Application of Fragment Growing and Fragment Linking to the Discovery of Inhibitors ofMycobacterium tuberculosisPantothenate Synthetase

Expanding FTMap for Fragment-Based Identification of Pharmacophore Regions in Ligand Binding Sites

The knowledge of ligand binding hot spots and of the important interactions within such hot spots is crucial for the design of lead compounds in the early stages of structure-based drug discovery. The computational solvent mapping server FTMap can reliably identify binding hot spots as consensus clusters, free energy minima that bind a variety of organic probe molecules. However, in its current implementation, FTMap provides limited information on regions within the hot spots that tend to interact with specific pharmacophoric features of potential ligands. E-FTMap is a new server that expands on the original FTMap protocol. E-FTMap uses 119 organic probes, rather than the 16 in the original FTMap, to exhaustively map binding sites, and identifies pharmacophore features as atomic consensus sites where similar chemical groups bind. We validate E-FTMap against a set of 109 experimentally derived structures of fragment-lead pairs, finding that highly ranked pharmacophore features overlap with the corresponding atoms in both fragments and lead compounds. Additionally, comparisons of mapping results to ensembles of bound ligands reveal that pharmacophores generated with E-FTMap tend to sample highly conserved protein-ligand interactions. E-FTMap is available as a web server at https://eftmap.bu.edu.

GRELinker: A Graph-Based Generative Model for Molecular Linker Design with Reinforcement and Curriculum Learning

Fragment-based drug discovery (FBDD) is widely used in drug design. One useful strategy in FBDD is designing linkers for linking fragments to optimize their molecular properties. In the current study, we present a novel generative fragment linking model, GRELinker, which utilizes a gated-graph neural network combined with reinforcement and curriculum learning to generate molecules with desirable attributes. The model has been shown to be efficient in multiple tasks, including controlling log P, optimizing synthesizability or predicted bioactivity of compounds, and generating molecules with high 3D similarity but low 2D similarity to the lead compound. Specifically, our model outperforms the previously reported reinforcement learning (RL) built-in method DRlinker on these benchmark tasks. Moreover, GRELinker has been successfully used in an actual FBDD case to generate optimized molecules with enhanced affinities by employing the docking score as the scoring function in RL. Besides, the implementation of curriculum learning in our framework enables the generation of structurally complex linkers more efficiently. These results demonstrate the benefits and feasibility of GRELinker in linker design for molecular optimization and drug discovery.

Medicinal Chemistry Strategies for the Modification of Bioactive Natural Products

Natural bioactive compounds are valuable resources for drug discovery due to their diverse and unique structures. However, these compounds often lack optimal drug-like properties. Therefore, structural optimization is a crucial step in the drug development process. By employing medicinal chemistry principles, targeted molecular operations can be applied to natural products while considering their size and complexity. Various strategies, including structural fragmentation, elimination of redundant atoms or groups, and exploration of structure-activity relationships, are utilized. Furthermore, improvements in physicochemical properties, chemical and metabolic stability, biophysical properties, and pharmacokinetic properties are sought after. This article provides a concise analysis of the process of modifying a few marketed drugs as illustrative examples.

Mycobacterium tuberculosis: Pathogenesis and therapeutic targets

Tuberculosis (TB) remains a significant public health concern in the 21st century, especially due to drug resistance, coinfection with diseases like immunodeficiency syndrome (AIDS) and coronavirus disease 2019, and the lengthy and costly treatment protocols. In this review, we summarize the pathogenesis of TB infection, therapeutic targets, and corresponding modulators, including first-line medications, current clinical trial drugs and molecules in preclinical assessment. Understanding the mechanisms of Mycobacterium tuberculosis (Mtb) infection and important biological targets can lead to innovative treatments. While most antitubercular agents target pathogen-related processes, host-directed therapy (HDT) modalities addressing immune defense, survival mechanisms, and immunopathology also hold promise. Mtb's adaptation to the human host involves manipulating host cellular mechanisms, and HDT aims to disrupt this manipulation to enhance treatment effectiveness. Our review provides valuable insights for future anti-TB drug development efforts.

Linkers in fragment-based drug design: an overview of the literature

INTRODUCTION

In fragment-based drug design, fragment linking is a popular strategy where two fragments binding to different sub-pockets of a target are linked together. This attractive method remains challenging especially due to the design of ideal linkers.

AREAS COVERED

The authors review the types of linkers and chemical reactions commonly used to the synthesis of linkers, including those utilized in protein-templated fragment self-assembly, where fragments are directly linked in the presence of the protein. Finally, they detail computational workflows and software including generative models that have been developed for fragment linking.

EXPERT OPINION

The authors believe that fragment linking offers key advantages for compound design, particularly for the design of bivalent inhibitors linking two distinct pockets of the same or different subunits. On the other hand, more studies are needed to increase the potential of protein-templated approaches in FBDD. Important computational tools such as structure-based de novo software are emerging to select suitable linkers. Fragment linking will undoubtedly benefit from developments in computational approaches and machine learning models.

Pantothenate biosynthesis in Toxoplasma gondii tachyzoites is not a drug target

Toxoplasma gondii is a pervasive apicomplexan parasite that can cause severe disease and death in immunocompromised individuals and the developing foetus. The treatment of toxoplasmosis often leads to serious side effects and novel drugs and drug targets are therefore actively sought. In 2014, Mageed and colleagues suggested that the T. gondii pantothenate synthetase, the enzyme responsible for the synthesis of the vitamin B₅ (pantothenate), the precursor of the important cofactor, coenzyme A, is a good drug target. Their conclusion was based on the ability of potent inhibitors of the M. tuberculosis pantothenate synthetase to inhibit the proliferation of T. gondii tachyzoites. They also reported that the inhibitory effect of the compounds could be antagonised by supplementing the medium with pantothenate, supporting their conclusion that the compounds were acting on the intended target. Contrary to these observations, we find that compound SW314, one of the compounds used in the Mageed et al. study and previously shown to be active against M. tuberculosis pantothenate synthetase in vitro, is inactive against the T. gondii pantothenate synthetase and does not inhibit tachyzoite proliferation, despite gaining access into the parasite in situ. Furthermore, we validate the recent observation that the pantothenate synthetase gene in T. gondii can be disrupted without detrimental effect to the survival of the tachyzoite-stage parasite in the presence or absence of extracellular pantothenate. We conclude that the T. gondii pantothenate synthetase is not essential during the tachyzoite stage of the parasite and it is therefore not a target for drug discovery against T. gondii tachyzoites.

The pathogenic mechanism of Mycobacterium tuberculosis: implication for new drug development

Mycobacterium tuberculosis (Mtb), the causative agent of tuberculosis (TB), is a tenacious pathogen that has latently infected one third of the world's population. However, conventional TB treatment regimens are no longer sufficient to tackle the growing threat of drug resistance, stimulating the development of innovative anti-tuberculosis agents, with special emphasis on new protein targets. The Mtb genome encodes ~4000 predicted proteins, among which many enzymes participate in various cellular metabolisms. For example, more than 200 proteins are involved in fatty acid biosynthesis, which assists in the construction of the cell envelope, and is closely related to the pathogenesis and resistance of mycobacteria. Here we review several essential enzymes responsible for fatty acid and nucleotide biosynthesis, cellular metabolism of lipids or amino acids, energy utilization, and metal uptake. These include InhA, MmpL3, MmaA4, PcaA, CmaA1, CmaA2, isocitrate lyases (ICLs), pantothenate synthase (PS), Lysine-ε amino transferase (LAT), LeuD, IdeR, KatG, Rv1098c, and PyrG. In addition, we summarize the role of the transcriptional regulator PhoP which may regulate the expression of more than 110 genes, and the essential biosynthesis enzyme glutamine synthetase (GlnA1). All these enzymes are either validated drug targets or promising target candidates, with drugs targeting ICLs and LAT expected to solve the problem of persistent TB infection. To better understand how anti-tuberculosis drugs act on these proteins, their structures and the structure-based drug/inhibitor designs are discussed. Overall, this investigation should provide guidance and support for current and future pharmaceutical development efforts against mycobacterial pathogenesis.

DRlinker: Deep Reinforcement Learning for Optimization in Fragment Linking Design

Fragment-based drug discovery is a widely used strategy for drug design in both academic and pharmaceutical industries. Although fragments can be linked to generate candidate compounds by the latest deep generative models, generating linkers with specified attributes remains underdeveloped. In this study, we presented a novel framework, DRlinker, to control fragment linking toward compounds with given attributes through reinforcement learning. The method has been shown to be effective for many tasks from controlling the linker length and log P, optimizing predicted bioactivity of compounds, to various multiobjective tasks. Specifically, our model successfully generated 91.0% and 93.9% of compounds complying with the desired linker length and log P and improved the 7.5 pChEMBL value in bioactivity optimization. Finally, a quasi-scaffold-hopping study revealed that DRlinker could generate nearly 30% molecules with high 3D similarity but low 2D similarity to the lead inhibitor, demonstrating the benefits and applicability of DRlinker in actual fragment-based drug design.

A Formylglycine-Peptide for the Site-Directed Identification of Phosphotyrosine-Mimetic Fragments

Discovery of protein-binding fragments for precisely defined binding sites is an unmet challenge to date. Herein, formylglycine is investigated as a molecular probe for the sensitive detection of fragments binding to a spatially defined protein site . Formylglycine peptide 3 was derived from a phosphotyrosine-containing peptide substrate of protein tyrosine phosphatase PTP1B by replacing the phosphorylated amino acid with the reactive electrophile. Fragment ligation with formylglycine occurred in situ in aqueous physiological buffer. Structures and kinetics were validated by NMR spectroscopy. Screening and hit validation revealed fluorinated and non-fluorinated hit fragments being able to replace the native phosphotyrosine residue. The formylglycine probe identified low-affinity fragments with high spatial resolution as substantiated by molecular modelling. The best fragment hit, 4-amino-phenyl-acetic acid, was converted into a cellularly active, nanomolar inhibitor of the protein tyrosine phosphatase SHP2.

Discovery and Structural Characterization of Small Molecule Binders of the Human CTLH E3 Ligase Subunit GID4

Targeted protein degradation (TPD) strategies exploit bivalent small molecules to bridge substrate proteins to an E3 ubiquitin ligase to induce substrate degradation. Few E3s have been explored as degradation effectors due to a dearth of E3-binding small molecules. We show that genetically induced recruitment to the GID4 subunit of the CTLH E3 complex induces protein degradation. An NMR-based fragment screen followed by structure-guided analog elaboration identified two binders of GID4, 16 and 67, with K_d values of 110 and 17 μM in vitro. A parallel DNA-encoded library (DEL) screen identified five binders of GID4, the best of which, 88, had a K_d of 5.6 μM in vitro and an EC₅₀ of 558 nM in cells with strong selectivity for GID4. X-ray co-structure determination revealed the basis for GID4–small molecule interactions. These results position GID4-CTLH as an E3 for TPD and provide candidate scaffolds for high-affinity moieties that bind GID4.

Fragment-Based Drug Discovery against Mycobacteria: The Success and Challenges

The emergence of drug-resistant mycobacteria, including Mycobacterium tuberculosis (Mtb) and non-tuberculous mycobacteria (NTM), poses an increasing global threat that urgently demands the development of new potent anti-mycobacterial drugs. One of the approaches toward the identification of new drugs is fragment-based drug discovery (FBDD), which is the most ingenious among other drug discovery models, such as structure-based drug design (SBDD) and high-throughput screening. Specialized techniques, such as X-ray crystallography, nuclear magnetic resonance spectroscopy, and many others, are part of the drug discovery approach to combat the Mtb and NTM global menaces. Moreover, the primary drawbacks of traditional methods, such as the limited measurement of biomolecular toxicity and uncertain bioavailability evaluation, are successfully overcome by the FBDD approach. The current review focuses on the recognition of fragment-based drug discovery as a popular approach using virtual, computational, and biophysical methods to identify potent fragment molecules. FBDD focuses on designing optimal inhibitors against potential therapeutic targets of NTM and Mtb (PurC, ArgB, MmpL3, and TrmD). Additionally, we have elaborated on the challenges associated with the FBDD approach in the identification and development of novel compounds. Insights into the applications and overcoming the challenges of FBDD approaches will aid in the identification of potential therapeutic compounds to treat drug-sensitive and drug-resistant NTMs and Mtb infections.

Design, synthesis and biological evaluation of benzo-[d]-imidazo-[2,1-b]-thiazole and imidazo-[2,1-b]-thiazole carboxamide triazole derivatives as antimycobacterial agents

In the search for new anti-mycobacterial agents, we revealed the importance of imidazo-[2,1-b]-thiazole and benzo-[d]-imidazo-[2,1-b]-thiazole carboxamide derivatives. We designed, in silico ADMET predicted and synthesized four series of novel imidazo-[2,1-b]-thiazole and benzo-[d]-imidazo-[2,1-b]-thiazole carboxamide analogues in combination with piperazine and various 1,2,3 triazoles. All the synthesized derivatives were characterized by 1H NMR, 13C NMR, HPLC and MS spectral analysis and evaluated for in vitro antitubercular activity. The most active benzo-[d]-imidazo-[2,1-b]-thiazole derivative IT10, carrying a 4-nitro phenyl moiety, displayed IC90 of 7.05 μM and IC50 of 2.32 μM against Mycobacterium tuberculosis (Mtb) H37Ra, while no acute cellular toxicity was observed (>128 μM) towards the MRC-5 lung fibroblast cell line. Another benzo-[d]-imidazo-[2,1-b]-thiazole compound, IT06, which possesses a 2,4-dichloro phenyl moiety, also showed significant activity with IC50 2.03 μM and IC90 15.22 μM against the tested strain of Mtb. Furthermore, the selected hits showed no activity towards a panel of non-tuberculous mycobacteria (NTM), thus suggesting a selective inhibition of Mtb by the tested imidazo-[2,1-b]-thiazole derivatives over the selected panel of NTM. Molecular docking and dynamics studies were also carried out for the most active compounds IT06 and IT10 in order to understand the putative binding pattern, as well as stability of the protein-ligand complex, against the selected target Pantothenate synthetase of Mtb.

Indole: A promising scaffold for the discovery and development of potential anti-tubercular agents

Indole-containing small molecules have been reported to have diverse pharmacological activities. The aromatic heterocyclic scaffold, which resembles various protein structures, has received attention from organic and medicinal chemists. Exploration of indole derivatives in drug discovery has rapidly yielded a vast array of biologically active compounds with broad therapeutic potential. Nature is the major source of indole scaffolds, but various classical and advanced synthesis methods for indoles have also been reported. One-pot synthesis is widely considered an efficient approach in synthetic organic chemistry and has been used to synthesize some indole compounds. The rapid emergence of drug-resistant tuberculosis is a major challenge to be addressed. Identifying novel targets and drug candidates for tuberculosis is therefore crucial. Researchers have extensively explored indole derivatives as potential anti-tubercular agents or drugs. Indole scaffolds containing the novel non-covalent (decaprenylphosphoryl-β-D-ribose2'-epimerase) DprE1 inhibitor 1,4-azaindole is currently in clinical trials to treat Mycobacterium tuberculosis. In addition, DG167 indazole sulfonamide with potent anti-tubercular activity is undergoing early-stage development in preclinical studies. Indole bearing cationic amphiphiles with high chemical diversity have been reported to depolarize and disrupt the mycobacterial membrane. Some indole-based compounds have potential inhibitory activities against distinct anti-tubercular targets, including the inhibition of cell wall synthesis, replication, transcription, and translation, as summarized in the graphical abstract. The success of computer-aided drug design in the fields of cancer and anti-viral drugs has accelerated in silico studies in antibacterial drug development. This review describes the sources of indole scaffolds, the potential for novel indole derivatives to serve as anti-tubercular agents, in silico findings, and proposed actions to facilitate the design of novel compounds with anti-tubercular activity.

Recent advances in DNA-encoded dynamic libraries

The DNA-encoded chemical library (DEL) has emerged as a powerful technology platform in drug discovery and is also gaining momentum in academic research. The rapid development of DNA-/DEL-compatible chemistries has greatly expanded the chemical space accessible to DELs. DEL technology has been widely adopted in the pharmaceutical industry and a number of clinical drug candidates have been identified from DEL selections. Recent innovations have combined DELs with other legacy and emerging techniques. Among them, the DNA-encoded dynamic library (DEDL) introduces DNA encoding into the classic dynamic combinatorial libraries (DCLs) and also integrates the principle of fragment-based drug discovery (FBDD), making DEDL a novel approach with distinct features from static DELs. In this Review, we provide a summary of the recently developed DEDL methods and their applications. Future developments in DEDLs are expected to extend the application scope of DELs to complex biological systems with unique ligand-discovery capabilities.

In Silico Structure-Based Approach for Group Efficiency Estimation in Fragment-Based Drug Design Using Evaluation of Fragment Contributions

The notion of a contribution of a specific group in an organic molecule's property and/or activity is both common in our thinking and is still not strictly correct due to the inherent non-additivity of free energy with respect to molecular fragments composing a molecule. The fragment- based drug discovery (FBDD) approach has proven to be fruitful in addressing the above notions. The main difficulty of the FBDD, however, is in its reliance on the low throughput and expensive experimental means of determining the fragment-sized molecules binding. In this article we propose a way to enhance the throughput and availability of the FBDD methods by judiciously using an in silico means of assessing the contribution to ligand-receptor binding energy of fragments of a molecule under question using a previously developed in silico Reverse Fragment Based Drug Discovery (R-FBDD) approach. It has been shown that the proposed structure-based drug discovery (SBDD) type of approach fills in the vacant niche among the existing in silico approaches, which mainly stem from the ligand-based drug discovery (LBDD) counterparts. In order to illustrate the applicability of the approach, our work retrospectively repeats the findings of the use case of an FBDD hit-to-lead project devoted to the experimentally based determination of additive group efficiency (GE)-an analog of ligand efficiency (LE) for a group in the molecule-using the Free-Wilson (FW) decomposition. It is shown that in using our in silico approach to evaluate fragment contributions of a ligand and to estimate GE one can arrive at similar decisions as those made using the experimentally determined activity-based FW decomposition. It is also shown that the approach is rather robust to the choice of the scoring function, provided the latter demonstrates a decent scoring power. We argue that the proposed approach of in silico assessment of GE has a wider applicability domain and expect that it will be widely applicable to enhance the net throughput of drug discovery based on the FBDD paradigm.

Comprehensive assessment of deep generative architectures for de novo drug design

Recently, deep learning (DL)-based de novo drug design represents a new trend in pharmaceutical research, and numerous DL-based methods have been developed for the generation of novel compounds with desired properties. However, a comprehensive understanding of the advantages and disadvantages of these methods is still lacking. In this study, the performances of different generative models were evaluated by analyzing the properties of the generated molecules in different scenarios, such as goal-directed (rediscovery, optimization and scaffold hopping of active compounds) and target-specific (generation of novel compounds for a given target) tasks. In overall, the DL-based models have significant advantages over the baseline models built by the traditional methods in learning the physicochemical property distributions of the training sets and may be more suitable for target-specific tasks. However, both the baselines and DL-based generative models cannot fully exploit the scaffolds of the training sets, and the molecules generated by the DL-based methods even have lower scaffold diversity than those generated by the traditional models. Moreover, our assessment illustrates that the DL-based methods do not exhibit obvious advantages over the genetic algorithm-based baselines in goal-directed tasks. We believe that our study provides valuable guidance for the effective use of generative models in de novo drug design.

Pantothenate and CoA biosynthesis in Apicomplexa and their promise as antiparasitic drug targets

The Apicomplexa phylum comprises thousands of distinct intracellular parasite species, including coccidians, haemosporidians, piroplasms, and cryptosporidia. These parasites are characterized by complex and divergent life cycles occupying a variety of host niches. Consequently, they exhibit distinct adaptations to the differences in nutritional availabilities, either relying on biosynthetic pathways or by salvaging metabolites from their host. Pantothenate (Pan, vitamin B5) is the precursor for the synthesis of an essential cofactor, coenzyme A (CoA), but among the apicomplexans, only the coccidian subgroup has the ability to synthesize Pan. While the pathway to synthesize CoA from Pan is largely conserved across all branches of life, there are differences in the redundancy of enzymes and possible alternative pathways to generate CoA from Pan. Impeding the scavenge of Pan and synthesis of Pan and CoA have been long recognized as potential targets for antimicrobial drug development, but in order to fully exploit these critical pathways, it is important to understand such differences. Recently, a potent class of pantothenamides (PanAms), Pan analogs, which target CoA-utilizing enzymes, has entered antimalarial preclinical development. The potential of PanAms to target multiple downstream pathways make them a promising compound class as broad antiparasitic drugs against other apicomplexans. In this review, we summarize the recent advances in understanding the Pan and CoA biosynthesis pathways, and the suitability of these pathways as drug targets in Apicomplexa, with a particular focus on the cyst-forming coccidian, Toxoplasma gondii, and the haemosporidian, Plasmodium falciparum.

Identification of isoform/domain-selective fragments from the selection of DNA-encoded dynamic library

DNA-encoded chemical library (DEL) has emerged to be a powerful ligand screening technology in drug discovery. Recently, we reported a DNA-encoded dynamic library (DEDL) approach that combines the principle of traditional dynamic combinatorial library (DCL) with DEL. DEDL has shown excellent potential in fragment-based ligand discovery with a variety of protein targets. Here, we further tested the utility of DEDL in identifying low molecular weight fragments that are selective for different isoforms or domains of the same protein family. A 10,000-member DEDL was selected against sirtuin-1, 2, and 5 (SIRT1, 2, 5) and the BD1 and BD2 domains of bromodomain 4 (BRD4), respectively. Albeit with modest potency, a series of isoform/domain-selective fragments were identified and the corresponding inhibitors were derived by fragment linking.

Use of molecular docking computational tools in drug discovery

Molecular docking has become an important component of the drug discovery process. Since first being developed in the 1980s, advancements in the power of computer hardware and the increasing number of and ease of access to small molecule and protein structures have contributed to the development of improved methods, making docking more popular in both industrial and academic settings. Over the years, the modalities by which docking is used to assist the different tasks of drug discovery have changed. Although initially developed and used as a standalone method, docking is now mostly employed in combination with other computational approaches within integrated workflows. Despite its invaluable contribution to the drug discovery process, molecular docking is still far from perfect. In this chapter we will provide an introduction to molecular docking and to the different docking procedures with a focus on several considerations and protocols, including protonation states, active site waters and consensus, that can greatly improve the docking results.

Schistosomiasis Drug Discovery in the Era of Automation and Artificial Intelligence

Schistosomiasis is a parasitic disease caused by trematode worms of the genus Schistosoma and affects over 200 million people worldwide. The control and treatment of this neglected tropical disease is based on a single drug, praziquantel, which raises concerns about the development of drug resistance. This, and the lack of efficacy of praziquantel against juvenile worms, highlights the urgency for new antischistosomal therapies. In this review we focus on innovative approaches to the identification of antischistosomal drug candidates, including the use of automated assays, fragment-based screening, computer-aided and artificial intelligence-based computational methods. We highlight the current developments that may contribute to optimizing research outputs and lead to more effective drugs for this highly prevalent disease, in a more cost-effective drug discovery endeavor.

General Theory of Fragment Linking in Molecular Design: Why Fragment Linking Rarely Succeeds and How to Improve Outcomes

Linking two fragments binding in nearby subpockets together has become an important technique in fragment-based drug discovery to optimize the binding potency of fragment hits. Despite the expected favorable translational and orientational entropic contribution to the binding free energy of the linked molecule, brute force enumeration of chemical linker for linking fragments is rarely successful, and the vast majority of linked molecules do not exhibit the expected gains of binding potency. In this paper, we examine the physical factors that contribute to the change of binding free energy from fragment linking and develop a method to rigorously calculate these different physical contributions. We find from these analyses that multiple confounding factors make successful fragment linking strategies rare, including (1) possible change of the binding mode of the fragments in the linked state compared to separate binding of the fragments, (2) unfavorable intramolecular strain energy of the bioactive conformation of the linked molecule, (3) unfavorable interaction between the linker and the protein, (4) favorable interaction energies between two fragments in solution when not chemically linked that offset the expected entropy loss for the formation of fragment pair, (5) complex compensating configurational entropic effects beyond the simplistic rotational and translational analysis. We here have applied a statistically mechanically rigorous approach to compute the fragment linking coefficients of 10 pharmaceutically interesting systems and quantify the contribution of each physical component to the binding free energy of the linked molecule. Based on these studies, we have found that the change in the relative configurational entropy of the two fragments in the protein binding pocket (a term neglected to our knowledge in all previous analyses) substantially offsets the favorable expected rotational and translational entropic contributions to the binding free energy of the linked molecule. This configurational restriction of the fragments in the binding pocket of the proteins is found to be, in our analysis, the dominant reason why most fragment linking strategies do not exhibit the expected gains of binding potency. These findings have further provided rich physical insights, which we expect should facilitate more successful fragment linking strategies to be formulated in the future.

Vitamin in the Crosshairs: Targeting Pantothenate and Coenzyme A Biosynthesis for New Antituberculosis Agents

Despite decades of dedicated research, there remains a dire need for new drugs against tuberculosis (TB). Current therapies are generations old and problematic. Resistance to these existing therapies results in an ever-increasing burden of patients with disease that is difficult or impossible to treat. Novel chemical entities with new mechanisms of action are therefore earnestly required. The biosynthesis of coenzyme A (CoA) has long been known to be essential in Mycobacterium tuberculosis (Mtb), the causative agent of TB. The pathway has been genetically validated by seminal studies in vitro and in vivo. In Mtb, the CoA biosynthetic pathway is comprised of nine enzymes: four to synthesize pantothenate (Pan) from l-aspartate and α-ketoisovalerate; five to synthesize CoA from Pan and pantetheine (PantSH). This review gathers literature reports on the structure/mechanism, inhibitors, and vulnerability of each enzyme in the CoA pathway. In addition to traditional inhibition of a single enzyme, the CoA pathway offers an antimetabolite strategy as a promising alternative. In this review, we provide our assessment of what appear to be the best targets, and, thus, which CoA pathway enzymes present the best opportunities for antitubercular drug discovery moving forward.

Inhibitors of pantothenate synthetase of Mycobacterium tuberculosis - a medicinal chemist perspective

Tuberculosis (TB), one of the most prevalent infections, is on the rise today. Although there are drugs available in the market to combat this lethal disorder, there are several shortcomings with the current drug regimen, such as prolonged treatment period, drug resistance, high cost, etc. Hence, it is inevitable for the current researchers across the globe to embark on new strategies for TB drug discovery, which will yield highly active low cost drugs with a shorter treatment period. To achieve this, novel strategies need to be adopted to discover new drugs. Pantothenate Synthetase (PS) is one such striking drug target in Mycobacterium tuberculosis (MTB). It was observed that the pantothenate biosynthetic pathway is crucial for the pathogenicity of MTB. Pantothenate is absent in mammals and needs to be obtained from dietary sources. Hence, the pantothenate biosynthesis pathway is an impending target for emerging new therapeutics to treat TB. Worldwide, several approaches have been implemented by researchers in the quest for these inhibitors such as high-throughput screening, simulating the reaction intermediate pantoyl adenylate, use of vibrant combinatorial chemistry, hybridization approach, virtual screening of databases, inhibitors based on the crystal structure of MTB PS, etc. The present review recapitulates current developments in PS inhibitors, important analogues of numerous metabolic intermediates, and newly established inhibitors with innumerable chemical structures.

Using a Fragment-Based Approach to Identify Alternative Chemical Scaffolds Targeting Dihydrofolate Reductase from Mycobacterium tuberculosis

Dihydrofolate reductase (DHFR), a key enzyme involved in folate metabolism, is a widely explored target in the treatment of cancer, immune diseases, bacteria, and protozoa infections. Although several antifolates have proved successful in the treatment of infectious diseases, they have been underexplored to combat tuberculosis, despite the essentiality of M. tuberculosis DHFR (MtDHFR). Herein, we describe an integrated fragment-based drug discovery approach to target MtDHFR that has identified hits with scaffolds not yet explored in any previous drug design campaign for this enzyme. The application of a SAR by catalog strategy of an in house library for one of the identified fragments has led to a series of molecules that bind to MtDHFR with low micromolar affinities. Crystal structures of MtDHFR in complex with compounds of this series demonstrated a novel binding mode that considerably differs from other DHFR antifolates, thus opening perspectives for the development of relevant MtDHFR inhibitors.

Selection of DNA-Encoded Dynamic Chemical Libraries for Direct Inhibitor Discovery

Dynamic combinatorial libraries (DCLs) is a powerful tool for ligand discovery in biomedical research; however, the application of DCLs has been hampered by their low diversity. Recently, the concept of DNA encoding has been employed in DCLs to create DNA-encoded dynamic libraries (DEDLs); however, all current DEDLs are limited to fragment identification, and a challenging process of fragment linking is required after selection. We report an anchor-directed DEDL approach that can identify full ligand structures from large-scale DEDLs. This method is also able to convert unbiased libraries into focused ones targeting specific protein classes. We demonstrated this method by selecting DEDLs against five proteins, and novel inhibitors were identified for all targets. Notably, several selective BD1/BD2 inhibitors were identified from the selections against bromodomain 4 (BRD4), an important anti-cancer drug target. This work may provide a broadly applicable method for inhibitor discovery.

From Substrate to Fragments to Inhibitor Active In Vivo against Staphylococcus aureus

Antibiotic resistance is a worldwide threat due to the decreasing supply of new antimicrobials. Novel targets and innovative strategies are urgently needed to generate pathbreaking drug compounds. NAD kinase (NADK) is essential for growth in most bacteria, as it supports critical metabolic pathways. Here, we report the discovery of a new class of antibacterials that targets bacterial NADK. We generated a series of small synthetic adenine derivatives to screen those harboring promising substituents in order to guide efficient fragment linking. This led to NKI1, a new lead compound inhibiting NADK that showed in vitro bactericidal activity against Staphylococcus aureus. In a murine model of infection, NKI1 restricted survival of the bacteria, including methicillin-resistant S. aureus. Collectively, these findings identify bacterial NADK as a potential drug target and NKI1 as a lead compound in the treatment of staphylococcal infections.

In silico Strategies to Support Fragment-to-Lead Optimization in Drug Discovery

Fragment-based drug (or lead) discovery (FBDD or FBLD) has developed in the last two decades to become a successful key technology in the pharmaceutical industry for early stage drug discovery and development. The FBDD strategy consists of screening low molecular weight compounds against macromolecular targets (usually proteins) of clinical relevance. These small molecular fragments can bind at one or more sites on the target and act as starting points for the development of lead compounds. In developing the fragments attractive features that can translate into compounds with favorable physical, pharmacokinetics and toxicity (ADMET-absorption, distribution, metabolism, excretion, and toxicity) properties can be integrated. Structure-enabled fragment screening campaigns use a combination of screening by a range of biophysical techniques, such as differential scanning fluorimetry, surface plasmon resonance, and thermophoresis, followed by structural characterization of fragment binding using NMR or X-ray crystallography. Structural characterization is also used in subsequent analysis for growing fragments of selected screening hits. The latest iteration of the FBDD workflow employs a high-throughput methodology of massively parallel screening by X-ray crystallography of individually soaked fragments. In this review we will outline the FBDD strategies and explore a variety of in silico approaches to support the follow-up fragment-to-lead optimization of either: growing, linking, and merging. These fragment expansion strategies include hot spot analysis, druggability prediction, SAR (structure-activity relationships) by catalog methods, application of machine learning/deep learning models for virtual screening and several de novo design methods for proposing synthesizable new compounds. Finally, we will highlight recent case studies in fragment-based drug discovery where in silico methods have successfully contributed to the development of lead compounds.

Theory and applications of differential scanning fluorimetry in early-stage drug discovery

Differential scanning fluorimetry (DSF) is an accessible, rapid, and economical biophysical technique that has seen many applications over the years, ranging from protein folding state detection to the identification of ligands that bind to the target protein. In this review, we discuss the theory, applications, and limitations of DSF, including the latest applications of DSF by ourselves and other researchers. We show that DSF is a powerful high-throughput tool in early drug discovery efforts. We place DSF in the context of other biophysical methods frequently used in drug discovery and highlight their benefits and downsides. We illustrate the uses of DSF in protein buffer optimization for stability, refolding, and crystallization purposes and provide several examples of each. We also show the use of DSF in a more downstream application, where it is used as an in vivo validation tool of ligand-target interaction in cell assays. Although DSF is a potent tool in buffer optimization and large chemical library screens when it comes to ligand-binding validation and optimization, orthogonal techniques are recommended as DSF is prone to false positives and negatives.

Seeking potent anti-tubercular agents: design and synthesis of substituted-N-(6-(4-(pyrazine-2-carbonyl)piperazine/homopiperazine-1-yl)pyridin-3-yl)benzamide derivatives as anti-tubercular agents

We herein report 27 pyrazinamide analogues as anti-tubercular agents, of which six exhibited excellent activity with IC₅₀ ≤ 2.18 μM and these were less toxic against HEK 293 cells.

In Silico Appraisal, Synthesis, Antibacterial Screening and DNA Cleavage for 1,2,5-thiadiazole Derivative

Background: :

Thiadiazole not only acts as “hydrogen binding domain” and “two-electron donor system” but also as constrained pharmacophore.

Methods::

The maleate salt of 2-((2-hydroxy-3-((4-morpholino-1, 2,5-thiadiazol-3-yl) oxy) propyl) amino)- 2-methylpropan-1-ol (TML-Hydroxy)(4) has been synthesized. This methodology involves preparation of 4-morpholino-1, 2,5-thiadiazol-3-ol by hydroxylation of 4-(4-chloro-1, 2,5-thiadiazol-3-yl) morpholine followed by condensation with 2-(chloromethyl) oxirane to afford 4-(4-(oxiran-2-ylmethoxy)-1,2,5-thiadiazol- 3-yl) morpholine. Oxirane ring of this compound was opened by treating with 2-amino-2-methyl propan-1- ol to afford the target compound TML-Hydroxy. Structures of the synthesized compounds have been elucidated by NMR, MASS, FTIR spectroscopy.

Results: :

The DSC study clearly showed that the compound 4-maleate salt is crystalline in nature. In vitro antibacterial inhibition and little potential for DNA cleavage of the compound 4 were explored. We extended our study to explore the inhibition mechanism by conducting molecular docking, ADMET and molecular dynamics analysis by using Schrödinger. The molecular docking for compound 4 showed better interactions with target 3IVX with docking score of -8.508 kcal/mol with respect to standard ciprofloxacin (docking score= -3.879 kcal/mol). TML-Hydroxy was obtained in silico as non-carcinogenic and non-AMES toxic with good percent human oral absorption profile (69.639%). TML-Hydroxy showed the moderate inhibition against Mycobacteria tuberculosis with MIC 25.00 μg/mL as well as moderate inhibition against S. aureus, Bacillus sps, K. Pneumoniae and E. coli species.

Conclusion: :

In view of the importance of the 1,2,5-thiadiazole moiety involved, this study would pave the way for future development of more effective analogs for applications in medicinal field.

Finding New Ways to Combat Multidrug-Resistant Tuberculosis

Tuberculosis (TB) is a major issue in global health and affects millions of people each year. Multidrug-resistant tuberculosis (MDR-TB) annually causes many deaths worldwide. Development of a way to diagnose and treat patients with MDR-TB can potentially reduce the incidence of the disease. The current study reviews the risk factors, pattern of progression, mechanism of resistance, and interaction between bacteria and the host immune system, which disrupts the immune response. It also targets the components of Mycobacterium tuberculosis (Mtb) and diagnosis and treatment options that could be available for clinical use in the near future. Mutations play an important role in development of MDR-TB and the selection of appropriate mutations can help to understand the type of resistance in patients to anti-TB drugs. In this way, they can be initially treated with proper and effective therapeutic choices, which can accelerate the course of treatment and improve patient health. Targeting the components and enzymes of Mtb is necessary for understanding bacterial survival and finding a way to destroy the pathogen and allow patients to recover faster and prevent the spread of disease, especially resistant strains.

Approaching Target Selectivity by De Novo Drug Design

Introduction: The development of drug candidates with a defined selectivity profile and a unique molecular structure is of fundamental interest for drug discovery. In contrast to the costly screening of large substance libraries, the targeted de novo design of a drug by using structural information of either the biological target and/or structure-activity relationship data of active modulators offers an efficient and intellectually appealing alternative. Areas covered: This review provides an overview on the different techniques of de novo drug design (ligand-based drug design, structure-based drug design, and fragment-based drug design) and highlights successful examples of this targeted approach toward selective modulators of therapeutically relevant targets. Expert opinion: De novo drug design has established itself as a very efficient method for the development of potent and selective modulators for a variety of different biological target classes. The ever-growing wealth of structural data on therapeutic targets will certainly further enhance the importance of de novo design for the drug discovery process in the future. However, a consistent use of the terminology of de novo drug design in the scientific literature should be sought.

Virtual Compound Libraries in Computer-Assisted Drug Discovery

The use of virtual compound libraries in computer-assisted drug discovery has gained in popularity and has already lead to numerous successes. Here, we examine key static and dynamic virtual library concepts that have been developed over the past decade. To facilitate the search for new drugs in the vastness of chemical space, there are still several hurdles to overcome, including the current difficulties in screening and parsing efficiency and the need for more reliable vendors and accurate synthesis prediction tools. These challenges should be tackled by both the developers of virtual libraries and by their users, in order for the exploration of chemical space to live up to its potential.

The Expanding Diversity of Mycobacterium tuberculosis Drug Targets

After decades of relative inactivity, a large increase in efforts to discover antitubercular therapeutics has brought insights into the biology of Mycobacterium tuberculosis (Mtb) and promising new drugs such as bedaquiline, which inhibits ATP synthase, and the nitroimidazoles delamanid and pretomanid, which inhibit both mycolic acid synthesis and energy production. Despite these advances, the drug discovery pipeline remains underpopulated. The field desperately needs compounds with novel mechanisms of action capable of inhibiting multi- and extensively drug -resistant Mtb (M/XDR-TB) and, potentially, nonreplicating Mtb with the hope of shortening the duration of required therapy. New knowledge about Mtb, along with new methods and technologies, has driven exploration into novel target areas, such as energy production and central metabolism, that diverge from the classical targets in macromolecular synthesis. Here, we review new small molecule drug candidates that act on these novel targets to highlight the methods and perspectives advancing the field. These new targets bring with them the aspiration of shortening treatment duration as well as a pipeline of effective regimens against XDR-TB, positioning Mtb drug discovery to become a model for anti-infective discovery.

Validating and enabling phosphoglycerate dehydrogenase (PHGDH) as a target for fragment-based drug discovery in PHGDH-amplified breast cancer

3-Phosphoglycerate dehydrogenase (PHGDH) has recently been identified as an attractive target in cancer therapy as it links upregulated glycolytic flux to increased biomass production in cancer cells. PHGDH catalyses the first step in the serine synthesis pathway and thus diverts glycolytic flux into serine synthesis. We have used siRNA-mediated suppression of PHGDH expression to show that PHGDH is a potential therapeutic target in PHGDH-amplified breast cancer. Knockdown caused reduced proliferation in the PHGDH-amplified cell line MDA-MB-468, whereas breast cancer cells with low PHGDH expression or with elevated PHGDH expression in the absence of genomic amplification were not affected. As a first step towards design of a chemical probe for PHGDH, we report a fragment-based drug discovery approach for the identification of PHGDH inhibitors. We designed a truncated PHGDH construct that gave crystals which diffracted to high resolution, and could be used for fragment soaking. 15 fragments stabilising PHGDH were identified using a thermal shift assay and validated by X-ray crystallography and ITC competition experiments to exhibit 1.5-26.2 mM affinity for PHGDH. A structure-guided fragment growing approach was applied to the PHGDH binders from the initial screen, yielding greater understanding of the binding site and suggesting routes to achieve higher affinity NAD-competitive inhibitors.

Hit Generation in TB Drug Discovery: From Genome to Granuloma

Current tuberculosis (TB) drug development efforts are not sufficient to end the global TB epidemic. Recent efforts have focused on the development of whole-cell screening assays because biochemical, target-based inhibitor screens during the last two decades have not delivered new TB drugs. Mycobacterium tuberculosis (Mtb), the causative agent of TB, encounters diverse microenvironments and can be found in a variety of metabolic states in the human host. Due to the complexity and heterogeneity of Mtb infection, no single model can fully recapitulate the in vivo conditions in which Mtb is found in TB patients, and there is no single “standard” screening condition to generate hit compounds for TB drug development. However, current screening assays have become more sophisticated as researchers attempt to mirror the complexity of TB disease in the laboratory. In this review, we describe efforts using surrogates and engineered strains of Mtb to focus screens on specific targets. We explain model culture systems ranging from carbon starvation to hypoxia, and combinations thereof, designed to represent the microenvironment which Mtb encounters in the human body. We outline ongoing efforts to model Mtb infection in the lung granuloma. We assess these different models, their ability to generate hit compounds, and needs for further TB drug development, to provide direction for future TB drug discovery.

Binding-Site Compatible Fragment Growing Applied to the Design of β2-Adrenergic Receptor Ligands

Fragment-based drug discovery is intimately linked to fragment extension approaches that can be accelerated using software for de novo design. Although computers allow for the facile generation of millions of suggestions, synthetic feasibility is however often neglected. In this study we computationally extended, chemically synthesized, and experimentally assayed new ligands for the β₂-adrenergic receptor (β₂AR) by growing fragment-sized ligands. In order to address the synthetic tractability issue, our in silico workflow aims at derivatized products based on robust organic reactions. The study started from the predicted binding modes of five fragments. We suggested a total of eight diverse extensions that were easily synthesized, and further assays showed that four products had an improved affinity (up to 40-fold) compared to their respective initial fragment. The described workflow, which we call "growing via merging" and for which the key tools are available online, can improve early fragment-based drug discovery projects, making it a useful creative tool for medicinal chemists during structure-activity relationship (SAR) studies.

Impact of a Central Scaffold on the Binding Affinity of Fragment Pairs Isolated from DNA-Encoded Self-Assembling Chemical Libraries

The screening of encoded self-assembling chemical libraries allows the identification of fragment pairs that bind to adjacent pockets on target proteins of interest. For practical applications, it is necessary to link these ligand pairs into discrete organic molecules, devoid of any nucleic acid component. Here we describe the discovery of a synergistic binding pair for acid alpha-1 glycoprotein and a chemical strategy for the identification of optimal linkers, connecting the two fragments. The procedure yielded a set of small organic ligands, the best of which exhibited a dissociation constant of 9.9 nm, as measured in solution by fluorescence polarization.

Ligand efficiency metrics in drug discovery: the pros and cons from a practical perspective

INTRODUCTION

Ligand efficiency metrics are almost universally accepted as a valuable indicator of compound quality and an aid to reduce attrition. Areas covered: In this review, the authors describe ligand efficiency metrics giving a balanced overview on their merits and points of weakness in order to enable the readers to gain an informed opinion. Relevant theoretical breakthroughs and drug-like properties are also illustrated. Several recent exemplary case studies are discussed in order to illustrate the main fields of application of ligand efficiency metrics. Expert opinion: As a medicinal chemist guide, ligand efficiency metrics perform in a context- and chemotype-dependent manner; thus, they should not be used as a magic box. Since the 'big bang' of efficiency metrics occurred more or less ten years ago and the average time to develop a new drug is over the same period, the next few years will give a clearer outlook on the increased rate of success, if any, gained by means of these new intriguing tools.

Enabling faster Go/No-Go decisions through secondary screens in anti-mycobacterial drug discovery

Management of tuberculosis, already a global health emergency, is becoming increasingly challenging with extensive misuse of second line drugs and their inaccessibility to eighty percent of the eligible patients. Rising statistics of antimicrobial resistance underscores the need for a set of completely new and more effective class of compounds with novel mechanisms of action that can be administered in combination to replace and shorten the present intensive six months regimen. In this review, we stress on the importance and the successes of phenotypic screening for discovery of anti-mycobacterial compound and discuss the importance of performing secondary screens and counter screens to get early estimate on compound's potentials for a successful development. We also highlight the recent advances and the related caveats in the assays that have been developed and discuss new screening modalities that can be incorporated during hit-selection to gain a quick insight into the mechanism of action, thus enabling quicker decisions in a hit triage.

Design, synthesis, molecular-docking and antimycobacterial evaluation of some novel 1,2,3-triazolyl xanthenones

As part of an ongoing effort to develop new antitubercular and antimicrobial agents, a series of substituted xanthenone derivatives (7a-p) were synthesized. Xanthenone derivatives (7a-p) were prepared via a one-pot three-component thermal cyclization reaction of β-naphthol (5), substituted 1-aryl-1H-[1,2,3]triazole-4-carbaldehydes (4a-h), and cyclic-1,3-diones (6a, b) in the presence of a catalytic amount of iodine. The newly synthesized compounds were characterized by IR, NMR, mass spectral data, and elemental analysis. These compounds (4a-h and 7a-p) were screened for in vitro antitubercular activity against the M. tuberculosis H37Rv (ATCC 27294) strain, for antibacterial activity against Gram-positive and Gram-negative strains, and for antifungal activity against a pathogenic strain of fungi. Among the compounds tested, most of them showed good to excellent antimicrobial and antitubercular activity. The active compounds displaying good potency in the MTB were further examined for toxicity in a HEK cell line. In addition, the structure and antitubercular activity relationship were further supported by in silico molecular-docking studies of the active compounds against the pantothenate synthetase (PS) enzyme of M. tuberculosis.