[show abstract][hide abstract] ABSTRACT: Tuberculosis is an ongoing threat to global health, especially with the emergence of multi drug-resistant (MDR) and extremely drug-resistant strains that are motivating the search for new treatment strategies. One potential strategy is immunotherapy using Innate Defence Regulator (IDR) peptides that selectively modulate innate immunity, enhancing chemokine induction and cell recruitment while suppressing potentially harmful inflammatory responses. IDR peptides possess only modest antimicrobial activity but have profound immunomodulatory functions that appear to be influential in resolving animal model infections. The IDR peptides HH2, 1018 and 1002 were tested for their activity against two M. tuberculosis strains, one drug-sensitive and the other MDR in both in vitro and in vivo models. All peptides showed no cytotoxic activity and only modest direct antimicrobial activity versus M. tuberculosis (MIC of 15-30 µg/ml). Nevertheless peptides HH2 and 1018 reduced bacillary loads in animal models with both the virulent drug susceptible H37Rv strain and an MDR isolate and, especially 1018 led to a considerable reduction in lung inflammation as revealed by decreased pneumonia. These results indicate that IDR peptides have potential as a novel immunotherapy against TB.
PLoS ONE 01/2013; 8(3):e59119. · 3.73 Impact Factor
[show abstract][hide abstract] ABSTRACT: Increased multiple antibiotic resistance in the face of declining antibiotic discovery is one of society's most pressing health issues. Antimicrobial peptides represent a promising new class of antibiotics. Here we ask whether it is possible to make small broad spectrum peptides employing minimal assumptions, by capitalizing on accumulating chemical biology information. Using peptide array technology, two large random 9-amino-acid peptide libraries were iteratively created using the amino acid composition of the most active peptides. The resultant data was used together with Artificial Neural Networks, a powerful machine learning technique, to create quantitative in silico models of antibiotic activity. On the basis of random testing, these models proved remarkably effective in predicting the activity of 100,000 virtual peptides. The best peptides, representing the top quartile of predicted activities, were effective against a broad array of multidrug-resistant "Superbugs" with activities that were equal to or better than four highly used conventional antibiotics, more effective than the most advanced clinical candidate antimicrobial peptide, and protective against Staphylococcus aureus infections in animal models.
ACS Chemical Biology 01/2009; 4(1):65-74. · 5.44 Impact Factor