Literature DB >> 25872900

Applications of physiologically based pharmacokinetic modeling for the optimization of anti-infective therapies.

Darren Michael Moss1, Catia Marzolini, Rajith K R Rajoli, Marco Siccardi.   

Abstract

INTRODUCTION: The pharmacokinetic properties of anti-infective drugs are a determinant part of treatment success. Pathogen replication is inhibited if adequate drug levels are achieved in target sites, whereas excessive drug concentrations linked to toxicity are to be avoided. Anti-infective distribution can be predicted by integrating in vitro drug properties and mathematical descriptions of human anatomy in physiologically based pharmacokinetic models. This method reduces the need for animal and human studies and is used increasingly in drug development and simulation of clinical scenario such as, for instance, drug-drug interactions, dose optimization, novel formulations and pharmacokinetics in special populations. AREAS COVERED: We have assessed the relevance of physiologically based pharmacokinetic modeling in the anti-infective research field, giving an overview of mechanisms involved in model design and have suggested strategies for future applications of physiologically based pharmacokinetic models. EXPERT OPINION: Physiologically based pharmacokinetic modeling provides a powerful tool in anti-infective optimization, and there is now no doubt that both industry and regulatory bodies have recognized the importance of this technology. It should be acknowledged, however, that major challenges remain to be addressed and that information detailing disease group physiology and anti-infective pharmacodynamics is required if a personalized medicine approach is to be achieved.

Entities:  

Keywords:  anti-infectives; pharmacokinetics; physiologically based pharmacokinetic; systems pharmacology

Mesh:

Substances:

Year:  2015        PMID: 25872900     DOI: 10.1517/17425255.2015.1037278

Source DB:  PubMed          Journal:  Expert Opin Drug Metab Toxicol        ISSN: 1742-5255            Impact factor:   4.481


  6 in total

1.  Physiologically based pharmacokinetic modelling prediction of the effects of dose adjustment in drug-drug interactions between levonorgestrel contraceptive implants and efavirenz-based ART.

Authors:  Owain Roberts; Rajith K R Rajoli; David J Back; Andrew Owen; Kristin M Darin; Courtney V Fletcher; Mohammed Lamorde; Kimberly K Scarsi; Marco Siccardi
Journal:  J Antimicrob Chemother       Date:  2018-04-01       Impact factor: 5.790

2.  Physiologically Based Pharmacokinetic Modeling to Predict Drug-Drug Interactions with Efavirenz Involving Simultaneous Inducing and Inhibitory Effects on Cytochromes.

Authors:  Catia Marzolini; Rajith Rajoli; Manuel Battegay; Luigia Elzi; David Back; Marco Siccardi
Journal:  Clin Pharmacokinet       Date:  2017-04       Impact factor: 6.447

3.  Validation of Computational Approaches for Antiretroviral Dose Optimization.

Authors:  Marco Siccardi; Laura Dickinson; Andrew Owen
Journal:  Antimicrob Agents Chemother       Date:  2016-05-23       Impact factor: 5.191

4.  Predicting Drug-Drug Interactions between Rifampicin and Ritonavir-Boosted Atazanavir Using PBPK Modelling.

Authors:  Maiara Camotti Montanha; Francesc Fabrega; Alice Howarth; Nicolas Cottura; Hannah Kinvig; Fazila Bunglawala; Andrew Lloyd; Paolo Denti; Catriona Waitt; Marco Siccardi
Journal:  Clin Pharmacokinet       Date:  2021-10-12       Impact factor: 5.577

Review 5.  Pharmacologic Considerations for Preexposure Prophylaxis in Transgender Women.

Authors:  Peter L Anderson; Daniel Reirden; Jose Castillo-Mancilla
Journal:  J Acquir Immune Defic Syndr       Date:  2016-08-15       Impact factor: 3.731

6.  Human cytochrome P450 2B6 genetic variability in Botswana: a case of haplotype diversity and convergent phenotypes.

Authors:  Leabaneng Tawe; Thato Motshoge; Pleasure Ramatlho; Naledi Mutukwa; Charles Waithaka Muthoga; Ghyslaine Bruna Djeunang Dongho; Axel Martinelli; Elias Peloewetse; Gianluca Russo; Isaac Kweku Quaye; Giacomo Maria Paganotti
Journal:  Sci Rep       Date:  2018-03-20       Impact factor: 4.379

  6 in total

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