Mw) of 161 kDa could be obtained. The rate equations that describe polycondensation kinetics were recast in a condensed form that allowed very fast numerical solution and calculation of the number-average molecular weight with time. Deviations with respect to the experiment were minimized in a least-squares fashion to determine rate constants. The optimized kinetics parameters are shown to reproduce the experimental data accurately.We present synthetic experiments of lactic acid (LA) polycondensation to produce poly(lactic acid) (PLA) as well as kinetic modeling calculations that capture the polymer molecular weight increase with time, given the initial concentrations. Tin-octoate-catalyzed polycondensation of - or L-lactic acid was carried out in pre-dried toluene after azeotropic dehydration for 48–120 h at 130–137 °C. The polymerization was optimized by varying lactic acid and catalyst concentrations as well as the temperature. Gel permeation chromatography was used to experimentally follow the evolution of molecular weights and the products were characterized by NMR, TGA, DSC and IR. Under optimal conditions, PLLA with weight-average molecular weight ( Plastic pollution has emerged as a major threat to the environment and public health. It has been shown that out of all plastic ever manufactured globally, a mere 6.1% has been recycled, another 8.4% has been incinerated, 30.1% remains in use and a majority of 55.4% has been disposed of into the environment. With the lifetime of plastic ranging between a few years and several centuries, the above situation has resulted in a waste crisis intertwined with other major challenges such as the depletion of natural resources because of oil consumed to produce plastic, and the carbon footprint associated with such products , b = [Q-B], c = [P-Q]  and w = [A-B]. Given that A-P and Q-B bonds are actually situated at the two ends of each lactic acid or higher chain molecule, their concentrations should be identical, b = a. Then, we arrive at the following simplified set of rate equations:Then, we can describe the process as a reaction between chemical bonds of HOOCRc(0) = 0, a(0) = a0  and w(0) ≈ 0. Then, an estimator of the average molecular weight with time can be given by the expressionmono is the repeat unit’s formal weight. This result comes from the simple fact that the number of P-Q bonds (concentration c) that corresponds to a single A-P or Q-B bond (concentration a) is merely the degree of polymerization. Given that there is just one bond of A-P type per molecule, the denominator in the above expression counts the molecules whereas the numerator sums their degrees of polymerization. In other words, the above estimator corresponds to the number-average molecular weight. Most notably, the simplified model does not require the definition of an arbitrary upper bound to the degree of polymerization –(4), which cannot be solved in practice without setting such an upper limit). Of course, there is a price to pay for this simplification, which lies in the model’s inherent inability to predict the molecular weight distribution and the index of polydispersity.The set of Equations (7)–(9) should be considerably easier to solve than Equations (2)–(4). Our initial conditions now read Mn, from Equation (11), implying that the estimate based on the total number of P-Q bonds represents an upper bound to Mn. However, for non-negligible fractions of ring polymers to form, very low reactant concentrations are required. The conditions of our experiments are expected to favor the formation of linear products over ring ones, so it is safe to assume that the above estimator, Equation (11), should be a very good approximation to the actual number-average molecular weight of linear PLA.In principle, the end groups of a long enough chain can react with each other to give rise to a ring polymer. Thus, an unknown fraction of the ester bonds present in the system should be subtracted before calculating the number-average molecular weight, k1, k−1 and kw. Given a set of N experimentally determined number-average molecular weights versus time it, i = 1, 2, …, N, we seek such rate and water removal constant values that the solution of Equations (7)–(9) yields predictions that minimize the error with respect to the measurements. Here, this was achieved by defining the objective function, F.k1 and k−1 and proportionality coefficient kw are collectively denoted by p, and the subscript ‘n’ denotes the number average of molecular weight of the i-th experimental data point and superscripts ‘exp’ and ‘num’ denote experimentally measured and numerically computed values, respectively. The numerically predicted molecular weights are calculated as functions of time, t, by solving the rate equations, Equations (7)–(9), and then applying Equation (11). Thus, the proposed minimization problem can be formulated asTo proceed with the solution of the above set of ODEs, we need to estimate the rate and water removal constants: F, was computed by comparing each experimental point with the value computed during the timestep coinciding with the corresponding experimental time.We solved the above minimization problem with the aid of the derivative-free Nelder–Mead minimization algorithm (Simplex)  as implex, from the current value x(t) of a given variable x to its updated value x(t + h) where h denotes the variable step, should not exceed a certain fractional threshold (set to 10%) of the old value. Thus, h would be reset to a smaller value and a new updated value would be calculated until achieving the desired, smaller than our threshold, change rate, Δx. On the other hand, a compromise was made by imposing a lower bound of 10−10 to h to avoid extremely small steps that would cause the solver to stall. It was shown in practice that this constraint to the range of h values had only a minimal effect on the solutions in the form of the values occasionally ‘jittering’ around their main trend line. The user-defined timestep, Δt, that was set to 1 s served as an upper bound to h.The rate equations were solved by a simple Euler method with a timestep of 1 s for a time span of 200 h . Initial conditions were set according to the initial concentrations calculated as shown in the example of F, by a large multiplier, in the case of unstable solutions . A number of initial values were tried for the rate and water removal constants until attaining an optimal solution that exhibited good reproduction of our experimental measurements.Overall, the solver performed very well, taking fractions of a second to integrate the rate equations within the desired time span on an ordinary laptop computer. When calling the solver through the optimizer, an additional safety measure was taken by penalizing the objective function, w/w of lactic acid was converted to L-lactide as determined by 1H-NMR  . In our y 1H-NMR .1H-NMR and GPC.The polymerization process was performed in a second step during which the Dean–Stark apparatus was replaced by a glass tube packed with activated molecular sieves 3 Å and the catalyst tin octoate catalyst was added. The molecular-sieve-packed tube was used to remove both residual water and water produced during polymerization as it was previously shown that recycling of the solvent through the molecular sieves may reduce the water content of the mixture to as low as 3 ppm or less ,32. Addi1H-NMR spectrum of PLLA  of PLA was 41 kDa  and dispersity (Đ) at 1.52 were detected. The formation of low-molecular-weight PLLA (Mn = 5766 kDa) was detected after 52.5 h total polycondensation time (Đ = 2.40). Further heating (68 h) led to a significant increase in the average-molecular-weight PLLA . The polymerization was terminated after 120 h, providing high-molecular-weight PLLA .To get insight into the course of polycondensation and provide data for modeling, the progress of lactic acid polycondensation was monitored with GPC at the optimum conditions , Entry 2−1 along with their deformation vibrations around 1380 and 1360 cm−1, and the strong stretching and bending vibration of ester carbonyl groups at ca. 1755 and 1265 cm−1, respectively, accompanied by the C-O stretching vibrations between 1080 and 1230 cm−1 , as well as their maximum decomposition temperature  and rate were recorded  for the high-Mn polymer was 296.5 °C, for the low-Mn PLA was 278.1 °C and for the medium-Mn PLA was 283.7 °C. The maximum rate of the main decomposition step for the high-Mn polymer was determined with dTG analysis at 275.7 °C while for the medium- and low-molecular-weight PLLAs it was at 261.6 °C and 256.2 °C, respectively  of the medium-Mn PLLA (41 kDa) was 59.9 °C, 5.3 °C less than the Tg of high-Mn PLLA  was calculated to be 61.1 °C  and the lowest degree of crystallinity (16.9%), in agreement with what has been previously reported in the literature .,52.51,52 70 kDa, , Entry 7 kDa, Mn , Entry 2terature . The degterature :(13)C%=∆Interestingly, the DSC graphs provided two melting peaks for the polymers with medium molecular weight  leaves 7.11 g of dry LA. However, this is actually an upper bound because of further conversion to lactide (by 5% wt.) and oligolactic acid (of known mass evidenced by 1H-NMR). Taking the conversion to lactide into account, we obtain an intermediate estimated dry LA mass of 6.75 g, under the assumptions that lactide does not react under the conditions of our experiments and no conversion to oligomers has taken place. To determine a lower bound that takes the early formation of oligolactic acid into account, we account for its own share of reactive bonds that will be included as an appropriate correction. We consider an unknown average degree of polymerization, np, of the molecules formed according to the reaction Initial concentrations were determined according to the data shown in nLA,0–nw where nLA,0 is the number of LA moles prior to the early formation of its oligomers, and nw are the moles of water formed . By subtracting the residual water from the 1.2 g total water removed during the initial stage of the experiment, and further considering a small amount of water produced during the partial conversion to lactide (0.0355 g), we end up with 0.2345 g of water produced by oligomerization. Then, we can find the estimated effective initial moles, and finally the corresponding estimated initial mass of dry LA (lower bound) equal to 5.58105 g. The material balance allows to determine an effective number of LA moles  in the spirit of our simplified kinetic model. This is equal to Our calculations based on the measurements taken under optimal experimental conditions for all three estimated dry LA masses, as above, resulted in the rate and water removal constants summarized in w = 0. Then, the set of rate equations is simplified to a becomes It can be argued that the actual trend, concealed by experimental errors and premature interruption of the process, should look more like a sigmoid function converging to a very high yet finite molecular weight. This scenario sounds more convincing when looking at the weight-average experimental values, which cannot be predicted by the simplified version of our model. Numerical predictions of number-average molecular weight, on the other hand, tend to grow linearly with time, as verified by running the simulation for longer time scales. This is indeed what happens when water concentration falls to negligible levels as we can easily verify by setting In other words, chain growth in the absence of water is predicted to obey a linear trend and proceed uninterrupted until reaching the physical limit of depleting all available pairs of -OH and -COOH groups in the mixture . k−1, the polymerization rate constant would be expected to exhibit a sigmoid dependence on molecular weight. In a simplified approach, we adopt a stepwise change; as an example, we try a 10-fold decrease when Mn exceeds the entanglement molecular weight. An optimization calculation under these assumptions yielded similar values for k−1 and kw as in k−1 was about 10 times lower. The objective function, F, is more sensitive to errors in the range of high molecular weights, and the depolymerization constant ‘adapted’ to the decrease in the k1 to keep the total error low. The linear trend with time was preserved but a sublinear one cannot be ruled out if more complex diffusion-related mechanisms are present. Regardless of the above scenaria that concern time scales even longer than our experiments, it is useful to look at the change of water content with time as a means of reevaluating the applicability of our assumptions about the constancy of the kinetic parameter governing water removal. Whether the scenario of negligible water concentration holds can be examined by looking at the concentrations calculated as functions of time. In practice, the prevalent presence of very long chains in the mixture would bring to the fore mechanisms related to mass transport (low diffusivity) of the emerging polymer species, affecting the overall kinetics in manners that were not considered when setting up our model. The increasing impact of these mechanisms on the mixture’s kinetics could indeed lead to sublinear dependence of molecular weight on time. The number of our experimental data is too small to allow more complex modeling without overfitting. However, reasonable assumptions and simplifications can be used to illustrate the impact of diffusion-controlled mechanisms. Mass-transport effects are expected to arise when chain size exceeds the entanglement molecular weight of PLLA, which is in the range of 9000 g/mol . Assuminkw. Then, we expect the model to behave during the slow-growth period in one of the following ways: (i) water content in actual experiments remaining high for a prolonged period: in this case, the chemical equilibrium would shift towards depolymerization and our model would overestimate the molecular weight throughout the run; (ii) water content falling rapidly to very low levels: should this be the case, the equilibrium would shift towards polymerization early on, and our model would underestimate the molecular weight throughout the simulation; (iii) water content falling at a rate comparable to the one predicted numerically: in this last case, the model is expected to work well at long enough time scales but would tend to deviate more strongly  during the slow-growth period. This is indeed the case, as numerical predictions in kw, varying with water content, allowing a scenario in which water content would decrease slowly until falling under some ‘critical’ value, thus shifting equilibrium towards polymerization and accelerating chain growth. Concentrations of P-A (and Q-B) bonds, repeat units  and water, as well as water’s mole fraction with time, are shown in a coincides with the initial lactic acid concentration. The curves in c/a, not quite far from the ten-mers measured by GPC during the first 20 h of experiment under optimal conditions). The concentration of available pairs of reactive -OH and -COOH groups providing P-A and Q-B bonds falls by that same ratio, and the corresponding amount of water is produced; these two factors immediately restrict chain growth until enough water is removed and the equilibrium is shifted towards polymerization.Another interesting observation has to do with the rapid change of concentrations during the first few steps of the simulation. Keeping in mind that the concentrations refer to bonds that take part in the polymerization–depolymerization reaction, it is only at the beginning of the simulation that the concentration variable keq = k1/k−1 as compared to the impact of the magnitude of the rate constants themselves , the sum of fractional errors squared  and the sum of absolute fractional differences. It was found that when decreasing the rate constants in a uniform manner even by orders of magnitude (multiplying by down to 10−3), the objective function remained largely unaffected (increased negligibly) whereas the other two measures improved (decreased) further, albeit by a very small amount. In other words, the values of the rate constants themselves play a minor role in reproducing the polymerization data as long as they have the same ratio, i.e., equilibrium constant—which is not a surprising result.Finally, it is worth discussing the role of the equilibrium constant emselves . We multk1 with k−1 fixed, would yield overestimated long-term predictions of number-average molecular weight. A uniform change of the rate constants, on the other hand, implies that the existing balance is preserved between chain formation and chain destruction. For instance, doubling both rate constants results in twice faster chain building, but also twice faster chain destruction. In view of the above, the optimal values of the rate constants should be understood as upper bounds of a range of values of k1 and k−1, constrained by the condition of an optimal equilibrium constant, keq = k1/k−1; on top of them, comes the optimal value . For instance, increasing al value  for the We have implemented a process for the synthesis of poly(L-lactic acid) via catalytic polycondensation carried out under relatively mild conditions, with the aid of azeotropic dehydration to shift the chemical equilibrium towards polymer formation. To optimize this process, lactic acid and catalyst (tin octoate) concentration in dry toluene, as well as the temperature, were systematically varied. Under the optimal conditions, i.e., lactic acid concentration 58.4 wt.% with respect to toluene and catalyst concentration 0.56 wt.% with respect to the lactic acid, the polymerization was found to proceed at temperatures between 134 and 137 °C and prolonged reaction times (ca. 120 h). Under these conditions, PLLAs with weight-average molecular weight of as high as 161 kDa and dispersity 1.81 were isolated at near quantitative yields.We have also developed a simplified kinetic model that can capture the observed experimental kinetics when adjusting for the rate constants and an effective water removal rate parameter. Our approach relied on the same assumptions as a previous model by Harshe et al. , which wThe PLLA polycondensation setup presented in this work is aimed at the production of PLLA from recycled food waste that has been converted to lactic acid through an existing fermentation process. The conditions achieved during the process satisfy local regulations and allow reduced operating costs. Furthermore, our kinetic model can assist in the design of the process during the subsequent scale-up steps by simplifying the calculations and allowing quick comparison of alternative scenaria based on data from pilot-plant production runs. The way is thus paved to the development of environmentally friendly and economically feasible processes in terms of operating conditions, valorization of recycled waste and production of recyclable bioplastic materials.


Informative representation of molecules is a crucial prerequisite in AI-driven drug design and discovery. Pharmacophore information including functional groups and chemical reactions can indicate molecular properties, which have not been fully exploited by prior atom-based molecular graph representation. To obtain a more informative representation of molecules for better molecule property prediction, we propose the Pharmacophoric-constrained Heterogeneous Graph Transformer (PharmHGT). We design a pharmacophoric-constrained multi-views molecular representation graph, enabling PharmHGT to extract vital chemical information from functional substructures and chemical reactions. With a carefully designed pharmacophoric-constrained multi-view molecular representation graph, PharmHGT can learn more chemical information from molecular functional substructures and chemical reaction information. Extensive downstream experiments prove that PharmHGT achieves remarkably superior performance over the state-of-the-art models the performance of our model is up to 1.55% in ROC-AUC and 0.272 in RMSE higher than the best baseline model) on molecular properties prediction. The ablation study and case study show that our proposed molecular graph representation method and heterogeneous graph transformer model can better capture the pharmacophoric structure and chemical information features. Further visualization studies also indicated a better representation capacity achieved by our model. Informative molecular representation is a vital prerequisite in artificial intelligence-driven de novo drug discovery, however, mapping the pharmacophoric information is underexploited by the atom-level based molecular graph representation. Here, the authors design a multi-level based Pharmacophoric-constrained heterogeneous graph transformer (PharmHGT) to better capture the pharmacophore structure and chemical information. The key step of molecule properties prediction is how to represent the molecules that map the molecular information to a feature vector. Conventional methods learn the representations depending on the expert-crafted physic-chemical descriptors7 have achieved remarkable results. Molecules can be naturally expressed as a graph structure, so the GNNs method can effectively capture molecular structure information, including nodes (atoms) and edges (bonds)8. Compared with the conventional methods, deep learning methods can use SMILES or molecular graph as input which is more informative and lead to significant improvement in downstream tasks such as molecules properties prediction. The graph-based molecular property prediction models view the molecules as graphs and use graph neural networks (GNN) to learn the representations and try to capture the topological structure information from atoms and bonds. Due to their ability to represent molecules as graphs, they are an important research area for molecular property prediction tasks. The most representative GNNs, including GCN14, GAT17, and MPNN20 etc., have been actively used in the field of molecular graphs-based for molecular properties prediction. However, these models ignore the information of fragments that contain functional groups. Recently, Zhang et al.21 works have begun to focus on molecular fragment information to predict the properties of molecules.In recent decades, deep learning methods have shown strong potential to compete with or even outperform conventional approaches. Graph neural networks (GNNs) have gained increasing more popular due to their capability of modeling graph-structured data. For the association prediction task of biological network data, the heterogeneous graph neural network algorithmsAlthough incorporating fragment information into graph architectures to benefit some molecular property estimation tasks has attracted research attention in recent years, there still are two issues that impede the usage of GNNs in this field: (1) those models have not provided a global chemical perspective method to better integrate atom and fragment information and both ignore the reaction information between fragments; (2) lacking the generalization ability of the different types and feature dimensions of atoms, fragments, and bonds. To address those two issues, more comprehensive information from different levels needs to be embedded and there is still a demand to develop a heterogeneous GNNs model for molecular property prediction.https://github.com/mindrank-ai/PharmHGT). Firstly, we use the reaction information of BRICS to divide the molecule into fragments that contain functional groups and retain the reaction information between these fragments to construct a heterogeneous molecular graph containing two types of nodes and three types of edges  to comprehensively learn different views of heterogeneous molecular graph features and boost the performance of molecule property prediction , which first proposed the definition of molecule graph fragments that may contain functional groups. However, FraGAT directly adopted the Attentive FP15 to get molecular graph embeddings, and the obtained fragments by this method also is rough because there are multiple substructures in one molecular. Zhang et al.21 proposed the Motif-based Graph Self-supervised Learning (MGSSL) model, which designed a molecule fragmentation method that leverages a retrosynthesis-based algorithm BRICS and additional rules for controlling the size of motif vocabulary and used GNNs to capture the rich structural and semantic information from graph motifs. However, this work still did not consider the reaction information between substructures obtained through BRICS and effectively combine the information of the atom and the substructures. Nevertheless, these works still prove that considering more information from molecular substructures with functional groups can provide a more informative representation that can significantly improve the performance of downstream tasks, but how and which kinds of information to be embedded needs more exploratory work.It has been demonstrated that many characteristics of molecules are related to molecular substructures which contain functional group information. Zhang et al.18 proposed Message Passing Neural Networks (MPNNs), which is the first general framework for supervised learning on graphs, and can effectively predict the quantum mechanical properties of small organic molecules. The MPNNs framework is capable of learning the representations from molecular graphs directly. Many researchers made improvements on this basis and proposed many models based on MPNN. Yang et al.23 introduced a graph convolutional model, called Directed MPNN (D-MPNN), which used messages associated with directed bonds to learn molecular representations. Song et al.19 proposed a directed graph-based Communicative Message Passing Neural Network (CMPNN) that comprehensively considered the information of atoms and bonds to improve the performance of molecular properties prediction. However, those MPNNs have ignored the chemical reactions information, which is vital for molecular properties from the knowledge of chemistry and pharmacy.Gilmer et al.24. Ying et al.25 have explored several simple coding methods of the graph, mathematically showing that many popular GNN variants are actually just special cases of Graph transformers. In the field of representation learning of molecules, Rong et al.8 proposed Graph Representation frOm self-superVised mEssage passing tRansformer (GROVER), which can learn the rich structure and semantic information of molecular from a large amount of unlabeled molecular data. Chen et al.26 proposed the Communicative Message Passing Transformer (CoMPT), which reinforces message interactions between nodes and edges based on the Transformer architecture.Researchers proposed the Transformer architecture eschewing recurrence and convolutions entirely and instead based solely on the attention mechanism29. Heterogeneous graphs are notoriously difficult to mine because of the bewildering combination of heterogeneous contents and structures. The current representation learning for molecules is still at the level of homogeneous graphs, but in addition to the basic atom-based molecular graph representation, some fragment-based and motif-based representation schemes have been proposed to represent a molecule. Obviously, if these representation schemes can be constructed for a comprehensive heterogeneous molecular graph representation, it will be more conducive to capturing the characterization of molecules and potentially improve the performance of downstream tasks. In this paper, we propose a new molecular heterogeneous graph construction method and propose a heterogeneous graph transformers model that can efficiently learn molecular representations.In the field of recommendation systems, heterogeneous graph models are popular for mining scenarios with nodes and/or edges of various typesThe key idea of PharmHGT is additionally capturing the pharmacophoric structure and chemical information feature from the heterogeneous molecular graph. Generally, the heterogeneous graph is associated with the node and edge attributes, while different node and edge types have unequal dimensions of features. As shown in Fig. Classification tasks. The BBBP dataset has 2035 molecules with binary labels of permeability properties, which are often used to predict the ability of molecules to penetrate the blood-brain barrier. The BACE dataset has 1513 molecules, which provides quantitative (IC50) and qualitative (binary label) binding results for a set of inhibitors of human β-secretase 1 (BACE-1). The ClinTox dataset has 1468 approved drug molecules and a list of molecules that failed due to toxicity during clinical trials. The Tox21 dataset has 7821 molecules for 12 different targets relevant to drug toxicity and was originally used in the Tox21 data challenge. The SIDER dataset has 1379 approved drug molecules and their side-effect, which are divided into 27 system organ classes. The HIV dataset has 41127 molecules and these molecules are tested for their ability to inhibit HIV replication.Regression tasks. The ESOL dataset records the solubility of 1128 compounds. The FreeSolv includes a total of 642 molecules are selected from the Free Solvation Database. The Lipophilicity dataset provides the experimental result of octanol/water distribution coefficient (logD at pH 7.4) of 4198 compounds.In order to better compare and prove the effectiveness of PharmHGT, we select nine benchmark molecular datasets for experiments including Blood-brain barrier permeability (BBBP), BACE, ClinTox, Tox21, SIDER, and HIV for classification tasks, and ESOL, Freesolv and Lipophilicity for regression tasks. Below, we include a brief introduction of these datasets.23. The node and edge features are processed by the open-source package RDKit, and the detail is demonstrated in Supplemental Experimental Procedures  by introducing a novel self-supervised motif generation framework for GNNs.MPNN baselines: The MPNN18 abstracts the commonalities between several of the most promising existing neural models into a single common framework, and focused on obtaining effective vertices (atoms) embedding by message passing module and message updating module; The DMPNN23 used messages associated with directed bonds rather than those with vertices; The CMPNN19 introduced a new message booster module to rich the message generation process.Graph transformer baseline: The CoMPT26, with a Transformer architecture, has learned a more attentive molecular representation by reinforcing the message interactions between nodes and edges; The GROVER model8 standard for Graph Representation frOm self-superVised mEssage passing tRansformer, which can learn rich structural and semantic information of molecules from enormous unlabeled molecular data by carefully designed self-supervised tasks in node-level, edge-level, and graph-level. In addition, the Graphormer model is also based on graph transformer, but Graphormer is a 3D model, which requires the 3D conformation of each small molecules. There are some inconsistencies between our model and Graphormer in terms of target tasks and inputs. For better comparability, we have not added Graphormer into the benchmark, but we give the computational results in the Supplemental Experimental Procedures  PharmHGT_β: by only retaining the pharm-level graph with reaction information.(2) PharmHGT_γ: by only retaining the junction-level graph.(3) PharmHGT_βα: by aggregating features of the pharm-level graph with reaction information to the atom-level graph.(4) PharmHGT_γα: by aggregating features of the junction-level graph to the atom-level graph.(5) PharmHGT_βγ: by aggregating features of the pharm-level with reaction information to the junction-level graph.(6) PharmHGT_γαβ: by aggregating features of the junction-level graph with the atom-level graph, then to the pharm-level graph.(7) PharmHGT_We conducted ablation studies on PharmHGT to explore the effect of atom-level view, pharm-level view, and junction-level view. Under the same experimental setup, we implement seven simplified variants of PharmHGT on the two benchmarks:β performs the worst when only retaining the pharm-level graph with reaction information. It indicates that lacking information from the atoms can not effectively represent the characteristics of the molecule. When combining two kinds of feature information, PharmHGT_γα aggregates the junction-level graph into an atom-level graph and it has the best performance among the models with one or two views. It proves that integrating the feature information from molecular fragments can improve the prediction performance. The results of PharmHGT demonstrate that further integrating the information from the reaction can obtain the most effective molecular characterization.As shown in Fig. To investigate the molecular representations learning ability of PharmHGT, we used t-distributed Stochastic Neighbor Embedding (t-SNE) with default hyper-parameters to visualize molecular representations of the Tox21 dataset in Fig. α cannot well aggregate molecules with similar toxic pharmacophores, and have limited discrimination from negative samples without the pharm-level view . Figure iew Fig. b; The priew Fig. a and theIn this paper, we propose PharmHGT, a pharmacophoric-constrained heterogeneous graph transformer model for molecular property prediction. We use the reaction information of BRICS to decompose molecules into several fragments and construct a heterogeneous molecular graph. Furthermore, we develop a heterogeneous graph transformation model to capture global information from multi-views of heterogeneous molecules. Extensive experiments demonstrate that our PharmHGT model achieves state-of-the-art performance on molecular properties prediction. The ablation study and case study also demonstrate the effectiveness of using pharmacophore group information and heterogeneous molecules information of molecules.30 to decompose molecules into several fragments with pharmacophore, and retain the reaction information between fragments to construct a heterogeneous molecular graph. The heterogeneous molecular graph is denoted G = {V, E}, the G associated with a node type mapping function We use the BRICSmentclass2pt{minimGα = , for each atom i ≤ ∣Nα∣ and ∣Nα∣ is the total number of atoms, while for each bond i, j ≤ ∣Nα∣. For featurization, the Vα is represented as Eα is represented as Mα∣ is the total number of directed bonds, (Atom-level view.) An atom-level view can be denoted as graph Gβ = , for each pharmacophore i ≤ ∣Nβ∣ and ∣Nβ∣ is the total number of pharmacophores, while for each BRICS reaction type i, j ≤ ∣Nβ∣. For featurization, the Vβ is represented as Eβ is represented as Mβ∣ is the total number of BRICS reaction types, (Pharm-level view.) A pharm-level view can be denoted as graph Gγ = , for each node i ≤ ∣Nγ∣ and ∣Nγ∣ is the total number of atoms and pharmacophores, while for each edge i, j ≤ ∣Nγ∣. For featurization, the Vγ is represented as Eγ is represented as Mγ∣ is the total number of atoms and pharmacophores junction relationships, (Junction-level view.) A junction-level view can be denoted as graph An example of the heterogeneous molecular graph and its multi-view is illustrated in Fig. The key idea of PharmHGT is additionally capturing the pharmacophoric structure and chemical information feature from heterogeneous molecular graphs. Generally, the heterogeneous graph is associated with node and edge attributes, while different node and edge types have unequal dimensions of features. The framework consists of three parts: multi-view molecular graph construction Fig. , aggregaXV and the feature matrix of edge XE, the features of all nodes can be obtained according to the intensity of the attention between the node and the related edge. The multi-head self-attention mechanism enhances the signal of the node in each view. Specifically, the basic block of PharmHGT is the usual attention module:h(X) is the hidden features, WQ, WK, WV are the projection matrices. The normal attention module is the dot product self-attention, the Q, K, V is considered in the same semantic vector space, which is not adapted in heterogeneous graph. Therefore, we build a multi-view attention function to get more information from different views, and the function can be formulated as:σ is active function, P is the view type set, p ∈ P is a view and Ωp is a learnable view type weight matrix. KpT is the transpose matrix of view p key matrix, and dk is the variance of Q and K. In addition, our model assumes that a single qi and ki satisfy the mean of 0 and the variance of 1. Considering the more general case, qi and ki satisfy the mean value of 0 and the variance is σ, then D(QKT) = dkσ4. In any case, divide by Wo is the weight matrix of each head. Therefore, we can get the hidden nodes and edges features embedding matrix:The inputs of PharmHGT are the feature matrix of node vi ∈ V, and the all edge nodes’ features are eij ∈ E. PharmHGT interactively operates on edge hidden states t denotes the current depth of the message passing, each step proceeds as follows:vi. Considering of the vanishing gradient issue, we set a simple residual block to make module training more stable during multi-views message passing:For each molecular graph view, we use graph transformer to obtain all nodes and edges features. All nodes’ features is Zα, Zβ, Zγ} of the molecule, where Zα is the vector of atom-level view, Zβ is the vector of pharm-level view and Zγ is the vector of junction-level view.For a given molecule, we obtain all types of representations of the three views of molecule atom-level, pharm-level, and junction-level by the above steps. Besides, the Gated Recurrent Unit is applied as a vision readout operator to get all three views feature vector {Then, the acquired three views features are aggregated to the final features through the attention layer again, and the final representation vector of a molecule is obtained. The readout attention function is:Specifically, the pharm-level-based contains the features of the reaction information, and we first aggregate it with the junction-level-based features to capture the associated information of pharmacophores and atoms and the reaction information between pharmacophores. The formula is as follows:f(⋅) is a fully connected layer for classification or regression.Then we are aggregating this information with atom-level-based feature information to obtain the final molecular global feature representation Fig. . The attPeer Review FileSupplementary Information


Trachycladus laevispirulifer; (ii) dysidealactams, an unprecedented class of sesquiterpene glycinyl-lactam and glycinyl-imide from a Dysidea sp., a sponge genera often discounted as having been exhaustively studied; (iii) cacolides, an unprecedented family of sesterterpene α-methyl-γ-hydroxybutenolides from a Cacospongia sp., all too easily mischaracterized and deprioritized during dereplication as a well-known class of sponge sesterterpene tetronic acids; and (iv) thorectandrins, a new class of indole alkaloid which revealed unexpected insights into the chemical and biological properties of the aplysinopsins, one of the earliest and more extensively reported class of sponge natural products.In reviewing a selection of recent case studies from our laboratory, we revealed some lessons learned and benefits accrued from the application of mass spectrometry (MS/MS) molecular networking in the field of marine sponge natural products. Molecular networking proved pivotal to our discovery of many new natural products and even new classes of natural product, some of which were opaque to alternate dereplication and prioritization strategies. Case studies included the discovery of: (i) trachycladindoles, an exceptionally rare class of bioactive indole alkaloid previously only known from a single southern Australia sample of  With dereplication based on sophisticated mass spectrometric analysis generating an almost bewildering wealth of data, it was a logical  next step to develop innovative computational tools to process and visualise these data to more easily and rapidly interrogate very large data sets incorporating many 1000s even 10,000s of compounds. Importantly, these mass spectrometric approaches required only a few μg of crude extract (or fractions or pure compounds) with individual data acquisitions taking only a few minutes and at minimal cost. In recent years, one of the more popular mass-spectrometry-based dereplication methodologies is that of Global Natural Products Social (GNPS) molecular networking . Further investigations of these sponges resulted in the isolation of new classes of natural products: dysidealactams and dysidealactones [Analysis of GNPS molecular families in lactones , cacolidlactones , and tholactones . The GNPlactones . All the39–45) from Trachycladus laevispirulifer (CMB-03397) collected in 2001 during commercial trawling operations in the Great Australian Bight. We subsequently patented the trachycladindoles as potent and selective kinase inhibitors with potential application in the treatment of neurodegenerative diseases  [Trachycladus sponge impractical, combined with no other literature reports of trachycladindoles, further investigations of this promising pharmacophore were stalled. Efforts at investigating structure activity relationships using available material were also compromised by the observation that on storage (−30 °C in DMSO) trachycladindoles E (43) and F (44) underwent quantitative deformylation to trachycladindoles A (39) and C (41) [In 2008 , we repoiseases) . Unfortud C (41) .39–41) and E (43) (dark green), and Family B clustered around authentic samples of trachycladindoles D (42) and F (44) (dark green), with both families featuring additional nodes (blue) attributed to new trachycladindoles associated with a Geodia sp. (CMB-01063) collected in 1990 during commercial trawling operations in the Great Australian Bight. Encouraged by this discovery, in 2020 [39–44) and an array of new trachycladindoles H–M (46–51) from CMB-01063. As an interesting aside, the discovery of trachycladindoles in two taxonomically unrelated sponge genera (Trachycladus and Geodia), and only from Great Australian Bight specimens of these sponges, does fuel speculation that the trachycladindoles may not be sponge natural products but may be microbial in origin.The GNPS molecular network shown in  in 2020 , we repoDysidea have a history of producing structurally diverse natural products, many featuring novel chemical scaffolds and functionality. For example, as early as the 1970s, Dysidea specimens collected off the Great Barrier Reef near Townsville and Cooktown, Queensland, were reported to yield brominated diphenyl ethers [Dysidea, including sesquiterpenes [N-acyl amino esters [Dysidea.Australian sponges of the genus l ethers ,44 and tl ethers ,46, whill ethers , and thol ethers . Since tterpenes ,51,52,53terpenes , thiosesterpenes , chlorino esters , sesterto esters , mycospoo esters , chlorolo esters ,60,61, bo esters ,63,64, do esters , 9,11-seo esters , and polo esters ,68. GiveDysidea sp. (CMB-01171) collected in 1991 by SCUBA (−25 m) off Port Phillip Heads, Victoria. Although this specimen had been in our collection for over 30 years, it had failed to be noticed. Consequently, we were intrigued to have the GNPS analysis reveal natural product nodes unique to this Dysidea sp., especially given the extensive history of Australian Dysidea sp. chemistry (see above). Following a chemical investigation, in 2020 [Dysidea sp. (CMB-01171), including dysidealactams A–F (52–57), dysidealactones A–B (58–59), and two solvolysis artifacts 60–61. Of particular note, dysidealactams A–D (52–55) incorporate a rare glycinyl lactam moiety , and 56 incorporates an unprecedented glycinyl imide moiety. Due to a prolonged storage in EtOH, it is quite common to observe the esterifaction of carboxylic acid groups to the corresponding ethyl esters [The GNPS molecular network shown in  in 2020 , we repol esters . The dysl esters .Ircinia oros followed shortly after in 1973 [Ircinia variabilis. Following re-isolation from Australian Ircinia spp., absolute configurations were assigned to all three of these earliest exemplars in 1994 [Ircinia [Psammocinia [Sarcotragus spp. [1H NMR resonances, particularly for the furan and tetronic acid termini. As our experience with the ircinialactams revealed (described above), there is still scope for discovery within the sesterterpene tetronic acid motif. Hence, we were keen to determine if a GNPS molecular network approach could provide a more reliable means of dereplication. To this end, we interrogated our GNPS molecular network of 960 marine extracts using authentic standards of -variabilin (62) (15)   and irci62) (15) . UnsurprIrcinia sp. (CMB-01064) collected in 1990 during commercial trawling operations in the Great Australian Bight was known to produce -variabilin (62), -variabilin (63), -8-hydroxy-variabilin (64), ircinialactam A (15), and 8-hydroxyircinialactams A (17) and B (18) (Ircinia sp. (CMB-03363) collected in 2001 by SCUBA near Port Phillip Heads, Victoria, was known to produce 62, 63, 64, and 15. Three additional Ircinia spp CMB-01058 were collected in 1990 by SCUBA near Flinders, Victoria; CMB-01693 collected in 1994 by benthic grab (−71 m) during a scientific operations off Port Lincoln, South Australia, and CMB-02014 collected in 1995 by SCUBA in Port Phillip Bay, Victoria, had not been previously subjected to chemical analysis. The GNPS molecular network shown in Ircinia spp. revealed a common molecular family co-clustering with authentic samples of 62 and 15 (dark green)—confirming that GNPS could be used to detect/dereplicate Ircinia sesterterpene tetronic acids.The sponge d B (18) ; while IPsammocinia sp. (CMB-03231) collected in 2000 by SCUBA off Lonsdale, Victoria, Psammocinia sp. (CMB-01018) collected in 1988 by SCUBA off Durras, New South Wales, and Psammocinia sp. (CMB-03344) collected in 2001 by SCUBA off Port Phillip Heads, Victoria, were all known to produce sesterterpene teronic acids; while Psammocinia sp. (CMB-01757) collected in 1995 by SCUBA (−30 m) off Barwon Heads, Victoria, and Psammocinia sp. (CMB-02026) collected in 1995 during scientific trawling operations (−100 m) in the Great Australian Bight, was not subjected to chemical analysis. The GNPS molecular network shown in Psammocinia spp. revealed a common molecular family co-clustering with authentic samples of 62 and 15 (dark green)—confirming that GNPS could be used to detect/dereplicate Psammocinia sesterterpene tetronic acids.The sponges Sarcotragus sp. (CMB-01788) and Sarcotragus sp. (CMB-01848) collected in 1995 during scientific trawling operations  in the Great Australian Bight, Sarcotragus sp. (CMB-02707) and Sarcotragus sp. (CMB-02717) collected in 1998 by SCUBA (−10 m) off Deal Island, Bass Strait, and Sarcotragus sp. (CMB-03390) collected in 2001 during commercial trawling operations in the Great Australian Bight, had not been subjected to chemical analysis. The GNPS molecular network shown in Sarcotragus spp. revealed a common molecular family co-clustering with authentic samples of 62 and 15 (dark green)—confirming that GNPS could be used to detect/dereplicate Sarcotragus sesterterpene tetronic acids.The sponges Cacospongia sp. (CMB-03404) collected in 2001 during commercial trawling operations in the Great Australian Bight featured a unique molecular family , together with the biosynthetically related cacolic acids A–C (77–79) from Cacospongia sp. (CMB-03404)  . Had it 80), first reported in 1977 [Thorecta sp., heralded the discovery of a great many related and uniquely sponge-derived natural products, including from the genera Verongia, Dercitus, Smenospongia, Dictyoceratida, Aplysina, Hyrtios, and Thorectandra [Ianthella cf. flabelliformis (CMB-03322)—examples of which exhibited promising isoform selective GlyRs potentiation with possible application in the development of a new class of analgesic for the treatment of chronic inflammatory pain . Nevertheless, with such an extensive literature on aplysinopsins, the prospect of new discoveries seemed slight—that is, until we reviewed our GNPS data.The bright yellow tryptophan alkaloid aplysinopsin  collected in 1995 during deep water (−45 m) scientific trawling operations in the Great Australian Bight. As a result, this sponge was targeted for detailed chemical analysis and, in 2021 [81), along with an array of known analogues, including 6-bromoaplysinopsin (82), 6-bromo-1′,8-dihydroaplysinospin (83), and an array of solvolysis adducts 84–87. The GNPS thorectandrin molecular family clusters were particularly useful at revealing solvolysis artifacts. Close examination of these compounds led us to speculate that 82 (and 80) could serve as a substrate for an indoleamine 2,3-dioxygenase-like (IDO) enzyme, resulting in transformation to the intermediate ring opened produce 82a, which, as a potent Michael acceptor, could act as a cellular toxin (explaining some of the observed “aplysinopsin” biology). Alternatively, the intermediate Michael acceptor 82a could form solvolysis adducts during handling, such as 83–86. By contrast, the reduced aplysinopsin analogue 83 might also be viewed as a prospective IDO substrate, being transformed to the thorectandin 81 which was not a Michael acceptor and, hence, not toxic. As a result, the discovery of 81 shed light on a previously unappreciated “aplysinopsin” chemical reactivity and ecology, where some aplysinopsins  can be viewed as pro-drug Michael acceptors that are activated by IDO’s in the tissues of species that prey on sponges, allowing them to act as potent Michael acceptor toxins. This same mechanism of action likely plays out in mammalian and microbial cells, explaining the anticancer and anti-infective properties of selected aplysinopsins that are pro-drug Michael acceptors.The GNPS molecular network shown in  in 2021 , we repoHopefully the case studies summarized above revealed how GNPS molecular networking can extract new value from a library of 960 southern Australian marine extracts including;Disclosing the rarity of a structure class ;Finding new sources/examples of rare chemistry ;Prioritizing otherwise stranded extracts to help find new analogues of known structure classes ;Finding entirely new structure classes ; andDiscovering new natural products that shed light on ecological properties and pharmacological mechanisms of action .Access to UPLC-MS/MS technology: While UPLC-MS/MS technology is increasingly accessible, the levels of access (and cost) can vary greatly between laboratories. To best apply GNPS as a routine biodiscovery dereplication tool requires hands on and reliable access to appropriate instrumentation.Access to HRESIMS: In our hands, the accuracy of the m/z values acquired for each GNPS node was insufficient to assign definitive molecular formula (MF). As MF assignments are critical to online database searches  that are the key to rapidly discriminating known from new natural products, this necessitates separate measurements on targeted molecular families and individual nodes. In due course this technological limitation may be solved by better instrumentation.Clusters versus singletons: In any GNPS molecular network, what stands out most are the molecular families . That said, all GNPS molecular networks feature a large number of singletons—nodes that are not clustered. While it may be tempting to disregard singletons during any GNPS analysis, this runs the risk of overlooking interesting (even remarkable) natural products—that simply are so unique in their MS/MS fragment that they have no cluster partners. Singletons can (on occasion) count!Molecular families: Molecular families are clusters of natural products (nodes) that share a common/related MS/MS fragmentation. As such, members of any given structure class can be incorporated into one or more molecular families, depending on the nature of structure diversity and its impact on MS/MS fragmentation. Do not assume all members of the same structure class will co-cluster.Molecular adducts: Some natural products classes can exist as multiple adducts , which can cluster separately into different molecular families. This can create the illusion of more structure diversity than is actually the case.Ionization: Some natural products do not ionize well under ESI(+) conditions, and as such, would not feature prominently  in a +ve mode GNPS molecular network. Depending on the structure class you are looking for, you might consider reacquiring a –ve mode GNPS molecular network.Structure Proof: Finally, and arguably of greatest importance, there are occurrences in the literature where researchers seemed to defer to GNPS as a definitive proof of structure assignment. This is unwise, especially for chiral natural products, as MS/MS fragmentation is silent on matters of stereochemistry. GNPS can be a valuable guide for dereplication and prioritization, but unless you have access to an authentic natural product standard, it is not a substitute for isolation and more traditional structure elucidation based on spectroscopic analysis.In conclusion, we would like to share observations on some challenges/limitations of GNPS molecular networking:


Despite being acknowledged as a major global health challenge, growing levels of antimicrobial resistance (AMR) in pathogenic and commensal organisms have proven an awkward fit for international health frameworks. This article surveys the history of attempts to coordinate international responses to AMR alongside the origins and evolution of the current international health regulations (IHR). It argues that AMR, which encompasses a vast range of microbial properties and ecological reservoirs, is an awkward fit for the ‘organismal’ philosophies that centre on the rapid control of individual pathogens that have characterised international policy-making since the 19th century. The health challenge of antimicrobial resistance (AMR) is as old as the antimicrobial era, with the first known clinical reports of emerging microbial ‘drug fastness’ in response to prolonged organoarsenic exposure dating back to 1907.Rather than solely trying to make AMR fit into traditional pathogen-focused policy frameworks, we should aim for a new structural mode of international policymaking, and one that moves beyond conceptualizing AMR solely in terms of present overuse and treatment failure and adopts a more long-term conception of stewarding microbial ecosystems.This article examines why AMR has proven so difficult to regulate. Focusing on the period between 1945 and 2015, it contrasts the evolution of AMR stewardship initiatives with the parallel development of international legal frameworks like the International Health Regulations (IHR), which have enjoyed varying levels of success. The article shows that AMR proved an awkward fit for a legal system that was developed around individual pathogens or substances. Traditional contain-and-control strategies developed to stop the spread of well-characterized bacterial and viral entities such as cholera, rinderpest, or influenza.The roots and evolution of our contemporary international health system are intimately tied to the history of pandemics and have consistently focused on containing the spread of individual pathogenic viruses or bacteria across national borders.Office International d’Hygiène Publique  and International Sanitary Conventions (ISCs) .Starting in the 19th century, and building on centuries of more local approaches to quarantine, pandemic events like the repeated waves of cholera and plague prompted imperial powers and emerging nation states to organize a total of 14 International Sanitary Conferences. The goal of these conferences was to develop an internationally standardized way of defining, notifying, and reacting to the spread of pathogenic threats. Despite early tensions, the period around 1900 saw the rise of germ-based explanations of disease and mutual concerns about pandemic events’ impact on trade lead to an initial formalization of international health politics in the form of permanent institutions like the From the 1920s onwards, new permanent international bodies with greater powers like the League of Nations Health Office  and World Health Organisation (WHO 1948-), as well as affiliated national and non-governmental organizations, launched various social medicine-oriented programs  alongside vaccination, environmental control, and disease eradication campaigns. However, all of these non-statutory schemes remained dependent on the often unstable political and financial support of sovereign nation states and imperial powers.In 1951, the International Sanitary Regulations (ISR) consolidated the previous ISC patchwork of reporting regulations for six already quarantinable diseases . Passed in the context of the ‘Hong Kong Flu’ pandemic, the 1969 International Health Regulations (IHR) replaced the ISR but did not include significant new powers. Indeed, reporting requirements were soon limited to three ‘classic’ diseases . Meanwhile, critics bemoaned lack of compliance with ISR/IHR provisions and international legal frameworks’ failure to provide means to react to new or re-emerging pathogens such as HIV/AIDS or multiple drug resistant tuberculosis. In practice, the IHRs were thus increasingly overshadowed by the passage of health-relevant international treaties in the fields of human rights, environmental protection, and trade.Between 1995 and 2005, concerns about pandemic preparedness, bioterrorism, the increasing displacement of international health by trade concerns, and the 2003 SARS outbreak led to sustained attempts to reform the IHRs. Passed in 2005, the revised IHR replaced many classic elements of international disease surveillance with a new concept of integrated biosecurity governance. Although they continued to focus on concerning established pathogens, the 2005 IHR required member states to survey and notify the WHO regarding a far broader range of potential public health risks of urgent international concern — including emergent diseases, intentional bioterrorist releases of disease, and nuclear and chemical hazards. Minimum national surveillance capacities were defined and the WHO could now also use non-governmental data to declare a public health emergency of international concern (PHEIC). In case of a PHEIC, the WHO could issue non-binding recommendations for member states to tackle the emergency, which would theoretically enable a standardized global response.Throughout this long history of trying to contain infectious diseases at the international level, the systemic health threat posed by AMR has been curiously absent from statutory health frameworks. Estimated to have caused over 1.27 million human deaths in 2019,The fact that AMR or ‘drug fastness’ could emerge as a result of exposing organisms to antimicrobial substances was nothing new by the time the first permanent international health institutions emerged. Concerns about the threat it could pose to new generations of antimicrobial treatments emerged soon after the launch and mass-marketing of sulfonamides (1930s) and antibiotics (1940s) and led to repeated calls for ‘rational therapy.’Staphylococcus aureus phage type 80/81, regulators followed a classic ‘organismal’ control approach aimed at stopping the pathogen with hygiene and updated treatment regimes rather than seeking to limit the evolutionary selection pressure being brought to bear on microbial ecosystems.When they did appear, reform responses to emerging AMR were limited. During the 1950s crisis surrounding the pandemic spread of penicillin resistant Despite warnings about the ecological impacts of mass antimicrobial usage by prominent microbiologists like gramicidin-discoverer René Dubos,Statement Regarding Worldwide Antibiotic Misuse that Levy coordinated and had signed by 147 scientists from 27 countries, was multisectoral in its concerns  and was picked up by many press outlets.It was only during the 1980s that sustained calls for wider international efforts to contain AMR emerged. Initially, these calls were voiced not by international organizations but by non-governmental actors. Tufts University clinician-researcher Stuart Levy, especially concerned with resistance-conferring plasmids that respected the boundaries of neither bacterial species nor nation-states, first drew attention to the shared, global nature of AMR. The 1981 Emerging Infections report,Yet despite the inclusion of AMR within the U.S. Institute of Medicine’s influential 1992 Staphylococcus aureus (MRSA), AMR appeared on the policy agendas of powerful nation states and transnational organisations such as the US and the European Union (EU).status quo — were insufficient and tended to follow traditional molds rather than address AMR’s genetic and ecological dimensions. Attempting to fix rather than restructure existing food production and health systems, high-income countries adopted selective usage reductions in human and animal health but mostly neglected the environmental domain whereas new surveillance systems once again focused on resistance in priority pathogens rather than environmental background levels of AMR. Although high-income levels of medical and agricultural usage began to decline by around 2010, interventions’ long-term impact on AMR proved difficult to assess. In some cases, local AMR levels had declined but quickly recovered once drugs were reintroduced; in other cases, interventions came too late to prevent the permanent establishment of AMR in clinical, agricultural, or broader microbial ecologies. Meanwhile, AMR genes and organisms continued to flow freely across borders. As highlighted by reports on New Delhi metallo-betalactamase 1 (2008) and mobilised colistin resistance (2015), addressing AMR required a broader international restructuring of health, food production, and pharmaceutical systems.By the end of the millennium, the ongoing rise of AMR levels was increasingly acknowledged as a severe global health threat that spanned all three domains of human, animal, and ecological health.The systems-dimension of would-be AMR reform efforts went far beyond anything ever attempted within the conservative legal frameworks of international health. Starting with an ill-fated attempt to galvanise AMR action on September 11, 2001, international organizations such as the WHO, the Food and Agriculture Organisation (FAO), and World Organisation for Animal Health (OIE) began to move from simply reporting on AMR to proposing novel surveillance and stewardship frameworks.However, similar to problems regarding the 2005 IHR,In 2000, Lederberg called for a reconsideration of the Manichean “good/bad” mindset regarding bacteria, and for a focus on the broader ecology of the microbial world we inhabit.Amidst the wider reordering of Global Health following COVID-19, a growing number of voices are now calling for an integration of the “silent pandemic”However, there are well-known limitations. At one level, the described measures do not address the upstream forces — from sanitation to the structures of care delivery — that have differentially shaped the worldwide incidence of AMR and disease burdens. New antibiotics will temporarily reduce AMR pressures, and improved surveillance and infection prevention can address the threat posed by individual pathogens. However, addressing the structural drivers of antibiotic dependency and disease will require far broader ‘antibiotic sensitive’ upstream investment in health and food systems resilience.Moreover, the invocation of Lederberg calls to mind a second set of considerations. In 2000, and echoing earlier warnings by René Dubos, Lederberg called for a reconsideration of the Manichean “good/bad” mindset regarding bacteria, and for a focus on the broader ecology of the microbial world we inhabit.If AMR signals an “Anthropocene in the cell,”


The complexity, urgent timeframe, and lack of clear solution to AMR have contributed to its classification as a ‘super wicked problem’. Yet knowledge surveys of the general public have found that they still harbour numerous misconceptions linked to both the sources and impact of AMR. This confusion is compounded by AMR being a One Health issue, and therefore a factor in not just human health but in other industries, such as farming. This can further inhibit understanding and knowledge transfer around AMR for those without a prior knowledge base.Antimicrobial resistance (AMR) is now regarded as one of the greatest global challenges of the 21In order to address the escalating risk that AMR presents, however, it is essential to address this knowledge gap and engage with the public to support wide scale changes in behaviour and consumer choice. The WHO now requires national action plans tackling AMR to include patient and public involvement/engagement (PPI/E) to support changing the trajectory of AMR. Despite this, little detail is available as part of strategic plans on how PPI/E should be undertaken in order to aid implementation. This paper discusses a number of approaches to support the design and delivery of PPI/E in relation to AMR, including the different social behaviour models underlying successful PPI/E strategies, and key considerations linked to specific activity types. The framework produced includes features for steps from initial planning and design through to evaluation. The aim is to help improve the ability of scientists and healthcare professionals to produce high quality AMR PPI/E. Antimicrobial resistance (AMR) was estimated to contribute to 1.27 million deaths in 2019 and is expected to result in 10 million deaths per year by 2050 .One obstacle to addressing AMR as an increasingly serious issue, is that it is a ‘super wicked problem’. Super wicked problems typically combine inherent complexity, with numerous interrelated biological and social drivers. Multiple stakeholders impede a single agency taking charge, with limited time available to reduce potential impact . AMR is Within human health, usage of antimicrobials is influenced by the knowledge, attitudes, and beliefs of both healthcare workers and the public . It is eUndertaking patient and public involvement/engagement (PPI/E) is a recognised key pathway to improving education and awareness, but also in supporting behaviour change within the public and healthcare professionals. The National Institute of Health Research defines patient and public involvement (PPI) as research done with or by patients and the public, not to, about, or for them. Involvement aims to work collaboratively with patients and the public including them in shared decision-making. Meanwhile, patient and public engagement (PPE) focuses on raising awareness and sharing research knowledge and findings; to tackle AMR as a ‘super wicked problem’, both approaches are required.Published evaluated examples of PPI/E activities and interventions encompass material or meetings delivered in various ways. Common examples include: face to face training, brochures, advertisements and billboards, educational videos, social media messaging, media engagement via interviews or films/TV, and educational conferences or science festivals .Several high-profile approaches to AMR engagement have been evaluated and published including the Antibiotic Guardian Campaign and European Antibiotic Awareness Day. The Antibiotic Guardian campaign launched in 2014 using a One Health approach to change behaviour through collective action via the use of pledges . The firScience, technology, engineering, arts and mathematics (STEAM) approaches, such as comic books, gamification or theatre, have become increasingly common . While tCognisant selection of the most appropriate approach for the outcome sought in conjunction with these factors should be undertaken . The PPIA review of published manuscripts demonstrates that few planned AMR PPI/E interventions were developed with behavioural theory embedded during the design, but were retrospectively mapped onto pre-existing theoretical constructs . As mostCommon objectives amongst published interventions included shaping knowledge, impacting beliefs, changing environment, and improving effectiveness of professional roles on social influencing . There aParticipatory learning and action approaches (PLA) are based around engaging community stakeholders in action-orientated, creative projects such as film, visual arts, photography, in order to produce meaningful change. Action orientated approaches are often introduced in synergy with education-based intervention to empower participants. This offers a bottom-up approach where communities are engaged to generate locally appropriate solutions. Linked to local engagement this aims to generate sustainable community wide changes in knowledge and behaviour by encompassing local social, cultural and economic factors .Information-Motivation-Behavioural (IMB) skills approaches assume that behaviour in relation to health interventions is dependent on individuals being informed about the behaviour and its impacts, having the motivation to engage with changing behaviour, and adequate skills to effect change . This apTranstheoretical approaches are based on the concept that cognition (awareness and knowledge) is required to achieve behaviour change. Therefore, interventions that disregard cognition will have limited sustainability and longevity, and therefore impaired long-term impacts. This theory encourages developing awareness of where the target participants are on the 6-stage intentional behaviour change spectrum  and designing interventions based on the surveyed knowledge, with an aim to move between phases and re-visit and modify interventions as appropriate .There are several different stages in planning the PPI/E activity, and decisions around planning interplay with whichever social behavioural model may be the most appropriate.Stage one: Define the outcome/objective of the intervention and associated success criteria.The main decisions during this stage are linked to whether the activity/intervention will be targeting education or behaviour or both. Even if the target is behaviour change alone, the approach may still need to undertake some aspects of knowledge increase, as behaviour change theories typically rely on different levels of pre-existing knowledge for the intervention to succeed . EducatiStage two: Define the target audience and identify factors which may impact intervention efficacy.-Gender distribution-Geographical location-Age-Family status -Professional status-Socio-economic status-Education level-Shared experiences -Social and cultural norms (beliefs or religious practices)Factors to consider that may define your audience and influence impact include :-Gender Stage three: Select the most appropriate PPI/E activity.The combination of intervention outcome and selected target audience will guide what type of activity might be the most appropriate . If the It is often challenging to get high levels of engagement, leading to longevity of impact, with an approach that will reach a large number of individuals. High impact activities require a level of personalisation that can be challenging to attain via social media or large-scale communications. The right choice of intervention will also require consideration of sustainability, resource availability, acceptability and scalability, with different options likely to be appropriate at different stages of engagement .Stage four: Design intervention evaluation tools.Evaluation tools should be designed with measurement of the identified outcome in mind . These tMessaging and communication around AMR are considered to be a ‘Goldilocks’ problem, where messaging is required to be ‘just right’ in order to be successful. Mis-managed messaging can actually increase confusion and widen knowledge gaps, leading to a possible increase in health inequalities in certain communities. If incorrect, communication can then lead to fear, avoidance and reduced engagement. Individuals may feel blamed by the media or politicians and therefore reciprocal blaming of experts and systems can develop . AnotherAdditionally health communication messaging is not linear; experts may construct and transmit the message, but it will be interpreted by the recipients in the light of pre-existing experience, knowledge and beliefs, embedded evaluation and feedback are therefore key . Health Finally, AMR interventions occur within an environment of ethical complexity, with intragenerational and intergenerational challenges. The interrelation of individual versus collective responsibility may be even more complex; for instance interventions linked to AMR have encountered challenges in implementation of behaviour change when parents engaged with healthcare interactions linked to their child's wellbeing, rather than their own . InequalPPI/E is increasingly acknowledged as key to addressing AMR as a super wicked problem and has proven to be an effective method of gathering both behavioural change and educational awareness. Despite this, design of activities and interventions can be demanding with multiple factors to consider. As experts in the subject of AMR, we do not always possess the requisite skills in pedagogy or behaviour change to deliver interventions with maximum impact. We therefore need to learn from or collaborate beyond our disciplinary silos to implement the vital change we aspire to achieve.None declared.


The long-term patency of arterial and venous grafts is crucial for the success of CABG. This study was designed to investigate graft patency using 128-slice coronary computed tomography angiography (CCTA) and compared the results with those obtained using invasive coronary angiography (ICA). In this observational cross-sectional study, we included 40 symptomatic post-CABG patients underwent CCTA and ICA within the same month.2. Mean duration since CABG was 5.25 ± 4.04 years. In total, 124 native vessels and 97 grafts were assessed using CCTA and ICA. CCTA delineated 8 non-cannulated venous grafts and 6 non-cannulated left internal mammary artery grafts. CCTA required a significantly lower radiation dose .Fifty-five percent were aged more than 60 years, and 80% were males. 67.5% had diabetes, 90% had hypertension, and 30% were smokers. Mean body mass index was 28.89 ± 5.17 kg/mCCTA can be as accurate as ICA in assessing bypass grafts with less radiation dose, providing a non-invasive reliable tool for evaluation. Coronary artery bypass grafting (CABG) is the gold standard of treatment of complex and multi-vessel coronary artery disease . Long-teSymptomatic patients after CABG represent a challenging diagnosis to angiographers. The calcified, diffusely diseased, and tortuous coronary arterial tree limits precise delineation of the lesion. Performing this angiographic evaluation non-invasively is even more challenging. High-quality CT angiographic images and a good understanding of the coronary arterial anatomy are required to properly determine revascularization techniques used .ICA carries the risk of arrhythmia, embolic stroke, dissection of native vessels or grafts, and myocardial infarction. There is up to 2% risk of morbidity , and 0.14–0.28% risk of mortality .However, CCTA is a reliable, non-invasive technique for assessing post-CABG patients as reported by Koplay et al., who revealed that CCTA findings were the same as ICA findings in evaluating stenosis more than 50%, with sensitivity, specificity, and diagnostic accuracy of MSCT were 90%, 99.3%, and 98.7%, respectively [We conducted our observational prospective cross-sectional study by involving 40 post-CABG patients evaluated using both CCTA and ICA. The study was approved by our Institutional Review Board (no. 17101604) and complied with the Declaration of Helsinki. Confidentiality and privacy of all data were assured. Before conducting the study, informed consent was obtained from each participant after explaining the aim. The research project is registered on ClinicalTrial.gov (registration no. NCT03265041).All patients who underwent CABG surgery more than 1 year ago, presented to our center, and met the inclusion criteria  were included in this study. The patients with prior CABG were assessed using both CCTA and ICA. Patients with renal insufficiency (serum creatinine level of more than 1.5 mg/dl), congestive heart failure, atrial fibrillation, hypersensitivity to the iodinated contrast agent or history of allergy to certain medications, and those who could not perform breath-hold for 15 s were excluded from the study. We also excluded patients with inadequate CCTA study quality.Included patients were subjected to through history taking which included age; sex; diabetes mellitus (DM); hypertension; smoking; dyslipidemia; heart failure symptoms; renal insufficiency; contraindications to contrast agent or other medications ; and any prior allergic reactions. Full examination was performed, including general examination; height, weight, body mass index (BMI), blood pressure measurement, heart rate assessment, and cardiac examination to detect any signs of heart failure. The ability to follow breath-hold instructions and perform good inspiratory breath-hold was evaluated. A 12-lead electrocardiogram was performed for all patients. Before CCTA assessment, baseline venous blood samples were collected for evaluating serum creatinine levels and lipid profile. A baseline transthoracic echocardiography study was conducted for all patients (for ejection fraction (EF) estimation as well as cardiac dimensions and valves assessment). All enrolled patients underwent CCTA and ICA within 1 month after the aforementioned tests.Patients were placed in the supine position on the CT examination table. Three ECG leads were connected to obtain an excellent ECG tracing. To synchronize the ECG signal with the raw image data, a noise-free ECG signal was maintained.A sizable intravenous line  was inserted in the right antecubital fossa to allow easy injection of the contrast agent. A 90–120 mL bolus of water-soluble non-ionic contrast  was injected through the cannula at a flow rate of 5 mL/s, followed by the injection of approximately 40–50-mL saline at a flow rate of 4 mL/s using a programmed dual-head power injector pump.The patients were braced for the sensations caused by the injection of the contrast agent as well as repeated breath-hold tests.All CT scans were done using a 128-slice dual-source CT scanner .The dataset for CCTA was acquired in three steps: topogram, determination of the appropriate time for initiating CCTA image acquisition to secure adequate homogeneous contrast enhancement of all coronary artery tree, and CCTA scan.Calcium scoring was not performed for all patients. The axial images were obtained using the bolus-tracing technique with a low radiation dose of 120 kV and 40 mAs..Retrospective ECG gating was used depending on the least motion phases for image reconstructionThe scans were analyzed by a CCTA expert (more than 12 years’ of experience in CCTA) using a dedicated workstation that is equipped with specialized cardiac post-processing software . The datasets were evaluated to detect significant coronary stenosis using original axial, multi-planar reformatted images (MPR), maximum intensity projection images (MIP), and volume-rendering techniques.Blinded to the results of CCTA, one experienced interventional cardiologist analyzed the native coronary segments, all grafts segments with their anastomosis and distal runoffs. Significant stenosis was defined as lesions with more than 50% lumen diameter stenosis in two orthogonal planes.p-values of less than 0.05.We performed data entry and data analysis using Statistical Package for the Social Sciences . Data were presented as numbers, percentages, means, medians, and standard deviations. The Chi-square and Fisher’s exact tests were used to compare qualitative variables, while to compare quantitative variables, the independent samples t-test for parametric data and the Mann–Whitney test for nonparametric data were used. The kappa test was used to detect agreement between CCTA and ICA. Differences were considered of statistical significance when We enrolled 40 patients with prior CABG assessed using CCTA and ICA to detect graft patency and native vessel disease progression.2. Regarding the lipid profile results of the study group, the mean LDL level was 84.28 ± 27.34 mg/dL, and the mean triglyceride (TG) level was 142.40 ± 66.95 mg/dl. In total, 67.5% of the study patients were on statin therapy. The mean left ventricular ejection fraction (LVEF) in this study was 57.23% ± 8.25% (range 36%–71%). The mean duration since CABG was 5.25 ± 4.04 years (range 2–21 years)  with a male predominance (80%). Among the population under study, 27 patients had DM (67.5%), 36 patients had hypertension (90%), and 12 patients were smokers (30%). In this study, the mean BMI was 28.89 ± 5.17 kg/mIn total, 124 native vessels were assessed using both CCTA and ICA and classified as 40 LADs, 40 left circumflex arteries (LCXs), 40 right coronary arteries (RCAs), and 4 ramus intermedius arteries. The posterior descending artery was assessed as part of the RCA or LCX according to origin. The main findings of CCTA for native LADs were as follows: one vessel showed an insignificant lesion, 15 vessels showed significant lesions, and 24 vessels were occluded. The same results were found on ICA indicating a good agreement between CCTA and ICA .For native LCXs, CCTA revealed one normal vessel, one vessel with an insignificant lesion, 22 vessels with significant lesions, 12 totally occluded vessels, three vessels with diffuse atherosclerosis, and one non-evaluable vessel due to severe calcification, whereas ICA revealed two normal vessels, one vessel with an insignificant lesion, 23 vessel with significant lesions, 12 occluded vessels, and two vessels with diffuse atherosclerosis; therefore, agreement between CCTA and ICA was good .CCTA findings for native RCAs revealed five normal vessels, one narrowed vessel with an insignificant lesion, 16 vessels with significant lesions, 15 totally occluded vessels, one vessel with diffuse atherosclerosis, and two non-evaluable vessels due to severe calcifications, whereas ICA revealed six normal vessels, one vessel with an insignificant lesion, 12 vessels with significant lesions,16 totally occluded vessels, three vessels with diffuse atherosclerosis, and two vessels that failed to be cannulated or visualized ; therefore, the agreement between CCTA and ICA was accepted .For the ramus intermedius, CCTA revealed two vessels with significant lesions and two totally occluded vessels, which were findings consistent with the results of ICA, indicating that the agreement between CCTA and ICA was good .In our study, there were 97 evaluable grafts. The most frequently evaluated grafts were venous (59 SVGs) and arterial (38 LIMA grafts) grafts.We evaluated 38 LIMA grafts; CCTA detected 29 patent grafts, seven non-patent grafts, and two non-evaluable grafts due to artifacts caused by metallic clips, whereas ICA detected 28 patent grafts, four non-patent grafts, and six grafts that failed to be cannulated Fig. .Fig. 1a In terms of venous grafts, 59 venous grafts were evaluated. CCTA revealed 20 patent grafts and 39 non-patent grafts, whereas ICA revealed only 12 patent grafts, 39 non-patent grafts, and eight grafts that failed to be cannulated.2) and found a significantly lower radiation dose in CCTA than in ICA .In this study, we calculated the radiation dose in both CCTA and ICA as dose area product (DAP)  has been evaluated, and all LIMA grafts were anastomosed to either significant stenosis or total occlusion, so we could not assess the relation between LAD stenosis severity and graft patency Tables , 3.Tablep = 0.002) . Furthermore, lower LVEF values have been associated with lower SVG patency  vs. 55.4% ± 7.45% )  . Graft fHowever, recurrence of ischemic symptoms may happen due to graft failure, the progression of native coronary artery atherosclerosis, or a combination of both .With the increased survival of CABG patients, there is a greater interest in the best diagnostic procedures for patients who present with ischemic symptoms following surgery .ICA remains the gold standard diagnostic test for evaluating symptomatic post-CABG patients However, this invasive procedure carries the risk of cardiac arrhythmia, stroke and vascular complications like dissection, bleeding, pseudo-aneurysm . CCTA haThis study used CCTA and ICA to assess graft patency and native vessel lesions in 40 symptomatic patients following CABG and correlated the different risk factors with graft patency.Regarding the patency of venous grafts assessed using CCTA, most venous grafts were non-patent, representing 66.1% of these grafts with a mean duration after CABG of 5.25 ± 4.04 years. This was consistent with the findings of a large Veterans Affairs trial by Goldman et al. who reported a 10-year patency rate for SVGs of 61%. Meanwhile, most arterial grafts (LIMA) assessed using CCTA were patent (76.3%), which is less than the patency rate stated by Goldman et al. for LIMA grafts 10 years after CABG (85%). This can be explained by the wide range of duration after CABG, ranging from 2 to 21 years, and the frequency of smoking and uncontrolled dyslipidemia in our cohort .We studied the variable cardiovascular risk factors, such as age, sex, hypertension, DM, and smoking status, and we found that they are not predictive for long-term graft occlusion; this finding is consistent with Harskamp et al. who evaluated the determinants of LIMA graft stenosis or occlusion in a study of 1539 CABG patients undergoing angiographic follow-up from the PREVENT IV trial .Regarding DM, 67.5% of the patients in this study had DM. According to Harskamp et al., the absence of DM was the only patient-specific predictor of LIMA graft failure as patients with DM might have more severe proximal coronary artery stenosis . MoreoveIn contrast, Deb et al. reported that compared with non-diabetic patients, SVG occlusion was higher in diabetic patients 5 years or more after CABG . HoweverLDL and TG levels were evaluated, and we found a significant association between LDL, TG levels, and SVG patency. This is consistent with the results of Hata et al. who noted yellow plaque and thrombus using intracoronary angioscopy in the vein grafts of patients with high LDL levels (more than 100 mg/dL) one year after surgery. However, they found that plaque and thrombus were absent in patients with low LDL levels (less than 80 mg/dL), suggesting that aggressive lipid-lowering treatment following CABG may prevent the development of SVG failure .. have reported similar findings, stating that only 79% of the LIMA grafts under study were cannulated [The diagnostic performance of CCTA for assessing both arterial and venous grafts as well as native coronary arteries was compared with that of ICA. We used CCTA to evaluate the non-cannulated grafts and found that one-third of them were patent. Krysztofiak et alnnulated . The usennulated . Secondlnnulated . The trannulated .In this study, eight non-cannulated venous grafts were found patent using CCTA, and this is of course a game changer in the planned revascularization strategy.Our results are concordant with the results of Weustink et al. who stated that MSCT has sensitivity of 100% for the detection of significant per segment graft obstruction and 95% for the detection of significant distal runoffs lesions .In this study, the two modalities had good agreement in detecting significant stenosis and total occlusion of native vessels with an average limit of agreement of 0.8 . Similar results were reported by Weustink et al. and Radwan et al. using 256-slice dual-source MDCT who stated per-patient, per-vessel, and per-segment CCTA accuracy of 95%, 88%, and 96%, respectively .The radiation dose was calculated for both CCTA and ICA, and we found that the radiation dose in CCTA was significantly lower, which is consistent with the findings of Koplay et al. using 128-slice MDCT .In this study, we used Siemens Somatom Definition 128-slice CT scanner with broad detector array and dual-source high helical pitch scanning to capture whole entire heart in a single beat, which resulted in a significant dose reduction. Its rotation speeds are faster, which improved the temporal resolution. Using ECG-based tube current modulation with a retrospective ECG-gated technique to reduce the applied tube current by 20%, iterative reconstruction techniques achieved 30–41% reduction in radiation dose.In conclusion, CCTA has a comparable diagnostic accuracy in assessing bypass grafts and native coronary arteries to ICA and thus provides a non-invasive reliable tool for evaluating patients following CABG with less radiation dose. As a result, we advocate for the use of CCTA at the outset to divert management away from ICA, with expected significant reduction of patients’ exposure to contrast material and radiation.Because this study only included symptomatic patients 1 year after CABG, we detected a higher percentage of graft failure.Due to the coronavirus disease 2019 pandemic, the sample size was limited with restriction to some elective investigations while maintaining the emergency procedure.


Antimicrobial resistance (AMR) has a significant impact on human, animal, and environmental health, being spread in diverse settings. Antibiotic misuse and overuse in the food chain are widely recognized as primary drivers of antibiotic-resistant bacteria. However, other antimicrobials, such as metals and organic acids, commonly present in agri-food environments , may also contribute to this global public health problem, although this remains a debatable topic owing to limited data. This review aims to provide insights into the current role of metals  and organic acids in the emergence and spread of AMR in the food chain. Based on a thorough literature review, this study adopts a unique integrative approach, analyzing in detail the known antimicrobial mechanisms of metals and organic acids, as well as the molecular adaptive tolerance strategies developed by diverse bacteria to overcome their action. Additionally, the interplay between the tolerance to metals or organic acids and AMR is explored, with particular focus on co-selection events. Through a comprehensive analysis, this review highlights potential silent drivers of AMR within the food chain and the need for further research at molecular and epidemiological levels across different food contexts worldwide. Antimicrobial resistance (AMR) is a critical global health challenge, ranked among the top ten public health threats worldwide . This biSince the discovery of antibiotics in the 1940s, the global threat of AMR has evolved dramatically over the past century . While aAlthough the link between human or animal antimicrobial use and AMR seems clear- cut, this association is a complex process involving multiple events, including pathogen–drug and pathogen–host interactions, the high mutation rates of particular strains, the emergence and expansion of successful antimicrobial-resistant clones and/or mobile genetic elements (MGE), co-selection events by unrelated antimicrobials , and the variable transmission rates of pathogens between humans, animals, and the environment . DiverseIn addition to antimicrobial misuse, other factors are also important drivers of AMR spread, including poor infection control practices , hygiene or biosecurity measures in healthcare facilities or animal production settings, limited access to clean water and sanitation, environmental waste discharges, and poor food hygiene and safety practices . Also, tAlthough these multiple drivers occur in different settings, the food production sector has long been recognized as one of the main environments for AMR expansion through ARB and MGE selection and evolution . Such ARChanges in consumption trends due to rapid human population growth have led to the increase and globalization of the food supply , with thStaphylococcus aureus has been identified in livestock and poultry meat  and the oxidized cupric form [Cu(II) or Cu2+], critical for a wide range of metabolic and regulatory cellular functions , which are responsible for lipid peroxidation, protein oxidation, and DNA damage  of P1B-ATPases  have also been described in the same Enterococcus plasmids as the tcrYAZB operon and other metals in Enterococcus spp. of the food chain and other niches , sulfonamides (sul), trimethoprim (dfrA), chloramphenicol (cmlA), and tetracyclines (tet) have also been described in E. coli, Klebsiella pneumoniae, and Salmonella isolates from food-production environments and human sources , as coccidiostats, and as a feed additive, mainly in the poultry and swine industries . Aon genes . ArsB ismulation . ArsB acr degree . Finallyxtrusion  192]..ars operars clusters . I. Iars clrsB gene . HoweverrsB gene . As withrsB gene . A phenoaerobic) ,180.arsA/arsB/acr3) have been predominantly found in bacteria from natural environmental sites, regardless of whether they had a history of arsenic contamination, including soils , creek water, and sewage  and of mobilizable or MGE  [aac(6′)-Ia], macrolides (erm35), bacitracin (bacA) and, in particular, with resistance genes to tetracycline (tet genes), probably promoted by co-selection events  and mercurous [Hg+/Hg(I)] forms  are dominant in water, soil, and sediments and methylmercury (MeHg) in biota [Mercury (Hg) is a highly toxic heavy metal widely dispersed in nature . Like ar)] forms . It can ms of Hg ,220. Thems of Hg . While Hin biota .4), mercury oxide (HgO), mercury chloride (HgCl2), or as elemental mercury  , sulfonamides , or tetracycline  were described in the plasmids of Klebsiella, Escherichia, Salmonella, and Enterobacter isolated from diverse sources  , tetracycline , aminoglycosides , and vancomycin (vanA)   and in Eectively ,262. Theentified ,263, aloentified . In receentified ,265,266. sludge) ,134,267. samples ,269,270.n (vanA) ,253. The3H) determines the compounds’ acidity  and the urease system are also important acid response mechanisms, associated with the production of basic compounds such as ammonia , im ureases . Further and CO2  [305,308asmic pH .+-ATPase, symporter, and antiporter) that promotes proton efflux in a proton motive force (PMF) dependence system [1-F0-ATPase is a bifunctional proton pump, that catalyzes the synthesis and hydrolysis of ATP [1-F0 operon by exposure to acidic pH suggests that this enzyme plays a critical role in acid resistance in several bacteria [Another important mechanism relies on the activity of proton pumps  [S. enterica [S. enterica strains [In contrast to inorganic acids , which primarily lower cytoplasmatic pH, organic acids have the additional ability to accumulate as intracellular anions . When thacillus) ,319. In acillus) . Interesacillus) . In the enterica , can indenterica . In fact strains .pmrA/B/C polymyxin resistance genes and actP acid resistance gene [gadE, hdeA, mdtE, mdtF, gadW, gadX, and gadA) were co-located with metal tolerance genes (mainly arsenic—arsA/B/R) in the same contig [S. enterica strains to subinhibitory concentrations of PAA  [S. enterica isolates from Danish pig slaughterhouses found little evidence of an association between increased MIC for PAA and antibiotic resistance [E. faecium exposed to low doses of PAA did not show changes in the abundance of ARGs [L. monocytogenes from food production plants exposed to PAA [Unlike metals, limited information on the co-selection of antibiotic resistance and acid tolerance is available. A recent study using metagenomic approaches revealed the co-occurrence of the nce gene . Furthere contig . In the 0 mg/mL)  resulted0 mg/mL) , but thi0 mg/mL) . On the sistance . Likewis of ARGs . In addid to PAA . Indeed,d to PAA . Howeverd to PAA , a mechaWhile antibiotic misuse and overuse remains the main driving force behind the emergence of AMR in the agri-food sector, there is growing recognition of the potential role of other antimicrobial compounds in this problem. Metals, including copper commonly found in feed, as well as pollutants such as arsenic and mercury that enter the food chain, can potentially contribute to the co-selection of ARB, often sharing metal tolerance genes and ARGs in diverse mobile genetic contexts. However, for organic acids, only a limited number of studies exploring the potential link between bacterial tolerance to these compounds and the emergence of AMR are available. Moreover, such studies provide limited data on the impact of organic acids on the selection of particular MDR strains  as they only include a small number of isolates with non-characterized epidemiological and genetic backgrounds. More data on the molecular mechanisms explaining adaptive features to different organic acids are also still needed for different bacteria.To effectively address the challenge of AMR in the food chain, future research should provide better and larger data on the epidemiological and genomic backgrounds of bacteria tolerant to metals or organic acids, as well as on the characterization of the genetic contexts accumulating and mobilizing tolerance genes to these stresses. It is also critical to assess the minimum selective concentrations of metals and organic acids for particular MDR bacterial clones, serotypes, and MGE in food contexts worldwide with diverse selective pressures and to investigate the ecological factors promoting the horizontal gene transfer events of AMR or ARG expression. Additionally, understanding the long-term impact of continued metal and organic acid exposure on AMR dynamics, especially in bacteria of clinical relevance to humans and animals, is essential. Employing advanced genomic and metagenomic techniques in longitudinal studies will help unravel the genetic basis and shifts of bacterial communities’ tolerance under the same stresses and after different controlling interventions, whose data are currently limited. Thus, future approaches to the problem will need to adopt a One Health strategy to examine the interconnectedness of metal and organic acid tolerance and AMR across humans, animals, and the environment. This will provide comprehensive insights, allowing the optimization of the use of metals and organic acids to mitigate the microbial risks associated with food production and to prevent pollution. It will enable more effective management practices, ensuring the long-term effectiveness of antimicrobial treatments for all.