Why we did the review
Isoniazid is one of the two main first-line tuberculosis drugs, along with rifampicin. Its plasma concentrations vary a lot between patients, and low concentrations of anti-TB drugs have been linked to treatment failure and to multidrug-resistant TB. Several population PK models of isoniazid had been published, but nobody had put them side by side. We wanted to compare the models and see which covariates kept coming up as explaining the variability.
How we did it
We followed PRISMA and searched PubMed and Scopus for population PK analyses of isoniazid in volunteers or TB patients from 2011 to 2021. Reviews, methodology papers, non-compartmental analyses, in vitro and animal work, and studies that reused a previously published model were excluded. From 107 records, 14 went to full evaluation and 12 studies (published 2013 to 2021) were included.
Together they covered 1,444 people, from 33 to 466 per study, aged 0 to 72. Four studies included children, eight were adults only, and one of those was in healthy volunteers. All gave isoniazid orally.
What we found
Ten of the twelve models were two-compartment and two were one-compartment. One of the one-compartment models was built from a single sample taken 3 hours after the dose, which probably isn’t enough data to support a second compartment. Absorption was handled in different ways: first-order, transit compartments (mean transit time 0.66 h, range 0.179 to 0.924 h across studies) or a lag time. Transit compartments describe the physiology of absorption better than a lag time does.
Mean reported clearance was 20.55 L/h (SD 16.60). Children cleared the drug more slowly: median 6.5 L/h (range 4.44 to 11.3) against 22.8 L/h (11.4 to 40.5) in adults. This fits with how NAT2, the enzyme that acetylates isoniazid, matures. At birth the slow-acetylator phenotype predominates, and the fast phenotype develops over roughly the first four years of life in children with the relevant genotypes. Central volume was also smaller in children.
Across the twelve studies, 29 covariates were tested. NAT2 genotype was the main one, affecting clearance in 11 studies. Creatinine clearance and body mass index each came up once on clearance, and body weight (two studies), body mass index and mid-upper arm circumference (one each) on central volume. Most studies used a mixture model to split patients into acetylator groups.
The NAT2 effect also tracks ethnicity. In a South African study more than 80% of patients were slow or intermediate acetylators, and in a Tanzanian study 48% were slow and 48% intermediate. Studies from Singapore and China found mostly fast and intermediate acetylators, and those two studies reported some of the highest clearances in the review.
A few gaps showed up. Only one study modelled the main metabolite, acetylisoniazid, even though acetylation status matters for hepatotoxicity (slow acetylators are at higher risk). Only one study checked adherence, by pill count and medication diaries. Every study did basic internal evaluation such as goodness-of-fit plots and VPCs, five added bootstrap or NPDE, and only two tested their model on an external dataset.
What it means
To dose isoniazid well, a patient’s NAT2 genotype and age should be taken into account, and body weight and BMI matter for dose adjustment. Genotyping isn’t routine everywhere, so mixture models are a practical way to estimate acetylator status when genetic data aren’t available. They still need external validation: one study that did genotype some patients found no significant difference in Cmax or AUC between slow-intermediate and rapid acetylators. Larger studies with better sampling would help, and both existing and new models should be evaluated on external data.
The paper: Tan WR, Sheikh Ghadzi SM, Hyder Ali IA, Harun SN. Systematic review of population pharmacokinetic models of isoniazid in children and adults with tuberculosis. Malaysian Journal of Pharmacy. 2022;8(2):1-15. doi:10.52494/DJIQ7058. It’s free to read on the journal’s site.