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Research on the Construction of an Accurate Dose Prediction Model for Mycophenolic Acid Drugs Based on Machine Learning

Research on the Construction of an Accurate Dose Prediction Model for Mycophenolic Acid Drugs Based on Machine Learning

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2500108261
Enrollment
Unknown
Registered
2025-08-27
Start date
2025-09-10
Completion date
Unknown
Last updated
2025-09-01

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Kidney transplantation, heart transplantation, liver transplantation

Interventions

Observation group:NA

Sponsors

The Second Affiliated Hospital of the Army Medical University
Lead Sponsor

Eligibility

Sex/Gender
All
Age
18 Years to 70 Years

Inclusion criteria

Inclusion criteria: 1.Hospitalized patients using mycophenolic acids; 2.The age ranged from 18 to 70 years; 3.Taking mycophenolic acids at a fixed dose for more than 5 days (i.e. reaching a steady state); 4.Patients with mycophenolate treatment drug monitoring data and clinical data;

Exclusion criteria

Exclusion criteria: 1.Those who do not take mycophenolate drugs regularly; 2.Patients with mycophenolate drugs not monitored during hospitalization; 3.Pregnant and lactating women; 4.Those with tumor.

Design outcomes

Primary

MeasureTime frame
Mycophenolic acid plasma concentration;—

Countries

China

Contacts

Public ContactJingbin Huang

The Second Affiliated Hospital of the Army Medical University

hjb20091364@126.com+86 23 68755580

Outcome results

None listed

Source: ChiCTR (via WHO ICTRP) · Data processed: Feb 4, 2026