Displaying publications 21 - 27 of 27 in total

Abstract:
Sort:
  1. Chaw SH, Lo YL, Goh SL, Cheong CC, Tan WK, Loh PS, et al.
    Obes Surg, 2021 10;31(10):4305-4315.
    PMID: 34282569 DOI: 10.1007/s11695-021-05564-x
    BACKGROUND: Transversus abdominis plane (TAP) block and intraperitoneal local anesthetics (IPLA) are widely investigated techniques that potentially improve analgesia after bariatric surgery. The analgesic efficacy of TAP block has been shown in previous studies, but the performance of TAP block can be difficult in patients with obesity. We performed a systematic review and meta-analysis to compare the analgesic efficacy of TAP block and IPLA. An alternative technique is useful in clinical setting when TAP block is not feasible.

    METHODS: We searched PubMed, Embase, and CENTRAL from inception until August 2020 for randomized controlled trials comparing both techniques. The primary outcome was cumulative morphine consumption at 24 h. Secondary pain-related outcomes included pain score at rest and on movement at 2, 6, 12, and 24 h; postoperative nausea and vomiting; and length of hospital stay.

    RESULTS: We included 23 studies with a total of 2,178 patients. TAP block is superior to control in reducing opioid consumption at 24 h, improving pain scores at all the time points and postoperative nausea and vomiting. The cumulative opioid consumption at 24 h for IPLA is less than control, while the indirect comparison between IPLA with PSI and control showed a significant reduction in pain scores at rest, at 2 h, and on movement at 12 h, and 24 h postoperatively.

    CONCLUSIONS: Transversus abdominis plane block is effective for reducing pain intensity and has superior opioid-sparing effect compared to control. Current evidence is insufficient to show an equivalent analgesic benefit of IPLA to TAP block.

  2. Holford N, O'Hanlon CJ, Allegaert K, Anderson B, Falcão A, Simon N, et al.
    Br J Clin Pharmacol, 2024 Apr;90(4):1066-1080.
    PMID: 38031322 DOI: 10.1111/bcp.15978
    AIMS: We propose using glomerular filtration rate (GFR) as the physiological basis for distinguishing components of renal clearance.

    METHODS: Gentamicin, amikacin and vancomycin are thought to be predominantly excreted by the kidneys. A mixed-effects joint model of the pharmacokinetics of these drugs was developed, with a wide dispersion of weight, age and serum creatinine. A dataset created from 18 sources resulted in 27,338 drug concentrations from 9,901 patients. Body size and composition, maturation and renal function were used to describe differences in drug clearance and volume of distribution.

    RESULTS: This study demonstrates that GFR is a predictor of two distinct components of renal elimination clearance: (1) GFR clearance associated with normal GFR and (2) non-GFR clearance not associated with normal GFR. All three drugs had GFR clearance estimated as a drug-specific percentage of normal GFR (gentamicin 39%, amikacin 90% and vancomycin 57%). The total clearance (sum of GFR and non-GFR clearance), standardized to 70 kg total body mass, 176 cm, male, renal function 1, was 5.58 L/h (95% confidence interval [CI] 5.50-5.69) (gentamicin), 7.77 L/h (95% CI 7.26-8.19) (amikacin) and 4.70 L/h (95% CI 4.61-4.80) (vancomycin).

    CONCLUSIONS: GFR provides a physiological basis for renal drug elimination. It has been used to distinguish two elimination components. This physiological approach has been applied to describe clearance and volume of distribution from premature neonates to elderly adults with a wide dispersion of size, body composition and renal function. Dose individualization has been implemented using target concentration intervention.

  3. Colin PJ, Allegaert K, Thomson AH, Touw DJ, Dolton M, de Hoog M, et al.
    Clin Pharmacokinet, 2019 06;58(6):767-780.
    PMID: 30656565 DOI: 10.1007/s40262-018-0727-5
    BACKGROUND AND OBJECTIVES: Uncertainty exists regarding the optimal dosing regimen for vancomycin in different patient populations, leading to a plethora of subgroup-specific pharmacokinetic models and derived dosing regimens. We aimed to investigate whether a single model for vancomycin could be developed based on a broad dataset covering the extremes of patient characteristics. Furthermore, as a benchmark for current dosing recommendations, we evaluated and optimised the expected vancomycin exposure throughout life and for specific patient subgroups.

    METHODS: A pooled population-pharmacokinetic model was built in NONMEM based on data from 14 different studies in different patient populations. Steady-state exposure was simulated and compared across patient subgroups for two US Food and Drug Administration/European Medicines Agency-approved drug labels and optimised doses were derived.

    RESULTS: The final model uses postmenstrual age, weight and serum creatinine as covariates. A 35-year-old, 70-kg patient with a serum creatinine level of 0.83 mg dL-1 (73.4 µmol L-1) has a V1, V2, CL and Q2 of 42.9 L, 41.7 L, 4.10 L h-1 and 3.22 L h-1. Clearance matures with age, reaching 50% of the maximal value (5.31 L h-1 70 kg-1) at 46.4 weeks postmenstrual age then declines with age to 50% at 61.6 years. Current dosing guidelines failed to achieve satisfactory steady-state exposure across patient subgroups. After optimisation, increased doses for the Food and Drug Administration label achieve consistent target attainment with minimal (± 20%) risk of under- and over-dosing across patient subgroups.

    CONCLUSIONS: A population model was developed that is useful for further development of age and kidney function-stratified dosing regimens of vancomycin and for individualisation of treatment through therapeutic drug monitoring and Bayesian forecasting.

  4. Jacqz-Aigrain E, Leroux S, Thomson AH, Allegaert K, Capparelli EV, Biran V, et al.
    J Antimicrob Chemother, 2019 08 01;74(8):2128-2138.
    PMID: 31049551 DOI: 10.1093/jac/dkz158
    OBJECTIVES: In the absence of consensus, the present meta-analysis was performed to determine an optimal dosing regimen of vancomycin for neonates.

    METHODS: A 'meta-model' with 4894 concentrations from 1631 neonates was built using NONMEM, and Monte Carlo simulations were performed to design an optimal intermittent infusion, aiming to reach a target AUC0-24 of 400 mg·h/L at steady-state in at least 80% of neonates.

    RESULTS: A two-compartment model best fitted the data. Current weight, postmenstrual age (PMA) and serum creatinine were the significant covariates for CL. After model validation, simulations showed that a loading dose (25 mg/kg) and a maintenance dose (15 mg/kg q12h if <35 weeks PMA and 15 mg/kg q8h if ≥35 weeks PMA) achieved the AUC0-24 target earlier than a standard 'Blue Book' dosage regimen in >89% of the treated patients.

    CONCLUSIONS: The results of a population meta-analysis of vancomycin data have been used to develop a new dosing regimen for neonatal use and to assist in the design of the model-based, multinational European trial, NeoVanc.

  5. Chai HJ, Kiew LV, Chin Y, Norazit A, Mohd Noor S, Lo YL, et al.
    Int J Nanomedicine, 2017;12:577-591.
    PMID: 28144140 DOI: 10.2147/IJN.S111284
    BACKGROUND AND PURPOSE: Poly-l-glutamic acid (PG) has been used widely as a carrier to deliver anticancer chemotherapeutics. This study evaluates PG as a selective renal drug carrier.

    EXPERIMENTAL APPROACH: 3H-deoxycytidine-labeled PGs (17 or 41 kDa) and 3H-deoxycytidine were administered intravenously to normal rats and streptozotocin-induced diabetic rats. The biodistribution of these compounds was determined over 24 h. Accumulation of PG in normal kidneys was also tracked using 5-(aminoacetamido) fluorescein (fluoresceinyl glycine amide)-labeled PG (PG-AF). To evaluate the potential of PGs in ferrying renal protective anti-oxidative stress compounds, the model drug 4-(2-aminoethyl)benzenesulfonyl fluoride hydrochloride (AEBSF) was conjugated to 41 kDa PG to form PG-AEBSF. PG-AEBSF was then characterized and evaluated for intracellular anti-oxidative stress efficacy (relative to free AEBSF).

    RESULTS: In the normal rat kidneys, 17 kDa radiolabeled PG (PG-Tr) presents a 7-fold higher, while 41 kDa PG-Tr shows a 15-fold higher renal accumulation than the free radiolabel after 24 h post injection. The accumulation of PG-AF was primarily found in the renal tubular tissues at 2 and 6 h after an intravenous administration. In the diabetic (oxidative stress-induced) kidneys, 41 kDa PG-Tr showed the greatest renal accumulation of 8-fold higher than the free compound 24 h post dose. Meanwhile, the synthesized PG-AEBSF was found to inhibit intracellular nicotinamide adenine dinucleotide phosphate oxidase (a reactive oxygen species generator) at an efficiency that is comparable to that of free AEBSF. This indicates the preservation of the anti-oxidative stress properties of AEBSF in the conjugated state.

    CONCLUSION/IMPLICATIONS: The favorable accumulation property of 41 kDa PG in normal and oxidative stress-induced kidneys, along with its capabilities in conserving the pharmacological properties of the conjugated renal protective drugs, supports its role as a potential renal targeting drug carrier.

  6. Tang BH, Zhang JY, Allegaert K, Hao GX, Yao BF, Leroux S, et al.
    Clin Pharmacokinet, 2023 Aug;62(8):1105-1116.
    PMID: 37300630 DOI: 10.1007/s40262-023-01265-z
    BACKGROUND AND OBJECTIVE: High variability in vancomycin exposure in neonates requires advanced individualized dosing regimens. Achieving steady-state trough concentration (C0) and steady-state area-under-curve (AUC0-24) targets is important to optimize treatment. The objective was to evaluate whether machine learning (ML) can be used to predict these treatment targets to calculate optimal individual dosing regimens under intermittent administration conditions.

    METHODS: C0 were retrieved from a large neonatal vancomycin dataset. Individual estimates of AUC0-24 were obtained from Bayesian post hoc estimation. Various ML algorithms were used for model building to C0 and AUC0-24. An external dataset was used for predictive performance evaluation.

    RESULTS: Before starting treatment, C0 can be predicted a priori using the Catboost-based C0-ML model combined with dosing regimen and nine covariates. External validation results showed a 42.5% improvement in prediction accuracy by using the ML model compared with the population pharmacokinetic model. The virtual trial showed that using the ML optimized dose; 80.3% of the virtual neonates achieved the pharmacodynamic target (C0 in the range of 10-20 mg/L), much higher than the international standard dose (37.7-61.5%). Once therapeutic drug monitoring (TDM) measurements (C0) in patients have been obtained, AUC0-24 can be further predicted using the Catboost-based AUC-ML model combined with C0 and nine covariates. External validation results showed that the AUC-ML model can achieve an prediction accuracy of 80.3%.

    CONCLUSION: C0-based and AUC0-24-based ML models were developed accurately and precisely. These can be used for individual dose recommendations of vancomycin in neonates before treatment and dose revision after the first TDM result is obtained, respectively.

  7. Tang BH, Guan Z, Allegaert K, Wu YE, Manolis E, Leroux S, et al.
    Clin Pharmacokinet, 2021 11;60(11):1435-1448.
    PMID: 34041714 DOI: 10.1007/s40262-021-01033-x
    BACKGROUND: Population pharmacokinetic evaluations have been widely used in neonatal pharmacokinetic studies, while machine learning has become a popular approach to solving complex problems in the current era of big data.

    OBJECTIVE: The aim of this proof-of-concept study was to evaluate whether combining population pharmacokinetic and machine learning approaches could provide a more accurate prediction of the clearance of renally eliminated drugs in individual neonates.

    METHODS: Six drugs that are primarily eliminated by the kidneys were selected (vancomycin, latamoxef, cefepime, azlocillin, ceftazidime, and amoxicillin) as 'proof of concept' compounds. Individual estimates of clearance obtained from population pharmacokinetic models were used as reference clearances, and diverse machine learning methods and nested cross-validation were adopted and evaluated against these reference clearances. The predictive performance of these combined methods was compared with the performance of two other predictive methods: a covariate-based maturation model and a postmenstrual age and body weight scaling model. Relative error was used to evaluate the different methods.

    RESULTS: The extra tree regressor was selected as the best-fit machine learning method. Using the combined method, more than 95% of predictions for all six drugs had a relative error of < 50% and the mean relative error was reduced by an average of 44.3% and 71.3% compared with the other two predictive methods.

    CONCLUSION: A combined population pharmacokinetic and machine learning approach provided improved predictions of individual clearances of renally cleared drugs in neonates. For a new patient treated in clinical practice, individual clearance can be predicted a priori using our model code combined with demographic data.

Filters
Contact Us

Please provide feedback to Administrator (afdal@afpm.org.my)

External Links