Rapid Gene Silencing Followed by Antimicrobial Susceptibility Testing for Target Validation in Antibiotic Discovery.

Mycobacterium tuberculosis is the main causative agent of tuberculosis (TB)-an ancient yet widespread global infectious disease to which 1.6 million people lost their lives in 2021. Antimicrobial resistance (AMR) has been an ongoing crisis for decades; 4.95 million deaths were associated with antibiotic resistance in 2019. While AMR is a multi-faceted problem, drug discovery is an urgent part of the solution and is at the forefront of modern research.The landscape of drug discovery for TB has undoubtedly been transformed by the development of high-throughput gene-silencing techniques that enable interrogation of every gene in the genome, and their relative contribution to fitness, virulence, and AMR. A recent advance in this area is CRISPR interference (CRISPRi). The application of this technique to antimicrobial susceptibility testing (AST) is the subject of ongoing research in basic science.CRISPRi technology can be used in conjunction with the high-throughput SPOT-culture growth inhibition assay (HT-SPOTi) to rapidly evaluate and assess gene essentiality including non-essential, conditionally essential (by using appropriate culture conditions), and essential genes. In addition, the HT-SPOTi method can develop drug susceptibility and drug resistance profiles.This technology is further useful for drug discovery groups who have designed target-based inhibitors rationally and wish to validate the primary mechanisms of their novel compounds' antibiotic action against the proposed target.

Adapt-cMolGPT: A Conditional Generative Pre-Trained Transformer with Adapter-Based Fine-Tuning for Target-Specific Molecular Generation.

Small-molecule drug design aims to generate compounds that target specific proteins, playing a crucial role in the early stages of drug discovery. Recently, research has emerged that utilizes the GPT model, which has achieved significant success in various fields to generate molecular compounds. However, due to the persistent challenge of small datasets in the pharmaceutical field, there has been some degradation in the performance of generating target-specific compounds. To address this issue, we propose an enhanced target-specific drug generation model, Adapt-cMolGPT, which modifies molecular representation and optimizes the fine-tuning process. In particular, we introduce a new fine-tuning method that incorporates an adapter module into a pre-trained base model and alternates weight updates by sections. We evaluated the proposed model through multiple experiments and demonstrated performance improvements compared to previous models. In the experimental results, Adapt-cMolGPT generated a greater number of novel and valid compounds compared to other models, with these generated compounds exhibiting properties similar to those of real molecular data. These results indicate that our proposed method is highly effective in designing drugs targeting specific proteins.

The Millennia-Long Development of Drugs Associated with the 80-Year-Old Artificial Intelligence Story: The Therapeutic Big Bang?

The journey of drug discovery (DD) has evolved from ancient practices to modern technology-driven approaches, with Artificial Intelligence (AI) emerging as a pivotal force in streamlining and accelerating the process. Despite the vital importance of DD, it faces challenges such as high costs and lengthy timelines. This review examines the historical progression and current market of DD alongside the development and integration of AI technologies. We analyse the challenges encountered in applying AI to DD, focusing on drug design and protein-protein interactions. The discussion is enriched by presenting models that put forward the application of AI in DD. Three case studies are highlighted to demonstrate the successful application of AI in DD, including the discovery of a novel class of antibiotics and a small-molecule inhibitor that has progressed to phase II clinical trials. These cases underscore the potential of AI to identify new drug candidates and optimise the development process. The convergence of DD and AI embodies a transformative shift in the field, offering a path to overcome traditional obstacles. By leveraging AI, the future of DD promises enhanced efficiency and novel breakthroughs, heralding a new era of medical innovation even though there is still a long way to go.

Discovery of CDC42 Inhibitors with a Favorable Pharmacokinetic Profile and Anticancer In Vivo Efficacy.

We previously reported trisubstituted pyrimidine lead compounds, namely, ARN22089 and ARN25062, which block the interaction between CDC42 with its specific downstream effector, a PAK protein. This interaction is crucial for the progression of multiple tumor types. Such inhibitors showed anticancer efficacy in vivo. Here, we describe a second class of CDC42 inhibitors with favorable drug-like properties. Out of the 25 compounds here reported, compound 15 (ARN25499) stands out as the best lead compound with an improved pharmacokinetic profile, increased bioavailability, and efficacy in an in vivo PDX tumor mouse model. Our results indicate that these CDC42 inhibitors represent a promising chemical class toward the discovery of anticancer drugs, with ARN25499 as an additional lead candidate for preclinical development.

Induced proximity in biology and therapeutics.

In celebration of Cell Chemical Biology's 30th anniversary, the editorial team presents a special issue, "Induced proximity in biology and therapeutics," to highlight a rapidly growing research area with significant promise for scientific discovery in fundamental biology and the development of innovative therapeutic strategies for modulating the "undruggable" targets.

Application of induced proximity for therapeutic discovery.

In this Voices piece, the Cell Chemical Biology editors ask researchers from a range of backgrounds: what are some exciting discoveries in the induced proximity field and the next frontier for therapeutic development?

Discovery of oral chemotherapeutic reversal agents for treating multidrug resistance cancer.

The major limitation of cancer treatment is multidrug resistance (MDR), which leads to the inactivation of chemotherapeutic drugs and greater than 90% mortality. To solve this ordeal, we applied ligand-based drug design and bioiosteric replacement strategy from an indazole to a pyrazole ring to discover compounds 27 and 43 with good potential for reversing drug resistance in combination with paclitaxel, and their reversal fold values were 53.2 and 51.0 at 5 µM, respectively, against an MDR cancer cell line (KBvin). Based on the PK profile results, we selected compound 43 with a longer half-life for mechanistic and animal experiments. Combination treatment with compound 43 and paclitaxel-induced apoptosis and enhanced subG1 by decreasing mitochondrial membrane potential in KBvin cells. In addition, 43 also inhibited P-gp function by interfering with ATPase activity. Meanwhile, cotreatment with compound 43 and paclitaxel significantly suppressed tumor growth (TGI = 55.5%) at a dose of 200 mg/kg (PO) in a xenograft model and showed no obvious liver or kidney toxicity by H&E staining. Overall, compound 43 may serve as a safe and effective oral resistance reversal chemotherapeutic agent.

The recent discovery of a promising pharmacological scaffold derived from carvacrol: A review.

Carvacrol, called CA, is a dynamic phytoconstituent characterized by a phenol ring abundantly sourced from various natural reservoirs. This versatile scaffold serves as a pivotal template for the design and synthesis of novel drug molecules, harboring promising biological activities. The active sites positioned at C-4, C-6, and the hydroxyl group (-OH) of CA offer fertile ground for creating potent drug candidates from a pharmacological standpoint. In this comprehensive review, we delve into diverse synthesis pathways and explore the biological activity of CA derivatives. We aim to illuminate the potential of these derivatives in discovering and developing efficacious treatments against a myriad of life-threatening diseases. By scrutinizing the structural modifications and pharmacophore placements that enhance the activity of CA derivatives, we aspire to inspire the innovation of novel therapeutics with heightened potency and effectiveness.

Discovery of a potent and selective JAK1-targeting PROTAC degrader with anti-tumor activities.

Aberrant activation of the JAK-STAT pathway is evident in various human diseases including cancers. Proteolysis targeting chimeras (PROTACs) provide an attractive strategy for developing novel JAK-targeting drugs. Herein, a series of CRBN-directed JAK-targeting PROTACs were designed and synthesized utilizing a JAK1/JAK2 dual inhibitor-momelotinib as the warhead. The most promising compound 10c exhibited both good enzymatic potency and cellular antiproliferative effects. Western blot analysis revealed that compound 10c effectively and selectively degraded JAK1 in a proteasome-dependent manner (DC50 = 214 nM). Moreover, PROTAC 10c significantly suppressed JAK1 and its key downstream signaling. Together, compound 10c may serve as a novel lead compound for antitumor drug discovery.

Antibacterial properties of natural products from marine fungi reported between 2012 and 2023: a review.

The oceans are rich in diverse microorganisms, animals, and plants. This vast biological complexity is a major source of unique secondary metabolites. In particular, marine fungi are a promising source of compounds with unique structures and potent antibacterial properties. Over the last decade, substantial progress has been made to identify these valuable antibacterial agents. This review summarizes the chemical structures and antibacterial activities of 223 compounds identified between 2012 and 2023. These compounds, effective against various bacteria including drug-resistant strains such as methicillin-resistant Staphylococcus aureus, exhibit strong potential as antibacterial therapeutics. The review also highlights the relevant challenges in transitioning from drug discovery to product commercialization. Emerging technologies such as metagenomics and synthetic biology are proposed as viable solutions. This paper sets the stage for further research on antibacterial compounds derived from marine fungi and advocates a multidisciplinary approach to combat drug-resistant bacteria.

Key challenges in TB drug discovery: A perspective.

Over the past 2000 years, tuberculosis (TB) has been responsible for more deaths than any other infectious disease. In recent years, there has been a recovery of research and development (R&D) efforts focused on TB drugs. This is driven by the pressing need to combat the global spread of the disease and develop improved therapies for both drug-sensitive and drug-resistant strains. Many new TB drug candidates have recently entered clinical trials, marking the beginning of a rebirth in this area after decades of neglect. The problem is that very few of the hundreds of compounds identified each year as potential anti-TB drugs really make it to the clinical development stage. This perspective focuses on the primary obstacles and approaches involved in the development of new medications for TB. This will help medicinal chemists better understand TB drug challenges and develop novel drug candidates.

Discovery of a potent, orally available furopyridine derivative as a novel selective bromodomain and extra-terminal domain (BET)-first bromodomain (BD1) inhibitor.

We explored novel immunosuppressive agents with immune tolerance using a phenotypic drug discovery strategy, focusing on costimulatory molecules in T cells, and obtained triazolothienodiazepine derivatives. Their mechanism of action is to inhibit the bromodomain and extra-terminal domain (BET) family, as we have previously reported. Selective inhibition of the first bromodomain (BD1) of the BET family is expected to exert antitumor and immunosuppressive effects, similar to BET inhibitors. This study identified furopyridine derivatives 7 and 8 with high BD1 inhibitory activity and high selectivity over BD2. Compound 7 was found to be orally bioavailable and exhibited anti-inflammatory activity in a lipopolysaccharide-induced model.

Discovery of a potent orally available pyrazolopyridone derivative as a novel selective bromodomain and extra-terminal domain (BET)-first bromodomain (BD1) inhibitor.

Clinical studies have shown that inhibitors of bromodomain and extra-terminal domain (BET) proteins, particularly BRD4, have antitumor activity and efficacy. The BET protein has two domains, BD1 and BD2, and we previously focused on BD1 and reported orally bioavailable BD1-selective inhibitors. In this study, we obtained a BD1 inhibitor, a more potent and highly selective pyrazolopyridone derivative 13a, and confirmed its in vivo efficacy.

Morphological profiling for drug discovery in the era of deep learning.

Morphological profiling is a valuable tool in phenotypic drug discovery. The advent of high-throughput automated imaging has enabled the capturing of a wide range of morphological features of cells or organisms in response to perturbations at the single-cell resolution. Concurrently, significant advances in machine learning and deep learning, especially in computer vision, have led to substantial improvements in analyzing large-scale high-content images at high throughput. These efforts have facilitated understanding of compound mechanism of action, drug repurposing, characterization of cell morphodynamics under perturbation, and ultimately contributing to the development of novel therapeutics. In this review, we provide a comprehensive overview of the recent advances in the field of morphological profiling. We summarize the image profiling analysis workflow, survey a broad spectrum of analysis strategies encompassing feature engineering- and deep learning-based approaches, and introduce publicly available benchmark datasets. We place a particular emphasis on the application of deep learning in this pipeline, covering cell segmentation, image representation learning, and multimodal learning. Additionally, we illuminate the application of morphological profiling in phenotypic drug discovery and highlight potential challenges and opportunities in this field.

Small molecule in situ resin capture provides a compound first approach to natural product discovery.

Culture-based microbial natural product discovery strategies fail to realize the extraordinary biosynthetic potential detected across earth's microbiomes. Here we introduce Small Molecule In situ Resin Capture (SMIRC), a culture-independent method to obtain natural products directly from the environments in which they are produced. We use SMIRC to capture numerous compounds including two new carbon skeletons that were characterized using NMR and contain structural features that are, to the best of our knowledge, unprecedented among natural products. Applications across diverse marine habitats reveal biome-specific metabolomic signatures and levels of chemical diversity in concordance with sequence-based predictions. Expanded deployments, in situ cultivation, and metagenomics facilitate compound discovery, enhance yields, and link compounds to candidate producing organisms, although microbial community complexity creates challenges for the later. This compound-first approach to natural product discovery provides access to poorly explored chemical space and has implications for drug discovery and the detection of chemically mediated biotic interactions.

Drug repurposing: A multi targetted approach to treat cardiac disease from existing classical drugs to modern drug discovery.

Cardiovascular diseases (CVDs) are characterized by abnormalities in the heart, blood vessels, and blood flow. CVDs comprise a diverse set of health issues. There are several types of CVDs like stroke, endothelial dysfunction, thrombosis, atherosclerosis, plaque instability and heart failure. Identification of a new drug for heart disease takes longer duration and its safety efficacy test takes even longer duration of research and approval. This chapter explores drug repurposing, nano-therapy, and plant-based treatments for managing CVDs from existing drugs which saves time and safety issues with testing new drugs. Existing drugs like statins, ACE inhibitor, warfarin, beta blockers, aspirin and metformin have been found to be useful in treating cardiac disease. For better drug delivery, nano therapy is opening new avenues for cardiac research by targeting interleukin (IL), TNF and other proteins by proteome interactome analysis. Nanoparticles enable precise delivery to atherosclerotic plaques, inflammation areas, and damaged cardiac tissues. Advancements in nano therapeutic agents, such as drug-eluting stents and drug-loaded nanoparticles are transforming CVDs management. Plant-based treatments, containing phytochemicals from Botanical sources, have potential cardiovascular benefits. These phytochemicals can mitigate risk factors associated with CVDs. The integration of these strategies opens new avenues for personalized, effective, and minimally invasive cardiovascular care. Altogether, traditional drugs, phytochemicals along with nanoparticles can revolutionize the future cardiac health care by identifying their signaling pathway, mechanism and interactome analysis.

Optimizing in silico drug discovery: simulation of connected differential expression signatures and applications to benchmarking.

BACKGROUND: We present a novel simulation method for generating connected differential expression signatures. Traditional methods have struggled with the lack of reliable benchmarking data and biases in drug-disease pair labeling, limiting the rigorous benchmarking of connectivity-based approaches. OBJECTIVE: Our aim is to develop a simulation method based on a statistical framework that allows for adjustable levels of parametrization, especially the connectivity, to generate a pair of interconnected differential signatures. This could help to address the issue of benchmarking data availability for connectivity-based drug repurposing approaches. METHODS: We first detailed the simulation process and how it reflected real biological variability and the interconnectedness of gene expression signatures. Then, we generated several datasets to enable the evaluation of different existing algorithms that compare differential expression signatures, providing insights into their performance and limitations. RESULTS: Our findings demonstrate the ability of our simulation to produce realistic data, as evidenced by correlation analyses and the log2 fold-change distribution of deregulated genes. Benchmarking reveals that methods like extreme cosine similarity and Pearson correlation outperform others in identifying connected signatures. CONCLUSION: Overall, our method provides a reliable tool for simulating differential expression signatures. The data simulated by our tool encompass a wide spectrum of possibilities to challenge and evaluate existing methods to estimate connectivity scores. This may represent a critical gap in connectivity-based drug repurposing research because reliable benchmarking data are essential for assessing and advancing in the development of new algorithms. The simulation tool is available as a R package (General Public License (GPL) license) at https://github.com/cgonzalez-gomez/cosimu.

Mining biosynthetic gene clusters in Paenibacillus genomes to discover novel antibiotics.

BACKGROUND: Bacterial antimicrobial resistance poses a severe threat to humanity, necessitating the urgent development of new antibiotics. Recent advances in genome sequencing offer new avenues for antibiotic discovery. Paenibacillus genomes encompass a considerable array of antibiotic biosynthetic gene clusters (BGCs), rendering these species as good candidates for genome-driven novel antibiotic exploration. Nevertheless, BGCs within Paenibacillus genomes have not been extensively studied. RESULTS: We conducted an analysis of 554 Paenibacillus genome sequences, sourced from the National Center for Biotechnology Information database, with a focused investigation involving 89 of these genomes via antiSMASH. Our analysis unearthed a total of 848 BGCs, of which 716 (84.4%) were classified as unknown. From the initial pool of 554 Paenibacillus strains, we selected 26 available in culture collections for an in-depth evaluation. Genomic scrutiny of these selected strains unveiled 255 BGCs, encoding non-ribosomal peptide synthetases, polyketide synthases, and bacteriocins, with 221 (86.7%) classified as unknown. Among these strains, 20 exhibited antimicrobial activity against the gram-positive bacterium Micrococcus luteus, yet only six strains displayed activity against the gram-negative bacterium Escherichia coli. We proceeded to focus on Paenibacillus brasilensis, which featured five new BGCs for further investigation. To facilitate detailed characterization, we constructed a mutant in which a single BGC encoding a novel antibiotic was activated while simultaneously inactivating multiple BGCs using a cytosine base editor (CBE). The novel antibiotic was found to be localized to the cell wall and demonstrated activity against both gram-positive bacteria and fungi. The chemical structure of the new antibiotic was elucidated on the basis of ESIMS, 1D and 2D NMR spectroscopic data. The novel compound, with a molecular weight of 926, was named bracidin. CONCLUSIONS: This study outcome highlights the potential of Paenibacillus species as valuable sources for novel antibiotics. In addition, CBE-mediated dereplication of antibiotics proved to be a rapid and efficient method for characterizing novel antibiotics from Paenibacillus species, suggesting that it will greatly accelerate the genome-based development of new antibiotics.

Advances in Integrated Multi-omics Analysis for Drug-Target Identification.

As an essential component of modern drug discovery, the role of drug-target identification is growing increasingly prominent. Additionally, single-omics technologies have been widely utilized in the process of discovering drug targets. However, it is difficult for any single-omics level to clearly expound the causal connection between drugs and how they give rise to the emergence of complex phenotypes. With the progress of large-scale sequencing and the development of high-throughput technologies, the tendency in drug-target identification has shifted towards integrated multi-omics techniques, gradually replacing traditional single-omics techniques. Herein, this review centers on the recent advancements in the domain of integrated multi-omics techniques for target identification, highlights the common multi-omics analysis strategies, briefly summarizes the selection of multi-omics analysis tools, and explores the challenges of existing multi-omics analyses, as well as the applications of multi-omics technology in drug-target identification.

OLB-AC: toward optimizing ligand bioactivities through deep graph learning and activity cliffs.

MOTIVATION: Deep graph learning (DGL) has been widely employed in the realm of ligand-based virtual screening. Within this field, a key hurdle is the existence of activity cliffs (ACs), where minor chemical alterations can lead to significant changes in bioactivity. In response, several DGL models have been developed to enhance ligand bioactivity prediction in the presence of ACs. Yet, there remains a largely unexplored opportunity within ACs for optimizing ligand bioactivity, making it an area ripe for further investigation. RESULTS: We present a novel approach to simultaneously predict and optimize ligand bioactivities through DGL and ACs (OLB-AC). OLB-AC possesses the capability to optimize ligand molecules located near ACs, providing a direct reference for optimizing ligand bioactivities with the matching of original ligands. To accomplish this, a novel attentive graph reconstruction neural network and ligand optimization scheme are proposed. Attentive graph reconstruction neural network reconstructs original ligands and optimizes them through adversarial representations derived from their bioactivity prediction process. Experimental results on nine drug targets reveal that out of the 667 molecules generated through OLB-AC optimization on datasets comprising 974 low-activity, noninhibitor, or highly toxic ligands, 49 are recognized as known highly active, inhibitor, or nontoxic ligands beyond the datasets' scope. The 27 out of 49 matched molecular pairs generated by OLB-AC reveal novel transformations not present in their training sets. The adversarial representations employed for ligand optimization originate from the gradients of bioactivity predictions. Therefore, we also assess OLB-AC's prediction accuracy across 33 different bioactivity datasets. Results show that OLB-AC achieves the best Pearson correlation coefficient (r2) on 27/33 datasets, with an average improvement of 7.2%-22.9% against the state-of-the-art bioactivity prediction methods. AVAILABILITY AND IMPLEMENTATION: The code and dataset developed in this work are available at github.com/Yueming-Yin/OLB-AC.