Veshni Pillay-Fuentes Lorente1, Ahmed A. Abulfathi2, Adrie Bekker3, Eric Decloedt1, Thomas Dorlo4
1Division of Clinical Pharmacology, Faculty of Medicine and Health Sciences, Stellenbosch University, 2CDDS, Certara USA, 3Department of Paediatrics and Child Health, Stellenbosch University, 4Department of Pharmacy, Uppsala University
Introduction/Objectives: The growing global burden of infections caused by multidrug resistant gram-negative bacteria (MDR-GNB) necessitates appropriate choice and dosing of antimicrobial agents [1]. Colistin, a polymyxin E antibiotic, is used as last-resort treatment for MDR-GNB infections in many low-income settings, including South Africa, mainly due to its efficacy and affordability [2-4]. However, there is a paucity of pharmacokinetic data to guide appropriate dosing in understudied populations, including critically ill Africans. Colistin is administered as colistimethate sodium (CMS) which is partially hydrolysed to active colistin (approximately 20-25%). Additionally, approximately 60-70% of CMS is excreted unchanged through the kidneys [5]. Critically ill patients, who are the primary recipients of colistin, often have pathophysiological changes such as altered renal function which can further impact CMS exposures. The objectives of this study were i) to characterize the pharmacokinetics of CMS in critically ill South African adults using a population approach, and ii) to explore covariates that influence CMS pharmacokinetics. Methods Pharmacokinetic data from an observational study conducted in 24 critically ill, adult patients at Tygerberg Hospital, Cape Town, South Africa were used to develop a CMS model. Patients received intravenous infusions of either 9 million units (MU) or 12 MU CMS loading dose, followed by maintenance infusions of 3 MU 8-hourly or 4 to 5 MU 12-hourly. Blood samples were collected at 1, 2, 4, 8, 12, 24 and 48 hours post-loading dose. CMS concentrations were analysed using liquid chromatography mass spectrometry at the Division of Clinical Pharmacology at Stellenbosch University. Covariates including weight, creatinine clearance (CRCL) calculated using the Cockroft-Gault equation, estimated glomerular filtration rate (eGFR) based on the 2021 CKD-EPI formula, albumin, inflammatory markers, ward-type (e.g. medical, surgical, or burns patients), and sex were evaluated in the model. Nonlinear mixed effects modelling was carried out using NONMEM (version, 7.5.1, ICON development solutions). Results Fifty percent (12/24) of the cohort were recruited from burns intensive care units (ICU), 42% (10/24) from surgical ICU and 8% (2/24) from the medical ICU. The mean age of patients was 42 years (± 16.3 standard deviation) and 17/24 (71%) were male. One hundred and fifty-four concentrations, of which 31% were below limit of quantification (BLQ), were used to develop the CMS model. BLQ data was handled using the M3 method. A two-compartment model with zero-order infusion and linear elimination adequately describes the CMS concentration-time data. All clearances and volumes were allometrically scaled based on body weight, normalized to 80kg with fixed exponents of 0.75 and 1 for clearance and volume terms, respectively. Final volume and clearance parameter estimates were 18.9 litres(L) (relative standard error [RSE], 13%) and 8.95 L/hr, (RSE 16%), respectively. Peripheral volume estimate was 61.2 L (RSE, 43%) and intercompartmental clearance 5.79 L/hr (RSE, 16%). Inter-individual variability was added on CMS Clearance (51% co-efficient of variation, RSE 32%). Both CRCL and eGFR influenced the clearance of CMS. eGFR was incorporated as a covariate in the model since it is the preferred method of assessing renal function in clinical practice. A 50% increase in eGFR increased CMS clearance by 69.8%. Inflammatory markers, sex, weight, ward-type, and albumin were not identified as statistically significant covariates. Conclusions The parameter estimates in this study are comparable to previously published data in non-African critically ill adult patients [6]. eGFR was found to be a significant covariate. Critically ill patients are prone to changes in renal function including both renal impairment and augmented renal function. Future analyses will integrate colistin data (active metabolite) to further explore the exposure of colistin and the influence of eGFR. Simulation scenarios with both augmented and impaired renal function will be conducted for dose optimization of CMS in critically ill patients.
1. World Health Organization. (2024, May 17). WHO updates list of drug-resistant bacteria most threatening to human health. World Health Organization. https://www.who.int/news/item/17-05-2024-who-updates-list-of-drug-resistant-bacteria-most-threatening-to-human-health 2. Majavie L, Johnston D, Messina A. A retrospective review of colistin utilisation at a tertiary care academic hospital in South Africa. S Afr J Infect Dis. 2021 Jun 18;36(1):205. doi: 10.4102/sajid.v36i1.205. PMID: 34485491; PMCID: PMC8378127. 3. Matshediso GP, Durojaiye OC, Adeniyi OV. Colistin utilization at a tertiary hospital in South Africa: an opportunity for antimicrobial stewardship practices. J Med Microbiol. 2024 Jun;73(6). doi: 10.1099/jmm.0.001840. PMID: 38842435. 4. Muhammad S Moolla, Andrew Whitelaw, Eric H Decloedt, Coenraad F N Koegelenberg, Arifa Parker, Opportunities to enhance antibiotic stewardship: colistin use and outcomes in a low-resource setting, JAC-Antimicrobial Resistance, Volume 3, Issue 4, December 2021, dlab169, https://doi.org/10.1093/jacamr/dlab169 5. Grégoire, N., Aranzana-Climent, V., Magréault, S. et al. Clinical Pharmacokinetics and Pharmacodynamics of Colistin. Clin Pharmacokinet 56, 1441–1460 (2017). https://doi.org/10.1007/s40262-017-0561-1 6. Garonzik SMLi JThamlikitkul V, Paterson DL, Shoham S, Jacob J, Silveira FPForrest A, Nation RL2011.Population Pharmacokinetics of Colistin Methanesulfonate and Formed Colistin in Critically Ill Patients from a Multicenter Study Provide Dosing Suggestions for Various Categories of Patients . Antimicrob Agents Chemother55:.https://doi.org/10.1128/aac.01733-10
Reference: PAGE 33 (2025) Abstr 11631 [www.page-meeting.org/?abstract=11631]
Poster: Drug/Disease Modelling - Infection