Design principles to assemble drug combinations for effective tuberculosis therapy using interpretable pairwise drug response measurements

Chemical genetic interactions elucidate pathways controlling tuberculosis antibiotic efficacy during infection

Successful tuberculosis therapy requires treatment with an unwieldy multidrug combination for several months. Thus, there is a growing need to identify novel genetic vulnerabilities that can be leveraged to develop new, more effective antitubercular drugs. Consequently, recent efforts to optimize tuberculosis (TB) therapy have exploited Mycobacterium tuberculosis (Mtb) chemical genetics to identify pathways influencing antibiotic efficacy, novel mechanisms of antibiotic action, and new targets for TB drug discovery. However, the influence of the complex host environment on these interactions remains largely unknown, leaving the therapeutic potential of the identified targets unclear. In this study, we leveraged a library of conditional mutants targeting 467 essential Mtb genes to characterize the chemical-genetic interactions (CGIs) with TB drugs directly in the mouse infection model. We found that these in vivo CGIs differ significantly from those identified in vitro. Both drug-specific and drug-agnostic effects were identified, and many were preserved during treatment with a multidrug combination, suggesting numerous strategies for enhancing therapy. This work also elucidated the complex effects of pyrazinamide (PZA), a drug that relies on aspects of the infection environment for efficacy. Specifically, our work supports the importance of coenzyme A synthesis- inhibition during infection, as well as the antagonistic effect of iron limitation on PZA activity. In addition, we found that inhibition of thiamine and purine synthesis increases PZA efficacy, suggesting additional therapeutically exploitable metabolic dependencies. Our findings present a map of the unique in vivo CGIs, characterizing the mechanism of PZA activity in vivo and identifying potential targets for TB drug development.

DeepDrug as an expert guided and AI driven drug repurposing methodology for selecting the lead combination of drugs for Alzheimer's disease

Alzheimer's Disease (AD) significantly aggravates human dignity and quality of life. While newly approved amyloid immunotherapy has been reported, effective AD drugs remain to be identified. Here, we propose a novel AI-driven drug-repurposing method, DeepDrug, to identify a lead combination of approved drugs to treat AD patients. DeepDrug advances drug-repurposing methodology in four aspects. Firstly, it incorporates expert knowledge to extend candidate targets to include long genes, immunological and aging pathways, and somatic mutation markers that are associated with AD. Secondly, it incorporates a signed directed heterogeneous biomedical graph encompassing a rich set of nodes and edges, and node/edge weighting to capture crucial pathways associated with AD. Thirdly, it encodes the weighted biomedical graph through a Graph Neural Network into a new embedding space to capture the granular relationships across different nodes. Fourthly, it systematically selects the high-order drug combinations via diminishing return-based thresholds. A five-drug lead combination, consisting of Tofacitinib, Niraparib, Baricitinib, Empagliflozin, and Doxercalciferol, has been selected from the top drug candidates based on DeepDrug scores to achieve the maximum synergistic effect. These five drugs target neuroinflammation, mitochondrial dysfunction, and glucose metabolism, which are all related to AD pathology. DeepDrug offers a novel AI-and-big-data, expert-guided mechanism for new drug combination discovery and drug-repurposing across AD and other neuro-degenerative diseases, with immediate clinical applications.

Characterizing the immune response to Mycobacterium tuberculosis: a comprehensive narrative review and implications in disease relapse

Introduction

Tuberculosis remains the leading cause of death from infectious diseases among adults worldwide. To date, an overarching review of the immune response to Mtb in humans has not been fully elucidated, with innate immunity remaining poorly understood due to historic focus on adaptive immunity. Specifically, there is a major gap concerning the contribution of the immune system to overall bacterial clearance, particularly residual bacteria. This review aims to describe the time course of interactions between the host immune system and Mtb, from the start of the infection to the development of the adaptive response. Concordantly, we aim to crystallize the pathogenic effects and immunoevasive mechanisms of Mtb. The translational value of animal data is also discussed.

Methods

The literature search was conducted in the PubMed, ScienceDirect, and Google Scholar databases, which included reported research from 1990 until 2024. A total of 190 publications were selected and screened, of which 108 were used for abstraction and 86 were used for data extraction. Graphical summaries were created using the narrative information (i.e., recruitment, recognition, and response) to generate clear visual representations of the immune response at the cellular and molecular levels.

Results

The key cellular players included airway epithelial cells, alveolar epithelial cells, neutrophils, natural killer cells, macrophages, dendritic cells, T cells, and granulomatous lesions; the prominent molecular players included IFN-γ, TNF-α, and IL-10. The paper also sheds light on the immune response to residual bacteria and applications of the data.

Discussion

We provide a comprehensive characterization of the key immune players that are implicated in pulmonary tuberculosis, in line with the organs or compartments in which mycobacteria reside, offering a broad vignette of the immune response to Mtb and how it responds to residual bacteria. Ultimately, the data presented could provide immunological insights to help establish optimized criteria for identifying efficacious treatment regimens and durations for relapse prevention in the modeling and simulation space and wider fields.

Catalase activity deficiency sensitizes multidrug-resistant Mycobacterium tuberculosis to the ATP synthase inhibitor bedaquiline

Multidrug-resistant tuberculosis (MDR-TB), defined as resistance to the first-line drugs isoniazid and rifampin, is a growing source of global mortality and threatens global control of tuberculosis disease. The diarylquinoline bedaquiline has recently emerged as a highly efficacious drug against MDR-TB and kills Mycobacterium tuberculosis by inhibiting mycobacterial ATP synthase. However, the mechanisms underlying bedaquiline's efficacy against MDR-TB remain unknown. Here we investigate bedaquiline hyper-susceptibility in drug-resistant Mycobacterium tuberculosis using systems biology approaches. We discovered that MDR clinical isolates are commonly sensitized to bedaquiline. This hypersensitization is caused by several physiological changes induced by deficient catalase activity. These include enhanced accumulation of reactive oxygen species, increased susceptibility to DNA damage, induction of sensitizing transcriptional programs, and metabolic repression of several biosynthetic pathways. In this work we demonstrate how resistance-associated changes in bacterial physiology can mechanistically induce collateral antimicrobial drug sensitivity and reveal druggable vulnerabilities in antimicrobial resistant pathogens.

Spectinamide MBX-4888A exhibits favorable lesion and tissue distribution and promotes treatment shortening in advanced murine models of tuberculosis

The spectinamides are novel, narrow-spectrum semisynthetic analogs of spectinomycin, modified to avoid intrinsic efflux by Mycobacterium tuberculosis. Spectinamides, including lead MBX-4888A (Lee-1810), exhibit promising therapeutic profiles in mice, as single drugs and as partner agents with other anti-tuberculosis antibiotics including rifampin and/or pyrazinamide. Here, we show that MBX-4888A, given by injection with the front-line standard of care regimen, is treatment shortening in multiple murine tuberculosis infection models. The positive treatment responses to MBX-4888A combination therapy in multiple mouse models, including mice exhibiting advanced pulmonary disease, can be attributed to favorable distribution in tissues and lesions, retention in caseum, along with favorable effects with rifampin and pyrazinamide under conditions achieved in necrotic lesions. This study also provides an additional data point regarding the safety and tolerability of spectinamide MBX-4888A in long-term murine efficacy studies.

Predicting bacterial fitness in Mycobacterium tuberculosis with transcriptional regulatory network-informed interpretable machine learning

Mycobacterium tuberculosis (Mtb) is the causative agent of tuberculosis disease, the greatest source of global mortality by a bacterial pathogen. Mtb adapts and responds to diverse stresses such as antibiotics by inducing transcriptional stress-response regulatory programs. Understanding how and when these mycobacterial regulatory programs are activated could enable novel treatment strategies for potentiating the efficacy of new and existing drugs. Here we sought to define and analyze Mtb regulatory programs that modulate bacterial fitness. We assembled a large Mtb RNA expression compendium and applied these to infer a comprehensive Mtb transcriptional regulatory network and compute condition-specific transcription factor activity profiles. We utilized transcriptomic and functional genomics data to train an interpretable machine learning model that can predict Mtb fitness from transcription factor activity profiles. We demonstrated that this transcription factor activity-based model can successfully predict Mtb growth arrest and growth resumption under hypoxia and reaeration using only RNA-seq expression data as a starting point. These integrative network modeling and machine learning analyses thus enable the prediction of mycobacterial fitness under different environmental and genetic contexts. We envision these models can potentially inform the future design of prognostic assays and therapeutic intervention that can cripple Mtb growth and survival to cure tuberculosis disease.

Distinguishing mutants that resist drugs via different mechanisms by examining fitness tradeoffs

There is growing interest in designing multidrug therapies that leverage tradeoffs to combat resistance. Tradeoffs are common in evolution and occur when, for example, resistance to one drug results in sensitivity to another. Major questions remain about the extent to which tradeoffs are reliable, specifically, whether the mutants that provide resistance to a given drug all suffer similar tradeoffs. This question is difficult because the drug-resistant mutants observed in the clinic, and even those evolved in controlled laboratory settings, are often biased towards those that provide large fitness benefits. Thus, the mutations (and mechanisms) that provide drug resistance may be more diverse than current data suggests. Here, we perform evolution experiments utilizing lineage-tracking to capture a fuller spectrum of mutations that give yeast cells a fitness advantage in fluconazole, a common antifungal drug. We then quantify fitness tradeoffs for each of 774 evolved mutants across 12 environments, finding these mutants group into classes with characteristically different tradeoffs. Their unique tradeoffs may imply that each group of mutants affects fitness through different underlying mechanisms. Some of the groupings we find are surprising. For example, we find some mutants that resist single drugs do not resist their combination, while others do. And some mutants to the same gene have different tradeoffs than others. These findings, on one hand, demonstrate the difficulty in relying on consistent or intuitive tradeoffs when designing multidrug treatments. On the other hand, by demonstrating that hundreds of adaptive mutations can be reduced to a few groups with characteristic tradeoffs, our findings may yet empower multidrug strategies that leverage tradeoffs to combat resistance. More generally speaking, by grouping mutants that likely affect fitness through similar underlying mechanisms, our work guides efforts to map the phenotypic effects of mutation.

Chemical genetic interactions elucidate pathways controlling tuberculosis antibiotic efficacy during infection

Successful tuberculosis therapy requires treatment with an unwieldy multidrug combination for several months. Thus, there is a growing need to identify novel genetic vulnerabilities that can be leveraged to develop new, more effective antitubercular drugs. Consequently, recent efforts to optimize TB therapy have exploited Mtb chemical genetics to identify pathways influencing antibiotic efficacy, novel mechanisms of antibiotic action, and new targets for TB drug discovery. However, the influence of the complex host environment on these interactions remains largely unknown, leaving the therapeutic potential of the identified targets unclear. In this study, we leveraged a library of conditional mutants targeting 467 essential Mtb genes to characterize the chemical-genetic interactions (CGIs) with TB drugs directly in the mouse infection model. We found that these in vivo CGIs differ significantly from those identified in vitro . Both drug-specific and drug-agnostic effects were identified, and many were preserved during treatment with a multidrug combination, suggesting numerous strategies for enhancing therapy. This work also elucidated the complex effects of pyrazinamide (PZA), a drug that relies on aspects of the infection environment for efficacy. Specifically, our work supports the importance of coenzyme A synthesis inhibition during infection, as well as the antagonistic effect of iron limitation on PZA activity. In addition, we found that inhibition of thiamine and purine synthesis increases PZA efficacy, suggesting novel therapeutically exploitable metabolic dependencies. Our findings present a map of the unique in vivo CGIs, characterizing the mechanism of PZA activity in vivo and identifying novel targets for TB drug development.

Significance

The inevitable rise of multi-drug-resistant tuberculosis underscores the urgent need for new TB drugs and novel drug targets while prioritizing synergistic drug combinations. Chemical-genetic interaction (CGI) studies have delineated bacterial pathways influencing antibiotic efficacy and uncovered druggable pathways that synergize with TB drugs. However, most studies are conducted in vitro , limiting our understanding of how the host environment influences drug-mutant interactions. Using an inducible mutant library targeting essential Mtb genes to characterize CGIs during infection, this study reveals that CGIs are both drug-specific and drug-agnostic and differ significantly from those observed in vitro . Synergistic CGIs comprised distinct metabolic pathways mediating antibiotic efficacy, revealing novel drug mechanisms of action, and defining potential drug targets that would synergize with frontline antitubercular drugs.

The Combination of Membrane Disruption and FtsZ Targeting by a Chemotherapeutic Hydrogel Synergistically Combats Pathogens Infections

The emergence of multidrug-resistant (MDR) bacteria poses a significant challenge to global health. Due to a shortage of antibiotics, alternative therapeutic strategies are urgently needed. Unfortunately, colistin, the last-resort antibiotic, has unavoidable nephrotoxicity and hepatotoxicity, and its single killing mechanism is prone to drug resistance. To address this challenge, a promising combinatorial approach that includes colistin, a membrane-disrupting antimicrobial agent, and chelerythrine (CHE), a FtsZ protein inhibitor is proposed. This approach significantly reduces antibiotic dose and development of resistance, leading to almost complete inactivation of MDR pathogens in vitro. To address solubility issues and ensure transport, the antimicrobial hydrogel system LNP-CHE-CST@hydrogel, which induced reactive oxygen species (ROS) and apoptosis-like cell death by targeting the FtsZ protein, is used. In an in vivo mouse skin infection model, the combination therapy effectively eliminated MDR bacteria within 24 h, as monitored by fluorescence tracking. The findings demonstrate a promising approach for developing multifunctional hydrogels to combat MDR bacterial infections.

Distinguishing mutants that resist drugs via different mechanisms by examining fitness tradeoffs

There is growing interest in designing multidrug therapies that leverage tradeoffs to combat resistance. Tradeoffs are common in evolution and occur when, for example, resistance to one drug results in sensitivity to another. Major questions remain about the extent to which tradeoffs are reliable, specifically, whether the mutants that provide resistance to a given drug all suffer similar tradeoffs. This question is difficult because the drug-resistant mutants observed in the clinic, and even those evolved in controlled laboratory settings, are often biased towards those that provide large fitness benefits. Thus, the mutations (and mechanisms) that provide drug resistance may be more diverse than current data suggests. Here, we perform evolution experiments utilizing lineage-tracking to capture a fuller spectrum of mutations that give yeast cells a fitness advantage in fluconazole, a common antifungal drug. We then quantify fitness tradeoffs for each of 774 evolved mutants across 12 environments, finding these mutants group into 6 classes with characteristically different tradeoffs. Their unique tradeoffs may imply that each group of mutants affects fitness through different underlying mechanisms. Some of the groupings we find are surprising. For example, we find some mutants that resist single drugs do not resist their combination, while others do. And some mutants to the same gene have different tradeoffs than others. These findings, on one hand, demonstrate the difficulty in relying on consistent or intuitive tradeoffs when designing multidrug treatments. On the other hand, by demonstrating that hundreds of adaptive mutations can be reduced to a few groups with characteristic tradeoffs, our findings may yet empower multidrug strategies that leverage tradeoffs to combat resistance. More generally speaking, by grouping mutants that likely affect fitness through similar underlying mechanisms, our work guides efforts to map the phenotypic effects of mutation.

Spectinamide MBX-4888A exhibits favorable lesion and tissue distribution and promotes treatment shortening in advanced murine models of tuberculosis

The spectinamides are novel, narrow-spectrum semisynthetic analogs of spectinomycin, modified to avoid intrinsic efflux by Mycobacterium tuberculosis . Spectinamides, including lead MBX-4888A (Lee-1810), exhibit promising therapeutic profiles in mice, as single drugs and as partner agents with other anti-tuberculosis antibiotics including rifampin and/or pyrazinamide. To demonstrate that this translates to more effective cure, we first confirmed the role of rifampin, with or without pyrazinamide, as essential to achieve effective bactericidal responses and sterilizing cure in the current standard of care regimen in chronically infected C3HeB/FeJ mice compared to BALB/c mice. Thus, demonstrating added value in testing clinically relevant regimens in murine models of increasing pathologic complexity. Next we show that MBX-4888A, given by injection with the front-line standard of care regimen, is treatment shortening in multiple murine tuberculosis infection models. The positive treatment responses to MBX-4888A combination therapy in multiple mouse models including mice exhibiting advanced pulmonary disease can be attributed to favorable distribution in tissues and lesions, retention in caseum, along with favorable effects with rifampin and pyrazinamide under conditions achieved in necrotic lesions. This study also provides an additional data point regarding the safety and tolerability of spectinamide MBX-4888A in long-term murine efficacy studies.

Use of multiple pharmacodynamic measures to deconstruct the Nix-TB regimen in a short-course murine model of tuberculosis

A major challenge for tuberculosis (TB) drug development is to prioritize promising combination regimens from a large and growing number of possibilities. This includes demonstrating individual drug contributions to the activity of higher-order combinations. A BALB/c mouse TB infection model was used to evaluate the contributions of each drug and pairwise combination in the clinically relevant Nix-TB regimen [bedaquiline-pretomanid-linezolid (BPaL)] during the first 3 weeks of treatment at human equivalent doses. The rRNA synthesis (RS) ratio, an exploratory pharmacodynamic (PD) marker of ongoing Mycobacterium tuberculosis rRNA synthesis, together with solid culture CFU counts and liquid culture time to positivity (TTP) were used as PD markers of treatment response in lung tissue; and their time-course profiles were mathematically modeled using rate equations with pharmacologically interpretable parameters. Antimicrobial interactions were quantified using Bliss independence and Isserlis formulas. Subadditive (or antagonistic) and additive effects on bacillary load, assessed by CFU and TTP, were found for bedaquiline-pretomanid and linezolid-containing pairs, respectively. In contrast, subadditive and additive effects on rRNA synthesis were found for pretomanid-linezolid and bedaquiline-containing pairs, respectively. Additionally, accurate predictions of the response to BPaL for all three PD markers were made using only the single-drug and pairwise effects together with an assumption of negligible three-way drug interactions. The results represent an experimental and PD modeling approach aimed at reducing combinatorial complexity and improving the cost-effectiveness of in vivo systems for preclinical TB regimen development.

Therapeutic developments for tuberculosis and nontuberculous mycobacterial lung disease

Tuberculosis (TB) drug discovery and development has undergone nothing short of a revolution over the past 20 years. Successful public-private partnerships and sustained funding have delivered a much-improved understanding of mycobacterial disease biology and pharmacology and a healthy pipeline that can tolerate inevitable attrition. Preclinical and clinical development has evolved from decade-old concepts to adaptive designs that permit rapid evaluation of regimens that might greatly shorten treatment duration over the next decade. But the past 20 years also saw the rise of a fatal and difficult-to-cure lung disease caused by nontuberculous mycobacteria (NTM), for which the drug development pipeline is nearly empty. Here, we discuss the similarities and differences between TB and NTM lung diseases, compare the preclinical and clinical advances, and identify major knowledge gaps and areas of cross-fertilization. We argue that applying paradigms and networks that have proved successful for TB, from basic research to clinical trials, will help to populate the pipeline and accelerate curative regimen development for NTM disease.

Use of Multiple Pharmacodynamic Measures to Deconstruct the Nix-TB Regimen in a Short-Course Murine Model of Tuberculosis

A major challenge for tuberculosis (TB) drug development is to prioritize promising combination regimens from a large and growing number of possibilities. This includes demonstrating individual drug contributions to the activity of higher-order combinations. A BALB/c mouse TB infection model was used to evaluate the contributions of each drug and pairwise combination in the clinically relevant Nix-TB regimen (bedaquiline-pretomanid-linezolid [BPaL]) during the first three weeks of treatment at human equivalent doses. RS ratio, an exploratory pharmacodynamic (PD) marker of ongoing Mycobacterium tuberculosis rRNA synthesis, to-gether with solid culture CFU and liquid culture time to positivity (TTP) were used as PD markers of treatment response in lung tissue; and their time course profiles were mathematically modeled using rate equations with pharmacologically interpretable parameters. Antimicrobial interactions were quantified using Bliss independence and Isserlis formulas. Subadditive (or antagonistic) and additive effects on bacillary load, assessed by CFU and TTP, were found for bedaquiline-pretomanid and linezolid-containing pairs, respectively. In contrast, subadditive and additive effects on rRNA synthesis were found for pretomanid-linezolid and bedaquiline-containing pairs, respectively. Additionally, accurate predictions of the response to BPaL for all three PD markers were made using only the single-drug and pairwise effects together with an assumption of negligible three-way drug interactions. The results represent an experimental and PD modeling approach aimed at reducing combinatorial complexity and improving the cost-effectiveness of in vivo systems for preclinical TB regimen development.

KatG catalase deficiency confers bedaquiline hyper-susceptibility to isoniazid resistant Mycobacterium tuberculosis

Multidrug-resistant tuberculosis (MDR-TB) is a growing source of global mortality and threatens global control of tuberculosis (TB) disease. The diarylquinoline bedaquiline (BDQ) recently emerged as a highly efficacious drug against MDR-TB, defined as resistance to the first-line drugs isoniazid (INH) and rifampin. INH resistance is primarily caused by loss-of-function mutations in the catalase KatG, but mechanisms underlying BDQ's efficacy against MDR-TB remain unknown. Here we employ a systems biology approach to investigate BDQ hyper-susceptibility in INH-resistant Mycobacterium tuberculosis . We found hyper-susceptibility to BDQ in INH-resistant cells is due to several physiological changes induced by KatG deficiency, including increased susceptibility to reactive oxygen species and DNA damage, remodeling of transcriptional programs, and metabolic repression of folate biosynthesis. We demonstrate BDQ hyper-susceptibility is common in INH-resistant clinical isolates. Collectively, these results highlight how altered bacterial physiology can impact drug efficacy in drug-resistant bacteria.

Advancing tuberculosis management: the role of predictive, preventive, and personalized medicine

Tuberculosis is a major global health issue, with approximately 10 million people falling ill and 1.4 million dying yearly. One of the most significant challenges to public health is the emergence of drug-resistant tuberculosis. For the last half-century, treating tuberculosis has adhered to a uniform management strategy in most patients. However, treatment ineffectiveness in some individuals with pulmonary tuberculosis presents a major challenge to the global tuberculosis control initiative. Unfavorable outcomes of tuberculosis treatment (including mortality, treatment failure, loss of follow-up, and unevaluated cases) may result in increased transmission of tuberculosis and the emergence of drug-resistant strains. Treatment failure may occur due to drug-resistant strains, non-adherence to medication, inadequate absorption of drugs, or low-quality healthcare. Identifying the underlying cause and adjusting the treatment accordingly to address treatment failure is important. This is where approaches such as artificial intelligence, genetic screening, and whole genome sequencing can play a critical role. In this review, we suggest a set of particular clinical applications of these approaches, which might have the potential to influence decisions regarding the clinical management of tuberculosis patients.

Design, Synthesis, and Activity Evaluation of Novel Dual-Target Inhibitors with Antifungal and Immunoregulatory Properties

Dual-target (CYP51/PD-L1) plays an important role in the process of fungal proliferation and immune suppression. A series of novel quinazoline compounds with dual-target inhibition function was constructed using the skeleton growth method, and their structures were synthesized, characterized, and evaluated. Among them, the perfected compounds (L11, L20, L21) were selected for further study, which exhibited remarkable biological activity against different fungal strains (MIC50, 0.25-2.0 μg/mL) in vitro. On the one hand, these compounds inhibited CYP51 activity, induced ROS aggregation, and mitochondrial damage; this ultimately caused fungal lysis and death. On the other hand, they also effectively activated the body's immune ability by blocking the interaction between PD-L1 and PD-1, slowed down the inflammatory reaction, and accelerated the recovery process of fungal infections.

Novel Synergies and Isolate Specificities in the Drug Interaction Landscape of Mycobacterium abscessus

Mycobacterium abscessus infections are difficult to treat and are often considered untreatable without tissue resection. Due to the intrinsic drug-resistant nature of the bacteria, combination therapy of three or more antibiotics is recommended. A major challenge in treating M. abscessus infections is the absence of a universal combination therapy with satisfying clinical success rates, leaving clinicians to treat infections using antibiotics lacking efficacy data. We systematically measured drug combinations in M. abscessus to establish a resource of drug interaction data and identify patterns of synergy to help design optimized combination therapies. We measured 191 pairwise drug combination effects among 22 antibacterials and identified 71 synergistic pairs, 54 antagonistic pairs, and 66 potentiator-antibiotic pairs. We found that commonly used drug combinations in the clinic, such as azithromycin and amikacin, are antagonistic in the lab reference strain ATCC 19977, whereas novel combinations, such as azithromycin and rifampicin, are synergistic. Another challenge in developing universally effective multidrug therapies for M. abscessus is the significant variation in drug response between isolates. We measured drug interactions in a focused set of 36 drug pairs across a small panel of clinical isolates with rough and smooth morphotypes. We observed strain-dependent drug interactions that cannot be predicted from single-drug susceptibility profiles or known drug mechanisms of action. Our study demonstrates the immense potential to identify synergistic drug combinations in the vast drug combination space and emphasizes the importance of strain-specific combination measurements for designing improved therapeutic interventions.

Spatiotemporal perspectives on tuberculosis chemotherapy

Mycobacterium tuberculosis (Mtb), the causative agent of tuberculosis (TB), accounts for over ten million infections and over 1.5 million deaths every year [1]. Upon infection, the seesaw between Mtb and our immune systems creates microenvironments that are compositionally distinctive and changing over time. While the field has begun to better understand the spatial complexity of TB disease, our understanding and experimental dissection of the temporal dynamics of TB and TB drug treatment is much more rudimentary. However, it is the combined spatiotemporal heterogeneity of TB disease that creates niches and time windows within which the pathogen can survive and thrive during treatment. Here, we review the emerging data on the interactions of spatial and temporal dynamics as they relate to TB disease and treatment. A better understanding of the interactions of Mtb, host, and antibiotics through space and time will elucidate treatment failure and potentially identify opportunities for new TB treatment regimens.

Miltefosine and Nifuratel Combination: A Promising Therapy for the Treatment of Leishmania donovani Visceral Leishmaniasis

Visceral leishmaniasis is a neglected vector-borne tropical disease caused by Leishmania donovani and Leishmania infantum that is endemic not only in East African countries, but also in Asia, regions of South America and the Mediterranean Basin. For the pharmacological control of this disease, there is a limited number of old and, in general, poorly adherent drugs, with a multitude of adverse effects and low oral bioavailability, which favor the emergence of resistant pathogens. Pentavalent antimonials are the first-line drugs, but due to their misuse, resistant Leishmania strains have emerged worldwide. Although these drugs have saved many lives, it is recommended to reduce their use as much as possible and replace them with novel and more friendly drugs. From a commercial collection of anti-infective drugs, we have recently identified nifuratel-a nitrofurantoin used against vaginal infections-as a promising repurposing drug against a mouse model of visceral leishmaniasis. In the present work, we have tested combinations of miltefosine-the only oral drug currently used against leishmaniasis-with nifuratel in different proportions, both in axenic amastigotes from bone marrow and in intracellular amastigotes from infected Balb/c mouse spleen macrophages, finding a potent synergy in both cases. In vivo evaluation of oral miltefosine/nifuratel combinations using a bioimaging platform has revealed the potential of these combinations for the treatment of this disease.

Tools to develop antibiotic combinations that target drug tolerance in Mycobacterium tuberculosis

Combination therapy is necessary to treat tuberculosis to decrease the rate of disease relapse and prevent the acquisition of drug resistance, and shorter regimens are urgently needed. The adaptation of Mycobacterium tuberculosis to various lesion microenvironments in infection induces various states of slow replication and non-replication and subsequent antibiotic tolerance. This non-heritable tolerance to treatment necessitates lengthy combination therapy. Therefore, it is critical to develop combination therapies that specifically target the different types of drug-tolerant cells in infection. As new tools to study drug combinations earlier in the drug development pipeline are being actively developed, we must consider how to best model the drug-tolerant cells to use these tools to design the best antibiotic combinations that target those cells and shorten tuberculosis therapy. In this review, we discuss the factors underlying types of drug tolerance, how combination therapy targets these populations of bacteria, and how drug tolerance is currently modeled for the development of tuberculosis multidrug therapy. We highlight areas for future studies to develop new tools that better model drug tolerance in tuberculosis infection specifically for combination therapy testing to bring the best drug regimens forward to the clinic.

Digital Research Environment(DRE)-enabled Artificial Intelligence (AI) to facilitate early stage drug development

Early-stage drug discovery is highly dependent upon drug target evaluation, understanding of disease progression and identification of patient characteristics linked to disease progression overlaid upon chemical libraries of potential drug candidates. Artificial intelligence (AI) has become a credible approach towards dealing with the diversity and volume of data in the modern drug development phase. There are a growing number of services and solutions available to pharmaceutical sponsors though most prefer to constrain their own data to closed solutions given the intellectual property considerations. Newer platforms offer an alternative, outsourced solution leveraging sponsors data with other, external open-source data to anchor predictions (often proprietary algorithms) which are refined given data indexed upon the sponsor's own chemical libraries. Digital research environments (DREs) provide a mechanism to ingest, curate, integrate and otherwise manage the diverse data types relevant for drug discovery activities and also provide workspace services from which target sharing and collaboration can occur providing yet another alternative with sponsors being in control of the platform, data and predictive algorithms. Regulatory engagement will be essential in the operationalizing of the various solutions and alternatives; current treatment of drug discovery data may not be adequate with respect to both quality and useability in the future. More sophisticated AI/ML algorithms are likely based on current performance metrics and diverse data types (e.g., imaging and genomic data) will certainly be a more consistent part of the myriad of data types that fuel future AI-based algorithms. This favors a dynamic DRE-enabled environment to support drug discovery.

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.

Predicting in vivo activity of combination therapies from in vitro drug pairs in diverse environments

New antibiotic combinations are needed to improve the treatment of tuberculosis. Larkins-Ford and colleagues share a framework that combines in vitro pairwise drug response data and machine learning to rationally prioritize combinations for clinical development.¹