Jacob K. McPherson, Surbhi Sharma, Biswajit Mishra, Fadi Shehadeh, Eleftherios Mylonakis
The convergence of artificial intelligence (AI) and antimicrobial pharmacology is creating unprecedented opportunities to accelerate antimicrobial discovery, optimize therapeutic strategies, and reduce the global burden of infectious diseases. A critical determinant of the successful integration of AI into antimicrobial pharmacology is the accurate and interpretable prediction of pharmacokinetic (PK) and pharmacodynamic (PD) properties, including measures such as the minimum inhibitory concentration (MIC), to identify the PK/PD indices and dosing regimens that maximize antimicrobial efficacy while ensuring safety. We review recent developments in AI models for antimicrobial PK/PD and critically examine their performance, interpretability, generalizability, and readiness for clinical translation. We provide a practical framework to help researchers, microbiologists, pharmacologists, and clinicians evaluate the strengths, limitations, and appropriate applications of these emerging approaches. We anticipate that rigorous development, transparent validation, and thoughtful implementation of AI-driven PK/PD models will accelerate antimicrobial discovery, optimize precision dosing, and ultimately improve patient outcomes.
Biosurfactants are amphiphilic biomolecules that are produced by microorganisms and have important industrial, environmental, and biomedical applications because of their biodegradability, low toxicity, and high surface activity. In the present study, the potential of biosurfactant production by a Lactobacillus helveticus indigenous strain (MTCC 5463) was investigated using integrated in silico, molecular, biochemical, and analytical approaches focused on the conserved non-ribosomal peptide synthetase (NRPS) adenylation domain. Comparative sequence and domain analysis of the Bacillus velezensis FZB42 reference identified domains for adenylation, condensation, and peptidyl carrier proteins (PCP) that are shared by biosurfactant biosynthetic genes. The AMP-binding domain was amplified, and then the resulting fragment was directionally cloned into the pET-28a expression vector and expressed in Escherichia coli BL21(DE3). A recombinant 14-kDa AMP-binding protein, which was associated with the production of a biosurfactant-like compound, was produced. The recombinant biosurfactant was extracted using chloroform:methanol solvent systems and found to have significant emulsification activity against petrol, diesel, mustard oil, and refined oil, and also its E24 value was found to be stable for 1 week. Thin-layer chromatography confirmed the presence of lipidic and glycolipid components, while FTIR analysis indicated the presence of hydroxyl, amide, aliphatic, and carbohydrate-associated functional groups, which are characteristics of a glycolipopeptide structure. The hydrophobic lipid architecture of the biosurfactant was supported by GC–MS profiling, which identified the presence of significant amounts of C16/C18 long-chain fatty acid derivatives and amides. Moreover, ¹H NMR and ¹³C NMR analyses indicated the presence of peptide bonds, glycerol/carbohydrate groups, and long hydrocarbon chains, giving further confirmation of the amphiphilic glycolipopeptide nature of the recombinant biosurfactant. In conclusion, the study shows that conserved NRPS adenylation domains can be utilized to produce biosurfactants in probiotic lactic acid bacteria and suggests that the recombinant glycolipopeptide biosurfactants produced by L. helveticus MTCC 5463 have potential for use in the industry.
Biofilms are a substantial problem across many sectors, with a frequently cited estimated global economic cost of >$5 trillion. In the healthcare setting, biofilm-associated infections have a significant impact on patient management and quality of life. They are difficult to eradicate and treat and contribute to escalating antimicrobial resistance. There are a multitude of methods for investigating biofilms, most designed to mimic in vivo conditions in an in vitro environment, but without standardisation, assay reproducibility and confidence is lost. The community needs validated, reproducible and harmonised approaches to support research and innovation underpinned by physical reference materials and written standards. Furthermore, clarity around the type of evidence needed to make and substantiate antibiofilm claims is needed; despite growing innovation in antibiofilm research, regulatory pathways remain unclear. This review discusses challenges and opportunities for biofilm innovation and highlights where regulatory science can aid and support progress.
The global food system is constantly being constrained by biotic and abiotic challenges, resulting in instability and insecurity, particularly in regions like sub-Saharan Africa, where agricultural productivity often remains below global averages. Microbial biotechnology includes many sustainable ways of leveraging the metabolic potential of microorganisms, such as bacteria, fungi, and viruses, to address food insecurity through enhanced crop resilience, precision fermentation, and improved soil health. Keeping up with these demands now requires constant innovative multidisciplinary approaches in the fast-growing field of artificial intelligence (AI). AI is reinventing microbial biotechnology via various applications in the areas of taxonomic profiling, metabolic modeling, and the design of microbial cell factories. This review evaluates the transformative role of AI in optimizing these microbial systems. Current advancements showcase the use of machine learning and deep learning architectures, such as convolutional neural networks and transformers, to accelerate the discovery of novel biofertilizers and biocontrol agents. In precision fermentation, AI-driven models and reinforcement learning are increasingly used to optimize the clustered regularly interspaced short palindromic repeats (CRISPR)-based microbial engineering, as well as bioprospecting for microbes that can improve soil health. However, challenges that beset the current landscape still include overall adoption, difficult-to-understand models or algorithm interpretability, quality input of training data, good ethical practices, high computational cost associated with complex structural simulations, and the need for standardized processes to make sure that AI applications are reliable and applicable in different microbiological settings. While AI is an essential ingredient for futuristic microbial biotechnology, tackling these technical and ethical hurdles is key to achieving stable food security.
With the emergence of post-antibiotic era, antivirulence therapy against Pseudomonas aeruginosa was conceived as a rational escape from the global antimicrobial resistance crisis. Built on the foundation of virulence attenuation/disarmament rather than pathogen elimination, antivirulence therapy has not achieved any tangible clinical translation. This misfortune shows a reproducible pattern rooted in a series of compounding conceptual limitations. This perspective argues that the translational challenges of antivirulence strategies reflect not pharmacological inadequacy, but a fundamental misalignment between therapeutic design and infection biology. We dissect four structurally-interconnected explanations driving this impasse. First, antivirulence agents have been evaluated using defined endpoints that are engineered for bactericidal drugs, rendering precision disarmament strategies invisible by design. Second, P. aeruginosa itself outsmarts and resists single-node perturbations of virulence pathways not through conventional resistance mechanisms but by deploying intricately connected networks that rewire bacterial virulence in response to therapeutic pressure. Thirdly, the aspiration for broad-spectrum antivirulence therapy negates that pseudomonal virulence is neither fixed nor universal, but a shifting, niche-sculpted, and strain-variable phenomenon that defies a universal target. Finally, antivirulence therapies rely heavily on host immunity-mediated bacterial clearance, which is grossly eroded/compromised in patients infected with P. aeruginosa. We argue that clinical translation of antivirulence therapy requires abandoning the reductionist, target-centric paradigm and adopting a state-control framework that treats pseudomonal infections as dynamic, multifactorial, and coupled host-pathogen interactions. This strategic shift, from targeting molecules to redirecting biological trajectories, can potentially reposition antivirulence agents as precision tools for destabilising pathological states and combating P. aeruginosa infections.
