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somatropin (Valtropin / somatropin, LG Chem / Eutropin Pen)

✓ Approved

Takeda · GHR · Recombinant Proteins

What is somatropin?

somatropin is a recombinant proteins developed by Takeda. It is approved for therapeutic indications via injectable (others) or subcutaneous injection.

Drug Profile

Brand NamesValtropin, somatropin, LG Chem, Eutropin Pen
CompanyTakeda
Drug ClassRecombinant Proteins
Molecular TargetGHR
RouteInjectable (Others), Subcutaneous Injection
StatusApproved
Sources: ClinicalTrials.gov, Takeda

Mechanism of Action

Molecular Targets

somatropin acts on 1 molecular target:

GHRgrowth hormone receptor (GHBP, GHIP)
Want deeper analysis?Noah AI can explain complex mechanisms and compare to similar drugs.

Therapeutic Indications

somatropin is developed for 4 unique indications across 2 therapeutic areas.

Therapeutic AreaConditionPhase
Endocrine disordersGrowth hormone deficiency✓ Approved
Congenital, familial and genetic disordersSex chromosome abnormality✓ Approved
Congenital, familial and genetic disordersOsteochondrodysplasia✓ Approved
Congenital, familial and genetic disordersPrader-Willi syndromePhase III

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The life sciences industry, and specifically the pharmaceutical sub-sector, is a major funder of private sector research and development. Canada's life sciences industry reinvests less of its revenues in research and development (R&D) activities than those in peer countries, which hinders innovation, creates supply risks and contributes to Canada's poor productivity growth. Traditional approaches to incentivizing reinvestment, including tax incentives, direct funding and public research spending, have failed. As an alternative, transforming Canada's health data systems would enable faster and more comprehensive secondary use, making R&D investments safer and more productive. Our peers are already pursuing this strategy, meaning Canada risks being left behind unless we act.

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Lipofuscin granules (LGs) in retinal pigment epithelium (RPE) cells contain bis-retinoids and their oxidation and degradation products, rendering them photo- and cytotoxic to intracellular structures. LGs are implicated in the pathogenesis of multiple visual pathologies, including age-related macular degeneration (AMD). They exhibit strong autofluorescence, which has led to the development of fundus autofluorescence (FAF) imaging as a non-invasive diagnostic method in ophthalmology. Spectral analysis of autofluorescence can expand the capabilities of this method, including for preclinical diagnostics, as pathological conditions are often associated with increased proportions of oxidized bis-retinoid derivatives that alter LG autofluorescence parameters. However, limited knowledge of age-dependent changes in LG bis-retinoid composition remains a key limitation. In this study, we combined fluorescence spectroscopy, confocal fluorescence microscopy, and fluorescence lifetime imaging (time-correlated single-photon counting) to demonstrate that, under physiological conditions, aging is accompanied by a progressive increase in the relative abundance of oxidation and degradation products of bis-retinoids in LGs. These findings provide an age-dependent baseline for distinguishing physiological and pathological states, thereby improving the potential of FAF imaging for early (preclinical) diagnosis.

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Two-dimensional chalcogenide semiconductors provide an attractive platform for deeply scaled transistors because their atomically thin bodies enable strong electrostatic control and effective suppression of short-channel effects. Here, we systematically investigate the ballistic transport limits of monolayer GeS2 and SnS2 double-gated metal-oxide-semiconductor field-effect transistors (MOSFETs) using density functional theory combined with the nonequilibrium Green-function formalism. Both n- and p-type devices are evaluated at gate lengths of 5, 3, and 1 nm under the 2028 International Technology Roadmap for Semiconductors high-performance (HP) and low-power (LP) requirements. By optimizing the source/drain doping concentration and underlap length, the n-type armchair GeS2 and SnS2 MOSFETs with Lg = 5 nm deliver ultrahigh on-state currents of 5064 and 4557 µA µm-1 for HP applications, and 1072 and 2042 µA µm-1 for LP applications, respectively. Moreover, the intrinsic delay time and power-delay product remain well below the corresponding ITRS limits. Further analysis reveals that the excellent current-driving capability originates from the favorable balance between carrier velocity and density of states associated with the transport-direction effective mass. These findings suggest the potential of monolayer GeS2 and SnS2 as promising channel candidates for extending two-dimensional electronics toward the deeply scaled, energy-efficient post-silicon technology node.

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Atherosclerosis (AS) is a complex systemic, immune-inflammatory vascular disease, and there is an urgent need to innovate its diagnostic and therapeutic strategies. The rapid advancement of artificial intelligence (AI) technology has opened up new avenues for the early diagnosis, risk prediction, and precision treatment of AS. However, a systematic quantitative analysis of global knowledge and collaboration in this field is still lacking. This study conducted a systematic search of the Web of Science Core Collection (WoSCC), Scopus, and PubMed databases for literature on the application of AI in AS research published between 2000 and 2025. Advanced bibliometric tools, such as CiteSpace, VOSviewer, and R-Bibliometrix, were employed to conduct multidimensional visual analyses of publication trends, transnational collaboration networks, knowledge flows in core journals, and the emergence of keywords. A total of 258 core publications were ultimately included, involving 1,900 authors, 44 countries/regions, and 1,152 research institutions. The spatio-temporal distribution revealed that the volume of publications in this field has grown significantly, with rapid growth beginning after 2021. Globally, a China-U.S. dual-center pattern has emerged: China leads in terms of output scale and growth rate, while the United States holds higher citation accumulation in terms of total citations and average citations per paper. However, raw citation counts and average citations per paper are influenced by publication year, document type, and denominator size; these cross-country differences should therefore be interpreted with caution, and future studies using normalized indicators are warranted. The evolution of research hotspots has undergone a distinct three-stage transition: from early basic algorithm development (2000-2014), through multimodal image intelligent analysis (2015-2020), to the current phase of clinical multicenter validation and in-depth mechanism decoding (2021-2025). Journal overlay analysis further confirms that clinical medicine is increasingly intersecting with basic life sciences, driving bidirectional knowledge transfer. This study systematically maps the global research landscape of AI in the field of AS, revealing its evolutionary path from methodological exploration to clinical translation. Future research should break down data barriers, advance multinational, multiethnic, and multicenter cohort validation, and focus on the development of interpretable AI models. The observed trends in the bibliometric data suggest that the integration of AI technology with systems biology and a holistic medical approach may play an increasingly important role in personalized, precision interventions for AS.

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