Empowering drug off-target discovery with metabolic and structural analysis

DrugMAP 2.0: molecular atlas and pharma-information of all drugs

The escalating costs and high failure rates have decelerated the pace of drug development, which amplifies the research interests in developing combinatorial/repurposed drugs and understanding off-target adverse drug reaction (ADR). In other words, it is demanded to delineate the molecular atlas and pharma-information for the combinatorial/repurposed drugs and off-target interactions. However, such invaluable data were inadequately covered by existing databases. In this study, a major update was thus conducted to the DrugMAP, which accumulated (a) 20831 combinatorial drugs and their interacting atlas involving 1583 pharmacologically important molecules; (b) 842 repurposed drugs and their interacting atlas with 795 molecules; (c) 3260 off-targets relevant to the ADRs of 2731 drugs and (d) various types of pharmaceutical information, including diverse ADMET properties, versatile diseases, and various ADRs/off-targets. With the growing demands for discovering combinatorial/repurposed therapies and the rapidly emerging interest in AI-based drug discovery, DrugMAP was highly expected to act as an indispensable supplement to existing databases facilitating drug discovery, which was accessible at: https://idrblab.org/drugmap/.

MolBiC: the cell-based landscape illustrating molecular bioactivities

The measurement of cell-based molecular bioactivity (CMB) is critical for almost every step of drug development. With the booming application of AI in biomedicine, it is essential to have the CMB data to promote the learning of cell-based patterns for guiding modern drug discovery, but no database providing such information has been constructed yet. In this study, we introduce MolBiC, a knowledge base designed to describe valuable data on molecular bioactivity measured within a cellular context. MolBiC features 550 093 experimentally validated CMBs, encompassing 321 086 molecules and 2666 targets across 988 cell lines. Our MolBiC database is unique in describing the valuable data of CMB, which meets the critical demands for CMB-based big data promoting the learning of cell-based molecular/pharmaceutical pattern in drug discovery and development. MolBiC is now freely accessible without any login requirement at: https://idrblab.org/MolBiC/.

Revealing novel protein interaction partners of glyphosate in Escherichia coli

Despite all debates about its safe use, glyphosate remains the most widely applied active ingredient in herbicide products, with renewed approval in the European Union until 2033. Non-target organisms are commonly exposed to glyphosate as a matter of its mode of application, with its broader environmental and biological impacts remaining under investigation. Glyphosate displays structural similarity to phosphoenolpyruvate (PEP), thereby competitively inhibiting the 5-enolpyruvylshikimate-3-phosphate synthase (EPSPS), crucial for the synthesis of aromatic amino acids in plants, fungi, bacteria, and archaea. Most microbes, including the gut bacterium Escherichia coli (E. coli), possess a glyphosate-sensitive class I EPSPS, making them vulnerable to glyphosate's effects. Yet, little is known about glyphosate's interactions with other bacterial proteins or its broader modes of action at the proteome level. Here, we employed a quantitative proteomics and thermal proteome profiling (TPP) approach to identify novel protein binding partners of glyphosate in the E. coli proteome. Glyphosate exposure significantly altered amino acid synthesizing pathways. The abundance of shikimate pathway proteins was increased, suggesting a compensatory mechanism. Extracellular riboflavin concentrations were elevated upon glyphosate exposure, while intracellular levels remained stable. Beyond the target enzyme EPSPS, thermal proteome profiling indicated an effect of glyphosate on the thermal stability of certain proteins, including AroH and ProA, indicating interactions. Similar to the competitive binding between PEP and glyphosate at EPSPS, one reason for the interaction of AroH and ProA with the herbicide could be a high structural similarity between their substrates and glyphosate. Overall, glyphosate induced metabolic disturbances in E. coli, extending beyond its primary target, thereby providing new insights into glyphosate's broader impact on microbial systems.

New drug discovery and development from natural products: Advances and strategies

Natural products (NPs) have a long history as sources for drug discovery, more than half of approved drugs are related to NPs, which also exhibit multifaceted advantages in the clinical treatment of complex diseases. However, bioactivity screening of NPs, target identification, and design optimization require continuously improved strategies, the complexity of drug mechanism of action and the limitations of technological strategies pose numerous challenges to the development of new drugs. This review begins with an overview of bioactivity- and target-based drug development patterns for NPs, advances in NP screening and derivatization, and the advantages and problems of major targets such as genes and proteins. Then, target-based drugs as well as identification and validation methods are further discussed to elucidate their mechanism of action. Subsequently, the current status and development trend of the application of traditional and emerging technologies in drug discovery and development of NPs are systematically described. Finally, the collaborative strategy of multi-technology integration and multi-disciplinary intersection is emphasized for the challenges faced in the identification, optimization, activity evaluation, and clinical application of NPs. It is hoped to provide a systematic overview and inspiration for exploring new drugs from natural resources in the future.

Recent Advances and Challenges in Targeted Drug Delivery Using Biofunctional Coatings

Globally, clinics are overwhelmed by drugs targeting undesired cells and organs, causing adverse systemic effects on the body. This shortfall in targeting specificity, safety, and efficiency has noticeably contributed to the failure of the bench-to-bedside transition. Activation or impairment of immune activity due to a misdirected drug and its carrier fuels complications, extending the range of destruction which can convert the course of disease into a life-threatening route. To address these great challenges, advanced coatings as indispensable components of future medicine have been investigated over the last few decades for precisely targeted drug delivery to achieve favorable prognoses in the treatment of a broad spectrum of diseases. Complemented by advancements in the pharmacological parameters, these systems hold great promise for the field. This chapter aims to discuss recent progress on new coatings for targeted drug delivery and the parameters for manufacturing these platforms for their cargo based on major determinants such as biocompatibility and bioactivity. A brief overview of the various applications of targeted drug delivery with functional coatings is also provided to offer a new perspective on the field.

Polypharmacology prediction: the long road toward comprehensively anticipating small-molecule selectivity to de-risk drug discovery

INTRODUCTION

Small molecules often bind to multiple targets, a behavior termed polypharmacology. Anticipating polypharmacology is essential for drug discovery since unknown off-targets can modulate safety and efficacy - profoundly affecting drug discovery success. Unfortunately, experimental methods to assess selectivity present significant limitations and drugs still fail in the clinic due to unanticipated off-targets. Computational methods are a cost-effective, complementary approach to predict polypharmacology.

AREAS COVERED

This review aims to provide a comprehensive overview of the state of polypharmacology prediction and discuss its strengths and limitations, covering both classical cheminformatics methods and bioinformatic approaches. The authors review available data sources, paying close attention to their different coverage. The authors then discuss major algorithms grouped by the types of data that they exploit using selected examples.

EXPERT OPINION

Polypharmacology prediction has made impressive progress over the last decades and contributed to identify many off-targets. However, data incompleteness currently limits most approaches to comprehensively predict selectivity. Moreover, our limited agreement on model assessment challenges the identification of the best algorithms - which at present show modest performance in prospective real-world applications. Despite these limitations, the exponential increase of multidisciplinary Big Data and AI hold much potential to better polypharmacology prediction and de-risk drug discovery.

Identification of Ribonuclease Inhibitors for the Control of Pathogenic Bacteria

Bacteria are known to be constantly adapting to become resistant to antibiotics. Currently, efficient antibacterial compounds are still available; however, it is only a matter of time until these compounds also become inefficient. Ribonucleases are the enzymes responsible for the maturation and degradation of RNA molecules, and many of them are essential for microbial survival. Members of the PNPase and RNase II families of exoribonucleases have been implicated in virulence in many pathogens and, as such, are valid targets for the development of new antibacterials. In this paper, we describe the use of virtual high-throughput screening (vHTS) to identify chemical compounds predicted to bind to the active sites within the known structures of RNase II and PNPase from Escherichia coli. The subsequent in vitro screening identified compounds that inhibited the activity of these exoribonucleases, with some also affecting cell viability, thereby providing proof of principle for utilizing the known structures of these enzymes in the pursuit of new antibacterials.

Metabolic pathways and antimicrobial peptide resistance in bacteria

Antimicrobial resistance is a pressing concern that poses a significant threat to global public health, necessitating the exploration of alternative strategies to combat drug-resistant microbial infections. Recently, antimicrobial peptides (AMPs) have gained substantial attention as possible replacements for conventional antibiotics. Because of their pharmacodynamics and killing mechanisms, AMPs display a lower risk of bacterial resistance evolution compared with most conventional antibiotics. However, bacteria display different mechanisms to resist AMPs, and the role of metabolic pathways in the resistance mechanism is not fully understood. This review examines the intricate relationship between metabolic genes and AMP resistance, focusing on the impact of metabolic pathways on various aspects of resistance. Metabolic pathways related to guanosine pentaphosphate (pppGpp) and guanosine tetraphosphate (ppGpp) [collectively (p)ppGpp], the tricarboxylic acid (TCA) cycle, haem biosynthesis, purine and pyrimidine biosynthesis, and amino acid and lipid metabolism influence in different ways metabolic adjustments, biofilm formation and energy production that could be involved in AMP resistance. By targeting metabolic pathways and their associated genes, it could be possible to enhance the efficacy of existing antimicrobial therapies and overcome the challenges exhibited by phenotypic (recalcitrance) and genetic resistance toward AMPs. Further research in this area is needed to provide valuable insights into specific mechanisms, uncover novel therapeutic targets, and aid in the fight against antimicrobial resistance.

Multifunctional-separation-mode ion chromatography method for determining major metabolites during multiple parallel fermentation of rice wine

Facile and effective analysis methods are desirable for elucidating the behaviours of metabolites during fermentation reactions. Herein, a multifunctional-separation-mode ion chromatography (MFS-IC) method was developed for the simultaneous monitoring of major metabolites during multiple parallel fermentation, including those related to central carbon metabolism (saccharification, glycolysis, alcoholic fermentation, and the tricarboxylic acid (TCA) cycle). The use of two types of sulfo-modified size-exclusion columns and phthalic acid as the eluent allowed the separation of oligosaccharides (disaccharides, trisaccharides, and tetrasaccharides), glucose, pyruvate, and major organic acids during the TCA cycle (cis-aconitate, citrate, iso-citrate, malate, fumarate, and succinate but not α-ketoglutarate) from other non-target analytes. The MFS-IC method was successfully applied to monitoring the major metabolites in the rice wine brewing process. This approach can contribute to an improved understanding of metabolite behaviour during fermentation without requiring the use of expensive advanced instrumentation methods such as liquid chromatography-mass spectrometry and gas chromatography-mass spectrometry.

An allosteric mechanism for potent inhibition of SARS-CoV-2 main proteinase

The main proteinase (Mpro) of SARS-CoV-2 plays a critical role in cleaving viral polyproteins into functional proteins required for viral replication and assembly, making it a prime drug target for COVID-19. It is well known that noncompetitive inhibition offers potential therapeutic options for treating COVID-19, which can effectively reduce the likelihood of cross-reactivity with other proteins and increase the selectivity of the drug. Therefore, the discovery of allosteric sites of Mpro has both scientific and practical significance. In this study, we explored the binding characteristics and inhibiting process of Mpro activity by two recently reported allosteric inhibitors, pelitinib and AT7519 which were obtained by the X-ray screening experiments, to probe the allosteric mechanism via molecular dynamic (MD) simulations. We found that pelitinib and AT7519 can stably bind to Mpro far from the active site. The binding affinity is estimated to be -24.37 ± 4.14 and - 26.96 ± 4.05 kcal/mol for pelitinib and AT7519, respectively, which is considerably stable compared with orthosteric drugs. Furthermore, the strong binding caused clear changes in the catalytic site of Mpro, thus decreasing the substrate accessibility. The community network analysis also validated that pelitinib and AT7519 strengthened intra- and inter-domain communication of Mpro dimer, resulting in a rigid Mpro, which could negatively impact substrate binding. In summary, our findings provide the detailed working mechanism for the two experimentally observed allosteric sites of Mpro. These allosteric sites greatly enhance the 'druggability' of Mpro and represent attractive targets for the development of new Mpro inhibitors.

The Rise of Hypothesis-Driven Artificial Intelligence in Oncology

Cancer is a complex disease involving the deregulation of intricate cellular systems beyond genetic aberrations and, as such, requires sophisticated computational approaches and high-dimensional data for optimal interpretation. While conventional artificial intelligence (AI) models excel in many prediction tasks, they often lack interpretability and are blind to the scientific hypotheses generated by researchers to enable cancer discoveries. Here we propose that hypothesis-driven AI, a new emerging class of AI algorithm, is an innovative approach to uncovering the complex etiology of cancer from big omics data. This review exemplifies how hypothesis-driven AI is different from conventional AI by citing its application in various areas of oncology including tumor classification, patient stratification, cancer gene discovery, drug response prediction, and tumor spatial organization. Our aim is to stress the feasibility of incorporating domain knowledge and scientific hypotheses to craft the design of new AI algorithms. We showcase the power of hypothesis-driven AI in making novel cancer discoveries that can be overlooked by conventional AI methods. Since hypothesis-driven AI is still in its infancy, open questions such as how to better incorporate new knowledge and biological perspectives to ameliorate bias and improve interpretability in the design of AI algorithms still need to be addressed. In conclusion, hypothesis-driven AI holds great promise in the discovery of new mechanistic and functional insights that explain the complexity of cancer etiology and potentially chart a new roadmap to improve treatment regimens for individual patients.

Personalized redox biology: Designs and concepts

Personalized interventions are regarded as a next-generation approach in almost all fields of biomedicine, such as clinical medicine, exercise, nutrition and pharmacology. At the same time, an increasing body of evidence indicates that redox processes regulate, at least in part, multiple aspects of human physiology and pathology. As a result, the idea of applying personalized redox treatments to improve their efficacy has gained popularity among researchers in recent years. The aim of the present primer-style review was to highlight some crucial yet underappreciated methodological, statistical, and interpretative concepts within the redox biology literature, while also providing a physiology-oriented perspective on personalized redox biology. The topics addressed are: (i) the critical issue of investigating the potential existence of inter-individual variability; (ii) the importance of distinguishing a genuine and consistent response of a subject from a chance finding; (iii) the challenge of accurately quantifying the effect of a redox treatment when dealing with 'extreme' groups due to mathematical coupling and regression to the mean; and (iv) research designs and analyses that have been implemented in other fields, and can be reframed and exploited in a redox biology context.