分离品质,寻找出影响HPLC实验过程较显著的因子,以及色谱条件的最佳化趋势.结果 以感冒止咳糖浆中黄芩苷的检测为例,系统设计实验,确定流动相甲醇百分比为影响检测过程最显著的因子,并优选出最佳色谱条件趋势为柱温30℃,流速0.6mL·min-1,流动相甲醇百分比为45%;结论本法科学地设计实验和快速有效提升实验效率与品质,可作为优化HPLC中药成分检测条件的有效方法.     

The effect of methylphenidate on information processing

Models of information processing currently popular in cognitive psychology divide the reaction process into a series of discrete separable stages. The distinction between one stage and another is verified by the additive factors method (AFM) as defined by Sternberg (1969). Task factors that do not interact with each other are inferred to affect different stages. The distinction between stimulus evaluation stages and response selection stages has been supported by brain event related potential (ERP) studies. The latency of the P300 component of the ERP is sensitive to changes in stimulus complexity but not to to changes in response complexity. The focus of this research is to determine the effects of stimulant drugs on stages of information processing using both reaction time (RT) and P300 latency within an AFM framework. four doses of methylphenidate (MP) were used in a within-subjects design to examine the effects of MP on stimulus and response processing. We found that MP speeds RT, and that this effect does not interact with the effect of stimulus complexity on RT. MP dose interacts with response complexity, the dose for optimal speeding varying with the level of complexity. The latency of P300 is increased by stimulus complexity, and not by response complexity, nor is it affected by MP. These results show that the stimulant drug acts on processes involved in response selection, rather than in stimulus evaluation. Individual differences in drug response are dose dependent, but also point to an effect on response processing.     

正交试验法优选"如意金黄散"巴布剂基质配方

目的 优选"如意金黄散"巴布剂基质最佳配方.方法 通过正交设计试验,以配方中聚丙烯酸钠、卡波姆、聚乙烯醇、CMC-Na、明胶、甘油为因素,以用量为水平,设计L18(37)正交表,以巴布剂基质的剥离强度和内聚力为量化指标,综合感观评分为综合考察指标,通过数据分析,优选出最佳的基质配比.结果 巴布剂基质最佳配方为聚丙烯酸钠∶卡波姆∶聚乙烯醇∶CMC-Na∶明胶∶甘油配比为0.5∶0.5∶0.12∶0.10∶0.08∶6.5.配方中各因素对剥离强度作用大小依次为聚丙烯酸钠、卡波姆、聚乙烯醇、CMC-Na、明胶、甘油,对内聚力作用大小依次卡波姆、CMC-Na、明胶、聚乙烯醇、聚丙烯酸钠、甘油.结论 采用优化后配方巴布剂剥离性能优良,内聚力适中、涂展性良好等,各项指标均符合外用贴敷要求.     

Multi-objective optimization methods in drug design

Drugs must satisfy multiple objectives to be approved by licensing authorities. Each of these objectives must be optimized during a drug discovery program. Multi-objective optimization methods enable searching for solutions meeting numerous objectives simultaneously.

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儿童Miller Fisher综合征的临床分析

目的：分析儿童Miller Fisher综合征的临床特点,加深对本病的认识,避免误诊、误治。方法：回顾性分析近5年我院诊断为Miller Fisher综合征的6倒惠儿的临床特点,并结合文献进行讨论。结果：6倒Miller Fisher综合征患儿中眼肌麻痹5例,共济失调6倒,面瘫3例,球麻痹3例,腱反射消失5例;4例头颅CT／MRI正常,1例脊髓MPd提示神经根肿胀,神经传导异常4例,脑脊液有蛋白一细胞分离5例。结论：Miller Fisher综合征是格林．巴利综合征的变异型,以眼肌麻痹、共济失调、腱反射消失为特点。伴脑脊液蛋白增高和神经传导异常．该病预后多良好。     

The bayesian bias correction method of the first-order approximation of nonlinear mixed-effects models for population pharmacokinetics

Population pharmacokinetic analysis usually employs nonlinear mixed-effects models. To estimate the parameters, Beal and Sheiner (1982) proposed the first-order method that employs a first-order Taylor series expansion around the means of random individual parameters. Because of the small computational burden and the high convergence proportion of maximization of the log likelihood function, this method is often used in practice. However, it is known that the estimates are biased. This paper proposes a simple procedure to reduce the bias. The proposed method maximizes the nonapproximated log likelihood functions of each individual given estimates of the population parameters derived from the first-order method, and the derived Bayes estimates of the random individual parameters are utilized to improve the estimates of the population mean parameters. We confirmed that the proposed method reduced the bias using simulated data and actual erythropoietin concentration data.     

Recent advances in the design of matrix metalloprotease inhibitors

Inhibition of matrix metalloproteases (MMPs) for the treatment of diseases, such as cancer, arthritis and other diseases associated with tissue remodeling, has become an area of intense interest in the pharmaceutical industry in recent years. Despite tremendous efforts over the last decade to explore individual members of this target family, along with multiple inhibitor classes, simple and effective drugs for inhibiting individual MMPs have not yet emerged. This review highlights the major developments in research into MMPs and their inhibitors, from the recent medicinal chemistry literature, with a focus on structure-based design, selectivity and pharmacokinetic (PK) properties. The increasing availability of high-resolution X-ray crystal structures for many members of this protein family makes MMPs ideally suited for structure-based design approaches, which are now routinely used in this area. The most challenging aspect of lead optimization for MMP inhibitors is in finding candidates having acceptable pharmacological, PK and selectivity profiles. Clinical trials in cancer giving disappointing results have led to discussions on how to gain adequate MMP selectivity in order to minimize side effects. Unfortunately, careful analysis of X-ray crystal structures has not suggested any simple solutions. These areas collectively constitute the main challenges in the current search for orally available MMP inhibitors, and will be discussed in this review.     

The stage-specific effect of alcohol on human information processing

Rationale Research on the limits of information processing shows that when a dual task is performed in quick succession, performance on the second task is increasingly degraded as the temporal gap between task 1 and task 2 decreases. The carry-over effect on task 2 is assumed to occur because the central cognitive stage of processing must be completed before the processing of task 2 can begin. This creates a bottleneck when the tasks are performed in close succession, but with longer delays task 2 is no longer affected by task 1.Objectives It was predicted that if alcohol disturbs (slows) the central stage of processing, shorter delays between task 1 and task 2 should reveal more intense disruption in the performance of task 2.Methods Two groups (n=16) of healthy male social drinkers performed a baseline test on a dual task. On each trial, task 1 was followed by task 2 at one of four delays (50, 200, 500, and 1100 ms). The groups then received either 0.65 g alcohol/kg or a placebo and performed the task again.Results The RT of the alcohol and placebo groups did not differ on the baseline test. In accord with the hypothesis, the alcohol group performed task 2 more slowly on the treatment test than did the placebo group at the three shortest delays (P<0.02). At the longest delay, the RT of the groups did not differ (P>0.15).Conclusions This pattern of task 2 RTs indicates that a moderate dose of alcohol can significantly impair (slow) the central, cognitive stage of information processing.