Tests a new pyrazinamide and hydroxychloroquine plan against standard TB treatment

AI summary

From the trial's eligibility criteria. Machine-read from the registry text on this page, so it can be wrong. The registry text below and the trial team decide who can take part.

The trial is looking for adults with newly diagnosed lung tuberculosis that is not drug-resistant. It compares a new two-drug schedule with the standard four-drug treatment.

Age

18 to 65 years

Sex

All sexes can join.

Disease stage

Drug-sensitive tuberculosis of the lungs, confirmed by a positive sputum smear or GeneXpert MTB/RIF test.

Earlier treatment

No earlier TB treatment, or less than 1 month of TB treatment in the past.

Biomarker or test result

HIV-negative, or HIV-positive with CD4 count ≥200 cells/mm³ on stable antiretroviral therapy.

Main reasons for not taking part

• Drug-resistant TB (resistance to Rifampicin or Isoniazid)
• Severe liver problems (Child-Pugh Class C) or ALT/AST >3 times the upper limit of normal
• Severe kidney problems (eGFR <30 mL/min/1.73m²)
• Pregnancy or breastfeeding
• Retinal disease or other reasons not to take Hydroxychloroquine

The registry criteria, word for word

Check if you might fit

Answer for yourself. This is not a decision: only the study team can say whether you can take part.

• Are you between 18 and 65 years old?YesNo

What the trial wants to find out

Tuberculosis (TB) kills 1.3 million people annually and remains the world's deadliest bacterial disease. The standard four-drug RIPE regimen achieves only 85% cure rates and causes drug-induced hepatotoxicity in 25-37% of patients. Hydroxychloroquine (HCQ), an FDA-approved antimalarial, has been shown to synergise with pyrazinamide (PZA) by inhibiting the BCRP-1 efflux pump and raising phagolysosomal pH, increasing intracellular PZA concentrations (FICI 0.38 in vitro). The AIPH-TB computational framework (Artificial Intelligence Physicochemical Harmonisation for Tuberculosis) uses multi-objective reinforcement learning, Gaussian process regression, and a digital twin macrophage simulator to identify an AI-optimised dosing schedule that maximises this synergy (PZA 1,500 mg + HCQ 200 mg at 0800 and HCQ 200 mg at 2000), maintaining phagolysosomal pH within 5.2-5.8 for 18 of 24 hours. The computational model predicts FICI 0.28 (strongly synergistic), 9.4-fold increase in intracellular PZA concentration, 99.5% cure rate, and <1.5% hepatotoxicity. This Phase II randomised controlled trial will test whether the AI-optimised PYZ-HCQ protocol is superior to standard RIPE in 200 newly-diagnosed drug-sensitive pulmonary TB patients over 6 months of treatment with 6 months of follow-up.

The 2 groups in the trial

• AI-Optimised PYZ-HCQ ArmTreatment being tested

  Participants receive AI-optimised combination of Pyrazinamide (PYZ) and Hydroxychloroquine (HCQ) for drug-sensitive pulmonary tuberculosis. AI algorithms determine optimal dosing and duration based on patient pharmacokinetic and pharmacogenomic parameters over a 4-month intensive phase followed by 2-month continuation phase.

  Treatment: Pyrazinamide and Hydroxychloroquine (AI-Optimised)

• Standard RIPE Regimen ArmExisting treatment, for comparison

  Participants receive standard WHO-recommended RIPE regimen (Rifampicin, Isoniazid, Pyrazinamide, Ethambutol) for drug-sensitive pulmonary tuberculosis. Standard 2-month intensive phase followed by 4-month continuation phase with Rifampicin and Isoniazid.

  Treatment: Standard RIPE Regimen (Rifampicin, Isoniazid, Pyrazinamide, Ethambutol)