12
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Population Pharmacokinetic Properties of Piperaquine in Falciparum Malaria: An Individual Participant Data Meta-Analysis

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Background

          Artemisinin-based combination therapies (ACTs) are the mainstay of the current treatment of uncomplicated Plasmodium falciparum malaria, but ACT resistance is spreading across Southeast Asia. Dihydroartemisinin-piperaquine is one of the five ACTs currently recommended by the World Health Organization. Previous studies suggest that young children (<5 y) with malaria are under-dosed. This study utilised a population-based pharmacokinetic approach to optimise the antimalarial treatment regimen for piperaquine.

          Methods and Findings

          Published pharmacokinetic studies on piperaquine were identified through a systematic literature review of articles published between 1 January 1960 and 15 February 2013. Individual plasma piperaquine concentration–time data from 11 clinical studies (8,776 samples from 728 individuals) in adults and children with uncomplicated malaria and healthy volunteers were collated and standardised by the WorldWide Antimalarial Resistance Network. Data were pooled and analysed using nonlinear mixed-effects modelling. Piperaquine pharmacokinetics were described successfully by a three-compartment disposition model with flexible absorption. Body weight influenced clearance and volume parameters significantly, resulting in lower piperaquine exposures in small children (<25 kg) compared to larger children and adults (≥25 kg) after administration of the manufacturers’ currently recommended dose regimens. Simulated median (interquartile range) day 7 plasma concentration was 29.4 (19.3–44.3) ng/ml in small children compared to 38.1 (25.8–56.3) ng/ml in larger children and adults, with the recommended dose regimen. The final model identified a mean (95% confidence interval) increase of 23.7% (15.8%–32.5%) in piperaquine bioavailability between each piperaquine dose occasion. The model also described an enzyme maturation function in very young children, resulting in 50% maturation at 0.575 (0.413–0.711) y of age. An evidence-based optimised dose regimen was constructed that would provide piperaquine exposures across all ages comparable to the exposure currently seen in a typical adult with standard treatment, without exceeding the concentration range observed with the manufacturers’ recommended regimen. Limited data were available in infants and pregnant women with malaria as well as in healthy individuals.

          Conclusions

          The derived population pharmacokinetic model was used to develop a revised dose regimen of dihydroartemisinin-piperaquine that is expected to provide equivalent piperaquine exposures safely in all patients, including in small children with malaria. Use of this dose regimen is expected to prolong the useful therapeutic life of dihydroartemisinin-piperaquine by increasing cure rates and thereby slowing resistance development. This work was part of the evidence that informed the World Health Organization technical guidelines development group in the development of the recently published treatment guidelines (2015).

          Abstract

          Joel Tarning and colleagues combine individual participant data from clinical trials to develop a pharmacokinetic model and derive an optimized dose regimen for malaria treatment in children.

          Author Summary

          Why Was This Study Done?
          • Despite expansion of malaria prevention and treatment in the last decade, malaria still kills around 1,200 people each day, mostly children below the age of five.

          • Reduced drug exposure to one of the commonly used antimalarial drug combinations, dihydroartemisinin-piperaquine, has been reported in small children.

          • It is crucial to develop an optimised dose regimen that achieves similar drug exposure in all patient groups, in order to give all patients an equal chance of cure.

          What Did the Researchers Do and Find?
          • Drug concentration–time data (8,776 piperaquine concentration measurements) from 728 individuals in 11 separate clinical trials were collated and pooled for an individual participant data meta-analysis.

          • A pharmacokinetic model was developed to describe the pharmacological properties of piperaquine, the expected variability between patients, and the influence of biologically important covariates.

          • Small children had a substantially lower piperaquine exposure after recommended dosing regimens.

          • The developed pharmacokinetic model was used to derive a new optimised dose regimen.

          What Do These Findings Mean?
          • The proposed improved dose regimen of dihydroartemisinin-piperaquine is expected to provide equivalent piperaquine exposures safely in all patients, including in small children with malaria.

          • An optimised dose regimen should prolong the useful therapeutic life of dihydroartemisinin-piperaquine by increasing cure rates and thereby slowing resistance development.

          Related collections

          Most cited references44

          • Record: found
          • Abstract: found
          • Article: not found

          Prediction-corrected visual predictive checks for diagnosing nonlinear mixed-effects models.

          Informative diagnostic tools are vital to the development of useful mixed-effects models. The Visual Predictive Check (VPC) is a popular tool for evaluating the performance of population PK and PKPD models. Ideally, a VPC will diagnose both the fixed and random effects in a mixed-effects model. In many cases, this can be done by comparing different percentiles of the observed data to percentiles of simulated data, generally grouped together within bins of an independent variable. However, the diagnostic value of a VPC can be hampered by binning across a large variability in dose and/or influential covariates. VPCs can also be misleading if applied to data following adaptive designs such as dose adjustments. The prediction-corrected VPC (pcVPC) offers a solution to these problems while retaining the visual interpretation of the traditional VPC. In a pcVPC, the variability coming from binning across independent variables is removed by normalizing the observed and simulated dependent variable based on the typical population prediction for the median independent variable in the bin. The principal benefit with the pcVPC has been explored by application to both simulated and real examples of PK and PKPD models. The investigated examples demonstrate that pcVPCs have an enhanced ability to diagnose model misspecification especially with respect to random effects models in a range of situations. The pcVPC was in contrast to traditional VPCs shown to be readily applicable to data from studies with a priori and/or a posteriori dose adaptations.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Dihydroartemisinin-piperaquine resistance in Plasmodium falciparum malaria in Cambodia: a multisite prospective cohort study.

            Artemisinin resistance in Plasmodium falciparum threatens to reduce the efficacy of artemisinin combination therapies (ACTs), thus compromising global efforts to eliminate malaria. Recent treatment failures with dihydroartemisinin-piperaquine, the current first-line ACT in Cambodia, suggest that piperaquine resistance may be emerging in this country. We explored the relation between artemisinin resistance and dihydroartemisinin-piperaquine failures, and sought to confirm the presence of piperaquine-resistant P falciparum infections in Cambodia.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Importance of shrinkage in empirical bayes estimates for diagnostics: problems and solutions.

              Empirical Bayes ("post hoc") estimates (EBEs) of etas provide modelers with diagnostics: the EBEs themselves, individual prediction (IPRED), and residual errors (individual weighted residual (IWRES)). When data are uninformative at the individual level, the EBE distribution will shrink towards zero (eta-shrinkage, quantified as 1-SD(eta (EBE))/omega), IPREDs towards the corresponding observations, and IWRES towards zero (epsilon-shrinkage, quantified as 1-SD(IWRES)). These diagnostics are widely used in pharmacokinetic (PK) pharmacodynamic (PD) modeling; we investigate here their usefulness in the presence of shrinkage. Datasets were simulated from a range of PK PD models, EBEs estimated in non-linear mixed effects modeling based on the true or a misspecified model, and desired diagnostics evaluated both qualitatively and quantitatively. Identified consequences of eta-shrinkage on EBE-based model diagnostics include non-normal and/or asymmetric distribution of EBEs with their mean values ("ETABAR") significantly different from zero, even for a correctly specified model; EBE-EBE correlations and covariate relationships may be masked, falsely induced, or the shape of the true relationship distorted. Consequences of epsilon-shrinkage included low power of IPRED and IWRES to diagnose structural and residual error model misspecification, respectively. EBE-based diagnostics should be interpreted with caution whenever substantial eta- or epsilon-shrinkage exists (usually greater than 20% to 30%). Reporting the magnitude of eta- and epsilon-shrinkage will facilitate the informed use and interpretation of EBE-based diagnostics.
                Bookmark

                Author and article information

                Contributors
                Role: Academic Editor
                Journal
                PLoS Med
                PLoS Med
                plos
                plosmed
                PLoS Medicine
                Public Library of Science (San Francisco, CA USA )
                1549-1277
                1549-1676
                10 January 2017
                January 2017
                : 14
                : 1
                : e1002212
                Affiliations
                [1 ]WorldWide Antimalarial Resistance Network, Oxford, United Kingdom
                [2 ]Mahidol Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand
                [3 ]Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
                [4 ]Division of Clinical Pharmacology, Department of Medicine, University of Cape Town, Cape Town, South Africa
                [5 ]Department of Drug Evaluation, Australian Army Malaria Institute, Brisbane, Queensland, Australia
                [6 ]Department of Malaria, Military Institute of Hygiene and Epidemiology, Hanoi, Viet Nam
                [7 ]Department of Infectious Diseases, Military Hospital 108, Hanoi, Viet Nam
                [8 ]Institut de Recherche en Sciences de la Santé, Direction Régionale de l’Ouest, Bobo-Dioulasso, Burkina Faso
                [9 ]London School of Hygiene & Tropical Medicine, London, United Kingdom
                [10 ]Kenya Medical Research Institute–Wellcome Trust Research Programme, Kilifi, Kenya
                [11 ]Institute for Tropical Medicine, University of Tübingen, Tübingen, Germany
                [12 ]Joanna Briggs Affiliate Centre for Evidence-Based Health Care, Evidence Synthesis and Translation Unit, Afya Research Africa, Nairobi, Kenya
                [13 ]Global and Tropical Health Division, Menzies School of Health Research and Charles Darwin University, Darwin, Northern Territory, Australia
                [14 ]Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, California, United States of America
                [15 ]Yale School of Public Health and Medicine, New Haven, Connecticut, United States of America
                [16 ]Shoklo Malaria Research Unit, Faculty of Tropical Medicine, Mahidol University, Mae Sot, Thailand
                ISGlobal, SPAIN
                Author notes

                KIB and NJW are members of the WHO Technical Expert Group (TEG) on Malaria Chemotherapy. KIB is also a member of the WHO TEG on Drug Resistance and Containment. KIB, NJW, JT and SP are members of the WHO Malaria Chemotherapy sub-group on dosage recommendations. None of the authors declare any other conflict of interest.

                • Conceptualization: PJG NJW KIB JT.

                • Data curation: RMH LW JT.

                • Formal analysis: RMH JT.

                • Funding acquisition: NPJD PJG JT.

                • Investigation: RMH MDE NXT NNQ IZ JBO SB LM CN RNP PD NCS SP FN EAA APP KML RM NPJD NJW JT.

                • Methodology: RMH JT.

                • Project administration: LW NPJD PJG NJW KIB JT.

                • Resources: MDE IZ JBO SB LM RNP SP FN EAA APP KML RM NPJD NJW JT.

                • Supervision: NPJD NJW JT.

                • Validation: RMH LW JT.

                • Visualization: RMH JT.

                • Writing – original draft: RMH JT.

                • Writing – review & editing: RMH LW MDE NXT NNQ IZ JBO SB LM CN RNP PD NCS SP FN EAA APP KML RM NPJD PJG NJW KIB JT.

                Author information
                http://orcid.org/0000-0002-3433-6106
                http://orcid.org/0000-0001-8703-0891
                http://orcid.org/0000-0003-2000-2874
                http://orcid.org/0000-0002-2158-846X
                http://orcid.org/0000-0003-1333-4838
                http://orcid.org/0000-0002-7951-0745
                http://orcid.org/0000-0002-0383-9624
                http://orcid.org/0000-0002-0383-9624
                http://orcid.org/0000-0003-1621-3257
                http://orcid.org/0000-0002-6008-2963
                http://orcid.org/0000-0002-1897-1978
                http://orcid.org/0000-0003-4566-4030
                Article
                PMEDICINE-D-16-01628
                10.1371/journal.pmed.1002212
                5224788
                28072872
                a7a2cd8b-af47-40c9-9681-afaeec5f0991
                © 2017 Hoglund et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 23 May 2016
                : 29 November 2016
                Page count
                Figures: 5, Tables: 3, Pages: 23
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/100004440, Wellcome Trust;
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100000865, Bill and Melinda Gates Foundation;
                Award Recipient :
                Funded by: ExxonMobil Foundation
                Award Recipient :
                The Mahidol-Oxford Tropical Medicine Research Unit (RMH, NPJD, NJW, and JT) is partly funded by the Wellcome Trust of Great Brittan. The WorldWide Antimalarial Resistance Network (RMH, LW, RNP, PD, PJG, KIB, and JT) is funded by a Bill & Melinda Gates Foundation grant and the ExxonMobil Foundation. The funders did not participate in the study protocol development, the writing of the paper, and had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Medicine and Health Sciences
                Parasitic Diseases
                Malaria
                Medicine and Health Sciences
                Tropical Diseases
                Malaria
                Medicine and Health Sciences
                Pharmacology
                Pharmacokinetics
                Medicine and Health Sciences
                Pharmaceutics
                Dose Prediction Methods
                Biology and Life Sciences
                Microbiology
                Microbial Control
                Antimicrobial Resistance
                Medicine and Health Sciences
                Pharmacology
                Antimicrobial Resistance
                Biology and Life Sciences
                Physiology
                Physiological Parameters
                Body Weight
                Medicine and Health Sciences
                Physiology
                Physiological Parameters
                Body Weight
                Medicine and Health Sciences
                Pharmacology
                Drugs
                Antimalarials
                Medicine and Health Sciences
                Pharmacology
                Pharmacokinetics
                Drug Absorption
                Medicine and Health Sciences
                Pharmaceutics
                Dose Prediction Methods
                Population-Based Pharmacokinetic Dosing
                Custom metadata
                Data are available from the WWARN data repository ( http://www.wwarn.org/working-together/sharing-data/accessing-data) for researchers who meet the criteria for access to confidential data.

                Medicine
                Medicine

                Comments

                Comment on this article