Targeting Properties Research Articles

Advancing robust underwater acoustic target recognition through multitask learning and multi-gate mixture of experts.

Underwater acoustic target recognition has emerged as a prominent research area within the field of underwater acoustics. However, the current availability of authentic underwater acoustic signal recordings remains limited, which hinders data-driven acoustic recognition models from learning robust patterns of targets from a limited set of intricate underwater signals, thereby compromising their stability in practical applications. To overcome these limitations, this study proposes a recognition framework called M3 (multitask, multi-gate, multi-expert) to enhance the model's ability to capture robust patterns by making it aware of the inherent properties of targets. In this framework, an auxiliary task that focuses on target properties, such as estimating target size, is designed. The auxiliary task then shares parameters with the recognition task to realize multitask learning. This paradigm allows the model to concentrate on shared information across tasks and identify robust patterns of targets in a regularized manner, thus, enhancing the model's generalization ability. Moreover, M3 incorporates multi-expert and multi-gate mechanisms, allowing for the allocation of distinct parameter spaces to various underwater signals. This enables the model to process intricate signal patterns in a fine-grained and differentiated manner. To evaluate the effectiveness of M3, extensive experiments were implemented on the ShipsEar underwater ship-radiated noise dataset. The results substantiate that M3 has the ability to outperform the most advanced single-task recognition models, thereby achieving the state-of-the-art performance.

HaloTag display enables quantitative single-particle characterisation and functionalisation of engineered extracellular vesicles.

Extracellular vesicles (EVs) play key roles in diverse biological processes, transport biomolecules between cells and have been engineered for therapeutic applications. A useful EV bioengineering strategy is to express engineered proteins on the EV surface to confer targeting, bioactivity and other properties. Measuring how incorporation varies across a population of EVs is important for characterising such materials and understanding their function, yet it remains challenging to quantitatively characterise the absolute number of engineered proteins incorporated at single-EV resolution. To address these needs, we developed a HaloTag-based characterisation platform in which dyes or other synthetic species can be covalently and stoichiometrically attached to engineered proteins on the EV surface. To evaluate this system, we employed several orthogonal quantification methods, including flow cytometry and fluorescence microscopy, and found that HaloTag-mediated quantification is generally robust across EV analysis methods. We compared HaloTag-labelling to antibody-labelling of EVs using single vesicle flow cytometry, enabling us to measure the substantial degree to which antibody labelling can underestimate proteins present on an EV. Finally, we demonstrate the use of HaloTag to compare between protein designs for EV bioengineering. Overall, the HaloTag system is a useful EV characterisation tool which complements and expands existing methods.

Property-guided generation of complex polymer topologies using variational autoencoders

The complexity and diversity of polymer topologies, or chain architectures, present substantial challenges in predicting and engineering polymer properties. Although machine learning is increasingly used in polymer science, applications to address architecturally complex polymers are nascent. Here, we use a generative machine learning model based on variational autoencoders and data generated from molecular dynamics simulations to design polymer topologies that exhibit target properties. Following the construction of a dataset featuring 1342 polymers with linear, cyclic, branch, comb, star, or dendritic structures, we employ a multi-task learning framework that effectively reconstructs and classifies polymer topologies while predicting their dilute-solution radii of gyration. This framework enables the generation of polymer topologies with target size, which is subsequently validated through molecular simulation. These capabilities are then exploited to contrast rheological properties of topologically distinct polymers with otherwise similar dilute-solution behavior. This research opens avenues for engineering polymers with more intricate and tailored properties with machine learning.

Research on hot spot tracking and development trend of NMR quantum chemical calculation in structure elucidation: A bibliometric analysis

Surging numbers of researches in nuclear magnetic resonance (NMR) quantum chemical calculation in structure elucidation of natural products have appeared in recent years, making it a hot spot. But so far, the effective summarization of the numerous and jumbled body of research is still lacking. In this case, this study seeks to show the research structure of this discipline from a integral and scientific viewpoint utilizing bibliometric analysis approach, which is an effective tool to analyze literature resources based on statistical and mathematical methodologies. Literature related to the NMR quantum chemical calculation in structure elucidation was obtained from Web of Science Core Collection (WOSCC) database. The annual publication, collaboration, hot spots, discipline structure and future development trend were analyzed by bibliometric software (Bibliometrix and VOSviewer). 1620 initial research articles published from 2013 to 2023 in this field were included in the analysis. 2015 is a breaking point. China and India are the countries with most contributions, and Russia Academy of Science is the affiliation with the most output. Journal of Molecular Structure (total publication = 348) is the journal with the most output while Journal of the American Chemical Society (local citation = 4090) is the one with the most citations. Muthu, Sambantham is the author who have most articles and citations. The research could be roughly divided into the calculation parameters, the source and properties of the research targets and theory or method of NMR quantum chemical calculation in structure elucidation based on the keywords analysis. There are numerous illustrations to support that quantum chemical calculation of NMR parameters can be used in structure elucidation, which builds firm cornerstone for this field. Three branches, including research target, basis support and application, have made some achievements, but still have wild development space. In recent years, the keywords “hardness”, “docking”, “surface”, “MEP”, “LUMO” appeared most frequently, demonstrating that more concepts in quantum chemistry as well as methods based on them were introduced in structure elucidation and became hot spots in recent years. These results provided an objective and data-based introduction to the NMR quantum chemical calculation in structure elucidation. It can be observed that growing numbers of quantum chemical theories as well as related methods were introduced in NMR parameters calculation in structure elucidation, making the process more efficient and the results more accurate. However, great efforts still needed to pay to archive more precise structures elucidation and high-throughput error correction.

Image-based non-isotropic point light source calibration using digital fringe projection

Accurate light source calibration is critical in numerous applications including physics-based computer vision and graphics. We propose an image-based method for calibrating non-isotropic point light sources, addressing challenges posed by their non-radially symmetric radiant intensity distribution and the non-Lambertian properties of the calibration target. We deduce an image formation model, and capture the intensity of the calibration target at multiple poses, coupled with accurate 3D geometry acquisition using the digital fringe projection technique. Finally, we design an iterative computational framework to optimize all the light source parameters simultaneously. The experiment demonstrated the high accuracy of the calibrated light source model and showcased its application by estimating the relative reflectance of diverse surfaces.

Biomimetic Analogues of the Desferrioxamine E Siderophore for PET Imaging of Invasive Aspergillosis: Targeting Properties and Species Specificity.

The pathogenic fungus Aspergillus fumigatus utilizes a cyclic ferrioxamine E (FOXE) siderophore to acquire iron from the host. Biomimetic FOXE analogues were labeled with gallium-68 for molecular imaging with PET. [68Ga]Ga(III)-FOXE analogues were internalized in A. fumigatus cells via Sit1. Uptake of [68Ga]Ga(III)-FOX 2-5, the most structurally alike analogue to FOXE, was high by both A. fumigatus and bacterial Staphylococcus aureus. However, altering the ring size provoked species-specific uptake between these two microbes: ring size shortening by one methylene unit (FOX 2-4) increased uptake by A. fumigatus compared to that by S. aureus, whereas lengthening the ring (FOX 2-6 and 3-5) had the opposite effect. These results were consistent both in vitro and in vivo, including PET imaging in infection models. Overall, this study provided valuable structural insights into the specificity of siderophore uptake and, for the first time, opened up ways for selective targeting and imaging of microbial pathogens by siderophore derivatization.

Bioengineered Mesenchymal Stem/Stromal Cells in Anti-Cancer Therapy: Current Trends and Future Prospects.

Mesenchymal stem/stromal cells (MSCs) are one of the most widely used cell types in advanced therapies due to their therapeutic potential in the regulation of tissue repair and homeostasis, and immune modulation. However, their use in cancer therapy is controversial: they can inhibit cancer cell proliferation, but also potentially promote tumour growth by supporting angiogenesis, modulation of the immune milieu and increasing cancer stem cell invasiveness. This opposite behaviour highlights the need for careful and nuanced use of MSCs in cancer treatment. To optimize their anti-cancer effects, diverse strategies have bioengineered MSCs to enhance their tumour targeting and therapeutic properties or to deliver anti-cancer drugs. In this review, we highlight the advanced uses of MSCs in cancer therapy, particularly as carriers of targeted treatments due to their natural tumour-homing capabilities. We also discuss the potential of MSC-derived extracellular vesicles to improve the efficiency of drug or molecule delivery to cancer cells. Ongoing clinical trials are evaluating the therapeutic potential of these cells and setting the stage for future advances in MSC-based cancer treatment. It is critical to identify the broad and potent applications of bioengineered MSCs in solid tumour targeting and anti-cancer agent delivery to position them as effective therapeutics in the evolving field of cancer therapy.

Nanostructured TiO2 synthesized by different methods: Relationship between TiO2 porosity and crystalline-amorphous structure

The present manuscript focuses on important aspects regarding TiO2, which is typically used in many applications. On one hand, a thorough review of methods for synthesizing nanostructured TiO2 is presented. Using these methods, and different post-synthesis heat-treatment conditions, close to thirty TiO2 materials have been prepared, and their porous texture and crystalline and amorphous structure have been characterized. In this large number of samples, the porous texture characterization has revealed a high contribution of mesoporosity in a large number of the synthesized materials, being in many materials around 60–70 %. Moreover, different percentages of crystalline phases, anatase, brookite, rutile, and amorphous titania are developed depending on the TiO2 preparation conditions. These fractions have been determined from X-ray diffraction data using the “simple” characterization method reported by Cano-Casanova et al., inspired by Jensen et al., and Bellardita et al., also exploring its limitations. This analysis has allowed assessing a linear relationship between porosity, particularly between the surface area, and the amorphous phase content in titanium dioxide. A relationship had been previously suggested in the literature, but never correlated in such a way. In summary, the present study constitutes a tool to help other researchers select the most suitable method for synthesizing nanostructured TiO2 materials with target properties and, also, to characterize them, and have a very easy and straightforward estimation of TiO2 amorphous phase content after the determination of their surface areas.

How does a structurally similar background language influence L3 grammars? A study on the syntax of L2/L3 Mandarin [SI 21/3]

ABSTRACT This article reports on an empirical study of L3 Mandarin, aiming to shed light on transfer effects and their interaction with other factors throughout the L3 acquisition trajectory. A fill-in-the-blank task was employed to examine L2 and L3 acquisition of three types of Mandarin sentence-final particle clusters. Participants in the study were Mandarin native speakers, L1 English-L2 Mandarin speakers, L1 English-L2 Cantonese-L3 Mandarin speakers, and L1 Cantonese-L2 English-L3 Mandarin speakers at low and high Mandarin proficiency levels. Our data support arguments in the L3 literature that transfer at the L3 initial stages is from a structurally more similar language. Facilitation takes place when the instantiation of the target property in the L3 is exactly the same as that in the transfer source language. Overall typological similarity might be helpful when transfer is from the L1, but it does not always facilitate the L3 acquisition of individual properties. For later L3 development, construction frequency and mapping paradigms have a great impact on the acquisition outcome. Moreover, a certain kind of non-facilitative transfer from an L1 is found to persist in L3 Mandarin grammars, which qualitatively supports the Cumulative Input Threshold Hypothesis.

Fabrication and mechanical characterization of near field electrospun bioresorbable vascular grafts with fibrous architecture mimicking the arterial extracellular matrix

Cardiovascular disease, arteriosclerosis, is characterized by the thickening of blood vessel (arteries) walls restricting blood flow and is a global health problem. One treatment option is a surgical procedure utilizing an autologous or synthetic vascular graft to bypass or replace the diseased arterial segment. The goal of this study was to fabricate and mechanically characterize near field electrospun bioresorbable vascular grafts with a fibrous architecture that mimics the arterial extracellular matrix. Polydioxanone vascular constructs with circumferential fiber alignment angles of 15°/75° and 30°/60° (0° representing circumferential fiber alignment) were fabricated using a custom built near-field electrospinning (NFES) system. The vascular construct mechanical properties were compared to the saphenous vein (SV) and internal mammary artery (IMA) through longitudinal and circumferential uniaxial mechanical testing, suture retention, and burst pressure evaluations. The results demonstrated that the 15°/75° templates were closest to mimicking the native vessel target properties as compared to the 30°/60°; however, neither of the vascular template designs achieved or exceeded all the target values. For the ultimate tensile strength, both the constructs met the SV value on the circumferential axis (2.61 MPa) and the IMA value on the longitudinal axis (4.3 MPa). In terms of suture retention, the 15°/75° template was the only construct that was in the IMA and SV targeted range of 138–200 gf. Finally, the burst pressure testing results indicated that neither of the vascular constructs achieved the level of the SV or IMA (1600–3196 mmHg), however, the 15° constructs were within the lower error of the SV. In conclusion, with further design modifications, NFES constructs have demonstrated promise as small-diameter vascular grafts by mimicking native arterial architecture and mechanical properties.

Low-dose doxorubicin loaded extracellular vesicles combined Fas/FasL pathway-mediated chemo-sensitization and immunotherapy against tumor

The clinical application of doxorubicin (DOX) is mainly restricted by its serious side effects, poor drug delivery efficiency, and limited immunogenic death (ICD) effect. To improve DOX-based chemotherapy and ameliorate its adverse effects, we utilized 3LL cell-derived extracellular vesicles to encapsulate DOX and sodium nitroprusside (SNP) to obtain DOX/SNP@CM, which could effectively target the tumor site by harnessing the inherent homologous targeting property of tumor cell membranes. DOX performed its role on chemotherapy, and SNP successfully respond to the intracellular GSH to continuously generate nitric oxide (NO). The in situ-produced NO upregulated the Fas expression on the tumor cell surface, thereby sensitizing the Fas/FasL pathway-mediated tumor cell apoptosis of DOX. Furthermore, NO also boosted the intratumoral infiltration of cytotoxic T cells by promoted ICD effect towards tumor cells. Importantly, the anti-tumor immunity tightly cooperated with Fas/FasL mediated tumor cell apoptosis by NO-mediated manipulation on Fas/FasL interaction, collectively making DOX/SNP@CM exert significant tumor growth inhibition with low-dose DOX. Remarkably, DOX and SNP both are widely used clinical medicines, ensuring DOX/SNP@CM a potential opportunity for future practical applications.

MRI-based microthrombi detection in stroke with polydopamine iron oxide

In acute ischemic stroke, even when successful recanalization is obtained, downstream microcirculation may still be obstructed by microvascular thrombosis, which is associated with compromised brain reperfusion and cognitive decline. Identifying these microthrombi through non-invasive methods remains challenging. We developed the PHySIOMIC (Polydopamine Hybridized Self-assembled Iron Oxide Mussel Inspired Clusters), a MRI-based contrast agent that unmasks these microthrombi. In a mouse model of thromboembolic ischemic stroke, our findings demonstrate that the PHySIOMIC generate a distinct hypointense signal on T2*-weighted MRI in the presence of microthrombi, that correlates with the lesion areas observed 24 hours post-stroke. Our microfluidic studies reveal the role of fibrinogen in the protein corona for the thrombosis targeting properties. Finally, we observe the biodegradation and biocompatibility of these particles. This work demonstrates that the PHySIOMIC particles offer an innovative and valuable tool for non-invasive in vivo diagnosis and monitoring of microthrombi, using MRI during ischemic stroke.

Liquid Interfacial Gating of Superhydrophobic Plasmonic Metal-Organic Frameworks for Three-in-One Separation, Enrichment, and Recognition in Bacterial Quorum Sensing.

Poor adsorption properties of nonadsorbing targets and competing adsorption of nontargets at a liquid interface always hamper the development of interface sensing techniques. There is a need to fabricate materials that are applicable to various interface assemblies and, meanwhile, could be employed as interfacial gating to improve the performance of interface sensing by separating, enriching, and recognizing targets at the liquid interface. Here, superhydrophobic zeolite imidazole frameworks-8@gold nanoparticles-1H,1H,2H,2H-perfluorodecanethiol (ZIF-8@GNPs-PFDT) with a static water contact angle (WCA) of 155° was constructed via electrostatic self-assembly and surface graft modification. The plasmonic metal-organic framework (PMOF) nanohybrid realized all-purpose self-assembly at air/liquid and liquid/liquid interfaces and also facilely assembled on the surface of liquid droplets, hydrogels, and foams. The self-assembled porous materials displayed the capability for separating, enriching, and recognizing analytes at various oil/water interfaces and thus could be used to adsorb nonadsorbing targets and block the competing adsorption of nontargets. The self-assembled ZIF-8@GNPs-PFDT structures were employed as a three-in-one interfacial gating to endow the excellent surface-enhanced Raman scattering (SERS) sensing capability and has become a promising tool for dye molecular analysis, oil/water separation, organic phase identification, and in situ cultivation and monitoring of bacterial quorum sensing (QS).

The use of omics technologies in creating LBP and postbiotics based on the Limosilactobacillus fermentum U-21.

In recent years, there has been an increasing tendency to create drugs based on certain commensal bacteria of the human microbiota and their ingredients, primarily focusing on live biotherapeutics (LBPs) and postbiotics. The creation of such drugs, termed pharmacobiotics, necessitates an understanding of their mechanisms of action and the identification of pharmacologically active ingredients that determine their target properties. Typically, these are complexes of biologically active substances synthesized by specific strains, promoted as LBPs or postbiotics (including vesicles): proteins, enzymes, low molecular weight metabolites, small RNAs, etc. This study employs omics technologies, including genomics, proteomics, and metabolomics, to explore the potential of Limosilactobacillus fermentum U-21 for innovative LBP and postbiotic formulations targeting neuroinflammatory processes. Proteomic techniques identified and quantified proteins expressed by L. fermentum U-21, highlighting their functional attributes and potential applications. Key identified proteins include ATP-dependent Clp protease (ClpL), chaperone protein DnaK, protein GrpE, thioredoxin reductase, LysM peptidoglycan-binding domain-containing protein, and NlpC/P60 domain-containing protein, which have roles in disaggregase, antioxidant, and immunomodulatory activities. Metabolomic analysis provided insights into small-molecule metabolites produced during fermentation, revealing compounds with anti-neuroinflammatory activity. Significant metabolites produced by L. fermentum U-21 include GABA (γ-aminobutyric acid), niacin, aucubin, and scyllo-inositol. GABA was found to stabilize neuronal activity, potentially counteracting neurodegenerative processes. Niacin, essential for optimal nervous system function, was detected in vesicles and culture fluid, and it modulates cytokine production, maintaining immune homeostasis. Aucubin, an iridoid glycoside usually secreted by plants, was identified as having antioxidant properties, addressing issues of bioavailability for therapeutic use. Scyllo-inositol, identified in vesicles, acts as a chemical chaperone, reducing abnormal protein clumps linked to neurodegenerative diseases. These findings demonstrate the capability of L. fermentum U-21 to produce bioactive substances that could be harnessed in the development of pharmacobiotics for neurodegenerative diseases, contributing to their immunomodulatory, anti-neuroinflammatory, and neuromodulatory activities. Data of the HPLC-MS/MS analysis are available via ProteomeXchange with identifier PXD050857.

Permanently Charged Cationic Lipids—Evolution from Excipients to Therapeutic Lipids

Cationic lipids are crucial in medical and biotechnological applications including cellular transfection and gene delivery. Ionizable cationic lipids are critical components of the mRNA‐based COVID vaccines while permanently charged cationic lipids have shown promise in cancer treatment. Despite significant research progress over the past few decades in designing improved, biocompatible cationic lipids, their transfection efficiency remains lower than that of viral vectors. Cationic lipids with additional functionalities like fusogenicity, stimuli‐responsiveness, targeting capabilities, and therapeutic activity have been engineered to improve their performance. This review highlights the importance of molecular hybridization toward the design of biocompatible cationic lipids having fusogenic, stimuli‐responsive, targeting, or therapeutic properties. This review mainly focuses on cationic lipids, having a permanent positive charge in the headgroup region, as these are typically employed to both increase cellular interactions and for improved loading, particularly for anionic nucleic acid‐based therapeutics and vaccines. Structure–activity relationships between the lipid chemical structure (headgroup, spacer, hydrocarbon chain) and, to a lesser extent, the self‐assembled nanostructure and the intrinsic biological activity of the multi‐functional cationic lipids are described. Finally, the challenges involved in developing smart lipids without affecting their inherent capacity to self‐assemble into structured nano‐carriers are discussed.

Precisely targeted drug delivery by mesenchymal stem cells-based biomimetic liposomes to cerebral ischemia-reperfusion injured hemisphere

The precise and targeted delivery of therapeutic agents to the lesion sites remains a major challenge in treating brain diseases represented by ischemic stroke. Herein, we modified liposomes with mesenchymal stem cells (MSC) membrane to construct biomimetic liposomes, termed MSCsome. MSCsome (115.99 ± 4.03 nm) exhibited concentrated accumulation in the cerebral infarcted hemisphere of mice with cerebral ischemia-reperfusion injury, while showing uniform distribution in the two cerebral hemispheres of normal mice. Moreover, MSCsome exhibited high colocalization with damaged nerve cells in the infarcted hemisphere, highlighting its advantageous precise targeting capabilities over liposomes at both the tissue and cellular levels. Leveraging its superior targeting properties, MSCsome effectively delivered Dl-3-n-butylphthalide (NBP) to the injured hemisphere, making a single-dose (15 mg/kg) intravenous injection of NBP-encapsulated MSCsome facilitate the recovery of motor functions in model mice by improving the damaged microenvironment and suppressing neuroinflammation. This study underscores that the modification of the MSC membrane notably enhances the capacity of liposomes for precisely targeting the injured hemisphere, which is particularly crucial in treating cerebral ischemia-reperfusion injury.

Machine learning-guided realization of full-color high-quantum-yield carbon quantum dots

Carbon quantum dots (CQDs) have versatile applications in luminescence, whereas identifying optimal synthesis conditions has been challenging due to numerous synthesis parameters and multiple desired outcomes, creating an enormous search space. In this study, we present a novel multi-objective optimization strategy utilizing a machine learning (ML) algorithm to intelligently guide the hydrothermal synthesis of CQDs. Our closed-loop approach learns from limited and sparse data, greatly reducing the research cycle and surpassing traditional trial-and-error methods. Moreover, it also reveals the intricate links between synthesis parameters and target properties and unifies the objective function to optimize multiple desired properties like full-color photoluminescence (PL) wavelength and high PL quantum yields (PLQY). With only 63 experiments, we achieve the synthesis of full-color fluorescent CQDs with high PLQY exceeding 60% across all colors. Our study represents a significant advancement in ML-guided CQDs synthesis, setting the stage for developing new materials with multiple desired properties.

Accurate Density Prediction of Sesquiterpenoid HEDFs and the Multiproperty Computing Server SesquiterPre.

Accurate and rapid evaluation of density is crucial for evaluating the packing and combustion characteristics of high-energy-density fuels (HEDFs). This parameter is pivotal in the selection of high-performance HEDFs. Our study leveraged a polycyclic compound density data set and quantum chemical (QC) descriptors to establish a correlation with the target properties using the XGBoost algorithm. We utilized a recursive feature elimination method to simplify the model and developed a concise and interpretable density prediction model incorporating only six QC descriptors. The model demonstrated robust performance, achieving coefficients of determination (R 2) of 0.967 and 0.971 for internal and external test sets, respectively, and root-mean-square errors (RMSE) of 0.031 and 0.027 g/cm3, respectively. Compared to the other two mainstream methods, the marginal discrepancy between the predicted and actual molecular densities underscores the model's superior predictive ability and more usefulness for energy density calculation. Furthermore, we developed a web server (SesquiterPre, https://sespre.cmdrg.com/#/) that can simultaneously calculate the density, enthalpy of combustion, and energy density of sesquiterpenoid HEDFs, which greatly facilitates the use of researchers and is of great significance for accelerating the design and screening of novel sesquiterpenoid HEDFs.

Interpretable Machine Learning Prediction of Voltage and Specific Capacity for Electrode Materials

AbstractThe application of machine learning (ML) is becoming widespread and playing an important role in the property prediction and design of battery. Average voltage and specific capacity are two important indicators of electrode materials, which determine suitability of electrode materials in batteries. In this study, 4351 data are collected from Material Project and 324 features are extracted from Material Project or calculated by Matminer. Four ML models are discussed for prediction of average voltage and specific capacity. In order to ensure the prediction accuracy and efficiency of ML models, sequential backward selection (SBS) method is introduced to select optimal feature set, which can reduce redundancy features. The combination of Deep Neural Network model and optimal feature set selected by the SBS method achieves accurate prediction and outperforms Crystal Graph Convolutional Neural Network and Graph‐attention Graph Neural Network. The interpretability analysis provides insight into the relationship between features and two target properties. To cope with the poor prediction performance for metal‐ion battery with scarce data, transfer learning is adopted and an excellent improvement is achieved in the prediction in Na‐ion, Mg‐ion and Ca‐ion electrode material. It can be concluded that ML is an effective approach to battery property prediction and design.

Evolutionary druggability for low-dimensional fitness landscapes toward new metrics for antimicrobial applications

The term ‘druggability’ describes the molecular properties of drugs or targets in pharmacological interventions and is commonly used in work involving drug development for clinical applications. There are no current analogues for this notion that quantify the drug-target interaction with respect to a given target variant’s sensitivity across a breadth of drugs in a panel, or a given drug’s range of effectiveness across alleles of a target protein. Using data from low-dimensional empirical fitness landscapes composed of 16 β-lactamase alleles and 7 β-lactam drugs, we introduce two metrics that capture (i) the average susceptibility of an allelic variant of a drug target to any available drug in a given panel (‘variant vulnerability’), and (ii) the average applicability of a drug (or mixture) across allelic variants of a drug target (‘drug applicability’). Finally, we (iii) disentangle the quality and magnitude of interactions between loci in the drug target and the seven drug environments in terms of their mutation by mutation by environment (G x G x E) interactions, offering mechanistic insight into the variant variability and drug applicability metrics. Summarizing, we propose that our framework can be applied to other datasets and pathogen-drug systems to understand which pathogen variants in a clinical setting are the most concerning (low variant vulnerability), and which drugs in a panel are most likely to be effective in an infection defined by standing genetic variation in the pathogen drug target (high drug applicability).