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AI adjuvant therapy
AI adjuvant therapy is a Small molecule drug developed by Tao OUYANG. It is currently in Phase 2 development for Adjuvant cancer therapy optimization (indication-specific application pending).
AI adjuvant therapy uses artificial intelligence to optimize and personalize adjuvant treatment strategies in cancer patients.
Researchers are studying the use of artificial intelligence (AI) to personalize treatment for various types of cancer, including prostate cancer, gastric cancer, and gastroesophageal junction adenocarcinoma. AI is being investigated as an adjuvant therapy in combination with treatments such as androgen deprivation therapy, high-dose-rate interstitial brachytherapy, and radiotherapy.
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Baseline phase 2 → approval rate
+15.3pp
Industry-wide phase 2 drugs reach approval ~15.3% of the time (BIO/Informa 2023 industry benchmark across all therapeutic areas). -
Oncology Phase 2 attrition
-2.0pp
Oncology drugs have higher Phase 2-to-Phase 3 attrition than average — many fail to show OS benefit in larger studies.
| Regulator | Country | Likely year | Lag vs FDA |
|---|---|---|---|
| FDA | US | 2031–2034 | — |
| EMA | EU | 2032–2035 | +0.7 yr |
| MHRA | GB | 2032–2035 | +0.7 yr |
| Health Canada | CA | 2032–2036 | +0.9 yr |
| TGA | AU | 2032–2036 | +1.2 yr |
| PMDA | JP | 2032–2036 | +1.5 yr |
| NMPA | CN | 2033–2037 | +2.3 yr |
| MFDS | KR | 2032–2036 | +1.4 yr |
| CDSCO | IN | 2032–2037 | +1.8 yr |
| ANVISA | BR | 2033–2037 | +2.3 yr |
Hover any row for the lag rationale. Lag estimates are reduced when the drug has FDA Breakthrough or EMA PRIME designation (sponsors file globally in parallel).
Estimate based on the BIO/Informa industry phase transition rates plus per-drug modifiers for therapeutic area, sponsor type, FDA designations, mechanism, and trial design. Per-jurisdiction lags from Tufts CSDD international approval studies. Not investment, clinical or regulatory advice. Methodology: /methodology#likelihood.
At a glance
| Generic name | AI adjuvant therapy |
|---|---|
| Sponsor | Tao OUYANG |
| Modality | Small molecule |
| Therapeutic area | Oncology |
| Phase | Phase 2 |
Mechanism of action
This investigational approach leverages machine learning algorithms to analyze patient data, tumor characteristics, and treatment response patterns to guide adjuvant therapy selection and dosing. The goal is to improve outcomes by tailoring adjuvant regimens to individual patient and tumor profiles, potentially reducing unnecessary toxicity while maximizing efficacy.
Approved indications
- Adjuvant cancer therapy optimization (indication-specific application pending)
Common side effects
Key clinical trials
- A Study of Elacestrant Versus Standard Endocrine Therapy in Women and Men With ER+,HER2-, Early Breast Cancer With High Risk of Recurrence (PHASE3)
- Non-interventional Study to Assess the Effectiveness and Safety of Ribociclib in the Adjuvant Therapy of Hormone Receptor Positive (HR+) HER2-negative Stage II and III Breast Cancer in Real Clinical Practice in Russia
- A Non-interventional Study for Kisqali (Ribociclib) in Combination With an Aromatase Inhibitor for Adjuvant Treatment in Patients With HR+/HER2- Early Breast Cancer at High Risk of Recurrence
- Evaluating the Addition of Adjuvant Chemotherapy to Ovarian Function Suppression Plus Endocrine Therapy in Premenopausal Patients With pN0-1, ER-Positive/HER2-Negative Breast Cancer and an Oncotype Recurrence Score Less Than or Equal to 25 (PHASE3)
- A Study of Camizestrant in ER+/HER2- Early Breast Cancer After at Least 2 Years of Standard Adjuvant Endocrine Therapy (PHASE3)
- Implementation Study to Describe and Compare Retention Rate and Adherence to Adjuvant Therapy With Ribociclib With and Without Usage of Mobile Application in Patients With HR+ HER2-negative Stage II and III Breast Cancer in Real-world Practice
- A Study of Changes in Ki67 Expression in People With Breast Cancer Receiving Endocrine Therapy Before Surgery (NA)
- Evaluating the Use of a Medication 'Switch' vs Guideline-directed Interventions for Relieving Side Effects of Aromatase Inhibitors Among Breast Cancer Patients (PHASE2)
Primary sources
Every claim on this page is sourced from regulatory or scientific primary sources. See our editorial policy for full methodology.
| Source | Used for |
|---|---|
| ClinicalTrials.gov | Trial enrolment, design, endpoints, results |
Competitive intelligence
For the full competitive landscape — auto-detected comparators, recent regulatory actions across the set, upcoming PDUFA, patent timeline, sponsor landscape:
- AI adjuvant therapy CI brief — competitive landscape report
- AI adjuvant therapy updates RSS · CI watch RSS
- Tao OUYANG portfolio CI
Frequently asked questions about AI adjuvant therapy
What is AI adjuvant therapy?
How does AI adjuvant therapy work?
What is AI adjuvant therapy used for?
Who makes AI adjuvant therapy?
What development phase is AI adjuvant therapy in?
Related
- Manufacturer: Tao OUYANG — full pipeline
- Therapeutic area: All drugs in Oncology
- Indication: Drugs for Adjuvant cancer therapy optimization (indication-specific application pending)
Primary sources · FDA · ClinicalTrials.gov · EMA · SEC EDGAR · ChEMBL · Wikidata · full sourcing