Quantitative modeling of dose-response and drug combination based on pathway network

Nano-hydroxyapatite radiolabeled with radium dichloride [223Ra] RaCl2 for bone cancer targeted alpha therapy: In vitro assay and radiation effect on the nanostructure

The use of targeted alpha therapy (TAT) for bone cancer is increasing each year. Among the alpha radionuclides, radium [²²³Ra]Ra⁺² is the first one approved for bone cancer metastasis therapy. The development of novel radiopharmaceutical based on [²²³Ra]Ra⁺² is essential to continuously increase the arsenal of new TAT drugs. In this study we have developed, characterized, and in vitro evaluated [²²³Ra] Ra-nano-hydroxyapatite. The results showed that [²²³Ra] Ra-nano-hydroxyapatite has a dose-response relationship for osteosarcoma cells and a safety profile for human fibroblast cells, corroborating the application as a radiopharmaceutical.

Preventing postpartum hemorrhage with combined therapy rather than oxytocin alone

Postpartum hemorrhage is the leading cause of maternal morbidity and mortality worldwide, with uterine atony estimated to account for 70% to 80% of cases, thereby remaining the single most common cause. Pharmacotherapy remains the first-line preventative therapy for postpartum hemorrhage. These therapies may be single (oxytocin, carbetocin, methylergonovine, ergometrine, misoprostol, prostaglandin analogs, or tranexamic acid) or combination therapies, acting in an additive, infra-additive, or synergistic fashion to prevent postpartum hemorrhage. Evidence is strong for the use of oxytocin, the first-line uterotonic agent in the United States for prevention of postpartum hemorrhage. Although carbetocin, a long-acting analog of oxytocin, is not yet available for use in the United States, it is likely the most effective single pharmacologic therapy for prevention of postpartum hemorrhage and need for additional uterotonics. Use of second-line uterotonics such as methylergonovine, misoprostol, and carboprost in combination with oxytocin has an additive or synergistic effect and a greater risk reduction for postpartum hemorrhage prevention compared with oxytocin alone. Therefore, combined therapy rather than oxytocin alone should be advised for preventing postpartum hemorrhage. Tranexamic acid has been found to be both effective and safe for decreasing maternal mortality in women with postpartum hemorrhage, and prophylactic use of tranexamic acid may decrease the need for packed red blood cell transfusions and/or uterotonics. The WOMAN-2 Trial, designed to assess if tranexamic acid prevents postpartum hemorrhage in women with moderate to severe anemia undergoing vaginal delivery, is currently recruiting participants. The additive, infra-additive, or synergistic action of oxytocin in combination with other second-line therapies deserves further study.

Systematic review of computational methods for drug combination prediction

Synergistic effects between drugs are rare and highly context-dependent and patient-specific. Hence, there is a need to develop novel approaches to stratify patients for optimal therapy regimens, especially in the context of personalized design of combinatorial treatments. Computational methods enable systematic in-silico screening of combination effects, and can thereby prioritize most potent combinations for further testing, among the massive number of potential combinations. To help researchers to choose a prediction method that best fits for various real-world applications, we carried out a systematic literature review of 117 computational methods developed to date for drug combination prediction, and classified the methods in terms of their combination prediction tasks and input data requirements. Most current methods focus on prediction or classification of combination synergy, and only a few methods consider the efficacy and potential toxicity of the combinations, which are the key determinants of therapeutic success of drug treatments. Furthermore, there is a need to further develop methods that enable dose-specific predictions of combination effects across multiple doses, which is important for clinical translation of the predictions, as well as model-based identification of biomarkers predictive of heterogeneous drug combination responses. Even if most of the computational methods reviewed focus on anticancer applications, many of the modelling approaches are also applicable to antiviral and other diseases or indications.

Prediction and identification of synergistic compound combinations against pancreatic cancer cells

Resistance to current therapies is common for pancreatic cancer and hence novel treatment options are urgently needed. In this work, we developed and validated a computational method to select synergistic compound combinations based on transcriptomic profiles from both the disease and compound side, combined with a pathway scoring system, which was then validated prospectively by testing 30 compounds (and their combinations) on PANC-1 cells. Some compounds selected as single agents showed lower GI50 values than the standard of care, gemcitabine. Compounds suggested as combination agents with standard therapy gemcitabine based on the best performing scoring system showed on average 2.82-5.18 times higher synergies compared to compounds that were predicted to be active as single agents. Examples of highly synergistic in vitro validated compound pairs include gemcitabine combined with Entinostat, thioridazine, loperamide, scriptaid and Saracatinib. Hence, the computational approach presented here was able to identify synergistic compound combinations against pancreatic cancer cells.

ZINC40099027 activates human focal adhesion kinase by accelerating the enzymatic activity of the FAK kinase domain

Focal adhesion kinase (FAK) regulates gastrointestinal epithelial restitution and healing. ZINC40099027 (Zn27) activates cellular FAK and promotes intestinal epithelial wound closure in vitro and in mice. However, whether Zn27 activates FAK directly or indirectly remains unknown. We evaluated Zn27 potential modulation of the key phosphatases, PTP-PEST, PTP1B, and SHP2, that inactivate FAK, and performed in vitro kinase assays with purified FAK to assess direct Zn27-FAK interaction. In human Caco-2 cells, Zn27-stimulated FAK-Tyr-397 phosphorylation despite PTP-PEST inhibition and did not affect PTP1B-FAK interaction or SHP2 activity. Conversely, in vitro kinase assays demonstrated that Zn27 directly activates both full-length 125 kDa FAK and its 35 kDa kinase domain. The ATP-competitive FAK inhibitor PF573228 reduced basal and ZN27-stimulated FAK phosphorylation in Caco-2 cells, but Zn27 increased FAK phosphorylation even in cells treated with PF573228. Increasing PF573228 concentrations completely prevented activation of 35 kDa FAK in vitro by a normally effective Zn27 concentration. Conversely, increasing Zn27 concentrations dose-dependently activated kinase activity and overcame PF573228 inhibition of FAK, suggesting the direct interactions of Zn27 with FAK may be competitive. Zn27 increased the maximal activity (Vmax ) of FAK. The apparent Km of the substrate also increased under laboratory conditions less relevant to intracellular ATP concentrations. These results suggest that Zn27 is highly potent and enhances FAK activity via allosteric interaction with the FAK kinase domain to increase the Vmax of FAK for ATP. Understanding Zn27 enhancement of FAK activity will be important to redesign and develop a clinical drug that can promote mucosal wound healing.

Multicellular Models Bridging Intracellular Signaling and Gene Transcription to Population Dynamics

Cell signaling and gene transcription occur at faster time scales compared to cellular death, division, and evolution. Bridging these multiscale events in a model is computationally challenging. We introduce a framework for the systematic development of multiscale cell population models. Using message passing interface (MPI) parallelism, the framework creates a population model from a single-cell biochemical network model. It launches parallel simulations on a single-cell model and treats each stand-alone parallel process as a cell object. MPI mediates cell-to-cell and cell-to-environment communications in a server-client fashion. In the framework, model-specific higher level rules link the intracellular molecular events to cellular functions, such as death, division, or phenotype change. Cell death is implemented by terminating a parallel process, while cell division is carried out by creating a new process (daughter cell) from an existing one (mother cell). We first demonstrate these capabilities by creating two simple example models. In one model, we consider a relatively simple scenario where cells can evolve independently. In the other model, we consider interdependency among the cells, where cellular communication determines their collective behavior and evolution under a temporally evolving growth condition. We then demonstrate the framework’s capability by simulating a full-scale model of bacterial quorum sensing, where the dynamics of a population of bacterial cells is dictated by the intercellular communications in a time-evolving growth environment.

Deciphering metabonomics biomarkers-targets interactions for psoriasis vulgaris by network pharmacology

OBJECTIVES

Psoriasis vulgaris is a chronic inflammatory and immune-mediated skin disease. 44 metabonomics biomarkers were identified by high-throughput liquid chromatography coupled to mass spectrometry in our previous work, but the roles of metabonomics biomarkers in the pathogenesis of psoriasis is unclear.

METHODS

The metabonomics biomarker-enzyme network was constructed. The key metabonomics biomarkers and enzymes were screened out by network analysis. The binding affinity between each metabonomics biomarker and target was calculated by molecular docking. A binding energy-weighted polypharmacological index was introduced to evaluate the importance of target-related pathways.

RESULTS

Long-chain fatty acids, phospholipids, Estradiol and NADH were the most important metabonomics biomarkers. Most key enzymes belonged hydrolase, thioesterase and acyltransferase. Six proteins (TNF-alpha, MAPK3, iNOS, eNOS, COX2 and mTOR) were extensively involved in inflammatory reaction, immune response and cell proliferation, and might be drug targets for psoriasis. PI3K-Akt signaling pathway and five other pathways had close correlation with the pathogenesis of psoriasis and could deserve further research.

CONCLUSIONS

The inflammatory reaction, immune response and cell proliferation are mainly involved in psoriasis. Network pharmacology provide a new insight into the relationships between metabonomics biomarkers and the pathogenesis of psoriasis. KEY MESSAGES   • Network pharmacology was adopted to identify key metabonomics biomarkers and enzymes.   • Six proteins were screened out as important drug targets for psoriasis.   • A binding energy-weighted polypharmacological index was introduced to evaluate the importance of target-related pathways.

Network-based technologies for early drug discovery

Although the traditional drug discovery approach has led to the development of many successful drugs, the attrition rates remain high. Recent advances in systems-oriented approaches (systems-biology and/or pharmacology) and 'omics technologies has led to a plethora of new computational tools that promise to enable a more-informed and successful implementation of the reductionist, one drug for one target for one disease, approach. These tools, based on biomolecular pathways and interaction networks, offer a systematic approach to unravel the mechanism(s) of a disease and link them to the chemical space and network footprint of a drug. Drug discovery can draw upon this holistic approach to identify the most-promising targets and compounds during the early phases of development.

Computational and experimental prediction of molecules involved in the anti-melanoma action of berberine

ETHNOPHARMACOLOGIC RELEVANCE

Berberine (BBR) is a naturally occurring alkaloid compound that can be found in Chinese medicinal herbs such as Rhizoma Coptidis and Phellodendri Cortex. These BBR containing herbs are commonly used by Chinese medicine doctors to treat cancers including melanoma. In this study, we explored proteins potentially involved in the anti-melanoma effects of BBR using computational and experimental approaches.

MATERIALS AND METHODS

Target proteins of BBR were predicted using the reverse pharmacophore screening, molecular docking and molecular dynamics. Anti-melanoma activities of BBR in melanoma cells were examined by MTT and EdU proliferation assays. Effects of BBR on activities of target proteins in melanoma cells were examined by Western blotting or fluorescence assay.

RESULTS

Ten proteins implicated in cancer and with high fit-score in the reverse pharmacophore screening were selected as potential targets of BBR. Molecular docking and molecular dynamics revealed that BBR could stably bind to four of the ten proteins, namely 3-phosphoinositide-dependent protein kinase 1 (PDK1), glucocorticoid receptor (GR), p38 mitogen-activated protein kinase (p38) and dihydroorotate dehydrogenase (DHODH). Cellular experiments showed that BBR inhibited cell proliferation, increased the phosphorylation of GR and p38, and inhibited the activity of DHODH in A375 human melanoma cells.

CONCLUSIONS

These findings suggest that p38, GR and DHODH are potentially involved in the anti-melanoma action of BBR. This study provided a chemical and pharmacological justification for the clinical use of BBR-containing herbs in melanoma treatment.

Detecting similar binding pockets to enable systems polypharmacology

In the era of systems biology, multi-target pharmacological strategies hold promise for tackling disease-related networks. In this regard, drug promiscuity may be leveraged to interfere with multiple receptors: the so-called polypharmacology of drugs can be anticipated by analyzing the similarity of binding sites across the proteome. Here, we perform a pairwise comparison of 90,000 putative binding pockets detected in 3,700 proteins, and find that 23,000 pairs of proteins have at least one similar cavity that could, in principle, accommodate similar ligands. By inspecting these pairs, we demonstrate how the detection of similar binding sites expands the space of opportunities for the rational design of drug polypharmacology. Finally, we illustrate how to leverage these opportunities in protein-protein interaction networks related to several therapeutic classes and tumor types, and in a genome-scale metabolic model of leukemia.

Traditionally, the fact that most drugs are promiscuous binders has been a major concern in pharmacology, due to the occurrence of undesired off-target clinical events. In the recent years, however, the realization that many diseases are the result of complex biological processes has encouraged rethinking of drug promiscuity as a promising feature, since it is sometimes necessary to interfere with multiple receptors in order to overcome the robustness of disease-related networks. One way to identify groups of proteins that could be targeted simultaneously is to look for similar binding sites. We have massively done so for all human proteins with a known high-resolution three-dimensional structure, unveiling a vast space of ‘polypharmacology’ opportunities. Of these, we know, a great majority is not of therapeutic interest. To pinpoint promising multi-target combinations, we advocate for the use of computational tools that are able to rapidly simulate the effect of drug-target interactions on biological networks.

Herb-target interaction network analysis helps to disclose molecular mechanism of traditional Chinese medicine

Though many studies have been performed to elucidate molecular mechanism of traditional Chinese medicines (TCMs) by identifying protein-compound interactions, no systematic analysis at herb level was reported. TCMs are prescribed by herbs and all compounds from a certain herb should be considered as a whole, thus studies at herb level may provide comprehensive understanding of TCMs. Here, we proposed a computational strategy to study molecular mechanism of TCM at herb level and used it to analyze a TCM anti-HIV formula. Herb-target network analysis was carried out between 17 HIV-related proteins and SH formula as well as three control groups based on systematic docking. Inhibitory herbs were identified and active compounds enrichment was found to contribute to the therapeutic effectiveness of herbs. Our study demonstrates that computational analysis of TCMs at herb level can catch the rationale of TCM formulation and serve as guidance for novel TCM formula design.

Combination therapeutics in complex diseases

The biological redundancies in molecular networks of complex diseases limit the efficacy of many single drug therapies. Combination therapeutics, as a common therapeutic method, involve pharmacological intervention using several drugs that interact with multiple targets in the molecular networks of diseases and may achieve better efficacy and/or less toxicity than monotherapy in practice. The development of combination therapeutics is complicated by several critical issues, including identifying multiple targets, targeting strategies and the drug combination. This review summarizes the current achievements in combination therapeutics, with a particular emphasis on the efforts to develop combination therapeutics for complex diseases.

Inflammatory pathway network-based drug repositioning and molecular phenomics

Inflammation is a protective biological response to body/tissue damage that involves immune cells, blood vessels and molecular mediators. In this work, we constructed the pathway network of inflammation, including 11 sub-pathways of inflammatory factors. Pathway-based network efficiency and network flux were adopted to evaluate drug efficacy. By using approved and experimentally validated anti-inflammatory drugs as training sets, a predictive model was built to screen potential anti-inflammatory drugs from approved drugs in DrugBank. This drug repositioning approach would bring a fast and cheap way to find new indications for approved drugs. Moreover, molecular phenomics profiles of the expression of inflammatory factors will provide new insight into the drug mechanism of action.