Sermorelin

Prakash, Amitabh; Goa, Karen L.

doi:10.2165/00063030-199912020-00007

Cited by 24 publications

(7 citation statements)

References 48 publications

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“…Sermorelin is a synthetic analog of growth hormone-releasing hormone (GHRH) and is used to treat children with growth hormone deficiency [ 62 ]. MultiPep classifies the peptide sequence of Sermorelin as a peptide hormone and a neuropeptide.…”

Section: Resultsmentioning

confidence: 99%

“…MultiPep classifies the peptide sequence of Sermorelin as a peptide hormone and a neuropeptide. This is fascinating when considering the general mechanism of action of the drug and when considering the findings that indicate that intravenous and subcutaneous sermorelin has been found to stimulate growth hormone secretion from the anterior pituitary [ 62 ].…”

Section: Resultsmentioning

confidence: 99%

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MultiPep: a hierarchical deep learning approach for multi-label classification of peptide bioactivities

2021

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Peptide-based therapeutics are here to stay and will prosper in the future. A key step in identifying novel peptide-drugs is the determination of their bioactivities. Recent advances in peptidomics screening approaches hold promise as a strategy for identifying novel drug targets. However, these screenings typically generate an immense number of peptides and tools for ranking these peptides prior to planning functional studies are warranted. Whereas a couple of tools in the literature predict multiple classes, these are constructed using multiple binary classifiers. We here aimed to use an innovative deep learning approach to generate an improved peptide bioactivity classifier with capacity of distinguishing between multiple classes. We present MultiPep: a deep learning multi-label classifier that assigns peptides to zero or more of 20 bioactivity classes. We train and test MultiPep on data from several publically available databases. The same data are used for a hierarchical clustering, whose dendrogram shapes the architecture of MultiPep. We test a new loss function that combines a customized version of Matthews correlation coefficient with binary cross entropy (BCE), and show that this is better than using class-weighted BCE as loss function. Further, we show that MultiPep surpasses state-of-the-art peptide bioactivity classifiers and that it predicts known and novel bioactivities of FDA-approved therapeutic peptides. In conclusion, we present innovative machine learning techniques used to produce a peptide prediction tool to aid peptide-based therapy development and hypothesis generation.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

MultiPep: a hierarchical deep learning approach for multi-label classification of peptide bioactivities

2021

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“…Chemie afforded the corresponding cyclic peptide natural products in good yields (Figures S2-S19). Encouraged by the above results for short peptide SAL ester cases, we further undertook the synthesis of linear peptide drugs (teriparatide, [20] sermorelin, [21] salmon calcitonin, [22] pramlintide [23] ) via STL. Meanwhile, the amino acid compatibility with our Boc-SPPS based methodology was further explored.…”

Section: Methodsmentioning

confidence: 99%

Chemical Synthesis of Proteins with Base‐Labile Posttranslational Modifications Enabled by a Boc‐SPPS Based General Strategy Towards Peptide C‐Terminal Salicylaldehyde Esters

et al. 2022

Angew Chem Int Ed

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Chemical synthesis of proteins bearing base‐labile post‐translational modifications (PTMs) is a challenging task. For instance, O‐acetylation and S‐palmitoylation PTMs cannot survive Fmoc removal conditions during Fmoc‐solid phase peptide synthesis (SPPS). In this work, we developed a new Boc‐SPPS‐based strategy for the synthesis of peptide C‐terminal salicylaldehyde (SAL) esters, which are the key reaction partner in Ser/Thr ligation and Cys/Pen ligation. The strategy utilized the semicarbazone‐modified aminomethyl (AM) resin, which could support the Boc‐SPPS and release the peptide SAL ester upon treatment with TFA/H2O and pyruvic acid. The non‐oxidative aldehyde regeneration was fully compatible with all the canonical amino acids. Armed with this strategy, we finished the syntheses of the O‐acetylated protein histone H3(S10ac, T22ac) and the hydrophobic S‐palmitoylated peptide derived from caveolin‐1.

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“…The half‐life of sermorelin was short, 11–12 min after either intravenous or subcutaneous administration. Because sermorelin, intravenously or subcutaneously administered, specifically stimulates GH secretion from the pituitary gland without any significant change in prolactin, luteinizing hormone, follicle‐stimulating hormone, insulin, cortisol, glucose, glucagon, or thyroid hormone levels, sermorelin was approved for the treatment of growth retardation in children in 1997. However, sermorelin was discontinued in 2008 due to difficulties in the manufacturing process of the active ingredient used to produce commercially supplied sermorelin but not due to safety issues.…”

Section: Pharmacologymentioning

confidence: 99%

Growth hormone secretagogues: history, mechanism of action, and clinical development

Ishida

Saitoh

Ebner

et al. 2020

JCSM Rapid Communications

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Growth hormone secretagogues (GHSs) are a generic term to describe compounds that increase growth hormone (GH) release. GHSs include agonists of the growth hormone secretagogue receptor (GHS‐R), whose natural ligand is ghrelin, and agonists of the growth hormone‐releasing hormone (GHRH) receptor, to which the GHRH binds as a native ligand. Several GHSs have been developed with a view to treating or diagnosing of GH deficiency, which causes growth retardation, gastrointestinal dysfunction, and altered body composition, in parallel with extensive research to identify GHRH, GHS‐R, and ghrelin. This review will focus on the research history and the pharmacology of each GHS, which reached randomized clinical trials. Furthermore, we will highlight the publicly disclosed clinical trials regarding GHSs.

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Sermorelin

Cited by 24 publications

References 48 publications

MultiPep: a hierarchical deep learning approach for multi-label classification of peptide bioactivities

MultiPep: a hierarchical deep learning approach for multi-label classification of peptide bioactivities

Chemical Synthesis of Proteins with Base‐Labile Posttranslational Modifications Enabled by a Boc‐SPPS Based General Strategy Towards Peptide C‐Terminal Salicylaldehyde Esters

Growth hormone secretagogues: history, mechanism of action, and clinical development

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