Dose Schedule Optimization and the Pharmacokinetic Driver of Neutropenia

Toxicity often limits the utility of oncology drugs, and optimization of dose schedule represents one option for mitigation of this toxicity. Here we explore the schedule-dependency of neutropenia, a common dose-limiting toxicity. To this end, we analyze previously published mathematical models of neutropenia to identify a pharmacokinetic (PK) predictor of the neutrophil nadir, and confirm this PK predictor in an in vivo experimental system. Specifically, we find total AUC and C_max are poor predictors of the neutrophil nadir, while a PK measure based on the moving average of the drug concentration correlates highly with neutropenia. Further, we confirm this PK parameter for its ability to predict neutropenia in vivo following treatment with different doses and schedules. This work represents an attempt at mechanistically deriving a fundamental understanding of the underlying pharmacokinetic drivers of neutropenia, and provides insights that can be leveraged in a translational setting during schedule selection.     

Pharmacogenomics and systems biology of membrane transporters

Membrane transporters are essential for fundamental cellular functions and normal physiological processes. These molecules influence drug absorption and distribution, and play key roles in drug therapeutic effects. A primary goal of current research in drug discovery and development is to fully understand the interaction between transporters and drugs at both system level and individual level for personalized therapy. Pharmacogenomics studies the genetic basis of the individual variations in response to drug therapy, whereas systems biology provides the understanding of biological processes at the system level. The integration of pharmacogenomics with systems biology in membrane transporter study is necessary to solve complex problems in diseases and drug effects. Such integration provides insight to key issues of pharmacogenomics and systems biology of membrane transporters. These key issues include the correlations between structure and function, genotype and phenotype, and systematic interactions between different transporters, between transporters and other proteins, and between transporters and drugs. The exploration in these key issues may ultimately contribute to the personalized medicine with high efficacy but less toxicity, which is the overall goal of pharmacogenomics and systems biology.     

Protein–protein interaction network analysis of insecticide resistance molecular mechanism in Drosophila melanogaster

The problem of resistance has not been solved fundamentally at present, because the development speed of new insecticides can not keep pace with the development speed of resistance, and the lack of understanding of molecular mechanism of resistance. Here we collected seed genes and their interacting proteins involved in insecticide resistance molecular mechanism in Drosophila melanogaster by literature mining and the String database. We identified a total of 528 proteins and 13514 protein–protein interactions. The protein interaction network was constructed by String and Pajek, and we analyzed the topological properties, such as degree centrality and eigenvector centrality. Proteasome complexes and drug metabolism—cytochrome P450 were an enrichment by Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. This is the first time to explore the insecticide resistance molecular mechanism of D. melanogaster by the methods and tools of network biology, it can provide the bioinformatic foundation for further understanding the mechanisms of insecticide resistance.     

Protein–protein interaction networks suggest different targets have different propensities for triggering drug resistance

Emergence of drug resistance is a major problem in the treatment of many diseases including tuberculosis. To tackle the problem from a wholistic perspective, it is essential to understand the molecular mechanisms by which bacteria acquire drug resistance using a systems approach. Availability of genome-scale data of expression profiles under different drug exposed conditions and protein–protein interactions, makes it feasible to reconstruct and analyze systems-level models. A number of proteins involved in different resistance mechanisms, referred to as the resistome are identified from literature. The interaction of the drug directly with the resistome is unable to explain most resistance processes adequately, including that of increased mutations in the target’s binding site. We recently hypothesized that some communication might exist from the drug environment to the resistome to trigger emergence of drug resistance. We report here a network based approach to identify most plausible paths of such communication in Mycobacterium tuberculosis. Networks capturing both structural and functional linkages among various proteins were weighted based on gene expression profiles upon exposure to specific drugs and betweenness centrality of the interactions. Our analysis suggests that different drug targets and hence different drugs could trigger the resistome to different extents and through different routes. The identified paths correlate well with the mechanisms known through experiment. Some examples of the top ranked hubs in multiple drug specific networks are PolA, FadD1, CydA, a monoxygenase and GltS, which could serve as co-targets, that could be inhibited in order to retard resistance related communication in the cell.     

A topological framework for the computation of the HOMFLY polynomial and its application to proteins

Polymers can be modeled as open polygonal paths and their closure generates knots. Knotted proteins detection is currently achieved via high-throughput methods based on a common framework insensitive to the handedness of knots. Here we propose a topological framework for the computation of the HOMFLY polynomial, an handedness-sensitive invariant. Our approach couples a multi-component reduction scheme with the polynomial computation. After validation on tabulated knots and links the framework was applied to the entire Protein Data Bank along with a set of selected topological checks that allowed to discard artificially entangled structures. This led to an up-to-date table of knotted proteins that also includes two newly detected right-handed trefoil knots in recently deposited protein structures. The application range of our framework is not limited to proteins and it can be extended to the topological analysis of biological and synthetic polymers and more generally to arbitrary polygonal paths.     

Target network analysis of adiponectin,a multifaceted adipokine

Application of network analysis to dissect the potential molecular mechanisms of biological processes and complicated diseases has been the new trend in biology and medicine in recent years. Among which, the protein–protein interactions (PPI) networks attract interests of most researchers. Adiponectin, a cytokine secreted from adipose tissue, participates in a number of metabolic processes, including glucose regulation and fatty acid metabolism and involves in a series of complicated diseases from head to toe. Hundreds of proteins including many identified and potential drug targets have been reported to be involved in adiponectin related signaling pathways, which comprised a complicated regulation network. Therapeutic target database (TTD) provides extensive information about the known and explored therapeutic protein targets and the signaling pathway information. In this study, adiponectin associated drug targets based PPI was constructed and its topological properties were analyzed, which might provide some insight into the dissection of adiponectin action mechanisms and promote adiponectin signaling based drug target identification and drug discovery. J. Cell. Biochem. 114: 1145–1152, 2013. © 2012 Wiley Periodicals, Inc.     

Differential toxicity of anticancer drugs on late (GM-CFC) and early (LTC-IC) hemopoietic progenitors in vitro

The clinical hematological toxicity of cytotoxic drugs can be acute, with a nadir of neutrophil count after 2 weeks and recovery the following week, or subacute, with a nadir of neutrophil count after 3 weeks and recovery in the following 2–3 weeks. The explanation usually given for this difference is that drugs in the first group are more toxic to mature hemopoietic precursors, while drugs of the second type are more toxic to undifferentiated cells. In an attempt to verify this hypothesis, we compared in vitro the effect of toxic doses of etoposide and tallimustine as representatives of drugs with acute toxicity, and of BCNU, melphalan, and carzelesin as representatives of drugs with subacute toxicity. Their effects were studied separately on more differentiated and earlier progenitors represented by granulocyte–macrophage colony-forming cells (GM-CFC) and long-term culture-initiating cells (LTC-IC), respectively. Etoposide, melphalan, BCNU, and carzelesin showed higher toxicity in differentiated than in early precursors: the concentration of drug inhibiting 70% (ID₇₀) of GM-CFC inhibited only by 10–40% the growth of LTC-IC. Tallimustine, in contrast, inhibited both GM-CFC and LTC-IC at comparable levels. These results do not correspond to the clinical pattern of myelotoxicity observed for those drugs. We conclude that the differential effects of antitumor drugs on later (GM-CFC) or earlier (LTC-IC) hemopoietic precursors may not represent a valid model for the pattern of myelotoxicity observed in humans. This revised version was published online in July 2006 with corrections to the Cover Date.     

Internet Resources Related to Drug Action and Human Response: A Review

It has been demonstrated that numerous proteins interact with drugs or their metabolites. Knowledge of these proteins is necessary to understand the mechanisms of drug action and human response. Progress in modern genetics, molecular biology, biochemistry and pharmacology is generating a comprehensive mechanistic understanding of drug-target interaction on the molecular level. This is valuable for researchers and pharmaceutical companies in their efforts to improve the efficacy of existing drugs and to discover new ones. Most recently, the integration of a systems biology approach into drug discovery processes calls for more holistic knowledge and easily accessible resources of the proteins that are important in drug action and human response. We have reviewed many publicly accessible internet resources of these proteins, according to their roles in drug action and human response, such as therapeutic effect, adverse reaction, absorption, distribution, metabolism and excretion.     

Proteomic analysis in NSAIDs-treated primary cardiomyocytes

NSAIDs (non-steroidal anti-inflammatory drugs) are widely used for the treatment of a variety of inflammatory diseases, but many of them were withdrawn from the market due to their cardiovascular toxicity. In this study, we tried to identify proteins responding to the cellular toxicity in NSAIDs-treated primarily cultured cardiomyocytes using 2-D proteomic analysis. We used seven different NSAIDs (celecoxib, rofecoxib, valdecoxib, diclofenac, naproxen, ibuprofen, and meloxicam) possessing each different degree of cardiovascular risk. Overall protein spots were similar in all NSAIDs-treated cells although numbers of decreased proteins were about 2-fold higher in celecoxib or rofecoxib-treated cells than in cells incubated with other NSAIDs. Many stress-related proteins, cardiac muscle movement proteins and proteins involved in membrane organization have been isolated. Among them, Septin-8, a filament scaffolding protein, showed its specific expression pattern depending on the extent of drug toxicity. Its expression level was low in cells treated by relatively high toxic drugs such as celecoxib, diclofenac, valdecoxib, and rofecoxib. On the contrary, Septin-8 was similarly expressed in control cells in the presence of less toxic drugs such ibuprofen, naproxen, and meloxicam. This data suggests that Septin-8 differentially responds to each NSAID.     

Effects of lithium carbonate on hematopoietic cells in patients with persistent neutropenia following chemotherapy or radiotherapy.

Neoplasm therapy is restricted by the haematological side effects of tumour-destructive therapy, requiring expensive supportive care to some extent to overcome and treat leucopenia and its consequences. An effective and very cost-effective alternative for treating neutropenia is to administer lithium carbonate. Lithium leads to a release of hematopoietic growth factors (CSF) and therefore to proliferation of neutrophil granulocytes. Normally, recombinant CSF is only administered when there are indications of severe neutropenia because of the high costs involved, all the more evident in the long-term treatment of persistent leucocytopenia. On the other hand, CSF and leucocytes play an essential role in tumour immunology and with regard to response rates to cytostatic drugs. Lithium salts have shown that they can increase the number of neutrophil granulocytes quite significantly and, to a lesser extent, the number of eosinophil granulocytes and lymphocytes as well. The average number of erythrocytes does not change significantly. Patient tolerability to lithium carbonate therapy is very good. It can be used to treat patients with chronic leucopenia following chemotherapy or radiotherapy extremely cost-effectively. Unfortunately this treatment has not won acceptance in clinical oncology in the face of highly cost-intensive treatment with recombinant CSF.     

Allostery and Missense Mutations as Intermittently Linked Promising Aspects of Modern Computational Drug Discovery

Drug research and development is a multidisciplinary field with its own successes. Yet, given the complexity of the process, it also faces challenges over the long development stages and even includes those that develop once a drug is marketed, i.e. drug toxicity and drug resistance. Better success can be achieved via well designed criteria in the early drug development stages. Here, we introduce the concepts of allostery and missense mutations, and argue that incorporation of these two intermittently linked biological phenomena into the early computational drug discovery stages would help to reduce the attrition risk in later stages of the process. We discuss the individual or in concert mechanisms of actions of mutations in allostery. Design of allosteric drugs is challenging compared to orthosteric drugs, yet they have been gaining popularity in recent years as alternative systems for the therapeutic regulation of proteins with an action-at-a-distance mode and non-invasive mechanisms. We propose an easy-to-apply computational allosteric drug discovery protocol which considers the mutation effect, and detail it with three case studies focusing on (1) analysis of effect of an allosteric mutation related to isoniazid drug resistance in tuberculosis; (2) identification of a cryptic pocket in the presence of an allosteric mutation of falcipain-2 as a malarial drug target; and (3) deciphering the effects of SARS-CoV-2 evolutionary mutations on a potential allosteric modulator with changes to allosteric communication paths.     

Genomes2Drugs: Identifies Target Proteins and Lead Drugs from Proteome Data

Background

Genome sequencing and bioinformatics have provided the full hypothetical proteome of many pathogenic organisms. Advances in microarray and mass spectrometry have also yielded large output datasets of possible target proteins/genes. However, the challenge remains to identify new targets for drug discovery from this wealth of information. Further analysis includes bioinformatics and/or molecular biology tools to validate the findings. This is time consuming and expensive, and could fail to yield novel drugs if protein purification and crystallography is impossible. To pre-empt this, a researcher may want to rapidly filter the output datasets for proteins that show good homology to proteins that have already been structurally characterised or proteins that are already targets for known drugs. Critically, those researchers developing novel antibiotics need to select out the proteins that show close homology to any human proteins, as future inhibitors are likely to cross-react with the host protein, causing off-target toxicity effects later in clinical trials.

Methodology/Principal Findings

To solve many of these issues, we have developed a free online resource called Genomes2Drugs which ranks sequences to identify proteins that are (i) homologous to previously crystallized proteins or (ii) targets of known drugs, but are (iii) not homologous to human proteins. When tested using the Plasmodium falciparum malarial genome the program correctly enriched the ranked list of proteins with known drug target proteins.

Conclusions/Significance

Genomes2Drugs rapidly identifies proteins that are likely to succeed in drug discovery pipelines. This free online resource helps in the identification of potential drug targets. Importantly, the program further highlights proteins that are likely to be inhibited by FDA-approved drugs. These drugs can then be rapidly moved into Phase IV clinical studies under ‘change-of-application’ patents.     

Network pharmacology: the next paradigm in drug discovery

The dominant paradigm in drug discovery is the concept of designing maximally selective ligands to act on individual drug targets. However, many effective drugs act via modulation of multiple proteins rather than single targets. Advances in systems biology are revealing a phenotypic robustness and a network structure that strongly suggests that exquisitely selective compounds, compared with multitarget drugs, may exhibit lower than desired clinical efficacy. This new appreciation of the role of polypharmacology has significant implications for tackling the two major sources of attrition in drug development--efficacy and toxicity. Integrating network biology and polypharmacology holds the promise of expanding the current opportunity space for druggable targets. However, the rational design of polypharmacology faces considerable challenges in the need for new methods to validate target combinations and optimize multiple structure-activity relationships while maintaining drug-like properties. Advances in these areas are creating the foundation of the next paradigm in drug discovery: network pharmacology.     

A metabolic network approach for the identification and prioritization of antimicrobial drug targets

For many infectious diseases, novel treatment options are needed in order to address problems with cost, toxicity and resistance to current drugs. Systems biology tools can be used to gain valuable insight into pathogenic processes and aid in expediting drug discovery. In the past decade, constraint-based modeling of genome-scale metabolic networks has become widely used. Focusing on pathogen metabolic networks, we review in silico strategies used to identify effective drug targets and highlight recent successes as well as limitations associated with such computational analyses. We further discuss how accounting for the host environment and even targeting the host may offer new therapeutic options. These systems-level approaches are beginning to provide novel avenues for drug targeting against infectious agents.     

Genomics, systems biology and drug development for infectious diseases

Although a variety of drugs are available for many infectious diseases that predominantly affect the developing world reasons remain for continuing to search for new chemotherapeutics. First, the development of microbial resistance has made some of the most effective and inexpensive drug regimes unreliable and dangerous to use on severely ill patients. Second, many existing antimicrobial drugs show toxicity or are too expensive for countries where the per capita income is in the order of hundreds of dollars per year. In recognition of this, new publicly and privately financed drug discovery efforts have been established to identify and develop new therapies for diseases such as tuberculosis, malaria and AIDS. This in turn, has intensified the need for tools to facilitate drug identification for those microbes whose molecular biology is poorly understood, or which are difficult to grow in the laboratory. While much has been written about how functional genomics can be used to find novel protein targets for chemotherapeutics this review will concentrate on how genome-wide, systems biology approaches may be used following whole organism, cell-based screening to understand the mechanism of drug action or to identify biological targets of small molecules. Here we focus on protozoan parasites, however, many of the approaches can be applied to pathogenic bacteria or parasitic helminths, insects or disease-causing fungi.     

Proteomic analysis of pancreatic ductal carcinoma cells after combined treatment with gemcitabine and trichostatin A

The human pancreatic adenocarcinoma cell line T3M4 has been treated with two agents, gemcitabine (2',2'-difluorodeoxycytidine, a drug interfering with DNA synthesis) and trichostatin A (a drug interfering with histone acetylation), both separately and in association. The association of the two drugs showed a marked cooperative effect in inhibiting proliferation and inducing apoptosis of the cells. In an effort to identify differentially expressed proteins in the different drug treatments, the proteomic expression has been studied by two-dimensional gel electrophoresis comparing untreated cells with cells treated with trichostatin A and/or gemcitabine. A total of 81 differentially expressed polypeptide chains have been visualized by setting a 2.5-fold threshold value. Of these, 56 were identified via MALDI-TOF and Q-TOF MS analyses. Most of the regulated proteins are involved in two major biological processes, namely apoptotic cell death and proliferation. Our results demonstrate that the level of activation/repression of the proteins involved in these processes correlates with the growth inhibition and the apoptotic response of the cells subjected to single or combined drug treatment.     

Using a Label Free Quantitative Proteomics Approach to Identify Changes in Protein Abundance in Multidrug-Resistant Mycobacterium tuberculosis

Reports in recent years indicate that the increasing emergence of resistance to drugs be using to TB treatment. The resistance to them severely affects to options for effective treatment. The emergence of multidrug-resistant tuberculosis has increased interest in understanding the mechanism of drug resistance in M. tuberculosis and the development of new therapeutics, diagnostics and vaccines. In this study, a label-free quantitative proteomics approach has been used to analyze proteome of multidrug-resistant and susceptible clinical isolates of M. tuberculosis and identify differences in protein abundance between the two groups. With this approach, we were able to identify a total of 1,583 proteins. The majority of identified proteins have predicted roles in lipid metabolism, intermediary metabolism, cell wall and cell processes. Comparative analysis revealed that 68 proteins identified by at least two peptides showed significant differences of at least twofolds in relative abundance between two groups. In all protein differences, the increase of some considering proteins such as NADH dehydrogenase, probable aldehyde dehydrogenase, cyclopropane mycolic acid synthase 3, probable arabinosyltransferase A, putative lipoprotein, uncharacterized oxidoreductase and six membrane proteins in resistant isolates might be involved in the drug resistance and to be potential diagnostic protein targets. The decrease in abundance of proteins related to secretion system and immunogenicity (ESAT-6-like proteins, ESX-1 secretion system associated proteins, O-antigen export system and MPT63) in the multidrug-resistant strains can be a defensive mechanism undertaken by the resistant cell.

Electronic supplementary material

The online version of this article (doi:10.1007/s12088-015-0511-2) contains supplementary material, which is available to authorized users.