Metabolic network analysis predicts efficacy of FDA-approved drugs targeting the causative agent of a neglected tropical disease

Drug Discovery for Cutaneous Leishmaniasis: A Review of Developments in the Past 15 Years

Leishmaniasis is a group of vector-borne, parasitic diseases caused by over 20 species of the protozoan Leishmania spp. The three major disease classifications, cutaneous, visceral, and mucocutaneous, have a range of clinical manifestations from self-healing skin lesions to hepatosplenomegaly and mucosal membrane damage to fatality. As a neglected tropical disease, leishmaniasis represents a major international health challenge, with nearly 350 million people living at risk of infection a year. The current chemotherapeutics used to treat leishmaniasis have harsh side effects, prolonged and costly treatment regimens, as well as emerging drug resistance, and are predominantly used for the treatment of visceral leishmaniasis. There is an undeniable need for the identification and development of novel chemotherapeutics targeting cutaneous leishmaniasis (CL), largely ignored by concerted drug development efforts. CL is mostly non-lethal and the most common presentation of this disease, with nearly 1 million new cases reported annually. Recognizing this unaddressed need, substantial yet fragmented progress in early drug discovery efforts for CL has occurred in the past 15 years and was outlined in this review. However, further work needs to be carried out to advance early discovery candidates towards the clinic. Importantly, there is a paucity of investment in the translation and development of therapies for CL, limiting the emergence of viable solutions to deal with this serious and complex international health problem.

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.

Carbohydrate metabolism in trypanosomatids: New insights revealing novel complexity, diversity and species-unique features

The human pathogenic trypanosomatid species collectively called the "TriTryp parasites" - Trypanosoma brucei, Trypanosoma cruzi and Leishmania spp. - have complex life cycles, with each of these parasitic protists residing in a different niche during their successive developmental stages where they encounter diverse nutrients. Consequently, they adapt their metabolic network accordingly. Yet, throughout the life cycles, carbohydrate metabolism - involving the glycolytic, gluconeogenic and pentose-phosphate pathways - always plays a central role in the biology of these parasites, whether the available carbon and free energy sources are saccharides, amino acids or lipids. In this paper, we provide an updated review of the carbohydrate metabolism of the TriTryps, highlighting new data about this metabolic network, the interconnection of its pathways and the compartmentalisation of its enzymes within glycosomes, cytosol and mitochondrion. Differences in the expression of the branches of the metabolic network between the successive life-cycle stages of each of these parasitic trypanosomatids are discussed, as well as differences between them. Recent structural and kinetic studies have revealed unique regulatory mechanisms for some of the network's key enzymes with important species-specific variations. Furthermore, reports of multiple post-translational modifications of trypanosomal glycolytic enzymes suggest that additional mechanisms for stage- and/or environmental cues that regulate activity are operational in the parasites. The detailed comparison of the carbohydrate metabolism of the TriTryps has thus revealed multiple differences and a greater complexity, including for the reduced metabolic network in bloodstream-form T. brucei, than previously appreciated. Although these parasites are related, share many cytological and metabolic features and are grouped within a single taxonomic family, the differences highlighted in this review reflect their separate evolutionary tracks from a common ancestor to the extant organisms. These differences are indicative of their adaptation to the different insect vectors and niches occupied in their mammalian hosts.

An Overview on the Therapeutics of Neglected Infectious Diseases-Leishmaniasis and Chagas Diseases

Neglected tropical diseases (NTDs) as termed by WHO include twenty different infectious diseases that are caused by bacteria, viruses, and parasites. Among these NTDs, Chagas disease and leishmaniasis are reported to cause high mortality in humans and are further associated with the limitations of existing drugs like severe toxicity and drug resistance. The above hitches have rendered researchers to focus on developing alternatives and novel therapeutics for the treatment of these diseases. In the past decade, several target-based drugs have emerged, which focus on specific biochemical pathways of the causative parasites. For leishmaniasis, the targets such as nucleoside analogs, inhibitors targeting nucleoside phosphate kinases of the parasite’s purine salvage pathway, 20S proteasome of Leishmania, mitochondria, and the associated proteins are reviewed along with the chemical structures of potential drug candidates. Similarly, in case of therapeutics for Chagas disease, several target-based drug candidates targeting sterol biosynthetic pathway (C14-ademethylase), L-cysteine protease, heme peroxidation, mitochondria, farnesyl pyrophosphate, etc., which are vital and unique to the causative parasite are discussed. Moreover, the use of nano-based formulations towards the therapeutics of the above diseases is also discussed.

Repurposing of known drugs for leishmaniasis treatment using bioinformatic predictions, in vitro validations and pharmacokinetic simulations

Leishmaniasis is a neglected tropical disease caused by Leishmania parasites and is associated to more than 1.3 million cases annually. Some of the pharmacological options for treating the disease are pentavalent antimonials, pentamidine, miltefosine, and amphotericin B. However, all are associated with a wide range of adverse effects and contraindications, as well as resistance from the parasite. In the present study, we looked for pharmacological alternatives to treat leishmaniasis, with a focus on drug repurposing. This was done by detecting potential homologs between proteins targeted by approved drugs and proteins of the parasite. The proteins were analyzed using an interaction network, and the drugs were subjected to in vitro evaluations and pharmacokinetics simulations to compare probable plasma concentrations with the effective concentrations detected experimentally. This strategy yielded a list of 33 drugs with potential anti-Leishmania activity, and more than 80 possible protein targets in the parasite. From the drugs tested, two reported high in vitro activity (perphenazine EC₅₀ = 1.2 µg/mL and rifabutin EC₅₀ = 8.5 µg/mL). These results allowed us to propose these drugs as candidates for further in vivo studies and evaluations of the effectiveness on their topical forms.

Drug repurposing for antimicrobial discovery

Antimicrobial resistance continues to be a public threat on a global scale. The ongoing need to develop new antimicrobial drugs that are effective against multi-drug-resistant pathogens has spurred the research community to invest in various drug discovery strategies, one of which is drug repurposing-the process of finding new uses for existing drugs. While still nascent in the antimicrobial field, the approach is gaining traction in both the public and private sector. While the approach has particular promise in fast-tracking compounds into clinical studies, it nevertheless has substantial obstacles to success. This Review covers the art of repurposing existing drugs for antimicrobial purposes. We discuss enabling screening platforms for antimicrobial discovery and present encouraging findings of novel antimicrobial therapeutic strategies. Also covered are general advantages of repurposing over de novo drug development and challenges of the strategy, including scientific, intellectual property and regulatory issues.

Synergy from gene expression and network mining (SynGeNet) method predicts synergistic drug combinations for diverse melanoma genomic subtypes

Systems biology perspectives are crucial for understanding the pathophysiology of complex diseases, and therefore hold great promise for the discovery of novel treatment strategies. Drug combinations have been shown to improve durability and reduce resistance to available first-line therapies in a variety of cancers; however, traditional drug discovery approaches are prohibitively cost and labor-intensive to evaluate large-scale matrices of potential drug combinations. Computational methods are needed to efficiently model complex interactions of drug target pathways and identify mechanisms underlying drug combination synergy. In this study, we employ a computational approach, SynGeNet (Synergy from Gene expression and Network mining), which integrates transcriptomics-based connectivity mapping and network centrality analysis to analyze disease networks and predict drug combinations. As an exemplar of a disease in which combination therapies demonstrate efficacy in genomic-specific contexts, we investigate malignant melanoma. We employed SynGeNet to generate drug combination predictions for each of the four major genomic subtypes of melanoma (BRAF, NRAS, NF1, and triple wild type) using publicly available gene expression and mutation data. We validated synergistic drug combinations predicted by our method across all genomic subtypes using results from a high-throughput drug screening study across. Finally, we prospectively validated the drug combination for BRAF-mutant melanoma that was top ranked by our approach, vemurafenib (BRAF inhibitor) + tretinoin (retinoic acid receptor agonist), using both in vitro and in vivo models of BRAF-mutant melanoma and RNA-sequencing analysis of drug-treated melanoma cells to validate the predicted mechanisms. Our approach is applicable to a wide range of disease domains, and, importantly, can model disease-relevant protein subnetworks in precision medicine contexts.

Network-Based Drug Repositioning: Approaches, Resources, and Research Directions

The wealth of knowledge and omic data available in drug research allowed the rising of several computational methods in drug discovery field yielding a novel and exciting application called drug repositioning. Several computational methods try to make a high-level integration of all the knowledge in order to discover unknown mechanisms. In this chapter we present an in-depth review of data resources and computational models for drug repositioning.

Transcriptomic Data Mining and Repurposing for Computational Drug Discovery

Conventional drug discovery in general is costly and time-consuming with extremely low success and relatively high attrition rates. The disparity between high cost of drug discovery and vast unmet medical needs resulted in advent of an increasing number of computational approaches that can "connect" disease with a candidate therapeutic. This includes computational drug repurposing or repositioning wherein the goal is to discover a new indication for an approved drug. Computational drug discovery approaches that are commonly used are similarity-based wherein network analysis or machine learning-based methods are used. One such approach is matching gene expression signatures from disease to those from small molecules, commonly referred to as connectivity mapping. In this chapter, we will focus on how publicly available existing transcriptomic data from diseases can be reused to identify novel candidate therapeutics and drug repositioning candidates. To elucidate these, we will present two case studies: (1) using transcriptional signature similarity or positive correlation to identify novel small molecules that are similar to an approved drug and (2) identifying candidate therapeutics via reciprocal connectivity or negative correlation between transcriptional signatures from a disease and small molecule.

Genome-Scale Modeling of NADPH-Driven β-Lapachone Sensitization in Head and Neck Squamous Cell Carcinoma

AIMS

The purpose of this study was to investigate differential nicotinamide adenine dinucleotide phosphate, reduced (NADPH) production between radiation-sensitive and -resistant head and neck squamous cell carcinoma (HNSCC) cell lines and whether these differences are predictive of sensitivity to the chemotherapeutic β-lapachone.

RESULTS

We have developed a novel human genome-scale metabolic modeling platform that combines transcriptomic, kinetic, thermodynamic, and metabolite concentration data. Upon incorporation of this information into cell line-specific models, we observed that the radiation-resistant HNSCC model redistributed flux through several major NADPH-producing reactions. Upon RNA interference of canonical NADPH-producing genes, the metabolic network can further reroute flux through alternate NADPH biosynthesis pathways in a cell line-specific manner. Model predictions of perturbations in cellular NADPH production after gene knockdown match well with experimentally verified effects of β-lapachone treatment on NADPH/NADP⁺ ratio and cell viability. This computational approach accurately predicts HNSCC-specific oxidoreductase genes that differentially affect cell viability between radiation-responsive and radiation-resistant cancer cells upon β-lapachone treatment.

INNOVATION

Quantitative genome-scale metabolic models that incorporate multiple levels of biological data are applied to provide accurate predictions of responses to a NADPH-dependent redox cycling chemotherapeutic drug under a variety of perturbations.

CONCLUSION

Our modeling approach suggests differences in metabolism and β-lapachone redox cycling that underlie phenotypic differences in radiation-sensitive and -resistant cancer cells. This approach can be extended to investigate the synergistic action of NAD(P)H: quinone oxidoreductase 1 bioactivatable drugs and radiation therapy. Antioxid. Redox Signal. 29, 937-952.

Systems Approach Reveals Nuclear Factor Erythroid 2-Related Factor 2/Protein Kinase R Crosstalk in Human Cutaneous Leishmaniasis

Leishmania parasites infect macrophages, causing a wide spectrum of human diseases, from cutaneous to visceral forms. In search of novel therapeutic targets, we performed comprehensive in vitro and ex vivo mapping of the signaling pathways upstream and downstream of antioxidant transcription factor [nuclear factor erythroid 2-related factor 2 (Nrf2)] in cutaneous leishmaniasis (CL), by combining functional assays in human and murine macrophages with a systems biology analysis of in situ (skin biopsies) CL patient samples. First, we show the PKR pathway controls the expression and activation of Nrf2 in Leishmania amazonensis infection in vitro. Nrf2 activation also required PI3K/Akt signaling and autophagy mechanisms. Nrf2- or PKR/Akt-deficient macrophages exhibited increased levels of ROS/RNS and reduced expression of Sod1 Nrf2-dependent gene and reduced parasite load. L. amazonensis counteracted the Nrf2 inhibitor Keap1 through the upregulation of p62 via PKR. This Nrf2/Keap1 observation was confirmed in situ in skin biopsies from Leishmania-infected patients. Next, we explored the ex vivo transcriptome in CL patients, as compared to healthy controls. We found the antioxidant response element/Nrf2 signaling pathway was significantly upregulated in CL, including downstream target p62. In silico enrichment analysis confirmed upstream signaling by interferon and PI3K/Akt, and validated our in vitro findings. Our integrated in vitro, ex vivo, and in silico approach establish Nrf2 as a central player in human cutaneous leishmaniasis and reveal Nrf2/PKR crosstalk and PI3K/Akt pathways as potential therapeutic targets.

DETC-based bacterial cellulose bio-curatives for topical treatment of cutaneous leishmaniasis

The treatment of leishmaniasis still relies on drugs with potentially serious adverse effects. Herein, we tested a topical formulation of bacterial cellulose (BC) membranes containing Diethyldithiocarbamate (DETC), a superoxide dismutase 1 inhibitor. Leishmania-infected macrophages exposed to BC-DETC resulted in parasite killing, without pronounced toxic effects to host cells. This outcome was associated with lower SOD1 activity and higher production of superoxide and cytokine mediators. Topical application of BC-DETC significantly decreased lesion size, parasite load and the inflammatory response at the infection site, as well as the production of both IFN-γ and TNF. Combination of topical BC-DETC plus intraperitoneal Sb^v also significantly reduced disease development and parasite load. The leishmanicidal effect of BC-DETC was extended to human macrophages infected with L. braziliensis, highlighting the feasibility of BC-DETC as a topical formulation for chemotherapy of cutaneous leishmaniasis caused by L. braziliensis.

Multiscale Model of Mycobacterium tuberculosis Infection Maps Metabolite and Gene Perturbations to Granuloma Sterilization Predictions

Granulomas are a hallmark of tuberculosis. Inside granulomas, the pathogen Mycobacterium tuberculosis may enter a metabolically inactive state that is less susceptible to antibiotics. Understanding M. tuberculosis metabolism within granulomas could contribute to reducing the lengthy treatment required for tuberculosis and provide additional targets for new drugs. Two key adaptations of M. tuberculosis are a nonreplicating phenotype and accumulation of lipid inclusions in response to hypoxic conditions. To explore how these adaptations influence granuloma-scale outcomes in vivo, we present a multiscale in silico model of granuloma formation in tuberculosis. The model comprises host immunity, M. tuberculosis metabolism, M. tuberculosis growth adaptation to hypoxia, and nutrient diffusion. We calibrated our model to in vivo data from nonhuman primates and rabbits and apply the model to predict M. tuberculosis population dynamics and heterogeneity within granulomas. We found that bacterial populations are highly dynamic throughout infection in response to changing oxygen levels and host immunity pressures. Our results indicate that a nonreplicating phenotype, but not lipid inclusion formation, is important for long-term M. tuberculosis survival in granulomas. We used virtual M. tuberculosis knockouts to predict the impact of both metabolic enzyme inhibitors and metabolic pathways exploited to overcome inhibition. Results indicate that knockouts whose growth rates are below ∼66% of the wild-type growth rate in a culture medium featuring lipid as the only carbon source are unable to sustain infections in granulomas. By mapping metabolite- and gene-scale perturbations to granuloma-scale outcomes and predicting mechanisms of sterilization, our method provides a powerful tool for hypothesis testing and guiding experimental searches for novel antituberculosis interventions.

In silico methods for drug repurposing and pharmacology

Data in the biological, chemical, and clinical domains are accumulating at ever-increasing rates and have the potential to accelerate and inform drug development in new ways. Challenges and opportunities now lie in developing analytic tools to transform these often complex and heterogeneous data into testable hypotheses and actionable insights. This is the aim of computational pharmacology, which uses in silico techniques to better understand and predict how drugs affect biological systems, which can in turn improve clinical use, avoid unwanted side effects, and guide selection and development of better treatments. One exciting application of computational pharmacology is drug repurposing-finding new uses for existing drugs. Already yielding many promising candidates, this strategy has the potential to improve the efficiency of the drug development process and reach patient populations with previously unmet needs such as those with rare diseases. While current techniques in computational pharmacology and drug repurposing often focus on just a single data modality such as gene expression or drug-target interactions, we argue that methods such as matrix factorization that can integrate data within and across diverse data types have the potential to improve predictive performance and provide a fuller picture of a drug's pharmacological action. WIREs Syst Biol Med 2016, 8:186-210. doi: 10.1002/wsbm.1337 For further resources related to this article, please visit the WIREs website.

Pathogenesis--Thoughts from the Front Line

The field of bacterial pathogenesis has advanced dramatically in the last decade. High throughput molecular technologies have empowered scientists as never before. However, there remain some limitations, misconceptions and ambiguities in the field that may bedevil even the experienced investigator. Here, I consider some of the unanswered questions that are not readily tractable to even the most powerful technology.

Dissecting Leishmania infantum Energy Metabolism - A Systems Perspective

Leishmania infantum, causative agent of visceral leishmaniasis in humans, illustrates a complex lifecycle pertaining to two extreme environments, namely, the gut of the sandfly vector and human macrophages. Leishmania is capable of dynamically adapting and tactically switching between these critically hostile situations. The possible metabolic routes ventured by the parasite to achieve this exceptional adaptation to its varying environments are still poorly understood. In this study, we present an extensively reconstructed energy metabolism network of Leishmania infantum as an attempt to identify certain strategic metabolic routes preferred by the parasite to optimize its survival in such dynamic environments. The reconstructed network consists of 142 genes encoding for enzymes performing 237 reactions distributed across five distinct model compartments. We annotated the subcellular locations of different enzymes and their reactions on the basis of strong literature evidence and sequence-based detection of cellular localization signal within a protein sequence. To explore the diverse features of parasite metabolism the metabolic network was implemented and analyzed as a constraint-based model. Using a systems-based approach, we also put forth an extensive set of lethal reaction knockouts; some of which were validated using published data on Leishmania species. Performing a robustness analysis, the model was rigorously validated and tested for the secretion of overflow metabolites specific to Leishmania under varying extracellular oxygen uptake rate. Further, the fate of important non-essential amino acids in L. infantum metabolism was investigated. Stage-specific scenarios of L. infantum energy metabolism were incorporated in the model and key metabolic differences were outlined. Analysis of the model revealed the essentiality of glucose uptake, succinate fermentation, glutamate biosynthesis and an active TCA cycle as driving forces for parasite energy metabolism and its optimal growth. Finally, through our in silico knockout analysis, we could identify possible therapeutic targets that provide experimentally testable hypotheses.

Modelling of compound combination effects and applications to efficacy and toxicity: state-of-the-art, challenges and perspectives

The development of treatments involving combinations of drugs is a promising approach towards combating complex or multifactorial disorders. However, the large number of compound combinations that can be generated, even from small compound collections, means that exhaustive experimental testing is infeasible. The ability to predict the behaviour of compound combinations in biological systems, whittling down the number of combinations to be tested, is therefore crucial. Here, we review the current state-of-the-art in the field of compound combination modelling, with the aim to support the development of approaches that, as we hope, will finally lead to an integration of chemical with systems-level biological information for predicting the effect of chemical mixtures.

Systems analysis of host-parasite interactions

Parasitic diseases caused by protozoan pathogens lead to hundreds of thousands of deaths per year in addition to substantial suffering and socioeconomic decline for millions of people worldwide. The lack of effective vaccines coupled with the widespread emergence of drug‐resistant parasites necessitates that the research community take an active role in understanding host–parasite infection biology in order to develop improved therapeutics. Recent advances in next‐generation sequencing and the rapid development of publicly accessible genomic databases for many human pathogens have facilitated the application of systems biology to the study of host–parasite interactions. Over the past decade, these technologies have led to the discovery of many important biological processes governing parasitic disease. The integration and interpretation of high‐throughput ‐omic data will undoubtedly generate extraordinary insight into host–parasite interaction networks essential to navigate the intricacies of these complex systems. As systems analysis continues to build the foundation for our understanding of host–parasite biology, this will provide the framework necessary to drive drug discovery research forward and accelerate the development of new antiparasitic therapies. WIREs Syst Biol Med 2015, 7:381–400. doi: 10.1002/wsbm.1311

For further resources related to this article, please visit the WIREs website.

Antileishmanial Activity of Disulfiram and Thiuram Disulfide Analogs in an Ex Vivo Model System Is Selectively Enhanced by the Addition of Divalent Metal Ions

Current treatments for cutaneous and visceral leishmaniasis are toxic, expensive, difficult to administer, and limited in efficacy and availability. Disulfiram has primarily been used to treat alcoholism. More recently, it has shown some efficacy as therapy against protozoan pathogens and certain cancers, suggesting a wide range of biological activities. We used an ex vivo system to screen several thiuram disulfide compounds for antileishmanial activity. We found five compounds (compound identifier [CID] 7188, 5455, 95876, 12892, and 3117 [disulfiram]) with anti-Leishmania activity at nanomolar concentrations. We further evaluated these compounds with the addition of divalent metal salts based on studies that indicated these salts could potentiate the action of disulfiram. In addition, clinical studies suggested that zinc has some efficacy in treating cutaneous leishmaniasis. Several divalent metal salts were evaluated at 1 μM, which is lower than the normal levels of copper and zinc in plasma of healthy individuals. The leishmanicidal activity of disulfiram and CID 7188 were enhanced by several divalent metal salts at 1 μM. The in vitro therapeutic index (IVTI) of disulfiram and CID 7188 increased 12- and 2.3-fold, respectively, against L. major when combined with ZnCl2. The combination of disulfiram with ZnSO4 resulted in a 1.8-fold increase in IVTI against L. donovani. This novel combination of thiuram disulfides and divalent metal ions salts could have application as topical and/or oral therapies for treatment of cutaneous and visceral leishmaniasis.

Drug target identification through systems biology

To rationalise drug target selection, we should understand the role of putative targets in biological pathways quantitatively. We review here how experimental and computational network-based approaches can aid more rational drug target selection and illustrate this with results obtained for microbes and for cancer. Comparison of the drug response of biochemical networks in target cells and (healthy) host cells can reveal network-selective targets.

A multi-scale approach to designing therapeutics for tuberculosis

Approximately one third of the world's population is infected with Mycobacterium tuberculosis. Limited information about how the immune system fights M. tuberculosis and what constitutes protection from the bacteria impact our ability to develop effective therapies for tuberculosis. We present an in vivo systems biology approach that integrates data from multiple model systems and over multiple length and time scales into a comprehensive multi-scale and multi-compartment view of the in vivo immune response to M. tuberculosis. We describe computational models that can be used to study (a) immunomodulation with the cytokines tumor necrosis factor and interleukin 10, (b) oral and inhaled antibiotics, and

Rapid countermeasure discovery against Francisella tularensis based on a metabolic network reconstruction

In the future, we may be faced with the need to provide treatment for an emergent biological threat against which existing vaccines and drugs have limited efficacy or availability. To prepare for this eventuality, our objective was to use a metabolic network-based approach to rapidly identify potential drug targets and prospectively screen and validate novel small-molecule antimicrobials. Our target organism was the fully virulent Francisella tularensis subspecies tularensis Schu S4 strain, a highly infectious intracellular pathogen that is the causative agent of tularemia and is classified as a category A biological agent by the Centers for Disease Control and Prevention. We proceeded with a staggered computational and experimental workflow that used a strain-specific metabolic network model, homology modeling and X-ray crystallography of protein targets, and ligand- and structure-based drug design. Selected compounds were subsequently filtered based on physiological-based pharmacokinetic modeling, and we selected a final set of 40 compounds for experimental validation of antimicrobial activity. We began screening these compounds in whole bacterial cell-based assays in biosafety level 3 facilities in the 20th week of the study and completed the screens within 12 weeks. Six compounds showed significant growth inhibition of F. tularensis, and we determined their respective minimum inhibitory concentrations and mammalian cell cytotoxicities. The most promising compound had a low molecular weight, was non-toxic, and abolished bacterial growth at 13 µM, with putative activity against pantetheine-phosphate adenylyltransferase, an enzyme involved in the biosynthesis of coenzyme A, encoded by gene coaD. The novel antimicrobial compounds identified in this study serve as starting points for lead optimization, animal testing, and drug development against tularemia. Our integrated in silico/in vitro approach had an overall 15% success rate in terms of active versus tested compounds over an elapsed time period of 32 weeks, from pathogen strain identification to selection and validation of novel antimicrobial compounds.

Mechanistic systems modeling to guide drug discovery and development

A crucial question that must be addressed in the drug development process is whether the proposed therapeutic target will yield the desired effect in the clinical population. Pharmaceutical and biotechnology companies place a large investment on research and development, long before confirmatory data are available from human trials. Basic science has greatly expanded the computable knowledge of disease processes, both through the generation of large omics data sets and a compendium of studies assessing cellular and systemic responses to physiologic and pathophysiologic stimuli. Given inherent uncertainties in drug development, mechanistic systems models can better inform target selection and the decision process for advancing compounds through preclinical and clinical research.

Structural and dynamical analysis of biological networks

Biological networks are currently being studied with approaches derived from the mathematical and physical sciences. Their structural analysis enables to highlight nodes with special properties that have sometimes been correlated with the biological importance of a gene or a protein. However, biological networks are dynamic both on the evolutionary time-scale, and on the much shorter time-scale of physiological processes. There is therefore no unique network for a given cellular process, but potentially many realizations, each with different properties as a consequence of regulatory mechanisms. Such realizations provide snapshots of a same network in different conditions, enabling the study of condition-dependent structural properties. True dynamical analysis can be obtained through detailed mathematical modeling techniques that are not easily scalable to full network models.