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A model-informed preclinical approach for prediction of clinical pharmacodynamic interactions of anti-TB drug combinations
Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap.ORCID-id: 0000-0002-8621-7344
Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap.
Erasmus MC, Dept Med Microbiol & Infect Dis, Rotterdam, Netherlands.
Erasmus MC, Dept Med Microbiol & Infect Dis, Rotterdam, Netherlands.
Vise andre og tillknytning
2018 (engelsk)Inngår i: Journal of Antimicrobial Chemotherapy, ISSN 0305-7453, E-ISSN 1460-2091, Vol. 73, nr 2, s. 437-447Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Background: Identification of pharmacodynamic interactions is not reasonable to carry out in a clinical setting for many reasons. The aim of this work was to develop a model-informed preclinical approach for prediction of clinical pharmacodynamic drug interactions in order to inform early anti-TB drug development.

Methods: In vitro time-kill experiments were performed with Mycobacterium tuberculosis using rifampicin, isoniazid or ethambutol alone as well as in different combinations at clinically relevant concentrations. The multistate TB pharmacometric (MTP) model was used to characterize the natural growth and exposure-response relationships of each drug after mono exposure. Pharmacodynamic interactions during combination exposure were characterized by linking the MTP model to the general pharmacodynamic interaction (GPDI) model with successful separation of the potential effect on each drug's potency (EC50) by the combining drug(s).

Results: All combinations showed pharmacodynamic interactions at cfu level, where all combinations, except isoniazid plus ethambutol, showed more effect (synergy) than any of the drugs alone. Using preclinical information, the MTP-GPDI modelling approach was shown to correctly predict clinically observed pharmacodynamic interactions, as deviations from expected additivity.

Conclusions: With the ability to predict clinical pharmacodynamic interactions, using preclinical information, the MTP-GPDI model approach outlined in this study constitutes groundwork for model-informed input to the development of new and enhancement of existing anti-TB combination regimens.

sted, utgiver, år, opplag, sider
2018. Vol. 73, nr 2, s. 437-447
HSV kategori
Identifikatorer
URN: urn:nbn:se:uu:diva-386326DOI: 10.1093/jac/dkx380ISI: 000424144300023PubMedID: 29136155OAI: oai:DiVA.org:uu-386326DiVA, id: diva2:1329030
Forskningsfinansiär
Swedish Research Council, 521-2011-3442EU, FP7, Seventh Framework ProgrammeTilgjengelig fra: 2019-06-24 Laget: 2019-06-24 Sist oppdatert: 2019-06-24bibliografisk kontrollert

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