Population pharmacokinetic and pharmacodynamic modelling of artemisinin and mefloquine enantiomers in patients with falciparum malaria

Svensson, U; Alin, Hassan; Karlsson, Mats O.; Bergqvist, Yngve; Ashton, Michael

doi:10.1007/s00228-002-0485-y

Cited by 48 publications

(57 citation statements)

References 29 publications

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“…For artemisinin (elimination half-life of 2 h (31)), k drug was modeled as a near-instantaneous removal of a large proportion of circulating parasites to the spleen at the time of drug administration (Eq. 6 (17), where one must note Fig. 1.…”

Section: Within-host Anti-malarial Pharmacokinetic-pharmacodynamic Momentioning

confidence: 99%

“…The above age-structured continuous-time model was extended to a model which represented parasite burden at time t in four compartments (P 1 (t), P 2 (t), P 3 (t), and P 4 (t)), each representing 12 h of the 48-h life cycle, and in a further fifth compartment (P 5 (t)) for removal of the damaged circulating parasites by the spleen (17). As for the previous models, no host immune response or resource limitations of the erythrocyte population were included as separate parameters.…”

Section: Within-host Anti-malarial Pharmacokinetic-pharmacodynamic Momentioning

confidence: 99%

“…2c, where k pq denotes the transition rate constant for movement from compartment p to q. Variations of this five-compartment model have also been published (10,21), and of note, both papers model the relationship between artemisinin (10) (and artemether/dihydroartemisinin (21)) concentration and killing differently to (17), thereby avoiding the issue just discussed.…”

Section: Within-host Anti-malarial Pharmacokinetic-pharmacodynamic Momentioning

confidence: 99%

“…These models can be incorporated in population-level analyses to predict the selection and spread of drug resistance (8). A variety of within-host PK-PD models have been described for falciparum malaria, and there is a wide variation in the number of biological parameters describing the parasite life cycle and PD effect of the anti-malarial treatment (5)(6)(7)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22). The accuracy of these models in predicting the change in parasite burden following treatment depends on many factors.…”

Section: Introductionmentioning

confidence: 99%

“…Some of these mechanistic PK-PD models have been fitted formally to patient parasite-time profiles to obtain parameter estimates (10,11,14,16,17,21,22), whereas others have simply selected parameter values that generate parasitetime profiles that accord well with the profiles from clinical studies (5)(6)(7)9,12,13,15,(18)(19)(20). Both of these approaches are dependent on empirical data defining the dynamics of the parasite burden.…”

Section: Introductionmentioning

confidence: 99%

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Making the Most of Clinical Data: Reviewing the Role of Pharmacokinetic-Pharmacodynamic Models of Anti-malarial Drugs

Simpson

Zaloumis

Livera

et al. 2014

AAPS J

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Abstract. Mechanistic within-host models integrating blood anti-malarial drug concentrations with the parasite-time profile provide a valuable decision tool for determining dosing regimens for anti-malarial treatments, as well as a formative component of population-level drug resistance models. We reviewed published anti-malarial pharmacokinetic-pharmacodynamic models to identify the challenges for these complex models where parameter estimation from clinical field data is limited. The inclusion of key pharmacodynamic processes in the mechanistic structure adopted varies considerably. These include the life cycle of the parasite within the red blood cell, the action of the anti-malarial on a specific stage of the life cycle, and the reduction in parasite growth associated with immunity. With regard to estimation of the pharmacodynamic parameters, the majority of studies simply compared descriptive summaries of the simulated outputs to published observations of host and parasite responses from clinical studies. Few studies formally estimated the pharmacodynamic parameters within a rigorous statistical framework using observed individual patient data. We recommend three steps in the development and evaluation of these models. Firstly, exploration through simulation to assess how the different parameters influence the parasite dynamics. Secondly, application of a simulation-estimation approach to determine whether the model parameters can be estimated with reasonable precision based on sampling designs that mimic clinical efficacy studies. Thirdly, fitting the mechanistic model to the clinical data within a Bayesian framework. We propose that authors present the model both schematically and in equation form and give a detailed description of each parameter, including a biological interpretation of the parameter estimates.

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Section: Within-host Anti-malarial Pharmacokinetic-pharmacodynamic Momentioning

confidence: 99%

Section: Within-host Anti-malarial Pharmacokinetic-pharmacodynamic Momentioning

confidence: 99%

Section: Within-host Anti-malarial Pharmacokinetic-pharmacodynamic Momentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Making the Most of Clinical Data: Reviewing the Role of Pharmacokinetic-Pharmacodynamic Models of Anti-malarial Drugs

Simpson

Zaloumis

Livera

et al. 2014

AAPS J

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The Role of Clinical Pharmacology in Chemotherapy of Multidrug‐Resistant Plasmodium falciparum

Karbwang

Na‐Bangchang

2020

The Journal of Clinical Pharma

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Malaria remains one of the major global public health problems due to the emergence and spread of multidrug‐resistant Plasmodium falciparum. In recent years, clinical pharmacology has significantly contributed to the optimal dosing regimens of antimalarial drugs. The application of pharmacometric modeling and simulation has assisted in the accurate characterization of pharmacokinetic‐pharmacodynamic relationships and the optimization of the dosage regimens of existing antimalarial drugs, including new antimalarial candidates for multidrug‐resistant P falciparum in different populations.

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Application of the Multistate Tuberculosis Pharmacometric Model in Patients With Rifampicin‐Treated Pulmonary Tuberculosis

Svensson

Simonsson

2016

CPT Pharmacom & Syst Pharma

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This is the first clinical implementation of the Multistate Tuberculosis Pharmacometric (MTP) model describing fast‐, slow‐, and nonmultiplying bacterial states of Mycobacterium tuberculosis. Colony forming unit data from 19 patients treated with rifampicin were analyzed. A previously developed rifampicin population pharmacokinetic (PK) model was linked to the MTP model previously developed using in vitro data. Drug effect was implemented as exposure‐response relationships tested at several effect sites, both alone and in combination. All MTP model parameters were fixed to in vitro estimates except Bmax. Drug effect was described by an on/off effect inhibiting growth of fast‐multiplying bacteria in addition to linear increase of the stimulation of the death rate of slow‐ and nonmultiplying bacteria with increasing drug exposure. Clinical trial simulations predicted well three retrospective clinical trials using the final model that confirmed the potential utility of the MTP model in antitubercular drug development.

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Population pharmacokinetic and pharmacodynamic modelling of artemisinin and mefloquine enantiomers in patients with falciparum malaria

Abstract: No significant pharmacokinetic interactions were observed after co-administration of artemisinin and mefloquine. The P. falciparum malaria pharmacodynamic model successfully described the antimalarial effect of artemisinin, mefloquine and a combination of the two drugs.

Cited by 48 publications

References 29 publications

Making the Most of Clinical Data: Reviewing the Role of Pharmacokinetic-Pharmacodynamic Models of Anti-malarial Drugs

Making the Most of Clinical Data: Reviewing the Role of Pharmacokinetic-Pharmacodynamic Models of Anti-malarial Drugs

The Role of Clinical Pharmacology in Chemotherapy of Multidrug‐Resistant Plasmodium falciparum

Application of the Multistate Tuberculosis Pharmacometric Model in Patients With Rifampicin‐Treated Pulmonary Tuberculosis

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