Background. Isoniazid (INH) is one of the drugs in the classical therapy for pulmonary tuberculosis. Several INH population pharmacokinetic models were established in the last ten years. This systemic review was performed i) to summarize published population pharmacokinetic (PPK) models of INH and ii) to summarize and explore identified covariates influencing the INH pharmacokinetic models. Methods. A search of publication for population pharmacokinetic analyses of INH either in healthy volunteers or patients from 2011 to 2021 was conducted in PubMed databases. Reviews, methodology articles, non-compartmental analysis, in-vitro, and animal studies, were excluded. Results. 12 studies were included in this review. INH pharmacokinetics was described as two-compartment with first-order absorption and elimination in most of the included studies. In most of the studies, N-acetyltransferase 2 (NAT2 ) genotype polymorphism (n=11) was the most common identified covariates affecting INH pharmacokinetic parameters. Body weight (n=2) and body mass index (BMI) (n=1) were the most common significant covariates retained in the final model. Conclusions . The population pharmacokinetic of INH has been extensively reviewed and the published PPK models of INH and its parameters were summarized in this review. The PPK variability of INH was reported to be explained mainly by the NAT2 genotype polymorphism on the clearance (CL) parameter. This review shows that to optimise and rationalise the dosing regimen of INH, a patient’s NAT2 genotype should be considered. Body weight and BMI value should be taken into consideration when making dosing adjustments for INH to achieve therapeutic range.

Background: Vancomycin is a glycopeptide antibiotic used for gram-positive infections. Several vancomycin population pharmacokinetic models have been introduced in the last decades. Thus, a systematic review was performed to compare published pharmacokinetics models and (ii) to summarise and explore identified covariates influencing the vancomycin pharmacokinetics models. Methods: A search of publications for population pharmacokinetic analyses of vancomycin in critically ill obese patients from inception to October 2022 was conducted in PubMed and SCOPUS databases. Reviews, methodology articles, in vitro and animal studies, and noncompartmental analyses were excluded. Data on study characteristics, patient demographics, clinical parameters, pharmacokinetic parameters, and outcomes were collected. Results: Six studies were included in this review. Vancomycin pharmacokinetics was described as one-compartment in most of the studies. Significant interindividual variations of vancomycin pharmacokinetic parameters were found in most of the included studies. Age, sex, body weight, fibrinogen, aspartate aminotransferase, blood urea nitrogen, cystatin, and concomitant nephrotoxic drugs were the most commonly identified covariates affecting these parameters. External validation was only performed in one study to determine the predictive performance of the models. Conclusions. Large pharmacokinetic variability remains despite the inclusion of several covariates. This can be improved by including other potential factors, such as metabolic factors and significant drug-drug interactions in a well-designed population pharmacokinetic model in the future, taking into account the incorporation of a larger sample size and a more stringent sampling strategy. External validation should also be performed to the previously published models to compare their predictive performances.

Aim Viral blips that occur among virally suppressed HIV-positive patients suggest immune activation and inflammation and associated with slower CD4 count and CD4/CD8 ratio normalisation. With the advances in HIV treatment, lifestyle and comorbidities begin to be a concern despite successful antiretroviral therapy. We reported a study incorporating the effect of CD4 and CD4/CD8 ratio normalisation on viral blips in joint disease progression (DP) and time-to-event (TTE) model. Methods A total of 152 HIV-positive patients receiving efavirenz therapy were recruited. Joint DP and TTE models on viral blip were developed for CD4 and CD4/CD8 ratio separately. Risk factors, such as smoking status, pack-year and comorbidity scores, were included in the analysis. Results Gompertz model best described the CD4 and CD4/CD8 ratio DP models, while viral blips data were fitted with the Cox proportional hazard model. History of opportunistic infections and changing of antiretroviral regimen significantly affect the baseline CD4 and CD4/CD8 ratio. Comorbidity score was significant in both CD4 (asymptote CD4) and CD4/CD8 ratio DP model (recovery rate). Increase in cumulative pack-year resulted in lower CD4/CD8 ratio recovery rate (β -0.02, 95%CI: -0.03 to -0.01; p<0.001). Active smokers with slow CD4 or CD4/CD8 ratio normalisation associated with more viral blips. Conclusion CD4 and CD4/CD8 ratio are significant risk factors of viral blips and potential markers of non-AIDS related morbidities in virally suppressed patients. Early identification of high-risk group with repeated viral load testing, lifestyle modification and comorbidities management should be emphasised in the HIV treatment long-term care plan.

Aim: Efavirenz is still widely used as the preferred first-line antiretroviral agent in the middle- and low- income countries, including Malaysia. The efavirenz population pharmacokinetic profile among HIV-positive smokers is still unknown. We aimed to assess the association of smoking with efavirenz and the differences in HIV clinical outcomes. Methods: A total of 154 stable HIV-positive patients on efavirenz in northern Malaysia were recruited with a sparse sampling for this multicentre prospective cohort study. The association between smoking and efavirenz pharmacokinetic parameters was determined using the non-linear mixed-effect model (NONMEM). A mixture model of clearance was adopted to describe the metaboliser status because genetic data is unavailable. The effect of smoking on HIV clinical markers (CD4, CD4 / CD8 ratio and viral blips) for at least two years after the antiretroviral initiation was also investigated. Results: Our data were best fitted with a one-compartment mixture model with first-order absorption without lag time. Smoking significantly associated with higher clearance (CL/F) (β = 1.39; 95% confidence interval (CI): 1.07 to 1.91), while weight affected both CL/F and volume (V/F). From the mixture model, 20% of patients were in the slow clearance group, which mimic the genotype distribution of slow metaboliser. An efavirenz dose reduction is not recommended for smokers ≥60kg with normal metabolism rate. Smoking significantly associated with slower normalisation of CD4 and CD4 / CD8 ratio. Conclusion: HIV-positive smokers presented with significantly higher efavirenz clearance and unfavourable clinical outcomes. Close monitoring of adherence and clinical response among smokers is warranted.