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`
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`UNITED STATES PATENT AND TRADEMARK OFFICE
`_____________________________
`
`BEFORE THE PATENT TRIAL AND APPEAL BOARD
`
`_____________________________
`
`
`MYLAN PHARMACEUTICALS INC., TEVA PHARMACEUTICALS USA,
`INC. and AKORN INC.,1
`Petitioners,
`
`v.
`
`ALLERGAN, INC.
`Patent Owner.
`
`_____________________________
`
`Case IPR2016-01127 (US 8,685,930 B2)
`Case IPR2016-01128 (US 8,629,111 B2)
`Case IPR2016-01129 (US 8,642,556 B2)
`Case IPR2016-01130 (US 8,633,162 B2)
`Case IPR2016-01131 (US 8,648,048 B2)
`Case IPR2016-01132 (US 9,248,191 B2)
`_____________________________
`
`DECLARATION OF DANIEL A. BLOCH, PH.D
`
`
`1 Cases IPR2017-00576 and IPR2017-00594, IPR2017-00578 and IPR2017 -
`00596, IPR2017 -00579 and IPR2017-00598, IPR2017 -00583 and IPR2017-
`00599, IPR2017-00585 and IPR2017-00600, and IPR2017 -00586 and IPR2017-
`00601, have respectively been joined with the captioned proceedings. The word-
`for-word identical paper is filed in each proceeding identified in the caption
`pursuant to the Board’s Scheduling Order (Paper 10).
`
`MYLAN - EXHIBIT 1040
`Mylan Pharmaceuticals Inc. et al. v. Allergan, Inc.
`IPR2016-01127, -01128, -01129, -01130, -01131, & -01132
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`PROTECTIVE ORDER MATERIAL
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`TABLE OF CONTENTS
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`I.
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`QUALIFICATIONS ........................................................................................... 1
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`II.
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`SCOPE OF WORK ............................................................................................ 4
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`III.
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`PHASE 2 CLINICAL TRIAL ............................................................................... 5
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`IV. PHASE 3 CLINICAL TRIAL ............................................................................. 11
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`V.
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`PHARMACOKINETIC STUDIES ........................................................................ 22
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`VI. CONCLUDING STATEMENTS .......................................................................... 35
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`VII. APPENDIX - LIST OF EXHIBITS ...................................................................... 36
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`I.
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`I, Daniel A. Bloch, Ph.D., declare as follows:
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`QUALIFICATIONS
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`1.
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`I am an Emeritus Research Professor in the Department of Health
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`Research and Policy, Division of Biostatistics at Stanford University. I obtained a
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`Ph.D. in mathematical statistics in 1967 from Johns Hopkins University. I began
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`my career in biostatistics as a Research Fellow in the department of
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`biomathematics at Cornell University Medical School in 1967 and as a Research
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`Associate at the Sloan-Kettering Institute for Cancer Research in New York. I
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`became an Assistant Professor of biomathematics at the Cornell University
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`Medical School in 1969 and became an Assistant Professor of Mathematics at
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`California’s Sonoma State University in 1970.
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`2.
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`In 1984 I became the Head Biostatistician for the Arthritis,
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`Rheumatism, and Aging Medical Information System (ARAMIS) and for the
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`Stanford Arthritis Center (SAC) at Stanford University. I was appointed Associate
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`Professor in 1993 and have been a full professor of Health Research and Policy,
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`Division of Biostatistics since 2001. I became Emeritus in 2007.
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`3. My primary responsibilities include application of mathematical
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`statistics to scientific studies and to advance biostatistical research methodology.
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`Before becoming Emeritus, my salary support at Stanford was fully funded
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`through research grants in various fields of medicine, with grants covering a broad
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`range of topics, including rheumatic diseases, substance abuse, sleep apnea, basic
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`immunology, juvenile arthritis, vascular structure and stem cell transplantation as
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`immunotherapy.
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`4.
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`I have published more than 200 original articles in peer-reviewed
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`journals and have additional articles either in press or recently submitted for
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`publication. Approximately 170 of these articles have appeared in medical
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`journals, mostly co-authored with medical faculty at Stanford and at other
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`universities in the United States, Canada and Europe. Often these collaborations
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`have afforded me the opportunity to explore the application of recently developed
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`statistical methods by illustrating their use on real data, and to explore the
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`development of new statistical methods.
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`5.
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`Among the several dozen articles I have published devoted to
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`statistical methods, topics include efficiency and unbiasedness of estimators,
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`sample size estimation, kappa statistics, non-parametrics, methods of assessing
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`multi-parameter endpoints, and, most recently, designing Phase II and Phase III
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`trials to estimate rare events. I believe that recognition of my expertise as a
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`statistical researcher and at-large applicator to diverse fields of medicine were
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`behind a Nobel Prize committee’s invitation to me to provide nominations for the
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`Nobel Prize in Medicine.
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`6.
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`I have extensive teaching experience, having taught courses offered
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`by both the Mathematical Statistics and the Health Research & Policy departments
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`at Stanford. At Stanford I have supervised the statistical efforts of numerous post-
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`doctoral fellows and medical students. I have also been invited to give dozens of
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`biostatistical presentations, in such diverse settings as Harvard, Johns Hopkins
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`University, the Mayo Clinic, the FDA Center for Biologics Evaluation and
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`Research, the College of Problems for the Drug Dependence annual meetings and
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`at Institute of Mathematical Statistics meetings.
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`7.
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`Since 1987, I have been a consultant on an ad hoc basis to
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`pharmaceutical and biotechnical firms, including both start-up and established
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`companies. Herein I participate in all aspects of applying statistics to implement
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`investigational plans, e.g., for protocol development, design of trials, data base
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`design, analysis of data, and reporting results. I have served on numerous data
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`safety monitoring boards and have been a member of the FDA Statistical Advisors
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`Panel.
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`8.
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`A significant focus of mine has been the use of statistics to plan,
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`conduct and evaluate experimental research, particularly medical research. I have
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`extensive experience in reviewing published papers. At the request of an editor, my
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`review deals with evaluating all aspects of how the author planned and applied
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`statistics to meet study objectives. Peer-reviewed journals for whom I have
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`reviewed manuscripts include but are not limited to: The New England Journal of
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`Medicine, The Journal of the American Medical Association, Lancet, The Archives
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`of Internal Medicine, The American Journal of Medicine, Arthritis and
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`Rheumatism, The Journal of Rheumatology, The Journal of Clinical Epidemiology,
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`Medical Care, Cancer, Radiology, Osteoarthritis and Cartilage, Addiction, The
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`Journal of Vascular Surgery, Statistics in Medicine, Biometrics, and The Journal
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`of the American Statistical Association.
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`9. My education, academic and professional appointments, a listing of
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`my prior deposition and trial testimony over the past four years, and publications
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`are set forth in my curriculum vitae. EX1043.
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`II.
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`SCOPE OF WORK
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`10.
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`I have been retained by the Petitioner to provide statistical analyses
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`for certain data reported in Stevenson, Sall Figures 1-2 and to provide statistical
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`analyses for Allergan’s animal PK studies testing cyclosporine ophthalmic
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`emulsions as used by Dr. Attar in her Exhibit B to her Declaration presented to the
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`USPTO. In forming my opinions, I relied on my knowledge, education, skills,
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`experience, and training, in addition to the documents and materials cited in this
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`Report. A full list of materials I considered in preparation of this Report is
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`provided in the Appendix in Section VIII.
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`11.
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`I am being compensated at the rate of $500 per hour. I am also being
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`reimbursed for any necessary and reasonable travel expenses and other expenses
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`incurred as a result of my involvement in this proceeding. I have no financial
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`interest in the outcome of this matter.
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`III. BIOSTATISTICS BACKGROUND
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`12.
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`Statistics refers to a body of methods for making valid scientific
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`inferences from data about a “population” of interest. The data are but a sample of
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`evidence from this population. Scientists use the techniques of statistics, among
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`other reasons, to estimate the confidence with which one may draw conclusions
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`based on the sample of evidence. Statistical inference may be thought of as the art
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`of using the sample to confidently draw conclusions about this population. Often a
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`conclusion is based on a data summary; for example, the difference in two sample
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`averages (e.g., one sample from a group treated with a formulation containing
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`0.05% cyclosporin, the other sample from a group treated with a formulation
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`containing 0.1% cyclosporin). With a proper choice of statistical method, the
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`scientist can estimate the chance that an observed result, for example a difference
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`in averages, is not the result of random chance.
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`13. Clearly a person can be more confident that an observed effect is real
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`if the study yields a sample which is both representative of the population of
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`interest and is large enough to capture the variation of data values in the population
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`of interest. If the set of observations from a group cluster closely, the sample
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`average is a good indicator of individual observations; if, on the other hand, the
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`observations were widely spread, the sample average would be a poor indicator of
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`most individual observations. So the representativeness of the average depends
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`upon the variability among sample values.
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`14. The most widely used measure of variability is called the standard
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`deviation. It is defined as the square root of the quantity called the variance. The
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`standard deviation directly quantifies the degree to which individual measurements
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`cluster near the mean. Brown, B.W. and Hollander M. “Statistics: A Biomedical
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`Introduction.” John Wiley & Sons, N.Y. 1977 (“Brown, et al.”).
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`15.
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`If the number of individuals sampled is n, then the variance of a
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`sample average is 1/n times the variance. The square root of the variance of the
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`sample average is called the standard error; the smaller the standard error, the
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`more confident one can be that the sample average is close to the population mean.
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`
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`A.
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`Statistical Significance
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`16. The statistical significance of the study result, for example the
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`observed difference in two group averages, is obtained by applying a statistical
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`test. A very important mathematical statistical principle is that, if the sample size is
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`“large,” then the distribution of averages of like samples is nearly a normal
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`distribution (the so-called “bell-shaped” curve), centered at the “true” population
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`average with standard deviation equal to the standard error.
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`17. This is called the central limit theorem of statistics. When two
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`averages are compared, the numerical value of T= (difference in averages) /
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`(standard error of the difference) is the quantity that determines statistical
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`significance. Large values of this ratio give statistical evidence that the difference
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`between the two groups to be compared is real. This will be large if the difference
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`in means is large compared to the standard error (the “signal to noise” ratio). If the
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`value of “T” is not large, then all we can conclude is “maybe the result is real,” but
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`not with the confidence desired. Brown, et al.
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`18. The “confidence desired” is obtained when the result is “statistically
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`significant.” If the desired confidence is 95%, by far the most common choice,
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`then the result is statistically significant only if the observed difference in averages
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`could possibly be the result of random chance at most 5% of the time. Through
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`applying the statistical test (e.g., the “T” test described above) and making
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`reference to an appropriate statistical distribution, one quantifies the achieved level
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`of significance derived from the data (the “P-value”). If the P-value is very small,
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`for example less than 1%, then we conclude the difference is very significant. And
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`if P=5.0%, then the result is on the broader line of statistical significance. Brown,
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`et al.
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`19. The Two Sample T-Test is used to determine whether the means of
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`two groups are equal to each other (e.g., compare averages of two study groups
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`receiving different treatments in the context of an experiment). The results from
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`applying the two-sample t-test is valid if one assumes that (a) the data from each
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`treatment group is normally distributed, or by assuming (b) the sample sizes are
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`large enough so one can assume the averages that are being compared are,
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`themselves, “close to” being normally distributed. Brown, et al.
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`20.
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`If the sample size is small and the data values themselves are not
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`normally distributed, use of a t-test will give inaccurate P-values. In those
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`circumstances, instead of using the two-sample t-test, the Wilcoxon 2-Sample Rank
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`Test is usually applied. The Wilcoxon test gives exact p-values and is “non-
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`parametric;” that is, it does not rely on normal theory.
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` Comparability of Treatments
`B.
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`21. The compared samples must also be properly comparable with respect
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`to the variable being studied. This is best achieved by ensuring that two groups are
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`similar except for the variable being studied. In that way, scientists may properly
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`draw conclusions that the variable being studied is responsible for the difference
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`between the compared groups.
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`22. For example, let’s say we want to compare the pharmacokinetic (PK)
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`performance of two formulations over a 12 hour period having differing % castor
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`oil, with the following compositions and study characteristics:
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`Formulation I
`0.05% cyclosporin
`0.625% castor oil
`0.5% polysorbate 80
`Administered as single dose
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`28.5 µL dose
`Assessed in Study I
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`Formulation II
`0.05% cyclosporin
`1.25% castor oil
`1.0% polysorbate 80
`Administered twice daily for
`9.5 days
`50 µL dose
`Assessed in Study II
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`Note that Formulation I and Formulation II differ with respect to amount of castor
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`oil, amount of polysorbate 80, dosing regimen, and dose size.
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`23.
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`If a statistical difference is observed in a measure evaluating these
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`formulations, it would be impossible to determine whether the difference was due
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`to % cyclosporin, % castor oil, % polysorbate 80, the combination of cyclosporin
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`and castor oil, the combination of the castor oil and polysorbate 80, or due to the
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`different dosing regimens, dose size or the difference in scale of the outcome
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`measure. Alternatively, let’s say the study involved identical formulations, except
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`for % castor oil as in the following formulations:
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`Formulation I
`0.05% cyclosporin
`0.625% castor oil
`1.0 % polysorbate 80
`Administered as single dose
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`Formulation II
`0.05% cyclosporin
`1.25% castor oil
`1.0% polysorbate 80
`Administered as single dose
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`28.5 µL dose
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`28.5 µL dose
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`24.
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`If “Study 1” and “Study 2” had identical investigational protocols, or
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`if the comparison was made with a single study, then if one concluded that those
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`two formulations resulted in a statistically significantly different performance with
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`respect to outcome, it would be scientifically reasonable to conclude that the
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`difference was due to percent castor oil, as that was the only difference between
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`the two formulations (assuming, of course, that the study was properly designed
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`and implemented).
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`25. By comparing two groups that are identical, except for the variable
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`that is being studied, scientists can attribute any statistically significant differences
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`between the two groups as being associated with the single difference between the
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`two groups. However, if there are multiple variables that differ between the two
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`groups to be compared, scientists cannot easily attribute any statistically significant
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`differences between the two groups.
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`IV. PHASE 2 CLINICAL TRIAL
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`26.
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`I have been asked to evaluate the results presented in Figures 1-5 of
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`Stevenson. I note that Stevenson does not provide data tables of the raw values
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`graphed in each of Figures 1-5, however this information can be gleaned through
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`precise measurements of the y-axis and bars within each bar graph. Stevenson also
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`discloses the number of patients in each treatment group. EX1015 (“Stevenson”) at
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`970, Table 1; see also id. at 969 (identifying “the efficacy analysis presented here
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`is confined to the evaluation of th[e] moderate-to-severe subgroup.”). However,
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`Stevenson does not provide, either in table or graphical form, the error associated
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`with each formulation’s reported “mean change.”
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`27. As noted by Allergan’s expert Dr. Sheppard, “[a] statistical analysis
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`depends on a lot more than arithmetic. It depends on the numbers that are fed in
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`and all of these parameters, you know, P values and ranges and significance and all
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`these numbers that statisticians apply to numbers.” EX1037 at 181:16-22 (and
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`continuing “we clinicians, who are not statisticians, which is the vast, vast majority
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`of us, have to depend on a statistical analysis.” Id.at 181:23-182:2). Due to the lack
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`of error bars, a person of ordinary skill in the art could not independently verify
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`any of the p-values presented by Stevenson or determine whether one formulation
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`outperformed the other. Thus, the only statistical analysis available from the
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`Stevenson reference is that reported by Stevenson. Importantly, Stevenson does
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`not disclose any statistically significantly different result between the 0.05% and
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`0.10% CsA formulations in any of the objective efficacy measures during the
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`treatment period. EX1015.
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`V.
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`PHASE 3 CLINICAL TRIAL
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`28.
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`I have been asked to evaluate the results presented in Figures 1-2 of
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`Sall (EX1007, “Sall”). Allergan’s experts Drs. Thorsteinn Loftsson and John D.
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`Sheppard have made conclusions that the performance of the 0.05% CsA
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`formulation in Figure 2 of Sall was superior to that of the 0.10% CsA formulation.
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`EX1036 (Loftsson), 200:5-201:22; EX1037 (Sheppard), 196:21-24 (claiming
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`“substantially superior performance by the 0.05 percent concentration” in “Figure
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`2 of Sall”), 246:2-20 (claiming “very different activity between the two
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`compositions” appears “clearly from the Sall article in Figure 2”).
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`29.
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` Both Drs. Loftsson and Sheppard admitted to not performing
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`statistical analyses on the Sall data, as well as noted that they are not statisticians.
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`EX1036 at 175:22; 176:18-19; 215:5-11; EX1037, 183:5-8 (“Q. You did not
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`perform, yourself, any statistical analyses of the data reported in the Sall paper;
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`correct? A. Correct.”). Indeed, when asked if a statistically significant finding
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`between one formulation and the vehicle, and an absence of a similar finding for a
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`second formulation is the same thing as a finding of statistically significant
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`differences between the two formulations, Dr. Loftsson responded that this type of
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`analysis was “outside the scope of my knowledge.” EX1036 at 218:16-219:4; see
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`also, id. at 176:15-20 (“Q. And at a high level, what is the standard deviation? A. It
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`indicates the variation, but I’m not a statistician. If you are testing me about that,
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`asking me about that, I can’t answer that.”); 178:7-10 (does not “have an
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`understanding as to the relationship between a standard deviation and an evaluation
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`of statistical significance.”); 241:19-242:9 (unable to define what a “median value”
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`is).
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`30. The conclusions of Drs. Loftsson and Sheppard that Sall Figure 2
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`demonstrated a difference between the two CsA formulations is incorrect. Unlike
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`Drs. Loftsson and Sheppard, I am skilled in the art of applying statistics to science
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`and I have performed a statistical analysis of the data in Figures 1-2 of Sall, which
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`Sall indicates reports results of two objective measures of dry eye disease: corneal
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`staining and categorized Schirmer Tear Test with anesthesia.
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`31.
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`I am informed that Drs. Sheppard and Schiffman believed that the Sall
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`study was not adequately statistically powered to make a pairwise comparison
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`between the 0.05% and 0.1% CsA treatment groups. See EX1037 at 200:10-14
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`(wherein Dr. Sheppard testified that Sall teaches the formulations comprising
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`cyclosporin can be compared “to baseline, and that is improved. You can compare
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`to vehicle. You are not comparing between the two groups. So it’s not statistically
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`powered to do that.”); EX1035 at 44:2-6 (“You’re often not powering these studies
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`to have enough power to demonstrate with a high degree of statistical significance
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`that there are – that these differences are highly likely to be due to the drug and not
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`chance alone.”). This is incorrect. Phase 3 consisted of two identical multicenter,
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`randomized, double-masked, parallel-group clinical trials. EX1007 at 632. Each
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`of the Phase 3 trials was powered separately to make statistical pairwise
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`comparisons between the 0.05%, 0.1%, and vehicle treatment groups. Id. at 634.
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`Pooling the two studies’ data, as was done in Sall, only increased the power. The
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`fact that Sall did not report any statistically significant differences between the
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`0.05% and 0.1% formulations (as will be discussed in detail below) reflects the
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`absence of such a finding and not, as Dr. Sheppard implicitly suggested, a lack of
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`statistical power for this comparison.
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`32. Unlike in Stevenson, however, the data presented in Sall Figures 1-2
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`include error bars. Thus the monthly averages and standard errors can be
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`measured. The sample sizes for each treatment group are contained in Sall Table 1.
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`With this information, the two sample t-test (described in the BioStatistics
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`Background Section) can be employed for evaluation of statistical significance,
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`and a biostatistician would have been able to perform his or her own analysis to
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`confirm Sall’s statistical findings, as I have done below.
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`33. Figure 1 of Sall depicts the mean change from baseline in corneal
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`staining.
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`34. Regarding this data, Sall reports that there was a statistically
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`significant improvement from baseline in corneal staining “within all treatment
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`groups at all follow-up visits (P < 0.001).” EX1007 at 635. Sall also notes both the
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`0.05% and 0.1% formulations showed statistically significant improvements
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`compared to vehicle at month 4. Id. At month 6, the 0.05% formulation is taught to
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`give a statistically significant improvement from baseline compared to vehicle. Id.
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`Sall also notes the 0.1% formulation showed “a trend (P=0.062) toward a
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`significantly greater improvement” than vehicle. Id.
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`35. Sall does not report a statistically significant difference in
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`performance between the 0.05% and 0.1% formulations in Figure 2. Based on my
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`own calculations2, summarized in Table 1, below, I too have found no statistically
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`significant differences between these two formulations at any time point.
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`Month
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`Value
`Description
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`P-Value
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`B vs A C vs A B vs C
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`Change from Baseline
`A
`B
`C
`Vehicle
`0.05/1.25
`0.1/1.25
`.239
`.159
`-0.53
`-0.64
`-0.63
`Mean Diff
` 1
`
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`0.06
`0.05
`0.06
`Std Error
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`.104
`.060
`-0.48
`-0.64
`-0.63
`Mean Diff
` 3
`
`
`0.06
`0.06
`0.07
`Std Error
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` ≤0.044 ≤0.044
`N/A
`-0.67
`-0.73
`Mean Diff
` 4
`
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`N/A
`0.06
`0.06
`Std Error
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` 0.008
` 0.062
`N/A
`-0.89
`-0.85
`Mean Diff
` 6
`
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`N/A
`0.06
`0.07
`Std Error
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`Table 1: Mean Change From Baseline in Corneal Staining – Phase 3
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`.898
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`.914
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` 0.480
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` 0.665
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`36. As can be seen above, by measuring the data presented in Figure 1,
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`the mean change from baseline and the standard error can be determined for the
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`vehicle (A), as well as for the 0.05% (B) and 0.1% (C) formulations. The three
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`right-most columns of the table include pairwise p-values for the 0.05%
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`2 The p-values in Table 1 for “B vs A” and “C vs A” at months 4 and 6 are the
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`values reported in Sall. Hence I did not need to measure the vehicle mean
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`difference and standard error at these months. In the Table “N/A” means not
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`applicable. I calculated the p-values for the remaining comparisons that were not
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`reported in Sall.
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`formulation versus vehicle (B vs A), the 0.1% formulation versus vehicle (C vs A),
`
`as well as the 0.05% formulation versus the 0.1% formulation (B vs C). As
`
`evidenced by Sall, the P-values in the comparisons of cyclosporin formulations to
`
`vehicle (B vs A, P≤ 0.044; C vs A, P ≤ 0.044), there is a statistically significant
`
`improvement from baseline compared to vehicle at month 4.
`
`37. Similarly, as evidenced by Sall, the 0.05% formulation showed a
`
`statistically significant improvement from baseline compared to vehicle at month 6
`
`(B vs A, P = 0.008); however, this statistically significant improvement was not
`
`observed in comparing the 0.1% formulation to vehicle (C vs A, P=0.062),
`
`although Sall reports this value as a “trend” toward significance. EX1007 at 635.
`
`38. Finally, a comparison of the two cyclosporin formulations against one
`
`another (B vs C) showed no statistically significant differences between the 0.05%
`
`and 0.1% formulations at any time point. In fact, the P-values are never close to
`
`approaching statistical significance.
`
`39. Figure 2 of Sall depicts the mean change from baseline in a second
`
`objective measure (categorized Schirmer Scores (with anesthesia)).
`
`-17-
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`PROTECTIVE ORDER MATERIAL
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`
`
`
`
`
`
`40. Drs. Loftsson and Sheppard base many of their conclusions on the
`
`above figure; however, both confirmed that they had not performed a statistical
`
`analysis of the data therein. EX1036 at 215:5-11; EX1037 at 183 (“Q. And with
`
`respect to Figure 2 of Sall, you have not evaluated whether there is a statistically
`
`significant difference in the Schirmer tear test with anesthesia values at month
`
`three between the 0.05 and 0.1 percent cyclosporin A treatment groups; correct? A.
`
`…Again, I have not performed any statistical analyses of this data myself.”); id. at
`
`184 (“I did not perform any statistical analysis of this data, nor do I have the data
`
`to do so.”). Again, when asked if he had an opinion as to “whether the studies
`
`-18-
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`PROTECTIVE ORDER MATERIAL
`
`
`
`
`
`reported in Sall Figure 2” were “adequately statistically powered to detect a
`
`statistical difference between the 0.05 and the 0.10 percent CsA formulations,” Dr.
`
`Loftsson responded, “Again, I have to tell you that I am not qualified to answer
`
`this question. I am not a specialist in statistics.” EX1036 at 222:16-25.
`
`41.
`
`It is important to note what values are actually being reported in Sall
`
`Figure 2. Figure 2 represents the Categorized Schirmer values measured with
`
`anesthesia. Categorized Schirmer values differ from raw Schirmer values. Raw
`
`Schirmer values are measured in mm/5minutes. EX1007 at 632. Categorized
`
`Schirmer values are obtained by converting raw Schirmer values using a
`
`Categorized scale as follows:
`
`Raw Schirmer
`Score
`
`< 3 mm / 5
`min
`
`3-6 mm / 5
`min
`
`7-10 mm / 5
`min
`
`11-14 mm /
`5 min
`
`> 14 mm / 5
`min
`
`Categorized
`Schirmer Value
`
`1
`
`
`
`2
`
`3
`
`4
`
`5
`
`Id. at 633. Sall does not report raw Schirmer values, only categorized values. Any
`
`conclusions based on these categorical values are one step removed from
`
`conclusions that can be reached about raw values. The uncertainty of conclusions
`
`reached about a comparison between two categorical values is compounded when
`
`the average difference is only a fraction of one categorized scale unit.
`
`42.
`
`I note that Sall does not report a statistically significant pairwise
`
`comparison between 0.05% and 0.1% at month 3. Rather, Sall teaches that the
`
`-19-
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`PROTECTIVE ORDER MATERIAL
`
`
`
`
`
`0.05% formulation showed statistically significantly greater improvement
`
`compared to vehicle (P = 0.009). Id. at 635. As evidenced by my analysis
`
`performed for Figure 1, it would be incorrect to make conclusions about the
`
`relative efficacy of 0.05% and 0.1% simply because 0.05% performed statistically
`
`better than vehicle and 0.1% did not at month 3.
`
`43.
`
`I performed the pairwise comparisons3 in Table 2, below, to confirm
`
`whether there is any statistically significant difference between the 0.05% and
`
`0.1% formulations at month 3. I have done the same analysis for the data provided
`
`for month 6, for which Sall notes that “there was a statistically significant
`
`improvement from baseline within both CsA groups. Moreover, the changes in
`
`Schirmer values in each of the CsA groups was significantly better than in the
`
`vehicle group (P < -0.007).” Id. at 635.
`
`Month
`
`Value
`Description
`
` 3
`
`Mean Diff
`
`Change from Baseline
`A
`B
`C
`Vehicle
`0.05/1.25
`0.1/1.25
`-0.24
`0.09
`-0.10
`
`P-Value
`
`B vs A C vs A B vs C
`
`0.009 0.246
`
`0.115
`
`
`3 The p-values in Table 2 for “B vs A” at months 3 and 6 and “C vs A” at
`
`month 6 are the values reported in Sall. Hence I did not need to measure the
`
`vehicle mean difference and standard error at month 6. In the Table “N/A” means
`
`not applicable. I calculated the p-values for the remaining comparisons that were
`
`not reported in Sall.
`
`-20-
`
`PROTECTIVE ORDER MATERIAL
`
`
`
`
`
`
` 6
`
`Std Error
`Mean Diff
`
`0.08
`N/A
`
`0.08
`0.39
`
`0.09
`0.26
`
`
`
`
`<0.007 <0.007 0.251
`
`
`
`0.08
`0.08
`N/A
`Std Error
`
`Table 2: Mean Change From Baseline in Categorized Schirmer Values
`Measured With Anesthesia – Phase 3
`
`
`
`44. As can be seen above, by measuring the data presented in Figure 2,
`
`the mean change from baseline, as well as the standard error can be determined for
`
`the vehicle (A), as well as for the 0.05% (B) and 0.1% (C) formulations. The three
`
`right-most columns of the table include pairwise p-values for the 0.05%
`
`formulation versus vehicle (B vs A), the 0.1% formulation versus vehicle (C vs A),
`
`as well as the 0.05% formulation versus the 0.1% formulation (B vs C). As
`
`evidenced by Sall, by the P-value in the comparison of the 0.05% formulation to
`
`vehicle (B vs A, P = 0.009), the 0.05% formulation showed a statistically
`
`significant improvement from baseline compared to vehicle at month 3.
`
`45. Similarly, as evidenced by Sall, both the 0.05% and 0.1%
`
`formulations showed statistically significant improvements from baseline
`
`compared to vehicle at month 6 (B vs A, P < 0.007; C vs A, P <0.007).
`
`46. Finally, a comparison of the two cyclosporin formulations against one
`
`another (B vs C) showed no statistically significant differences at either time point
`
`(B vs C, month 3, P = 0.115; B vs C, month 6, P = 0.251). The findings in Table 2
`
`are consistent with the fact that Sall does not report a statistically significant
`
`difference between the two formulations.
`
`-21-
`
`PROTECTIVE ORDER MATERIAL
`
`
`
`
`
`47. Dr. Sheppard testified that Sall teaches “no statistically significant
`
`differences among the groups,” at baseline, explaining that “that tells us something
`
`very important, and that is, that based upon this analysis, the three groups were the
`
`same when they started, and that’s pretty important to a clinical trial.” EX1037 at
`
`178: 19-25; See also id. at 179 (“So that range is statistically insignificant. So
`
`they’re the same.”). By the same token, the lack of statistically significant
`
`difference between the cyclosporin formulation groups in Sall Figures 1 and 2
`
`again tells us something very important – the two formulations performed the
`
`same.
`
`VI. PHARMACOKINETIC STUDIES
`
`48.
`
`I attended the deposition of Dr. Mayssa Attar. I understand from
`
`attending her deposition that she used data from two specific pharmacokinetic
`
`studies to generate Exhibit B of the Declaration she submitted to the patent office
`
`during patent prosecution of the patents at issue in these proceedings: PK-98-074
`
`(EX2027) and PK-00-163 (EX1027
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