Antimicrobial Effect of Low-Fluoride Toothpastes Containing Polyphosphate and Polyols: An In Vitro Assessment of Inhibition Zones.
Igor ZenAlberto Carlos Botazzo DelbemThayse Yumi HosidaCaio SampaioLeonardo Antônio de MoraisTamires Passadori MartinsDouglas Roberto MonteiroJuliano Pelim PessanPublished in: Antibiotics (Basel, Switzerland) (2023)
This study evaluated the antimicrobial effect of toothpastes containing 200 ppm fluoride (200F), xylitol (X, 16%), erythritol (E, 4%), and sodium trimetaphosphate (TMP, 0.25%), alone or in different associations, against Streptococcus mutans (SM), Lactobacillus casei (LC), Actinomyces israelii (AI), and Candida albicans (CA). Suspensions of the micro-organisms were added to a BHI Agar medium. Five wells were made on each plate to receive toothpaste suspensions at different dilutions. Toothpastes containing no actives (placebo) or 1100 ppm F (1100F) were used as negative and positive controls. Two-way ANOVA and Tukey's HDS test were used ( p < 0.05). For SM, the largest halo was for 200F+TMP at all dilutions, followed by the 200F+X+E toothpaste ( p < 0.001). For LC, the overall trend showed that the polyols effectively inhibited microbial growth, and the association with the other compounds enhanced such effects ( p < 0.001). For AI, a less-defined trend was observed. For CA, the experimental toothpaste (200F+X+E+TMP) was consistently more effective than the other treatments, followed by 200F+X+E ( p < 0.001). The association of polyols and TMP in a low-fluoride toothpaste effectively reduced the growth of cariogenic micro-organisms (SM, CA, and LC), suggesting that this formulation could be an interesting alternative for children due to its low fluoride content.
Keyphrases
- candida albicans
- biofilm formation
- drinking water
- staphylococcus aureus
- simultaneous determination
- artificial intelligence
- mass spectrometry
- protein kinase
- microbial community
- pseudomonas aeruginosa
- liquid chromatography
- clinical trial
- drug delivery
- escherichia coli
- cystic fibrosis
- machine learning
- solid phase extraction
- study protocol
- lactic acid