Why do people on the same HIV drug dose reach different blood levels?
Two variants of the liver enzyme gene CYP2B6, along with body weight and sex, together explained about 55% of the person-to-person variation in how quickly patients cleared efavirenz.
Source
CYP2B6*6, CYP2B6*18, Body weight and sex are predictors of efavirenz pharmacokinetics and treatment response: population pharmacokinetic modeling in an HIV/AIDS and TB cohort in Zimbabwe
Study at a glance
- Design
- Cross-sectional — Single-timepoint plasma efavirenz measurement (12-15 h post-dose) with TaqMan genotyping, analysed by nonlinear mixed-effects population PK modelling in NONMEM plus Monte Carlo simulations
- N
- N=185 · 185 patients analysed: 95 recruited on efavirenz-based ART alone and 90 with HIV/TB co-infection also receiving rifampicin-containing TB therapy (60 men, 125 women)
- Population
- Adults with HIV, some co-infected with tuberculosis, attending two hospitals in Zimbabwe while taking efavirenz-based antiretroviral therapy
- Outcome
- Apparent oral clearance of efavirenz and plasma concentration; secondary outcomes were reported central nervous system adverse effects
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What they did
The researchers recruited 185 patients taking efavirenz-based therapy at two Zimbabwean hospitals, 90 of whom were also on rifampicin-containing tuberculosis treatment. They measured plasma efavirenz 12-15 hours after a dose by HPLC and genotyped each patient for variants in CYP2B6, CYP2A6 and ABCB1. A one-compartment population pharmacokinetic model was fitted in NONMEM, testing genetic and demographic variables as covariates on drug clearance, and the fitted model was then used for simulation.
What they found
Patients homozygous for the CYP2B6*6, CYP2B6*18 and CYP2A6*9 variants had significantly higher average efavirenz levels, and those carrying at least two mutant alleles across CYP2B6*6 and *18 had roughly fourfold higher concentrations than wild-type patients. In the final model, CYP2B6*6, CYP2B6*18, body weight and sex together explained up to 55% of between-subject variability in clearance; clearance fell from 7.01 L/h in extensive metabolisers to 0.539 L/h in poor metabolisers, rose 21% per 10 kg of weight, and was 22% higher in women. CYP2A6 and ABCB1 variants were not retained, co-treatment with rifampicin made no significant difference, and average levels did not differ between patients with and without CNS side effects.
The limits
What it doesn't show
Each patient contributed a single sparse concentration measurement, and absorption rate and volume of distribution were fixed from earlier literature rather than estimated, so the clearance estimates depend on those assumptions. The design is observational and cross-sectional with no virological endpoint measured, so it cannot show that any particular exposure level improves treatment outcomes; the simulated exposures are model output, not trial results, and the authors call for controlled clinical studies to test them. CNS side effects were assessed once by score chart although symptoms in this cohort appeared anywhere from four weeks to many months, so the genotype-symptom analysis was poorly timed. About 45% of variability remained unexplained, and results come from one country and may not transfer to other populations.
Key terms
- Apparent oral clearance (CL/F)
- How fast the body removes a drug relative to the fraction absorbed; higher clearance means lower blood levels for the same dose.
- CYP2B6
- A liver enzyme that metabolises efavirenz; common gene variants reduce its activity.
- Poor metaboliser
- Someone whose genotype gives very low enzyme activity, so the drug is broken down slowly and accumulates.
- Population pharmacokinetic modelling
- Fitting a model to sparse concentration data from many patients at once to estimate typical parameters and how much individuals vary.
- Between-subject variability
- The share of differences in a pharmacokinetic parameter that is due to differences between people rather than measurement noise.
- Therapeutic range
- The concentration window in which a drug is generally expected to work with acceptable side effects.
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Quiz yourself
Which enzyme is mainly responsible for metabolising efavirenz?
Common questions
Why does genotype matter so much for this drug?
Efavirenz is cleared mainly by CYP2B6, so reduced-function variants of that one gene translate directly into slower clearance and much higher blood levels on the same dose.
Why didn't rifampicin change efavirenz levels here?
Rifampicin induces the enzymes that clear efavirenz, but other TB drugs such as isoniazid inhibit some of them, and the authors suggest these opposing effects may cancel out; the literature is conflicting.
Why couldn't the study link genotype to side effects?
Side effects were assessed at a single visit while symptoms in this group started as late as four weeks and sometimes lasted years, so a one-off assessment may miss the relevant window.
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