In silico-based high-throughput screen for discovery of novel combinations for tuberculosis treatment

Wollamide Cyclic Hexapeptides Synergize with Established and New Tuberculosis Antibiotics in Targeting Mycobacterium tuberculosis

Shorter and more effective treatment regimens as well as new drugs are urgent priorities for reducing the immense global burden of tuberculosis (TB). As treatment of TB currently requires multiple antibiotics with diverse mechanisms of action, any new drug lead requires assessment of potential interactions with existing TB antibiotics. We previously described the discovery of wollamides, a new class of Streptomyces-derived cyclic hexapeptides with antimycobacterial activity. To further assess the value of the wollamide pharmacophore as an antimycobacterial lead, we determined wollamide interactions with first- and second-line TB antibiotics by determining fractional inhibitory combination index and zero interaction potency scores. In vitro two-way and multiway interaction analyses revealed that wollamide B1 synergizes with ethambutol, pretomanid, delamanid, and para-aminosalicylic acid in inhibiting the replication and promoting the killing of phylogenetically diverse clinical and reference strains of the Mycobacterium tuberculosis complex (MTBC). Wollamide B1 antimycobacterial activity was not compromised in multi- and extensively drug-resistant MTBC strains. Moreover, growth-inhibitory antimycobacterial activity of the combination of bedaquiline/pretomanid/linezolid was further enhanced by wollamide B1, and wollamide B1 did not compromise the antimycobacterial activity of the isoniazid/rifampicin/ethambutol combination. Collectively, these findings add new dimensions to the desirable characteristics of the wollamide pharmacophore as an antimycobacterial lead compound. IMPORTANCE Tuberculosis (TB) is an infectious disease that affects millions of people globally, with 1.6 million deaths annually. TB treatment requires combinations of multiple different antibiotics for many months, and toxic side effects can occur. Therefore, shorter, safer, more effective TB therapies are required, and these should ideally also be effective against drug-resistant strains of the bacteria that cause TB. This study shows that wollamide B1, a chemically optimized member of a new class of antibacterial compounds, inhibits the growth of drug-sensitive as well as multidrug-resistant Mycobacterium tuberculosis isolated from TB patients. In combination with TB antibiotics, wollamide B1 synergistically enhances the activity of several antibiotics, including complex drug combinations that are currently used for TB treatment. These new insights expand the catalogue of the desirable characteristics of wollamide B1 as an antimycobacterial lead compound that might inspire the development of improved TB treatments.

Advances in the design of combination therapies for the treatment of tuberculosis

INTRODUCTION

Tuberculosis requires lengthy multi-drug therapy. Mycobacterium tuberculosis occupies different tissue compartments during infection, making drug access and susceptibility patterns variable. Antibiotic combinations are needed to ensure each compartment of infection is reached with effective drug treatment. Despite drug combinations' role in treating tuberculosis, the design of such combinations has been tackled relatively late in the drug development process, limiting the number of drug combinations tested. In recent years, there has been significant progress using in vitro, in vivo, and computational methodologies to interrogate combination drug effects.

AREAS COVERED

This review discusses the advances in these methodologies and how they may be used in conjunction with new successful clinical trials of novel drug combinations to design optimized combination therapies for tuberculosis. Literature searches for approaches and experimental models used to evaluate drug combination effects were undertaken.

EXPERT OPINION

We are entering an era richer in combination drug effect and pharmacokinetic/pharmacodynamic data, genetic tools, and outcome measurement types. Application of computational modeling approaches that integrate these data and produce predictive models of clinical outcomes may enable the field to generate novel, effective multidrug therapies using existing and new drug combination backbones.

Validated In Silico Model for Biofilm Formation in Escherichia coli

Using Escherichia coli as the representative biofilm former, we report here the development of an in silico model built by simulating events that transform a free-living bacterial entity into self-encased multicellular biofilms. Published literature on ∼300 genes associated with pathways involved in biofilm formation was curated, static maps were created, and suitably interconnected with their respective metabolites using ordinary differential equations. Precise interplay of genetic networks that regulate the transitory switching of bacterial growth pattern in response to environmental changes and the resultant multicomponent synthesis of the extracellular matrix were appropriately represented. Subsequently, the in silico model was analyzed by simulating time-dependent changes in the concentration of components by using the R and python environment. The model was validated by simulating and verifying the impact of key gene knockouts (KOs) and systematic knockdowns on biofilm formation, thus ensuring the outcomes were comparable with the reported literature. Similarly, specific gene KOs in laboratory and pathogenic E. coli were constructed and assessed. MiaA, YdeO, and YgiV were found to be crucial in biofilm development. Furthermore, qRT-PCR confirmed the elevation of expression in biofilm-forming clinical isolates. Findings reported in this study offer opportunities for identifying biofilm inhibitors with applications in multiple industries. The application of this model can be extended to the health care sector specifically to develop novel adjunct therapies that prevent biofilms in medical implants and reduce emergence of biofilm-associated resistant polymicrobial-chronic infections. The in silico framework reported here is open source and accessible for further enhancements.

Azaindole Based Potentiator of Antibiotics against Gram-Negative Bacteria

We discovered azaindole-based compounds with weak innate activity that exhibit substantial potentiation of antibacterial activities of different antibiotics, viz., rifampicin, erythromycin, solithromycin, and novobiocin in Gram-negative bacteria. In the presence of the azaindole derivatives, these antibiotics exhibited submicromolar minimum inhibitory concentrations (MICs) against Escherichia coli, Klebsiella pneumoniae, Pseudomonas aeruginosa, and Acinetobacter baumannii. The fold improvements in MIC of these antibiotics that were otherwise weak or inactive on their own against these bacteria were also observed against drug-resistant clinical isolates. Our studies indicate that this selective potentiation is probably through destabilization of the outer membrane's integrity, known to be regulated by the lipopolysaccharides (LPS). Thus, the azaindole based compounds described here open opportunities for those antibiotics that are otherwise ineffective due to LPS mediated entry barriers in Gram-negative bacteria.

Transcriptomic Signatures Predict Regulators of Drug Synergy and Clinical Regimen Efficacy against Tuberculosis

Multidrug combination therapy is an important strategy for treating tuberculosis, the world’s deadliest bacterial infection. Long treatment durations and growing rates of drug resistance have created an urgent need for new approaches to prioritize effective drug regimens. Hence, we developed a computational model called INDIGO-MTB that identifies synergistic drug regimens from an immense set of possible drug combinations using the pathogen response transcriptome elicited by individual drugs. Although the underlying input data for INDIGO-MTB was generated under in vitro broth culture conditions, the predictions from INDIGO-MTB correlated significantly with in vivo drug regimen efficacy from clinical trials. INDIGO-MTB also identified the transcription factor Rv1353c as a regulator of multiple drug interaction outcomes, which could be targeted for rationally enhancing drug synergy.

The rapid spread of multidrug-resistant strains has created a pressing need for new drug regimens to treat tuberculosis (TB), which kills 1.8 million people each year. Identifying new regimens has been challenging due to the slow growth of the pathogen Mycobacterium tuberculosis (MTB), coupled with the large number of possible drug combinations. Here we present a computational model (INDIGO-MTB) that identified synergistic regimens featuring existing and emerging anti-TB drugs after screening in silico more than 1 million potential drug combinations using MTB drug transcriptomic profiles. INDIGO-MTB further predicted the gene Rv1353c as a key transcriptional regulator of multiple drug interactions, and we confirmed experimentally that Rv1353c upregulation reduces the antagonism of the bedaquiline-streptomycin combination. A retrospective analysis of 57 clinical trials of TB regimens using INDIGO-MTB revealed that synergistic combinations were significantly more efficacious than antagonistic combinations (P value = 1 × 10⁻⁴) based on the percentage of patients with negative sputum cultures after 8 weeks of treatment. Our study establishes a framework for rapid assessment of TB drug combinations and is also applicable to other bacterial pathogens.

A novel series of enoyl reductase inhibitors targeting the ESKAPE pathogens, Staphylococcus aureus and Acinetobacter baumannii

S. aureus and A. baumannii are among the ESKAPE pathogens that are increasingly difficult to treat due to the rise in the number of drug resistant strains. Novel therapeutics targeting these pathogens are much needed. The bacterial enoyl reductase (FabI) is as potentially significant drug target for developing pathogen-specific antibiotics due to the presence of alternate FabI isoforms in many other bacterial species. We report the identification and development of a novel N-carboxy pyrrolidine scaffold targeting FabI in S. aureus and A. baumannii, two pathogens for which FabI essentiality has been established. This scaffold is unrelated to other known antibiotic families, and FabI is not targeted by any currently approved antibiotic. Our data shows that this scaffold displays promising enzyme inhibitory activity against FabI from both S. aureus and A. baumannii, as well as encouraging antibacterial activity in S. aureus. Compounds also display excellent synergy when combined with colistin and tested against A. baumannii. In this combination the MIC of colistin is reduced by 10-fold. Our first generation compound displays promising enzyme inhibition, targets FabI in S. aureus with a favorable selectivity index (ratio of cytotoxicity to MIC), and has excellent synergy with colistin against A. baumannii, including a multidrug resistant strain.

Chemogenomics and orthology-based design of antibiotic combination therapies

Combination antibiotic therapies are being increasingly used in the clinic to enhance potency and counter drug resistance. However, the large search space of candidate drugs and dosage regimes makes the identification of effective combinations highly challenging. Here, we present a computational approach called INDIGO, which uses chemogenomics data to predict antibiotic combinations that interact synergistically or antagonistically in inhibiting bacterial growth. INDIGO quantifies the influence of individual chemical–genetic interactions on synergy and antagonism and significantly outperforms existing approaches based on experimental evaluation of novel predictions in Escherichia coli. Our analysis revealed a core set of genes and pathways (e.g. central metabolism) that are predictive of antibiotic interactions. By identifying the interactions that are associated with orthologous genes, we successfully estimated drug‐interaction outcomes in the bacterial pathogens Mycobacterium tuberculosis and Staphylococcus aureus, using the E. coli INDIGO model. INDIGO thus enables the discovery of effective combination therapies in less‐studied pathogens by leveraging chemogenomics data in model organisms.

Unravelling the Secrets of Mycobacterial Cidality through the Lens of Antisense

One of the major impediments in anti-tubercular drug discovery is the lack of a robust grammar that governs the in-vitro to the in-vivo translation of efficacy. Mycobacterium tuberculosis (Mtb) is capable of growing both extracellular as well as intracellular; encountering various hostile conditions like acidic milieu, free radicals, starvation, oxygen deprivation, and immune effector mechanisms. Unique survival strategies of Mtb have prompted researchers to develop in-vitro equivalents to simulate in-vivo physiologies and exploited to find efficacious inhibitors against various phenotypes. Conventionally, the inhibitors are screened on Mtb under the conditions that are unrelated to the in-vivo disease environments. The present study was aimed to (1). Investigate cidality of Mtb targets using a non-chemical inhibitor antisense-RNA (AS-RNA) under in-vivo simulated in-vitro conditions.(2). Confirm the cidality of the targets under in-vivo in experimental tuberculosis. (3). Correlate in-vitro vs. in-vivo cidality data to identify the in-vitro condition that best predicts in-vivo cidality potential of the targets. Using cidality as a metric for efficacy, and AS-RNA as a target-specific inhibitor, we delineated the cidality potential of five target genes under six different physiological conditions (replicating, hypoxia, low pH, nutrient starvation, nitrogen depletion, and nitric oxide).In-vitro cidality confirmed in experimental tuberculosis in BALB/c mice using the AS-RNA allowed us to identify cidal targets in the rank order of rpoB>aroK>ppk>rpoC>ilvB. RpoB was used as the cidality control. In-vitro and in-vivo studies feature aroK (encoding shikimate kinase) as an in-vivo mycobactericidal target suitable for anti-TB drug discovery. In-vitro to in-vivo cidality correlations suggested the low pH (R = 0.9856) in-vitro model as best predictor of in-vivo cidality; however, similar correlation studies in pathologically relevant (Kramnik) mice are warranted. In the acute infection phase for the high fidelity translation, the compound efficacy may also be evaluated in the low pH, in addition to the standard replication condition.