statistical analysis plan final analysis nmp-cyz-p2-002 1/46

Statistical Analysis Plan Final Analysis NMP-CYZ-P2-002 1/46

BS-FRM-001_01-3.0 05SEP2019 SAP (version 2.0)

Statistical Analysis Plan for

final analysis

Version 2.0 Study: A Multicenter, Randomized, Double-blind, Placebo-controlled, Phase 2

Study to Evaluate the Efficacy and Safety of Cyclo-Z in Subjects with Type 2 Diabetes

Study-ID: NMP-CYZ-P2-002 Sponsor / Contact:

NovMetaPharma Co., Ltd.

Evaluation: FGK Clinical Research GmbH FGK code 3495 Version: 2.0 of 05SEP2019 Previous versions: 1.0 of 21JUN2019

The content of this Statistical Analysis Plan is confidential and must not be passed to any third party without permission of NovMetaPharma Co., Ltd.



Revision history Version Author Date Reason for Revision

1.0 CR 21JUN2019 Final version 2.0 CR 05SEP2019 Additional subgroup analysis added; imputation

method for GEE added; description of output for logistic regression adapted; minor adaption of units; pooling of sites added; other parts of SAP and SAP appendix clarified and updated



Table of Contents TABLE OF CONTENTS .................................................................................................................................. 3

LIST OF ABBREVIATIONS ............................................................................................................................ 5 1 GENERAL ................................................................................................................................................ 7

2 EFFICACY AND SAFETY ENDPOINTS ................................................................................................. 9

2.1 Primary Efficacy Endpoint ........................................................................................................... 9 2.2 Secondary Efficacy Endpoints .................................................................................................... 9 2.3 Exploratory Efficacy Endpoints ................................................................................................... 9 2.4 Safety Endpoints ......................................................................................................................... 9 2.5 Other Relevant Variables .......................................................................................................... 10

3 STATISTICAL ANALYSIS SETS .......................................................................................................... 11

3.1 Full Analysis Set (FAS) ............................................................................................................. 11 3.2 Efficacy Analysis Set (EAS) ...................................................................................................... 11 3.3 Per-Protocol Set (PPS) ............................................................................................................. 11 3.4 Safety Analysis Set (SAF) ......................................................................................................... 11 3.5 Assignment of Analysis Sets to Analysis .................................................................................. 12 3.6 Visit Terminology ....................................................................................................................... 12

4 DATA SCREENING AND ACCEPTANCE ............................................................................................ 13

4.1 General Principles ..................................................................................................................... 13 4.2 Data Handling and Electronic Transfer of Data ........................................................................ 13 4.3 Handling of Missing and Incomplete Data ................................................................................ 13

4.3.1 Patterns of Missing Data ..................................................................................................... 13 4.3.2 Missing HbA1c Level Measurements and Imputation ......................................................... 13 4.3.3 Missing Data Imputation for Other Endpoints ...................................................................... 14

4.4 Multicenter Studies .................................................................................................................... 15 4.5 Adjustments for Covariates ....................................................................................................... 15 4.6 Multiple Comparisons/Multiplicity .............................................................................................. 16 4.7 Detection of Bias ....................................................................................................................... 16 4.8 Outliers ...................................................................................................................................... 17 4.9 Distributional Characteristics .................................................................................................... 17 4.10 Validation of Statistical Analyses .............................................................................................. 17

5 STATISTICAL EVALUATION................................................................................................................ 18 5.1 Dispositions of Subjects and Analysis Sets .............................................................................. 18 5.2 Demographics and Other Covariates ........................................................................................ 18 5.3 Efficacy Analysis ....................................................................................................................... 19

5.3.1 Analysis of Primary Efficacy Endpoint ................................................................................. 20 5.3.2 Analysis of Secondary Efficacy Endpoints .......................................................................... 22 5.3.3 Analysis of Exploratory Efficacy Endpoints ......................................................................... 28

5.4 Safety Analysis .......................................................................................................................... 31 5.5 Interim Analysis ......................................................................................................................... 34 5.6 Analysis of Other Relevant Variables ....................................................................................... 35 5.7 Important Protocol Deviations ................................................................................................... 35 5.8 Analysis of Subgroup ................................................................................................................ 37 5.9 Analysis of Dose Response ...................................................................................................... 38



5.10 Analysis of Linear Trend ........................................................................................................... 39 5.11 Data Base Lock and Blind Data Review ................................................................................... 40 5.12 Miscellaneous ........................................................................................................................... 41

6 CHANGES FROM PROTOCOL ............................................................................................................ 43

7 REFERENCES ....................................................................................................................................... 45

8 SIGNATURES ........................................................................................................................................ 46



List of Abbreviations In the following abbreviations are listed as used within this statistical analysis plan or which might occur within the tables, listings and graphs outputs:

ADDQoL Audit of diabetes-dependent quality of life AE Adverse event ADaM Analysis data model ADR Adverse drug reaction ANCOVA Analysis of covariance AR Autoregressive covariance structure ATC Anatomical therapeutic chemical classification AUC Area under curve BMI Body mass index CHP Cyclic dipeptide (his-pro) CMH Cochran Mantel Haenszel CI Confidence interval CRF Case report form CS Clinically significant CS for SAS code Compound symmetry covariance structure CSR Clinical study report CTM Clinical trial manager DM Data management EAS Efficacy analysis set ECG Electrocardiogram EDC Electronic data capture FAS Full analysis set FPG Fasting plasma glucose GEE Generalized estimating equations ICH International council of harmonization IDE Insulin-degrading enzyme IP Investigational product IPD Important protocol deviations H0 Null hypothesis Ha Alternative hypothesis HbA1c Glycosylated hemoglobin LOCF Last observation carried forward LS Least squares MAR Missing at random MedDRA Medical dictionary for regulatory activities MNAR Missing not at random MMRM Mixed-model repeated measures N Number of subjects NCS Not clinically significant OGTT Oral glucose tolerance test PD Protocol deviation



PP Per-protocol PPS Per-protocol set PT Preferred term QoL Quality of life SAE Serious adverse event SAF Safety analysis set SAP Statistical analysis plan SD Standard deviation SDTM Study data tabulation model SEM Standard error of the mean SOC System organ class TAFGC Three-hour Average above Fasting Glucose Concentration TEAE Treatment emergent adverse event TLG Tables, listings, graphs UN Unstructured covariance structure



1 General

This Statistical Analysis Plan (SAP) was defined by the Sponsor and the responsible FGK Statistician without knowledge of the randomization code. It is based upon the Study Protocol (Version 1.0 of 05 December 2017) and contains detailed description of the statistical methods described therein.

Statistical analysis of this study will be the responsibility of Sponsor and FGK Statistician. Any other change to the data analysis methods described in the protocol, and the justification for making the change, will be described in the SAP or the clinical study report (CSR). Additional exploratory analyses of the data will be conducted as deemed appropriate.

This plan may be revised during the study to accommodate protocol amendments and to adapt to unexpected issues in study execution or data that affect the planned analyses. These revisions will be based on blinded review of the study and data. This plan will be finalized prior to the final database lock and unblinding.

This is a double-blind, randomized, placebo-controlled, parallel-group comparison study to evaluate the efficacy and safety of Cyclo-Z vs. placebo in adult subjects with type 2 diabetes. Approximately 20 clinical sites may be utilized in the United States so that approximately 300 subjects (a potential 20% screening failure rate) may be screened for total 28-week study period (2 weeks for screening, 24 weeks for treatment, and 2 weeks for safety follow-up).

Subjects who meet preliminary inclusion and exclusion criteria at Screening will undergo a 2-week assessment period of record-keeping compliance. Subjects will be asked to record daily blood glucose values (fasting before breakfast and two hours after dinner) and study medication adherence.

Two hundred fifty-five (255) qualified subjects will be assigned randomly to either the placebo arm or to one of the two Cyclo-Z treatment groups requiring the oral intake of a single tablet of Cyclo-Z or placebo once daily before bedtime for 24 consecutive weeks.

At Visits 2, 3, 4, 5, 6, 7, and 8 (Weeks 0, 2, 4, 8, 12, 16, and 20), each subject will be dispensed with study drug supply for a 4-week period (28 tablets plus 2 extra tablets, total 30 tablets). The subjects will be instructed to orally administer one tablet just before bedtime each day.

At Visit 2 (Week 0), subjects who meet the inclusion/ exclusion criteria will be randomly assigned to receive placebo or 1 of 2 doses of Cyclo-Z in accordance with the randomization code. Specifically, the subjects will be randomly assigned in a 1:1:1 ratio to the following:

• Dose A: Cyclo-Z containing 23 mg zinc plus 6 mg CHP – 85 subjects

• Dose B: Cyclo-Z containing 23 mg zinc plus 15 mg CHP – 85 subjects

• Dose C: Placebo – 85 subjects.

The primary objective is to assess the dose-dependent efficacy of Cyclo-Z for the treatment of subjects with type 2 diabetes. The secondary objective is to assess the safety of Cyclo-Z in the treatment of subjects with type 2 diabetes.



In addition, the trial will be used to support dose selection for further development in Phase 3 trials and guide design of these additional trials.

The primary comparisons in this study will be tested for each Cyclo-Z treatment group compared to the placebo group: Cyclo-Z containing 23 mg zinc plus 15 mg CHP (high dose) vs. placebo, Cyclo-Z containing 23 mg zinc plus 6 mg CHP (low dose) vs. placebo at the α = 0.05 level of significance. The comparison of Cyclo-Z containing 23 mg zinc plus 15 mg CHP (high dose) vs. Cyclo-Z containing 23 mg zinc plus 6 mg CHP (low dose) will also be conducted to explore the difference in the two active doses at the α = 0.05 level of significance. No sequential testing will be performed in this Phase 2 exploratory study. No adjustments for multiplicity will be performed in this Phase 2 exploratory study.

The primary endpoint is the change in Glycosylated Hemoglobin (HbA1c) from baseline at week 24. Assuming a treatment effect of -0.7% for Cyclo-Z group with a standard deviation of 1.132 and a placebo effect of -0.08% with a standard deviation of 1.068 in change of HbA1c level from baseline at week 24, a total sample size of 255 subjects (i.e., 85 subjects per treatment group) including 20% drop out rate will provide 90% power to detect the difference between an active and placebo group in change of HbA1c level from baseline at week 24 using a two-sample t-test with a 2-sided significance level of 0.05. The power calculation is derived using nQuery Advisor® 7.0.



2 Efficacy and Safety Endpoints

2.1 Primary Efficacy Endpoint

The primary efficacy endpoint for this study is the change in HbA1c from baseline at week 24.

2.2 Secondary Efficacy Endpoints

The following secondary efficacy endpoints will be analyzed:

Change in HbA1c from baseline over time

Change in fasting plasma glucose (FPG) levels from baseline over time

Change in plasma insulin from baseline over time

Proportion of subjects achieving HbA1c goal of < 7.0% at week 24


Proportion of subjects with decrease in HbA1c of ≥ 0.5% from baseline at week 24


Change in postprandial (2 hours after dinner) blood glucose level from baseline over time

Change in Three-hour Average above Fasting Glucose Concentration (TAFGC) from baseline at week 12 and week 24

Change in Oral Glucose Tolerance Test (OGTT) Area Under Curve (AUC) from baseline at week 12 and week 24

Proportion of subjects with marked hyperglycemia or hypoglycemia over time

Change in score of Audit of Diabetes-Dependent Quality of Life (ADDQoL) Questionnaire from baseline at week 24

2.3 Exploratory Efficacy Endpoints

The following exploratory efficacy endpoints will be separately evaluated for the change from baseline at week 12 and week 24:

Body weight

Body mass index (BMI)

Insulin-degrading enzyme (IDE)

Serum Parameters: zinc, adiponectin, C-peptide, glucagon, leptin

Urine Parameters: zinc, glucose, microalbumin, copper

2.4 Safety Endpoints

The following safety endpoints will be evaluated for the change from baseline assessment:

Hematology, chemistry, urinalysis

Thyroid Function Test (TSH, T3, and T4)

Electrocardiograms (ECGs)

Physical examination



Vital signs

Adverse events

Concomitant medications

Episodes of marked hyperglycemia and hypoglycemia

2.5 Other Relevant Variables

In a subset of subjects, Cyclic dipeptide (his-pro) (CHP) and zinc levels in blood will be measured for evaluation of population pharmacokinetics.

Another relevant variable is a major protocol deviation.



3 Statistical Analysis Sets

3.1 Full Analysis Set (FAS)

The FAS will include all subjects who were randomized in the study. Subjects will be analyzed according to their randomized treatment, regardless of treatment received.

3.2 Efficacy Analysis Set (EAS)

The EAS is a subset of FAS consisting of subjects who have received at least one dose of study medication, i.e. any dose of placebo or Cyclo-Z, and who had both baseline and any post-baseline efficacy data. Analyses for efficacy endpoints will utilize this analysis set.

3.3 Per-Protocol Set (PPS)

All subjects valid for EAS who meet ALL the following criteria will be ‘valid per protocol’ (also called ‘valid for efficacy’) and will be included in the PPS:

Complete at least 4 weeks of treatment after randomization. Have both baseline and at least 4 weeks of efficacy data. Subject did not have any significant violation (major protocol deviation) after randomization.

Major protocol deviations that will potentially impact the primary analysis of efficacy endpoint at week 24 or violate GCP at site will be discussed and decided prior to database lock and will be applied in the per protocol analysis:

The following cases will be excluded from the PPS:

Subjects whose informed consent is not provided. Subjects who are withdrawn/ early terminated without completing at least 4 weeks of

treatment after randomization. Subjects who are not meeting inclusion/ exclusion criteria. Subjects who receive excluded concomitant medications. Subjects who have major protocol deviations – all protocol deviations will be classified as

major or minor under the discussion with Sponsor before database is locked during a blinded data review (see Section 5.11).

Subjects who have overall compliance rate as ≤ 70% or ≥ 120%. Additional criteria maybe added prior to unblinding of the study database. Protocol deviations will be identified and classified for each subject during a blinded data review (see Section 5.11).

3.4 Safety Analysis Set (SAF)

The SAF will consist of all randomized subjects who received at least one dose of study medication, i.e. any dose of placebo or Cyclo-Z, and has any post-randomization safety data collected. If the administration of any study medication is not certain, the subject will be included in the SAF. The analyses based on the SAF will be conducted on an “as treated” basis, i.e., all subjects will be analyzed by the treatment they have actually received.



3.5 Assignment of Analysis Sets to Analysis

Tabulation of subject enrollment by site, subjects per visit, status at study termination, demographic and baseline characteristics and protocol deviations will utilize the FAS. The primary analysis of all primary, secondary, and exploratory efficacy endpoints will be conducted on the EAS. Also, some sensitivity analyses of the efficacy endpoints will be conducted on the EAS. The PPS will be used for sensitivity analyses of primary and secondary efficacy endpoints. The SAF will be used to analyze the safety endpoints. Analyses of demographics and baseline characteristics, efficacy endpoints at baseline, safety endpoints, and summary of study medication will utilize this analysis set.

3.6 Visit Terminology

Visit no.

Notation used on the case report form

Notation used for tables, listings and graphs

0 Screening (V1) Wk -2 Screening

101 Baseline (V2) Wk 0 Baseline

102 Wk 2 (V3) ± 2 days Week 2

104 Wk 4 (V4) ± 2 days Week 4

108 Wk 8 (V5) ± 2 days Week 8

112 Wk 12 (V6) ± 2 days Week 12

116 Wk 16 (V7) ± 2 days Week 16

120 Wk 20 (V8) ± 2 days Week 20

124 Wk 24 (V9) ± 2 days [includes early terminations entered in V9]

Week 24/ ET

124.1 Wk 24 (V9) ± 2 days Week 24

124.2 Wk 24 (V9) ± 2 days Week 24 - LOCF

126 Wk 26 (V10) + 7 days Week 26

Note: ET=Early termination; LOCF=Last observation carried forward.



4 Data Screening and Acceptance

4.1 General Principles

The objective of the data screening is to assess the quality and statistical characteristics of the data relative to the requirements of the planned final analyses.

4.2 Data Handling and Electronic Transfer of Data

The FGK Data Management (DM) department will provide all the data to be used in the planned final analyses. The study will use the eCRF database which will be set up by Trium Analysis Online GmbH (Trium) based on FGK’s BS-FRM-003_04_eCRF_Template. The eCRF database is part of Trium's CT-Engine application. All data collected in the eCRF will be extracted from Trium's CT-Engine application by FGK DM. Protocol deviations will be collected from the site and transferred to InClin’s CDMS (Smartsheet). Final PK data for subset group will be transferred from Celerion PK group to FGK DM. Unblinded subject and bottle ID randomization lists will be provided by FGK’s statistician after database is locked. Details on data handling and transfer of data will be provided in Data Management Plan and Data Transfer Specification(s).

4.3 Handling of Missing and Incomplete Data

4.3.1 Patterns of Missing Data

Subjects may be missing specific data points for various reasons. In general, data may be missing due to a subject’s early withdrawal from the study, a missed visit, or non-evaluable of data point at certain time point or visit. During the data review process, queries will be sent to the sites whether it is a true missing value or a data entry or sample processing error from the lab. All attempts will be made by the study team members to identify missing or partial data for this study prior to database lock. The frequency and pattern of missing data for efficacy endpoints will be assessed through descriptive summaries of measurements over time.

4.3.2 Missing HbA1c Level Measurements and Imputation For primary efficacy endpoint, the primary analysis will be performed using a mixed-model repeated measures (MMRM) which has an inherent mechanism of imputation under the assumption of Missing at Random (MAR). To evaluate the robustness of the analysis results, sensitivity analyses will be performed for the primary efficacy endpoint. The sensitivity analyses are:

1) The same analysis (MMRM) using the LOCF method to handle missing data using the EAS.

In the LOCF method, post-baseline missing continuous endpoints will be imputed using the last observed value including baseline value. For example, if a subject has all of the post-baseline values as missing, then all of the post-baseline values will be imputed using the observed baseline value (observed baseline value = valid value at week 0 visit). Missing baseline will not be imputed.

2) The same analysis (MMRM) without imputation of missing data using the PPS.



3) Sensitivity analysis with a tipping-point approach using the EAS.

This sensitivity analysis is in multiple imputation under the MNAR assumption by searching for a tipping point that reverses the study conclusion.

The tipping-point approach is like a progressive stress-test to assess how severe departures from MAR need to be in order to overturn conclusions from the primary analysis. In the Cyclo-Z active groups, all the imputed values will be replaced by the imputed value plus a shift parameter beginning from 0 with the increment of 0.05. Each value of the shift parameter will be tested sequentially until the value is reached at which the conclusion from the MMRM analysis is reversed. That value is the tipping point indicating that the conclusion under MAR is questionable if this shift parameter value is plausible. If implausible departures from MAR are required to change the results from statistically significant (p≤0.05) to not statistically significant (p>0.05), the results will be said to be robust to the departure from MAR assumption and provide more confidence in the results obtained based on statistical methods with the MAR assumptions (Yang Yuan, 2014).

4) Sensitivity analysis with control-based pattern imputation using the EAS.

This sensitivity analysis is in multiple imputation under the MNAR assumption by creating control-based pattern imputation. Ratitch and O’Kelly (2011) describe an implementation of the pattern-mixture model approach that uses a control-based pattern imputation. In this imputation model the missing observations in the treatment group (Cyclo-Z) are constructed not from the observed data in the treatment group but rather from the observed data in the placebo group. This model is also the imputation model that is used to impute missing observations in the placebo group (Yang Yuan, 2014). This particular MNAR assumption can be viewed as a “worst reasonable case” assessment of the primary analysis.

4.3.3 Missing Data Imputation for Other Endpoints

Primary analysis of continuous secondary efficacy endpoints will be analyzed based on observed data using MMRM which has an inherent mechanism of imputation under the assumption of MAR. In the sensitivity analyses of continuous secondary efficacy endpoints, missing continuous efficacy endpoints will be handled using the last observation carried forward (LOCF) method, a tipping-point approach, and a control-based pattern imputation (see Section 4.3.2). In the sensitivity analyses of continuous secondary efficacy endpoint “Change in score of ADDQoL Questionnaire from baseline at week 24”, missing values will be handled using the LOCF method only (see Section 4.3.2). In the sensitivity analyses of binary secondary efficacy endpoints missing data are imputed as non-responders for CMH tests, logistic regression and GEE. No sensitivity analyses of exploratory efficacy endpoints will be performed.



Missing or incomplete dates will be listed as is in any listings. Incomplete start date of adverse event or concomitant medication taken will be handled by following rule: Start Date Missing Imputation Exception

Adverse Event Concomitant Medication

Day 01 Default to date of first dose if an AE/ CM starts at the same year and month as date of first dose

Day/Month 01JAN Default to date of first dose if an AE/ CM started at the same year as date of first dose

Day/Month/Year No imputation

Incomplete end date of concomitant medication taken will be handled by following rule: End Date Missing Imputation Exception

Concomitant Medication Day 01 Default to date of first dose if a CM starts at the same year and month as date of first dose

Day/Month 01DEC Default to date of first dose if a CM started at the same year as date of first dose

Day/Month/Year No imputation

Other imputation may be applied if deemed necessary even after this SAP is approved. This addition or even change does not require an SAP revision. Instead, those detailed imputation rules or methods will be documented in a separate file such as statistical decision log or tracking log.

4.4 Multicenter Studies No planned analysis related to site will be conducted.

4.5 Adjustments for Covariates

This study is stratified by site as a block factor; therefore, this stratification factor will be fitted in the corresponding statistical model as a fixed effect. Pooling of study sites: For the purpose of using site as covariates, sites contributing with a low number of subjects in the EAS (site less than 10 subjects in the EAS) will be pooled with a comparable site based on geographical location, and the pooled study sites will be used as analysis sites in all efficacy analyses. The following sites will be pooled:



Site ID State City Pooling 10 California Montclair Site 10 and Site 23 will be combined.

11 Texas San Antonio

12 Florida Miami Beach Site 12 and Site 14 will be combined.

13 Florida Coral Gables

14 Florida Miami Site 12 and Site 14 will be combined.

15 Florida Orlando

16 Florida St. Petersburg Site 16 and Site 19 will be combined.

17 Georgia Snellville Sites 17, 21, 26, 29 will be combined.

18 Ohio Cleveland Sites 18 and 20 will be combined.

19 Florida DeLand Site 16 and Site 19 will be combined.

20 Massachusetts North Dartmouth Sites 18 and 20 will be combined.

21 Georgia Peachtree Corners Sites 17, 21, 26, 29 will be combined.

22 Texas San Antonio

23 California North Hollywood Site 10 and Site 23 will be combined.

24 Nevada Las Vegas

26 Alabama Birmingham Sites 17, 21, 26, 29 will be combined.

27 California Chula Vista Site 27 and Site 28 will be combined.

28 California La Mesa

29 Alabama Anniston Sites 17, 21, 26, 29 will be combined.

30 Michigan Caro Baseline HbA1c will be used as a covariate.

4.6 Multiple Comparisons/Multiplicity This is an exploratory Phase 2 study; thus, multiplicity adjustment will not be performed.

4.7 Detection of Bias

This study has been designed to minimize the potential bias by using the randomization schedule to randomize the subjects into treatment groups and using the blinding. Other factors that may bias the results of the study include:

• major protocol deviations likely to impact the analysis and interpretation of the efficacy endpoints; these will be tabulated in the clinical study report (CSR).

• subjects unblinding before the final database lock and formal unblinding; any unblinding of individual subject prior to formal unblinding of the study will be documented in the CSR.

Additional sensitivity analyses may be included to assess the impact of potential biases on the primary endpoint. If any of the sensitivity analyses are required to evaluate potential biases in the study’s

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conclusions, then the sources of the potential biases and results of the sensitivity analyses will be clearly documented in the CSR.

4.8 Outliers

Various methods including univariate summaries, histograms, scatter plots, box plots, and line graphs, may be used to identify outliers in the key efficacy and safety endpoints. Extreme data points will be identified during the blinded review of the data prior to database lock. Such data points will be reviewed with Data Management to ensure accuracy of the data. The primary analyses will include those outliers in the data. Sensitivity analyses may be undertaken if extreme outliers for a variable are observed and identified.

4.9 Distributional Characteristics

Distributional assumptions for the primary and secondary endpoints will be assessed. If the assumptions are not met, then alternative statistical methods will be utilized. The use of alternative methods will be fully justified in the CSR.

4.10 Validation of Statistical Analyses

SAS programs will be developed and maintained by FGK, and output will be verified in accordance with current FGK SOPs. Tables, listings, graphs (TLGs) will be generated and validated. SAS System version 9.4 or higher will be used to generate TLGs.



5 Statistical Evaluation

Baseline is defined as the last non-missing measurement taken before first study drug application. If date of first dose is missing in diary, the date of randomization will be used as date of first dose. If the date of first dose in the diary is much later than date of randomization but information on tablets taken on the drug accountability CRF indicate tablets taken prior to Week 2, the date of randomization will be used as date if first dose (this will be decided on a subject basis during the BDRM). If date of last dose is missing in diary, the date prior to the last visit at which the patient returned study medication and indicated that study medication was taken since last visit (according to information on drug accountability CRF) will be used as date of last dose. Visit 9 is a combined visit of either the regular week 24 visit or an early termination before week 24. In TLGs, two presentations of this visit will be shown:

- "Week 24": only regular week 24 visits will be included. - "Week 24/ ET": both regular week 24 visits and early termination visits before week 24 will be

included. Unscheduled visits will not be included in any tables or graphs, only in listings. In all tables the three different treatment groups (Cyclo-Z containing 23 mg zinc plus 6 mg CHP/ Cyclo-Z containing 23 mg zinc plus 15 mg CHP/ Placebo) will be separately tabulated. Total columns will additionally be displayed for all three treatment groups combined. If a laboratory value is given with a ‘<’ or ‘>’ or ‘<=’ or ‘>=’ sign, only the value without the sign is considered for any calculation in tables and graphs. In the listings the values including signs are displayed.

5.1 Dispositions of Subjects and Analysis Sets

Disposition of subjects and analysis sets

The disposition of subjects and analysis sets will be shown for all screened subjects. Subject enrollment per site and subject disposition will be presented for the FAS. Subjects per visit will be shown for the FAS and the SAF. Violation of inclusion and exclusion criteria will be shown for all screened subjects. The status at study termination will be presented for the FAS. No inferential assessments will be performed on disposition of subject’s data.

5.2 Demographics and Other Covariates

Demographic data

Demographic data (age, sex, ethnicity, race, height [cm]) will be tabulated for the FAS and the SAF. If body height is given in inches the value will be converted to cm before inclusion of the value into the table. Pregnancy test



The proportion of subjects with positive or negative pregnancy test results from females with childbearing potential will be tabulated for the FAS and the SAF. Medical history

Medical history data will only be listed for the FAS. Efficacy variables measured at baseline

Primary, secondary and exploratory efficacy variables at baseline will be summarized using basic statistics by treatment group for the SAF.

No inferential assessments will be performed on demographics and other covariates.

5.3 Efficacy Analysis

All primary, secondary and exploratory endpoints will be summarized using descriptive methods. The efficacy and sensitivity analyses will be conducted on the EAS and PPS, respectively. Unless otherwise specified, all tests of treatment effects will be conducted at a 2-sided alpha level of 0.05, and confidence intervals (CIs) will be calculated at 95%, 2-sided. All tests of interactions between treatments and visits will be conducted at a 2-sided alpha level of 0.10. No adjustments for multiplicity will be performed.

The primary comparisons in this study will be tested for each Cyclo-Z treatment group compared to the placebo group: Cyclo-Z containing 23 mg zinc plus 15 mg CHP (high dose) vs. placebo, Cyclo-Z containing 23 mg zinc plus 6 mg CHP (low dose) vs. placebo at the α = 0.05 level of significance. The comparison of Cyclo-Z containing 23 mg zinc plus 15 mg CHP (high dose) vs. Cyclo-Z containing 23 mg zinc plus 6 mg CHP (low dose) will be conducted to explore the difference in the two active doses at the α = 0.05 level of significance. No sequential testing will be performed in this Phase 2 exploratory study. For all continuous primary, secondary and exploratory efficacy endpoints (using MMRM model), least squares (LS) means and standard errors at week 24 and at other time points for each treatment group and the difference in LS means with the corresponding standard errors, 95% confidence interval, and the p-values for treatment comparisons will be displayed in a table format. For continuous secondary efficacy endpoint “Change in score of Audit of Diabetes-Dependent Quality of Life (ADDQoL) Questionnaire from baseline at week 24” (using ANCOVA model), the LS means and standard errors at week 24 for each treatment group, and the p-values for treatment comparisons will be displayed in a table format. For all primary, secondary and exploratory efficacy endpoints, the following figures will be created:

For all continuous endpoints, mean values +/- standard deviation of the absolute change from baseline will be displayed graphically over time by visit and treatment group.

The proportion of subjects meeting binary endpoints over time will be displayed in a figure format by visit and treatment group.



Note1: On the vertical axis of the graph will be response rate [%] per treatment group, n = number of subjects per visit per treatment group. Different types of shading for the different treatment groups will be applied.

Note2: Applicable for all figures for primary, secondary and exploratory endpoints: in one set of figures, p-values will be displayed, in another set of figures, no p-values will be displayed.

5.3.1 Analysis of Primary Efficacy Endpoint

Change in HbA1c from baseline at week 24 [%]

The primary analysis of the primary efficacy endpoint will be conducted on the EAS. The primary efficacy endpoint, absolute change in HbA1c from baseline at week 24 will be analyzed using a mixed-model repeated measures (MMRM) without any imputation for missing data and has an inherent mechanism of imputation under the assumption of MAR. The primary efficacy endpoint will be tested for each Cyclo-Z treatment group compared to the placebo group at a 2-sided significance level of 0.05; Cyclo-Z 15 mg CHP (high dose) vs. placebo, Cyclo-Z 6 mg CHP (low dose) vs. placebo. The comparison of Cyclo-Z containing 23 mg zinc plus 15 mg CHP (high dose) vs. Cyclo-Z containing 23 mg zinc plus 6 mg CHP (low dose) will be conducted to explore the difference in the two active doses at a 2-sided significance level of 0.05. Unless otherwise noted, all tests of treatment effects will be conducted at a 2-sided alpha level of 0.05, and confidence intervals (CIs) will be calculated at 95%, 2-sided. All tests of interactions between treatment groups and visits will be conducted at a 2-sided alpha level of 0.10. HbA1c is measured at baseline, week 4, week 8, week 12, week 16, week 20, and week 24. The absolute change in HbA1c from baseline at each timepoint [hba1c_chg] is included in the MMRM. Absolute change in HbA1c from baseline at week 24 will be tested for each Cyclo-Z treatment group compared to the placebo group and two Cyclo-Z treatment groups (high dose vs. low dose) will be compared at a 2-sided significance level of 0.05 with the following comparisons in the same model:

• Cyclo-Z containing 23 mg zinc plus 15 mg CHP (high dose) vs. placebo at a 2-sided significance level of 0.05

• Cyclo-Z containing 23 mg zinc plus 6 mg CHP (low dose) vs. placebo at a 2-sided significance level of 0.05

• Cyclo-Z containing 23 mg zinc plus 15 mg CHP (high dose) vs. Cyclo-Z containing 23 mg zinc plus 6 mg CHP (low dose) at a 2-sided significance level of 0.05

The model will include random subject effects and the following fixed effects:

• treatment group [trt] • baseline HbA1c [hba1c_base] • site (as this was a stratification factor for randomization) [site] • visit [timepoint] • treatment group (trt)-by-visit interaction [trt*timepoint]



An unstructured covariance structure (in SAS, type=un) will be used to model the within subject errors. If this model fails to converge, then the following covariance structure will be tested in order:

• autoregressive [type=AR(1)] • compound symmetry (type=cs)

The first covariance structure that converges will be used. In the following, an example SAS code is given, proc mixed data=FINAL; class trt timepoint site subjid; model HbA1c_chg=trt timepoint trt*timepoint site hba1c_base/solution ddfm=KR; random intercept / subject=subjid s; repeated timepoint / subject=subjid type=un rcorr; lsmeans trt*timepoint / diff cl alpha=0.05; run; If the model does not converge, the autoregressive covariance structure will be tried [type=un to be replaced by type=AR(1)]. If the model with the autoregressive covariance structure does not converge, then a compound symmetry covariance structure will be tried [type=un to be replaced by type=cs]. If the model with the compound symmetry covariance structure does also not converge, the interaction effect will be dropped. If this still does not lead to convergence, only the basic statistics described below will be presented. Least squares means (LS means) estimates (point estimate together with standard error), two-sided 95% confidence interval (lower and upper) will be provided for each treatment group, and the difference between each active dose and placebo, and the difference between 2 active doses (high dose vs. low dose) at the same visit along with the corresponding p-values. Before application of the MMRM, it will be checked if the residuals are approximately normally distributed. If there are no major violations of the normal distribution the model can be applied. The following null hypothesis will be tested:

H0: (Adjusted) mean absolute changes in HbA1c from baseline at week 24 are equal in both treatment groups

versus the alternative

Ha: (Adjusted) mean absolute changes in HbA1c from baseline at week 24 are not equal in both treatment groups.

To evaluate the robustness of the analysis results, sensitivity analyses will be performed for the primary efficacy endpoint (see Section 4.3.2).

Absolute HbA1c values and absolute changes in HbA1c from baseline will be summarized by visit and treatment group using descriptive statistics.



5.3.2 Analysis of Secondary Efficacy Endpoints

Change in HbA1c from baseline over time [%]

The primary analysis of the first secondary efficacy endpoint will be conducted on the EAS.

The same analysis methods as for “Change in HbA1c from baseline at week 24” will be used. The same sensitivity analysis methods as for “Change in HbA1c from baseline at week 24” will be used. Only the interpretation of the results of the model will focus on the change from baseline over time instead of the change from baseline at week 24. Change in fasting plasma glucose (FPG) levels from baseline over time (mg/dL) Fasting plasma glucose measured by central lab will be used. The values will be converted to mg/dL before inclusion in any analysis. The same analysis methods as for “Change in HbA1c from baseline at week 24” will be used. The same sensitivity analysis methods as for “Change in HbA1c from baseline at week 24” will be used. Change in plasma insulin from baseline over time (uIU/mL)

The same analysis methods as for “Change in HbA1c from baseline at week 24” will be used. The same sensitivity analysis methods as for “Change in HbA1c from baseline at week 24” will be used. Proportion of subjects achieving HbA1c goal of < 7.0% at week 24

The proportion of subjects achieving HbA1c goal of < 7.0% at week 24 will be analyzed using the Cochran-Mantel-Haenszel (CMH) test stratified by the site after the missing data are imputed as non-responder. The proportion of subjects achieving HbA1c goal of < 7.0% at week 24 will be tested separately for each Cyclo-Z treatment group compared to the placebo group, and two Cyclo-Z treatment groups (high dose vs. low dose) will be compared at a 2-sided significance level of 0.05:



• Cyclo-Z containing 23 mg zinc plus 15 mg CHP (high dose) vs. Cyclo-Z containing 23 mg zinc plus 6 mg CHP (low dose) at a 2-sided significance level of 0.05

The p-values, odds ratio along with its associated 95% confidence intervals will be reported. Sensitivity analyses for this endpoint are:

1) Marginal model in form of generalized estimating equations (GEE) approach after the missing data are imputed as non-responders using the EAS. Proportion of subjects achieving HbA1c goal of < 7.0% at week 24 will be tested for each Cyclo-Z treatment group compared to the placebo group and two Cyclo-Z treatment groups



(high dose vs. low dose) will be compared at a 2-sided significance level of 0.05 with the following comparison in the same model:



• Cyclo-Z containing 23 mg zinc plus 15 mg CHP (high dose) vs. Cyclo-Z containing 23 mg zinc plus 6 mg CHP (low dose) at a 2-sided significance level of 0.05 The response variable is “Achieving HbA1c goal < 7.0 % at week 24”. The following null hypothesis will be tested: H0: Proportion of subjects achieving HbA1c goal of < 7.0% at week 24 are equal in both treatment groups versus the alternative Ha: Proportion of subjects achieving HbA1c goal of < 7.0% at week 24 are not equal in both treatment groups.

Since the response data are binary, the logit link function will be used to model the marginal mean. Furthermore, the variance function for the binomial distribution will be assumed for the GEE. SAS procedure PROC GENMOD will be used. The response variable “Achieving HbA1c goal < 7.0 %” is available at baseline, week 4, week 8, week 12, week 16, week 20, and week 24. The response variable “Achieving HbA1c goal < 7.0 %” at each post-baseline timepoint [eff] is included in the GEE. An unstructured working correlation structure (in SAS, type = un) will be specified. If this model fails to converge, then the following structures will be tested in order:

• autoregressive [type=AR(1)] • exchangeable [type=exch]

The first working correlation structure that converges will be used. In the following, an example of SAS code is given: proc genmod data=FINAL descending; class subjid trt site timepoint; model eff= hba1c_base trt timepoint trt*timepoint site/link=logit dist=binomial; repeated sub = subjid / type=un; lsmeans trt*timepoint / ilink pdiff cl exp;



run; Where the variable of interest is “Achieving HbA1c goal < 7.0 % at week 24”. The odds ratio along with its associated 95% confidence intervals will be reported. Least squares means (LS means) estimates (point estimate together with standard error and two-sided 95% confidence interval) will be provided for each treatment group as well as for the difference between each dose and placebo, and difference between 2 active doses (high dose vs. low dose) at the same visit along with the corresponding p-values.

(2) CMH test stratified by site using the PPS (missing data are imputed as non-responders, i.e.

“Achieving HbA1c goal < 7.0% at week 24” = “no”).

(3) Logistic regression models at week 12 and at week 24 after the missing data are imputed as non-responders using the EAS.

For both visits (week 12/ week 24) a separate logistic model will be calculated. The response variable is “Achieving HbA1c goal < 7.0 %”. The following covariates will be included in the model:

o treatment group (reference category = placebo) o HbA1c at baseline o site (reference category = last site)

The odds ratio along with its associated 95% confidence intervals will be reported. Least square means (LS means) estimates (point estimate together with standard error and two-sided 95% confidence interval) will be provided for each treatment group as well as for the difference between each dose and placebo, and difference between 2 active doses (high dose vs. low dose) at the same visit along with the corresponding p-values..

The proportion of subjects achieving HbA1c goal < 7.0% (yes/no) will be tabulated by visit. Missing data will be imputed as non-responder (i.e. “Achieving HbA1c goal < 7.0%” = “no”).


The same analysis methods as for “Proportion of subjects achieving HbA1c goal of < 7.0% at week 24” will be used. The same sensitivity analysis methods as for “Proportion of subjects achieving HbA1c goal of < 7.0% at week 24” will be used.




The same analysis methods as for “Proportion of subjects achieving HbA1c goal of < 7.0% at week 24” will be used. The same sensitivity analysis methods as for “Proportion of subjects achieving HbA1c goal of < 7.0% at week 24” will be used. Proportion of subjects with decrease in HbA1c of ≥ 1.0% from baseline at week 24

The same analysis methods as for “Proportion of subjects achieving HbA1c goal of < 7.0% at week 24” will be used. The same sensitivity analysis methods as for “Proportion of subjects achieving HbA1c goal of < 7.0% at week 24” will be used. Change in postprandial (2 hours after dinner) blood glucose level from baseline over time [mg/dL]

The postprandial blood glucose level should be recorded daily in the subject diary (“Blood sugar, 2 hours after dinner”). Only the values recorded in the diary at the same date as the following visits will be used for inclusion in the MMRM: baseline, week 2, week 4, week 8, week 12, week 16, week 20, week 24/ ET.

The same analysis methods as for “Change in HbA1c from baseline at week 24” will be used. The same sensitivity analysis methods as for “Change in HbA1c from baseline at week 24” will be used. Change in Three-hour Average above Fasting Glucose Concentration (TAFGC) from baseline at week 12 and week 24 (mg/dL) TAFGC is calculated as follows: TAFGC = ∑n[(glucose value at (i) hour in OGTT) – (glucose value at 0 hour in OGTT)]/n, where i = 0.5, 1.0, 1.5, 2.0, 2.5, and 3; n = number of measurements after 0 hour. All OGTT values will be converted in the standard unit mg/dL before using the values in any analysis. In case of missing values, the TAFGC will be applied utilizing the remaining non-missing values between 0.5 and 3 hour measurements. In case of missing 0 hour or 3 hour value, the subject will be excluded from the evaluation. The same analysis methods as for “Change in HbA1c from baseline at week 24” will be used. The same sensitivity analysis methods as for “Change in HbA1c from baseline at week 24” will be used. Change in Oral Glucose Tolerance Test (OGTT) Area Under Curve (AUC) from baseline at week 12 and week 24 [mg*h/dL] All OGTT values will be converted in the standard unit mg/dL before using the values in any analysis. AUC0-3OGTT [mg*h/dL] is derived from the measurements given at time points 0, 0.5, 1, 1.5, 2, 2.5, and 3 hours. It will be calculated by the trapezoidal rule taking the actual measurement times. In case of missing values the trapezoidal rule will be applied utilizing the remaining non-missing values between 0 and 3 hour measurements. In case of missing 0 hour or 3 hour value the subject will be



excluded from the evaluation. The same analysis methods as for “Change in HbA1c from baseline at week 24” will be used. The same sensitivity analysis methods as for “Change in HbA1c from baseline at week 24” will be used. Proportion of subjects with marked hyperglycemia or hypoglycemia over time

The variable “Marked hyperglycemia or hypoglycemia” will be calculated as follows: if at a visit (baseline, week 2, week 4, week 8, week 12, week 16, week 20, week 24/ ET) at least one of the questions “Did hyperglycemic episode occur since the last visit” = “yes” or “Did hypoglycemic episode occur since the last visit” = “yes” then “Marked hyperglycemia or hypoglycemia” = “yes” for this visit.

The same analysis methods as for “Proportion of subjects achieving HbA1c goal of < 7.0% at week 24” will be used. The same sensitivity analysis methods as for “Proportion of subjects achieving HbA1c goal of < 7.0% at week 24” will be used. “Marked hyperglycemia or hypoglycemia” will be tabulated by visit. Missing data will not be imputed. Change in score of Audit of Diabetes-Dependent Quality of Life (ADDQoL) Questionnaire from baseline at week 24

The CRF variable “Average weighted impact score” which is calculated by the electronic data capture (EDC) system will be used as score to evaluate this endpoint. Possible values are between -9 (maximum negative impact of diabetes) to +3 (maximum positive impact of diabetes). The score is calculated by the EDC system according to the following rules:

• Individual domains: weighted impact score = impact rating (questions with part a) x importance rating (questions with part b)

• The two overview items are not to be considered for score calculation. • If one part of the question is missing, the score will be missing. • If any response is "0", the score will be "0". • If the domain is not applicable/ unknown/ not done, the score will be missing. • Average weighted impact score = sum of weighted ratings of applicable domains/ number of

applicable domains.

Since there are no repeated post-baseline measurements, an analysis of covariance (ANCOVA) model with factors treatment group and site, and baseline score of ADDQoL as a (linear) covariate using the EAS will be used to compare treatment groups for this endpoint. That means that three separate models will be fit (one including subjects of the high dose group and the placebo group, and one including subjects of the low dose group and the placebo group, the last one including subjects of the high dose group and the low group). The following null hypothesis will be tested:

H0: (Adjusted) mean absolute changes in score of ADDQoL from baseline at week 24 are equal in both treatment groups



versus the alternative

Ha: (Adjusted) mean absolute changes in score of ADDQoL from baseline at week 24 are not equal in both treatment groups.

The null hypothesis of equal (adjusted) treatment means in absolute change in score of ADDQoL from baseline at week 24 will be tested by applying the following analysis of covariance model:

Absolute change in score of ADDQoL from baseline at week 24 = µ + ß1 * group + ß2 * site + ß3

* ADDQoLb + e;

where µ=global mean group = treatment group ADDQoLb = baseline score of ADDQoL ßi = parameter estimate e = error term Estimates for least square means (LS means) and difference in LS means between the two treatment groups along with 95%-confidence intervals derived from the above model will be given. In addition, the p-value for testing this null hypothesis will be given. The following sensitivity analyses will be used: 1) ANCOVA using LOCF method to handle missing data using the EAS. 2) ANCOVA without imputation of missing data using the PPS.

In the listing, the data are presented for each quality of life (QoL) domain:

Item no. QoL domain 1 Leisure 2 Work 3 Journeys 4 Holidays 5 Physical 6 Family life 7 Friendship and social life 8 Personal relationship 9 Sex life

10 Physical appearance 11 Self-confidence 12 Motivation 13 Reactions of other people 14 Feelings about the future 15 Financial situations 16 Living conditions 17 Depend on others



5.3.3 Analysis of Exploratory Efficacy Endpoints The following exploratory efficacy endpoints will be evaluated for the change from baseline at week 12 and week 24: Body weight [kg]

Values will be converted to kg before inclusion in the analysis. Body mass index (BMI) [kg/m²] Insulin-degrading enzyme (IDE) will be measured as follows (note: serum samples, not plasma,

were validated for IDE measurements): o IDE Activity (measured) by Fluorescence [U/mL] o IDE Activity (normalized for total protein conc.) by Calculation [U of IDE/g protein] o IDE Mass measured by ELISA [ug/L] o IDE Mass (normalized for total protein conc) by Calculation [fg of IDE/ug protein] o IDE Total Protein [mg/mL]

Serum Parameters: o zinc [ug/dL] The zinc values measured by the central laboratory LabConnect will be used for this endpoint. o adiponectin [ug/mL] o C-peptide [ng/mL] o glucagon [ng/L] o leptin [ng/mL]

Values will be converted to the units given in brackets before inclusion in the analysis. Urine Parameters:

o zinc (per volume) [ug/dL] o glucose [mg/dL] Urine glucose has semi-quantitative values in mg/dL, e.g. “Negative”, “150”, “>=500”. The semi-quantitative values will be converted to quantitative values according to the following rules before inclusion of the values in any analysis: “Negative” 0 Any numeric value no conversion necessary “>=500” 500 o microalbumin [mg/dL] o copper (per volume) [ug/dL]

Values will be converted to the units given in brackets before inclusion in the analysis. For all exploratory endpoints the same analysis method (MMRM) as for “Change in HbA1c from baseline at week 24” will be used including all visits at which the exploratory endpoint is measured.

18 Freedom to eat 19 Freedom to drink



All exploratory variables will be summarized using basic statistics for absolute values and for changes from baseline. All exploratory analyses will be done for the EAS. Sensitivity analyses will not be performed.



Summary of Primary and Sensitivity Analyses for Efficacy Endpoints

Endpoints Primary Analysis Sensitivity Analysis Primary Endpoint

Change in HbA1c from baseline at week 24 [%]

Mixed-Model Repeated Measures (MMRM) based on observed data without imputation using the EAS

1) MMRM using LOCF method to handle missing data using the EAS 2) MMRM without imputation of missing data using the PPS 3) Tipping-point approach using the EAS [assumption; data is missing not at random (MNAR)] 4) Control-based pattern imputation using the EAS [assumption; data is missing not at random (MNAR)]

Secondary Endpoints All continuous endpoints


1) MMRM using LOCF method to handle missing data using the EAS 2) MMRM without imputation of missing data using the PPS 3) Tipping-point approach using the EAS [assumption; data is missing not at random (MNAR)] 4) Control-based pattern imputation using the EAS [assumption; data is missing not at random (MNAR)]

Secondary Endpoint “Change in score of ADDQoL Questionnaire from baseline at week 24”

Analysis of Covariance (ANCOVA) using the EAS

1) ANCOVA using LOCF method to handle missing data using the EAS 2) ANCOVA without imputation of missing data using the PPS

Secondary Endpoints All binomial endpoints

Cochran-Mantel-Haenszel (CMH) test stratified by site after imputation of missing data as non-responders using the EAS

1) Marginal model in form of generalized estimating equations (GEE) approach using the EAS (missing data are imputed as non-responders) 2) CMH test stratified by site using the PPS (missing data are imputed as non-responders) 3) Logistic regression model at week 12 and at week 24 using the EAS (missing data are imputed as non-responders)

Exploratory Endpoints Change from baseline at Week 12 and week 24 in the following endpoints: • Body weight • BMI • IDE • Serum Parameters • Urine Parameters


Not applicable



5.4 Safety Analysis

Safety parameters will be analyzed descriptively. No statistical tests will be done for safety parameters. Laboratory parameters Changes from baseline in laboratory findings will be summarized using shift tables. Complete combinations of baseline and post-baseline values are shown, i.e. both values are given. Continuous laboratory values will be analyzed using basic statistics for values and absolute changes from baseline. The following laboratory parameters were determined and will be summarized for the corresponding visits: Hematology (measured at screening, baseline, week 4, week 8, week 12, week 16, week 20, week 24/ ET): leukocytes (WBC), erythrocytes (RBC), hemoglobin, hematocrit, platelet count, neutrophils/leukocytes, lymphocytes/leukocytes, monocytes/leukocytes, eosinophils/leukocytes, basophils/leukocytes, neutrophils count, lymphocytes count, monocytes count, eosinophils count, basophils count, MCH, MCV, MCHC, red blood cell distribution width. Chemistry (measured at screening, baseline, week 4, week 8, week 12, week 16, week 20, week 24/ ET): albumin, BUN, calcium, bicarbonate, creatine kinase, creatinine, amylase, potassium, lactate dehydrogenase, magnesium, sodium, phosphorus, total protein, chloride, AST, ALT, alkaline phosphatase, total bilirubin. Urinalysis (measured at baseline, week 4, week 8, week 12, week 16, week 20, week 24/ ET): urine specific gravity, urine PH, U protein semi-quant random, urine glucose semi-quant, U ketones (acetone) random, U bilirubin, urine blood. As only urine specific gravity and urine pH are continuous parameters, only those two parameters will be summarized using basic statistics. For the other urinalysis parameters only the shift tables described above will be created. Thyroid function test (measured at baseline, week 24/ ET): TSH, T3, T4. Urine microscopy will only be listed. For laboratory data (hematology and chemistry) and thyroid function test the mean values +/- standard deviation of the absolute change from baseline will be displayed graphically over time by visit and treatment group.



12-lead ECG Result of 12-lead ECG will be collected at screening and week 24. This result [normal, abnormal [clinically significant (CS), not clinically significant (NCS)] will be summarized with count and percentage by treatment group and visit. Physical examinations Result of physical examination (examination criteria: general appearance, head, ears, eyes, nose, throat, neck, heart, lungs, abdomen, lymph nodes, genitourinary, extremities, neurological, skin, musculoskeletal, and others) will be collected at screening, baseline, week 4, week 8, week 12, week 16, week 20, week 24. If the question “Did the physical examination reveal any abnormal findings” is answered with “yes” the corresponding examination criterion is considered to be “abnormal”. All examination criteria not revealing any abnormal findings are considered as “normal” in the statistical analysis. The results (normal, abnormal) will be summarized by treatment group and visit separately for each examination criterion. Vital signs (blood pressure, heart rate) Blood pressure (systolic and diastolic) [mmHg], pulse rate [beats/min], breathing [breaths/ minute], and temperature [°C] will be analyzed descriptively by visit and treatment group for both values and absolute changes from baseline. The values will be converted in the units given in brackets before inclusion in any summary table or graph. For vital signs the mean values +/- standard deviation of the absolute change from baseline will be displayed graphically over time by visit and treatment group. Adverse events (AEs) A treatment-emergent AE (TEAE) will be defined as an AE that began or worsened on or after the date and time of the first treatment dose. AEs recorded prior to the first application of study treatment will be considered non-treatment-emergent. AEs with insufficient date or time information to determine whether or not they were treatment-emergent will be considered treatment-emergent. All reported AEs (treatment-emergent or not) will be listed. Only TEAEs will be included in tables. In the tables TEAEs will be labeled as adverse events (AEs), not TEAEs. TEAEs will be summarized with the number of entries, as well as the number and rate of affected subjects for each treatment group by categorizing as follows:

• AE/ ADR (Adverse event/ Adverse drug reaction) TEAEs which have the relationship to investigational product as ‘yes’ or missing are regarded as ADR.

• SAE/ SADR (Serious adverse event/ Serious adverse drug reaction) SAEs with a start date on or after the date and time of the first treatment dose are regarded as treatment-emergent SAE and included in the tables containing SAEs. This applies also in case that the corresponding AE has a start date prior to date and time of the first treatment dose.

TEAEs will also be summarized with the number of entries, as well as the number and rate of affected subjects for each treatment group by categorization as follows:



• Outcome • Severity • Relationship to investigational product • Action taken regarding investigational product • Action taken for AE • SAE

AEs will be coded by using the Medical dictionary for regulatory activities (MedDRA) version 20.0. AEs will be tabulated by system organ class (SOC) and preferred term (PT) (MedDRA). The number of entries, as well as the number and rate of affected subjects will be reported for each treatment group. Adverse drug reactions, SAEs, non-serious AEs and serious adverse drug reactions are tabulated separately by SOC and PT. In the listings the duration of the AE will be given in days. It is calculated as stop date – start date + 1. It will only be calculated if complete start and stop date of the AE are available. Prior and concomitant medication Concomitant medication during the course of the study will be described. Concomitant medications are defined as all medications with start date ≥ date of first dose or ongoing or end date ≥ date of first dose. Medications with insufficient date information to determine whether or not they were concomitant will be considered concomitant. Intake of any concomitant medications (no/ yes) will be summarized with a frequency table by treatment group. Medications will be coded by WHO-DD version 01MAR2018. Concomitant medications will be tabulated by anatomic group (ATC level 1), ATC level 4 and WHO-DD preferred term for each treatment group. Medications not classified as being concomitant will be regarded as Prior Medication and will be listed. Episodes of marked hypoglycemia and hyperglycemia

Hypoglycemia occurred since the last visit will be summarized with the number of episodes, as well as the number and rate of affected subjects by each visit separately. The hypoglycemic episode counts on the CRF page “Assessment of hypoglycemic and hyperglycemic episodes” is the basis for this endpoint. Hyperglycemia occurred since the last visit will be summarized with the number of episodes, as well as the number and rate of affected subjects by each visit separately. The hyperglycemic episode counts on the CRF page “Assessment of hypoglycemic and hyperglycemic episodes” is the basis for this endpoint. As there are cases where the diary entries are not entered at the correct visits in the eCRF, the diary entries are allocated to the correct visits using date of diary entries and dates of visits.



Study drug administration Number of tablets taken Number of tablets taken (= CRF variable “Number of tablets taken since last visit”) will be summarized using basic statistics by treatment group and visit. Total number of tablets taken is calculated as sum of the number of tablets taken at each visit. Compliance Subject compliance [%] is calculated as follows: Compliance since previous visit = (number of tablets taken since previous visit) / [study drug exposure [days] since previous visit] * 100. Study drug exposure [days] is calculated for each visit starting with week 2 up to week 24 as (date of visit) – (date of previous visit). Total study drug exposure [days] is calculated as (date of last available regular visit (max. date of week 24)) – (date of first available regular visit (min. date of week 0)). Total compliance = (total number of tablets taken) / (total study drug exposure [days]) * 100. For definitions of number of tablets taken see above. A basic statistic table for subject’s compliance will be displayed by visit and in total for each treatment group. Total dose of study medication taken Total number of tablets taken is calculated as sum of the number of tablets taken at each visit from Drug Accountability eCRF (see above). The total dose of study medication taken [mg] is calculated based on actual treatment as

• Treatment group = “Cyclo-Z containing 23 mg zinc plus 6 mg CHP”: (total number of tablets taken) * 6 mg

• Treatment group = “Cyclo-Z containing 23 mg zinc plus 15 mg CHP”: (total number of tablets taken) * 15 mg

• Treatment group = “Placebo”: 0 mg

The total number of tablets taken and the total dose of study medication taken will be summarized using descriptive statistics.

5.5 Interim Analysis

No planned analyses were planned for this Phase 2 study.



5.6 Analysis of Other Relevant Variables Population pharmacokinetics - CHP

In a subset of subjects, Cyclic dipeptide (his-pro) (CHP) levels in blood will be measured for evaluation of population pharmacokinetics. CHP levels [ng/mL] will be provided by Celerion to FGK and will be described using basic statistics by visit and treatment group. No statistical tests will be done. Population pharmacokinetics - zinc

In the same subset of subjects, zinc levels in blood will be measured. Along with the CHP levels, zinc levels will be provided by Celerion to FGK and will be described using basic statistics by visit and treatment group. The values received by Celerion will be converted to ug/dL before inclusion in the table. No statistical tests will be done.

5.7 Important Protocol Deviations

Important protocol deviations (IPDs) (in this study we call them as ”major“) will be listed for all randomized subjects (FAS) and summarized by treatment group. All IPDs related to study inclusion and exclusion criteria, conduct of the trial, subject management or subject assessment will be described in detail. The IPDs will be prospectively defined in the study specific monitoring requirements. Some of the protocol deviations that may not be prospectively identified may later be considered important by the Sponsor and InClin (CRO) study team, in which case they will be also reported. The IPDs will be captured from the clinical trial database along with an excel file managed by the study clinical trial manager (CTM) at InClin. The excel file generated by the CTM will be converted in a domain of the study data tabulation model (STDM) as a source in addition to other necessary SDTM domains to define PPS. All protocol deviations occurred during this study as defined and categorized during a blinded data review (see Section 5.11) will be summarized by treatment group.



Subjects who have protocol deviations will be categorized as follows and will be tabulated.

Protocol Deviation Guidelines Major Minor 1. Adverse Event

Adverse event not followed to resolution or stabilization. X

Serious adverse event is not reported and/or followed to resolution or stabilization. X

2. Inadequate/Incomplete Source Document and/or Study Logs

Insufficient source document(s) to support study data entered into EDC. Becomes significant if site is routinely missing source documentation or they are missing confirmation of inclusion and exclusion criteria and/or primary endpoints of eligibility.

X

Laboratory results not reviewed and signed by investigator within two weeks of receipt. X

Drug dispensation of the study drug is not adequately documented to clearly establish that the subject received correct study drug or dose. X

3. Inclusion/Exclusion Deviations

Subject did not meet all inclusion criteria or met one of the exclusion criteria or was inappropriately re-screened. Refer to current protocol for full list of inclusion and exclusion criteria. (Specify which criterion or criteria was/were met/not met.)

X

4. Informed Consent Issues

An ICF was not signed or not completed prior to any study-related procedure. X

Subject was randomized but the informed consent date was missing. X

Informed consent process not properly followed or documented (i.e., timing not noted, copy not given to subject, missing signature, missing pages). An outdated ICF version was administered.

X

5. Laboratory Issues

Part of the safety lab test results are missing. (Provide list of which test/analyte(s) are missing.) Becomes significant if no safety testing was performed at a visit where indicated or individual analytes are missed at multiple visits.

X

6. PK Issues

One or more of the PK samples (urine or blood) were not collected or were not drawn appropriately. One or more PK samples were not processed, labeled, or shipped appropriately to Central lab. Back-up samples not stored at the site.

X

7. Pregnancy

In the event of a subject pregnancy, a Pregnancy report form is not completed. Study drug is not discontinued immediately or subject is not followed for safety and pregnancy outcome according to the Protocol.

X



8. Procedure / Assessment Not Done

Any protocol required study visit assessments (e.g. vital signs, physical exam, ECG) were not performed or were performed outside specified protocol window. Becomes significant if the same assessments are repeatedly missed and/or out of window.

X

9. Prohibited Concomitant Medication Taken During Study

Subject took a prohibited medication/treatment X

10. Randomization Error

Subject was randomized incorrectly. X

Subject was not registered in IWRS. X

11. Study Treatment Administration/Dispensing/Compliance

Subject received an incorrect dose of study drug. X

Subject missed a dose of study drug. X

Subject’s overall dosing compliance was ≤ 70% or ≥ 120% X

Interval between doses fell outside specified protocol window. X

Study drug exposed to a temperature excursion was administered. (Unless excursion was previously approved by sponsor) X

12. Visits/Procedures Out of Window

Any protocol required laboratory assessments were not performed or were performed outside specified protocol window. X

If other protocol deviations are identified during the study, it will be discussed during the study and will be categorized and tabulated accordingly. Individual subjects with protocol deviations will be listed by site. No inferential assessments will be performed on protocol deviation data.

5.8 Analysis of Subgroup

Subgroup analyses of the treatment interaction for important factors, including age at baseline group (<65 years old, ≥65 years old), gender, race, duration of diabetes at baseline (<median, ≥median), HbA1c at baseline group I (<8%, ≥8%), BMI at baseline group I [kg/m2] (<median, ≥median), BMI at baseline group II [kg/m2] (<30, ≥30), will be conducted for the primary endpoint (change in HbA1c from baseline at week 24). Duration of diabetes at baseline will be calculated as (date of screening visit) – (date of onset of diabetes on medical history page). In case of incomplete day of onset, the day will be imputed as 01. In case of missing day and month of onset, the day and month will be imputed as 01JAN. These subgroup analyses will be conducted using the MMRM with treatment group, visit, subgroup, treatment-by-visit, treatment-by-subgroup, visit-by-subgroup, and treatment-by-visit-by-subgroup as fixed effects, baseline HbA1c as a covariate. If the MMRM fails to converge, the corresponding analysis of covariance (ANCOVA) or ANOVA model (LOCF) will be used.



When analyzing baseline HbA1c as a subgroup, the baseline HbA1c will not be included as a covariate to avoid collinearity. Absolute HbA1c values and absolute changes in HbA1c from baseline will be summarized by visit, treatment group and separately by age at baseline group (<65 years old, ≥65 years old), gender, race, HbA1c at baseline group II [%] (≤ 7.0, 7.0 < HbA1c ≤ 8.0, 8.0 < HbA1c ≤ 9.0, > 9.0), BMI at baseline group III [kg/m2] (< 25, 25 to < 30, 30 to < 35, ≥ 35), and duration of diabetes at baseline (<median, ≥median) using descriptive statistics. Additionally all primary analyses of all primary, secondary and exploratory endpoints will be repeated based on the subjects in the two subgroups obesity, and SAE cases excluded group. The subgroup obesity consists of all subjects with baseline BMI ≥ 30 kg/m². The subgroup SAE cases excluded group consists of all subjects that did not experience an SAE. For all primary, secondary and exploratory endpoints absolute values and absolute changes from baseline will be summarized by visit and treatment group separately for the subjects in the two subgroups obesity and SAE cases excluded group. TEAE exceeding 5% in any group will be summarized descriptively for the subjects with the top quartile of the body weight reduction from baseline at week 24 by treatment group. First, out of all subjects of the SAF, those subjects with the top quartile of the body weight reduction from baseline at week 24 will be identified. Body weight reduction from baseline at week 24 is defined as a negative value for change in body weight from baseline at week 24. This will result in x subjects. Second, out of all TEAEs those TEAEs that were experienced by

• at least 5% of the subjects in the high dose group or • at least 5% of the subjects in the low dose group or • at least 5% of the subjects in the placebo group will be identified.

This will result in y types of TEAEs (identifiably by preferred term). For those x subjects meeting above definition, the y types of TEAEs will be tabulated by SOC and PT (MedDRA) along with the number of entries, as well as the number and rate of affected subjects for each treatment group. Other exploratory subgroup analyses may be performed as seemed appropriate.

5.9 Analysis of Dose Response

The dose response analysis will be conducted to see whether there is any dose response among 3 treatment groups using dose response contrast values for the change from baseline in HbA1c and Body Weight (for obesity subjects only). The obesity is defined as subjects whose BMI is ≥30 kg/m² at baseline. A linear contrast (-1, 0, 1) and a dose adjusted contrast that is adjusted for different sample sizes and doses (Ruberg 1995) will be used. Specifically, the coefficients of the contrast will be calculated using the following equation:



𝐶𝐶𝑗𝑗 = 𝑛𝑛𝑗𝑗 ��𝑑𝑑𝑗𝑗𝛴𝛴𝑛𝑛𝑖𝑖

� −𝛴𝛴𝑛𝑛𝑖𝑖𝑑𝑑𝑖𝑖(𝛴𝛴𝑛𝑛𝑖𝑖)2

�

where ni, i=1,2,3 is defined as the sample size for each of the treatments as Placebo, Cyclo-Z containing 23 mg zinc plus 6 mg CHP, and Cyclo-Z containing 23 mg zinc plus 15 mg CHP respectively; di, i=1,2,3 is defined as the CHP dose of 0 mg, 6 mg, and 15 mg for each of the treatments: Placebo, Cyclo-Z containing 23 mg zinc plus 6 mg CHP, and Cyclo-Z containing 23 mg zinc plus 15 mg CHP; Σni = n1 + n2 + n3; and Σnidi = n1*d1 + n2*d2 + n3*d3. In the following, sample SAS code is given: proc mixed data=FINAL(where=(timepoint>1)); class trt(ref='d') timepoint site subjid; model chg = trt timepoint trt*timepoint site base/ s ddfm=kr; repeated timepoint / subject=subjid type=un rcorr; lsmeans trt*timepoint / pdiff cl alpha=0.05; estimate 'doseresp' trt -1 0 1 /e; estimate "doserespadj" trt &c1 &c2 &c3 /e; ods output lsmeans=ls diffs=lsdiff estimates=sscoeff; run;

An unstructured covariance structure (in SAS, type=un) will be used to model the within subject errors. If this model fails to converge, then the following covariance structure will be tested in order:

• autoregressive [type=AR(1)] • compound symmetry (type=cs)

The first covariance structure that converges will be used. If the model does not converge, the autoregressive covariance structure will be tried [type=un to be replaced by type=AR(1)]. If the model with the autoregressive covariance structure does not converge, then a compound symmetry covariance structure will be tried [type=un to be replaced by type=cs]. If the model with the compound symmetry covariance structure does also not converge, the interaction effect will be dropped. If this still does not lead to convergence, the model cannot be applied. Before application of the MMRM, it will be checked if the subject specific residuals are approximately normally distributed. If there are no major violations of the normal distribution the model can be applied.

5.10 Analysis of Linear Trend

The MANOVA (Multivariate Analysis of Variance) will be used to explore whether there are any linear relationships among all pairs of dependent variables [i.e., changes from baseline in HbA1c and Body Weight (for obesity subjects only) for each subject] by visit. The MANOVA will be used including the efficacy endpoints as dependent variables, and treatment group as explanatory variable for each visit. The obesity is defined as subjects whose BMI is ≥30 kg/m² at baseline. The SAS GLM procedure with the MANOVA statement will be used for this analysis.



Two separate MANOVA models will be calculated:

1) MANOVA for the change from Baseline in HbA1c [%] and Body Weight [kg] for Cyclo-Z (high Dose) vs. Placebo by each visit AND

2) MANOVA for the change from Baseline in HbA1c [%] and Body Weight [kg] for Cyclo-Z (Low Dose) vs. Placebo by each visit

In the following, sample SAS code is given: proc GLM data= FINAL(where=(timepoint>1)); class trt; model chg = trt; manova h=trt / printe printh; by timepoint; ods output ErrorSSCP=CORR; run; Note: where “chg” will be change from baseline in HbA1c and change from baseline in body weight (for obesity subjects only).

5.11 Data Base Lock and Blind Data Review

A database lock (closure) will be performed prior to the analysis. All parameters will be checked, as specified in the data validation plan, and all data queries will be resolved before database lock and final analysis. SDTM datasets will be used to create ADaM datasets using ADaM Implementation Guide (Version 1.0) and Analysis Data Model (Version 2.1). An ADaM specification document will be set-up as a Microsoft Excel spreadsheet, describing ADaM dataset to be created. A final define.xml will be created when the ADaM datasets and the specification documents are final. A blinded data review will be conducted prior to unblinding based on all data to check for protocol deviations and to allocate the subjects to the analysis sets. At least the following items will be examined: At least 4 weeks (= 28 days) of treatment after randomization completed Both baseline and at least 4 weeks of efficacy data available, i.e. at least one of the parameters

used for primary, secondary or exploratory endpoints (at least one of HbA1c, FPG, plasma insulin, postprandial blood glucose level, OGTT, hyperglycemia and hypoglycemia, ADDQoL, body weight, BMI, IDE, serum parameters: zinc, adiponectin, C-peptide, glucagon, leptin, urine parameters: zinc, glucose, microalbumin, copper)

Informed consent not provided Informed consent date not before baseline date or any other study related procedure Premature withdrawal/ early termination Any visit window violations Any violation of inclusion/ exclusion criteria Use of excluded concomitant medications [Listing of CMs needs to be searched for excluded

CMs by study medical monitor]



Overall compliance rate as ≤ 70% or ≥ 120% Incorrect allocation to treatment group [Check if project management/ monitors are aware of any

incorrect allocation/ use of wrong bottle; any CHP concentration measured for placebo subjects?] Unblinding within the conduct of the study [Check if project management is aware of any

unblinding] The protocol deviations will be identified and categorized as minor or major prior to unblinding. These evaluations and assessments will be done together and in agreement with the study team, including the Sponsor and InClin, however FGK will provide the study team with the appropriate subject listings. Data review can be done via a telephone conference or in writing. The assignment of subjects to the FAS, EAS, PPS, and the SAF will be done prior to unblinding. Data unblinding based on the randomization listing and the analysis will be done after database lock. Prior to database lock a blinded data review will be conducted and data review minutes will be signed by the Sponsor, InClin, and FGK. The affiliation of subjects to the treatment groups will be done after unblinding.

5.12 Miscellaneous

Continuous variables will be presented using number of subjects with non-missing observations (N), mean, standard deviation (SD), median, minimum (min), maximum (max), 1st quartile (Q1) and 3rd quartile (Q3). In the description of the tables this will be denoted by ”basic statistics“. For continuous efficacy endpoints the basic statistics will additionally include the standard error of the mean (SEM) and the two-sided 95% confidence interval of the mean.

In general, min and max will be presented to the same level of precision as the raw data; means and medians, and quartiles will be presented to one further decimal place. SD will be presented to two further decimal places. Categorical variables will be presented by frequency tables, using number and percentage. Percentages will be presented to one decimal place. P-values will be reported to four decimal places and if p-values are smaller than 0.0001 they will be written as ‘< 0.0001’.

The listings are always sorted by treatment group, site, and subject. If a different sorting order should be used for some listings, this will be remarked separately. The variables for the special listings are explicitly given in the table shells of listings. Enrolled but not treated subjects (e.g. withdrawal before treatment) and screening failures will be included in tables and listings describing disposition of subjects, analysis sets and inclusion/ exclusion criteria as well as listings for subject demographics, time schedules (visits), and protocol deviations. The listings including all screened subjects will be sorted by treatment group as treated.



The following title will be used for all generated tables, listings, and figures: NMP-CYZ-P2-002 Page # of # Table/Listing/Figure NNN: Description of contents Subtitle for description of contents - if applicable Analysis set [if not already included in the title]

The numbering NNN of the tables/listings/graphs will be stated in the detailed description (Appendix A).

The following footnote will be used for all generated tables, listings, and graphs: Date: Actual date (ddmmmyyyy) time (hh:mm) Program: Name of program

The statistical evaluation will be performed using SAS® version 9.4 or higher.



6 Changes from Protocol

In the following any changes on statistical aspects as described in the protocol are given: The primary efficacy analysis for the primary endpoint described in section 8.1.3 of the protocol

needed clarification and is clarified in the SAP.

Text in protocol: The primary endpoint will be tested for each Cyclo-Z treatment group compared to the placebo group sequentially at a 2-sided significance level of 0.05 in the order of Cyclo-Z 15 mg CHP (high dose) vs. placebo, Cyclo-Z 6 mg CHP (low dose) vs. placebo. The lower dose group will be tested only when the higher dose group is considered statistically significant.

Text in SAP: The primary efficacy endpoint will be tested for each Cyclo-Z treatment group compared to the placebo group at a 2-sided significance level of 0.05; Cyclo-Z 15 mg CHP (high dose) vs. placebo, Cyclo-Z 6 mg CHP (low dose) vs. placebo. The comparison of Cyclo-Z containing 23 mg zinc plus 15 mg CHP (high dose) vs. Cyclo-Z containing 23 mg zinc plus 6 mg CHP (low dose) will be conducted to explore the difference in the two active doses at the α = 0.05 level of significance. No sequential testing will be performed in this Phase 2 exploratory study.

The sensitivity analyses for the primary endpoint described in section 8.1.3 of the protocol needed clarification and are clarified in the SAP. The same sensitivity analyses should be done for the secondary continuous endpoints as well according to the protocol. Text in protocol: (1) the same analyses using the last observation carried forward (LOCF) to handle missing

data with analysis of covariance (ANCOVA) model, and

(2) the same analysis (repeated measures linear mixed effects model) without imputation of missing data using the PPS.

Text in SAP: The repeated measures linear mixed effects model will be written as mixed-model repeated measures (MMRM) in SAP and extra 2 sensitivity methods will be added. (1) The same analysis (MMRM) using LOCF method to handle missing data (using the EAS).

(2) The same analysis (MMRM) without imputation of missing data using the PPS.

(3) Sensitivity analysis with a tipping-point approach using the EAS.

This sensitivity analysis is in multiple imputation under the MNAR assumption by searching for a tipping point that reverses the study conclusion.

(4) Sensitivity analysis with control-based pattern imputation using the EAS.



This sensitivity analysis is in multiple imputation under the MNAR assumption by creating control-based pattern imputation.

For the sensitivity analyses of secondary efficacy endpoints (binomial), the clarification has been added using the analysis population set. (1) Marginal model in form of generalized estimating equations (GEE) approach without

imputation of missing data using the EAS.

(2) CMH test stratified by site (missing data are imputed as non-response) using the PPS. (3) Logistic regression model (missing data are imputed as non-response) using the EAS.

The analysis for the secondary efficacy endpoint ”Change in score of ADDQoL Questionnaire

from baseline at week 24” described in section 8.1.4 of the protocol needed clarification and is clarified in the SAP.

Text in SAP: Since there are no repeated post-baseline measurements, an analysis of covariance (ANCOVA) model, with factors treatment group and site, and baseline HbA1c as a (linear) covariate using the EAS will be used to compare treatment groups for this endpoint. That means that three separate models will be fit (one including subjects of the high dose group and the placebo group, and one including subjects of the low dose group and the placebo group, the last one including subjects of the high dose group and the low group). Estimates for least square means (LS means) and difference in LS means between the two treatment groups along with 95%-confidence intervals derived from the above model will be given. In addition, the p-value for testing this null hypothesis will be given. The following sensitivity analyses will be used: 1) ANCOVA using LOCF method to handle missing data using the EAS. 2) ANCOVA without imputation of missing data using the PPS.

Exploratory Efficacy Endpoints and Analysis of Exploratory Efficacy Endpoints – Plasma Insulin-

degrading enzyme (IDE) has been clarified.

Text in SAP: Insulin-degrading enzyme (IDE) will be measured as follows (note: serum samples, not plasma, were validated for IDE measurements).



7 References 1. ADaM Implementation Guide (version 1.0) and Analysis Data Model (version 2.1)

2. https://www.cdisc.org/.../adam/adam_implementation_guide_v1.0.pdf

3. NMP-CYZ-P2-002 Clinical Study Protocol (version 1.0 of 05 December 2017)

4. Yang Yuan, Sensitivity Analysis in Multiple Imputation for Missing Data, Paper SAS270-2014 SAS

Institute Inc.

5. Ratitch, B. and O’Kelly, M. (2011), “Implementation of Pattern-Mixture Models Using Standard SAS/STAT Procedures,” in Proceedings of PharmaSUG 2011 (Pharmaceutical Industry SAS Users Group), SP04, Nashville.

6. Ruberg SJ. Dose Response Studies. II. Analysis and Interpretation. J Biopharm Stat 5(1), 15-42 (1995).

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