COMPARE RENAL FUNCTION PRESERVATION OUTCOME OF SGLT2 INHIBITOR VS DPP4 PDB16 INHIBITOR IN PATIENTS WITH TYPE 2 DIABETES: A RETROSPECTIVE COHORT STUDY OF ISPOR Europe 2018 JAPANESE COMMERCIAL DATABASE WITH ADVANCED ANALYTICS APPROACH 10-14 November Fang Liz ZHOU1, Hirotaka WATADA2, Yuki TAJIMA3, Mathilde BERTHELOT4, Dian KANG4, Cyril ESNAULT4, Yujin SHUTO3, Hiroshi MAEGAWA5, Daisuke KOYA6 Barcelona, Spain 1Sanofi, Bridgewater, NJ, USA, 2Juntendo University Graduate School of Medicine, Tokyo, Japan, 3Sanofi K.K., Tokyo, Japan, 4Quinten, Paris, France, 5Shiga University of Medical Science, Otsu, Japan, 6Kanazawa Medical University, Uchinada, Japan

I INTRODUCTION M ETHODS Figure 1: Overview of the Methodology Used for the Analysis

• Background: • Data Source: Medical Data Vision database (EHR with claims - Japan) 2017 update Raw MDV Data • Tofogliflozin, a highly selective sodium/glucose cotransporter 2 inhibitor (SGLT2i), – MDV Database is based on health claims data and administrative data or Diagnosis Procedure Combination (DPC) data for more than 20 million 1 patients from over 300 Japanese acute hospitals since April 2010. was approved in Japan for Type 2 Diabetes Mellitus (T2DM) in May 2014 . It is Data Flattening, Consolidation and Enrichment marketed as Apleway/Deberza, by Sanofi K.K./Kowa respectively. Five other – Data acquired: 967,362 patients with diagnosis of diabetes mellitus (ICD 10: E10-E14) and any anti-diabetic medications (ATC Code A10) in the SGLT2is are marketed in Japan: Ipragliflozin, Dapagliflozin, Luseogliflozin, MDV (Apr. 2014- Sep. 2017) Canagliflozin and Empagliflozin. – Available data: age, sex, date of diagnosis, disease code, ICD-10 code, drug name, date of prescription, medical procedure, date of visit, laboratory test results (partially available for particular medical institutes) Randomly sample the database with a Learning dataset (70%) Global matrix • Study Rationale: stratification on the class and on Regimen • Endpoints and Outcomes: Validation dataset (30%) – Previous outcome trials and meta-analysis in T2DM patients have showed that – Renal Function Preservation (RFP) significant renal protection was observed of SGLT2i over placebo2. • The definition for RFP was defined as no change or a positive change in eGFR from baseline to LOCF. – However, SGLT2i comparison vs DPP4 inhibitor (DPP4i) has been mostly focused on • Baseline eGFR was the last eGFR measurement between 6 months and the start of treatment. Select the best profiles for validation HbA1c, FPG, body weight and hypoglycemia, instead of eGFR improvement. As well, • LOCF eGFR measurement was measured during follow-up under treatment and in the defined time window: 1 year after baseline ± 3 months. • Qfinder© : Profile’s generation and priorization (coverage, Learning dataset clear characterization on who would benefit the most from SGLT2i (RFP) therapy • If for some patients have several eGFR values, we considered the ones closest to 1-year. signal, homogeneity, significance, influence taking into

(class effect) compared to DPP4i in terms of renal function preservation is still account confounding factors) – Glycaemic Control • Clinical selection lacking in Japan. Additionally, profile of T2DM patients who have the highest benefit • We checked a superior or equivalent effect on HbA1c change for SGLT2i compared to DPP4i (through a Gaussian regression on the HbA1c change from Tofogliflozin among all the SGLT2i patients has not been explored. taking into account confounding factors) – Research addressing these questions could improve understanding of SGLT2i and • Data Analysis and Statistical Methods Validation dataset tofogliflozin treatment benefit in Japanese T2DM patient population and provide Statistical validation of profiles – Descriptive Analysis: additional insight to support clinical management of these patients. Study variables, baseline covariates and outcomes of interest were described (counts, percent, mean, standard deviations) comparing SGLT2i and DPP4i cohorts. P-values based on t-test and chi-square test for continuous data and categorical data, respectively, were used to detect statistical Expert review differences across cohorts. OBJECTIVE – Multivariate analysis: All Qfinder © profiles: This study aimed to evaluate renal function preservation • RFP was compared between the 2 classes using logistic regression adjusted for a propensity score based on socio-demographic and clinical Are identified through a comprehensive combinatory exploration of databases, without pre- (RFP) of SGLT2i vs DPP4i in Japanese type 2 diabetes covariates. defined assumptions Are controlled with regard to confounding factors • Q-Finder (proprietary supervised learning algorithm) was implemented to identify profiles of patients who showed additional RFP benefit of SGLT2i patients and identify patient sub-groups showing additional Ensure at least a comparable level of glycaemic control between treatment classes vs DPP4i. At profile level, RFP was assessed using logistic regression and HbA1c change using a Gaussian regression, both adjusted for RFP benefit of SGLT2i compared to DPP4i treatment using Are significant and robust through a validation analysis on an independent database confounding factors. taking into account multiple testing using Benjamin-Hochberg procedure real world data. • Other details were shown in Figure 1. Are submitted to sensitivity analyses

R ESULTS Global Results • Adjusted for propensity score based on baseline confounding factors, global analysis showed significant higher odds of achieving RFP with SGLT2i • Study population: class compared to DPP4i class. In terms of HbA1c reduction, there was no significant difference between SGLT2i class and DPP4i controlling for - A total of 5,247 (SGLT2i group: 990, DPP4i group:4,257) patients are included in the analysis after inclusion/exclusion criteria are applied confounders. (Figure 2).

• Baseline characteristics: Figure 3: Global Results - Baseline characteristics including demographics, laboratory results, medical history and treatments are presented by cohorts (Table 1). Percentage of patients with Renal Function Preservation Mean Change of HbA1c aOR*=1.27 [1.05,1.53] ∆am**(%)=0.0026 [-0.0106, 0.0159] Figure 2: Study Population – Patient Selection p-value=0.0116 p-value=0.6900 50 MDV patients 42% SGLT2i DPP4i 40 38% 772121 T2DM confirmed diagnosis and no T1DM diagnosis 0

Patients with at least 2 valid creatinine values 30 -0.02 87473 (between 0.2 and 20 mg/dL) -0.04 20 SGTL2i patients DPP4i patients -0.06

1st SGTL2i intake after 1st May 2014 and no Patients never treated with SGLT2i during the 10 -0.08 SGTL2i use in baseline. 1st intake is 4325 83105 identification period. 1st intake is considered -0.086% -0.10 -0.088% considered as index date as index date 0 SGLT2i DPP4i Age ≥ 18 at index date 4325 83105 Age ≥ 18 at index date Numbers on the graph are not the adjusted aOR*: adjusted OR At least 1 eGFR during 6 months baseline and 1st DPP4i intake after 1st May 2014 and no ∆am**: Delta of adjusted mean 1716 27731 1 eGFR value 9-15 months after index date DPP4i use in baseline

Continuous treatment with SGLT2i from index At least 1 eGFR during 6 months baseline and 1097 9739 date to LOCF eGFR date 1 eGFR value 9-15 months after index date Profile Results • 150 profiles were considered for the Q-finder analysis. Active in MDV database for at least 6 months Continuous treatment with DPP4i from index 1062 5094 prior to index date date to LOCF eGFR date • 43 profiles showed significant better renal function preservation as ”Class Effect”, namely profiles with a SGLT2i effect higher than DPP4i effect within the profile and also as “Class Benefit”, namely profiles with SGLT2i effect versus DPP4i effect higher within the profile than outside the profile. The Patients who are not newly initiated Active in MDV database for at least 6 months 990 4257 profiles were reviewed by experts. SGTL2i+DPP4i post index date prior to index date • Only 12 profiles with clinical relevance were selected. Four profiles shown in Table 2 were validated taking into account multiple testing correction and adjustment on confounding factors. These profiles also shown an equivalent effect on HbA1c change of SGLT2i compared to DPP4i. Table 1: Baseline Characteristics Table 2: Profile Results SGTL2i patients DPP4i patients P-value Total patients Indicators on learning dataset Indicators on validation dataset Overall N=990 N=4257 N=5247 Class Benefit Class Effect Size Class Effect Class Benefit N / Mean % / SD N / Mean % / SD N / Mean % / SD Profile Criteria Size within vs without within the profile Coverage within the profile within vs without the profile Coverage the profile Gender aOR* P-value aOR* P-value aOR* P-value P-value BH aOR* P-value P-value BH Female 362 37% 1686 40% 2048 39% Hyperlipidemia = NO 724 296 Age(Mean, SD) 58 12 69 11 <0.0001 67 12 2.2 0.0009 2.1 0.0025 2.7 0.0053 0.0639 2.8 0.0054 0.0646 eGFR ≥ 79 20% 19% Age Group eGFR ≥ 79 556 238 2.7 0.0023 2.4 0.0074 3.0 0.0184 0.0752 2.9 0.0293 0.1390 18-34 25 3% 24 1% 49 1% Disease duration (years) ≤ 1.2 15% 15% eGFR ≥ 75 430 198 35-44 105 11% 87 2% 192 4% 2.3 0.0032 2.2 0.0086 2.6 0.0212 0.0752 2.5 0.0348 0.1390 Antithrombotic Agents = YES 12% 13% 45-54 243 25% 355 8% <0.0001 598 11% Hemoglobin (g/dL) (Hb) ≤ 13.4 384 176 2.7 0.0009 2.0 0.0254 3.3 0.0251 0.0752 2.6 0.0730 0.2189 55-64 295 30% 892 21% 1187 23% Low density lipoprotein C (mg/dL) (LDL-C) ≥ 95.1 10% 11% ≥65 322 33% 2899 68% 3221 61% Signal: P-value: BH: Benjamin-Hochberg BMI (Mean, SD) 29 7 24 5 <0.0001 25 5 •aOR: adjusted OR on confounding factors ≥ 0.1 < 0.1 Disease Duration •Class Effect: Looking for profiles with SGLT2i effect higher than DPP4i effect within the profile <1 year 124 13% 1993 47% 2117 40% •Class Benefit: looking for SGLT2i effect versus DPP4i effect higher within the profile than outside the profile a×d 1-9 years 594 60% 1445 34% <0.0001 2039 39% RFP No RFP RFP No RFP Odd-ratio for class effect = b×c ≥10 years 272 27% 819 19% 1091 21% a×d SGLT2i a b SGLT2i a’ b’ b×c Odd-ratio for class benefit = Weight (Mean, SD) 78 22 61 16 <0.0001 62 17 a'×d' DPP4i c d DPP4i c’ d’ b'×c' Laboratory values (Mean, SD) (units) eGFR ml/min 76.0 22.5 65.7 26.2 <0.0001 67.6 25.8 HbA1c NGSP % 8.3 1.4 7.7 1.6 <0.0001 7.8 1.5 ISCUSSION Creatinine mg/dL 0.8 0.4 1.2 1.5 <0.0001 1.1 1.4 D Albumin g/dL 4.2 0.4 3.8 0.7 <0.0001 3.9 0.7 • Baseline characteristics of DPP4i treated group and SGLT2i treated group were quite different, which reflected the treatment pattern of real world Uric Acid mg/dL 5.6 1.4 5.5 1.6 0.1392 5.5 1.6 setting. Urea Nitrogen mg/dL 15.6 6.0 19.2 11.5 <0.0001 18.5 10.9 • RFP was defined as no change or a positive change in eGFR in 1 year, considering the number of patients to show RFP in MDV database in order HbA1c to apply this analytical methodology to obtain profiles. 4 <6,5 47 5% 593 14% 640 12% • In recent paper , the 30 or 40% decrease in eGFR over 2 or 3 years was indicated to be adopted as surrogate endpoints of progression to End- 6,5-6,9 74 7% 665 16% 739 14% stage renal disease in Japanese CKD patients. The relationship between RFP in our study and the renal protection is not comparable, since <0.0001 definition of outcome and follow-up period is different between studies. ≥7 864 87% 2670 63% 3534 67% • Although there were limitations mentioned below, the treatment of SGLT2i may contribute RFP in patients with relatively sustained renal function missing 5 1% 329 8% 334 6% and short duration of T2DM when compared with the DPP4i, based on the 4 profiles obtained in this study. Medical History (Comorbidities) • The analytical method used in this study was indicated to become potential methodology to identify patient sub-groups benefit from certain Charlson Comorbidity Index 2 2.0 2 2.3 <0.0001 2 2.3 intervention using large volume of real world data. Hyperlipidemia 687 69% 1656 39% <0.0001 2343 45% Hypertension 707 71% 2062 48% <0.0001 2769 53% Nephropathy 97 10% 164 4% <0.0001 261 5% L IMITATION Neuropathy 625 63% 1372 32% <0.0001 1997 38% • Confounding or bias might not be completely excluded after using propensity-score in regression model. Regimen • MDV database consists of patients from large acute hospitals, so might not be representative of the broader population with T2DM in Japan. Naive 44 4% 1962 46% 2006 38% • Diagnoses were based on ICD-10 code; as claim data might not link the actual diagnosis name, this could have resulted in misclassification. 1 OADs 124 13% 594 14% 718 14% <0.0001 • The study used a fixed follow-up approach, i.e., treatment for at least 9 months, could lead to selection bias. 2+ OADs 523 53% 446 10% 969 18% • MDV data was provided by hospitals and majority of them did not provide lab test to MDV. Therefore missing data in lab was due to systemic Insulin 299 30% 1255 29% 1554 30% reason for data capture. Number of prior OADs (Mean, SD) 2.2 1.2 0.5 0.8 <0.0001 0.8 1.1 Prior treatments (antidiabetics) CONCLUSION Alpha glucosidase inhibitors 208 21% 516 12% <0.0001 724 14% Biguanides 633 64% 682 16% <0.0001 1315 25% This study showed at different levels a favorable renal function preservation associated to SGLT2i treatment compared to Dipeptidyl peptidase 4 (DPP-4) inhibitors 708 72% 0 0% <0.0001 708 13% DPP4i in real-world clinical setting in Japan. Glucagon-like peptide-1 receptor (GLP-1) analogues 95 10% 26 1% <0.0001 121 2% Sulfonylureas 349 35% 624 15% <0.0001 973 19% REFERENCES 1. Kaku, K. et al. Efficacy and safety of monotherapy with the novel sodium/glucose cotransporter-2 inhibitor tofogliflozin in Japanese patients with type 2 diabetes mellitus: a Thiazolidinediones 162 16% 197 5% <0.0001 359 7% combined Phase 2 and 3 randomized, placebo-controlled, double-blind, parallel-group comparative study. Cardiovasc. Diabetol. 13, 65 (2014). Prior treatments (others) 2. Wanner, C. et. al. Empagliflozin and Progression of Kidney Disease in Type 2 Diabetes. N. Engl. J. Med. 375, 323-334 (2016). Agents Acting On The Renin-Angiotensin System 597 60% 1719 40% <0.0001 2316 44% 3. Min, S. H., Yoon, J.-H., Hahn, S. & Cho, Y. M. Comparison between SGLT2 inhibitors and DPP4 inhibitors added to insulin therapy in type 2 diabetes: a systematic review with Diuretics 160 16% 859 20% 0.0046 1019 19% indirect comparison meta-analysis. Diabetes Metab. Res. Rev. 33, (2017). 4. Kanda, E. et al. Guidelines for clinical evaluation of chronic kidney disease. Clin Exp Nephrol. 2018 Jul 13. doi: 10.1007/s10157-018-1615-x DISCLOSURE and FUNDING ̶ Hirotaka WATADA: ̶ Hiroshi MAEGAWA: ̶ Daisuke KOYA: • Funding: • Honoraria for scientific lectures: MSD, Eli Lilly, Takeda, Novartis, • Honoraria for scientific lectures: MSD, Nippon Boehringer Ingelheim, Astellas, Ono, Mitsubishi • Honoraria for scientific lectures: Astellas, AstraZeneca, MSD, Ono, Kyowa Hakko Kirin, Taisho- – This analysis was funded by Sanofi. Sumitomo Dainippon, Sanofi, and Daiichi Sankyo. Tanabe, Sanofi, Taisho-Toyama, Takeda, Kowa Pharmaceutical, Daiichi Sankyo, AstraZeneca, Toyama, Taisho, Takeda, Mitsubishi Tanabe, Eli Lilly, Nippon Boehringer Ingelheim. • Disclosure: • Research funds: MSD, Eli Lilly, Takeda, Kowa Pharmaceutical, Sanwa Kagaku Kenkyusho, Novartis, and Eli Lilly. • Research funds: Sanwa Kagaku Kenkyusho, Site Support Institute, Mitsubishi Tanabe, Morinaga – Fang Liz ZHOU: Employee of Sanofi, USA Mochida, Sanwa Kagaku Kenkyusho, Novo Nordisk, Kissei, • Research funds: Astellas, and AstraZeneca • Grants: Astellas, AstraZeneca, Ono, Kissei, Kyowa Hakko Kirin, Kowa Pharmaceutical, Sanofi, – Yuki TAJIMA and Yujin SHUTO: Novartis, Nippon Boehringer Ingelheim, AstraZeneca, Astellas, • Grants: Takeda, Astellas, MSD, Teijin Pharma, Nippon Boehringer Ingelheim, Kyowa Hakko Johnson & Johnson, Daiichi Sankyo, Taisho-Toyama, Sumitomo Dainippon, Takeda, Mitsubishi Employees of Sanofi K.K., Japan Mitsubishi Tanabe, Sumitomo Dainippon, Abbott Japan, Sanofi, Kirin, Taisho-Toyama, Kowa Pharmaceutical, Ono, Daiichi Sankyo, Sanofi, Mitsubishi Tanabe, Tanabe, Japan Tobacco, Novo Nordisk, Bayer, Pfizer. Pfizer, and Daiichi Sankyo. – Mathilde BERTHELOT, Dian KANG and Cyril ESNAULT: Shionogi, Chugai, Sunstar, Otsuka Pharmaceutical, Sanwa Kagaku Kenkyusho, Sumitomo • Courses endowed by companies: Ono, Kyowa Hakko Kirin, Taisho-Toyama, Mitsubishi Tanabe, Employees of Quinten, France Dainippon, Eisai, Pfizer, Novo Nordisk, Mochida, Novartis, and Bristol-Myers Squibb. Nippon Boehringer Ingelheim