Predicting Risk of Multidrug-Resistant Enterobacterales Infections Among People With HIV

Faecal carriage of extended-spectrum beta-lactamase and carbapenemase-producing enterobacterales among HIV patients at Jimma Medical Center, Southwest Ethiopia

BACKGROUND

Enterobacterales infections in immunocompromised individuals are associated with considerable morbidity, mortality, and health care costs. This study aimed to assess the faecal carriage of extended-spectrum β-lactamase (ESBL) and carbapenemase-producing Enterobacterales (CPE) among HIV-infected patients at Jimma Medical Center. A total of 344 stool samples were collected and inoculated on Mac-Conkey and Eosin-Methylene Blue agar and incubated at 35-37 °C aerobically. ESBL and carbapenemase production were detected using D68C ESBL/AmpC and D73C CARBA plus (Mast Group, UK).

RESULTS

A total of 376 Enterobacterales were isolated. The prevalence of ESBL-PE and CPE carriage rate was 13.3% (50/376) and 4.3% (16/376) respectively. The highest proportion of ESBL producing isolates were found in K. pneumoniae 29.0% (9/31) followed by E. coli 13.4% (39/292). Similarly, K. pneumoniae 12.9% (4/31) was the most common carbapenem-resistant isolate followed by E. coli 3.8% (11/292). Multi-drug resistance was observed in 66.5% (250/376) of the isolates. Prior cephalosporin use (AOR = 7.9; 2.31-27.29), CD4 count (≤ 350 cells/µL) (AOR = 3.8; 1.12-12.9), and comorbidities (AOR = 2.3; 1.24-4.32) were significantly associated with ESBL production. Additionally, cephalosporin use (AOR = 6.34; 1.27-31.66) was significantly associated with the presence of CRE.

CONCLUSIONS

This study revealed a high prevalence of ESBL-PE and CPE among HIV patients, with K. pneumoniae and E. coli being the dominant isolates. MDR was common, with key risk factors being prior cephalosporin use, low CD4 counts, and comorbidities. These findings emphasize the need for enhanced infection prevention and control, regular screening, and improved antibiotic stewardship to curb the spread of resistant bacteria in immunocompromised individuals.

Development of a gradient method for sulfamethoxazole, trimethoprim, isoniazid, and pyridoxine hydrochloride in rabbit plasma through QbD-driven investigation

The current study developed a method for quantifying four drugs-Sulfamethoxazole, Trimethoprim, Isoniazid, and Pyridoxine-in rabbit plasma. The method uses gradient liquid chromatography based on analytical quality by design. To achieve separation, a Eclip Plus C18 (250 mm × 5 mm, 4.6 µm) column with L1 packing was used, and analytes were detected at 254 nm at ambient temperature. The optimized mobile phase consisted of 50 mM potassium dihydrogen phosphate buffer (pH 6.5) and Methanol. The concentration of Methanol was 3% (0-5 min), 15% (5-15 min), 55% (15-27 min), and 3% Methanol until the end of the 30-min runtime, and the flow rate was set at 0.95 mL/min. Control Noise Experimentation was used to screen studies, revealing that flow rate, pH, and Methanol concentration significantly affected the analytical attributes. The study identified critical attributes (resolution and asymmetric factor) and developed a quality target method profile. A central composition design was used to optimize the essential parameters. The method developed for the drugs showed peaks at retention times of 6.990 min for Isoniazid, 7.880 min for Pyridoxine, 15.530 min for Sulfamethoxazole, and 26.890 min for Trimethoprim, respectively. The method was validated with linearity in the range of 10-640 ng ml-1, with R2 of 0.9993, 0.9987, 0.9993, and 0.9992 for Sulfamethoxazole, Trimethoprim, Isoniazid, and Pyridoxine, respectively.

Multidrug-Resistant Bacteria in Immunocompromised Patients

The increasing incidence of antibiotic resistance in bacteria is a major problem in terms of therapeutic options, especially in immunocompromised patients, such as patients from intensive care units (ICUs), HIV-positive patients, patients with malignancies or transplant patients. Commensal bacteria, especially anaerobes, serve to maintain microbial stability by preventing overpopulation with pathogenic bacteria. In immunocompromised patients, microbiota imbalance caused by antibiotic therapy and decreased host immunity favors intestinal overpopulation with pathogenic species, leading to increased bacterial translocation and susceptibility to systemic infections. Infections with multidrug-resistant (MDR) bacteria pose major challenges to the establishment of appropriate treatment and lead to increased mortality. Asymptomatic colonization with MDR bacteria usually precedes infection and tends to persist for long periods of time, and in immunocompromised patients, colonization with MDR bacteria is a risk factor for systemic infections. This review aims to assess the relation between colonization and infection with MDR bacteria in immunocompromised patients such as ICU patients, HIV-positive patients and cancer patients and to identify the prevalence and patterns of MDR bacterial colonization and infection in this category of patients.

Significant Publications on Infectious Diseases Pharmacotherapy in 2022

Purpose: To provide summarization of the most significant infectious diseases (ID) pharmacotherapy articles published in peer-reviewed literature in 2022. Summary: Members of the Houston Infectious Diseases Network (HIDN) nominated notable articles providing significant contributions to ID pharmacotherapy in 2022. Article nominations included those pertaining to general ID, as well as those including coronavirus disease 2019 (COVID-19), and human immunodeficiency virus/acquired immunodeficiency syndrome (HIV/AIDS) pharmacotherapy. A total of 71 articles were nominated by HIDN. Members: 68 articles pertaining to general ID pharmacotherapy and 3 articles focusing on HIV/AIDS. To aid selection of the most these most notable articles of 2022, a survey was created and distributed to members of the Society of Infectious Diseases Pharmacists (SIDP). Of the 153 SIDP members who participated in the survey, there were 128 recorded votes for the top 10 general ID pharmacotherapy articles and 30 votes recorded for the top HIV/AIDS article. The most notable publications are summarized. Conclusion: Post pandemic significant advances in antimicrobial stewardship and infectious disease states continues to occur in a world recently focused on the coronavirus disease 2019 (COVID-19) global pandemic. Continuous growth in publication of ID-related articles over the past year lends towards the aims of this review to aid clinicians in remaining current on key practice-changing ID pharmacotherapy publications from 2022.

Gut resistome linked to sexual preference and HIV infection

BACKGROUND

People living with HIV (PLWH) are at increased risk of acquisition of multidrug resistant organisms due to higher rates of predisposing factors. The gut microbiome is the main reservoir of the collection of antimicrobial resistance determinants known as the gut resistome. In PLWH, changes in gut microbiome have been linked to immune activation and HIV-1 associated complications. Specifically, gut dysbiosis defined by low microbial gene richness has been linked to low Nadir CD4 + T-cell counts. Additionally, sexual preference has been shown to strongly influence gut microbiome composition in PLWH resulting in different Prevotella or Bacteroides enriched enterotypes, in MSM (men-who-have-sex-with-men) or no-MSM, respectively. To date, little is known about gut resistome composition in PLWH due to the scarcity of studies using shotgun metagenomics. The present study aimed to detect associations between different microbiome features linked to HIV-1 infection and gut resistome composition.

RESULTS

Using shotgun metagenomics we characterized the gut resistome composition of 129 HIV-1 infected subjects showing different HIV clinical profiles and 27 HIV-1 negative controls from a cross-sectional observational study conducted in Barcelona, Spain. Most no-MSM showed a Bacteroides-enriched enterotype and low microbial gene richness microbiomes. We did not identify differences in resistome diversity and composition according to HIV-1 infection or immune status. However, gut resistome was more diverse in MSM group, Prevotella-enriched enterotype and gut micorbiomes with high microbial gene richness compared to no-MSM group, Bacteroides-enriched enterotype and gut microbiomes with low microbial gene richness. Additionally, gut resistome beta-diversity was different according to the defined groups and we identified a set of differentially abundant antimicrobial resistance determinants based on the established categories.

CONCLUSIONS

Our findings reveal a significant correlation between gut resistome composition and various host variables commonly associated with gut microbiome, including microbiome enterotype, microbial gene richness, and sexual preference. These host variables have been previously linked to immune activation and lower Nadir CD4 + T-Cell counts, which are prognostic factors of HIV-related comorbidities. This study provides new insights into the relationship between antibiotic resistance and clinical characteristics of PLWH.

Tackling the Antimicrobial Resistance "Pandemic" with Machine Learning Tools: A Summary of Available Evidence

Antimicrobial resistance is recognised as one of the top threats healthcare is bound to face in the future. There have been various attempts to preserve the efficacy of existing antimicrobials, develop new and efficient antimicrobials, manage infections with multi-drug resistant strains, and improve patient outcomes, resulting in a growing mass of routinely available data, including electronic health records and microbiological information that can be employed to develop individualised antimicrobial stewardship. Machine learning methods have been developed to predict antimicrobial resistance from whole-genome sequencing data, forecast medication susceptibility, recognise epidemic patterns for surveillance purposes, or propose new antibacterial treatments and accelerate scientific discovery. Unfortunately, there is an evident gap between the number of machine learning applications in science and the effective implementation of these systems. This narrative review highlights some of the outstanding opportunities that machine learning offers when applied in research related to antimicrobial resistance. In the future, machine learning tools may prove to be superbugs' kryptonite. This review aims to provide an overview of available publications to aid researchers that are looking to expand their work with new approaches and to acquaint them with the current application of machine learning techniques in this field.

In vitro antibacterial activity of antiretroviral drugs on key commensal bacteria from the human microbiota

Introduction

Antiretroviral therapy has improved life expectancy in HIV-infected patients. However, people living with HIV under antiretroviral therapy are at higher risks of developing chronic complications and acquiring multidrug resistant bacteria than healthy population. These factors have been associated with shifts in gut microbiome composition and immune activation. It is unclear how antiretroviral drugs affect gut microbiota composition, but it has been observed that antiretroviral treatment is not able to fully restore gut health after HIV infection. Additionally, some antiretroviral drugs have shown antibacterial activity suggesting that these drugs could have a direct impact on the human microbiome composition.

Methods

We determined the in vitro antibacterial activity of 16 antiretroviral drugs against a set of key clinically relevant and human commensal bacterial strains.

Results

Our results demonstrate that 5 antiretroviral drugs have in vitro antibacterial activity against gut and vaginal human commensal bacteria. Zidovudine has antibacterial activity against Escherichia coli, Klebsiella pneumoniae and Prevotella bivia, abacavir against Gardnerella vaginalis, efavirenz against G. vaginalis and P. bivia and bictegravir against Enterococcus spp. and G. vaginalis. Moreover, we describe for the first time that elvitegravir has antibacterial activity against G. vaginalis and P. bivia and, most importantly, against vancomycin-resistant Enterococcus spp. and methicillin-resistant Staphylococcus aureus strains with MIC values of 4-16 and 4 µg/mL, respectively showing high level of effectiveness against the tested multidrug-resistant bacteria.

Discussion

Our results underscore that some antiretroviral drugs may influence the human microbiota composition. In addition, we report the potential use of elvitegravir to treat multidrug-resistant Gram-positive bacteria warranting the need of clinical studies to repurpose this antiretroviral drug.

Using Machine Learning to Predict Antimicrobial Resistance-A Literature Review

Machine learning (ML) algorithms are increasingly applied in medical research and in healthcare, gradually improving clinical practice. Among various applications of these novel methods, their usage in the combat against antimicrobial resistance (AMR) is one of the most crucial areas of interest, as increasing resistance to antibiotics and management of difficult-to-treat multidrug-resistant infections are significant challenges for most countries worldwide, with life-threatening consequences. As antibiotic efficacy and treatment options decrease, the need for implementation of multimodal antibiotic stewardship programs is of utmost importance in order to restrict antibiotic misuse and prevent further aggravation of the AMR problem. Both supervised and unsupervised machine learning tools have been successfully used to predict early antibiotic resistance, and thus support clinicians in selecting appropriate therapy. In this paper, we reviewed the existing literature on machine learning and artificial intelligence (AI) in general in conjunction with antimicrobial resistance prediction. This is a narrative review, where we discuss the applications of ML methods in the field of AMR and their value as a complementary tool in the antibiotic stewardship practice, mainly from the clinician's point of view.