Pharmacological Acromegaly Treatment: Cost-Utility and Value of Information Analysis

Efficacy and Safety of Pegvisomant in the Treatment of Acromegaly

Pegvisomant, the first and currently only clinically available growth hormone receptor antagonist, is an effective therapeutic option for the medical treatment of acromegaly, a rare disorder characterized by excessive growth hormone secretion. With now over 20 years of real world experience, its safety and efficacy is well-established. However, several aspects of its clinical use are still controversially discussed. The high cost of pegvisomant has limited its use in several countries, and recent studies have reported a lower efficacy than the initial clinical trials. A reported increase in tumor volume under therapy varies between studies and has been attributed to either actual growth or re-expansion after cessation of somatostatin receptor ligand therapy. Furthermore, different combinations of pegvisomant and other therapeutic agents aiming at reduction of acromegaly disease activity have been proposed to increase or retain effectiveness while lowering side effects and cost. This review aims to assess current clinical data on the safety and efficacy of pegvisomant while also addressing controversies surrounding its use.

The socioeconomic burden of acromegaly

Acromegaly is a rare and insidious disease characterized by chronic excess growth hormone, leading to various morphological changes and systemic complications. Despite its low prevalence, acromegaly poses a significant socioeconomic burden on patients and healthcare systems. This review synthesizes the current state of knowledge on the psychosocial burden, disability, impact on daily life, and cost of acromegaly disease, focusing on the quality of life, partnership, medical care and treatment afflictions, participation in daily activities, professional and leisure impairment, and cost of treatment for acromegaly and its comorbidities. It also examines management strategies, coping mechanisms, and interventions aimed at alleviating this burden. A comprehensive understanding of the extent of the socioeconomic burden in acromegaly is crucial to develop effective strategies to improve treatment and care. Further research is warranted to explore the myriad factors contributing to this burden, as well as the efficacy of interventions to alleviate it, ultimately enhancing the quality of life for patients with acromegaly.

miR-383-5p, miR-181a-5p, and miR-181b-5p as Predictors of Response to First-Generation Somatostatin Receptor Ligands in Acromegaly

Acromegaly is a chronic systemic disease caused in the vast majority of cases by growth hormone (GH)-secreting adenoma, with surgery being the first-line treatment. When a cure is not attained with surgery, first-generation somatostatin receptor ligands (fg-SRLs) are the most common medication prescribed. Predictors of response to fg-SRLs have been studied; however, they cannot fully predict the response to fg-SRL. MicroRNAs are small RNAs, the main role of which is messenger RNA (mRNA) post-transcriptional regulation. This study aimed to identify the microRNAs involved in resistance to treatment with fg-SRLs in acromegaly. Ten patients with acromegaly undergoing treatment with fg-SRLs were selected to undergo miRNA sequencing: five controlled and five uncontrolled with treatment. Bioinformatic analysis was performed to detect differentially expressed miRNAs. Then, the same 10 samples were used for validation by qPCR and an additional 22 samples were analyzed, totaling 32 samples. e We found 59 differentially expressed miRNAs in the first analysis. miR-181a-5p and miR-181b-5p were downregulated, and miR-383-5p was upregulated in the uncontrolled group. Receiver operating characteristic (ROC) curve analysis of miR-383-5p showed an NPV of 84.3% and a PPV of 84.5%. In summary, miR-181a-5p, miR-181b-5p, and miR-383-5p are biomarkers of response to fg-SRLs, and they can be used individually or included in prediction models as tools to guide clinical decisions.

Machine Learning-based Prediction Model for Treatment of Acromegaly With First-generation Somatostatin Receptor Ligands

CONTEXT

Artificial intelligence (AI), in particular machine learning (ML), may be used to deeply analyze biomarkers of response to first-generation somatostatin receptor ligands (fg-SRLs) in the treatment of acromegaly.

OBJECTIVE

To develop a prediction model of therapeutic response of acromegaly to fg-SRL.

METHODS

Patients with acromegaly not cured by primary surgical treatment and who had adjuvant therapy with fg-SRL for at least 6 months after surgery were included. Patients were considered controlled if they presented growth hormone (GH) <1.0 ng/mL and normal age-adjusted insulin-like growth factor (IGF)-I levels. Six AI models were evaluated: logistic regression, k-nearest neighbor classifier, support vector machine, gradient-boosted classifier, random forest, and multilayer perceptron. The features included in the analysis were age at diagnosis, sex, GH, and IGF-I levels at diagnosis and at pretreatment, somatostatin receptor subtype 2 and 5 (SST2 and SST5) protein expression and cytokeratin granulation pattern (GP).

RESULTS

A total of 153 patients were analyzed. Controlled patients were older (P = .002), had lower GH at diagnosis (P = .01), had lower pretreatment GH and IGF-I (P < .001), and more frequently harbored tumors that were densely granulated (P = .014) or highly expressed SST2 (P < .001). The model that performed best was the support vector machine with the features SST2, SST5, GP, sex, age, and pretreatment GH and IGF-I levels. It had an accuracy of 86.3%, positive predictive value of 83.3% and negative predictive value of 87.5%.

CONCLUSION

We developed a ML-based prediction model with high accuracy that has the potential to improve medical management of acromegaly, optimize biochemical control, decrease long-term morbidities and mortality, and reduce health services costs.