Clinical trial · Interventional
Neural Network-based Treatment Decision Support Tool in Patients With Refractory Solid Organ Malignancies
A Phase II Trial of Neural Network-based Treatment Decision Support Tool in Patients With Refractory Solid Organ Malignancies
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 8, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260908-000001
Summary
Brief summary (as posted)
DRUID is a treatment decision support tool combining predictive models and public databases related to multi-gene markers, drug response screens, gene essentiality and clinical status of drugs to provide drug recommendations personalized based on an input genomic profile. We hypothesize that DRUID analysis of patients' somatic mutational profile from NGS diagnostic platform can be used as a treatment decision support tool in patients with refractory cancer without targetable mutations.
Conditions
Conditions (1)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Solid Organ Malignancies | — | UNRESOLVED | — |
Interventions
Interventions (1)
| Intervention | Type | Mapped drug | Match |
|---|---|---|---|
| DRUID AI Program | Other | — | UNRESOLVED |
Design
Arms and outcomes
Arms (1)
- type
- EXPERIMENTAL
- label
- DRUID
- description
- Patients NGS profile will be analysed with DRUID system to generate recommendations based on predicted efficacy. Patients with available archival tissue will have gene expression analysis performed to optimise DRUID recommendation. Patients will subsequently receive single agent therapy based on DRUID recommendations and criteria for therapy choice.
- interventionNames
- Other: DRUID AI Program
Primary outcomes (1)
- measure
- Objective response rate (ORR)
- timeFrame
- 10 months
- description
- Defined as patient exhibiting a best study response of complete or partial clinical response based on radiological imaging per RECIST 1.1 criteria.
Secondary outcomes (2)
- measure
- Clinical benefit
Eligibility
Eligibility (as posted)
- Sex
- All
- Minimum age
- 21 Years
- Maximum age
- 99 Years
Show eligibility criteria text
Inclusion Criteria:
Patients may be included in the study only if they meet all of the following criteria:
* Age ≥ 21 years.
* Histological or cytological diagnosis solid organ malignancy
* Available results of comprehensive NGS panel testing performed on either tumour tissue or blood-based assay. If results are from blood-based assay, test must have been performed in the metastatic setting.
* ECOG 0-2.
* At least 1 measurable tumour lesions based on RECIST 1.1 criteria
* Estimated life expectancy of at least 12 weeks.
* Has documented progressive disease from last line of therapy.
* Has received at least 2 lines of palliative systemic therapy with no available standard therapy:
* Adequate organ function including the following:
* Bone marrow:
* Absolute neutrophil (segmented and bands) count (ANC) ≥ 1.5 x 109/L
* Platelets ≥ 100 x 109/L
* Haemoglobin ≥ 8 x 109/L
* Hepatic:
* Bilirubin ≤ 1.5 x upper limit of normal (ULN),
* ALT or AST ≤ 2.5x ULN, (or ≤ 5 X with liver metastases)
* Renal:
* Creatinine ≤ 1.5x ULN
* Signed informed consent from patient or legal representative.
* Able to comply with study-related procedures.
Exclusion Criteria:
* Treatment within the last 30 days with any investigational drug.
* Concurrent administration of any other tumour therapy, including cytotoxic chemotherapy, hormonal therapy, and immunotherapy.
* Major surgery within 28 days of study drug administration.
* Active infection that in the opinion of the investigator would compromise the patient's ability to tolerate therapy.
* Pregnancy.
* Breast feeding.
* Serious concomitant disorders that would compromise the safety of the patient or compromise the patient's ability to complete the study, at the discretion of the investigator.
* Active bleeding disorder or bleeding site.
* Non-healing wound.
* Second primary malignancy that is clinically detectable at the time of consideration for study enrolment.
* Symptomatic brain metastasis.References
Publications (8)
- BACKGROUNDZehir A, Benayed R, Shah RH, Syed A, Middha S, Kim HR, Srinivasan P, Gao J, Chakravarty D, Devlin SM, Hellmann MD, Barron DA, Schram AM, Hameed M, Dogan S, Ross DS, Hechtman JF, DeLair DF, Yao J, Mandelker DL, Cheng DT, Chandramohan R, Mohanty AS, Ptashkin RN, Jayakumaran G, Prasad M, Syed MH, Rema AB, Liu ZY, Nafa K, Borsu L, Sadowska J, Casanova J, Bacares R, Kiecka IJ, Razumova A, Son JB, Stewart L, Baldi T, Mullaney KA, Al-Ahmadie H, Vakiani E, Abeshouse AA, Penson AV, Jonsson P, Camacho N, Chang MT, Won HH, Gross BE, Kundra R, Heins ZJ, Chen HW, Phillips S, Zhang H, Wang J, Ochoa A, Wills J, Eubank M, Thomas SB, Gardos SM, Reales DN, Galle J, Durany R, Cambria R, Abida W, Cercek A, Feldman DR, Gounder MM, Hakimi AA, Harding JJ, Iyer G, Janjigian YY, Jordan EJ, Kelly CM, Lowery MA, Morris LGT, Omuro AM, Raj N, Razavi P, Shoushtari AN, Shukla N, Soumerai TE, Varghese AM, Yaeger R, Coleman J, Bochner B, Riely GJ, Saltz LB, Scher HI, Sabbatini PJ, Robson ME, Klimstra DS, Taylor BS, Baselga J, Schultz N, Hyman DM, Arcila ME, Solit DB, Ladanyi M, Berger MF. Mutational landscape of metastatic cancer revealed from prospective clinical sequencing of 10,000 patients. Nat Med. 2017 Jun;23(6):703-713. doi: 10.1038/nm.4333. Epub 2017 May 8. PMID 28481359
- BACKGROUNDGarraway LA, Verweij J, Ballman KV. Precision oncology: an overview. J Clin Oncol. 2013 May 20;31(15):1803-5. doi: 10.1200/JCO.2013.49.4799. Epub 2013 Apr 15. No abstract available. PMID 23589545
- BACKGROUNDCalifano A, Alvarez MJ. The recurrent architecture of tumour initiation, progression and drug sensitivity. Nat Rev Cancer. 2017 Feb;17(2):116-130. doi: 10.1038/nrc.2016.124. Epub 2016 Dec 15. PMID 27977008
- BACKGROUNDMariappan R, Jayagopal A, Sien HZ, Rajan V. Neural Collective Matrix Factorization for integrated analysis of heterogeneous biomedical data. Bioinformatics. 2022 Sep 30;38(19):4554-4561. doi: 10.1093/bioinformatics/btac543. PMID 35929808
- BACKGROUNDNguyen T, Nguyen GTT, Nguyen T, Le DH. Graph Convolutional Networks for Drug Response Prediction. IEEE/ACM Trans Comput Biol Bioinform. 2022 Jan-Feb;19(1):146-154. doi: 10.1109/TCBB.2021.3060430. Epub 2022 Feb 3.