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Journal of Pharmacokinetics and Pharmacodynamics — Template for authors

Publisher: Springer

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Guideline source

Categories	Rank	Trend in last 3 yrs
Pharmacology	#140 of 297	up by 33 ranks

Journal quality:

Good

Last 4 years overview: 190 Published Papers | 740 Citations

Indexed in: Scopus

Last updated: 28/06/2020

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Journal Performance & Insights

Impact Factor

CiteRatio

Determines the importance of a journal by taking a measure of frequency with which the average article in a journal has been cited in a particular year.

A measure of average citations received per peer-reviewed paper published in the journal.

2.461

15% from 2018

Impact factor for Journal of Pharmacokinetics and Pharmacodynamics from 2016 - 2019
Year	Value
2019	2.461
2018	2.91
2017	2.196
2016	1.673

Graph view

3.9

3% from 2019

CiteRatio for Journal of Pharmacokinetics and Pharmacodynamics from 2016 - 2020
Year	Value
2020	3.9
2019	3.8
2018	3.2
2017	3.0
2016	3.3

Graph view

Insights

Impact factor of this journal has decreased by 15% in last year.
This journal’s impact factor is in the top 10 percentile category.

Insights

CiteRatio of this journal has increased by 3% in last years.
This journal’s CiteRatio is in the top 10 percentile category.

SCImago Journal Rank (SJR)

Source Normalized Impact per Paper (SNIP)

Measures weighted citations received by the journal. Citation weighting depends on the categories and prestige of the citing journal.

Measures actual citations received relative to citations expected for the journal's category.

0.827

13% from 2019

SJR for Journal of Pharmacokinetics and Pharmacodynamics from 2016 - 2020
Year	Value
2020	0.827
2019	0.735
2018	0.783
2017	0.784
2016	0.714

Graph view

0.881

3% from 2019

SNIP for Journal of Pharmacokinetics and Pharmacodynamics from 2016 - 2020
Year	Value
2020	0.881
2019	0.91
2018	0.945
2017	0.9
2016	0.778

Graph view

Insights

SJR of this journal has increased by 13% in last years.
This journal’s SJR is in the top 10 percentile category.

Insights

SNIP of this journal has decreased by 3% in last years.
This journal’s SNIP is in the top 10 percentile category.

Journal of Pharmacokinetics and Pharmacodynamics

Guideline source: View

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Springer

Journal of Pharmacokinetics and Pharmacodynamics

Journal of Pharmacokinetics and Pharmacodynamics is devoted to illustrating the importance of pharmacokinetics, pharmacodynamics, and pharmacometrics in the understanding of drug action, therapy, design, development, and evaluation. Experimental and theoretical papers deal with the kinetics of drug disposition and drug action; clinical pharmacokinetics; factors affecting formation and disposition of drug metabolites; dosage form evaluation from animals and man; bioavailabilty studies, scaling from animals to man, and in vitro and in vivo correlations. A special section is provided for papers addressing pharmacometrics, the computational and statistical methods used for the modeling, analysis, interpretation, and simulation of pharmacokinetic and pharmacodynamic data. Although purely theoretical papers can be accepted, most papers will illustrate and apply their finding to real-world observations in animals and/or man. Read Less

Pharmacology

Pharmacology, Toxicology and Pharmaceutics

Last updated on	28 Jun 2020
ISSN	1567-567X
Impact Factor	Medium - 0.794
Open Access	No
Sherpa RoMEO Archiving Policy	Green
Plagiarism Check	Available via Turnitin
Endnote Style	Download Available
Bibliography Name	SPBASIC
Citation Type	Author Year (Blonder et al, 1982)
Bibliography Example	Beenakker CWJ (2006) Specular andreev reflection in graphene. Phys Rev Lett 97(6):067,007, URL 10.1103/PhysRevLett.97.067007

Top papers written in this journal

Journal Article • DOI: 10.1023/A:1012299115260 •

Ways to fit a PK model with some data below the quantification limit.

Stuart L. Beal¹ •

01 Oct 2001 - Journal of Pharmacokinetics and Pharmacodynamics

Abstract:

Pharmacokinetic data consist of drug concentration measurements, as well as reports of some measured concentrations being below the quantification limit of the assay (BQL). A pharmacokinetic model may befit to these data, and for this purpose, the BQL observations must be either discarded or handled in a special way. In this ... Pharmacokinetic data consist of drug concentration measurements, as well as reports of some measured concentrations being below the quantification limit of the assay (BQL). A pharmacokinetic model may befit to these data, and for this purpose, the BQL observations must be either discarded or handled in a special way. In this paper, seven methods for dealing with BQL observations are evaluated. Both single-subject and population data are simulated from a one-compartment model. A moderate amount of data is simulated for each individual. The actual cv of concentration measurements at the quantification limit is assumed to be no greater than 20%, in accord with the FDA Guidance. The results of this paper should be interpreted in this context. The methods include handling BQL observations as fixed-point censored observations, i.e., by using the likelihoods that these observations are in fact BQL. This method is shown to have some overall statistical advantage. However, the gain in using this method over that of simply discarding the BQL observations is not always much, and this is especially so when the frequency of BQL observations is small. Some simple methods entailing (i) replacing one or more BQL observations with the value 0, or (ii) replacing them with the value QL/2, where QL is the quantification limit, are also included. The first of these two approaches should not be used With population data, use of the second approach can result in some noticeably improved estimation of the typical value of a parameter, but then there is also marked degradation in the estimation of the population variance of the parameter. read more

Topics:

Population variance (53%)53% related to the paper

833 Citations

Journal Article • DOI: 10.1023/A:1014414520282 •

General pharmacokinetic model for drugs exhibiting target-mediated drug disposition.

Donald E. Mager¹, William J. Jusko¹ •

01 Dec 2001 - Journal of Pharmacokinetics and Pharmacodynamics

Abstract:

Drugs that bind with high affinity and to a significant extent (relative to dose) to a pharmacologic target such as an enzyme, receptor, or transporter may exhibit nonlinear pharmacokinetic (PK) behavior. Processes such as receptor-mediated endocytosis may result in drug elimination. A general PK model for characterizing such... Drugs that bind with high affinity and to a significant extent (relative to dose) to a pharmacologic target such as an enzyme, receptor, or transporter may exhibit nonlinear pharmacokinetic (PK) behavior. Processes such as receptor-mediated endocytosis may result in drug elimination. A general PK model for characterizing such behavior is described and explored through computer simulations and applications to several therapeutic agents. Simulations show that model predicted plasma concentration vs. time profiles are expected to be polyexponential with steeper distribution phases for lower doses and similar terminal disposition phases. Noncompartmental parameters always show apparent Vss and CLD decreasing with dose, but apparent clearance decreases only when the binding process produces drug elimination. The proposed model well captured the time-course of drug concentrations for the aldose reductase inhibitor imirestat, the endothelin receptor antagonist bosentan, and recombinant human interferon-β 1a. This type of model has a mechanistic basis and considerable utility for fully describing the kinetics for various doses of relevant drugs. read more

Topics:

Pharmacokinetics (53%)53% related to the paper

509 Citations

Journal Article • DOI: 10.1007/S10928-007-9066-0 •

Implementation of a transit compartment model for describing drug absorption in pharmacokinetic studies

Radojka M. Savic¹, Daniël M. Jonker¹, •

26 Jul 2007 - Journal of Pharmacokinetics and Pharmacodynamics

Abstract:

Purpose: To compare the performance of the standard lag time model (LAG model) with the performance of an analytical solution of the transit compartment model (TRANSIT model) in the evaluation of four pharmacokinetic studies with four different compounds. Methods: The population pharmacokinetic analyses were performed using N... Purpose: To compare the performance of the standard lag time model (LAG model) with the performance of an analytical solution of the transit compartment model (TRANSIT model) in the evaluation of four pharmacokinetic studies with four different compounds. Methods: The population pharmacokinetic analyses were performed using NONMEM on concentration–time data of glibenclamide, furosemide, amiloride, and moxonidine. In the TRANSIT model, the optimal number of transit compartments was estimated from the data. This was based on an analytical solution for the change in drug concentration arising from a series of transit compartments with the same first-order transfer rate between each compartment. Goodness-of-fit was assessed by the decrease in objective function value (OFV) and by inspection of diagnostic graphs. Results: With the TRANSIT model, the OFV was significantly lower and the goodness-of-fit was markedly improved in the absorption phase compared with the LAG model for all drugs. The parameter estimates related to the absorption differed between the two models while the estimates of the pharmacokinetic disposition parameters were similar. Conclusion: Based on these results, the TRANSIT model is an attractive alternative for modeling drug absorption delay, especially when a LAG model poorly describes the drug absorption phase or is numerically unstable. read more

Topics:

NONMEM (52%)52% related to the paper, Population (51%)51% related to the paper

437 Citations

Journal Article • DOI: 10.1023/A:1011555016423 •

Evaluating pharmacokinetic/pharmacodynamic models using the posterior predictive check

Yoshitaka Yano¹, Stuart L. Beal, •

01 Apr 2001 - Journal of Pharmacokinetics and Pharmacodynamics

Abstract:

The posterior predictive check (PPC) is a model evaluation tool. It assigns a value (p PPC ) to the probability that the value of a given statistic computed from data arising under an analysis model is as or more extreme than the value computed from the real data themselves. If this probability is too small, the analysis mo... The posterior predictive check (PPC) is a model evaluation tool. It assigns a value (p PPC ) to the probability that the value of a given statistic computed from data arising under an analysis model is as or more extreme than the value computed from the real data themselves. If this probability is too small, the analysis model is regarded as invalid for the given statistic. Properties of the PPC for pharmacokinetic (PK) and pharmacodynamic (PD) model evaluation are examined herein for a particularly simple simulation setting: extensive sampling of a single individual's data arising from simple PK/PD and error models. To test the performance characteristics of the PPC, repeatedly, “real” data are simulated and for a variety of statistics, the PPC is applied to an analysis model, which may (null hypothesis) or may not (alternative hypothesis) be identical to the simulation model. Five models are used here: (PK1) mono-exponential with proportional error, (PK2) biexponential with proportional error, (PK2e) biexponential with additive error, (PD1) E max model with additive error under the logit transform, and (PD2) sigmoid E max model with additive error under the logit transform. Six simulation/analysis settings are studied. The first three, (PK1/PK1), (PK2/PK2), and (PD1/PD1) evaluate whether the PPC has appropriate type-I error level, whereas the second three (PK2/PK1), (PK2e/PK2), and (PD2/PD1) evaluate whether the PPC has adequate power. For a set of 100 data sets simulated/analyzed under each model pair according to a stipulated extensive sampling design, the p PPC is computed for a number of statistics in three different ways (each way uses a different approximation to the posterior distribution on the model parameters). We find that in general; (i) The PPC is conservative under the null in the sense that for many statistics, prob(p PPC ≤α)<α for small α. With respect to such statistics, this means that useful models will rarely be regarded incorrectly as invalid. A high correlation of a statistic with the parameter estimates obtained from the same data used to compute the statistic (a measure of statistical “sufficiency”) tends to identify the most conservative statistics. (ii) Power is not very great, at least for the alternative models we tested, and it is especially poor with “statistics” that are in part a function of parameters as well as data. Although there is a tendency for nonsufficient statistics (as we have measured this) to have greater power, this is by no means an infallible diagnostic. (iii) No clear advantage for one or another method of approximating the posterior distribution on model parameters is found. read more

Topics:

p-value (55%)55% related to the paper, Posterior probability (53%)53% related to the paper, Statistic (52%)52% related to the paper

385 Citations

Journal Article • DOI: 10.1023/B:JOPA.0000012998.04442.1F •

Simultaneous vs. sequential analysis for population PK/PD data I: best-case performance.

Liping Zhang¹, Stuart L. Beal¹, •

01 Dec 2003 - Journal of Pharmacokinetics and Pharmacodynamics

Abstract:

Dose [-concentration]-effect relationships can be obtained by fitting a predictive pharmacokinetic (PK)-pharmacodynamic (PD) model to both concentration and effect observations. Either a model can be fit simultaneously to all the data (“simultaneous” method), or first a model can be fit to the PK data and then a model can be ... Dose [-concentration]-effect relationships can be obtained by fitting a predictive pharmacokinetic (PK)-pharmacodynamic (PD) model to both concentration and effect observations. Either a model can be fit simultaneously to all the data (“simultaneous” method), or first a model can be fit to the PK data and then a model can be fit to the PD data, conditioning in some way on the PK data or on the estimates of the PK parameters (“sequential” method). Using simulated data, we compare the performance of the simultaneous method with that of three sequential method variants with respect to computation time, estimation precision, and inference. Using NONMEM, under various study designs, observations of one type of PK and one type of PD response from different numbers of individuals were simulated according to a one-compartment PK model and direct Emax PD model, with parameters drawn from an appropriate population distribution. The same PK and PD models were fit to these observations using simultaneous and sequential methods. Performance measures include computation time, fraction of cases for which estimates are successfully obtained, precision of PD parameter estimates, precision of PD parameter standard error estimates, and type-I error rates of a likelihood ratio test. With the sequential method, computation time is less, and estimates are more likely to be obtained. Using the First Order Conditional Estimation (FOCE) method, a sequential approach that conditions on both population PK parameter estimates and PK data, estimates PD parameters and their standard errors about as well as the “gold standard” simultaneous method, and saves about 40% computation time. Type-I error rates of likelihood ratio test for both simultaneous and sequential approaches are close to the nominal rates. read more

Topics:

PK/PD models (60%)60% related to the paper, Population (52%)52% related to the paper, Likelihood-ratio test (51%)51% related to the paper

304 Citations

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To be honest, the answer is no. The impact factor is one of the many elements that determine the quality of a journal. Few of these factors include review board, rejection rates, frequency of inclusion in indexes, and Eigenfactor. You need to assess all these factors before you make your final call.

13. What is Sherpa RoMEO Archiving Policy for Journal of Pharmacokinetics and Pharmacodynamics?

We extracted this data from Sherpa Romeo to help researchers understand the access level of this journal in accordance with the Sherpa Romeo Archiving Policy for Journal of Pharmacokinetics and Pharmacodynamics. The table below indicates the level of access a journal has as per Sherpa Romeo's archiving policy.

RoMEO Colour	Archiving policy
Green	Can archive pre-print and post-print or publisher's version/PDF
Blue	Can archive post-print (ie final draft post-refereeing) or publisher's version/PDF
Yellow	Can archive pre-print (ie pre-refereeing)
White	Archiving not formally supported

FYI:

Pre-prints as being the version of the paper before peer review and
Post-prints as being the version of the paper after peer-review, with revisions having been made.

14. What are the most common citation types In Journal of Pharmacokinetics and Pharmacodynamics?

The 5 most common citation types in order of usage for Journal of Pharmacokinetics and Pharmacodynamics are:.

S. No.	Citation Style Type
1.	Author Year
2.	Numbered
3.	Numbered (Superscripted)
4.	Author Year (Cited Pages)
5.	Footnote

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Journal of Pharmacokinetics and Pharmacodynamics — Template for authors

Related Journals

Journal Performance & Insights

Impact Factor

CiteRatio

2.461

3.9

Insights

Insights

SCImago Journal Rank (SJR)

Source Normalized Impact per Paper (SNIP)

0.827

0.881

Insights

Insights

Journal of Pharmacokinetics and Pharmacodynamics

Journal of Pharmacokinetics and Pharmacodynamics

Top papers written in this journal

Abstract:

Topics:

Abstract:

Topics:

Abstract:

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Speed and accuracy over MS Word

Plagiarism Reports via Turnitin

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Frequently asked questions

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No word template required

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Trusted by academicians

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