RARE DISEASE
Insulinoma
Insulinoma
Insulinoma
Drug discovery
2
drugs
With orphan designations
Overview
Insulinomas are rare pancreatic neuroendocrine tumors (incidence 1–4/million/year) causing hyperinsulinemic hypoglycemia via uncontrolled insulin secretion [1][2][17]. Over 90% are benign, solitary, and intrapancreatic [1][6][19], with malignant/metastatic forms seen in 6–15% of cases [1][2][6]. Diagnosis relies on Whipple’s triad (symptoms of hypoglycemia, glucose <55 mg/dL during symptoms, resolution with glucose) [6][14]. Surgical resection remains curative for most indolent cases [3][8][13], while malignant cases require multimodal management [1][8][16].
Therapies
Curative: Surgical enucleation (43%) or pancreatic resection (45%) with 89% cure rate [7][13]
Medical: Diazoxide (first-line), somatostatin analogs, or glucose monitoring for inoperable cases [3][8][16]
Malignant: Debulking, liver-directed therapies (embolization/ablation), and systemic therapies (everolimus, PRRT, chemotherapy) [1][8][16]
Categories: rare endocrine diseases, rare gastroenterological diseases, rare neoplastic diseases
Research Papers
2,860 drug discovery papers about Insulinoma, with 2 first-in-class and 4 next-in-class emerging drug candidates forecasted to outperform the average preclinical success rate. Recent publications:
2,860 drug discovery papers about Insulinoma, with 2 first-in-class and 4 next-in-class emerging drug candidates forecasted to outperform the average preclinical success rate. Recent publications:
2026-07-01 | High-yield, standardised automated radiosynthesis process for routine clinical insulinoma PET with [⁶⁸Ga]Ga-NODAGA-exendin-4.
Insulinoma is a rare pancreatic neuroendocrine tumour characterised by inappropriate insulin secretion and recurrent hypoglycaemia. Owing to their small size and intrapancreatic localisation, insulinomas are frequently difficult to detect using conventional anatomical imaging techniques. Functional imaging based on positron emission tomography has therefore gained increasing interest, particularly through targeting of the glucagon-like peptide-1 receptor, which is highly overexpressed in most insulinomas. Among available tracers, gallium-68-labelled NODAGA-exendin-4 has demonstrated excellent diagnostic performance. However, its routine clinical implementation remains limited by the absence of a harmonised, robust and transferable radiosynthesis protocol compatible with hospital radiopharmacy practice. The aim of this work was to optimise and harmonise the automated preparation of gallium-68-labelled NODAGA-exendin-4 by systematically evaluating critical synthesis parameters and validating the optimised process across multiple commercially available gallium-68 generators. Using an automated synthesis module, key variables including precursor amount, labelling temperature and duration, formulation additives, workflow sequence and quality control conditions were investigated. The optimised protocol was subsequently validated using generators from three different manufacturers. Optimisation studies demonstrated that formulation-related parameters, particularly the post-labelling addition of polysorbate 20, resulted in improved radiochemical conversion and reduced residual activity within the synthesis cassette. An optimal precursor amount of 20 µg was identified as a compromise between radiochemical yield and clinical injectability constraints. Radiolabelling at 95 °C for 12 min ensured high conversion while reducing overall synthesis time. Validation runs showed excellent reproducibility, with non-decay-corrected yields ranging from 60 to 73% and decay-corrected yields reaching up to 93%, independent of generator type. Radiochemical purity consistently exceeded 95%, and all quality control parameters complied with established specifications. This study establishes a simplified, efficient and generator-independent automated synthesis of gallium-68-labelled NODAGA-exendin-4. By addressing key translational and regulatory constraints, the proposed protocol provides a practical foundation for the routine clinical implementation of glucagon-like peptide-1 receptor imaging in patients with suspected insulinoma.
2026-06-29 | Artificial Intelligence-Assisted Endoscopic Ultrasound-Guided Ablation of Pancreatic Neuroendocrine Tumors: Toward Precision Diagnosis, Risk Stratification, and Personalized Therapy.
Pancreatic neuroendocrine tumors (pNETs) are increasingly detected at an early stage because of the wider use of cross-sectional imaging and endoscopic ultrasound. Their management remains challenging, particularly for small functioning tumors and selected non-functioning lesions, where the risks of pancreatic surgery must be balanced against tumor biology, symptoms, progression risk, and patient preference. Endoscopic ultrasound (EUS)-guided ablation, particularly radiofrequency ablation, has emerged as a minimally invasive, organ-preserving option for carefully selected patients with small pNETs, especially insulinomas and low-risk non-functioning lesions. However, current evidence is limited by small cohorts, heterogeneous techniques, variable follow-up protocols, and uncertainty regarding long-term oncological outcomes. Artificial intelligence (AI) may enhance this evolving field by supporting EUS-based lesion detection, characterization, grading prediction, risk stratification, patient selection, procedural planning, and post-ablation surveillance. AI-assisted models using EUS images, radiomics, pathology, and multimodal clinical data may help identify patients most likely to benefit from ablation while avoiding inappropriate local therapy in biologically aggressive disease. This review summarizes the current role of EUS-guided ablation for pNETs and explores the emerging potential of AI to support precision diagnosis, individualized risk assessment, and personalized minimally invasive therapy.
2026-06-23 | Effect of the FGF1-B promoter on spontaneous beta-cell tumorigenesis in F1B-Tag mice: A robust translational model for insulinoma and metabolic crosstalk.
153 Background: Fibroblast growth factor 1 (FGF1) is a key regulator of glucose homeostasis and β-cell physiology. The F1B-Tag transgenic mouse, utilizing the FGF1-B promoter to drive SV40 T-antigen expression, provides a unique opportunity to study spontaneous tumorigenesis. This study characterizes the F1B-Tag model as a clinically relevant platform for human insulinoma, focusing on the molecular interplay between the FGF1-B promoter and pancreatic β-cell transformation. Methods: F1B-Tag mice were monitored longitudinally to evaluate oncogenic progression. Immunohistochemical (IHC) analysis was performed to co-localize SV40 T-antigen with FGF1 and proliferation markers. Metabolic profiling included blood glucose monitoring, fasting insulin ELISA, and Glucose Tolerance Tests (GTT). Results: F1B-Tag mice developed spontaneous tumors, with pancreatic lesions appearing at approximately 5 months of age. Tumorigenesis was specifically localized to the insulin-secreting β-cells within the pancreatic islets, rather than the ductal cells. IHC confirmed that the FGF1-B promoter actively drives T-antigen expression within these cells, triggering uncontrolled proliferation. Progressed F1B-Tag mice exhibited a statistically significant and marked reduction in both random and fasting blood sugar levels compared to age-matched wild-type (WT) controls. This hypoglycemic state was driven by profound hyperinsulinemia, where fasting plasma insulin levels in F1B-Tag mice were substantially and significantly elevated, reaching nearly twenty-fold higher than WT levels after normalization. GTT revealed a persistent, flattened low-glucose response curve following glucose administration, confirming autonomous and pathological insulin over-secretion. Conclusions: The F1B-Tag mouse is a robust translational model that accurately recapitulates the metabolic disturbances and oncogenic progression of insulinoma. Our findings demonstrate that the FGF1-B promoter is a critical driver in β-cell oncogenesis and glucose-sensing circuits. This model offers a valuable therapeutic screening platform for investigating novel FGF1-targeted therapies and modulating tumor-induced metabolic dysfunction.
2026-07-01 | High-yield, standardised automated radiosynthesis process for routine clinical insulinoma PET with [⁶⁸Ga]Ga-NODAGA-exendin-4.
Insulinoma is a rare pancreatic neuroendocrine tumour characterised by inappropriate insulin secretion and recurrent hypoglycaemia. Owing to their small size and intrapancreatic localisation, insulinomas are frequently difficult to detect using conventional anatomical imaging techniques. Functional imaging based on positron emission tomography has therefore gained increasing interest, particularly through targeting of the glucagon-like peptide-1 receptor, which is highly overexpressed in most insulinomas. Among available tracers, gallium-68-labelled NODAGA-exendin-4 has demonstrated excellent diagnostic performance. However, its routine clinical implementation remains limited by the absence of a harmonised, robust and transferable radiosynthesis protocol compatible with hospital radiopharmacy practice. The aim of this work was to optimise and harmonise the automated preparation of gallium-68-labelled NODAGA-exendin-4 by systematically evaluating critical synthesis parameters and validating the optimised process across multiple commercially available gallium-68 generators. Using an automated synthesis module, key variables including precursor amount, labelling temperature and duration, formulation additives, workflow sequence and quality control conditions were investigated. The optimised protocol was subsequently validated using generators from three different manufacturers. Optimisation studies demonstrated that formulation-related parameters, particularly the post-labelling addition of polysorbate 20, resulted in improved radiochemical conversion and reduced residual activity within the synthesis cassette. An optimal precursor amount of 20 µg was identified as a compromise between radiochemical yield and clinical injectability constraints. Radiolabelling at 95 °C for 12 min ensured high conversion while reducing overall synthesis time. Validation runs showed excellent reproducibility, with non-decay-corrected yields ranging from 60 to 73% and decay-corrected yields reaching up to 93%, independent of generator type. Radiochemical purity consistently exceeded 95%, and all quality control parameters complied with established specifications. This study establishes a simplified, efficient and generator-independent automated synthesis of gallium-68-labelled NODAGA-exendin-4. By addressing key translational and regulatory constraints, the proposed protocol provides a practical foundation for the routine clinical implementation of glucagon-like peptide-1 receptor imaging in patients with suspected insulinoma.
2026-06-29 | Artificial Intelligence-Assisted Endoscopic Ultrasound-Guided Ablation of Pancreatic Neuroendocrine Tumors: Toward Precision Diagnosis, Risk Stratification, and Personalized Therapy.
Pancreatic neuroendocrine tumors (pNETs) are increasingly detected at an early stage because of the wider use of cross-sectional imaging and endoscopic ultrasound. Their management remains challenging, particularly for small functioning tumors and selected non-functioning lesions, where the risks of pancreatic surgery must be balanced against tumor biology, symptoms, progression risk, and patient preference. Endoscopic ultrasound (EUS)-guided ablation, particularly radiofrequency ablation, has emerged as a minimally invasive, organ-preserving option for carefully selected patients with small pNETs, especially insulinomas and low-risk non-functioning lesions. However, current evidence is limited by small cohorts, heterogeneous techniques, variable follow-up protocols, and uncertainty regarding long-term oncological outcomes. Artificial intelligence (AI) may enhance this evolving field by supporting EUS-based lesion detection, characterization, grading prediction, risk stratification, patient selection, procedural planning, and post-ablation surveillance. AI-assisted models using EUS images, radiomics, pathology, and multimodal clinical data may help identify patients most likely to benefit from ablation while avoiding inappropriate local therapy in biologically aggressive disease. This review summarizes the current role of EUS-guided ablation for pNETs and explores the emerging potential of AI to support precision diagnosis, individualized risk assessment, and personalized minimally invasive therapy.
2026-06-23 | Effect of the FGF1-B promoter on spontaneous beta-cell tumorigenesis in F1B-Tag mice: A robust translational model for insulinoma and metabolic crosstalk.
153 Background: Fibroblast growth factor 1 (FGF1) is a key regulator of glucose homeostasis and β-cell physiology. The F1B-Tag transgenic mouse, utilizing the FGF1-B promoter to drive SV40 T-antigen expression, provides a unique opportunity to study spontaneous tumorigenesis. This study characterizes the F1B-Tag model as a clinically relevant platform for human insulinoma, focusing on the molecular interplay between the FGF1-B promoter and pancreatic β-cell transformation. Methods: F1B-Tag mice were monitored longitudinally to evaluate oncogenic progression. Immunohistochemical (IHC) analysis was performed to co-localize SV40 T-antigen with FGF1 and proliferation markers. Metabolic profiling included blood glucose monitoring, fasting insulin ELISA, and Glucose Tolerance Tests (GTT). Results: F1B-Tag mice developed spontaneous tumors, with pancreatic lesions appearing at approximately 5 months of age. Tumorigenesis was specifically localized to the insulin-secreting β-cells within the pancreatic islets, rather than the ductal cells. IHC confirmed that the FGF1-B promoter actively drives T-antigen expression within these cells, triggering uncontrolled proliferation. Progressed F1B-Tag mice exhibited a statistically significant and marked reduction in both random and fasting blood sugar levels compared to age-matched wild-type (WT) controls. This hypoglycemic state was driven by profound hyperinsulinemia, where fasting plasma insulin levels in F1B-Tag mice were substantially and significantly elevated, reaching nearly twenty-fold higher than WT levels after normalization. GTT revealed a persistent, flattened low-glucose response curve following glucose administration, confirming autonomous and pathological insulin over-secretion. Conclusions: The F1B-Tag mouse is a robust translational model that accurately recapitulates the metabolic disturbances and oncogenic progression of insulinoma. Our findings demonstrate that the FGF1-B promoter is a critical driver in β-cell oncogenesis and glucose-sensing circuits. This model offers a valuable therapeutic screening platform for investigating novel FGF1-targeted therapies and modulating tumor-induced metabolic dysfunction.
Access all drug discovery articles and probability of success in trials forecasts:
Access all drug discovery articles and probability of success in trials forecasts:
Drug Discovery Landscape
2 orphan drug designations for Insulinoma.
2 orphan drug designations for Insulinoma.
Drug | Therapy type | Regulator | Orphan designation | Approval | Sponsor |
|---|---|---|---|---|---|
Anti-(insulin receptor) human monoclonal antibody | antibodies | EMA | 2024-01-12 | — | Rezolute (Bio) Ireland Limited |
Lys40(NODAGA-68Ga)NH2-exendin-4 | peptides | EMA | 2020-06-26 | — | Stichting Katholieke Universiteit |
Let's accelerate rare disease drug discovery
Let's accelerate drug discovery
Get access to Explority AI's forecasts to outperform average preclinical success rates. Whether you're expanding your R&D pipeline, evaluating a partnership, or simply have a question — we'd love to hear from you.