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Sulfonylurea Compounds

Sulfonylurea compounds are a class of medications commonly used to treat type 2 diabetes.
These compounds work by stimulating the release of insulin from the pancreas, helping to lower blood glucose levels.
PubCompare.ai's AI-driven platform allows researchers to explore the latest literature, preprints, and patents related to sulfonylurea compounds, enhancing the reproducibility and accuracy of their studies.
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Discover new insights and advance your understanding of this important class of diabetes therapeutics.

Most cited protocols related to «Sulfonylurea Compounds»

To measure adherence, we first created a supply diary for each patient-day by stringing together consecutive fills of each medication class being studied based on dispensing dates and reported days' supply. All drugs dispensed within a therapeutic class (eg, glyburide and glipizide in the sulfonylurea class) were considered interchangeable. When a dispensing occurred before the previous dispensing should have run out, utilization of the new medication was assumed to begin the day after the end of the old dispensing. If a patient accumulated more than 180 days' supply on a given day, the accumulated supply was truncated at 180 days.
We estimated adherence by calculating the PDC for each drug class prescribed to each patient from the index date to the end of our assessment period (December 31, 2006) using 2 different methods that differ in how the denominator of the adherence measure is calculated (Figure 1). We defined prescription-based adherence based on medication possession for all drugs within a class during the time between 2 prescriptions; that is, the number of days of medication supplied between the first and last prescriptions in a given period (numerator) was divided by the number of days between these 2 prescriptions plus the accumulated days supplied from the last prescription (denominator). In contrast, we defined interval-based adherence based on medication possession during the interval from the index date to December 31, 2006. In this way, the number of days of medication supplied throughout the period (numerator) was divided by the number of days in it (denominator).
Publication 2009
Glipizide Glyburide Patients Pharmaceutical Preparations Sulfonylurea Compounds Therapeutics
All the primers used in this study are listed in S1 Table. The PCR-amplified regions and ligation junctions were confirmed by sequence analysis for all vectors.
Acetolactate synthase (ALS) is a key enzyme in the biosynthesis of the branched-chain amino acids leucine, isoleucine, and valine. Sulfonylurea herbicides, e.g. CS, bind reversibly to the ALS-FAD-thiamine pyrophosphate-Mg2+-decarboxylated pyruvate complex and also compete for the second pyruvate binding site in ALS [29 (link)]. Mutagenesis at these sites in ALS confers tolerance to sulfonylurea herbicides. For example, mALSs containing mutations at P197H, R198S, and W574L conferred resistance to CS in Arabidopsis [30 (link)]. To generate a CS-resistance marker gene for use in M. polymorpha, its ALS sequence was mutagenized to contain corresponding mutations (P207S/R208S/W582L) to those described above, as follows. First, M. polymorpha ALS cDNA was amplified by RT-PCR with the primer set ALS-P1 and ALS-P2 using first-strand cDNA synthesized from a 10-day-old thallus. The resultant ALS cDNA was cloned into the pENTR/D-TOPO vector (Life Technologies, Gaithersburg, MD, USA). A point mutation at W582L in the ALS sequence was introduced by PCR-based site-directed mutagenesis with the primer pair ALS-W582L-F and ALS-W582L-R, using the ALS cDNA plasmid as the template. The PCR product was digested with DpnI, and the digested PCR product was transformed into Escherichia coli competent cells (DH5α). The mutation in ALS was confirmed by sequencing the resultant plasmid. The mALS was prepared by repeating the same procedure with the primer set ALS-P207S-R208S-F and ALS-P207S-R208S-F using the W582L-mutated ALS as a template. The mALS cDNA was cloned into pCAMBIA1300 to replace the hpt gene, generating the plasmid pCAMBIA1300-mALS.
The marker cassettes pro35S×2:hpt:ter35S, pro35S×2:aacC1:ter35S, pro35S×2:mALS:ter35S, and pro35S×2:nptII:ter35S were prepared by PCR with the primer set Marker_LinF and Marker_RinF using pCAMBIA1300, pPZP221, pCAMBIA1300-mALS, and pCAMBIA2301, respectively, as the template. The marker cassettes were cloned into EcoRI-digested pGWB400 [31 (link)] using an In-Fusion HD cloning kit (Clontech, Mountain View, CA, USA) to replace the proNOS:nptII:terNOS cassette, generating pMpGWB100, pMpGWB200, pMpGWB300, and pMpGWB400, respectively. The E. coli DH5α cells harboring these plasmids were selected on LB medium containing 100 mg/l spectinomycin.
The Gateway-compatible binary vectors pMpGWBs were constructed using the same strategy as that used by Nakagawa et al. to construct pGWBs [31 (link),32 (link)]. Detailed procedures of pMpGWBs construction are described in S1 Text. The E. coli (strain DB3.1) cells harboring pMpGWBs were selected on LB media containing 100 mg/l spectinomycin and 30 mg/l chloramphenicol.
The vector for expression of TagRFP-LTI6b was constructed as follows: the GFP-LTI6b coding sequence was PCR-amplified using the primers GFP-LTI6b_GW_L1 and GFP-LTI6b_GW_R1 and cloned into pENTR/D-TOPO. The GFP coding sequence flanked by two NotI sites in this plasmid was replaced with the TagRFP-coding sequence with two similarly flanking NotI sites, which was PCR-amplified using the primers pENTRD_NotI_TagRFP_F and TagRFP_NotI_R. This plasmid was used for LR recombination with pMpGWB103 to generate pMpGWB103-TagRFP-LTI6b.
To construct the vector for expression of SP-GFP-HDEL, the SP-GFP-HDEL-coding sequence [33 ] was PCR-amplified using the primers SP_Lc and HDEL_R and cloned into pENTR/D-TOPO. The resulting vector was used for LR recombination with pMpGWB303 to generate pMpGWB303-SP-GFP-HDEL.
To construct the vectors for expression of tdTomato-NLS and GUS under the endogenous ELONGATION FACTOR1α promoter (proEF), the 1,729-bp promoter sequence of MpEF1α [34 (link)] was amplified by PCR using the primers MpEF-P_L1 and MpEF-P_R1 and cloned into pENTR/D-TOPO. The resulting vector was used for LR recombination with pMpGWB216 and pMpGWB404 to generate pMpGWB216-proEF and pMpGWB404-proEF, respectively.
Publication 2015
Acetolactate Synthase alpha-acetolactate Amino Acids, Branched-Chain Anabolism Arabidopsis Binding Sites Cells Chloramphenicol Cloning Vectors CXXC5 protein, human Deoxyribonuclease EcoRI DNA, Complementary Enzymes Escherichia coli Genes Genes, vif Herbicides histidyl-aspartyl-glutamyl-leucine Immune Tolerance Isoleucine Leucine Ligation Mutagenesis Mutagenesis, Site-Directed Mutation Oligonucleotide Primers Open Reading Frames Plasmids Point Mutation Pyruvate Recombination, Genetic Reverse Transcriptase Polymerase Chain Reaction Spectinomycin Strains Sulfonylurea Compounds Synthase, Pyruvate tdTomato Thiamine Pyrophosphate Topotecan Valine
We assembled a cohort of patients who received pharmacy benefits through Horizon Blue Cross Blue Shield of New Jersey and who were prescribed an oral hypoglycemic agent between January 1, 2005, and December 31, 2005. We considered only those patients who had 1 or more inpatient or outpatient claims with a diagnosis of diabetes (International Classification of Diseases, Ninth Revision, Clinical Modification code 250.x) and were prescribed 2 or more classes of oral hypoglycemic medications during this period. While all oral hypoglycemics may be considered members of 1 therapeutic class, we defined classes on a mechanistic basis (ie, sulfonylurea, metformin hydrochloride, glitazones, acarbose, and meglitinides), as many patients take agents belonging to these different classes concurrently. Furthermore, this strategy allows for the generalization of our methods to patients using noninterchangeable medication classes (eg, statins, angiotensin-converting enzyme inhibitors, antiplatelets, and β-blockers after myocardial infarction).
We excluded patients who lost eligibility or did not fill any prescriptions or use any medical services in 2006. We defined an index date for each oral hypoglycemic class prescribed to each patient as the first prescription date for any member of the class during the accrual period.
We combined filled prescription data for patients in our cohort with complete paid claims data and eligibility files to create a relational database consisting of data for all filled prescriptions, procedures, inpatient and outpatient physician encounters, hospitalizations, and deaths for the patients in our cohort. Prescription information in the claims data included drug name, dosage, date dispensed, quantity dispensed, and days supplied. All traceable person-specific identifying factors were transformed into anonymous coded study numbers. The institutional review board of Brigham and Women's Hospital approved the study.
Publication 2009
Acarbose Angiotensin-Converting Enzyme Inhibitors Diabetes Mellitus Diagnosis Eligibility Determination Ethics Committees, Research Generalization, Psychological Hospitalization Hydroxymethylglutaryl-CoA Reductase Inhibitors Hypoglycemic Agents Inpatient meglitinide Metformin Hydrochloride Myocardial Infarction Outpatients Patients Pharmaceutical Preparations Physicians Sulfonylurea Compounds Therapeutics Thiazolidinediones

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Publication 2018
Adult Angiotensin-Converting Enzyme Inhibitors Blood Glucose Blood Pressure Diabetes Mellitus Glycemic Control Insulin Metformin Patients Sulfonylurea Compounds
During the placebo run-in period, participants received single-blind placebo capsules matching study drug once daily. Participants were randomised to receive canagliflozin 100 mg or 300 mg, sitagliptin 100 mg or placebo (2:2:2:1) once daily for 26 weeks. The canagliflozin 100 mg and 300 mg once-daily doses were selected based on findings from a dose-ranging, Phase 2 study in patients with type 2 diabetes on background metformin [5 (link)]; a 300 mg twice-daily regimen provided only incremental benefits vs the once-daily regimen and was therefore not selected for further development. The use of placebo as a control for the 26 week core treatment period was done in accordance with US Food and Drug Administration and European Medicines Agency regulatory guidelines [15 , 16 ]. The computer-generated randomisation schedule was prepared by the sponsor before the study. Randomisation was balanced using permuted blocks of seven and stratified by whether a participant was on metformin monotherapy or metformin plus sulfonylurea at screening. After randomisation, HbA1c and FPG values were masked to the study centres unless they met glycaemic rescue criteria. After completion of period I, the database was locked and the study was unblinded by the sponsor for regulatory filing; the participants and the study centre and local sponsor personnel remained blinded throughout period II.
Participants who completed period I then entered period II, during which those randomised to canagliflozin (100 or 300 mg) or sitagliptin 100 mg continued on those treatments while those randomised to placebo switched to sitagliptin 100 mg in a blinded fashion. During the double-blind treatment period, glycaemic rescue therapy with glimepiride (added to study drug and background metformin) was initiated if FPG >15.0 mmol/l after day 1 to week 6, >13.3 mmol/l after week 6 to week 12, and >11.1 mmol/l after week 12 to week 26. Glimepiride therapy was also started if HbA1c >8.0% (64 mmol/mol) after week 26.
Publication 2013
Canagliflozin Capsule Diabetes Mellitus, Non-Insulin-Dependent Europeans glimepiride Metformin Patients Placebos Sitagliptin Sulfonylurea Compounds Therapeutics Treatment Protocols

Most recents protocols related to «Sulfonylurea Compounds»

To mitigate risk of confounding, we assessed and adjusted for > 30 baseline covariates that were assessed in the 12-month period prior to and including the index date. These covariates included patient sociodemographics (e.g., age at medication initiation, biological sex, and race, calendar year), complications of diabetes (e.g., diabetic neuropathy, nephropathy, retinopathy), oral and injectable glucose lowering therapies (e.g., metformin, sulfonylureas, insulin), diagnosis of cardiovascular conditions (e.g., myocardial infarction, stroke, HF), and cardiovascular medication use (e.g., dispensing of β-blockers, loop diuretics, statins). Frailty status was ascertained using the claims based frailty index, and using a threshold of ≥ 0.25 to define frailty [23 (link)].
Propensity scores were estimated using a logistic regression that modelled the probability of initiating SGLT2i (exposure) versus a non-gliflozin medication (control) conditional on the baseline covariates. These propensity scores were then used to estimate stabilized inverse probability of treatment weights (IPTW) to account for imbalances in patient characteristics [24 (link)].
Publication 2023
Biopharmaceuticals Cardiovascular Agents Cardiovascular Diseases Cerebrovascular Accident Complications of Diabetes Mellitus Diabetic Neuropathies Diagnosis Glucose Hydroxymethylglutaryl-CoA Reductase Inhibitors Insulin Kidney Diseases Loop Diuretics Metformin Myocardial Infarction Patients Pharmaceutical Preparations Retinal Diseases Sodium-Glucose Transporter 2 Inhibitors Sulfonylurea Compounds
The analyses included six pivotal randomized, double-blind trials of dulaglutide 1.5 mg in participants with T2D that measured sitting SBP and DBP from vital sign data around the timeline of 6 months (week 24 to week 26). Five placebo-controlled studies were used to estimate the effects between dulaglutide 1.5 mg and placebo. AWARD-1 (NCT01064687), AWARD-5 (NCT00734474), AWARD-8 (NCT01769378), and AWARD-10 (NCT02597049) were phase 3, placebo-controlled trials which investigated the safety and glycemic efficacy of dulaglutide with various background glycemic therapies (Table 1). Ferdinand et al. (NCT01149421) was a phase 2, randomized, double-blind, placebo-controlled trial which evaluated BP and heart rate effects of dulaglutide vs. placebo in participants with T2D with and without hypertension and BP < 140/90 mmHg. In addition, AWARD-11 (NCT03495102) was a phase 3, non-placebo-controlled trial to evaluate safety and glycemic efficacy of dulaglutide 3.0 mg and 4.5 mg to dulaglutide 1.5 mg.

Study design for placebo-controlled trials included in the meta-analysis

ParametersAWARD-1AWARD-5AWARD-8AWARD-10AWARD-11Ferdinand et al
PhasePhase IIIPhase II/IIIPhase IIIPhase IIIPhase IIIPhase II
RandomizationRandomizedRandomizedRandomizedRandomizedRandomizedRandomized
BlindingBlindingDouble-blindDouble-blindDouble-blindDouble-blindDouble-blind
Primary EndpointA1cA1cA1cA1cA1c24-h SBP
Study Treatment Period52 weeks24 months24 weeks24 weeks52 weeks26 weeks
Last scheduled visit with PBO26 weeks6 months24 weeks24 weeks52 weeks (no PBO)26 weeks
Background therapy (Add-ons)Met + TZDMet monoSU monoSGLT2i with or without metforminMet monoStable OAM
Key inclusion/ exclusion criteria
 Age ≥ 18 years18–75 years ≥ 18 years ≥ 18 years ≥ 18 years ≥ 18 years
 T2D durationNA ≥ 6 monthsNANAfor ≥ 6 monthsNA
 A1c7.0–11.07.0–9.57.5–9.57.0–9.57.5–117–9.5
 BMI23–4525–40 ≤ 45 ≤ 45 ≥ 25NA
 MedicationStable OAMDiet & exercise / metformin and/or other OAMStable SUSGLT2i with or without metformin for ≥ 3 monthsStable metformin for ≥ 3 monthsOAM

BMI body mass index, NA not applicable for the study’s design, Met metformin, mono monotherapy, OAM oral antihyperglycemic medication, PBO placebo, SBP systolic blood pressure, SGLT2i sodium-glucose cotransporter-2 inhibitors, SU sulfonylurea, T2D type 2 diabetes, TZD thiazolidinediones

Publication 2023
Diabetes Mellitus, Non-Insulin-Dependent dulaglutide High Blood Pressures Hypoglycemic Agents Index, Body Mass inhibitors Metformin Pharmaceutical Preparations Placebos Rate, Heart Safety Signs, Vital SLC5A2 protein, human Sulfonylurea Compounds Systolic Pressure Therapeutics Thiazolidinediones TimeLine
We searched the database of patients with type 2 diabetes who were referred for ECG-gated coronary computed tomography angiography (CCTA) examinations (Toshiba Aquilion CT scanner, Toshiba Medical, Tochigi, Japan; SOMATOM Definition or Force, Siemens Healthineers, Forchheim, Germany) for the first time and underwent abdominal CT scans (Toshiba Aquilion CT scanner, Toshiba Medical, Tochigi, Japan; Discovery CT750 HD, General Electric Healthcare, Chicago, IL, USA; SOMATOM Definition, Siemens Healthineers, Forchheim, Germany; GE Optima CT660, General Electric Healthcare, Chicago, IL, USA) within 1 year of CCTA at Osaka University Hospital or Sumitomo Hospital between January 2000 and March 2021. A total of 411 patients met these criteria. Among these patients, we excluded patients to avoid the influence of cardiac function or the myocardial CT value of pathological conditions other than myocardial fat accumulation. The excluded patients included those with heart failure with reduced ejection fraction (≤ 40%), those with valvular heart disease and those who had received past percutaneous coronary intervention (PCI) for coronary artery disease. Moreover, we excluded patients who had liver cirrhosis, renal failure (estimated glomerular filtration rate of < 30 mL/min/1.73 m2), malignant diseases and diseases requiring glucocorticoids for the treatment of other diseases. Furthermore, it is known that changes in X-ray tube voltage affect CT attenuation values [20 (link)]. Therefore, patients who underwent CCTA or abdominal CT examinations that were not performed at 120 kV were excluded. Using these criteria, 124 patients were finally included in our analyses. The flowchart for the recruitment of the patients is shown in Additional file 1: Fig S1. Among these 124 patients, the medications for diabetes at the time of CCTA were as follows: insulin for 42 patients, glucagon-like peptide-1 (GLP-1) receptor agonists for 9 patients, sulfonylureas for 31 patients, biguanides for 43 patients, dipeptidyl peptidase-4 inhibitors for 40 patients, α-glucosidase inhibitors for 26 patients, thiazolidinediones for 13 patients, glinides for 12 patients and sodium–glucose cotransporter 2 (SGLT2) inhibitors for 3 patients. The medications for dyslipidemia at the time of CCTA were as follows: statins for 69 patients, fibrates for 9 patients, ezetimibe for 5 patients, omega-3 fatty acids for 8 patients and probucol for 1 patient.
This study was approved by the Institutional Ethics Review Boards of Osaka University Hospital and Sumitomo Hospital and was carried out in accordance with the principles of the Declaration of Helsinki. The study was announced to the public on the websites of our department at Osaka University Hospital and Sumitomo Hospital, and all patients were allowed to participate or refuse to participate in the study.
Publication 2023
Abdomen agonists alpha-Glucosidase Inhibitors Biguanides Cardiomyopathies CAT SCANNERS X RAY Computed Tomography Angiography Congestive Heart Failure Coronary Artery Disease Diabetes Mellitus Diabetes Mellitus, Non-Insulin-Dependent Dipeptidyl-Peptidase IV Inhibitors Dyslipidemias Electricity Ezetimibe Fibrates Glomerular Filtration Rate Glucagon-Like Peptide-1 Receptor Glucocorticoids Heart Hydroxymethylglutaryl-CoA Reductase Inhibitors inhibitors Insulin Kidney Failure Liver Cirrhosis Myocardium Omega-3 Fatty Acids Patients Pharmaceutical Preparations Physical Examination Probucol Radiography SLC5A2 protein, human Sulfonylurea Compounds Thiazolidinediones Valve Disease, Heart X-Ray Computed Tomography
Comparisons among the following interventions were included: insulin, metformin, sulfonylureas, thiazolidinediones (TZDs), active comparator drugs (ACDs), dipeptidyl peptidase-4 (DPP-4) inhibitors, Glucagon-like peptide-1 (GLP-1) analogues or agonists, sodium/glucose cotransporter 2 (SGLT-2) inhibitors, α-glucosidase inhibitors, meglitinides, or placebo. There are no restrictions on the combination formula such as whether to plus other agents to metformin or sulfonylureas. There are also no restrictions on different doses or frequency of the same agent. We classify all eligible drugs according to the above drug categories and because different drugs in the same category may have a variable effect, we include studies that compare drugs in a same category either. If a network meta-analysis included drugs of interest but also included drugs that were not of interest, or if multiple interventions included glycemic control by non-pharmacological methods, such studies would also be included. Interventions includes some drugs but not any drugs of interest within the list except for comparator drugs will not be included.
Publication 2023
agonists alpha-Glucosidase Inhibitors Dipeptidyl-Peptidase IV Inhibitors Glucagon-Like Peptide 1 Glycemic Control inhibitors Insulin meglitinide Metformin Pharmaceutical Preparations Placebos SLC5A2 protein, human Sulfonylurea Compounds Thiazolidinediones
By using the Oracle Empirica Signal software (Oracle Health Sciences, Austin, TX), we calculated disproportionality statistics produced by four signal detection methodologies, to assess the occurrence of therapy failure (cases) in depressed patients, in association with the exposure to at least one antidiabetic drug, defined as the following ATC Level 4 codes: A10BA Biguanides; A10BB Sulfonylureas; A10BG Thiazolidinediones; A10BH DPP4-inhibitors; A10BJ GLP-1 analogues; A10BK SGLT2 inhibitors (i.e., those agents for which preliminary evidence from literature supports our pharmacological hypothesis).
Three of these disproportionality scores, based on 2 × 2 disproportionality analysis, are well-established and currently used worldwide by several organisations for routine safety surveillance, i.e:

i) The Reporting Odds Ratio (ROR), defined as the ratio of the odds of the occurrence of therapy failure with antidiabetic drugs versus the occurrence of therapy failure without antidiabetic agents (van Manen et al., 2007 (link));

ii) The Proportional Reporting Ratio (PRR), comparing the frequency of occurrence of therapy failure in reports referring to antidiabetic agents with the frequency of occurrence of reports of therapy failure in reports that do not mention antidiabetic agents. (van Manen et al., 2007 (link)).

iii) The Empirical Bayesian Geometric Mean (EBGM) calculated using the Multi-item Gamma Poisson Shrinker (MGPS) Algorithm, using Bayesian shrinkage to improve the reliability of the disproportionality score (DuMouchel, 1999 (link)). We generated both the point estimates (EBGM) and their associated 90% confidence intervals labelled EB05–EB95.

Moreover, we used a more advanced regression-based methodology designed to produce disproportionality statistics with adjusted background rates; it can control masking and more extensive confounding effects by fitting separate Bayesian logistic regression models to each target AE and by automatically selecting predictors to be included in each regression model:

iv) The Regression-enhanced Empirical Bayesian Geometric Mean (ERAM) calculated using the Regression-Adjusted Gamma Poisson Shrinker (RGPS) Algorithm (DuMouchel and Harpaz, 2012 ). We generated the point estimates (ERAM) and their associated 90% confidentiality intervals labelled ER05–ER95.

With the aim to investigate the antidepressant effects of antidiabetic drugs, disproportionality signals were considered clinically meaningful if.

i) The upper limit of the 90% confidence interval (CI) of the ROR for cases (ROR95) is less than one;

ii) The PRR score is less than one and the corresponding p-value is less than 0.05;

iii) The upper limit of the 90% confidence interval of the EBGM for cases (EB95) is less than one;

iv) The upper limit of the 90% confidence interval of the ERAM for cases (ER95) is less than one.

Publication 2023
Antidepressive Agents Antidiabetics austin Biguanides Dipeptidyl-Peptidase IV Inhibitors Gamma Rays Glucagon-Like Peptide 1 Patients Safety Signal Detection (Psychology) Sodium-Glucose Transporter 2 Inhibitors Sulfonylurea Compounds Therapeutics Thiazolidinediones

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More about "Sulfonylurea Compounds"

Sulfonylurea compounds are a class of medications commonly used to treat type 2 diabetes.
These drugs, also known as sulfonylureaa, work by stimulating the release of insulin from the pancreas, helping to lower blood glucose levels.
Researchers can utilize PubCompare.ai's AI-driven platform to explore the latest literature, preprints, and patents related to sulfonylurea compounds, enhancing the reproducibility and accuracy of their studies.
The platform's AI comparisons can help optimize the research workflow and uncover the best protocols for investigating this important class of diabetes therapeutics.
This includes discovering new insights and advancing the understanding of sulfonylureaa and their mechanisms of action.
Researchers may also find it useful to consider related terms and technologies, such as SAS version 9.4, SAS 9.4, SPSS version 18.0, AU5800 Platform, Hygromycin, Lantus, Bacterial genomic DNA extraction kits, Teneligliptin, Sitagliptin, and the Synchron DX600 analyzer.
These tools and compounds can provide additional context and capabilities for studying sulfonylureaa and their applications in diabetes management.
By leveraging the power of PubCompare.ai's AI platform and incorporating a holistic understanding of the sulfonylurea compound landscape, researchers can enhance the reproducibility, accuracy, and impact of their work in this critical area of diabetes research and treatment.