Tuberculosis Drug Discovery: A Decade of Hit Assessment for Defined Targets.
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ABSTRACT:
SUBMITTER: Oh S
PROVIDER: S-EPMC8005628 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Tuberculosis treatments need to be shorter and overcome drug resistance. Our previous large scale phenotypic high-throughput screening against Mycobacterium tuberculosis (Mtb) has identified 737 active compounds and thousands that are inactive. We have used this data for building computational models as an approach to minimize the number of compounds tested.A cheminformatics clustering approach followed by Bayesian machine learning models (based on publicly available Mtb screening data) was used to illustrate that application of these models for screening set selections can enrich the hit rate.In order to explore chemical diversity around active cluster scaffolds of the dose-response hits obtained from our previous Mtb screens a set of 1924 commercially available molecules have been selected and evaluated for antitubercular activity and cytotoxicity using Vero, THP-1 and HepG2 cell lines with 4.3%, 4.2% and 2.7% hit rates, respectively. We demonstrate that models incorporating antitubercular and cytotoxicity data in Vero cells can significantly enrich the selection of non-toxic actives compared to random selection. Across all cell lines, the Molecular Libraries Small Molecule Repository (MLSMR) and cytotoxicity model identified ~10% of the hits in the top 1% screened (>10 fold enrichment). We also showed that seven out of nine Mtb active compounds from different academic published studies and eight out of eleven Mtb active compounds from a pharmaceutical screen (GSK) would have been identified by these Bayesian models.Combining clustering and Bayesian models represents a useful strategy for compound prioritization and hit-to lead optimization of antitubercular agents.
| S-EPMC3946937 | biostudies-literature
Project description:High-throughput screening (HTS) in whole cells is widely pursued to find compounds active against Mycobacterium tuberculosis (Mtb) for further development towards new tuberculosis (TB) drugs. Hit rates from these screens, usually conducted at 10 to 25 µM concentrations, typically range from less than 1% to the low single digits. New approaches to increase the efficiency of hit identification are urgently needed to learn from past screening data. The pharmaceutical industry has for many years taken advantage of computational approaches to optimize compound libraries for in vitro testing, a practice not fully embraced by academic laboratories in the search for new TB drugs. Adapting these proven approaches, we have recently built and validated Bayesian machine learning models for predicting compounds with activity against Mtb based on publicly available large-scale HTS data from the Tuberculosis Antimicrobial Acquisition Coordinating Facility. We now demonstrate the largest prospective validation to date in which we computationally screened 82,403 molecules with these Bayesian models, assayed a total of 550 molecules in vitro, and identified 124 actives against Mtb. Individual hit rates for the different datasets varied from 15-28%. We have identified several FDA approved and late stage clinical candidate kinase inhibitors with activity against Mtb which may represent starting points for further optimization. The computational models developed herein and the commercially available molecules derived from them are now available to any group pursuing Mtb drug discovery.
| S-EPMC3647004 | biostudies-literature
Project description:Mycobacterium tuberculosis (Mtb), the etiological agent of tuberculosis, is one of the most devastating infectious agents in the world. Chemical-genetic characterization through in vitro evolution combined with whole genome sequencing analysis was used identify novel drug targets and drug resistance genes in Mtb associated with its intracellular growth in human macrophages. We performed a genome analysis of 53 Mtb mutants resistant to 15 different hit compounds. We found nonsynonymous mutations/indels in 30 genes that may be associated with drug resistance acquisitions. Beyond confirming previously identified drug resistance mechanisms such as rpoB and lead targets reported in novel anti-tuberculosis drug screenings such as mmpL3, ethA, and mbtA, we have discovered several unrecognized candidate drug targets including prrB. The exploration of the Mtb chemical mutant genomes could help novel drug discovery and the structural biology of compounds and associated mechanisms of action relevant to tuberculosis treatment.
| S-EPMC9324642 | biostudies-literature
Project description:The tuberculosis (TB) drug discovery pipeline is fueled by compounds identified in whole-cell screens against the causative agent, Mycobacterium tuberculosis Phenotypic screening enables the selection of molecules that inhibit essential cellular functions in live, intact bacilli grown under a chosen in vitro condition. However, deducing the mechanism of action (MOA), which is important to avoid promiscuous targets, often requires significant biological resources in a lengthy process that risks decoupling medicinal chemistry and biology efforts. Therefore, there is a need to develop methods enabling rapid MOA assessment of putative "actives" for triage decisions. Here, we describe a modified version of a bioluminescence reporter assay that allows nondestructive detection of compounds targeting either of two macromolecular processes in M. tuberculosis: cell wall biosynthesis or maintenance of DNA integrity. Coupling the luxCDABE operon from Photorhabdus luminescens to mycobacterial promoters driving expression of the iniBAC operon (PiniB-LUX) or the DNA damage-inducible genes, recA (PrecA-LUX) or radA (PradA-LUX), provided quantitative detection in real time of compounds triggering expression of any of these promoters over an extended 10- to 12-day incubation. Testing against known anti-TB agents confirmed the specificity of each reporter in registering the MOA of the applied antibiotic in M. tuberculosis, independent of bactericidal or bacteriostatic activity. Moreover, profiles obtained for experimental compounds indicated the potential to infer complex MOAs in which multiple cellular processes are disrupted. These results demonstrate the utility of the reporters for early triage of compounds based on the provisional MOA and suggest their application to investigate polypharmacology in known and experimental anti-TB agents.
| S-EPMC5075082 | biostudies-literature
Project description:Drug Discovery is a lengthy and costly process and has faced a period of declining productivity within the last two decades resulting in increasing importance of integrative data-driven approaches. In this paper, data mining and integration is leveraged to inspect target innovation trends in drug discovery. The study highlights protein families and classes that have received more attention and those that have just emerged in the scientific literature, thus highlighting novel opportunities for drug intervention. In order to delineate the evolution of target-driven research interest from a biological perspective, trends in biological process annotations from Gene Ontology and disease annotations from DisGeNET are captured. The analysis reveals an increasing interest in targets related to immune system processes, and a recurrent trend for targets involved in circulatory system processes. At the level of diseases, targets associated with cancer-related pathologies, intellectual disability, and schizophrenia are increasingly investigated in recent years. The methodology enables researchers to capture trends in research attention in target space at an early stage during the drug discovery process. Workflows, scripts, and data used in this study are publicly available from https://github.com/BZdrazil/Moving_Targets . An interactive web application allows the customized exploration of target, biological process, and disease trends (available at https://rguha.shinyapps.io/MovingTargets/ ).
| S-EPMC7677539 | biostudies-literature
Project description:Tuberculosis, caused by Mycobacterium tuberculosis (M. tuberculosis), is one of the leading causes of human deaths globally according to the WHO TB 2019 report. The continuous rise in multi- and extensive-drug resistance in M. tuberculosis broadens the challenges to control tuberculosis. The availability of a large number of completely sequenced genomes of M. tuberculosis has provided an opportunity to explore the pangenome of the species along with the pan-phylogeny and to identify potential novel drug targets leading to drug discovery. We attempt to calculate the pangenome of M. tuberculosis that comprises a total of 150 complete genomes and performed the phylo-genomic classification and analysis. Further, the conserved core genome (1251 proteins) is subjected to various sequential filters (non-human homology, essentiality, virulence, physicochemical parameters, and pathway analysis) resulted in identification of eight putative broad-spectrum drug targets. Upon molecular docking analyses of these targets with ligands available at the DrugBank database shortlisted a total of five promising ligands with projected inhibitory potential; namely, 2'deoxy-thymidine-5'-diphospho-alpha-d-glucose, uridine diphosphate glucose, 2'-deoxy-thymidine-beta-l-rhamnose, thymidine-5'-triphosphate, and citicoline. We are confident that with further lead optimization and experimental validation, these lead compounds may provide a sound basis to develop safe and effective drugs against tuberculosis disease in humans.
| S-EPMC7698547 | biostudies-literature
Project description:BACKGROUND:The problem of drug resistance and bacterial persistence in tuberculosis is a cause of global alarm. Although, the UN's Sustainable Development Goals for 2030 has targeted a Tb free world, the treatment gap exists and only a few new drug candidates are in the pipeline. In spite of large information from medicinal chemistry to 'omics' data, there has been a little effort from pharmaceutical companies to generate pipelines for the development of novel drug candidates against the multi drug resistant Mycobacterium tuberculosis. METHODS:In the present study, we describe an integrated methodology; utilizing systems level information to optimize ligand selection to lower the failure rates at the pre-clinical and clinical levels. In the present study, metabolic targets (Rv2763c, Rv3247c, Rv1094, Rv3607c, Rv3048c, Rv2965c, Rv2361c, Rv0865, Rv0321, Rv0098, Rv0390, Rv3588c, Rv2244, Rv2465c and Rv2607) in M. tuberculosis, identified using our previous Systems Biology and data-intensive genome level analysis, have been used to design potential lead molecules, which are likely to be non-toxic. Various in silico drug discovery tools have been utilized to generate small molecular leads for each of the 15 targets with available crystal structures. RESULTS:The present study resulted in identification of 20 novel lead molecules including 4 FDA approved drugs (droxidropa, tetroxoprim, domperidone and nemonapride) which can be further taken for drug repurposing. This comprehensive integrated methodology, with both experimental and in silico approaches, has the potential to not only tackle the MDR form of Mtb but also the most important persister population of the bacterium, with a potential to reduce the failures in the Tb drug discovery. CONCLUSION:We propose an integrated approach of systems and structural biology for identifying targets that address the high attrition rate issue in lead identification and drug development We expect that this system level analysis will be applicable for identification of drug candidates to other pathogenic organisms as well.
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Project description:Recent estimates suggest close to one million people per year die globally owing to HIV-related illnesses. Therefore, there is still a need to identify new targets to develop future treatments. Many of the more recently identified targets are host-related and these might be more difficult for the virus to develop drug resistance to. In addition, there are virus-related targets (capsid and RNAse H) that have yet to be exploited clinically. Several of the newer targets also address virulence factors, virus latency or target persistence. The targets highlighted in this review could represent the next generation of viable candidates for drug discovery projects as well as continue the search for a cure for this disease.
| S-EPMC6510608 | biostudies-literature
Project description:A mathematical model which predicts the intraerythrocytic stages of Plasmodium falciparum infection was developed using data from malaria-infected mice. Variables selected accounted for levels of healthy red blood cells, merozoite (Plasmodium asexual phase) infected red blood cells, gametocyte (Plasmodium sexual phase) infected red blood cells and a phenomenological variable which accounts for the mean activity of the immune system of the host. The model built was able to reproduce the behavior of three different scenarios of malaria. It predicts the later dynamics of malaria-infected humans well after the first peak of parasitemia, the qualitative response of malaria-infected monkeys to vaccination and the changes observed in malaria-infected mice when they are treated with antimalarial drugs. The mathematical model was used to identify new targets to be focused on drug design. Optimization methodologies were applied to identify five targets for minimizing the parasite load; four of the targets thus identified have never before been taken into account in drug design. The potential targets include: 1) increasing the death rate of the gametocytes, 2) decreasing the invasion rate of the red blood cells by the merozoites, 3) increasing the transformation of merozoites into gametocytes, 4) decreasing the activation of the immune system by the gametocytes, and finally 5) a combination of the previous target with decreasing the recycling rate of the red blood cells. The first target is already used in current therapies, whereas the remainders are proposals for potential new targets. Furthermore, the combined target (the simultaneous decrease of the activation of IS by gRBC and the decrease of the influence of IS on the recycling of hRBC) is interesting, since this combination does not affect the parasite directly. Thus, it is not expected to generate selective pressure on the parasites, which means that it would not produce resistance in Plasmodium.
| S-EPMC3610898 | biostudies-literature
Project description:Lysophosphatidic acid (LPA, 1-radyl-2-hydroxy-sn-glycero-3-phosphate) is the prototype member of a family of lipid mediators and second messengers. LPA and its naturally occurring analogues interact with G protein-coupled receptors on the cell surface and a nuclear hormone receptor within the cell. In addition, there are several enzymes that utilize LPA as a substrate or generate it as a product and are under its regulatory control. LPA is present in biological fluids, and attempts have been made to link changes in its concentration and molecular composition to specific disease conditions. Through their many targets, members of the LPA family regulate cell survival, apoptosis, motility, shape, differentiation, gene transcription, malignant transformation and more. The present review depicts arbitrary aspects of the physiological and pathophysiological actions of LPA and attempts to link them with select targets. Many of us are now convinced that therapies targeting LPA biosynthesis and signalling are feasible for the treatment of devastating human diseases such as cancer, fibrosis and degenerative conditions. However, successful targeting of the pathways associated with this pleiotropic lipid will depend on the future development of as yet undeveloped pharmacons.
| S-EPMC2989581 | biostudies-literature