tetano
Editor, Senior Moderator
J Pharmacokinet Pharmacodyn. 2016 Sep 27. [Epub ahead of print]
[h=1]Quantifying the therapeutic requirements and potential for combination therapy to prevent bacterial coinfection during influenza.[/h] Smith AM[SUP]1[/SUP].
[h=3]Author information[/h]
[h=3]Abstract[/h] Secondary bacterial infections (SBIs) exacerbate influenza-associated disease and mortality. Antimicrobial agents can reduce the severity of SBIs, but many have limited efficacy or cause adverse effects. Thus, new treatment strategies are needed. Kinetic models describing the infection process can help determine optimal therapeutic targets, the time scale on which a drug will be most effective, and how infection dynamics will change under therapy. To understand how different therapies perturb the dynamics of influenza infection and bacterial coinfection and to quantify the benefit of increasing a drug's efficacy or targeting a different infection process, I analyzed data from mice treated with an antiviral, an antibiotic, or an immune modulatory agent with kinetic models. The results suggest that antivirals targeting the viral life cycle are most efficacious in the first 2 days of infection, potentially because of an improved immune response, and that increasing the clearance of infected cells is important for treatment later in the infection. For a coinfection, immunotherapy could control low bacterial loads with as little as 20 % efficacy, but more effective drugs would be necessary for high bacterial loads. Antibiotics targeting bacterial replication and administered 10 h after infection would require 100 % efficacy, which could be reduced to 40 % with prophylaxis. Combining immunotherapy with antibiotics could substantially increase treatment success. Taken together, the results suggest when and why some therapies fail, determine the efficacy needed for successful treatment, identify potential immune effects, and show how the regulation of underlying mechanisms can be used to design new therapeutic strategies.
[h=4]KEYWORDS:[/h] Antibiotics; Antivirals; Coinfection; Combination therapy; Immunotherapy; Influenza; Kinetic modeling; Pneumococcus
PMID: 27679506 DOI: 10.1007/s10928-016-9494-9
[PubMed - as supplied by publisher]
[h=1]Quantifying the therapeutic requirements and potential for combination therapy to prevent bacterial coinfection during influenza.[/h] Smith AM[SUP]1[/SUP].
[h=3]Author information[/h]
[h=3]Abstract[/h] Secondary bacterial infections (SBIs) exacerbate influenza-associated disease and mortality. Antimicrobial agents can reduce the severity of SBIs, but many have limited efficacy or cause adverse effects. Thus, new treatment strategies are needed. Kinetic models describing the infection process can help determine optimal therapeutic targets, the time scale on which a drug will be most effective, and how infection dynamics will change under therapy. To understand how different therapies perturb the dynamics of influenza infection and bacterial coinfection and to quantify the benefit of increasing a drug's efficacy or targeting a different infection process, I analyzed data from mice treated with an antiviral, an antibiotic, or an immune modulatory agent with kinetic models. The results suggest that antivirals targeting the viral life cycle are most efficacious in the first 2 days of infection, potentially because of an improved immune response, and that increasing the clearance of infected cells is important for treatment later in the infection. For a coinfection, immunotherapy could control low bacterial loads with as little as 20 % efficacy, but more effective drugs would be necessary for high bacterial loads. Antibiotics targeting bacterial replication and administered 10 h after infection would require 100 % efficacy, which could be reduced to 40 % with prophylaxis. Combining immunotherapy with antibiotics could substantially increase treatment success. Taken together, the results suggest when and why some therapies fail, determine the efficacy needed for successful treatment, identify potential immune effects, and show how the regulation of underlying mechanisms can be used to design new therapeutic strategies.
[h=4]KEYWORDS:[/h] Antibiotics; Antivirals; Coinfection; Combination therapy; Immunotherapy; Influenza; Kinetic modeling; Pneumococcus
PMID: 27679506 DOI: 10.1007/s10928-016-9494-9
[PubMed - as supplied by publisher]