Linking High-Throughput Screens to Identify MoAs and Novel Inhibitors of Mycobacterium tuberculosis Dihydrofolate Reductase

Artificial intelligence applications in the diagnosis and treatment of bacterial infections

The diagnosis and treatment of bacterial infections in the medical and public health field in the 21st century remain significantly challenging. Artificial Intelligence (AI) has emerged as a powerful new tool in diagnosing and treating bacterial infections. AI is rapidly revolutionizing epidemiological studies of infectious diseases, providing effective early warning, prevention, and control of outbreaks. Machine learning models provide a highly flexible way to simulate and predict the complex mechanisms of pathogen-host interactions, which is crucial for a comprehensive understanding of the nature of diseases. Machine learning-based pathogen identification technology and antimicrobial drug susceptibility testing break through the limitations of traditional methods, significantly shorten the time from sample collection to the determination of result, and greatly improve the speed and accuracy of laboratory testing. In addition, AI technology application in treating bacterial infections, particularly in the research and development of drugs and vaccines, and the application of innovative therapies such as bacteriophage, provides new strategies for improving therapy and curbing bacterial resistance. Although AI has a broad application prospect in diagnosing and treating bacterial infections, significant challenges remain in data quality and quantity, model interpretability, clinical integration, and patient privacy protection. To overcome these challenges and, realize widespread application in clinical practice, interdisciplinary cooperation, technology innovation, and policy support are essential components of the joint efforts required. In summary, with continuous advancements and in-depth application of AI technology, AI will enable doctors to more effectivelyaddress the challenge of bacterial infection, promoting the development of medical practice toward precision, efficiency, and personalization; optimizing the best nursing and treatment plans for patients; and providing strong support for public health safety.

Redirecting raltitrexed from cancer cell thymidylate synthase to Mycobacterium tuberculosis phosphopantetheinyl transferase

There is a compelling need to find drugs active against Mycobacterium tuberculosis (Mtb). 4'-Phosphopantetheinyl transferase (PptT) is an essential enzyme in Mtb that has attracted interest as a potential drug target. We optimized a PptT assay, used it to screen 422,740 compounds, and identified raltitrexed, an antineoplastic antimetabolite, as the most potent PptT inhibitor yet reported. While trying unsuccessfully to improve raltitrexed's ability to kill Mtb and remove its ability to kill human cells, we learned three lessons that may help others developing antibiotics. First, binding of raltitrexed substantially changed the configuration of the PptT active site, complicating molecular modeling of analogs based on the unliganded crystal structure or the structure of cocrystals with inhibitors of another class. Second, minor changes in the raltitrexed molecule changed its target in Mtb from PptT to dihydrofolate reductase (DHFR). Third, the structure-activity relationship for over 800 raltitrexed analogs only became interpretable when we quantified and characterized the compounds' intrabacterial accumulation and transformation.

Pharmacological validation of dihydrofolate reductase as a drug target in Mycobacterium abscessus

The Mycobacterium abscessus drug development pipeline is poorly populated, with particularly few validated target-lead couples to initiate de novo drug discovery. Trimethoprim, an inhibitor of dihydrofolate reductase (DHFR) used for the treatment of a range of bacterial infections, is not active against M. abscessus. Thus, evidence that M. abscessus DHFR is vulnerable to pharmacological intervention with a small molecule inhibitor is lacking. Here, we show that the pyrrolo-quinazoline PQD-1, previously identified as a DHFR inhibitor active against Mycobacterium tuberculosis, exerts whole cell activity against M. abscessus. Enzyme inhibition studies showed that PQD-1, in contrast to trimethoprim, is a potent inhibitor of M. abscessus DHFR and over-expression of DHFR causes resistance to PQD-1, providing biochemical and genetic evidence that DHFR is a vulnerable target and mediates PQD-1's growth inhibitory activity in M. abscessus. As observed in M. tuberculosis, PQD-1 resistant mutations mapped to the folate pathway enzyme thymidylate synthase (TYMS) ThyA. Like trimethoprim in other bacteria, PQD-1 synergizes with the dihydropteroate synthase (DHPS) inhibitor sulfamethoxazole (SMX), offering an opportunity to exploit the successful dual inhibition of the folate pathway and develop similarly potent combinations against M. abscessus. PQD-1 is active against subspecies of M. abscessus and a panel of clinical isolates, providing epidemiological validation of the target-lead couple. Leveraging a series of PQD-1 analogs, we have demonstrated a dynamic structure-activity relationship (SAR). Collectively, the results identify M. abscessus DHFR as an attractive target and PQD-1 as a chemical starting point for the discovery of novel drugs and drug combinations that target the folate pathway in M. abscessus.

Exploration and Biological Evaluation of 1,3-Diamino-7H-pyrrol[3,2-f]quinazoline Derivatives as Dihydrofolate Reductase Inhibitors

Dihydrofolate reductase (DHFR), a core enzyme of folate metabolism, plays a crucial role in the biosynthesis of purines and thymidylate for cell proliferation and growth in both prokaryotic and eukaryotic cells. However, the development of new DHFR inhibitors is challenging due to the limited number of scaffolds available for drug development. Hence, we designed and synthesized a new class of DHFR inhibitors with a 1,3-diamino-7H-pyrrol[3,2-f]quinazoline derivative (PQD) structure bearing condensed rings. Compound 6r exhibited therapeutic effects on mouse models of systemic infection and thigh infection caused by methicillin-resistant Staphylococcus aureus (MRSA) ATCC 43300. Moreover, methyl-modified PQD compound 8a showed a strong efficacy in a murine model of breast cancer, which was better than the effects of taxol. The findings showcased in this study highlight the promising capabilities of novel DHFR inhibitors in addressing bacterial infections as well as breast cancer.

Sensing of Antibiotic-Bacteria Interactions

Sensing of antibiotic-bacteria interactions is an important area of research that has gained significant attention in recent years. Antibiotic resistance is a major public health concern, and it is essential to develop new strategies for detecting and monitoring bacterial responses to antibiotics in order to maintain effective antibiotic development and antibacterial treatment. This review summarizes recent advances in sensing strategies for antibiotic-bacteria interactions, which are divided into two main parts: studies on the mechanism of action for sensitive bacteria and interrogation of the defense mechanisms for resistant ones. In conclusion, this review provides an overview of the present research landscape concerning antibiotic-bacteria interactions, emphasizing the potential for method adaptation and the integration of machine learning techniques in data analysis, which could potentially lead to a transformative impact on mechanistic studies within the field.

Antibiotic discovery in the artificial intelligence era

As the global burden of antibiotic resistance continues to grow, creative approaches to antibiotic discovery are needed to accelerate the development of novel medicines. A rapidly progressing computational revolution-artificial intelligence-offers an optimistic path forward due to its ability to alleviate bottlenecks in the antibiotic discovery pipeline. In this review, we discuss how advancements in artificial intelligence are reinvigorating the adoption of past antibiotic discovery models-namely natural product exploration and small molecule screening. We then explore the application of contemporary machine learning approaches to emerging areas of antibiotic discovery, including antibacterial systems biology, drug combination development, antimicrobial peptide discovery, and mechanism of action prediction. Lastly, we propose a call to action for open access of high-quality screening datasets and interdisciplinary collaboration to accelerate the rate at which machine learning models can be trained and new antibiotic drugs can be developed.

An Insight on the Prospect of Quinazoline and Quinazolinone Derivatives as Anti-tubercular Agents

Multiple potential drugs have been developed based on the heterocyclic molecules for the treatment of different symptoms. Among the existing heterocyclic molecules, quinazoline and quinazolinone derivatives have been found to exhibit extensive pharmacological and biological characteristics. One significant property of these molecules is their potency as anti-tubercular agents. Thus, both quinazoline and quinazolinone derivatives are modified using different functional groups as substituents for investigating their anti-tubercular activities. We present a summary of the reported anti-tubercular drugs, designed using quinazoline and quinazolinone derivatives, in this review.

Use of Artificial Intelligence and Machine Learning for Discovery of Drugs for Neglected Tropical Diseases

Neglected tropical diseases continue to create high levels of morbidity and mortality in a sizeable fraction of the world’s population, despite ongoing research into new treatments. Some of the most important technological developments that have accelerated drug discovery for diseases of affluent countries have not flowed down to neglected tropical disease drug discovery. Pharmaceutical development business models, cost of developing new drug treatments and subsequent costs to patients, and accessibility of technologies to scientists in most of the affected countries are some of the reasons for this low uptake and slow development relative to that for common diseases in developed countries. Computational methods are starting to make significant inroads into discovery of drugs for neglected tropical diseases due to the increasing availability of large databases that can be used to train ML models, increasing accuracy of these methods, lower entry barrier for researchers, and widespread availability of public domain machine learning codes. Here, the application of artificial intelligence, largely the subset called machine learning, to modelling and prediction of biological activities and discovery of new drugs for neglected tropical diseases is summarized. The pathways for the development of machine learning methods in the short to medium term and the use of other artificial intelligence methods for drug discovery is discussed. The current roadblocks to, and likely impacts of, synergistic new technological developments on the use of ML methods for neglected tropical disease drug discovery in the future are also discussed.

Exploring new targets and chemical space with affinity selection-mass spectrometry

Affinity selection-mass spectrometry (AS-MS) is a high-throughput screening (HTS) technique for drug discovery that enables rapid screening of large collections of compounds to identify ligands for a specific biomolecular target. AS-MS is a binding assay that is insensitive to the functional effects a ligand might have, which is important because it lets us identify novel ligands irrespective of their binding site. This approach is gaining popularity, notably due to its role in the emergence of useful agents for targeted protein degradation. This Perspective highlights the use of AS-MS techniques to explore broad chemical space and identify small-molecule ligands for biological targets that have proven challenging to address with other screening paradigms. We present chemical structures of reported AS-MS hits to illustrate the potential of this screening approach to deliver high-quality hits for further optimization. AS-MS has, thus, evolved from being an infrequent alternative to traditional HTS or DNA-encoded library strategies to now firmly establishing itself as a HTS approach for drug discovery.

Design, Synthesis and Evaluation of 2,4-Diaminoquinazoline Derivatives as Potential Tubulin Polymerization Inhibitors

Microtubules are highly dynamic polymers composed of α- and β-tubulin proteins that have been shown to be potential therapeutic targets for the development of anticancer drugs. Currently, a wide variety of chemically diverse agents that bind to β-tubulin have been reported. Nocodazole (NZ) and colchicine (COL) are well-known tubulin-depolymerizing agents that have close binding sites in the β-tubulin. In this study, we designed and synthesized a set of nine 2,4-diaminoquinazoline derivatives that could occupy both NZ and COL binding sites. The synthesized compounds were evaluated for their antiproliferative activities against five cancer cell lines (PC-3, HCT-15, MCF-7, MDA-MB-231, and SK-LU-1), a noncancerous one (COS-7), and peripheral blood mononuclear cells (PBMC). The effect of compounds 4 e and 4 i on tubulin organization and polymerization was analyzed on the SK-LU-1 cell line by indirect immunofluorescence, western blotting, and tubulin polymerization assays. Our results demonstrated that both compounds exert their antiproliferative activity by inhibiting tubulin polymerization. Finally, a possible binding pose of 4 i in the NZ/COL binding site was determined by using molecular docking and molecular dynamics (MD) approaches. To our knowledge, this is the first report of non-N-substituted 2,4-diaminoquinazoline derivatives with the ability to inhibit tubulin polymerization.

The application of machine learning techniques to innovative antibacterial discovery and development

INTRODUCTION

After the initial wave of antibiotic discovery, few novel classes of antibiotics have emerged, with the latest dating back to the 1980's. Furthermore, the pace of antibiotic drug discovery is unable to keep up with the increasing prevalence of antibiotic drug resistance. However, the increasing amount of available data promotes the use of machine learning techniques (MLT) in drug discovery projects (e.g. construction of regression/classification models and ranking/virtual screening of compounds).

AREAS COVERED

In this review, the authors cover some of the applications of MLT in medicinal chemistry, focusing on the development of new antibiotics, the prediction of resistance and its mechanisms. The aim of this review is to illustrate the main advantages and disadvantages and the major trends from studies over the past 5 years.

EXPERT OPINION

The application of MLT to antibacterial drug discovery can aid the selection of new and potent lead compounds, with desirable pharmacokinetic and toxic profiles for further optimization. The increasing volume of available data along with the constant improvement in computational power and algorithms has meant that we are experiencing a transition in the way we face modern issues such as drug resistance, where our decisions are data-driven and experiments can be focused by data-suggested hypotheses.

Dihydrofolate reductase inhibitors for use as antimicrobial agents

Drug-resistant bacteria pose an increasingly serious threat to mankind all over the world. However, the currently available clinical treatments do not meet the urgent demand.Therefore, it is desirable to find new targets and inhibitors to overcome the problems of antibiotic resistance. Dihydrofolate reductase (DHFR) is an important enzyme required to maintain bacterial growth, and hence inhibitors of DHFR have been proven as effective agents for treating bacterial infections. This review provides insights into the recent discovery of antimicrobial agents targeting DHFR. In particular, three pathogens, Escherichia coli (E. coli), Mycobacterium tuberculosis(Mtb) and Staphylococcus aureus(S. aureus), and research strategies are emphasized. DHFR inhibitors are expected to be good alternatives to fight bacterial infections.

Targeting RNA with Small Molecules: Identification of Selective, RNA-Binding Small Molecules Occupying Drug-Like Chemical Space

Although the potential value of RNA as a target for new small molecule therapeutics is becoming increasingly credible, the physicochemical properties required for small molecules to selectively bind to RNA remain relatively unexplored. To investigate the druggability of RNAs with small molecules, we have employed affinity mass spectrometry, using the Automated Ligand Identification System (ALIS), to screen 42 RNAs from a variety of RNA classes, each against an array of chemically diverse drug-like small molecules (~50,000 compounds) and functionally annotated tool compounds (~5100 compounds). The set of RNA–small molecule interactions that was generated was compared with that for protein–small molecule interactions, and naïve Bayesian models were constructed to determine the types of specific chemical properties that bias small molecules toward binding to RNA. This set of RNA-selective chemical features was then used to build an RNA-focused set of ~3800 small molecules that demonstrated increased propensity toward binding the RNA target set. In addition, the data provide an overview of the specific physicochemical properties that help to enable binding to potential RNA targets. This work has increased the understanding of the chemical properties that are involved in small molecule binding to RNA, and the methodology used here is generally applicable to RNA-focused drug discovery efforts.

Identification of N-Substituted Triazolo-azetidines as Novel Antibacterials using pDualrep2 HTS Platform

Aim and Objective:

Antibiotic resistance is a serious constraint to the development of new effective antibacterials. Therefore, the discovery of the new antibacterials remains one of the main challenges in modern medicinal chemistry. This study was undertaken to identify novel molecules with antibacterial activity.

Materials and Methods:

Using our unique double-reporter system, in-house large-scale HTS campaign was conducted for the identification of antibacterial potency of small-molecule compounds. The construction allows us to visually assess the underlying mechanism of action. After the initial HTS and rescreen procedure, luciferase assay, C14-test, determination of MIC value and PrestoBlue test were carried out.

Results:

HTS rounds and rescreen campaign have revealed the antibacterial activity of a series of Nsubstituted triazolo-azetidines and their isosteric derivatives that has not been reported previously. Primary hit-molecule demonstrated a MIC value of 12.5 µg/mL against E. coli Δ tolC with signs of translation blockage and no SOS-response. Translation inhibition (26%, luciferase assay) was achieved at high concentrations up to 160 µg/mL, while no activity was found using C14-test. The compound did not demonstrate cytotoxicity in the PrestoBlue assay against a panel of eukaryotic cells. Within a series of direct structural analogues bearing the same or bioisosteric scaffold, compound 2 was found to have an improved antibacterial potency (MIC=6.25 µg/mL) close to Erythromycin (MIC=2.5-5 µg/mL) against the same strain. In contrast to the parent hit, this compound was more active and selective, and provided a robust IP position.

Conclusion:

N-substituted triazolo-azetidine scaffold may be used as a versatile starting point for the development of novel active and selective antibacterial compounds.

RNA-ALIS: Methodology for screening soluble RNAs as small molecule targets using ALIS affinity-selection mass spectrometry

Recent advances resulting from the completion of the human genome have shown that RNA has the promise to be a target for small molecule drugs, and therefore represents a previously unexploited class of targets for novel human therapeutics. We recently reported the adaptation of an affinity selection mass spectrometry screening technique, termed ALIS (Automatic Ligand Identification System), to screen and characterize a variety of RNA species from both prokaryotic and eukaryotic sources. We demonstrated that the ALIS technique, which had previously been used for protein targets, was also compatible for screening, ranking and characterizing small molecule ligands for RNA targets. We present here a detailed description of the use of ALIS for screening and characterizing ligands for RNA and discuss issues of validating and testing RNA for use in the ALIS system. We have also further elaborated on issues of RNA stability and testing in the ALIS system and demonstrate that the affinity-selection screening system has the potential to be a general solution for label-free screening and characterization of small molecule drug candidates for RNA targets.

A high-throughput whole cell screen to identify inhibitors of Mycobacterium tuberculosis

Tuberculosis is a disease of global importance for which novel drugs are urgently required. We developed a whole-cell phenotypic screen which can be used to identify inhibitors of Mycobacterium tuberculosis growth. We used recombinant strains of virulent M. tuberculosis which express far-red fluorescent reporters and used fluorescence to monitor growth in vitro. We optimized our high throughput assays using both 96-well and 384-well plates; both formats gave assays which met stringent reproducibility and robustness tests. We screened a compound set of 1105 chemically diverse compounds previously shown to be active against M. tuberculosis and identified primary hits which showed ≥ 90% growth inhibition. We ranked hits and identified three chemical classes of interest-the phenoxyalkylbenzamidazoles, the benzothiophene 1-1 dioxides, and the piperidinamines. These new compound classes may serve as starting points for the development of new series of inhibitors that prevent the growth of M. tuberculosis. This assay can be used for further screening, or could easily be adapted to other strains of M. tuberculosis.

Rapidly evolving changes and gene loss associated with host switching in Corynebacterium pseudotuberculosis

Phylogenomics and genome scale positive selection analyses were performed on 29 Corynebacterium pseudotuberculosis genomes that were isolated from different hosts, including representatives of the Ovis and Equi biovars. A total of 27 genes were identified as undergoing adaptive changes. An analysis of the clades within this species and these biovars, the genes specific to each branch, and the genes responding to selective pressure show clear differences, indicating that adaptation and specialization is occurring in different clades. These changes are often correlated with the isolation host but could indicate responses to some undetermined factor in the respective niches. The fact that some of these more-rapidly evolving genes have homology to known virulence factors, antimicrobial resistance genes and drug targets shows that this type of analysis could be used to identify novel targets, and that these could be used as a way to control this pathogen.

Role of Chemical Biology in Tuberculosis Drug Discovery and Diagnosis

The use of chemical techniques to study biological systems (often referred to currently as chemical biology) has become a powerful tool for both drug discovery and the development of novel diagnostic strategies. In tuberculosis, such tools have been applied to identifying drug targets from hit compounds, matching high-throughput screening hits against large numbers of isolated protein targets and identifying classes of enzymes with important functions. Metabolites unique to mycobacteria have provided important starting points for the development of innovative tools. For example, the unique biology of trehalose has provided both novel diagnostic strategies as well as probes of in vivo biological processes that are difficult to study any other way. Other mycobacterial metabolites are potentially valuable starting points and have the potential to illuminate new aspects of mycobacterial pathogenesis.