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WO2016201566A1 - Systèmes et procédés pour sélectionner des composés ayant un risque de cardiotoxicité réduit au moyen de modèles h-erg - Google Patents

Systèmes et procédés pour sélectionner des composés ayant un risque de cardiotoxicité réduit au moyen de modèles h-erg Download PDF

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WO2016201566A1
WO2016201566A1 PCT/CA2016/050691 CA2016050691W WO2016201566A1 WO 2016201566 A1 WO2016201566 A1 WO 2016201566A1 CA 2016050691 W CA2016050691 W CA 2016050691W WO 2016201566 A1 WO2016201566 A1 WO 2016201566A1
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atom
proa
compound
protein
information
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PCT/CA2016/050691
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Sergei NOSKOV
Serdar DURDAGI
Henry DUFF
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Uti Limited Partnership
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    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B35/00—ICT specially adapted for in silico combinatorial libraries of nucleic acids, proteins or peptides
    • G16B35/20—Screening of libraries
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/5005—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving human or animal cells
    • G01N33/5008—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving human or animal cells for testing or evaluating the effect of chemical or biological compounds, e.g. drugs, cosmetics
    • G01N33/5014—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving human or animal cells for testing or evaluating the effect of chemical or biological compounds, e.g. drugs, cosmetics for testing toxicity
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/68—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving proteins, peptides or amino acids
    • G01N33/6872—Intracellular protein regulatory factors and their receptors, e.g. including ion channels
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B15/00—ICT specially adapted for analysing two-dimensional or three-dimensional molecular structures, e.g. structural or functional relations or structure alignment
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B15/00—ICT specially adapted for analysing two-dimensional or three-dimensional molecular structures, e.g. structural or functional relations or structure alignment
    • G16B15/30—Drug targeting using structural data; Docking or binding prediction
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B35/00—ICT specially adapted for in silico combinatorial libraries of nucleic acids, proteins or peptides
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16C—COMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
    • G16C20/00—Chemoinformatics, i.e. ICT specially adapted for the handling of physicochemical or structural data of chemical particles, elements, compounds or mixtures
    • G16C20/60—In silico combinatorial chemistry

Definitions

  • the recommended in vitro drug screening process includes traditional patch clamp techniques, radiolabeled drug binding assays, 86RB-flux assays, and high-throughput cell-based fluorescent dyes and stably transfected hERGl ion channels from Chinese hamster ovary (CHO) cells (Stork et al., 2007, "State Dependent Dissociation of HERG Channel Inhibitors," Br. J.
  • methods include the steps of: a) providing structural information, including, for example, in the form of coordinates, describing one or more conformations of a hERGl channel protein; b) providing structural information describing conformers of one or more compounds; c) using a docking algorithm to dock the conformers of the one or more compounds of step b) to the one or more conformations of step a);
  • one or more of the steps a) through f) of the method are performed in the recited order.
  • the conformation of step a) corresponds to the open state of the hERGl protein. In certain embodiments, the conformation of step a) corresponds to the closed state of the hERGl protein. In certain embodiments, the conformation of step a) corresponds to the open-inactivated state of the hERGl protein.
  • providing the structural information in step a) comprises using the coordinates of one or more dominant conformations identified from an MD simulation of the hERGl protein.
  • the MD simulation incorporates implicit or explicit solvent molecules and ion molecules, and a hydrated lipid bilayer with explicit phospholipid, solvent and ion molecules.
  • the duration of the MD simulation is greater than 100 ns. In certain embodiments, the duration of the MD simulation is 100, 150, 200, 250, 300, 350, 400, 450 or 500 ns.
  • the coordinates of the one or more dominant conformations identified from the MD simulation correspond to the open state of the hERGl protein.
  • step b) comprises providing the chemical structure of a compound and determining the conformers of the compound.
  • the chemical structure of the compound defines the conformers.
  • steps a) through f) of the method are executed on one or more processors.
  • the compound is selected from the group consisting of an antihistamine, an antiarrhythmic, an antianginal, an antipsychotic, an anticholinergic, an antitussive, an antibiotic, an antispasmodic, a calcium antagonist, an inotrope, an ACE inhibitor, an antihypertensive, a beta-blocker, an antiepileptic, a gastroprokinetic agent, an alphal -blocker, an antidepressant, an aldosterone antagonist, an opiate, an anesthetic, an antiviral, a PDE inhibitor, an antifungal, a serotonin antagonist, an antiestrogen, and a diuretic.
  • an antihistamine an antiarrhythmic, an antianginal, an antipsychotic, an anticholinergic, an antitussive, an antibiotic, an antispasmodic, a calcium antagonist, an inotrope, an ACE inhibitor, an antihypertensive, a beta-blocker, an antiepileptic, a gastropro
  • the docking algorithm of step c) is selected from
  • the method further comprises the step of evaluating the potential of mean force for each of the combinations of hERGl protein and compound in the corresponding optimized preferred binding conformations.
  • the potential of mean force is evaluated using umbrella sampling.
  • the method further comprises the step of calculating binding energies for each of the combinations of hERGl protein and compound in the corresponding optimized preferred binding conformations. In certain embodiments, the method further comprises the step of selecting for each of the combinations of hERGl protein and compound the lowest calculated binding energy in the optimized preferred binding conformations, and outputting the selected calculated binding energies as the predicted binding energies for each of the combinations of protein and compound.
  • the compound if the compound blocks the hERGl ion channel in the preferred binding conformations, the compound is predicted to be cardiotoxic. In certain embodiments, if the compound is predicted to be cardiotoxic, the compound is not selected for further clinical development or for use in humans.
  • the method further comprises testing the
  • the method further comprises testing the
  • a processor-implemented system for designing a compound in order to reduce risk of cardiotoxicity.
  • the system includes one or more computer-readable mediums, a grid computing system, and a data structure.
  • the one or more computer-readable mediums are for storing protein structural information representative of a hERGl ion channel protein and for storing compound structural information describing conformers of the compound.
  • the grid computing system includes a plurality of processor-implemented compute nodes and a processor-implemented central coordinator, said grid computing system receiving the stored protein structural information and the stored compound structural information from the one or more computer- readable mediums.
  • Said grid computing system uses the received protein structural information to perform molecular dynamics simulations for determining configurations of target protein flexibility over a simulation length of greater than 50 ns.
  • the molecular dynamics simulations involve each of the compute nodes determining forces acting on an atom based upon an empirical force field that approximates intramolecular forces, where numerical integration is performed to update positions and velocities of atoms.
  • the central coordinator forms molecular dynamic trajectories based upon the updated positions and velocities of the atoms as determined by each of the compute nodes.
  • a computer-implemented system for selecting a compound with reduced risk of cardiotoxicity which includes one or more data processors and a computer-readable storage medium encoded with instructions for commanding the one or more data processors to execute certain operations.
  • the operations include: a) providing structural information describing one or more conformations of a hERGl channel protein; b) providing structural information describing conformers of one or more compounds; c) using a docking algorithm to dock the conformers of the one or more compounds of step b) to the one or more conformations of step a); d) identifying a plurality of preferred binding conformations for each of the combinations of protein and compound; e) optimizing the preferred binding conformations using Molecular Dynamics (MD) simulations; and f) determining if the compound blocks the channel of the hERGl protein in the preferred binding conformations. If the compound blocks the channel in the preferred binding conformations, the compound is predicted to be cardiotoxic. If the compound does not block the channel in the preferred binding conformations, the compound is predicted to have reduced risk of cardiotoxicity. Based on a prediction that the compound has reduced risk of cardiotoxicity, the compound is selected.
  • MD Molecular Dynamics
  • the structural information of step a) describes a conformation of the hERGl protein.
  • step a) uses coordinates selected from the group consisting of Table A, Table B and Table C, describing a conformation of the hERGl protein.
  • the coordinates are selected from Table A.
  • the coordinates are selected from Table B.
  • the coordinates are selected from Table C.
  • the preferred-binding-conformation fields are contained within the database schema and are configured to store information related to one or more preferred binding conformations for each combination of protein and compound determined based at least in part on information in the protein-structural- information fields and the compound-structural-information fields.
  • the one or more data processors are configured to: process a database query that operates over data related to the protein-structural-information fields, the compound-structural-information fields, and the preferred-binding-conformation fields and determine whether the one or more compounds are cardiotoxic by using information in the preferred-binding-conformation fields.
  • a non-transitory computer-readable storage medium for storing data for access by a compound-selection program which is executed on a data processing system.
  • the storage medium includes a protein-structural-information data structure, a candidate-compound-structural-information data structure, a molecular- dynamics-simulations data structure, a dominant-conformations data structure, and a binding- conformations data structure.
  • the protein-structural-information data structure has access to information stored in a database and includes protein structural information representative of a hERGl ion channel protein.
  • the candidate-compound-structural-information data structure has access to information stored in the database and includes compound structural information describing conformers of one or more compounds.
  • the molecular-dynamics- simulations data structure has access to information stored in the database and includes configuration information of target protein flexibility determined by performing molecular dynamics simulations on the protein structural information.
  • the dominant-conformations data structure has access to information stored in the database and is determined by using a first clustering algorithm based at least in part on the configuration information of target protein flexibility.
  • the binding-conformations data structure has access to information stored in the database and includes information related to one or more combinations of protein and compound determined by using a docking algorithm based at least in part on the compound structural information and the one or more dominant conformations, one or more preferred binding conformations being determined by using a second clustering algorithm based at least in part on the information related to the one or more combinations of protein and compound.
  • a compound is selected if the compound does not block the channel of the hERGl protein in the preferred binding conformations.
  • FIGURES 1A and IB System block diagrams for selecting a compound that has reduced risk of cardiotoxicity.
  • Processes illustrated in the system block diagrams (1 A) and (IB) are: Target Preparation (includes, e.g., combined de « ⁇ /homology protein modeling of hERG), Ligand Collection Preparation (includes, e.g., translation of the 2D information of the ligand into a 3D representative structure), Ensemble Generation (includes, e.g., Molecular Dynamics simulations, principal component analysis, and iterative clustering), Docking (includes, e.g., docking and iterative clustering), MP Simulations on Selected Complexes (includes, e.g., Molecular Dynamics simulations and preliminary ranking of docking hits), Rescoring using MM-PBSA (includes, e.g., binding free energy calculation and rescoring of top hits), and Experimental Testing (includes, e.g., hERGl channel inhibition
  • the top hits from the Rescoring step can act as positive controls for the next phase screening.
  • the Ensemble Generation. Docking. MP Simulations on Selected Complexes, and Rescoring using MM-PBSA steps may be performed on a supercomputer, for example, the "IBM Blue Gene/Q" supercomputer system at the Health Sciences Center for Computational Innovation, University of Rochester (e.g., as shown in the block diagram (IB)).
  • FIGURE 2 (Top) Schematic drawing illustrating the general topology of hERGl.
  • the six transmembrane helices (SI to S6), the pore helix (PH), the pore forming loop, the Per-Arnt-Sim (PAS) domain, the cyclic nucleotide-binding domain (CNBD) are shown.
  • the voltage sensor in S4 is indicated by the positive charges. Mutations studied are displayed with yellow stars.
  • the boxed numbers indicate the coding exons of the KNCH2 gene.
  • the gray box represent the unique region not present in hERGl a.
  • FIGURE 4 (A) Potential of mean force for the movement of neutral dofetilide. Two energy wells were chosen from each open (black: a, b) and open-inactivated (grey: a', b') hERG l . (B) Average locations of dofetilide in hERG l open and open- inactivated states, nearby interacting residue's names are shown in different grey shading for each monomer. All atoms within 3.9 A of dofetilide are shown with sticks. Water molecules are shown as balls and the hydrogen bonds as sticks.
  • ECG electrocardiogram
  • This electrical activity is the result of ions such as sodium and potassium passing through ion channels in the membranes surrounding heart cells.
  • a prolonged QT interval indicates an abnormality in electrical activity that leads to irregularities in heart muscle contraction.
  • One of these irregularities is a specific pattern of very rapid contractions (tachycardia) of the lower chambers of the heart called torsade de pointes, a type of ventricular tachycardia.
  • the rapid contractions which are not effective in pumping blood to the body, result in a decreased flow of oxygen-rich blood to the brain. This can result in a sudden loss of consciousness (syncope) and death.
  • membrane bound protein refers to any protein that is bound to a cell membrane under physiological pH and salt concentrations.
  • binding of the membrane bound protein can be either by direct binding to the phospholipid bilayer or by binding to a protein, glycoprotein, or other intermediary that is bound to the membrane.
  • the term "voltage-gated channel” or “voltage-gated ion channel” refers to a class of transmembrane ion channels that are activated by changes in electrical potential difference near the channel.
  • the voltage-gated ion channel is a voltage-gated potassium channel.
  • structural information refers to the three dimensional structural coordinates of the atoms within a macromolecule, for example, a protein macromolecule such as hERGl.
  • three-dimensional (3D) structure refers to the
  • EM energy minimization
  • EM refers to computational methods for computing stable states of interacting atoms, groups of atoms or molecules, including macromolecules, corresponding to global and local minima on their potential energy surface. Starting from a non-equilibrium molecular geometry, EM employs the mathematical procedure of optimization to move atoms so as to reduce the net forces (the gradients of potential energy) on the atoms until they become negligible.
  • PMF potential of mean force simulation
  • PFM simulation is a type of simulation which is often used in Monte Carlo or MD simulations to examine how a system's energy changes as a function of some specific reaction coordinate parameter.
  • PMF simulations may be used to examine how the system's energy changes as a function of the distance between two residues, or as a protein is pulled through a lipid bilayer.
  • PMF simulations are used in conjunction with umbrella sampling because the PMF simulation will typically fail to adequately sample the system space as it proceeds.
  • a model included in the term can be any of a variety of known representations of a molecule including, for example, a graphical representation of its three-dimensional structure, a set of coordinates, set of distance constraints, set of bond angle constraints or set of other physical or chemical properties or combinations thereof.
  • the ligand is a compound, for example a small molecule
  • the receptor is a protein macromolecule, for example, hERGl.
  • binding conformations refers to the orientation of a ligand to a receptor when bound or docked to each other.
  • conformations refers to most highly populated orientation(s) of a ligand to a receptor when bound or docked to each other.
  • a clustering algorithm is used to determine the
  • binding conformation refers to the energetically preferred orientation of a ligand to a receptor when bound or docked to each other to form a stable ligand-receptor complex.
  • EC5 0 generally describes the effective dose of the compound.
  • EC 50 is the dose of the compound that inhibits viral replication by 50%.
  • ECso's and ECc>o's can be measured according to any method known to one of ordinary skill in the art.
  • CC5 0 and CC 90 refer to the concentration of a compound that reduces the number of viable cells (e.g., kills the cells) compared to that for untreated controls, by 50% and 90%, respectively.
  • the term “CC50” generally describes the concentration of the compound that is cytotoxic to cells.
  • CC5 0 is the dose of the compound that is cytotoxic to uninfected cells.
  • CC5 0 is the dose of the compound that is cytotoxic to heart cells.
  • the methods disclosed herein select for compounds with reduced risk of cardiotoxicity, but which retain strong biological activity to their primary targets.
  • such compounds may have high EC50 values for the secondary biological target (e.g., hERGl ), high CC5 0 values for uninfected cells, but low EC5 0 values against the primary biological target (e.g., HCV NS3/4A protease, HCV NS5B polymerase, or HCV NS5a protein).
  • CCso's and CCgo's can be measured according to any method known to one of ordinary skill in the art.
  • SI selectiveivity index
  • processor and "central processing unit” or “CPU” are used interchangeably and refer to a device that is able to read a program from a computer memory (e.g., ROM or other computer memory) and perform a set of steps according to the program.
  • a computer memory e.g., ROM or other computer memory
  • computer readable medium refers to any device or system for storing and providing information (e.g., data and instructions) to a computer processor.
  • Examples of computer readable media include, but are not limited to, DVDs, CDs, hard disk drives, magnetic tape and servers for streaming media over networks.
  • hERGl Ether-a-go- go Related Gene 1
  • the model is combined with an atomistically detailed high throughput screening algorithm of test compounds in silico to predict cardiotoxicity and to select for compounds with reduced cardiotoxicities.
  • the hERGl channel is expressed in the heart as well as in various brain regions, smooth muscle cells, endocrine cells, and a wide range of tumor cell lines.
  • its role in the heart is the one that has been well characterized and extensively studied for two main reasons.
  • LQTS long QT syndrome
  • the blockade of hERGl by prescription medications causes drug-induced QT prolongation that shares the same risk of sudden cardiac arrest like LQTS.
  • Comput. Biol. 1, 95-117); may further comprise ab initio or de novo protein modeling methods using various algorithms, performable without limitation using the publically distributed "ROSETTA'" platform (Simons et al, 1999, Genetics 37, 171-176; Baker 2000, Nature 405, 39-42; Bradley et al, 2003, Proteins 53, 457-468; Rohl 2004, Methods Enzymol. 383, 66-93), the "I-TASSER” application (Wu et al, 2007, BMC Biol. 5, 17), or using physics-based prediction (see, e.g., Duan and Kollman 1998, Science 282, 740-744; Oldziej et al, 2005, Proc. Natl. Acad. Sci. USA 102, 7547-7552); or a combination of any such approaches.
  • Computational approaches applicable herein for structure prediction of biomolecules are evaluated annually within the Critical Assessment of Techniques for Protein Structure (CASP) experiment as published in the CASP Proceedings
  • the compound is selected from the group consisting of ivabradine, dofetilide, ibutilide, E4031, MK-499, KN-93, amiodarone, cisapride, haloperidol, droperidol, bepridil, terfenadine, E-4031, propafenone, domperidone, changrolin, and bertosamil.
  • the compound is selected from the group consisting of amiodarone, cisapride, droperidol and haloperidol.
  • the compound is selected from the group consisting of bepridil, domperidone, E-4031 and terfenadine.
  • the molecular simulation is conducted using the
  • AMBER Assisted Model Building with Energy Refinement
  • CHARMM Brooks et al, 2009, J. Comput.
  • LAMMPS Large-scale Atomic/Molecular Massively Parallel Simulator
  • NAMD NAMD
  • the molecular simulation incorporates solvent molecules.
  • the molecular simulation incorporates implicit or explicit solvent molecules.
  • implicit solvation also known as continuum solvation
  • continuum solvation is a method of representing solvent as a continuous medium instead of individual "explicit" solvent molecules most often used in MD
  • the method optionally comprises the step of principal component analysis (PCA) of the MD trajectory.
  • PCA is performed prior to identification of dominant conformations of the ion channel protein using clustering algorithms (see below).
  • PCA is performed using the software AMBER-ptraj (Case et al, 2012, AMBER 12, University of California, San Francisco; Salomon-Ferrer et al, 2013, "An Overview of the Amber Biomolecular Simulation
  • PCA reduces the system dimensionality toward a finite set of independent principal components covering the essential dynamics of the system.
  • the method optionally comprises the step of calculating the root mean square deviation (RMSD) of Ca atoms relative to a reference structure of the ion channel protein.
  • RMSD root mean square deviation
  • calculation of RMSD is performed to observe the overall behavior of the MD trajectory, prior to identification of dominant conformations of the ion channel protein using clustering algorithms (see below).
  • a clustering algorithm as described above, is used to identify the preferred binding conformations for each of the combinations of compound and protein.
  • the preferred binding conformations are those which have the largest cluster population and the lowest binding energy.
  • the preferred binding conformations are the energetically preferred orientation of the compound (ligand) docked to one or more conformations of the hERGl protein (receptor), for example, conformations that represent the open, closed or open-inactivated states of the hERGl protein, to form a stable complex.
  • a compound that blocks the channel in one of its preferred binding conformations is cardiotoxic. In certain embodiments, a compound that does not block the channel in any of its preferred binding conformations has reduced risk of cardiotoxicity.
  • the method comprises the step of optimizing the preferred binding conformations using MD, as described above.
  • the MD uses NAMD software. 6.2.3.11 Calculation of Binding Energys. AG r paragraphi r
  • the method comprises prediction of cardiotoxicity and selection of a compound based on (i) classification of the compound as "blocker” versus “nonblocker”; and/or (ii) calculated binding energies.
  • the compound wherein the compound blocks the hERGl ion channel in one of its preferred binding conformations, the compound is identified as a "blocker.” Under such circumstances, the compound is predicted to be cardiotoxic, and the compound is not selected for further clinical development or for use in humans. However, under such circumstances, the method may further comprise the step of using a molecular modeling algorithm to chemically modify or redesign the compound such that it does not block the ion channel in its preferred binding conformations and retains biological activity to its primary biological target, as described in Sections 5.2.3.13 and 5.2.3.14 below, respectively.
  • a new compound may also be selected from the collections of a chemical or compound library, for example, a library of new drug candidates generated by organic or medicinal chemists as part of a drug discovery program, as described in Section 5.2.3.15 below.
  • binding affinity is predicted to be moderate to strong.
  • the compound is predicted to be cardiotoxic at therapeutically relevant
  • the method further comprises the step of using a molecular modeling algorithm to chemically modify or design the compound such that it does not block the ion channel in any of its preferred binding conformations.
  • the method comprises repeating steps e') through i') for the modified or redesigned compound. In certain embodiments, the method comprises repeating steps a) through f) for the modified or redesigned compound.
  • a chemical moiety of a compound identified as a "blocker” is found to be responsible for blocking, obstructing, or partially obstructing the hERGl ion channel, that chemical moiety may be modified in silico using any one of the molecular modeling algorithms disclosed herein or known to one of ordinary skill in the art. The modified compound may then be retested by repeating steps a) through f) of the methods disclosed herein.
  • the modified or redesigned compound is tested in an in vitro biological assay for selective binding to its biological target.
  • the modified or redesigned compound binds with high affinity to its biological target and/or retains biological activity.
  • the computational models or screening algorithms disclosed herein for selecting compounds that have reduced risk of cardiotoxicity may be combined with any computational models or screening algorithms known to those of ordinary skill in the art for modeling the binding of the compound or modified/redesigned compound to its primary biological target.
  • the new compound may then be retested for cardiotoxicity by repeating steps e) through i) of the methods disclosed herein.
  • the new compound selected from the chemical library may also be tested for selective binding to a desired biological target, for example, a primary biological target, as described above in Section 5.2.3.14 above, for the modified/redesigned compound.
  • a desired biological target for example, a primary biological target, as described above in Section 5.2.3.14 above, for the modified/redesigned compound.
  • the methods disclosed herein include checking in silico predicted cardiotoxicities with the results of an in vitro biological assay, or in vivo in an animal model.
  • the methods disclosed herein may also include validating or confirming the in silico predicted cardiotoxicities with the results of an in vitro biological assay, or with the results of an in vivo study in an animal model.
  • the in vitro biological assay is a FluxORTM potassium ion channel assay (see, e.g. Beacham et al, 2010, “Cell-Based Potassium Ion Channel Screening Using FluxORTM Assay," J. Biomol. Screen., 15(4), 441-446), which allows high throughput screening of potassium ion channel and transporter activities.
  • the FluxORTM assay monitors the permeability of potassium channels to thallium (Tl + ) ions.
  • Tl + thallium
  • thallium flows down its concentration gradient into the cells, and channel or transporter activity is detected with a proprietary indicator dye that increases in cytosolic fluorescence. Accordingly, the fluorescence reported in the FluxORTM system is an indicator of any ion channel activity or transport process that allows thallium into cells.
  • the FluxORTM potassium channel assay is performed on HEK 293 cells stably expressing hERGlor mouse cardiomyocyte cell line HL-1 cells.
  • the FluxORTM potassium channel assay is performed on a human adult cardiomyocyte cell line expressing hERGl 6.2.4.2 Electrophysiology Measurements in Single Cells
  • electrophysiology measurements for example, patch clamp electrophysiology measurements, which use a high throughput single cell planar patch clamp approach (see, e.g., Schroeder et al , 2003, “Ionworks HT: A New High-Throughput Electrophysiology Measurement Platform,” J. Biomol. Screen. 8 (1), 50-64).
  • electrophysiology measurements are in single cells.
  • the single cells are Chinese hamster ovary (CHO) cells stably transfected with hERGl (CHO-hERG). In certain embodiments, the single cells are from a human adult cardiomyocyte cell line expressing hERGl.
  • the in vitro biological assay is a Cloe Screen IC 50 hERG Safety assay, for example, as provided by the company CYPROTEX (see, e.g., http://www.cyprotex.com/toxicology/cardiotoxicity/hergsafety/).
  • a second recording of the hERG current is performed.
  • Post-compound hERG currents are expressed as a percentage of pre-compound hERG currents (% control current) and plotted against concentration for each compound. Where concentration dependent inhibition is observed the Hill equation is used to fit a sigmoidal line to the data and an IC5 0 (concentration at which 50% inhibition is observed) is determined.
  • the method comprises testing the cardiotoxicity of the compound or modified compound in vivo by measuring ECG in a transgenic mouse model expressing human hERGl .
  • Target Preparation and Ligand Collection Preparation steps may be performed on local machines (e.g., in a Molecular Operating Environment (MOE)).
  • MOE Molecular Operating Environment
  • CCG Computing Group
  • MOE also generates variants of the same ligand with different tautomeric, stereochemical, and ionization properties. All generated structures are conformationally relaxed using energy minimization protocols included in MOE.
  • the software LigPrep from the Schrodinger package may be used to translate the 2D information of a compound (ligand) into a 3D representative structure.
  • LigPrep may also be used to generate variants of the same ligand with different tautomeric, stereochemical, and ionization properties. All generated structures may be conformationally relaxed using energy minimization protocols included in LigPrep. 7.3 EXAMPLE 3; MOLECULAR DYNAMICS SIMULATIONS
  • the optimal number of clusters is estimated by observing the values of the Davies-Bouldin index (DBI) (see, e.g., Davies et al., 1979, “A Cluster Separation Measure,” IEEE Trans. Pattern Anal. Intelligence 1, 224) and the percentage of data explained by the data (SSR/SST) (see, e.g., Shao et al, 2007, “Clustering Molecular Dynamics Trajectories: 1. Characterizing the Performance of Different Clustering Algorithms," J. Chem. Theory & Computation. 3, 231) for different cluster counts ranging from 5 to 600. At the optimal number of clusters, a plateau in the SSR/SST is expected to match a local minimum in the DBI (Shao et al, 2007). Using this methodology, distinct conformations for the intracellular hERG channel are identified.
  • DBI Davies-Bouldin index
  • SSR/SST the percentage of data explained by the data
  • average coordinates for the hERGl molecule may be calculated over the entire length of the MD trajectory, and this set of averaged coordinates used to select a single "snap shot" at one time step in the trajectory that best represents the conformational space explored by the trajectory.
  • Protonation states of all ionizable residues are calculated using the program PDB2PQR. All simulations are performed at 300 K and pH 7 using the NAMD program (Kale et al., 1999, "NAMD2: Greater Scalability for Parallel Molecular Dynamics," J. Comp. Phys. 151, 283-312). Following parameterization, the protein-ligand complexes are immersed in the center of a cube of TIP3P water molecules. The cube dimensions are chosen to provide at least a 10 A buffer of water molecules around each system. When required, chloride or sodium counter-ions are added to neutralize the total charge of the complex by replacing water molecules having the highest electrostatic energies on their oxygen atoms.
  • MM-PBSA molecular mechanics Poisson-Boltzmann surface area
  • the molecular mechanical (EMM) energy of each snapshot is calculated using the SANDER module of AMBERIO with all pair- wise interactions included using a dielectric constant ( ⁇ ) of 1.0.
  • the solvation free energy (G solv ) is estimated as the sum of electrostatic solvation free energy, calculated by the finite-difference solution of the Poisson-Boltzmann equation in the Adaptive Poisson-Boltzmann Solver (APBS) and non-polar solvation free energy, calculated from the solvent-accessible surface area (SASA) algorithm.
  • the solute entropy is approximated using the normal mode analysis. Applying the thermodynamic cycle for each protein-ligand complex, the binding free energy is calculated using the following equation:
  • the calculated binding energies, AG° ca / c can be compared directly to the physiologically relevant concentrations.
  • the IC5 0 (concentration at which 50% inhibition is observed) values measured from, for example, in vitro biological assays are converted to the observed free energy change of binding, AG a s (cal mol "1 ) using the equation:
  • the calculated binding energy of a tested compound may also compared to that of a known control (a known hERG blocker from a standardized panel of drugs).
  • a known control a known hERG blocker from a standardized panel of drugs.
  • K ' K ' (4) where K a and K j2 are the molar concentrations of the tested compound and the control, repectively.
  • binding energies may be estimated using umbrella sampling simulations (see, e.g., Kaster et al., "Umbrella Sampling,” WIREs Comput Mol Sci 2011, 1 : 932-942. doi: 10.1002/wcms.66) to evaluate the potential of mean force for the tested compound unbinding from the hERGl channel.
  • VMD Visual MD
  • Human MD Humphrey et al, 1996, “Visual Molecular Dynamics,” J. Mol. Graphics, 14 (1), 33-38
  • a channel blocker binds within the cavity so that the passage of the potassium ions through the selection filter is blocked.
  • a compound may bind to the channel in a way that it does not interfere with the potassium passage.
  • compounds may be classified as "blockers,” e.g., compound that blocked the hERGl ion channel, or as "non-blockers,” e.g., compounds that do not block the hERGl ion channel. 7.9 EXAMPLE 9; HERG1 CHANNEL INHIBITION (IC n DETERMINATION) IN MAMMALIAN CELLS
  • 384-well planar arrays and hERG tail-currents are measured by whole-cell voltage-clamping.
  • a range of concentrations (TBD) of the test compounds are then added to the cells and a second recording of the hERG current is made. The percent change in hERG current is calculated.
  • IC50 values are derived by fitting a sigmoidal function to concentration-response data, where concentration-dependent inhibition was observed.
  • the cells used are Chinese hamster ovary (CHO) cells stably transfected with hERG (cell-line obtained from Cytomyx, UK).
  • a single-cell suspension is prepared in extracellular solution (Dulbecco's phosphate buffered saline with calcium and magnesium pH 7-7.2) and aliquots are added automatically to each well of a PatchPlateTM.
  • the cells are then positioned over a small hole at the bottom of each well by applying a vacuum beneath the plate to form an electrical seal.
  • the vacuum is applied through a single compartment common to all wells which are filled with intracellular solution (buffered to pH 7.2 with HEPES).
  • the resistance of each seal is measured via a common ground-electrode in the intracellular compartment and individual electrodes placed into each of the upper wells.
  • Post-compound currents are then expressed as a percentage of pre-compound currents and plotted against concentration for each compound. Where concentration-dependent inhibition is observed, the data are fitted to the following equation and an IC5 0 value calculated:
  • Kidney 293 cells (HEK 293) cells stably expressing hERGl or mouse cardiomyocyte cell line HL-1 cells (a gift from Dr. William Clay comb, Louisiana, USA). Briefly, FluxORTM loading buffer is made from Hank's Balanced Saline Solution (HBSS) buffered with 20 mM HEPES and pH adjusted with NaOH to 7.4. PowerloadTM concentrate and water-soluble probenecid are used as directed by the kit to enhance the dye solubility and retention, respectively.
  • HBSS Hank's Balanced Saline Solution
  • PowerloadTM concentrate and water-soluble probenecid are used as directed by the kit to enhance the dye solubility and retention, respectively.
  • stimulation buffer is prepared from the 5X chloride-free buffer, thallium, and potassium sulfate reagents provided in the kit to contain 10 mM free thallium (5 mM T1 2 S0 4 ) and 50 mM free potassium (25 mM K 2 S0 4 ).
  • Electrocardiograpy to test anti-arrhythmic activity in transgenic mice expressing hERGl specifically in the heart may be performed using previously published protocols (Royer et al. , 2005, "Expression of Human ERG K+ Channels in the Mouse Heart Exerts Anti-Arrhythmic Activity,” Cardiovascular Res. 65, 128-137).
  • Dofetilide is a class III antiarrhythmic agent, which is marketed in the United
  • ROSETTA-membrane simulations were run to generate refined ensembles for open-inactivated and closed states.
  • ROSETTA output was clusted into 12 to 20 stable clusters and these clusters run on the "IBM Blue Gene/Q" supercomputer system for timescales around 100 ns.
  • the structural difference between open state and open-inactivated states is schematically illustrated in FIGURE 3.
  • HERGl -dofetilide complexes were embedded in a DPPC bilayer.
  • the system was solvated in TIP3P water molecules with 150 mM KC1.
  • All of the systems (4 complexes for charged/neutral dofetilide at open and open-inactivated states) were built and pre-equilibrated with the CHARMM program using CHARMM27 force field (see, e.g., Noskov et al., 2008, "Control of Ion Selectivity In LeuT: Two Na(+) Binding Sites with two Different Mechanisms," J. Mol. Biol., 377, 804-818).
  • CHARMM generalized force fields CGenFF
  • CHARMM General Force Field a Force Field for Drug- Like Molecules Compatible with the CHARMM All-Atom Additive Biological Force Fields
  • NAMD2.9 program package Phillips et al., 2005, "Scalable Molecular Dynamics with NAMD,” J.
  • Simulations were performed with harmonic biasing potentials with a force constant of 10 kcal/(mol A 2 ) along the z-axis.
  • the zero position along the z-axis was the center of mass of the Ca of residues 623-628 in the filter.
  • the flat-bottom cylindrical constraints with radius of 10 A was used to cap lateral displacement of the bound drug.
  • the reaction coordinate for each window was the distance along the z-axis between the center of mass of dofetilide and the zero position.
  • the sampling windows were spaced every 0.5 A from -7.5 A to -49.5 A resulting in 85 windows for open hERG and from -8.5 A to -38.0 A resulting in 60 windows for open-inactivated hERG.
  • the simulation time per window was set to 22 ns.
  • the binding site is located between Y652 and the filter. Both head groups are coordinated by S649 and water molecules (FIGURE 4B-a'). The outer binding site is at the gate and close to the hydrophobic residues Y652, F656 and 1655 (FIGURE 4B-b'). One arm of dofetilide points into the solvent.
  • ivabradine was found to blocks the hERGl current over a range of concentrations overlapping with those required to block HCN4 (Lees-Miller et al, 2015, "Ivabradine Prolongs Phase 3 of Cardiac Repolarization and Blocks the hERGl (KCNH2) Current over a Concentration-Range Overlapping with that Required to Block HCN4," J. Mol. Cell. Cardiology, 85, 71-78).
  • the atomistic details of this blockage are explored in the present example using the methods and systems disclosed herein.
  • Ivabradine was docked to an in silico hERG models representing open and closed states.
  • the best-scored binding poses for neutral and cationic ivabradine binding to an intra-cavitary site in the open-state of hERGl are shown in FIGURE 6.
  • the identified lipid- exposed binding site found in open and closed state of hERGl are shown in FIGURE 7.
  • Docking scores to an intra-cavitary site in the open state of hERGl are -6.4 kcal/mol and -6.7 kcal/mol for neutral and ionized forms of ivabradine, respectively.
  • the computed binding enthalpy for neutral and charged forms of ivabradine to an intra-cavitary sites were -16 ⁇ 4 kcal/mol and -14 ⁇ 5 kcal/mol, respectively.
  • the key residues involved in the intra-cavity binding site for ivabradine are F656, Y652 and A653.
  • the key residues important for stabilizing the polar groups in the drug are S624, T623 and S642.
  • the docking studies also mapped a well-defined binding site on the hERGl surface exposed to lipids.
  • the results of MD simulations support that ivabradine is stable at the lipid-exposed pocket in hERGl (shown in FIGURE 7).
  • the diffusion of the drug center of mass in 50 ns of equilibrium MD simulations is illustrated in FIGURE 7 along with metrics describing conformational dynamics of the bound drug.
  • binding enthalpies for neutral ivabradine binding to lipid-facing site were calculated.
  • the calculated binding enthalpy was -13 ⁇ 3 kcal/mol and -14 ⁇ 3 kcal/mol for site found in open and closed states, respectively.
  • the lipid exposed binding pocket is formed predominantly by F551, 1663,
  • FIGURE 8 depicts a grid computing environment for selecting a compound with reduced risk of cardiotoxicity.
  • user computers 1302 can interact with the grid computing environment 1306 through a number of ways, such as over one or more networks 1304.
  • the grid computing environment 1306 can assist users to select a compound with reduced risk of cardiotoxicity.
  • One or more data stores 1308 can store the data to be analyzed by the grid computing environment 1306 as well as any intermediate or final data generated by the grid computing environment.
  • the configuration of the grid computing environment 1306 allows its operations to be performed such that intermediate and final data results can be stored solely in volatile memory (e.g., RAM), without a requirement that intermediate or final data results be stored to non-volatile types of memory (e.g., disk).
  • the grid computing environment 1306 receives ad hoc queries from a user and when responses, which are generated by processing large amounts of data, need to be generated on-the-fly.
  • the grid computing environment 1306 is configured to retain the processed information within the grid memory so that responses can be generated for the user at different levels of detail as well as allow a user to interactively query against this information.
  • the grid computing environment 1306 receives structural information describing the structure of the ion channel protein, and performs a molecular dynamics simulation of the protein structure. Then, the grid computing environment 1306 uses a clustering algorithm to identify dominant conformations of the protein structure from the molecular dynamics simulation, and select the dominant conformations of the protein structure identified from the clustering algorithm. In addition, the grid computing environment 1306 receives structural information describing conformers of one or more compounds, and uses a docking algorithm to dock the conformers of the one or more compounds to the dominant conformations.
  • the grid computing environment 1306 further identifies a plurality of preferred binding conformations for each of the combinations of protein and compound, and optimizes the preferred binding conformations using molecular dynamics simulations so as to determine whether the compound blocks the ion channel of the protein in the preferred binding conformations.
  • the grid computing environment 1306, without an OLAP or relational database environment being required aggregates protein structural information and compound structural information from the data stores 1308. Then the grid computing environment 1306 uses the received protein structural information to perform molecular dynamics simulations for determining configurations of target protein flexibility (e.g., over a simulation length of greater than 50 ns).
  • the molecular dynamics simulations involve the grid computing environment 1306 determining forces acting on an atom based upon an empirical force field that approximates intramolecular forces, where numerical integration is performed to update positions and velocities of atoms.
  • the grid computing environment 1306 clusters molecular dynamic trajectories formed based upon the updated positions and velocities of the atoms into dominant conformations of the protein, and executes a docking algorithm that uses the compound's structural information in order to dock the compound's conformers to the dominant conformations of the protein. Based on information related to the docked compound's conformers, the grid computing environment 1306 identifies a plurality of preferred binding conformations for each of the combinations of protein and compound. If the compound does not block the ion channel of the protein in the preferred binding conformations, the grid computing environment 1306 predicts the compound has reduced risk of cardiotoxicity. Otherwise, the grid computing environment 1306 predicts the compound is cardiotoxic, and redesigns the compound in order to reduce risk of
  • FIGURE 9 illustrates hardware and software components for the grid computing environment 1306.
  • the grid computing environment 1306 includes a central coordinator software component 1406 which operates on a root data processor 1404.
  • the central coordinator 1406 of the grid computing environment 1306 communicates with a user computer 1402 and with node coordinator software components (1412, 1414) which execute on their own separate data processors (1408, 1410) contained within the grid computing environment 1306.
  • the grid computing environment 1306 can comprise a number of blade servers, and a central coordinator 1406 and the node coordinators (1412, 1414) are associated with their own blade server.
  • a central coordinator 1406 and the node coordinators (1412, 1414) execute on their own respective blade server.
  • each blade server contains multiple cores and a thread is associated with and executes on a core belonging to a node processor (e.g., node processor 1408).
  • a network connects each blade server together.
  • the central coordinator 1406 comprises a node on the grid. For example, there might be 100 nodes, with only 50 nodes specified to be run as node coordinators.
  • the grid computing environment 1306 will run the central coordinator 1406 as a 51st node, and selects the central coordinator node randomly from within the grid. Accordingly, the central coordinator 1406 has the same hardware configuration as a node coordinator.
  • the central coordinator 1406 may receive information and provide information to a user regarding queries that the user has submitted to the grid.
  • the central coordinator 1406 is also responsible for communicating with the 50 node coordinator nodes, such as by sending those instructions on what to do as well as receiving and processing information from the node coordinators.
  • the central coordinator 1406 is the central point of contact for the client with respect to the grid, and a user never directly communicates with any of the node coordinators.
  • the central coordinator 1406 communicates with the client (or another source) to obtain the input data to be processed.
  • the central coordinator 1406 divides up the input data and sends the correct portion of the input data for routing to the node coordinators.
  • the central coordinator 1406 also may generate random numbers for use by the node coordinators in simulation operations as well as aggregate any processing results from the node coordinators.
  • the central coordinator 1406 manages the node coordinators, and each node coordinator manages the threads which execute on their respective machines.
  • a node processor includes shared memory for use for a node coordinator and its threads.
  • the grid computing environment 1306 is structured to conduct its operations (e.g., matrix operations, etc.) such that as many data transfers as possible occur within a blade server (i.e., between threads via shared memory on their node) versus performing data transfers between threads which operate on different blades.
  • Such data transfers via shared memory are more efficient than a data transfer involving a connection with another blade server.
  • FIGURE 10 depicts example schematics of data structures utilized by a compound-selection system.
  • Multiple data structures are stored in a data store 1500, including a protein-structural-information data structure 1502, a candidate-compound- structural-information data structure 1504, a binding-conformations data structure 1506, a molecular-dynamics-simulations data structure 1508, a dominant-conformations data structure 1510, a cluster data structure 1512, and a cardiotoxi city-analysis data structure 1514.
  • These interrelated data structures can be part of the central coordinator 1406 by aggregating data from individual nodes. However, portions of these data structures can be distributed as needed, so that the individual nodes can store the process data.
  • the data store 1500 can be different types of storage devices and programming constructs (e.g., RAM, ROM, Flash memory, flat files, databases, programming data structures, programming variables, IF-THEN (or similar type) statement constructs, etc.).
  • the data store 1500 can be a single relational database or can be databases residing on a server in a distributed network.
  • the protein-structural-information data structure 1502 is configured to store data related to the structure of the hERGl ion channel protein, for example, special relationship data between different atoms.
  • the data related to the structure of the potassium ion channel protein may be obtained from a homology model, an NMR solution structure, an X-ray crystal structure, a molecular model, etc.
  • Molecular dynamics simulations can be performed on data stored in the protein-structural-information data structure 1502. For example, the molecular dynamics simulations involve solving the equation of motion according to the laws of physics, e.g., the chemical bonds within proteins being allowed to flex, rotate, bend, or vibrate.
  • Data stored in the molecular-dynamics-simulations data structure 1508 are processed using a clustering algorithm, and associated cluster population data are stored in the cluster data structure 1512. Dominant conformations of the potassium ion channel protein are identified based at least in part on the data stored in the molecular-dynamics- simulations data structure 1508 and the associated cluster population data stored in the cluster data structure 1512. Atomistic trajectory data (e.g., at different time slices) related to the identified dominant conformations are stored in the dominant-conformations data structure 1510. [00302] Data stored in the candidate-compound-structure-information data structure
  • conformations include those with a largest cluster population and a lowest binding energy.
  • binding energies are calculated (e.g., using salvation models, etc.) for each of the combinations of protein and compound (receptor and ligand) in the corresponding optimized preferred binding conformation(s).
  • the calculated binding energies are output as the predicted binding energies for each of the combinations of protein and compound.
  • the methods and systems described herein may be implemented on many different types of processing devices by program code comprising program instructions that are executable by the device processing subsystem.
  • the software program instructions may include source code, object code, machine code, or any other stored data that is operable to cause a processing system to perform the methods and operations described herein.
  • Other implementations may also be used, however, such as firmware or even appropriately designed hardware configured to carry out the methods and systems described herein.
  • the systems' and methods' data may be stored and implemented in one or more different types of computer-implemented data stores, such as different types of storage devices and programming constructs (e.g., RAM, ROM, Flash memory, flat files, databases, programming data structures, programming variables, IF-THEN (or similar type) statement constructs, etc.).
  • storage devices and programming constructs e.g., RAM, ROM, Flash memory, flat files, databases, programming data structures, programming variables, IF-THEN (or similar type) statement constructs, etc.
  • data structures describe formats for use in organizing and storing data in databases, programs, memory, or other computer-readable media for use by a computer program.
  • the systems and methods may be provided on many different types of computer-readable media including computer storage mechanisms (e.g., CD-ROM, diskette, RAM, flash memory, computer's hard drive, etc.) that contain instructions (e.g., software) for use in execution by a processor to perform the methods' operations and implement the systems described herein.
  • computer storage mechanisms e.g., CD-ROM, diskette, RAM, flash memory, computer's hard drive, etc.
  • instructions e.g., software
  • a module or processor includes but is not limited to a unit of code that performs a software operation, and can be implemented for example as a subroutine unit of code, or as a software function unit of code, or as an object (as in an object-oriented paradigm), or as an applet, or in a computer script language, or as another type of computer code.
  • the software components and/or functionality may be located on a single computer or distributed across multiple computers depending upon the situation at hand.
  • the computing system can include clients and servers.
  • a client and server are generally remote from each other and typically interact through a communication network.
  • the relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other.
  • ATOM 108 HD1 ILE X 560 -2 .963 22 .251 -6. .177 0. .00 0. .00 PROA
  • ATOM 109 HD2 ILE X 560 -3 .469 23 .950 -5. .655 0. .00 0. .00 PROA
  • ATOM 110 HD3 ILE X 560 -1 .722 23 .561 -6. .047 0. .00 0. .00 PROA
  • ATOM 218 HD1 ILE X 567 -10 .316 22 .675 3. .854 0. .00 0. .00 PROA
  • ATOM 249 CA TYR X 569 -9 .496 16 .609 10. .761 0. .00 0. .00 PROA
  • ATOM 280 CA ILE X 571 -12 .529 21 .075 13. .085 0. .00 0. .00 PROA
  • ATOM 292 HD1 ILE X 571 -13 .964 23 .931 13. .339 0. .00 0. .00 PROA
  • ATOM 294 HD3 ILE X 571 -15 .481 24 .346 12. .589 0. .00 0. .00 PROA
  • ATOM 306 CA ASN X 573 -10 .512 19 .078 17. .846 0. .00 0. .00 PROA
  • ATOM 307 HA ASN X 573 -10 .923 18 .767 18. .796 0. .00 0. .00 PROA
  • ATOM 314 HD21 ASN X 573 -10 .236 15 .206 16. .507 0. .00 0. .00 PROA
  • ATOM 315 HD22 ASN X 573 -9 .745 16 .664 15. .669 0. .00 0. .00 PROA
  • ATOM 348 CA GLN X 576 -13 .272 23 .602 22. .592 0. .00 0. .00 PROA
  • ATOM 440 HD1 ARG X 582 -10 .273 23 .642 29. .728 0. .00 0. .00 PROA
  • ATOM 458 HB ILE X 583 -9 .835 21 .362 36. .179 0. .00 0. .00 PROA

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Abstract

La présente invention concerne des systèmes et des procédés pour sélectionner des composés qui ont un risque de cardiotoxicité réduit ou qui ne sont pas susceptibles d'être cardiotoxiques. À titre d'exemple, un système et un procédé peuvent comprendre un modèle dynamique computationnel combiné à un criblage haut débit in silico qui imite l'un des canaux ioniques les plus importants associé à la cardiotoxicité, à savoir le canal du gène lié à éther-à-go-go humain (hERG). La présente invention concerne également des systèmes et des procédés pour reformuler des composés qui sont prédits comme étant cardiotoxiques sur la base du modèle et du criblage haut débit.
PCT/CA2016/050691 2015-06-17 2016-06-15 Systèmes et procédés pour sélectionner des composés ayant un risque de cardiotoxicité réduit au moyen de modèles h-erg WO2016201566A1 (fr)

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CN113764039A (zh) * 2021-09-13 2021-12-07 核工业湖州勘测规划设计研究院股份有限公司 一种单体聚合酶定向进化识别不同启动子的模拟预测方法

Citations (2)

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US20080213404A1 (en) * 2005-02-04 2008-09-04 Johnson Randall S Hif Modulating Compounds and Methods of Use Thereof
WO2015028597A1 (fr) * 2013-08-30 2015-03-05 Technical University Of Denmark Procédé pour prédire l'inhibition du canal potassique herg dans des composés acides et zwittérioniques

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20080213404A1 (en) * 2005-02-04 2008-09-04 Johnson Randall S Hif Modulating Compounds and Methods of Use Thereof
WO2015028597A1 (fr) * 2013-08-30 2015-03-05 Technical University Of Denmark Procédé pour prédire l'inhibition du canal potassique herg dans des composés acides et zwittérioniques

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113764039A (zh) * 2021-09-13 2021-12-07 核工业湖州勘测规划设计研究院股份有限公司 一种单体聚合酶定向进化识别不同启动子的模拟预测方法

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