Development of population pharmacokinetics model and Bayesian estimation of rifampicin exposure in Indonesian patients with tuberculosis

BACKGROUND

Interindividual variability in the pharmacokinetics (PK) of anti-tuberculosis (TB) drugs is the leading cause of treatment failure. Herein, we evaluated the influence of demographic, clinical, and genetic factors that cause variability in RIF PK parameters in Indonesian TB patients.

METHODS

In total, 210 Indonesian patients with TB (300 plasma samples) were enrolled in this study. Clinical data, solute carrier organic anion transporter family member-1B1 (SLCO1B1) haplotypes *1a, *1b, and *15, and RIF concentrations were analyzed. The population PK model was developed using a non-linear mixed effect method.

RESULTS

A one-compartment model with allometric scaling adequately described the PK of RIF. Age and SLCO1B1 haplotype *15 were significantly associated with variability in apparent clearance (CL/F). For patients in their 40s, each 10-year increase in age was associated with a 10% decrease in CL/F (7.85 L/h). Patients with the SLCO1B1 haplotype *15 had a 24% lower CL/F compared to those with the wild-type. Visual predictive checks and non-parametric bootstrap analysis indicated good model performance.

CONCLUSION

Age and SLCO1B1 haplotype *15 were significant covariates of RIF CL/F. Geriatric patients with haplotype *15 had significantly greater exposure to RIF. The model could optimize TB pharmacotherapy through its application in therapeutic drug monitoring (clinical trial no. NCT05280886).

Development of population pharmacokinetics model of isoniazid in Indonesian patients with tuberculosis

OBJECTIVES

No population pharmacokinetics (PK) model of isoniazid (INH) has been reported for the Indonesian population with tuberculosis (TB). Therefore, we aimed to develop a population PK model to optimize pharmacotherapy of INH on the basis of therapeutic drug monitoring (TDM) implementation in Indonesian patients with TB.

MATERIALS AND METHODS

INH concentrations, N-acetyltransferase 2 (NAT2) genotypes, and clinical data were collected from Dr. Soetomo General Academic Hospital, Indonesia. A nonlinear mixed-effect model was used to develop and validate the population PK model.

RESULTS

A total of 107 patients with TB (with 153 samples) were involved in this study. A one-compartment model with allometric scaling for bodyweight effect described well the PK of INH. The NAT2 acetylator phenotype significantly affected INH clearance. The mean clearance rates for the rapid, intermediate, and slow NAT2 acetylator phenotypes were 55.9, 37.8, and 17.7 L/h, respectively. Our model was well-validated through visual predictive checks and bootstrapping.

CONCLUSIONS

We established the population PK model for INH in Indonesian patients with TB using the NAT2 acetylator phenotype as a significant covariate. Our Bayesian forecasting model should enable optimization of TB treatment for INH in Indonesian patients with TB.

Case Report: Survival of A Coronavirus Disease-2019 (Covid-19) Patient with Acute Respiratory Distress Syndrome (ARDS) in Dr. Soetomo Hospital, Surabaya, Indonesia

An outbreak of coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that began in Wuhan, China has spread rapidly in multiple countries of the world and has become a pandemic. Currently, there is no vaccine or specific antiviral for COVID-19. A study reported 7.3% of critical patients admitted to ICU, 71% of them required mechanical ventilation, and 38.5% of them were survived. Herein, we reported a 54 year old man with Acute Respiratory Distress Syndrome (ARDS) of COVID-19 who survived the disease. Real-time reverse transcriptase-polymerase chain reaction (RT-PCR) assay of nasopharyngeal and oropharingeal swabs were positive for SARS-CoV-2. Diagnosis of ARDS was also according to clinical symptoms, laboratory, chest radiograph, and chest CT scan. Alcaligenes faecalis and Candida albicans were also identified from sputum culture. Treatment for this patient was causal and supportive therapy, including antibiotic, antiviral, and antifungal therapy according to the culture results, fluid resuscitation, and oxygen supply from the mechanical ventilator. This patient was survived and discharged on hospital day-29. A fibrosis in parenchyma pulmonary and sensory peripheral neuropathy occurred after survived from ARDS. Monitoring of clinical, laboratory, and chest radiograph were continued after the patient discharged from the hospital. This case highlights the importance of early diagnosis and effective treatment to the care of COVID-19 patient.

Case Report: Survival of A Coronavirus Disease-2019 (Covid-19) Patient with Acute Respiratory Distress Syndrome (ARDS) in Dr. Soetomo Hospital, Surabaya, Indonesia

An outbreak of coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that began in Wuhan, China has spread rapidly in multiple countries of the world and has become a pandemic. Currently, there is no vaccine or specific antiviral for COVID-19. A study reported 7.3% of critical patients admitted to ICU, 71% of them required mechanical ventilation, and 38.5% of them were survived. Herein, we reported a 54-year-old man with Acute Respiratory Distress Syndrome (ARDS) of COVID-19 who survived the disease. Real-time reverse transcriptase-polymerase chain reaction (RT-PCR) assay of nasopharyngeal and oropharingeal swabs were positive for SARS-CoV-2. Diagnosis of ARDS was also according to clinical symptoms, laboratory, chest radiograph, and chest CT scan. Alcaligenes faecalis and Candida albicans were also identified from sputum culture. Treatment for this patient was causal and supportive therapy, including antibiotic, antiviral, and antifungal therapy according to the culture results, fluid resuscitation, and oxygen supply from the mechanical ventilator. This patient was survived and discharged on hospital day-29. A fibrosis in parenchyma pulmonary and sensory peripheral neuropathy occurred after survived from ARDS. Monitoring of clinical, laboratory, and chest radiograph were continued after the patient discharged from the hospital. This case highlights the importance of early diagnosis and effective treatment to the care of COVID-19 patient.