Rational Drug Development Research Articles

Determination of protein-ligand binding modes using fast multi-dimensional NMR with hyperpolarization.

Elucidation of small molecule-protein interactions provides essential information for understanding biological processes such as cellular signaling, as well as for rational drug development. Here, multi-dimensional NMR with sensitivity enhancement by dissolution dynamic nuclear polarization (D-DNP) is shown to allow the determination of the binding epitope of folic acid when complexed with the target dihydrofolate reductase. Protein signals are selectively enhanced by polarization transfer from the hyperpolarized ligand. A pseudo three-dimensional data acquisition with ligand-side Hadamard encoding results in protein-side [13C, 1H] chemical shift correlations that contain intermolecular nuclear Overhauser effect (NOE) information. A scoring function based on this data is used to select pre-docked ligand poses. The top five poses are within 0.76 Å root-mean-square deviation from a reference structure for the encoded five protons, showing improvements compared with the poses selected by an energy-based scoring function without experimental inputs. The sensitivity enhancement provided by the D-DNP combined with multi-dimensional NMR increases the speed and potentially the selectivity of structure elucidation of ligand binding epitopes.

Characterization of cancer omics and drug perturbations in panels of lung cancer cells

To better understand the disruptions of transcriptional regulations and gene expression in lung cancers, we constructed a multi-omics catalogue of the responses of lung cancer cells to a series of chemical compounds. We generated and analyzed 3,240 RNA-seq and 3,393 ATAC-seq libraries obtained from 23 cell lines treated with 95 well-annotated compounds. To demonstrate the power of the created multi-omics resource, we attempted to identify drugs that could induce the designated changes alone or in combination. The basal multi-omics information was first integrated into co-expression modules. Among these modules, we identified a stress response module that may be a promising drug intervention target, as new combinations of compounds that could be used to regulate this module and the consequent phenotypic appearance of cancer cells have been identified. We believe that the multi-omics profiles generated in this study and the strategy used to stratify them will lead to more rational and efficient development of anticancer drugs.

Profiling the Immune Tumor Microenvironment of Newly Diagnosed Diffuse Large B Cell Lymphoma Identifies Three Major Immune Infiltration Patterns

Background: Diffuse large B-cell lymphoma (DLBCL) is a genetically heterogeneous disease of malignant B cells most often classified by tumor gene expression and/or mutations. DLBCL is also characterized by a tumor microenvironmental influence that has not been well described. Immuno-oncology-targeting agents such as immune checkpoint inhibitors have limited clinical activity in DLBCL, highlighting the need for a better understanding of the DLBCL tumor microenvironment for rational drug development, combinations, and disease stratification. Here, we systematically characterize the immune composition of more than 100 DLBCL tumors using 2 imaging technologies and provide insight into the complexity of DLBCL disease biology. Methods: A total of 110 cases of newly diagnosed DLBCL were analyzed by multiplex immunohistochemistry (IHC; n=70) and multiplexed ion beam imaging (MIBI) (n=40), each with 10 common markers (CD20, CD3, CD8, Foxp3, CD56, CD163, CD11c, CD56, PD-1, PD-L1) and a few platform-specific markers. Both IHC and MIBI images were digitalized to generate marker-positive cell counts (including single, double, or triple positivity), cell density (cell count/mm2), and population fraction (% of total nucleated cells). Marker density was analyzed for the major components of tumor-infiltrating cell types and correlation between any pair of markers. In addition, RNAseq data and fluorescence in situ hybridization (FISH) data on MYC, BCL-2, and BCL-6 translocation were generated. Results: In the IHC cohort, T cells (CD3+), dendritic cells (DCs; by CD11c+), and macrophages (CD163+)were the major immune components, with median population fractions of 22%, 16%, and 2.7%, respectively. Natural killer cells (CD56+CD20−) were a minor component at a median of 0.1%. A significant negative correlation was observed between tumor cells (CD20+) and CD4+ (Spearman ρ = −0.47; P = 1.3 × 10−04), and CD8+ T cells (Spearman ρ = −0.42; P = 1.4 × 10−03) cells, and an unexpectedly negative correlation between DCs (CD11c+) and macrophages (CD163+, r = −0.63; P = 9.8 × 10−06) was found. Similar to follicular lymphoma, 2 PD-1+ T-cell populations were identified: PD-1bright and PD-1dim. The PD-1bright cells co-stained with CXCR5, indicating T follicular helper cells (Tfh). The PD-1dim were expressed on exhausted effector cells that co-stained with Tim3 or Lag3. The median population fraction of Tim3+ or Lag3+ among T cells was 25%, whereas that of PD-1dim among T cells was 0.2%, indicating that PD-1 was not a useful marker for exhausted T cells. PD-L1 was predominantly found on DCs and macrophages, and the median population fraction of PD-L1+ among tumor cells was only 6.2%. By unsupervised hierarchical clustering on marker density, 3 major immune-infiltration patterns (P1, P2, and P3) were identified. The first 2 segments (P1 and P2) were 20% and 25% of the total cases, respectively. Both were characterized by high T-cell, macrophage, and DC infiltration. P1 was enriched for PD-1+ T cells, whereas P2 was void of any PD-1+ cells. The third segment (P3) that comprised 55% of the cases was predominantly tumor cells with low T-cell, macrophage, and DC infiltration. PD-L1+ cells were primarily found in segments P1 and P2 but rare in segment P3. Additional analysis on the associations between the 3 immune-infiltration patterns and prognostic DLBCL molecular features such as cell-of-origin, double-hit gene signature, and double-hit FISH will also be presented. Conclusions: These data show the complexity of DLBCL disease biology and show classification of DLBCL at the immune-infiltration level as 3 distinct patterns. The overall low expression of PD-1+ T cells and the restricted pattern of PD-L1+ tumor cells provide a possible explanation for the lack of clinical activity of PD-1/PD-L1 blockade in DLBCL. We also observed other exhausted T cells expressing Lag3 and Tim3, suggesting alternative therapeutic opportunities in stratified populations. These data also highlight the opportunity to develop rational immuno-oncology-targeted agents based on the immune infiltration pattern of DLBCL and selection of patients who may respond more favorably to particular agents. Disclosures Huang: Celgene Corporation: Employment, Equity Ownership. Nakayama:Celgene Corporation: Employment, Equity Ownership. Stokes:Celgene Corporation: Employment, Equity Ownership. Towfic:Celgene Corporation: Employment, Equity Ownership. Lee:Celgene Corporation: Employment, Equity Ownership. Ren:Celgene Corporation: Employment, Equity Ownership. Marella:Celgene Corporation: Employment, Equity Ownership. Wang:Celgene Corporation: Employment, Equity Ownership. Hagner:Celgene Corporation: Employment, Equity Ownership, Patents & Royalties. Couto:Celgene Corporation: Employment, Equity Ownership, Patents & Royalties. Newhall:Celgene Corporation: Employment, Equity Ownership. Gandhi:Celgene Corporation: Employment, Equity Ownership, Patents & Royalties.

Molecular Treatment Stratification for Newly Diagnosed High-Risk Myeloma, Including Plasma Cell Leukemia - Feasibility Results of the Ukmra Optimum: MUK9 Trial (NCT03188172)

Background High-risk myeloma patients have unsatisfactory outcomes with current treatments and are in urgent need of improved diagnostic and therapeutic strategies. We have recently validated specific markers predicting high-risk disease in newly diagnosed MM (NDMM), in particular double-hit with presence of ≥2 consensus high-risk markers t(4;14), t(14;16), t(14;20), del(1p), gain(1q), del(17p) (Shah V, et al., Leukemia 2018) and diagnostic GEP SKY92 high risk signature (Sherborne A, et al., IMW 2017). Diagnostic tests for these markers were implemented in the UK multi-center OPTIMUM: MUK9 trial to prospectively stratify therapy for high-risk NDMM. Trial design OPTIMUM: MUK9 is a phase 2 trial for transplant eligible NDMM, consisting of two inter-related protocols: a molecular screening protocol (MUK9A) and an interventional trial (MUK9B) for high-risk MM identified in MUK9A. Patients with suspected or confirmed MM fit for intensive therapy enrolled in MUK9A have central molecular profiling at ICR, London, of CD138-selected BM MM cells for translocations, copy number aberrations (qRT-PCR; MLPA P425, MRC Holland) and SKY92 signature status (MMprofiler; SkylineDx). If clinically indicated SOC therapy (VTD, max. 2 cycles) can be given whilst central results are generated. Patients found to have high-risk MM by double-hit and/or SKY92 are offered enrolment into MUK9B. All other patients receive SOC (VTD, HD-MEL+ASCT) for which clinical data is collected. Patients diagnosed with plasma cell leukemia (PCL) can be enrolled directly in MUK9B. MUK9B treatment consists of quintuplet daratumumab, cyclophosphamide, bortezomib, lenalidomide, dexamethasone (Dara-CVRd) induction (up to 6 cycles), bortezomib-augmented single HD-MEL+ASCT, Dara-VRd consolidation 1 (6 cycles), Dara-VR consolidation 2 (12 cycles) and Dara-R maintenance (until PD). Dose adjustments are permitted in order to maximize tolerability of long-term therapy. Patient reported outcomes (PRO) are recorded at baseline and throughout treatment. Response and MRD are centrally assessed (Birmingham, Leeds). Primary endpoint for MUK9A is feasibility of central molecular testing within 56 days turnaround time, which we report on here. Primary endpoint of MUK9B is treatment efficacy, comparing MUK9B PFS to near-concurrent molecularly matched high-risk patient outcomes from UK NCRI Myeloma XI using a Bayesian design. Secondary endpoints include safety, PFS2, MRD and OS and study of molecular evolution in high-risk disease. Results The protocol recruited 29/Sep/17 - 31/Jul/19 at 39 UK sites, achieving the recruitment target of 105 high-risk patients treated on MUK9B ahead of projections. At the time of analysis (12/Jul/19), 430 patients with suspected or confirmed NDMM have been recruited to MUK9A across 39 UK NHS hospitals. Of these, 376 (87%) patients were confirmed to have symptomatic MM (60.9% male; median age 61y (range 29-79)) as per updated IMWG diagnostic criteria (2014), including 9 (2%) PCL patients, with the remainder diagnosed as SMM/MGUS (31; 7%) or other (14; 3%). For 371 of the 376 symptomatic MM patients BM was received by the central laboratory and was of sufficient quality for profiling in 331 (89%) patients. Repeat samples were requested for all others and a sufficient sample received for 20/45 (44%). Central results were successfully reported within the pre-specified 56 day interval for all patients (median 17 days; IQR 13-22). Of 346 patients with a reported result, 128 (37.0%) have high-risk MM, with molecular characteristics mirroring Myeloma XI patients (Figure 1). PCL patients show expected characteristics as listed in Table 1. Basic demographics were not different between high-risk vs. non-high-risk. 101 high-risk patients have or are planning to enter MUK9B, 10 pending decision; 17 high-risk patients did not enter MUK9B, the majority due to ineligibility. 92 patients have started Dara-CVRD therapy. There are currently no safety concerns, the majority of patients are completing induction successfully; 1 patient stopped induction therapy due to adverse events. Updated results will be presented. Discussion Our data demonstrate feasibility of multi-center molecular stratified trial delivery for high-risk NDMM patients. These early trial results strongly support accelerated trial strategies for MM patient groups with high unmet need and rational drug development specifically for high-risk MM. Disclosures Jenner: Abbvie, Amgen, Celgene, Novartis, Janssen, Sanofi Genzyme, Takeda: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau. Hall:Celgene, Amgen, Janssen, Karyopharm: Other: Research funding to Institution. Walker:Janssen, Celgene: Other: Research funding to Institution. Croft:Celgene: Other: Travel expenses. Jackson:Celgene, Amgen, Roche, Janssen, Sanofi: Honoraria. Flanagan:Amgen, Celgene, Janssen, Karyopharm: Other: Research funding to Institution. Drayson:Abingdon Health: Consultancy, Equity Ownership. Owen:Celgene, Janssen: Consultancy; Celgene: Research Funding; Janssen: Other: Travel expenses; Celgene, Janssen: Honoraria. Pratt:Binding Site, Amgen, Takeda, Janssen, Gilead: Consultancy, Honoraria, Other: Travel support. Cook:Celgene, Janssen-Cilag, Takeda: Honoraria, Research Funding; Janssen, Takeda, Sanofi, Karyopharm, Celgene: Consultancy, Honoraria, Speakers Bureau; Amgen, Bristol-Myers Squib, GlycoMimetics, Seattle Genetics, Sanofi: Honoraria. Brown:Amgen, Celgene, Janssen, Karyopharm: Other: Research funding to Institution. Kaiser:Celgene, Janssen: Research Funding; Abbvie, Celgene, Takeda, Janssen, Amgen, Abbvie, Karyopharm: Consultancy; Takeda, Janssen, Celgene, Amgen: Honoraria, Other: Travel Expenses.

EXTH-21. DIFFERENTIAL PROTEIN EXPRESSION LEVELS OF ABC AND SOLUTE CARRIER TRANSPORTERS AT THE BLOOD-BRAIN BARRIER OF HUMAN BRAIN AND GLIOBLASTOMA

Abstract BACKGROUND Mechanistic understanding and quantitative prediction of drug penetration across the human blood-brain barrier (BBB) is critical to rational drug development and treatment for brain cancer especially glioblastoma. However, prediction of drug brain/tumor penetration has been significantly hindered mainly due to the lack of quantitation data on transporter protein expression levels at the human BBB. This study was to determine protein expression levels of major transporters and markers at the BBB of human brain and glioblastoma. METHOD The absolute protein expression levels of major transporters and markers were determined in isolated microvessels of human brain (N=30), glioblastoma (N=47), rat (N=10) and mouse brain (N=10), using liquid chromatography with tandem mass spectrometry (LC-MS/MS) based targeted proteomics. RESULTS In isolated microvessels of 30 human brain specimens, the median protein abundances for ABCB1, ABCG2, GLUT1, GLUT3, LAT1, MCT1, Na/K ATPase, and Claudin-5 were 3.38, 6.21, 54.51, 7.17, 3.42, 5.71, 32.14, and 1.15 fmol/µg protein, respectively. In glioblastoma microvessels, ABCB1, ABCG2, MCT1, GLUT1, Na/K ATPase, and Claudin-5 protein levels were significantly reduced, while LAT1 was increased and GLU1 remained the same. ABCC4, OATP1A2, OATP2B1, and OAT3 were undetectable in isolated microvessels of both human brain and glioblastoma. There was species difference in transporter protein expression levels in isolated microvessels of human, rat and mouse brain. Specifically, rodent BBB expressed significantly higher ABCB1 but similar ABCG2, as compared to human BBB. CONCLUSION The physical and biochemical barriers of the BBB in glioblastomas are largely disrupted, as indicated by the loss or significant reduction in protein expression of the tight junction marker (claudin-5), brain endothelial cell marker (GLUT1), and major efflux transporters (ABCB1 and ABCG2) as compared to normal human BBB. Differential BBB transporter protein expression levels provides mechanistic and quantitative basis for the prediction of heterogeneous drug penetration into human normal brain and glioblastoma.

Combining epigenetic drugs with other therapies for solid tumours - past lessons and future promise.

Epigenetic dysregulation has long been recognized as a key factor contributing to tumorigenesis and tumour maintenance that can influence all of the recognized hallmarks of cancer. Despite regulatory approvals for the treatment of certain haematological malignancies, the efficacy of the first generation of epigenetic drugs (epi-drugs) in patients with solid tumours has been disappointing; however, successes have now been achieved in selected solid tumour subtypes, thanks to the development of novel compounds and a better understanding of cancer biology that have enabled precision medicine approaches. Several lines of evidence support that, beyond their potential as monotherapies, epigenetic drugs could have important roles in synergy with other anticancer therapies or in reversing acquired therapy resistance. Herein, we review the mechanisms by which epi-drugs can modulate the sensitivity of cancer cells to other forms of anticancer therapy, including chemotherapy, radiation therapy, hormone therapy, molecularly targeted therapy and immunotherapy. We provide a critical appraisal of the preclinical rationale, completed clinical studies and ongoing clinical trials relating to combination therapies incorporating epi-drugs. Finally, we propose and discuss rational clinical trial designs and drug development strategies, considering key factors including patient selection, tumour biomarker evaluation, drug scheduling and response assessment and study end points, with the aim of optimizing the development of such combinations.

Big Data and Artificial Intelligence Modeling for Drug Discovery.

Due to the massive data sets available for drug candidates, modern drug discovery has advanced to the big data era. Central to this shift is the development of artificial intelligence approaches to implementing innovative modeling based on the dynamic, heterogeneous, and large nature of drug data sets. As a result, recently developed artificial intelligence approaches such as deep learning and relevant modeling studies provide new solutions to efficacy and safety evaluations of drug candidates based on big data modeling and analysis. The resulting models provided deep insights into the continuum from chemical structure to in vitro, in vivo, and clinical outcomes. The relevant novel data mining, curation, and management techniques provided critical support to recent modeling studies. In summary, the new advancement of artificial intelligence in the big data era has paved the road to future rational drug development and optimization, which will have a significant impact on drug discovery procedures and, eventually, public health.

Therapeutic Targeting of the Soluble Guanylate Cyclase.

The soluble guanylate cyclase (sGC) is the physiological sensor for nitric oxide and alterations of its function are actively implicated in a wide variety of pathophysiological conditions. Intense research efforts over the past 20 years have provided significant information on its regulation, culminating in the rational development of approved drugs or investigational lead molecules, which target and interact with sGC through novel mechanisms. However, there are numerous questions that remain unanswered. Ongoing investigations, with the critical aid of structural chemistry studies, try to further elucidate the enzyme's structural characteristics that define the association of "stimulators" or "activators" of sGC in the presence or absence of the heme moiety, respectively, as well as the precise conformational attributes that will allow the design of more innovative and effective drugs. This review relates the progress achieved, particularly in the past 10 years, in understanding the function of this enzyme, and focusses on a) the rationale and results of its therapeutic targeting in disease situations, depending on the state of enzyme (oxidized or not, heme-carrying or not) and b) the most recent structural studies, which should permit improved design of future therapeutic molecules that aim to directly upregulate the activity of sGC.

Exploring the arsenal of peptide toxins from the venoms of social wasps for the rational development of novel therapeutic drugs

Exploring the arsenal of peptide toxins from the venoms of social wasps for the rational development of novel therapeutic drugs

Advancing the development of new tuberculosis treatment regimens: The essential role of translational and clinical pharmacology and microbiology.

Application of clinical pharmacology best practices is essential to the efficient and rational development of drugs. In general, knowledge gained about exposure–response relationships in preclinical models aids drug and dose selection in human studies, and biomarkers and pharmacokinetic (PK) data one collects in early to middle drug development can be used to predict the dose and treatment response of promising therapeutics in definitive phase 3 trials. The essentiality of sound clinical pharmacology in tuberculosis (TB) drug and regimen development is heightened by unique challenges in assessing drugs for this disease—aspects of the organism’s biology, the variability in lung pathology, uncertainties about how to link treatment outcomes seen in preclinical models with those seen in humans (which thwarts preclinical–clinical translational work), the lack of predictive early clinical biomarkers, and the high variability in treatment response across patients and populations (Fig 1). In TB disease, Mycobacterium tuberculosis (M.tb) bacilli are detected in necrotic granulomas, large cavities with liquefied contents, and intracellularly within macrophages. We believe that drugs must access each of these compartments to achieve cure in patients [1]. We also believe that TB drugs and regimens must kill bacilli in different metabolic states, from actively multiplying to semidormant [2,3]. Both in vitro and in vivo preclinical models are leveraged to assess the clinical utility of new TB drugs and drug combinations. These models vary both in their ability to assess efficacy relative to the shifting metabolic states of M.tb infection and in their ability to recapitulate human disease. Still, two models are proving to be highly informative. The mouse model of infection has been invaluable in selecting rank-ordered drug combinations, whereas the now-validated in vitro pharmacodynamic (PD) system (IVPDS, or “hollow fiber model” for TB) has significantly improved our understanding of the PK drivers of treatment response in various growth and physiologic states [4,5]. In the IVPDS, an elaborate system of dialysis-like tubing allows the investigator to reproduce human-like concentration–time curves and see how different PK profiles affect killing of bacilli that are living in the system. Dose-fractionation studies, for example, can be carried out, and one can determine whether a drug’s activity is time dependent or, rather, concentration dependent. Or one can test a drug’s activity when the organism is nutrient starved, in log-phase growth, or intracellular. Whereas these models are informative, there remain gaps in our ability to bridge preclinical and clinical data using modern translational quantitative modeling [6]. There are also gaps in our ability to link surrogate end points in early-phase clinical trials (namely, longitudinally collected sputum cultures) and clinically relevant end points of treatment failure, relapse, and death in later-phase trials [7,8]. The identification of accurate tools that identify those patients who are unlikely to achieve cure with shortened regimens (specifically, patients with a disease phenotype that is “hard to treat”) would have immense value to both clinical trialists and TB clinicians [9]. The TB clinical pharmacology field has the opportunity to apply state-of-the-art quantitative pharmacology tools to bridge preclinical and clinical data more effectively and to enhance learning across the continuum of clinical development [9–13]. In this paper, based on discussions occurring at a WHO workshop held in March 2018, we describe our views on best practices for incorporating translational, PK-PD, and microbiologic assessments into drug development [14]. Open in a separate window Fig 1 Schema of preclinical and clinical pharmacology studies important for TB drug and regimen development. By phase of development, in green are the questions to be addressed, in blue are the tools to use to answer the questions, and in red are the outputs. ADME, absorption, distribution, metabolism, excretion; DDI, drug–drug interaction; Dz, disease; MIC, minimum inhibitory concentration; PBPK, physiologically based PK; PD, pharmacodynamic; Ph2A, phase 2A; Ph2B/C, phase 2B and C; Ph3, phase 3; PK, pharmacokinetic; TB, tuberculosis; y.o., year-olds.

Discovery, X-ray Crystallography and Antiviral Activity of Allosteric Inhibitors of Flavivirus NS2B-NS3 Protease.

Flaviviruses, including dengue, West Nile and recently emerged Zika virus, are important human pathogens, but there are no drugs to prevent or treat these viral infections. The highly conserved Flavivirus NS2B-NS3 protease is essential for viral replication and therefore a drug target. Compound screening followed by medicinal chemistry yielded a series of drug-like, broadly active inhibitors of Flavivirus proteases with IC50 as low as 120 nM. The inhibitor exhibited significant antiviral activities in cells (EC68: 300-600 nM) and in a mouse model of Zika virus infection. X-ray studies reveal that the inhibitors bind to an allosteric, mostly hydrophobic pocket of dengue NS3 and hold the protease in an open, catalytically inactive conformation. The inhibitors and their binding structures would be useful for rational drug development targeting Zika, dengue and other Flaviviruses.

Cheminformatics techniques in antimalarial drug discovery and development from natural products 1: basic concepts

Abstract A large number of natural products, especially those used in ethnomedicine of malaria, have shown varying in vitro antiplasmodial activities. Facilitating antimalarial drug development from this wealth of natural products is an imperative and laudable mission to pursue. However, limited manpower, high research cost coupled with high failure rate during preclinical and clinical studies might militate against the pursuit of this mission. These limitations may be overcome with cheminformatic techniques. Cheminformatics involves the organization, integration, curation, standardization, simulation, mining and transformation of pharmacology data (compounds and bioactivity) into knowledge that can drive rational and viable drug development decisions. This chapter will review the application of cheminformatics techniques (including molecular diversity analysis, quantitative-structure activity/property relationships and Machine learning) to natural products with in vitro and in vivo antiplasmodial activities in order to facilitate their development into antimalarial drug candidates and design of new potential antimalarial compounds.

Molecular modeling, dynamics simulation and characterization of human inositol hexakisphosphate kinase 1 (IP6K1) related to diabetes

Inositol hexakisphosphate kinase-1 (IP6K1) protein plays an important role in insulin signaling producing IP7 that inhibit the action of protein kinase B (Akt). Inhibition of IP6K1 has been proposed as a novel way to enhance insulin signaling. Characterization and binding interaction of IP6K1 is essential for rational anti-diabetic drug development targeting this protein. Computational tools were used to analyze the physicochemical characteristics of IP6K1. Homology modelling reliably predicts the tertiary structure of IP6K1. Derived three-dimensional models werethen used to predict the binding mode and interacting amino acid residues. MD simulation (30 ns) was employed to investigate the protein dynamics. The modeled IP6K1 exhibited secondary characteristics comprising of 63.3% helixes, 30.2% sheets and 13.4% turns with an aliphatic index of 65.83 and instability index 50.53 showing that the protein is relatively unstable without its appropriate environment. The extinction coefficient was 34560 while the grand average of hydropathicity was −0.724. Homology modelling was performed by SWISS-MODEL program and the proposed model was evaluated as reliable based on RAMPAGE’s Ramachandran plot, and ProSA analyses. RMSD, RMSF, Rg revealed that the protein attained stability around 20ns. This appeared to be the first attempt to portray molecular dynamic simulation of IP6K1 coupled with modeling and thorough characteristic analysis of the protein using parameters like Ramachandran plot, Chou and Fasman Secondary Structure prediction and Protparam. Studies like protein engineering, structure and function as well as activity analysis are suggested. Our computational studies reavealed the binding pocket and critical amino acid residues that can be exploited in the design of inhibitors of IP6K1 as antidiabetic drugs.Keywords: Insulin signaling, physicochemical characteristics, homology modelling, diabetes

In Silico Peptide Ligation: Iterative Residue Docking and Linking as a New Approach to Predict Protein-Peptide Interactions.

Peptide–protein interactions are corner-stones of living functions involved in essential mechanisms, such as cell signaling. Given the difficulty of obtaining direct experimental structural biology data, prediction of those interactions is of crucial interest for the rational development of new drugs, notably to fight diseases, such as cancer or Alzheimer’s disease. Because of the high flexibility of natural unconstrained linear peptides, prediction of their binding mode in a protein cavity remains challenging. Several theoretical approaches have been developed in the last decade to address this issue. Nevertheless, improvements are needed, such as the conformation prediction of peptide side-chains, which are dependent on peptide length and flexibility. Here, we present a novel in silico method, Iterative Residue Docking and Linking (IRDL), to efficiently predict peptide–protein interactions. In order to reduce the conformational space, this innovative method splits peptides into several short segments. Then, it uses the performance of intramolecular covalent docking to rebuild, sequentially, the complete peptide in the active site of its protein target. Once the peptide is constructed, a rescoring step is applied in order to correctly rank all IRDL solutions. Applied on a set of 11 crystallized peptide–protein complexes, the IRDL method shows promising results, since it is able to retrieve experimental binding conformations with a Root Mean Square Deviation (RMSD) below 2 Å in the top five ranked solutions. For some complexes, IRDL method outperforms two other docking protocols evaluated in this study. Hence, IRDL is a new tool that could be used in drug design projects to predict peptide–protein interactions.

Rational design and development of anti-venom drugs for snakebites based on the endogenous inhibitors from Japanese viper

Rational design and development of anti-venom drugs for snakebites based on the endogenous inhibitors from Japanese viper

A Novel Discovery: Holistic Efficacy at the Special Organ Level of Pungent Flavored Compounds from Pungent Traditional Chinese Medicine.

Pungent traditional Chinese medicines (TCMs) play a vital role in the clinical treatment of hepatobiliary disease, gastrointestinal diseases, cardiovascular diseases, diabetes, skin diseases and so on. Pungent TCMs have a vastness of pungent flavored (with pungent taste or smell) compounds. To elucidate the molecular mechanism of pungent flavored compounds in treating cardiovascular diseases (CVDs) and liver diseases, five pungent TCMs with the action of blood-activating and stasis-resolving (BASR) were selected. Here, an integrated systems pharmacology approach is presented for illustrating the molecular correlations between pungent flavored compounds and their holistic efficacy at the special organ level. First, we identified target proteins that are associated with pungent flavored compounds and found that these targets were functionally related to CVDs and liver diseases. Then, based on the phenotype that directly links human genes to the body parts they affect, we clustered target modules associated with pungent flavored compounds into liver and heart organs. We applied systems-based analysis to introduce a pungent flavored compound-target-pathway-organ network that clarifies mechanisms of pungent substances treating cardiovascular diseases and liver diseases by acting on the heart/liver organ. The systems pharmacology also suggests a novel systematic strategy for rational drug development from pungent TCMs in treating cardiovascular disease and associated liver diseases.

Rethinking to riluzole mechanism of action: the molecular link among protein kinase CK1δ activity, TDP-43 phosphorylation, and amyotrophic lateral sclerosis pharmacological treatment.

Rethinking to riluzole mechanism of action: the molecular link among protein kinase CK1δ activity, TDP-43 phosphorylation, and amyotrophic lateral sclerosis pharmacological treatment.

Drug repositioning in epilepsy reveals novel antiseizure candidates.

ObjectiveEpilepsy treatment falls short in ~30% of cases. A better understanding of epilepsy pathophysiology can guide rational drug development in this difficult to treat condition. We tested a low‐cost, drug‐repositioning strategy to identify candidate epilepsy drugs that are already FDA‐approved and might be immediately tested in epilepsy patients who require new therapies.MethodsBiopsies of spiking and nonspiking hippocampal brain tissue from six patients with unilateral mesial temporal lobe epilepsy were analyzed by RNA‐Seq. These profiles were correlated with transcriptomes from cell lines treated with FDA‐approved drugs, identifying compounds which were tested for therapeutic efficacy in a zebrafish seizure assay.ResultsIn spiking versus nonspiking biopsies, RNA‐Seq identified 689 differentially expressed genes, 148 of which were previously cited in articles mentioning seizures or epilepsy. Differentially expressed genes were highly enriched for protein–protein interactions and formed three clusters with associated GO‐terms including myelination, protein ubiquitination, and neuronal migration. Among the 184 compounds, a zebrafish seizure model tested the therapeutic efficacy of doxycycline, metformin, nifedipine, and pyrantel tartrate, with metformin, nifedipine, and pyrantel tartrate all showing efficacy.InterpretationThis proof‐of‐principle analysis suggests our powerful, rapid, cost‐effective approach can likely be applied to other hard‐to‐treat diseases.

Binding of platinum derivative, oxaliplatin to deoxyribonucleic acid: structural insight into antitumor action

Platinum-derived chemodrugs constitute an active class in cancer therapeutics. Besides being potent against various solid tumors, oxaliplatin has been recognized as the first platinum compound to be approved for the treatment of colorectal cancer. Structurally, oxaliplatin consists of a platinum metal complexed to oxalate and diaminocyclohexane (DACH) and exert its anticancer action by inhibiting DNA replication and transcription. The present study highlights the binding properties of oxaliplatin with calf thymus DNA using spectroscopic methods to comprehend its binding mechanism at molecular level to overcome associated cellular resistance and side effects. Attenuated total reflection-Fourier transform infrared (ATR-FTIR) spectroscopic outcomes confirm that oxaliplatin is a covalent binding agent and also provide sequence specificity in DNA molecule. Infrared spectral results further indicate that oxaliplatin alkylates purine nitrogenous bases majorly guanine residues (G) in the major groove via formation of either interstrand or intrastrand guanine–guanine d(GpG) and guanine–adenine d(GpA) (N7 position) crosslinks accompanied with a slight external binding to sugar–phosphate backbone. Again, circular dichroism (CD) spectroscopic results suggest subtle conformational changes in DNA molecule due to its complexation with oxaliplatin and duplex attains an intermediate conformational state, having characteristics of both B- and C-forms. Further, a moderate binding strength of 4.12 ± 0.2 × 104 M−1 for the interaction has been estimated via ultraviolet–visible spectroscopy. The inferences obtained from these investigations are encouraging and can form the basis for further exploration in the field of rational drug development based on platinum compounds possessing preferential binding for nucleic acid with improved competence.Communicated by Ramaswamy H. Sarma

Computer-aided Drug Design and Drug Pharmacokinetic Prediction: A Mini-review.

Prediction of pharmacokinetics and drug targeting is a challenge in drug design. There are different types of software that can help to predict the pharmacokinetic profile of a drug. Quantitative structure-activity relationship (QSAR) modeling is used for drug design with less cost. Drug-excipient interactions are predicted by docking tools. Computerized drug target prediction and docking programs offer additional options to predict potential effects and adverse reactions of a given candidate as well as the best orientation of the compound on the receptor active site. Information on the absorption, distribution, metabolism and excretion of the drug in the body can enhance prediction of drug release and distribution in the blood and central nervous system (CNS). Computer- aided drug design and delivery can help to save the time and cost in the process of rational drug development.