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PubMed2026

Prediction of antimicrobial minimum inhibitory concentration from bacterial genomes using a scalable and interpretable machine learning approach.

Although machine learning models can predict antimicrobial susceptibility from bacterial whole genome sequencing (WGS), state-of-the-art approaches are computationally demanding or dependent on knowledge of genetic resistance determinants. Here, we describe an efficient data-driven approach to predicting minimum inhibitory concentration (MIC) by progressively extending and refining predictive genome segments, independent of prior knowledge of resistance determinants. Resultant models had high interpretability - known and potentially novel resistance determinants were captured. Using 762 clinical E. coli strains, 71.6% of predictions were within one dilution of the measured MIC. Models trained with this algorithm generalised better onto external data (F1 score = 0.85) compared with alternative models trained on annotated resistance determinants (F1 = 0.82) or k-mer counts (F1 = 0.74). Computational demands were low (RAM usage 23.6GB vs 38.8GB for k-mer model). These advantages represent an important advance in predicting antimicrobial susceptibility from WGS, with potential applications for clinical diagnostics, drug development, and surveillance.

Journal Article↗

PubMed2026

Machine learning-based drug susceptibility prediction from Candida genomic data.

OBJECTIVES: Invasive Candida infection is an increasing clinical concern, with antifungal resistance rising across multiple species. However, rapid and accurate antifungal susceptibility testing (AFST) remains limited in routine practice. The study evaluated species distribution and antifungal susceptibility of invasive Candida isolates in China and assessed the feasibility of combining whole-genome sequencing (WGS) with machine learning to predict minimum inhibitory concentrations (MICs). METHODS: Consecutive non-repetitive isolates were collected from 20 hospitals in 13 provinces during 2022-2023. MICs of nine antifungal agents were determined by broth microdilution, and WGS was performed for species accounting for >5% of the total isolates. Genomic 11-mer features were extracted and used to train random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGBoost) models, followed by optimization of the best-performing algorithm. RESULTS: A total of 337 isolates were obtained from blood (n = 232) and sterile body fluids (n = 105), comprising C. albicans (n = 103), C. tropicalis (n = 71), C. parapsilosis (n = 67), and C. glabrata (n = 63). Non-albicans Candida showed higher azole and echinocandin resistance, with C. tropicalis notably resistant to azoles and C. glabrata to echinocandins. Among the three models, RF demonstrated the best performance on 304 sequenced isolates. The optimized RF model was evaluated by the receiver operating characteristic (ROC) curve analysis and achieved an average area under the ROC curve (AUC) of 0.979 (95% CI: 0.974-0.984), essential agreement over 90.1%, and categorical agreement over 93.2% across species. CONCLUSIONS: These findings underscore the clinical challenge posed by non-albicans Candida resistance, and indicate that WGS-based MIC prediction may offer a highly accurate reference for earlier antifungal therapy.

Antifungal Agents↗

PubMed2026

Comprehensive in silico genomics analysis of global trends and host-specific emergence of aminoglycoside resistance in Staphylococcus aureus: a One-Health perspective.

BACKGROUND: Aminoglycosides remain clinically valuable against Staphylococcus aureus. Aminoglycoside resistance in S. aureus represents a critical One Health concern and is primarily driven by aminoglycoside-modifying enzymes (AMEs), which are frequently plasmid-encoded. Although regional studies have provided valuable insights, the global epidemiology of aminoglycoside resistance determinants remains poorly characterized because comprehensive data integrating human, animal, and environmental reservoirs are still lacking. This study addresses this gap by analyzing over 110,000 S. aureus genomes (2000-2025) to map the global resistome, quantify temporal and host-specific trends, and assess the association between genetic determinants and phenotypic resistance. METHODS: We performed a retrospective One Health meta-analysis of 110,309 S. aureus genomes collected between 2000 and 2025 from 128 countries. Genomes were quality-filtered and aminoglycoside resistance determinants were identified using NCBI AMRFinderPlus (v4.0.23). Multilocus sequence typing and host-source harmonization (Human, Animal, Environment, Unknown) enabled clonal and reservoir stratification. Temporal trends in gene prevalence and resistance burden were modeled with robust regression. Geographic and host-associated structuring of key genes was assessed via χ2 and enrichment tests. Machine-learning models (elastic-net, random forests, XGBoost) were benchmarked for minimum inhibitory concentration (MIC) prediction via nested cross-validation, with performance evaluated by mean absolute error, RMSE, and SHAP-based feature importance. All analyses were conducted in R and Python using publicly available, de-identified genomic data. RESULTS: Aminoglycoside resistance-associated genes were dominated by modifying enzyme determinants, with ant(6)-Ia, ant(9)-Ia, aph(3')-IIIa, sat4, aadD1, and aac(6')-Ie/aph(2'')-Ia occurring in 14-22% of isolates worldwide. Temporal analysis revealed significant declines in several major determinants, most notably ant(9)-Ia (-2.22 percentage points per year, p < 0.001), whereas apmA exhibited a non-significant decreasing trend in animal isolates. Host structuring was marked: human clinical isolates concentrated common determinants, while animal and environmental isolates harbored rare alleles (apmA, spw, str, spd). Geographic mapping confirmed near-universal distribution of common genes but focal restriction of rare ones. Publicly available phenotypic data indicated strong activity of amikacin, whereas gentamicin showed a distinct resistant subpopulation that closely corresponded with AME gene carriage. Genotype-phenotype analyses demonstrated strong concordance, with gene-rich complements predicting resistant MIC strata and absence of determinants predicting susceptibility. Analysis across different gene classes revealed frequent co-occurrence of aminoglycoside resistance genes with determinants from other classes, such as mecA, blaZ, and MLS_B, embedding them within multidrug-resistant (MDR) genomic contexts. CONCLUSION: Over 25 years, the prevalence of aminoglycoside resistance-associated genes in S. aureus has declined for several common determinants, while rare veterinary-linked alleles are emerging in animal isolates. Strong genotype-phenotype concordance supports genomic prediction for gentamicin and amikacin, where MIC data are available, although phenotypic confirmation remains essential. The frequent co-occurrence of aminoglycoside resistance genes with other antimicrobial resistance determinants indicates their integration within co-occurrence patterns of MDR genes, defined here as clusters of co-occurring resistance genes often carried on shared mobile genetic elements. These patterns highlight the need for integrated One Health surveillance combining clinical, veterinary, and environmental monitoring with plasmid-context resolution to anticipate emerging threats.

Aminoglycosides↗

PubMed2025

Estimating the association of antimicrobial resistance genes with minimum inhibitory concentration in Escherichia coli: an observational study.

BACKGROUND: Surveillance and prediction of antibiotic resistance in Escherichia coli relies on curated databases of genes and mutations. We aimed to quantify the effect of acquiring specific genetic elements on minimum inhibitory concentrations (MICs) for particular antibiotic-species combinations, addressing the current scarcity of such data in existing databases. METHODS: For this observational study, we evaluated a collection of E coli isolates with linked whole-genome sequencing and MIC data, originating from human urinary or bloodstream infections obtained from the Oxford University Hospitals National Health Service Foundation Trust in Oxfordshire, UK. We used multivariable interval regression models to estimate the change in MIC (with 95% CIs) for specific antibiotics associated with the acquisition of antibiotic resistance genes and associated mutations in the National Center for Biotechnology Information AMRFinder database, with and without an adjustment for population structure. We then tested the ability of these models to predict MIC and binary resistance or susceptibility using leave-one-out cross-validation. FINDINGS: We evaluated 2875 E coli isolates obtained during 2013-2018 and 2020. Although most ARGs and resistance mutations (89 [80%] of 111) were associated with an increased MIC, a much smaller number (27 [24%] of 111) was found to be putatively independently resistance-conferring (ie, associated with an MIC above the European Committee on Antimicrobial Susceptibility Testing breakpoint) when acquired in isolation. We found evidence of differential effects of acquired ARGs and resistance mutations between different generations of cephalosporin antibiotics and showed that sub-breakpoint variation in MIC can be linked to genetic mechanisms of resistance. 20 697 (83·3%; range 52·9-97·7 across all antibiotics) of 24 858 MICs were correctly exactly predicted and 23 677 (95·2%; 87·3-97·7) of 24 858 MICs were predicted to within one doubling dilution. INTERPRETATION: Quantitative estimates of the independent effect of the acquisition of ARGs on MIC add to the interpretability and utility of existing databases. Compared with approaches using machine learning models, the use of these estimates yields similar or better performance in the prediction of antibiotic resistance phenotype with more readily interpretable results. The methods outlined here could be readily applied to other antibiotic-pathogen combinations. FUNDING: The National Institute for Health and Care Research (NIHR) and the Medical Research Council (MRC).

Escherichia coli↗

PubMed2024

Antibiotic resistance genotype, phenotype, and clinical outcomes in patients with Gram-negative infections at Rabin Medical Center in Israel.

UNLABELLED: Antibiotic resistance is a major cause of morbidity and mortality. However, a better understanding of the relationship between bacterial genetic markers, phenotypic resistance, and clinical outcomes is needed. We performed whole-genome sequencing on five medically important pathogens (Acinetobacter baumannii, Enterobacter cloacae, Escherichia coli, Klebsiella pneumoniae, and Pseudomonas aeruginosa) to investigate how resistance genes impact patient outcomes. A total of 168 isolates from 162 patients with Gram-negative infections admitted to Beilinson Hospital at Rabin Medical Center in Israel were included for final analysis. Genomes were analyzed for resistance determinants and correlated with microbiologic and clinical data. Thirty-day mortality from time of culture was 26.5% (43/162). Twenty-nine patients had carbapenem-resistant isolates (29/168, 17.2%), while 63 patients had multidrug-resistant isolates (63/168, 37.5%). Albumin levels were inversely associated with mortality and length of stay, while arrival from a healthcare facility and cancer chemotherapy predicted having a multidrug-resistant isolate. Sequencing revealed possible patient-to-patient transmission events. blaCTX-M-15 was associated with multidrug-resistance in E. coli (OR = 3.888, P = 0.023) on multivariate analysis. Increased blaOXA-72 copy number was associated with carbapenem-resistance in A. baumannii (P = 0.003) and meropenem minimum inhibitory concentration (P = 0.005), yet carbapenem-resistant isolates retained sensitivity to cefiderocol and sulbactam-durlobactam. RJX84154 was associated with multidrug-resistance across all pathogens (P = 0.0018) and in E. coli (P = 0.0024). Low albumin levels were associated with mortality and length of stay in this sample population. blaCTX-M-15 was correlated with multidrug-resistance in E. coli, and blaOXA-72 depth predicted meropenem minimum inhibitory concentration in A. baumannii. RJX84154 may play a role in multidrug-resistance. IMPORTANCE: While there have been several studies that attempt to find clinical predictors of outcomes in patients hospitalized with bacterial infections, less has been done to combine clinical data with genomic mechanisms of antibiotic resistance. This study focused on a hospitalized patient population in Israel with infections due to medically important bacterial pathogens as a way to build a framework that would unite clinical data with both bacterial antibiotic susceptibility and genomic data. Merging both clinical and genomic data allowed us to find both bacterial and clinical factors that impact certain clinical outcomes. As genome sequencing of bacteria becomes both rapid and commonplace, near real-time monitoring of resistance determinants could help to optimize clinical care and potentially improve outcomes in these patients.

Humans↗

PubMed2025

Uncovering encrypted antimicrobial peptides in health-associated Lactobacillaceae by large-scale genomics and machine learning.

BACKGROUND: Antimicrobial peptides (AMPs) are well known for their broad-spectrum activity and have shown great promise in addressing the antibiotic-resistant crisis. The Lactobacillaceae family, recognized for its health-promoting effects in humans, represents a valuable source of novel AMPs. However, the global prevalence and distribution of AMPs within Lactobacillaceae remains largely unknown, which limits the efficient discovery and development of novel AMPs. RESULTS: We analyzed all available genomes (10,327 genomes), encompassing 38 genera and 515 species, to investigate the biosynthetic potential (indicated by the number of AMP sequences in the genome) of AMP in the Lactobacillaceae family. We demonstrated Lactobacillaceae species had ubiquitous (69.90%) biosynthetic potential of AMPs. Overall, 9601 AMPs were identified, clustering into 2092 gene cluster families (GCFs), which showed strong interspecies specificity (95.27%), intraspecies heterogeneity (93.31%), and habitat uniqueness (95.83%), that greatly expanded on the AMP sequence landscape. Novelty assessment indicated that 1516 GCFs (72.47%) had no similarity to any known AMPs in existing databases. Machine learning predictions suggested that novel AMPs from Lactobacillaceae possessed strong antimicrobial potential, with 664 GCFs having an additive minimum inhibitory concentration (MIC) below 100 μM. We randomly synthesized 16 AMPs (with predicted MIC < 100 μM) and identified 10 AMPs exhibiting varied-spectrum activity against 11 common pathogens. Finally, we identified one Lactobacillus delbrueckii-originated AMP (delbruin_1) having broad-spectrum (all 11 pathogens) and high antimicrobial activity (average MIC = 38.56 µM), which proved its potential as a clinically viable antimicrobial agent. CONCLUSIONS: We uncovered the global prevalence of AMPs in Lactobacillaceae and proved that Lactobacillaceae is an untapped and invaluable source of novel AMPs to combat the antibiotic-resistance crisis. Meanwhile, we provided a machine learning-guided framework for AMP discovery, offering a scalable roadmap for identifying novel AMPs not only in Lactobacillaceae but also in other organisms. Video Abstract.

Machine Learning↗

PubMed2026

No phenotypic resistance observed for most group-3 and -4 variants in Mycobacterium tuberculosis genes related to bedaquiline, clofazimine, delamanid, and pretomanid in a Central and West African context.

The interpretation of genetic variants' association (or not) with phenotypic resistance to newly introduced and repurposed antituberculosis drugs remains challenging, as many mutations detected by whole-genome sequencing (WGS) are classified as of uncertain significance (group 3) or not associated with resistance-interim (group 4) by the World Health Organization (WHO) mutation catalog v2. We evaluated the phenotypic impact of such variants on minimum inhibitory concentrations (MICs) for bedaquiline (BDQ), clofazimine (CFZ), delamanid (DLM), and pretomanid (PA) in Mycobacterium tuberculosis complex isolates from the multi-country DIAMA cohort in sub-Saharan Africa (SSA), which recruited RR/RS-TB patients naïve to these drugs. Among 1,475 isolates with available WGS data, 163 variants met eligibility criteria; due to viable strain unavailability, 89 isolates carrying 29 unique BDQ/CFZ-related and 60 unique DLM/PA-related variants were tested for MIC determination using broth microdilution. Additional structural modeling was performed to explore potential effects of amino-acid substitutions on protein stability. Among BDQ/CFZ-related variants, MICs above the critical concentrations (CCs) were consistently associated with mmpR5 variants, whereas variants in atpE, pepQ, and Rv1979c were not. DLM/PA variants (ddn, fbiA-D, and fgd1) were frequently detected as non-fixed populations, yet rarely yielding MIC values above the CC. Predicted structural destabilization showed no consistent association with MIC values or variant fixation status. Under the conditions tested, phenotypic resistance was not detected for most group 3 and 4 variants detected by WGS. Our data provide evidence from SSA to support improved interpretation of resistance-associated mutations for new and repurposed antituberculosis drugs.IMPORTANCEWhole-genome sequencing increasingly detects Mycobacterium tuberculosis complex mutations classified by the World Health Organization (WHO) mutation catalog v2 as group 3 variants of uncertain significance or group 4 variants not associated with resistance-interim, limiting reliable prediction of resistance to new and repurposed antituberculosis drugs. By generating minimum inhibitory concentration (MIC) data for such variants identified in a multi-country sub-Saharan African cohort, this study provides phenotypic evidence to support future refinement and expansion of the WHO mutation catalog v2. Notably, mmpR5 variants associated with elevated bedaquiline/clofazimine MICs were identified in eight isolates, suggesting that some patients in this cohort may have harbored pre-existing resistance-associated variants yet remained potentially eligible for bedaquiline-containing regimens. These findings contribute to improving the interpretation of genomic resistance data and strengthening surveillance of resistance to bedaquiline, clofazimine, delamanid, and pretomanid.

Mycobacterium tuberculosis↗

PubMed1979

Pharmacokinetic determinants of penicillin cure of gonococcal urethritis.

In a 1964 study of the pharmacokinetic determinants of penicillin cure of gonococcal urethritis, 45 male prisoner volunteers were experimentally infected with strains of Neisseria gonorrhoeae having known in vitro penicillin susceptibility. After developing urethritis, subjects received intramuscular penicillin G and had serum samples obtained serially to determine penicillin concentration. Using a multiple regression technique, we studied patient-associated parameters and parameters of the serum penicillin curves to determine the best predictors of treatment results. Cure was best predicted by the time the serum penicillin concentration remained above three to four times the penicillin minimum inhibitory concentration of the infecting strain (probability of correct classification, >0.80). Those cured had serum penicillin concentrations which remained in this range for means of 7 to 10 h. Our findings confirm principles of antimicrobial therapy derived from animal models and may have application in studying therapy of gonorrhea and other infectious diseases.

Adult↗

PubMed2026

A mechanism-guided framework for prioritizing membrane-interaction anti-Vibrio peptides from peptidomics data.

A mechanism-guided framework for prioritizing membrane-interaction antimicrobial peptide candidates from proteomics-derived peptide mixtures is presented. The framework integrates conservative machine-learning-based antimicrobial peptide (AMP) screening with a literature-derived membrane-interaction plausibility (MAP) assessment and a data-driven membrane-interaction ranking function (AIPx), followed by structural visualization for interpretability. MAP encodes physicochemical characteristics commonly associated with peptide-membrane interaction and provides a graded plausibility assessment. Building upon this physicochemically interpretable framework, AIPx ranks peptides using feature weights calibrated from experimentally characterized anti-Vibrio peptides, where minimum inhibitory concentration (MIC) values are used as a coarse-grained ranking reference rather than a direct prediction target. In a peptidomics-based peptide fractionation study targeting Vibrio spp., AIPx exhibited a consistent relationship with experimentally observed antibacterial activity. Distributional analysis revealed that peptide fractions exhibiting high anti-Vibrio activity are characterized by enrichment of high-ranking peptides rather than by AMP abundance alone. By structuring AMP identification and prioritization as sequential stages, the MAP + AIPx framework enables interpretable and experimentally actionable candidate selection by reducing biologically implausible candidates. The framework facilitates species-oriented prioritization of AMP candidates, addressing a key challenge in antimicrobial peptide discovery where activity may depend on target-specific membrane characteristics. Moreover, the approach is extensible through species-specific calibration and supports interpretable, mechanism-informed prioritization in antimicrobial peptide discovery.

Proteomics↗

PubMed1979

Disk diffusion testing of susceptibility of Mycobacterium fortuitum and Mycobacterium chelonei to antibacterial agents.

Although recent studies have suggested that some antibacterial agents have good activity against the rapidly growing mycobacteria Mycobacterium fortuitum and Mycobacterium chelonei, an easily applicable method for susceptibility testing of clinical isolates is not yet available. We evaluated a disk diffusion method with Mueller-Hinton agar and 48-h readings with 59 strains of M. fortuitum and 11 strains of M. chelonei and compared the results to agar dilution susceptibilities for nine antimicrobial agents. All isolates were susceptible to 16 micrograms of amikacin or kanamycin per ml with minimum zone diameters of 14 and 18 mm, respectively. Amikacin inhibited 100% of isolates of M. fortuitum at 2 micrograms/ml, whereas 10 of 11 (91%) of M. chelonei strains had minimum inhibitory concentrations of 4.0 micrograms/ml or greater. Doxycycline and minocycline had almost identical activities, inhibiting 44% of strains at 4.0 micrograms/ml, and both allowed easy differentiation between susceptible and resistant strains by disk diffusion. Although most isolates of M. chelonei grew better on 7H10 agar, this media gave two- to eight-fold higher minimum inhibitory concentrations than were obtained with Mueller-Hinton agar. Disk diffusion susceptibility testing appears to be a simple and reliable means of predicting susceptibility results for M. fortuitum and most isolates of M. chelonei by the agar dilution method.

Aminoglycosides↗

PubMed2026

Evaluation of Oxford nanopore sequencing for antimicrobial resistance surveillance in Salmonella: comparison with phenotypic antimicrobial susceptibility in a large-scale study.

UNLABELLED: Salmonella is a major zoonotic foodborne pathogen, and antimicrobial resistance (AMR) in Salmonella presents a significant public health challenge. Compared with conventional antimicrobial susceptibility testing (AST), whole-genome sequencing (WGS) provides a more rapid and comprehensive approach to AMR characterization, thereby informing antimicrobial selection and supporting public health surveillance. In this study, Oxford Nanopore Technology (ONT)-based WGS was performed on 1,490 Salmonella isolates collected through nationwide surveillance in Taiwan in 2025. Genotypic resistance inferred from WGS data was compared with phenotypic AST results to assess the performance of ONT-WGS. Overall, WGS-inferred resistance showed high concordance with phenotypic resistance for most antimicrobials. However, major genotype-phenotype discordance was observed, attributed to four categories: (i) breakpoint-dependent classification, (ii) reduced or absent phenotypic expression of resistance genes, (iii) minimum inhibitory concentration (MIC) modulation by ramAp, and (iv) absence of known AMR determinants. Notable discrepancies included tigecycline resistance without known genetic determinants, nalidixic acid resistance linked to ramAp-mediated MIC elevation, and a high prevalence of colistin resistance (35.7%) in S. Enteritidis, with most resistant isolates lacking identifiable AMR determinants. Additionally, a significant proportion of ESBL- and AmpC-producing isolates were classified as susceptible or intermediate to cefotaxime and ceftazidime under CLSI criteria, highlighting the potential for misclassification and treatment failure. These findings demonstrate that ONT-WGS enables accurate and comprehensive AMR characterization by directly identifying resistance determinants and avoiding potential misclassification associated with breakpoint-based AST interpretations. When interpreted appropriately, WGS can support better antimicrobial selection and serve as a valuable alternative to conventional susceptibility testing. IMPORTANCE: Accurate prediction of antimicrobial resistance is essential for appropriate therapy and effective surveillance of Salmonella. However, discordance between genotype-based predictions and phenotypic antimicrobial susceptibility testing (AST) can complicate clinical interpretation. In this nationwide study of 1,490 Salmonella isolates, we show that Oxford Nanopore Technology-based whole-genome sequencing (ONT-WGS) provides rapid and comprehensive detection of antimicrobial resistance determinants with high concordance to phenotypic AST. We further identify four major mechanisms underlying genotype-phenotype discordance, including breakpoint-dependent classification, reduced or absent phenotypic expression of resistance genes, minimum inhibitory concentration (MIC) modulation by ramAp, and the absence of known AMR determinants. These findings demonstrate how WGS can complement conventional AST, improve interpretation of challenging susceptibility results, and strengthen genomic surveillance of emerging antimicrobial-resistant Salmonella.

Microbial Sensitivity Tests↗

PubMed2026

Efficacy of the NMIC-150 system in identifying extended-spectrum beta-lactamases in clinical isolates.

Extended-spectrum beta-lactamases (ESBLs) are significant contributors to the growing global crisis of antimicrobial resistance. This study evaluated the performance of the NMIC-150 System for susceptibility testing of third-generation cephalosporins (3GCs) and assessed whether ceftazidime-avibactam and aztreonam-avibactam could identify ESBL-producing carbapenem-resistant Enterobacterales (CREs). A total of 278 non-duplicate clinical isolates (Klebsiella pneumoniae, E. coli, and Proteus mirabilis) were analyzed. Antimicrobial susceptibility was determined using reference broth microdilution (BMD) and the NMIC-150 System. ESBL production was defined as an ≥eight-fold reduction in the minimum inhibitory concentration (MIC) of 3GCs in the presence of clavulanic acid, according to CLSI criteria. Whole-genome sequencing was performed to characterize ESBL and carbapenemase genes among 3GC-resistant isolates. A Random Forest model was used to predict ESBL-producing isolates based on MIC values. The NMIC-150 System demonstrated over 90% categorical and essential agreement with BMD for ceftazidime and ceftriaxone, along with robust predictive performance via Random Forest analysis. These findings suggest that the NMIC-150 System is a reliable platform for 3GC susceptibility testing and that an ≥eight-fold MIC reduction with ceftazidime-avibactam or aztreonam-avibactam may serve as a phenotypic indicator of ESBL production in CRE isolates. In conclusion, the NMIC-150 System shows potential for routine antimicrobial resistance surveillance and may facilitate the rapid identification of ESBL-producing CREs in clinical settings.

Microbial Sensitivity Tests↗

PubMed1977

Activity of spectinomycin against anaerobes.

The in vitro inhibitory activity of spectinomycin was tested against various anaerobic bacteria. Different results were obtained with different media and with different initial pH's of the media. The highest minimum inhibitory concentrations for Bacteroides fragilis ([Formula: see text] 128 mug/ml) were obtained with the use of Wilkins-Chalgren agar (pH 7.2) and Brucella blood agar (pH 7.0). Brucella blood agar at higher pH's (7.4 and 8.0) and Mueller-Hinton and Diagnostic Sensitivity Test agars produced lower minimum inhibitory concentrations (32 and 64 mug/ml). This same relationship between spectinomycin activity and pH of the medium was, in general, observed with these media and other anaerobes, including isolates of B. melaninogenicus, Fusobacterium, gram-positive cocci, Clostridium perfringens, and C. ramosum. The variable results observed in this study and in two others make it difficult to predict the clinical usefulness of spectinomycin in the treatment of anaerobic infections. It is probably most appropriate to be guided by results obtained with Wilkins-Chalgren agar and the method proposed as a reference to the National Committee for Clinical Laboratory Standards. These results indicate that spectinomycin is not a potent inhibitor of B. fragilis or other clinically significant anaerobes.

Anaerobiosis↗

PubMed1992

Growth response of several Candida albicans strains to inhibitory concentrations of heavy metals.

Prolonged exposure of several Candida albicans strains to inhibitory concentrations of Cd, Cu, or Zn resulted in the appearance of resistant colonies at frequencies and with kinetics significantly different than expected based solely upon the predicted spontaneous mutation rate. Characteristics of the response included: (i) a delay usually of 4-10 days in the emergence of the first resistant colonies; (ii) continued accumulation of resistant colonies for a minimum of 21 days after initial exposure to selection; and (iii) final mutation frequencies ranging from 7.0 x 10(-6) to 9.8 x 10(-4). Further examination of the response of one of the strains to Cd, demonstrated that pretreatment with either ultraviolet irradiation or hydroxyurea resulted in approximately a 10-fold increase in the number of resistant colonies detected. While the distribution and identity of colony phenotypes was altered for all strains after exposure to the heavy metals, no specific morphologies could be correlated to development of resistance.

Cadmium↗

PubMedDate not supplied

Drug absorption in gastrointestinal disease with particular reference to malabsorption syndromes.

There is a considerable range in the dose of many drugs that is required to produce a given pharmacological effect in an individual patient. This individual variation in dose requirement is sometimes reflected in the wide scatter in the steady state plasma concentration that follows the same oral dose of a drug given to any group of subjects. Such individual differences are largely due to variation in the rate of elimination of drugs. Gastrointestinal disease may also alter oral dose requirements by producing variation in both the amount and rate of drug absorption. These changes may be reflected in the plasma concentration/time curve that follows an oral dose. The amount of drug abosorbed is simultaneously affected by many factors. These include the physicochemical properties of the drug and the physiological factors that operate within the gut, as well as the presence of other substances such as food, or interaction with other drugs in the gut. The availability of the drug within the intestinal lumen is largely governed by its dissolution characteristics, particularly factors which can interfere with dissolution of the drug product in the gut. Physiological factors within the gut that affect oral drug absorption include gastric emptying rate and intestinal motility, the pH of the gastrointestinal fluids, the activity of gastrointestinal drug metabolising enzymes (e.g. monoamine oxidase and dopa decarboxylase) or drug metabolising bacteria and the surface area of the gut. Many factors affect gastric emptying. These include disease, surgery and other drugs. A change in the rate of gastric emptying alters the rate of drug delivery from the stomach to the duodenum and upper small intestine. This may profoundly alter the plasma concentration/time curve that follows oral administration of many drugs. For some drugs, proximal jejunal disease may reduce, delay or increase the apparent amount of drug absorbed. Reduced absorption of an antibiotic leads to a fall in the peak plasma concentration. If the peak falls below the minimum inhibitory concentration for a particular organism then therapeutic failure may occur, if it is assumed that the peak plasma concentration is all important for antimicrobial activity. Excessive drug absorption may lead to drug toxicity. Abnormal drug absorption is a feature of lower small intestinal conditions such as Crohn's disease. This suggests that drug absorption is not confined to the jejunum but continues throughout the small intestine. It is not always possible to predict the pattern of drug malabsorption from a knowledge of the physicochemical and pharmacokinetic properties of the drug and the pathophysiology of the disease. The rate and amount of drug absorbed be one patient may differ from that in another patient with the same condtion. Although these differences reflect normal individual variation, they are also related to the extent and activity of disease at the time of study...

Absorption↗