Judging the appropriate therapy for carbapenem-resistant Acinetobacter infections

Novel Antimicrobial Peptide SAAP Mutant as a Better Adjuvant to Sulbactam-Based Treatments Against Clinical Strains of XDR Acinetobacter baumannii

The production of extended spectrum β-lactamases (ESBLs) in extensively drug-resistant (XDR) strains of Acinetobacter baumannii has created havoc amongst clinicians making the treatment procedure challenging. Carbapenem-resistant strains have displayed total ineffectiveness towards newer combinations of β-lactam-β-lactamase inhibitors (βL-βLI) in tertiary healthcare settings. Therefore, the present study was aimed to design potential β-lactamase antimicrobial peptide (AMP) inhibitors against ESBLs produced by the strains. We have constructed an AMP mutant library with higher antimicrobial efficacy (range: ~ 15 to 27%) than their parent peptides. The mutants were thoroughly screened based on different physicochemical and immunogenic properties revealing three peptides, namely SAAP-148, HFIAP-1, myticalin-C6 and their mutants with safe pharmacokinetics profile. Molecular docking highlighted SAAP-148_M15 displaying maximum inhibitory potential with lowest binding energies against NDM1 (- 1148.7 kcal/mol), followed by OXA23 (- 1032.5 kcal/mol) and OXA58 (- 925.3 kcal/mol). The intermolecular interaction profiles displayed SAAP-148_M15 exhibiting hydrogen bonds and van der Waals hydrophobic interactions with the crucial residues of metallo β-lactamase [IPR001279] and penicillin-binding transpeptidase [IPR001460] domains. Coarse-grained clustering and molecular dynamics simulations (MDS) further validated the stable backbone profile and minimal residue-level fluctuations of the protein-peptide complex that were maintained throughout the simulation timeframe. The present study hypothesised that the combination of sulbactam (βL) with SAAP-148_M15 (βLI) holds immense potential in inhibiting the ESBLs alongside restoration of sulbactam activity. The current in silico findings upon further experimental validations can pave path towards designing of successful therapeutic strategy against XDR strains of A. baumannii.

Empirical antibiotic therapy for difficult-to-treat Gram-negative infections: when, how, and how long?

PURPOSE OF REVIEW

To discuss empirical therapy for severe infections due to Gram-negative bacteria with difficult-to-treat resistance (GNB-DTR) in current clinical practice, focusing in particular on the positioning of novel therapeutic agents and rapid diagnostic tests.

RECENT FINDINGS

The current era of novel agents active against GNB-DTR and showing differential activity against specific determinants of resistance is an unprecedented scenario, in which the clinical reasoning leading to the choice of the empirical therapy for treating severe GNB-DTR infections is becoming more complex, but it also allows for enhanced treatment precision.

SUMMARY

Novel agents should be used in line with antimicrobial stewardship principles, aimed at reducing selective pressure for antimicrobial resistance. However, this does not mean that they should not be used. Indeed, excesses in restrictive uses may be unethical by precluding access to the most effective and less toxic treatments for patients with severe GNB-DTR infections. Given these premises (the 'how'), empirical treatment with novel agents should be considered in all patients with risk factors for GNB-DTR and severe clinical presentation of acute infection (the 'when'). Furthermore, empirical novel agents should preferably be continued only for a few hours, until de-escalation, modification, or confirmation (as targeted therapy) is made possible by the results of rapid diagnostic tests (the 'how long').

Genome sequencing and molecular characterisation of XDR Acinetobacter baumannii reveal complexities in resistance: Novel combination of sulbactam-durlobactam holds promise for therapeutic intervention

Emerging nosocomial strains of Acinetobacter baumannii are of recent concern as they are expressing extensive drug resistance (XDR). Using whole-genome sequencing and molecular characterisation analysis, the current study reveals the presence of carbapenemase genes in 92.86% of studied Indian isolates. These included bla_OXA-51 , bla_OXA-23 , bla_OXA-58 , and bla_NDM genes, with over a third expressing dual carbapenemase genes. As per the MLST scheme, IC2^Oxf /CC2^Pas was the predominant clone, with 57.14% isolates belonging to this lineage. The presence of these carbapenemase genes resulted in sulbactam (SUL) resistance (MIC: 16-256 µg/ml) in all of the studied isolates. The efficacy of durlobactam (DUR), a novel β-lactamase inhibitor that also inhibits PBP2 was assessed through in silico intermolecular interaction analysis. Several nonsynonymous single nucleotide polymorphisms were identified in PBP2 (G264S, I108V, S259T) and PBP3 (A515V, T526S) sequences. Minimal variations were recorded in the protein backbone dynamics in active-site motifs of wild-type and mutants, which correlated with negligible binding energy fluctuations for the PBP3-SUL (-5.85 ± 0.04 kcal/mol) and PBP2-DUR (-5.16 ± 0.66 kcal/mol) complexes. Furthermore, higher binding affinities and low inhibition constants were noted in OXA23-DUR (-7.36 kcal/mol; 4.01 µM), OXA58-DUR (-6.44 kcal/mol; 19.07 µM), and NDM-DUR (-6.82 kcal/mol; 10.01 µM) complexes when compared with the conventional drugs avibactam and aztreonam. Stable interaction profiles of DUR with carbapenemases can possibly restore SUL activity against both PBP3_WT and PBP3_MTs . The study establishes the efficacy of the novel SUL-DUR combination as a successful treatment strategy in combating emerging XDR strains of A. baumannii.

Therapeutic options for difficult-to-treat Acinetobacter baumannii infections: a 2020 perspective

INTRODUCTION

Treatment of severe infections due to Acinetobacter baumannii with difficult-to-treat resistance (DTR-AB), which exhibits resistance to all β-lactams, β-lactam/β-lactamases inhibitor combinations, and fluoroquinolones, remains a challenge for clinicians.

AREAS COVERED

The present perspective provides a personal view on both current and future agents for the treatment of severe DTR-AB infections.

EXPERT OPINION

We currently are in a transition era for the treatment of DTR-AB infections, where in the past 20 years, polymyxin-based regimens have become the most used approach (although possibly suboptimal, there were few or no alternatives) and where in the next 20 years, polymyxins will likely be replaced by less toxic novel agents as first-line choices. Two novel antimicrobial agents have been recently approved that show activity against DTR-AB, cefiderocol and eravacycline, while durlobactam/sulbactam is in phase-3 of clinical development. In the near future, these agents could become important first-line choices for the treatment of DTR-AB within approved indications, or for off-label indications in the absence of dependable alternatives. Good-quality post-marketing experiences remain necessary for arising clinically relevant questions and guiding the design of further dedicated randomized controlled trials to stably optimize the use of novel agents for DTR-AB infections in the next decades.

Machine Learning and Multidrug-Resistant Gram-Negative Bacteria: An Interesting Combination for Current and Future Research

The dissemination of multidrug-resistant Gram-negative bacteria (MDR-GNB) is associated with increased morbidity and mortality in several countries. Machine learning (ML) is a branch of artificial intelligence that consists of conferring on computers the ability to learn from data. In this narrative review, we discuss three existing examples of the application of ML algorithms for assessing three different types of risk: (i) the risk of developing a MDR-GNB infection, (ii) the risk of MDR-GNB etiology in patients with an already clinically evident infection, and (iii) the risk of anticipating the emergence of MDR in GNB through the misuse of antibiotics. In the next few years, we expect to witness an increasingly large number of research studies perfecting the application of ML techniques in the field of MDR-GNB infections. Very importantly, this cannot be separated from the availability of a continuously refined and updated ethical framework allowing an appropriate use of the large datasets of medical data needed to build efficient ML-based support systems that could be shared through appropriate standard infrastructures.