Statistical methods for automated drug susceptibility testing: Bayesian minimum inhibitory concentration prediction from growth curves

Zhou, Xi Kathy; Clyde, Merlise A.; Garrett, James; Lourdes, Viridiana; O’Connell, Michael; Parmigiani, Giovanni; Turner, David J.; Wiles, Tim

doi:10.1214/08-aoas217

Cited by 3 publications

(2 citation statements)

References 17 publications

(16 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…[a] Values represent technical triplicates and experimental triplicates. MIC: minimum inhibitory concentration values are the lowest compound concentration at which no visible growth was observed after incubation overnight (16 h) . MBC: minimum bactericidal concentration.…”

Section: Resultsmentioning

confidence: 99%

“…Thus, PKZ6 and PKZ7 did not exhibit activity profiles indicative of T-boxspecific inhibition. The minimum inhibitory concentrations (MIC) [40][41][42][43] and minimumb actericidal concentrations (MBC) [41,44] of each compound against B. subtilis and S. aureus were determined using ab roth microdilutiona ssay.C ompound PKZ18 exhibited MICs of approximately 64 mgmL À1 against both species (Table 1). PKZ6 and PKZ7 exhibitedM ICsi nt he range of 32-512 mgmL À1 (Supporting Information Ta ble S1).…”

Section: Compoundsselectedins Ilico Active Against Gram-positive Versmentioning

confidence: 99%

See 1 more Smart Citation

Discovery of Small‐Molecule Antibiotics against a Unique tRNA‐Mediated Regulation of Transcription in Gram‐Positive Bacteria

et al. 2019

View full text Add to dashboard Cite

The emergence of multidrug‐resistant bacteria necessitates the identification of unique targets of intervention and compounds that inhibit their function. Gram‐positive bacteria use a well‐conserved tRNA‐responsive transcriptional regulatory element in mRNAs, known as the T‐box, to regulate the transcription of multiple operons that control amino acid metabolism. T‐box regulatory elements are found only in the 5′‐untranslated region (UTR) of mRNAs of Gram‐positive bacteria, not Gram‐negative bacteria or the human host. Using the structure of the 5′UTR sequence of the Bacillus subtilis tyrosyl‐tRNA synthetase mRNA T‐box as a model, in silico docking of 305 000 small compounds initially yielded 700 as potential binders that could inhibit the binding of the tRNA ligand. A single family of compounds inhibited the growth of Gram‐positive bacteria, but not Gram‐negative bacteria, including drug‐resistant clinical isolates at minimum inhibitory concentrations (MIC 16–64 μg mL−1). Resistance developed at an extremely low mutational frequency (1.21×10−10). At 4 μg mL−1, the parent compound PKZ18 significantly inhibited in vivo transcription of glycyl‐tRNA synthetase mRNA. PKZ18 also inhibited in vivo translation of the S. aureus threonyl‐tRNA synthetase protein. PKZ18 bound to the Specifier Loop in vitro (Kd≈24 μm). Its core chemistry necessary for antibacterial activity has been identified. These findings support the T‐box regulatory mechanism as a new target for antibiotic discovery that may impede the emergence of resistance.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Compoundsselectedins Ilico Active Against Gram-positive Versmentioning

confidence: 99%

Discovery of Small‐Molecule Antibiotics against a Unique tRNA‐Mediated Regulation of Transcription in Gram‐Positive Bacteria

et al. 2019

View full text Add to dashboard Cite

show abstract

Identification of Nonlinear Differential Systems for Bacteria Population Under Antibiotics Influence

Serovajsky

Azimov

Il’in

et al. 2017

Trends in Mathematics

View full text Add to dashboard Cite

Modeling of Effectiveness of N3-Substituted Amidrazone Derivatives as Potential Agents against Gram-Positive Bacteria

Ćwiklińska-Jurkowska,

Paprocka,

Mwaura

et al. 2024

Molecules

View full text Add to dashboard Cite

Prediction of the antibacterial activity of new chemical compounds is an important task, due to the growing problem of bacterial drug resistance. Generalized linear models (GLMs) were created using 85 amidrazone derivatives based on the results of antimicrobial activity tests, determined as the minimum inhibitory concentration (MIC) against Gram-positive bacteria: Staphylococcus aureus, Enterococcus faecalis, Micrococcus luteus, Nocardia corallina, and Mycobacterium smegmatis. For the analysis of compounds characterized by experimentally measured MIC values, we included physicochemical properties (e.g., molecular weight, number of hydrogen donors and acceptors, topological polar surface area, compound percentages of carbon, nitrogen, and oxygen, melting points, and lipophilicity) as potential predictors. The presence of R1 and R2 substituents, as well as interactions between melting temperature and R1 or R2 substituents, were also considered. The set of potential predictors also included possible biological effects (e.g., antibacterial, antituberculotic) of tested compounds calculated with the PASS (Prediction of Activity Spectra for Substances) program. Using GLMs with least absolute shrinkage and selection (LASSO), least-angle regression, and stepwise selection, statistically significant models with the optimal value of the adjusted determination coefficient and of seven fit criteria were chosen, e.g., Akaike’s information criterion. The most often selected variables were as follows: molecular weight, PASS_antieczematic, PASS_anti-inflam, squared melting temperature, PASS_antitumor, and experimental lipophilicity. Additionally, relevant to the bacterial strain, the interactions between melting temperature and R1 or R2 substituents were selected, indicating that the relationship between MIC and melting temperature depends on the type of R1 or R2 substituent.

show abstract

Statistical methods for automated drug susceptibility testing: Bayesian minimum inhibitory concentration prediction from growth curves

Cited by 3 publications

References 17 publications

Discovery of Small‐Molecule Antibiotics against a Unique tRNA‐Mediated Regulation of Transcription in Gram‐Positive Bacteria

Discovery of Small‐Molecule Antibiotics against a Unique tRNA‐Mediated Regulation of Transcription in Gram‐Positive Bacteria

Identification of Nonlinear Differential Systems for Bacteria Population Under Antibiotics Influence

Modeling of Effectiveness of N3-Substituted Amidrazone Derivatives as Potential Agents against Gram-Positive Bacteria

Contact Info

Product

Resources

About