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selpercatinib

✓ Approved

Loxo Oncology · NTRK1 · Companion diagnostic

What is selpercatinib?

selpercatinib is a companion diagnostic developed by Loxo Oncology. It is approved for therapeutic indications via others.

Drug Profile

CompanyLoxo Oncology
Drug ClassCompanion diagnostic
Molecular TargetNTRK1
RouteOthers
StatusApproved
Sources: ClinicalTrials.gov, Loxo Oncology

Mechanism of Action

Molecular Targets

selpercatinib acts on 1 molecular target:

NTRK1neurotrophic receptor tyrosine kinase 1 (TRK1, TRK)
Want deeper analysis?Noah AI can explain complex mechanisms and compare to similar drugs.

Therapeutic Indications

selpercatinib is developed for 1 unique indication across 1 therapeutic area.

Therapeutic AreaConditionPhase
Neoplasms benign, malignant and unspecified (incl cysts and polyps)Uterine cancer✓ Approved

Related Research Articles

PubMedRespirology case reports2026-07-17

Selpercatinib-Induced Deep Skin Fissures Triggered by Mechanical Friction in a Patient With RET Fusion-Positive Lung Adenocarcinoma.

Ikeuchi Tomoyuki T, Yamamoto Mitsuhiro M, Toge Hirokazu H, Brioka Naoto N et al.

We report a case of selpercatinib-induced deep skin fissures in a patient with RET fusion-positive lung adenocarcinoma. Minor mechanical abrasions triggered severe fingertip fissures, which were likely exacerbated by selpercatinib-related VEGF receptor inhibition. The condition improved with drug withdrawal and corticosteroids administration, enabling the continuation of treatment with careful management of recurrent episodes.

PMID 42466436
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PubMedScientific reports2026-07-17

Characteristics of Staphylococcus lugdunensis isolated from humans and animals.

Prorok Paulina P, Skrok Milena M, Karwańska Magdalena M, Siedlecka Magdalena M et al.

Staphylococcus lugdunensis is an opportunistic coagulase-negative Staphylococcus increasingly reported in both humans and companion animals. In this study, we performed a comprehensive characterization of S. lugdunensis isolates obtained from different hosts and clinical backgrounds. Species identification was conducted using MALDI-TOF MS and confirmed by PCR targeting the species-specific fbl gene, complemented by partial rpoB sequencing. The isolates were analysed using multilocus sequence typing (MLST), PCR-based detection of antimicrobial resistance genes, and phenotypic antimicrobial susceptibility testing. Biofilm formation was assessed using a crystal violet microtiter plate assay under different incubation temperatures, and the virulence of selected strains was evaluated using the Galleria mellonella larvae infection model. The isolates exhibited genetic diversity and variable antimicrobial resistance and biofilm phenotypes. Among the analysed isolates, biofilm production was significantly influenced by incubation temperature and host origin, and selected strains caused differential larval survival in the G. mellonella model. Collectively, these findings highlight the heterogeneity of the analysed S. lugdunensis collection comprising human- and animal-derived isolates and support the need for further studies within the One Health framework.

PMID 42463811
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PubMedJournal of infection and chemotherapy : official journal of the Japan Society of Chemotherapy2026-07-17

Mycoplasma Pneumonia Diagnostic Prediction Score: Is it possible to differentiate between Mycoplasma pneumoniae pneumonia and SARS-CoV-2 pneumonia?

Miyashita Naoyuki N, Nakamori Yasushi Y, Ogata Makoto M, Fukuda Naoki N et al.

The Mycoplasma Pneumonia Diagnostic Prediction Score, recommended in pneumonia guidelines, is a useful method for differentiating Mycoplasma pneumoniae pneumonia from bacterial pneumonia. On the other hand, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a common microorganism in community-acquired pneumonia, so we investigated whether the Mycoplasma Score can differentiate between M. pneumoniae pneumonia and SARS-CoV-2 pneumonia. Analysis was performed on 162 patients with M. pneumoniae, 422 patients with the Ancestral strain, 262 with the Alpha variant, 274 with the Delta variant, and 1,241 with the Omicron variant. When using the Mycoplasma Score, the sensitivity for predicting M. pneumoniae pneumonia was 71.8%. The diagnostic specificity was 81.3% for the Ancestral strain, 81.7% for the Alpha variant, 77.4% for the Delta variant, and 88.2% for the Omicron variant. The specificity for all SARS-CoV-2 pneumonia cases was 84.8%. When targeting the currently circulating Omicron variant, the diagnostic specificity ranged from 83.9% to 92.7%, showing differences among subvariants. Differences between the two groups were identified using four parameters: age, underlying disease, severity of cough, and use of rapid diagnostic methods. When comparing M. pneumoniae pneumonia and SARS-CoV-2 pneumonia, the diagnostic sensitivity of the Mycoplasma Pneumonia Diagnostic Prediction Score was 71.8%, and the diagnostic specificity was 84.8%. However, when targeting the currently circulating SARS-CoV-2 Omicron variant pneumonia, the diagnostic specificity increased to 88.2%, suggesting that it is possible to differentiate between M. pneumoniae pneumonia and SARS-CoV-2 pneumonia. However, the specificity is lower than that for differentiating bacterial pneumonia.

PMID 42463011
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PubMedCureus2026-07-17

Aggressive B-cell Lymphoma Masquerading as Benign Epstein-Barr Virus (EBV)-Related Splenomegaly: An Analysis of Diagnostic Anchoring Bias.

Savadkar Amrut A, Chauhan Ipsita I, Sharma Mansi M, Rallabandi Suhasini S et al.

We report a case involving a 52-year-old male who was initially diagnosed with Epstein-Barr virus (EBV)-related splenomegaly and subsequently identified as having an aggressive B-cell lymphoproliferative disorder. Despite multiple initial diagnostic tests yielding negative results, persistent clinical suspicion due to the worsening patient's condition warranted further investigation, ultimately establishing the correct diagnosis. This case underscores the diagnostic challenges in distinguishing benign viral-associated splenomegaly from underlying malignant lymphoproliferative disorders and highlights the importance of maintaining clinical vigilance when initial diagnostic findings are discordant with the clinical presentation.

PMID 42465140
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PubMedBMC medical education2026-07-17

Enhancing ultrasound training for breast cancer diagnosis: a controlled study of AI-assisted learning.

Wu Shuang S, Wang Weihao W, Wu Jian J, Zhou Hong H et al.

This study aimed to develop and evaluate an AI-assisted teaching platform to enhance diagnostic competency in breast ultrasound. The goal was to assess whether AI integration improves diagnostic accuracy, learning efficiency, and participant satisfaction within a residency training program. We conducted a cohort-based study at our hospital. Twelve junior residents (experimental group) underwent AI-assisted training via a newly implemented platform, while twelve senior residents (control group) completed conventional training. Diagnostic performance was evaluated before and after the one-month intervention using consistent assessments. Participant satisfaction was surveyed across domains including learning engagement, skill development, and confidence. In the experimental group, post-intervention diagnostic scores (90.50 ± 9.82) were significantly higher than pre-intervention diagnostic scores(70.00 ± 17.55, P = 0.003,95%CI[-32.54,-8.46], Cohen's d=-1.44). Survey results indicated high satisfaction: 83.33% strongly agreed the platform facilitated learning, 66.67% reported improved pattern recognition, and 66.67% noted increased engagement in self-learning. A majority also reported gains in clinical reasoning and confidence when facing a real patient. We integrated an AI-assisted platform into ultrasound residency training, creating an educational tool. In this single-center exploratory study, the AI-assisted platform shows potential to improve residents' diagnostic skills for breast ultrasound.

PMID 42464241
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PubMedBMC medical education2026-07-17

Effect of AI-assisted caries annotation on dental students' performance in caries detection on panoramic radiographs.

Pornprasertsuk-Damrongsri Suchaya S, Kitisubkanchana Jira J, Vachmanus Sirawich S, Mongkolwat Pattanasak P et al.

Dental caries remains one of the most prevalent oral diseases globally. The integration of artificial intelligence (AI) into dental radiographic interpretation, particularly for caries detection, has expanded rapidly. AI-assisted caries annotation may help dental students identify carious lesions on panoramic radiographs-a commonly used diagnostic tool for evaluating teeth and surrounding structures-thereby improving diagnostic accuracy, efficiency, and confidence. This study aimed to assess the effect of AI-assisted caries annotation on dental students' diagnostic performance, confidence, and time efficiency in detecting caries on panoramic radiographs. Fifty panoramic radiographs with multistage carious lesions, verified by bite-wing radiographs as the gold standard, were randomly selected. Caries were annotated using recently developed AI-assisted software. Forty fourth-year dental students participated after calibration with ten sets of unannotated and AI-annotated radiographs. In Session 1, participants identified carious lesions on 40 unannotated panoramic radiographs. One month later (Session 2), the same radiographs were re-evaluated with AI-assisted caries annotation, alongside an unannotated radiograph. For each radiograph, the location and depth of detected caries, diagnostic time, and self-reported confidence (0-10 scale) were recorded. Diagnostic accuracy, sensitivity, specificity, balanced accuracy, precision, negative predictive value, and miss rate between the sessions were compared using paired t-tests or Wilcoxon tests. AI-assisted caries annotation significantly enhanced diagnostic performance compared with conventional interpretation. Accuracy increased from 0.91 to 0.96, sensitivity from 0.35 to 0.67, specificity from 0.96 to 0.99, balanced accuracy from 0.65 to 0.83, precision from 0.33 to 0.77, negative predictive value from 0.95 to 0.98, while the miss rate decreased from 0.65 to 0.33 (p < 0.001). Students' confidence improved notably for enamel caries (4.0 to 6.0), dentin caries (5.0 to 7.0), and pulp-involved caries (8.0 to 8.5), as well as overall detection (5.0 to 7.0) (p < 0.001). Moreover, the mean diagnostic time per radiograph significantly decreased from 67.89 to 53.92 s (p < 0.001). AI-assisted caries annotation substantially enhanced diagnostic efficiency, reduced interpretation time, and improved dental students' confidence in detecting dental caries across all depths. These findings highlight the potential of AI-assisted annotation as an effective educational adjunct for developing diagnostic competence in dental radiology.

PMID 42464347
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