Cytokinetics, Incorporated (Form: 8-K)  

 

UNITED STATES
SECURITIES AND EXCHANGE COMMISSION
WASHINGTON, D.C. 20549

FORM 8-K

CURRENT REPORT

Pursuant to Section 13 or 15(d) of the Securities Exchange Act of 1934

     
Date of Report (Date of Earliest Event Reported):   December 7, 2016

Cytokinetics, Incorporated
__________________________________________
(Exact name of registrant as specified in its charter)

     
Delaware 000-50633 94-3291317
_____________________
(State or other jurisdiction 		_____________
(Commission 		______________
(I.R.S. Employer
of incorporation) File Number) Identification No.)
      
280 East Grand Avenue, South San Francisco, California   94080
_________________________________
(Address of principal executive offices) 		  ___________
(Zip Code)
     
Registrant’s telephone number, including area code:   (650) 624 - 3000

Not Applicable
______________________________________________
Former name or former address, if changed since last report

 

Check the appropriate box below if the Form 8-K filing is intended to simultaneously satisfy the filing obligation of the registrant under any of the following provisions:

[  ]  Written communications pursuant to Rule 425 under the Securities Act (17 CFR 230.425)
[  ]  Soliciting material pursuant to Rule 14a-12 under the Exchange Act (17 CFR 240.14a-12)
[  ]  Pre-commencement communications pursuant to Rule 14d-2(b) under the Exchange Act (17 CFR 240.14d-2(b))
[  ]  Pre-commencement communications pursuant to Rule 13e-4(c) under the Exchange Act (17 CFR 240.13e-4(c))

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Item 8.01 Other Events.

On December 7, 2016, Origent Data Sciences, Inc. (Origent) and Cytokinetics, Inc. announced results from the first part of a research collaboration to refine and prospectively validate an Origent computer model to predict the course of ALS disease progression leveraging data from Cytokinetics’ clinical trials of tirasemtiv. The poster, presented by David L. Ennist, Ph.D., MBA, Chief Science Officer, Origent Data Sciences and Jinsy A. Andrews, M.D., Director of Neuromuscular Clinical Trials, Columbia University, at the 27th International Symposium on ALS/MND in Dublin, Ireland, showed that the Gradient Boosting Machine (GBM) algorithm was the optimal model to predict slow vital capacity (SVC) at times subsequent to baseline and that forced vital capacity (FVC) records could be used to predict slow vital capacity (SVC) scores of ALS patients using this machine learning technique.

A copy of the press release is filed as Exhibit 99.1 to this Current Report on Form 8-K, and is incorporated herein by reference.





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SIGNATURES

Pursuant to the requirements of the Securities Exchange Act of 1934, the registrant has duly caused this report to be signed on its behalf by the undersigned hereunto duly authorized.

         
    Cytokinetics, Incorporated
          
December 7, 2016   By:   /s/ Sharon A. Barbari
       
        Name: Sharon A. Barbari
        Title: Executive Vice President, Finance and Chief Financial Officer

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Exhibit Index


     
Exhibit No.   Description
 
99.1
  Press Release, Dated December 7, 2016