WO2010094753A1 - Prédiction de cardiotoxicité - Google Patents

Prédiction de cardiotoxicité Download PDF

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Publication number
WO2010094753A1
WO2010094753A1 PCT/EP2010/052092 EP2010052092W WO2010094753A1 WO 2010094753 A1 WO2010094753 A1 WO 2010094753A1 EP 2010052092 W EP2010052092 W EP 2010052092W WO 2010094753 A1 WO2010094753 A1 WO 2010094753A1
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WO
WIPO (PCT)
Prior art keywords
cardiotoxicity
compound
kinases
kinase
inhibition
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Application number
PCT/EP2010/052092
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English (en)
Inventor
Hans Marcus Ludwig Bitter
Preeti Dhawan
Nina Gonzaludo
Kyle L. Kolaja
Hirdesh Uppal
Original Assignee
F. Hoffmann-La Roche Ag
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Application filed by F. Hoffmann-La Roche Ag filed Critical F. Hoffmann-La Roche Ag
Publication of WO2010094753A1 publication Critical patent/WO2010094753A1/fr

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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
    • C12Q1/48—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving transferase
    • C12Q1/485—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving transferase involving kinase

Definitions

  • This invention relates generally to the field of toxicology. More particularly, the invention relates to methods for predicting cardiotoxicity, and methods for screening compounds for potential cardiotoxicity.
  • the heart is an adaptive organ for pumping blood, responding to changing needs by modifying contractile strength and beating rate.
  • the cardiac myocyte is the principal cell in the heart; it coordinates contraction and has the capability to sense a large number of hormonal, neural, electrical and mechanical inputs through a variety of cell surface and nuclear receptors.
  • Myocytes are also targets of an extraordinary number of physiological and pharmacological agents, because of the critical need to regulate contraction strength and heart rate, and their importance in several cardiovascular diseases.
  • determining the mechanisms of toxicity requires the identification of the specific target responsible for cardiotoxicity.
  • the identification of targets mediating cardiotoxicity can also help to guide future drug development, because some of these molecules or proteins are likely to be 'bystander' targets that have no role in the disease indication that a given drug is being developed for and there is therefore no need for the drug to inhibit them.
  • kinase inhibitors creates many opportunities for toxicity, not only as a result of the inhibition of desired targets but, probably much more importantly, due to the inhibition of off-target kinases.
  • Cardiotoxicity of a targeted agent was first reported for trastuzumab, the monoclonal antibody that targets the ERBB2 receptor and adverse cardiac effects have also been reported after treatment of patients with imatinib, and are mentioned in the prescribing information for dasatinib (Sprycel), sunitinib (Sutent), sorafenib (Nexavar) and bevacizumab (Avastin). Cardiotoxicity is not associated with all kinase inhibitors because it is not observed with certain other KIs, such as those that target the epidermal growth factor receptor. Therefore, cardiotoxicity needs to be determined for each agent on a case-by-case basis.
  • the present invention relates to a method for predicting the cardiotoxicity of a compound, said method comprising: a) providing a test compound;
  • the present invention provides a method for screening compounds for potential cardiotoxicity, said method comprising:
  • the inhibition by over 80% of at least five of said kinases in the model kinase group indicates a likelihood that said compound will demonstrate cardiotoxicity.
  • the inhibition by over 80% of all eleven of said kinases in the model kinase group indicates a likelihood that said test compound will demonstrate cardiotoxicity.
  • the ability of the compound to inhibit the kinase activity is determined by measuring the binding affinity of the compound for said kinases.
  • step b) further comprises determining the ability of the compound at 10 ⁇ M to inhibit the kinase activity in the potential model kinase group consisting of AB L2, LYN, ZAK, Ephrin Receptor B2, YESl, MAP4K4, PKNl, BRAF, DDR2, MAP4K5, and STK24, wherein inhibition by over 80% of at least one of said kinases in the potential model kinase group indicates a likelihood that said test compound will demonstrate cardiotoxicity.
  • test substrate comprising:
  • a solid support and immobilized on said solid support, the kinases CSFlR, KIT, FYN, PDGFR beta, FGR, LCK, Ephrin Receptor Bl, FRK, ABLl, PDGFR alpha, and HCK.
  • test substrate further comprising:
  • cardiotoxicity refers to compounds that cause direct or indirect injury to cardiomyocytes and the myocardium that may manifest in certain clinical symptoms which may include: congestive heart failure, ischemia, hypotension, hypertension, arrhythmias (e.g. bradycardia), edema, QT prolongation and conduction disorders, and thromboembolism.
  • test compound refers to a substance which is to be tested for cardiotoxicity.
  • the test compound can be a candidate drug or lead compound, a chemical intermediate, environmental pollutant, a mixture of compounds, and the like.
  • kinase refers to an enzyme capable of attaching and/or removing a phosphate group from a protein or molecule.
  • Inhibition of kinase activity refers to the ability of a compound to reduce or interfere with such phosphatase activity.
  • binding affinity of a small molecule for a given kinase correlates well with the ability of said molecule to inhibit the kinase activity, binding affinity is considered synonymous with kinase activity herein, and high binding affinity is considered equivalent to high kinase inhibitory activity.
  • model kinase refers to the following set of kinases (also identified by accession numbers in parentheses): CSFlR (NP 005202.2) [Seq. Id. No. 1], KIT (NP 000213.1) [Seq. Id. No. 2], FYN (NP 694592.1) [Seq. Id. No. 3], PDGFR beta (NP 002600.1) [Seq. Id. No. 4], FGR (NP 005239.1) [Seq. Id. No. 5], LCK (NP 005347.3) [Seq. Id. No. 6], Ephrin Receptor Bl (NP_004432.1) [Seq. Id.
  • FRK NP_002022.1
  • ABLl NP_005148.2
  • PDGFR alpha NP 006197.1
  • HCK NP 002101.2
  • potential model kinase refers to the following set of kinases (also identified by accession numbers in parentheses): ABL2 (NP 005149.3) [Seq. Id. No. 12], LYN (NP 002341.1) [Seq. Id. No.
  • the invention provides a method for quickly determining the likelihood that a given compound will exhibit cardiotoxicity in an in vivo or in vitro toxicity assay by examining the interaction between the compound and a number of kinases (kinase binding and/or inhibition). As kinase inhibition and/or binding can be determined quickly, and by using automated methods, the method of the invention enables high-throughput screening of compounds for cardiotoxicity (or lack thereof).
  • binding and inhibition can be determined using methods known in the art. See, for example, M.A. Fabian et al, Nature Biotechnol (2005) 23:329-36, incorporated herein by reference in full.
  • binding affinity may be determined by a variety of methods known in the art; for example by competitive assay using an immobilized kinase (or an immobilized test compound, or an immobilized competing ligand, any of which may be labeled).
  • Compounds and kinases can be immobilized by standard methods, for example by biotinylation and capture on a streptavidin-coated substrate.
  • test substrate having, for example, a plurality of immobilized kinases, preferably comprising the set of model kinases identified herein: .
  • the kinases can be immobilized directly (i.e., by adsorption, covalent bond, or biotin- avidin binding or the like) to the surface, or indirectly (for example by binding to a ligand that is tethered to the surface by adsorption, covalent bond, biotin-avidin or other linkage).
  • the kinases are then contacted with the test compound(s), and the affinity (or enzyme inhibition) determined, for example by measuring the binding of labeled compound or loss of labeled competitor.
  • the kinase affinity of each compound is measured against at the kinases comprising the model kinase group.
  • a compound with high total activity (for example, demonstrating high affinity for five of the eleven kinases) has a high likelihood of cardiotoxicity: this compound is predicted to test positive for cardiotoxicity in either an in vitro or in vivo test system.
  • a compound having low total activity (for example, showing only low affinity for the kinases in the model kinase group, or showing high affinity to less than five kinases in the model kinase group) is predicted to test negative in either the in vitro or in vivo toxicity assay.
  • Candidate drugs that test positive in the assay of the invention i.e., that are predicted to demonstrate cardiotoxicity in the in vitro or in vivo assays
  • such compounds can be flagged as potentially (for example, by the software managing the system in the case of an automated high-throughput system), thus enabling earlier decision making.
  • a plurality of compounds e.g. 50 or more
  • the method of the invention is fast and easily automated, it enables the bulk screening of compounds that would otherwise not be possible or practical.
  • Environmental pollutants and the like can also be identified using the method of the invention, in which case such compounds are typically identified for further study into their toxic properties.
  • the aim of the analysis was (1) to build a model using kinase inhibition profiles to predict cardiotoxicity and (2) to identify the kinases correlated with cardiotoxicity.
  • the analysis was carried out in several steps: first, eighteen suitable internal and marketed small molecule kinase inhibitors (SMKIsO) were selected to form a training set with which to build the model; second, for each compound in the training set, a cardiotoxicity assessment (positive or negative) and single point inhibition profiles against 290 kinases were acquired; and third, a statistical analysis was performed to build a predictive model.
  • SMKIsO small molecule kinase inhibitors
  • Pre-processing was first performed across the set of all inhibition profiles to remove uninformative kinases. Kinases with no variance in percent inhibition across the set of 18 compounds were removed, as they were not informative in separating the cardiotoxicity labels.
  • Feature selection (FS) and pattern recognition (PR) were performed in several phases in order to build the model.
  • cross validation was used to assess the model performance over several trials. Each trial randomly split the initial data into a training set and a test set; the training set was used to build the temporary model, and the test set was used to predict results and then verify performance. Each cross validation fold was stratified, that is, the proportion of positive to negative compounds was kept roughly equal across all folds.
  • FS methods were then used to determine which kinases, or "features", were likely to correlate most with cardiotoxicity labels. In each trial, the inhibition values against the features chosen were used as input for a PR method, when then predicted the positive or negative result.
  • FS methods were divided into two groups: univariate filter methods appropriate for a large input data set (FSl), and multivariate methods that performed better with less data (FS2).
  • FSl univariate filter methods appropriate for a large input data set
  • FS2 multivariate methods that performed better with less data
  • PR methods Different combinations of FSl, FS2, and PR methods were tested over several trials using 25 four-fold stratified cross-validations.
  • the combination of methods with the lowest mean error rate was chosen as the final method set for the model.
  • This combination included Single Train Error for FSl, Random Forests for FS2, and Random Forests for classification for PR. This combination of machine learning methods correctly predicted cardiotoxicity labels with an accuracy of 87%, sensitivity of 85%, and specificity of 88%.
  • model kinases Based on the statistical analysis of the original dataset of 18 compounds, it may be possible to expand the model kinases to include other kinases that were chosen as significant predictors in less than 50% of the test runs.
  • This set of eleven additional kinases include: AB L2, LYN, ZAK, EPHB2, YES, MAP4K4, PKNl, BRAF, DDR2, MAP4K5, and STK24.
  • the model consists of single point kinase inhibition profiles against the following eleven kinases: CSFlR, KIT, FYN, PDGFRB, FGR, LCK, EPHBl, FRK, ABLl, PDGFRA, and HCK.
  • the eleven kinases in the model were chosen based on the statistical analysis of an initial training set of 18 compounds. Over multiple runs of testing, the kinase percent inhibition profiles against these eleven kinases were found to be significant in predicting cardiotoxicity at least 50% of the time. That is, compounds that were determined to be cardiotoxic tended to have high levels of inhibition against the eleven kinases.
  • a Random Forest classifier an accurate prediction of cardiotoxicity can be determined.
  • the model Given the kinase inhibition profile of a compound against the eleven kinases, the model is used to predict whether that compound will be cardiotoxic. The information from the model results would be useful as a pre-screening for compounds, given the assessment difficulty and lack of mechanistic understanding of cardiotoxicity. Based on a preliminary training set of compounds with kinase inhibition profiles and known cardiotoxicity assessment, the model has performed with an accuracy of approximately 87%, with a sensitivity and specificity of 85% and 88%, respectively. With 50% accuracy being equivalent to random classification, this model has performed well and has proved its utility in predicting cardiotoxicity.

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  • Chemical & Material Sciences (AREA)
  • Organic Chemistry (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Zoology (AREA)
  • Wood Science & Technology (AREA)
  • Proteomics, Peptides & Aminoacids (AREA)
  • Health & Medical Sciences (AREA)
  • Engineering & Computer Science (AREA)
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  • Physics & Mathematics (AREA)
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  • Bioinformatics & Cheminformatics (AREA)
  • General Engineering & Computer Science (AREA)
  • General Health & Medical Sciences (AREA)
  • Genetics & Genomics (AREA)
  • Measuring Or Testing Involving Enzymes Or Micro-Organisms (AREA)

Abstract

Le procédé ci-décrit permet de prédire la cardiotoxicité d'un composé, ledit procédé comprenant les étapes consistant à a) utiliser un composé d'essai; b) déterminer la capacité dudit composé à une concentration d'environ 10 µM à inhiber l'activité kinase dans un groupe-type de kinases constitué par CSF1R, KIT, FYN, PDGFR bêta, FGR, LCK, le récepteur B1 de l'éphrine, FRK, ABL1, PDGFR alpha et HCK, toute inhibition de plus de 80 % d'au moins une desdites kinases indiquant que ledit composé d'essai fera vraisemblablement preuve de cardiotoxicité.
PCT/EP2010/052092 2009-02-23 2010-02-19 Prédiction de cardiotoxicité WO2010094753A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
US15464009P 2009-02-23 2009-02-23
US61/154,640 2009-02-23

Publications (1)

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WO2010094753A1 true WO2010094753A1 (fr) 2010-08-26

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Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
CHINTALGATTU VISHNU ET AL: "Cardiomyocyte PDGFR-beta signaling is an essential component of the mouse cardiac response to load-induced stress.", THE JOURNAL OF CLINICAL INVESTIGATION 1 FEB 2010, vol. 120, no. 2, 1 February 2010 (2010-02-01), pages 472 - 484, XP009131629, ISSN: 1558-8238 *
FORCE T ET AL: "Cardiotoxicity of the new cancer therapeutics - mechanisms of, and approaches to, the problem", DRUG DISCOVERY TODAY, ELSEVIER, RAHWAY, NJ, US, vol. 13, no. 17-18, 1 September 2008 (2008-09-01), pages 778 - 784, XP025409776, ISSN: 1359-6446, [retrieved on 20080902] *
THOMAS FORCE ET AL: "Molecular mechanisms of cardiotoxicity of tyrosine kinase inhibition", NATURE REVIEWS. CANCER, NATUR PUBLISHING GROUP, LONDON, GB, vol. 7, no. 5, 1 May 2007 (2007-05-01), pages 332 - 344, XP007905632, ISSN: 1474-175X *

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