Publications

A curated list of scientific journal articles on related software and contributions from the Rx Studio Team members.

Socio-Economic Validation of Precision Dosing Software

1.
Wannee Kantasiripitak, Ruth Van Daele, Matthias Gijsen, Marc Ferrante, Isabel Spriet, Erwin Dreesen (2020): Software Tools for Model-Informed Precision Dosing: How Well Do They Satisfy the Needs?. Frontiers in Pharmacology. 11(620): 1-13. doi:10.3389/fphar.2020.00620

Model-informed precision dosing (MIPD) software tools are used to optimize dosage regimens in individual patients, aiming to achieve drug exposure targets associated with desirable clinical outcomes. Over the last few decades, numerous MIPD software tools have been developed. However, they have still not been widely integrated into clinical practice. This study focuses on identifying the requirements for and evaluating the performance of the currently available MIPD software tools. First, a total of 22 experts in the field of precision dosing completed a web survey to assess the importance (from 0; do not agree at all, to 10; completely agree) of 103 pre-established software tool criteria organized in eight categories: user-friendliness and utilization, user support, computational aspects, population models, quality and validation, output generation, privacy and data security, and cost. Category mean ± pooled standard deviation importance scores ranged from 7.2 ± 2.1 (user-friendliness and utilization) to 8.5 ± 1.8 (privacy and data security). The relative importance score of each criterion within a category was used as a weighting factor in the subsequent evaluation of the software tools. Ten software tools were identified through literature and internet searches: four software tools were provided by companies (DoseMeRx, InsightRX Nova, MwPharm++, and PrecisePK) and six were provided by non-company owners (AutoKinetics, BestDose, ID-ODS, NextDose, TDMx, and Tucuxi). All software tools performed well in all categories, although there were differences in terms of in-built software features, user interface design, the number of drug modules and populations, user support, quality control, and cost. Therefore, the choice for a certain software tool should be made based on these differences and personal preferences. However, there are still improvements to be made in terms of electronic health record integration, standardization of software and model validation strategies, and prospective evidence for the software tools’ clinical and cost benefits.
2.
Polasek TM, Kirkpatrick CMJ, Rostami-Hodjegan A (2019): Precision dosing to avoid adverse drug reactions. Therapeutic Advances in Drug Safety. 10: 1-6. doi:10.1177/2042098619894147

Adverse drug reactions (ADRs) have traditionally been managed by trial and error, adjusting drug and dose selection reactively following patient harm. With an improved understanding of ADRs, and the patient characteristics that increase susceptibility, precision medicine technologies enable a proactive approach to ADRs and support clinicians to change prescribing accordingly. This commentary revisits the famous pharmacology–toxicology continuum first postulated by Paracelsus 500 years ago and explains why precision dosing is needed to help avoid ADRs in modern clinical practice. Strategies on how to improve precision dosing are given, including more research to establish better precision dosing targets in the cases of greatest need, easier access to dosing instructions via e-prescribing, improved monitoring of patients with novel biomarkers of drug response, and further application of model-informed precision dosing.
3.
Rachel J Tyson, Christine C Park, J Robert Powell, J Herbert Patterson, Daniel Weiner, Paul B Watkins 2, Daniel Gonzalez (2020): Precision Dosing Priority Criteria: Drug, Disease, and Patient Population Variables. Frontiers in Pharmacology. 11: 1-18. doi:10.3389/fphar.2020.00420

The administered dose of a drug modulates whether patients will experience optimal effectiveness, toxicity including death, or no effect at all. Dosing is particularly important for diseases and/or drugs where the drug can decrease severe morbidity or prolong life. Likewise, dosing is important where the drug can cause death or severe morbidity. Since we believe there are many examples where more precise dosing could benefit patients, it is worthwhile to consider how to prioritize drug–disease targets. One key consideration is the quality of information available from which more precise dosing recommendations can be constructed. When a new more precise dosing scheme is created and differs significantly from the approved label, it is important to consider the level of proof necessary to either change the label and/or change clinical practice. The cost and effort needed to provide this proof should also be considered in prioritizing drug–disease precision dosing targets. Although precision dosing is being promoted and has great promise, it is underutilized in many drugs and disease states. Therefore, we believe it is important to consider how more precise dosing is going to be delivered to high priority patients in a timely manner. If better dosing schemes do not change clinical practice resulting in better patient outcomes, then what is the use? This review paper discusses variables to consider when prioritizing precision dosing candidates while highlighting key examples of precision dosing that have been successfully used to improve patient care.
4.
Daniel Gonzalez, Gauri G Rao, Stacy C Bailey, Kim L R Brouwer, Yanguang Cao, Daniel J Crona, Angela D M Kashuba, Craig R Lee, Kathryn Morbitzer, J Herbert Patterson, Tim Wiltshire, Jon Easter, Scott W Savage, J Robert Powell (2017): Precision Dosing: Public Health Need, Proposed Framework, and Anticipated Impact. Clinical and translational science. 10(6):443-454. doi:10.1111/cts.12490

“Precision dosing” focuses on the individualization of drug treatment regimens based on patient factors known to alter drug disposition and/or response. In 2015, over 8 in 10 nonfederal acute care hospitals in the United States had adopted a basic electronic health record (EHR) system, which will facilitate the application of precision dosing. Expanding on recent publications, we outline the public health need, a proposed framework, and the anticipated impact for the adoption of precision dosing.

Peer-Reviewed Drug Models Available on Rx Studio Platform

1.
Anthony M Nicasio, Robert E Ariano, Sheryl A Zelenitsky, Aryun Kim, Jared L Crandon, Joseph L Kuti, David P Nicolau (2009): Population Pharmacokinetics of High-Dose, Prolonged-Infusion Cefepime in Adult Critically Ill Patients with Ventilator-Associated Pneumonia. Antimicrobial Agents and Chemotherapy. 53(4): 1476-1481. doi:10.1128/AAC.01141-08

A population pharmacokinetic model of cefepime was constructed from data from adult critical care patients with ventilator-associated pneumonia (VAP). A total of 32 patients treated with high-dose cefepime, 2 g every 8 h (3-h infusion) or a renal function-adjusted equivalent dose, were randomized into two groups--26 for the initial model and 6 for model validation. Serum samples of cefepime were collected at steady state. Nonparametric adaptive grid population modeling was employed using a two-compartment K(slope) pharmacokinetic model relating the elimination rate constant (K(10)) to renal function, as defined by creatinine clearance (CL(CR)), and central distribution volume (V(1)) to total body weight (TBW). The final model was described by the following equations: K(10) = 0.0027 x CL(CR) + 0.071 h(-1) and V(1) = TBW x 0.21 liter/kg. The median intercompartmental transfer constants K(12) and K(21) were 0.780 h(-1) and 0.472 h(-1), respectively. Using these median parameter estimates, the bias, precision, and coefficient of determination for the initial model were 11.3 microg/ml, 24.0 microg/ml, and 26%, respectively. The independent validation group displayed a bias, precision, and coefficient of determination of -1.64 microg/ml, 17.1 microg/ml, and 62%, respectively. Time-concentration profiles were assessed for various dosing regimens, using 5,000-patient Monte Carlo simulations. Among the regimens, the likelihoods of 2 g every 8 h (3-h infusion) achieving free drug concentrations above the MIC for 50% of the dosing interval were 91.8%, 78.1%, and 50.3% for MICs of 8, 16, and 32 microg/ml, respectively. This study provides a pharmacokinetic model capable of predicting cefepime concentrations in critically ill patients with VAP.
2.
Vincent H Tam, Peggy S McKinnon, Ronda L Akins, George L Drusano, Michael J Rybak (2003): Pharmacokinetics and Pharmacodynamics of Cefepime in Patients with Various Degrees of Renal Function. Antimicrobial Agents and Chemotherapy. 47(6): 1853-1861. doi:10.1128/aac.47.6.1853-1861.2003

This study evaluated the pharmacokinetics of cefepime in 36 patients with different levels of renal function. Pharmacokinetic and pharmacodynamic parameters were calculated using samples obtained at steady state. Patients with creatinine clearance (CL(CR)) of >100 ml/min had more rapid clearance (CL) and a lower minimum concentration in serum (C(min)). C(min) in this group was found to be 3.3 +/- 3.6 mg/liter (mean and standard deviation), compared to 19.5 +/- 21.5 mg/liter in patients with a CL(CR) of between 60 and 100 ml/min (P = 0.025) and 14.0 +/- 11.5 mg/liter in patients with a CL(CR) of <60 ml/min (P = 0.009). Patient data were also analyzed by the nonparametric expectation maximization method and Bayesian forecasting. The median volume of distribution in the central compartment was 27.08 liters. CL and CL(CR) were highly correlated (P = 0.00033) according to the equation CL= 0.324 liters/h + (0.0551 x CL(CR)). The median rate constants from the central compartment to the peripheral compartment and from the peripheral compartment to the central compartment were 12.58 and 41.09 h(-1), respectively. The time-concentration profiles for 1,000 patients (CL(CR)s, 120, 60, and 30 ml/min) each receiving various dosing regimens were simulated by using Monte Carlo simulations. Standard dosing resulted in a C(min) that was greater than or equal to the MIC in more than 80% of the simulated profiles with MICs < or = 2 mg/liter. Current dosing recommendations may be suboptimal for monotherapy of infections due to less susceptible pathogens (e.g., those for which MICs are > or = 4 mg/liter), particularly when CL(CR) exceeds 120 ml/min.
3.
Vineet Goti, Ayyappa Chaturvedula, Michael J Fossler, Steve Mok, Jesse T Jacob (2018): Hospitalized Patients With and Without Hemodialysis Have Markedly Different Vancomycin Pharmacokinetics: A Population Pharmacokinetic Model-Based Analysis. Therapeutic Drug Monitoring. 40(2): 212-221. doi:10.1097/FTD.0000000000000490

Background
Despite being in clinical use for about 6 decades, vancomycin dosing remains perplexing and complex.
Methods
A population pharmacokinetic modeling and simulation approach was used to evaluate the efficiency of the current nomogram-based dosing of vancomycin. Serum vancomycin concentrations were obtained as a part of routine therapeutic drug monitoring from two 500-bed academic medical centers. A population pharmacokinetic model was first built using these therapeutic drug monitoring data. Population pharmacokinetic modeling was conducted using NONMEM (7.2 and 7.3). The forward addition-backward elimination approach was used to test the covariate effects. Appropriate numerical and visual criteria were used as model diagnostics for checking model appropriateness and model qualification. The current nomogram efficiency was evaluated by determining the percentage of subjects in the therapeutic range (10-20 mg/L).
Results
A 2-compartment model with between-subject variability on clearance (CL), central volume of distribution (Vc), and peripheral volume of distribution best fit the data. Blood urea nitrogen, age, creatinine clearance, and hemodialysis status were significant covariates on clearance. Hemodialysis status was a significant covariate on Vc and peripheral volume of distribution. In the final model, creatinine clearance was retained as a covariate on CL whereas hemodialysis status was retained as covariate on both CL and Vc. Using Monte Carlo simulations, the current nomogram was optimized by the addition of a loading dose and reducing the maintenance doses. The current nomogram is suboptimal. Optimization of the nomogram resulted in >40% subjects consistently being in the therapeutic range at troughs collected after the first 6 doses.
Conclusions
CL and Vc differ markedly between patients undergoing hemodialysis and those not undergoing hemodialysis. Dosing nomogram based on these covariate relationships may potentially help in accurate dosing of vancomycin.
4.
Dolores Santos Buelga, María del Mar Fernandez de Gatta, Emma V. Herrera, Alfonso Dominguez-Gil, María José García (2005): Population Pharmacokinetic Analysis of Vancomycin in Patients with Hematological Malignancies. Antimicrobial Agents and Chemotherapy. 49(12): 4934-4941. doi:10.1128/AAC.49.12.4934-4941.2005

This study determines vancomycin (VAN) population pharmacokinetics (PK) in adult patients with hematological malignancies. VAN serum concentration data (n = 1,004) from therapeutic drug monitoring were collected retrospectively from 215 patients. A one-compartment PK model was selected. VAN pharmacokinetics population parameters were generated using the NONMEM program. A graphic approach and stepwise generalized additive modeling were used to elucidate the preliminary relationships between PK parameters and clinical covariates analyzed. Covariate selection revealed that total body weight (TBW) affected V, whereas renal function, estimated by creatinine clearance, and a diagnosis of acute myeloblastic leukemia (AML) influenced VAN clearance. We propose one general and two AML-specific models. The former was defined by CL (liters/h) = 1.08 × CLCR(Cockcroft and Gault) (liters/h); CVCL = 28.16% and V (liters) = 0.98 × TBW; CVV =37.15%. AML models confirmed this structure but with a higher clearance coefficient (1.17). The a priori performance of the models was evaluated in another 59 patients, and clinical suitability was confirmed. The models were fairly accurate, with more than 33% of the measured concentrations being within ±20% of the predicted value. This therapeutic precision is twofold higher than that of a noncustomized population model (16.1%). The corresponding standardized prediction errors included zero and a standard deviation close to unity. The models could be used to estimate appropriate VAN dosage guidelines, which are not clearly defined for this high-risk population. Their simple structure should allow easy implementation in clinical software and application in dosage individualization using the Bayesian approach.

After nearly 4 decades of clinical use, vancomycin (VAN) has maintained an important and uncontested niche in the antibacterial arsenal owing to its consistent activity against almost all gram-positive bacteria. However, the emergence and gradually increasing prevalence of vancomycin-resistant organisms in recent years have led to its administration being limited to specific indications.

The empirical use of VAN in persistently febrile neutropenic patients remains controversial. Currently, the prevalent opinion is for a restrictive use of glycopeptides, i.e., only for patients whose infection requires them, based on the microbiological data and a rigorous clinical evaluation of the patient. From a practical standpoint, this postulate implies, first, a rational antibiotic selection based on potential pathogens and, second, optimal use, including the drug dose and duration of therapy. In this sense, the population approach and pharmacodynamic criteria have become available as tools in individualized antimicrobial therapy, leading to increased efficacy and reduced selection of resistance. In order to apply such a strategy in everyday clinical practice, the precise pharmacokinetic (PK)-pharmacodynamic index determining efficacy and its target value as well as population PK parameters obtained from specific cohorts (oncology, intensive care unit, etc.) must be known or estimated.

A specific glycopeptide-treated population benefiting from this approach could be patients with hematological malignancies, owing to their high risk of developing life-threatening bacterial infections and the need for higher-than-expected dosages. However, little is known about the VAN pharmacokinetics in these patients since only one population PK analysis has been published. The methodological and sampling size constraints of this work suggested the need for studies aimed at improving our knowledge about the PK behavior of this drug in this particular group of patients. Other populations of patients with nonhematological diseases, mainly pediatric, treated with VAN have been appropriately characterized using the most usual and suitable population approach of mixed-effect modeling implemented in the NONMEM program.

On this basis, the information obtained should provide specific PK parameters to estimate appropriate dosage guidelines, which are not clearly defined for this high-risk population.

5.
Michael J Rybak, Jennifer Le, Thomas P Lodise, Donald P Levine, John S Bradley, Catherine Liu, Bruce A Mueller, Manjunath P Pai, Annie Wong-Beringer, John C Rotschafer, Keith A Rodvold, Holly D Maples, Benjamin M Lomaestro (2020): Therapeutic monitoring of vancomycin for serious methicillin-resistant Staphylococcus aureus infections: A revised consensus guideline and review by the American Society of Health-System Pharmacists, the Infectious Diseases Society of America, the Pediatric Infectious Diseases Society, and the Society of Infectious Diseases Pharmacists. American Journal of Health-System Pharmacy. 77(11): 835-864. doi:10.1093/ajhp/zxaa036

The first consensus guideline for therapeutic monitoring of vancomycin in adult patients was published in 2009. A committee representing 3 organizations (the American Society for Health-System Pharmacists [ASHP], Infectious Diseases Society of America [IDSA], and Society for Infectious Diseases Pharmacists [SIDP]) searched and reviewed all relevant peer-reviewed data on vancomycin as it related to in vitro and in vivo pharmacokinetic and pharmacodynamic (PK/PD) characteristics, including information on clinical efficacy, toxicity, and vancomycin resistance in relation to serum drug concentration and monitoring. The data were summarized, and specific dosing and monitoring recommendations were made. The primary recommendations consisted of eliminating routine monitoring of serum peak concentrations, emphasizing a ratio of area under the curve over 24 hours to minimum inhibitory concentration (AUC/MIC) of ≥400 as the primary PK/PD predictor of vancomycin activity, and promoting serum trough concentrations of 15 to 20 mg/L as a surrogate marker for the optimal vancomycin AUC/MIC if the MIC was ≤1 mg/L in patients with normal renal function. The guideline also recommended, albeit with limited data support, that actual body weight be used to determine the vancomycin dosage and loading doses for severe infections in patients who were seriously ill.

Since those recommendations were generated, a number of publications have evaluated the impact of the 2009 guidelines on clinical efficacy and toxicity in patients receiving vancomycin for the treatment of methicillin-resistant Staphylococcus aureus (MRSA) infections. It should be noted, however, that when the recommendations were originally published, there were important issues not addressed and gaps in knowledge that could not be covered adequately because of insufficient data. In fact, adequate data were not available to make recommendations in the original guideline for specific dosing and monitoring for pediatric patients outside of the neonatal age group; specific recommendations for vancomycin dosage adjustment and monitoring in the morbidly obese patient population and patients with renal failure, including specific dialysis dosage adjustments; recommendations for the use of prolonged or continuous infusion (CI) vancomycin therapy; and safety data on the use of dosages that exceed 3 g per day. In addition, there were minimal to no data on the safety and efficacy of targeted trough concentrations of 15 to 20 mg/L.

This consensus revision evaluates the current scientific data and controversies associated with vancomycin dosing and serum concentration monitoring for serious MRSA infections (including but not limited to bacteremia, sepsis, infective endocarditis, pneumonia, osteomyelitis, and meningitis) and provides new recommendations based on recent available evidence. Due to a lack of data to guide appropriate targets, the development of this guideline excluded evaluation of vancomycin for methicillin-susceptible S. aureus (MSSA) strains, coagulase-negative staphylococci, and other pathogens; thus, the extrapolation of guideline recommendations to these pathogens should be viewed with extreme caution. Furthermore, serious invasive MRSA infections exclude nonbacteremic skin and skin structure and urinary tract infections. Since this guideline focuses on optimization of vancomycin dosing and monitoring, recommendations on the appropriateness of vancomycin use, combination or alternative antibiotic therapy, and multiple medical interventions that may be necessary for successful treatment of invasive MRSA infections are beyond the scope of this guideline and will not be presented.

6.
Manjunath P. Pai, Anne N. Nafziger, Joseph S. Bertino Jr. (2009): Simplified Estimation of Aminoglycoside Pharmacokinetics in Underweight and Obese Adult Patients. Antimicrobial Agents and Chemotherapy. 55(9): 4006-4011. doi:10.1128/AAC.00174-11

Aminoglycosides are an important class of agents that are used in combination antimicrobial regimens to treat bacterial pathogens. Dosing of aminoglycosides is typically based on total body weight. However, the most appropriate alternative body size descriptor for dosing aminoglycosides at the extremes of weight (underweight and obese) is not known. Also, the predictive performance of newer formulas to assess kidney function, such as the modification of diet in renal disease (MDRD) and chronic kidney disease-epidemiology (CKD-EPI) equations compared to the Cockcroft-Gault equation to predict aminoglycoside clearance, is not known. We sought to examine dosing of aminoglycosides across the extremes of weight using a variety of formulas to assess kidney function. Pharmacokinetic data were obtained from a set of prospectively collected data (1982 to 2003) of 2,073 (53.5% male) adult patients that included 497 tobramycin- and 1,576 gentamicin-treated cases. The median (minimum, maximum) age, weight, and body mass index were 66 (18, 98) years, 70.0 (29.7, 206.7) kg, and 24.4 (11.3, 73.8) kg/m2, respectively. The percentage of underweight, normal-weight, overweight, and obese cases based on the World Health Organization classification were 8.8%, 45.5%, 26.5%, and 19.2%, respectively. The aminoglycoside volume of distribution was normalized to several alternative body size descriptors. Only lean body weight estimated by the method of S. Janmahasatian et al. (Clin. Pharmacokinet. 44:1051–1065, 2005) normalized the volume of distribution for both tobramycin and gentamicin across all weight strata, with the estimate being approximately 0.45 liter/kg. Aminoglycoside dosing can be simplified across all weight strata with the use of lean body weight. The CKD-EPI equation best predicts aminoglycoside clearance.
7.
Eva María Sáez Fernández, Jonás Samuel Pérez-Blanco, José M Lanao, M Victoria Calvo, Ana Martín-Suárez (2019): Evaluation of renal function equations to predict amikacin clearance. Expert Review of Clinical Pharmacology. 12(8): 805-813. doi:10.1080/17512433.2019.1637253

Objective
To evaluate the predictive performance of eight renal function equations to describe amikacin elimination in a large standard population with a wide range of age.
Methods
Retrospective study of adult hospitalized patients treated with amikacin and monitored in the clinical pharmacokinetics laboratory of a pharmacy service. Renal function was calculated as Cockcroft-Gault with total, adjusted and ideal body weight, MDRD-4, CKD-EPI, rLM, BIS1, and FAS. One compartment model with first-order elimination, including interindividual variability on clearance and volume of distribution and combined residual error model was selected as a base structural model. A pharmaco-statistical analysis was performed following a non-linear mixed effects modeling approach (NONMEM 7.3 software).
Results
198 patients (61 years [18–93]) and 566 measured amikacin plasma concentrations were included. All the estimated glomerular filtration rate and creatinine clearance equations evaluated described properly the data. The linear relationship between clearance and glomerular filtration rate based on rLM showed a statistically significant improvement in the fit of the data. rLM must be evaluated carefully in renal failure for amikacin dose adjustment.
Conclusions
Revised Lund-Malmö (rLM) and CKD-EPI showed the superior predictive performance of amikacin drug elimination comparing to all the alternative metrics evaluated.
8.
Jared L Crandon, Robert E Ariano, Sheryl A Zelenitsky, Anthony M Nicasio, Joseph L Kuti, David P Nicolau (2011): Optimization of meropenem dosage in the critically ill population based on renal function. Antimicrobial Agents and Chemotherapy. 37(4):632-8. doi:10.1007/s00134-010-2105-0

Purpose
To develop a meropenem population pharmacokinetic model in critically ill patients with particular focus on optimizing dosing regimens based on renal function.
Methods
Population pharmacokinetic analysis was performed with creatinine clearance (CrCl) and adjusted body weight to predict parameter estimates. Initial modeling was performed on 21 patients (55 samples). Validation was conducted with 12 samples from 5 randomly selected patients excluded from the original model. A 5,000-patient Monte Carlo simulation was used to ascertain optimal dosing regimens for three CrCl ranges.
Results
Mean ± SD age, APACHE, and CrCl were 59.2 ± 16.8 years, 13.6 ± 7, and 78.3 ± 33.7 mL/min. Meropenem doses ranged from 0.5 g every 8 h (q8h)-2 g q8h as 0.5-3 h infusions. Median estimates for volume of the central compartment, K₁₂ and K₂₁ were 0.24 L/kg, 0.49 h⁻¹, and 0.65 h⁻¹, respectively. K₁₀ was described by the equation: K₁₀= 0.3922 + 0.0025 × CrCl. Model bias and precision were -1.9 and 8.1 mg/L. R², bias, and precision for the validation were 93%, 1.1, and 2.6 mg/L. At minimum inhibitory concentrations (MICs) up to 8 mg/L, the probability of achieving 40% fT > MIC was 96, 90, and 61% for 3 h infusions of 2 g q8h, 1 g q8h, and 1 g q12h in patients with CrCl ≥50, 30-49, and 10-29, respectively. Target attainment was 75, 65, and 44% for these same dosing regimens as 0.5 h infusions.
Conclusions
This pharmacokinetic model is capable of accurately estimating meropenem concentrations in critically ill patients over a range of CrCl values. Compared with 0.5 h infusions, regimens employing prolonged infusions improved target attainment across all CrCl ranges.
9.
Chonghua Li, Joseph L. Kuti, Charles H. Nightingale, David P. Nicolau (2013): Population Pharmacokinetic Analysis and Dosing Regimen Optimization of Meropenem in Adult Patients. The Journal of Clinical Pharmacology. 46(10): 1171-1178. doi:10.1177/0091270006291035

The objectives of this study were to develop a meropenem population pharmacokinetic model using patient data and use it to explore alternative dosage regimens that could optimize the currently used dosing regimen to achieve higher likelihood of pharmacodynamic exposure against pathogenic bacteria. We gathered concentration data from 79 patients (ages 18–93 years) who received meropenem 0.5, 1, or 2 g over 0.5‐ or 3‐hour infusion every 8 hours. Meropenem population pharmacokinetic analysis was performed using the NONMEM program. A 2‐compartment model fit the data best. Creatinine clearance, age, and body weight were the most significant covariates to affect meropenem pharmacokinetics. Monte Carlo simulation was applied to mimic the concentration‐time profiles while 1 g meropenem was administrated via infusion over 0.5, 1, 2, and 3 hours. The 3‐hour prolonged infusion improved the likelihood of obtaining both bacteriostatic and bactericidal exposures most notably at the current susceptibility breakpoints.
10.
Kichan Doh, Heungjeong Woo, Jun Hur, Haejun Yim, Jimyon Kim, Hongseok Chae, Seunghoon Han, Dong-Seok Yim (2010): Population pharmacokinetics of meropenem in burn patients. Journal of Antimicrobial Chemotherapy. 65(11): 2428-2435. doi:10.1093/jac/dkq317

Objectives
To develop a population pharmacokinetic model of meropenem in burn patients and to explore the appropriateness of current dosage regimens.
Patients and methods
Fifty-nine patients with burns ranging from 3% to 97% of total body surface area treated with meropenem were analysed. The population pharmacokinetic parameters of meropenem in 59 burn patients were estimated, and concentrations were simulated by using a mixed effect method (NONMEM, ver. 6.2).
Results
The final model was a two-compartment model with first-order elimination where creatinine clearance (CLCR) and oedema contributed. The mean population pharmacokinetic parameters were clearance (L/h) = 4.45 + 10.5 × CLCR (mL/min)/138, V1 (central volume) = 17.0 + 11.1 × presence of oedema (0 or 1) L, V2 (peripheral volume) = 10.1 L and Q (intercompartmental clearance) = 5.25 L/h with interindividual variability (CV%) of 31.5%, 44.4%, 67.2% and 0% (not estimated), respectively.
Conclusions
The population clearance and volume of distribution in our burn patients were significantly greater than those reported in non-burn patients. The simulation of 1000 virtual patients’ plasma meropenem concentration treated with 1000 mg (30 min infusion) every 8 h based upon the model predicted the probability of achieving the time above MIC > 40% of the dosing interval as 58.9% for Pseudomonas aeruginosa isolated from three university hospitals in Korea.
11.
T. W. Felton, J. A. Roberts, T. P. Lodise, M. Van Guilder, E. Boselli, M. N. Neely, W. W. Hope (2014): Individualization of Piperacillin Dosing for Critically Ill Patients: Dosing Software To Optimize Antimicrobial Therapy. Antimicrobial Agents and Chemotherapy. 58(7): 4094-4102. doi:10.1128/AAC.02664-14

Piperacillin-tazobactam is frequently used for empirical and targeted therapy of infections in critically ill patients. Considerable pharmacokinetic (PK) variability is observed in critically ill patients. By estimating an individual's PK, dosage optimization Bayesian estimation techniques can be used to calculate the appropriate piperacillin regimen to achieve desired drug exposure targets. The aim of this study was to establish a population PK model for piperacillin in critically ill patients and then analyze the performance of the model in the dose optimization software program BestDose. Linear, with estimated creatinine clearance and weight as covariates, Michaelis-Menten (MM) and parallel linear/MM structural models were fitted to the data from 146 critically ill patients with nosocomial infection. Piperacillin concentrations measured in the first dosing interval, from each of 8 additional individuals, combined with the population model were embedded into the dose optimization software. The impact of the number of observations was assessed. Precision was assessed by (i) the predicted piperacillin dosage and by (ii) linear regression of the observed-versus-predicted piperacillin concentrations from the second 24 h of treatment. We found that a linear clearance model with creatinine clearance and weight as covariates for drug clearance and volume of distribution, respectively, best described the observed data. When there were at least two observed piperacillin concentrations, the dose optimization software predicted a mean piperacillin dosage of 4.02 g in the 8 patients administered piperacillin doses of 4.00 g. Linear regression of the observed-versus-predicted piperacillin concentrations for 8 individuals after 24 h of piperacillin dosing demonstrated an r2 of >0.89. In conclusion, for most critically ill patients, individualized piperacillin regimens delivering a target serum piperacillin concentration is achievable. Further validation of the dosage optimization software in a clinical trial is required.
12.
N. Patel, M. H. Scheetz, G. L. Drusano, T. P. Lodise (2010): Identification of Optimal Renal Dosage Adjustments for Traditional and Extended-Infusion Piperacillin-Tazobactam Dosing Regimens in Hospitalized Patients. Antimicrobial Agents and Chemotherapy. 54(1): 460-465. doi:10.1128/AAC.00296-09

This study examined the effect of various levels of renal impairment on the probability of achieving free drug concentrations that exceed the MIC for 50% of the dosing interval (50% fT > MIC) for traditional and extended-infusion piperacillin-tazobactam (TZP) dosing strategies. It also identified optimal renal dosage adjustments for traditional and extended-infusion dosing schemes that yielded probability of target attainment (PTA) and exposure profiles that were isometric to those of the parent regimens. Data from 105 patients were analyzed using the population pharmacokinetic modeling program BigNPAG. To assess the effect of creatinine clearance (CLCR) on overall clearance, TZP clearance was made proportional to the estimated CLCR. A Monte Carlo simulation (9,999 subjects) was performed for the traditional dosing scheme (4.5 g infused during 30 min every 6 h) and the extended-infusion TZP dosing scheme (3.375 g infused during 4 h every 8 h). The fraction of simulated subjects who achieved 50% fT > MIC was calculated for the range of piperacillin MICs from 0.25 to 32 mg/liter and stratified by CLCR. The traditional TZP regimen displayed the greatest variability in PTA across MIC values, especially for MIC values exceeding 4 mg/liter, when stratified by CLCR. In contrast, the PTA for the extended-infusion TZP regimen exceeded ≥80% for MIC values of ≤8 mg/liter across all CLCR strata. All regimens were associated with suboptimal PTA for MIC values of ≥32 mg/liter irrespective of the CLCR. The CLCR adjustments for traditional and extended-infusion TZP dosing regimens should be considered at a CLCR of ≤20 ml/min.

Piperacillin-tazobactam (TZP), a combination product of a semisynthetic penicillin and a beta-lactamase inhibitor, exhibits broad-spectrum activity and low toxicity, and it is indicated for a variety of clinical infections (1). TZP is excreted primarily from the body via the kidney, with the majority (∼70%) being eliminated as unchanged drug in the urine (1). Because TZP is eliminated primarily by the kidneys, dose alterations are required for patients with renal impairment.

Despite 15 years of clinical experience, the effect of renal impairment on the pharmacodynamic profile of TZP has not been well evaluated. It is well known that beta-lactam drugs such as TZP exert bactericidal activity in a time-dependent manner, with the time the free drug concentrations exceed the MIC during the dosing interval (fT > MIC) being the key pharmacodynamic parameter. For beta-lactams like TZP, it appears that bactericidal activity is optimized when fT > MIC exceeds 50% of the dosing interval (designated 50% fT > MIC). Among the studies that have characterized the ability of various TZP dosing strategies to achieve 50% fT > MIC, all have reported the overall probability of target attainment (PTA); we are unaware of any previous study that stratified PTA by renal function. Additionally, no study has assessed the effect of renal dose adjustments on the ability to achieve the desired pharmacodynamic target. An understanding of the effects of renal impairment and renal dose adjustment schemes on the pharmacodynamic profile are clinically important, because the majority of patients receiving TZP have some degree of renal impairment and often are administered a TZP regimen that is adjusted accordingly. While effectiveness is often the primary interest, minimizing TZP accumulation or excessive exposure also is of great importance. In the presence of renal dysfunction, TZP accumulation may occur and result in unnecessary toxicity. Quantifying the degree of TZP accumulation that results from diminished renal function will assist in identifying the creatinine clearance (CLCR) breakpoint and dose adjustment that would leave the PTA substantially unaltered and still not result in profound accumulation.

This study had two specific aims. First, we examined the effect of various levels of renal impairment on the probability of achieving 50% fT > MIC for traditional and extended-infusion TZP dosing strategies. Second, we sought to identify renal dosage adjustments for traditional and extended-infusion dosing schemes that yielded PTA and exposure profiles for the TZP renal dosing strategies that were isometric to parent regimens. For the purpose of the analysis, we characterized only the pharmacodynamic profile of piperacillin. We did not examine the pharmacodynamic profile of tazobactam, because current doses of tazobactam in the TZP formulation have been shown to be sufficient for an antibacterial effect when the target is attaining a free-drug concentration exceeding the MIC for 50% of the dosing interval.

13.
Thomas P. Lodise, Nimish Patel, Ben M. Lomaestro, Keith A. Rodvold, George L. Drusano (2009): Relationship between Initial Vancomycin Concentration-Time Profile and Nephrotoxicity among Hospitalized Patients. Clinical Infectious Diseases. 49(4): 507-514. doi:10.1086/600884

Background
Data suggest that higher doses of vancomycin can increase the risk of nephrotoxicity. No study has been undertaken to determine the pharmacodynamic index (ie, the area under the curve [AUC] or the trough value) that best describes the relationship between vancomycin exposure and onset of nephrotoxicity.
Methods
A retrospective study was conducted among patients who received vancomycin for a suspected or proven gram-positive infection during the period from 1 January 2005 through 31 December 2006 at Albany Medical Center Hospital. Patients were included in our study if they (1) were ⩾18 years old, (2) had an absolute neutrophil count of ⩾1000 cells/mm3, (3) received vancomycin for >48 h, (4) had ⩾1 vancomycin trough level collected within 96 h of vancomycin therapy, and (5) had a baseline serum creatinine level of <2.0 mg/dL. Patients were excluded if they (1) had a diagnosis of cystic fibrosis, (2) received intravenous contrast dye within 7 days of starting vancomycin or during therapy, or (3) required vasopressor support during therapy. Demographics, comorbid conditions, and treatment data were collected. The highest observed vancomycin trough value within 96 h of initiation of vancomycin therapy and the estimated vancomycin AUC were analyzed as measures of vancomycin exposure. The vancomycin AUC value from 0 to 24 h at steady state (in units of mg × h/L) for each patient was estimated by use of the maximum a posteriori probability Bayesian procedure in ADAPT II. Nephrotoxicity was defined as an increase in serum creatinine level of 0.5 mg/dL or 50%, whichever was greater, following initiation of vancomycin therapy. Logistic and Cox proportional hazards regression models identified the vancomycin pharmacodynamic index that best describes the relationship between vancomycin exposure and toxicity.
Results
During the study period, 166 patients met the inclusion criteria. Both initial vancomycin trough values and 0–24-h at steady state AUC values were associated with nephrotoxicity in the bivariate analyses. However, the vancomycin trough value, modeled as a continuous variable, was the only vancomycin exposure variable associated with nephrotoxicity in the multivariate analyses.
Conclusions
The results indicate that a vancomycin exposure-toxicity response relationship exists. The vancomycin trough value is the pharmacodynamic index that best describes this association.

Relevant Rx Studio Team Publications

Peer-reviewed scientific journal articles by our team members:

1.
Silva SD, Alves GCS, Chequer FMD, Farkas A, Daróczi G, Roberts JA, Sanches C (2020): Linguistic and cultural adaptation to the Portuguese language of antimicrobial dose adjustment software. einstein (São Paulo). 18. eAO5023. doi:10.31744/einstein_journal/2020ao5023

Objective
To adapt an antibiotic dose adjustment software initially developed in English, to Portuguese and to the Brazilian context.
Methods
This was an observational, descriptive study in which the Delphi method was used to establish consensus among specialists from different health areas, with questions addressing the visual and operational aspects of the software. In a second stage, a pilot experimental study was performed with the random comparison of patients for evaluation and adaptation of the software in the real environment of an intensive care unit, where it was compared between patients who used the standardized dose of piperacillin/tazobactam, and those who used an individualized dose adjusted through the software Individually Designed and Optimized Dosing Strategies.
Results
Twelve professionals participated in the first round, whose suggestions were forwarded to the software developer for adjustments, and subsequently submitted to the second round. Eight specialists participated in the second round. Indexes of 80% and 90% of concordance were obtained between the judges, characterizing uniformity in the suggestions. Thus, there was modification in the layout of the software for linguistic and cultural adequacy, minimizing errors of understanding and contradictions. In the second stage, 21 patients were included, and there were no differences between doses of piperacillin in the standard dose and adjusted dose Groups.
Conclusion
The adapted version of the software is safe and reliable for its use in Brazil.
2.
Kovacs K, Szakmar E, Meder U, Szakacs L, Cseko A, Vatai B, Szabo JA, McNamara PJ, Szabo M, Jermendy A (2019): A Randomized Controlled Study of Low-Dose Hydrocortisone Versus Placebo in Dopamine-Treated Hypotensive Neonates Undergoing Hypothermia Treatment for Hypoxic-Ischemic Encephalopathy. J Pediatr. 211:13-19.e3. doi:10.1016/j.jpeds.2019.04.008

Objective
To investigate whether hydrocortisone supplementation increases blood pressure and decreases inotrope requirements compared with placebo in cooled, asphyxiated neonates with volume-resistant hypotension.
Study Design
A double-blind, randomized, placebo-controlled clinical trial was conducted in a Level III neonatal intensive care unit in 2016-2017. Thirty-five asphyxiated neonates with volume-resistant hypotension (defined as a mean arterial pressure [MAP] < gestational age in weeks) were randomly assigned to receive 0.5 mg/kg/6 hours of hydrocortisone or placebo in addition to standard dopamine treatment during hypothermia.
Results
More patients reached the target of at least 5-mm Hg increment of MAP in 2 hours after randomization in the hydrocortisone group, compared with the placebo group (94% vs 58%, P = .02, intention-to-treat analysis). The duration of cardiovascular support (P = .001) as well as cumulative (P < .001) and peak inotrope dosage (P < .001) were lower in the hydrocortisone group. In a per-protocol analysis, regression modeling predicted that a 4-mm Hg increase in MAP in response to hydrocortisone treatment was comparable with the effect of 15 μg/kg/min of dopamine in this patient population. Serum cortisol concentrations were low before randomization in both the hydrocortisone and placebo groups (median 3.5 and 3.3 μg/dL, P = .87; respectively), suggesting inappropriate adrenal function. Short-term clinical outcomes were similar in the 2 groups.
Conclusion
Hydrocortisone administration was effective in raising the blood pressure and decreasing inotrope requirement in asphyxiated neonates with volume-resistant hypotension during hypothermia treatment.
3.
Farkas A, Daroczi G, Villasurda P, Dolton M, Nakagaki M, Roberts JA (2016): Comparative evaluation of the predictive performance of three different structural population pharmacokinetic models to predict future voriconazole concentrations. Antimicrobial Agents and Chemotherapy. 60(11):6806-6812. doi:10.1128/AAC.00970-16

Bayesian methods for voriconazole therapeutic drug monitoring (TDM) have been reported previously, but there are only sparse reports comparing the accuracy and precision of predictions of published models. Furthermore, the comparative accuracy of linear, mixed linear and nonlinear, or entirely nonlinear models may be of high clinical relevance. In this study, models were coded into individually designed optimum dosing strategies (ID-ODS) with voriconazole concentration data analyzed using inverse Bayesian modeling. The data used were from two independent data sets, patients with proven or suspected invasive fungal infections (n = 57) and hematopoietic stem cell transplant recipients (n = 10). Observed voriconazole concentrations were predicted whereby for each concentration value, the data available to that point were used to predict that value. The mean prediction error (ME) and mean squared prediction error (MSE) and their 95% confidence intervals (95% CI) were calculated to measure absolute bias and precision, while ΔME and ΔMSE and their 95% CI were used to measure relative bias and precision, respectively. A total of 519 voriconazole concentrations were analyzed using three models. MEs (95% CI) were 0.09 (−0.02, 0.22), 0.23 (0.04, 0.42), and 0.35 (0.16 to 0.54) while the MSEs (95% CI) were 2.1 (1.03, 3.17), 4.98 (0.90, 9.06), and 4.97 (−0.54 to 10.48) for the linear, mixed, and nonlinear models, respectively. In conclusion, while simulations with the linear model were found to be slightly more accurate and similarly precise, the small difference in accuracy is likely negligible from the clinical point of view, making all three approaches appropriate for use in a voriconazole TDM program.
4.
Wong G, Farkas A, Sussman R, Daroczi G, Hope WW, Lipman J, Roberts JA (2014): Comparison of the accuracy and precision of pharmacokinetic equations to predict free meropenem concentrations in critically ill patients. Antimicrobial Agents and Chemotherapy. 59(3):1411-1417. doi:10.1128/AAC.04001-14

Population pharmacokinetic analyses can be applied to predict optimized dosages for individual patients. The aim of this study was to compare the prediction performance of the published population pharmacokinetic models for meropenem in critically ill patients. We coded the published population pharmacokinetic models with covariate relationships into dosing software to predict unbound meropenem concentrations measured in a separate cohort of critically ill patients. The agreements between the observed and predicted concentrations were evaluated with Bland-Altman plots. The absolute and relative bias and precision of the models were determined. The clinical implications of the results were evaluated according to whether dose adjustments were required from the predictions to achieve a meropenem concentration of >2 mg/liter throughout the dosing interval. A total of 157 free meropenem concentrations from 56 patients were analyzed. Eight published population pharmacokinetic models were compared. The models showed an absolute bias in predicting the unbound meropenem concentrations from a mean percent difference (95% confidence interval [CI]) of −108.5% (−119.9% to −97.3%) to 19.9% (7.3% to 32.7%), while absolute precision ranged from −249.1% (−263.4% to −234.8%) to 31.9% (17.6% to 46.2%) and −178.9% (−196.9% to −160.9%) to 175.0% (157.0% to 193.0%). A dose change was required in 44% to 64% of the concentration results. Seven of the eight equations evaluated underpredicted free meropenem concentrations. In conclusion, the overall accuracy of these models supports their inclusion in dosing software and application for individualizing meropenem doses in critically ill patients to increase the likelihood of achievement of optimal antibiotic exposures.

Posters presented at conferences contributed by our team members:

1.
Farkas A, Woods KL, Ciummo F, Shah A, Sassine J, Freites CO, Daroczi G, Yassin A (2019): Development of a Linear Mixed-Effect Pharmacodynamic Model to Quantify the Effects of Frequently Prescribed Antimicrobials on QT Interval Prolongation in Hospitalized Patients. .

Background
Torsades de pointes is a life-threatening ventricular tachycardia associated with prolongation of the QT interval. Many diseases and medications have been implicated as potentially prolonging the QT interval, but little data exists regarding the means of quantifying this risk. The aim of this study was to describe the impact of commonly used antimicrobials on the QT interval in hospitalized patients.
Methods
Demographic, diseases, laboratory, medication administration history and ECG recording data were collected from the electronic records of adult patients admitted, from July 2018 to December 2018, to two urban hospitals. A model for the QT interval comprised of four sub-models: gender, heart rate, circadian rhythm, and the drug and disease effects. Fixed and random effects with between occasion variability were estimated for the parameters. A Bayesian approach using the NUTS in STAN was used via the brms package in the R® software.
Results
Data from 1,353 patients were used with baseline characteristics shown in Table 1. Observed vs. predicted plots based on the training (Figure 1A) and validation data set (Figure 1B) showed a great fit. The parameters for QTc0, α, gender, and circadian rhythm were accurately identified (Table 2). Similarly, the model correctly described the expected impact of acute or chronic diseases on the QT interval. Uncertainty interval estimates (Figure 2) show that patients treated with fluconazole and levofloxacin are likely to present with a QT interval [mean (95% CI) of 6.84 (0.22,21.45) and 5.05 (0.15, 16.70), respectively], that is > 5 ms longer vs. no treatment, the minimum cutoff that should evoke further risk assessment of QT interval prolongation.
Conclusions
The model developed correctly describes the impact baseline risk factors have on the QT interval. Point estimates of QT interval prolongation show that patients treated with fluconazole and levofloxacin may be at considerable risk; while those treated with azithromycin or ciprofloxacin are more likely to be at an insignificant risk for QT interval prolongation during hospital admission. Further workup to quantify the impact of concomitant treatment with these and other at-risk medications is underway.
2.
Duttra S, Sanches GC, Roberts JA, Daroczi G, Farkas A (2017): Development of an on-line application to support a program aimed to evaluate antimicrobial dosing optimization without therapeutic drug monitoring in critically ill patients in Brazil. .

Background
Enhancing the quality of prescribing and administration of antibiotics should be considered a key priority for improving therapeutic outcomes and suppressing the increasing rates of resistance that is presently observed worldwide. The availability of therapeutic drug monitoring (TDM) for many of the commonly used antibiotics is a rarity in a considerable number of centers around the world. Alternatively, the use of a widely available web based application utilizing population PK models and sophisticated simulation algorithms may have the potential to be a valuable tool in optimizing PKPD indices. The aim of this study was to describe the process of modifying an on-line dose optimization application to meet the needs of a program designed to evaluate the adaptation of published population PK models for dose optimization in the absence of TDM into the care of Brazilian critically ill patients.
Materials/Methods
Piperacillin and tazobactam PK models are coded into Individually Designed Optimum Dosing Strategies (ID-ODSTM) on - line using the R language to provide the necessary background for the high - level computations to estimate concentration - time profiles for 5000 virtual patients per simulation. The user provides patient demographic and laboratory information (including MICs) via a user friendly html interface in international units. PTAs for up to 200 short and extended infusion regimens from 2000 to 8000 mg of piperacillin at 50 mg intervals given every 12, 8, 6 and 4 hours for the target ƒT>MIC of 50% for MICs up to 32 µg/ml in serum are established assuming 70% protein binding and lognormal distribution for all pharmacokinetic parameters.
Results
PTAs for all simulated regimens are evaluated and a subset reaching 90% or more is separated for further analysis to provide the dosing regimen that achieves the optimal target at the pre-specified MIC with the least amount of drug in mgs to be administered in a 24 h time period. Once computation is accomplished, clinically relevant information including patient demographics, suggested dosing regimens, PTAs, and creatinine clearance will be displayed using uncomplicated and adequately descriptive plotting designs and in the Portuguese language (Figure 1. and 2.).
Conclusions
The development of this modified application provides the foundations for a multi-model based, point of care clinical decision support tool on the web and mobile devices for clinicians focusing on optimizing antimicrobial therapy. In the absence of available and affordable TDM, this system will be used to evaluate the adaptation of published population PK models for dose optimization into the care of the Brazilian critically ill patients. By setting the application to give the dosing regimen that uses the lowest amount of drug per day, the cost will also be kept to the minimum necessary to provide optimal exposure.
3.
Farkas A, Daroczi G (2015): Development of a Smart Phone application to individualize antibiotic dosing in critically ill patients using Monte Carlo simulations, Bayesian feedback and drug interaction modeling approaches. .

Objective
Smart phone technology can help facilitate mobile computing at the point of care. The objective of this study was to develop a mobile phone application to provide individual dosing recommendations based on Cumulative Fraction of Response (CFR), Probabilities of Target Attainment (PTA), Bayesian feedback and combination drug interaction modeling for several antibiotics. Here the example of Cefepime is presented.
Methods
Population pharmacokinetic (popPK) model for CEF in critically ill patients is used as the Bayesian prior and to estimate concentration - time profiles for 5000 virtual patients per simulation. Additionally, the Greco interaction equation is employed and linked to simulated concentration - time profiles to generate the curve of killing effect for combination therapy. The models and conditions are coded into Individually Designed Optimum Dosing Strategies (ID-ODS TM ) on - line to provide the necessary background for the high - level computations.
Results
The user provides patient demographic and laboratory information (including institution specific MIC distribution) via a user friendly interface in conventional units, which is then passed through the template of conditions in ID-ODS TM . PTAs for short, extended, or continuous infusion regimens for the target ƒT>MIC of 60% for MICs up to 32 μg/ml in serum are established assuming lognormal distribution for all pharmacokinetic parameters. These PTAs are also used to calculate CFRs, allowing to compare up to 4 different regimens side by side at a time. For Bayesian dose individualization, a total of 5000 iterations are completed using a sequential approach allowing for the change of PK parameters from time to time. After the computation, clinically useful information including individual PK parameter estimates and suggested dosing regimens, PTAs, CFRs, and the predicted killing effect of the candidate dosing strategies will be displayed using uncomplicated and adequately descriptive plotting designs.
Conclusions
This mobile-platform application provides the opportunity for clinicians interested in optimizing antimicrobial therapy at the point of care. This system can be used to improve antibiotic dosing practices at the bedside via the utilization of modern principles of antimicrobial pharmacodynamics, popPK model based approach, Bayesian feedback and Monte Carlo simulation.
4.
Farkas A, Daroczi G, Roberts JA (2014): Usage statistics of Individually Designed Optimum Dosing Strategies, a multi-model based online application to individualize antibiotic dosing in critically ill patients. .

Objective
In order to help overcome some of the current challenges for the wide-spread implementation of model based goal oriented dosing of antibiotics, we have launched Individually Designed Optimum Dosing Strategies (ID-ODS ® ) on the web in late 2012. The objective of this study was to evaluate the utilization of this on-line dosing tool used to facilitate the optimal dosing of sixteen antibiotics via estimation of patient specific Probabilities of Target Attainment (PTA) and Bayesian adaptive feedback.
Methods
Continuous data collection on individual queries was supported by Rapporter®, a data analysis and reporting application for the use of the R ® statistical software environment in the cloud. The number of queries on specific antibiotic templates by geolocalized IP address and anonymised parameters were evaluated for CPU time and for the frequency of successfully generated reports.
Results
The website applications were successfully queried 5678 times during the time of evaluation, 85.9 % of all users connected from North America. The remaining 14.1 % of users joined the site mainly from Europe (47.7%), Asia - Pacific region (25.9%), South America (24.2%) and the rest of the world (2.2%). PTAs for Piperacillin and Tazobactam and estimations of empiric dosing regimens for Vancomycin were the most common reasons for utilization, followed by Bayesian analysis of individual Vancomycin and Aminoglycoside concentration information. They accounted for a combined 54.5% of all data management. Cefepime and Meropenem were the second and third most commonly accessed templates for dose optimization, representing 39.1 % of the entire beta - lactam queries together. CPU times differed substantially for templates running PTAs versus the Bayesian models with a mean + SD of 6.88 + 2.08 seconds and 19.16 + 17.12 seconds, respectively. Generating the reports was aborted early 13.5 % of the time, where the reasons for failure were most commonly linked to inaccurate data entry.
Conclusions
The world-wide web availability of this cross-platform application provides the framework for a point of care clinical decision support tool on mobile and stationery devices for practitioners interested in optimizing antimicrobial therapy. During the first year in live environment, the system was ran successfully over five thousand occasions, providing computational results of high complexity under the average of 20 seconds of time. The utilization information collected during this period will also help us further improve the system to minimize rates of template failure due to inaccurate data entry.
5.
Farkas A, Wong G, Daroczi G, Lipman J, Roberts JA (2014): Comparative evaluation of the accuracy and precision of pharmacokinetic equations to predict free meropenem concentrations in critically ill patients. .

Objective
Population pharmacokinetic analyses that quantify the effect of demographic, pathophysiological, and other drug-related factors on pharmacokinetic behavior are valuable for accurately predicting individualized and optimized doses for patients. The aim of this study was to establish the agreement between observed and population pharmacokinetic equations based predictions of concentrations to rank the predictive performance of available pharmacokinetic models.
Methods
Unbound meropenem concentrations were measured in critically ill patients as part of a clinical therapeutic drug monitoring (TDM) program. Published one and two compartment population models with covariate relationships were coded in the R language into Individually Designed Optimum Dosing Strategies – Scientific (ID-ODS - S®) and were used to predict meropenem concentrations. Difference plots were produced to visually evaluate the agreement between the observed and predicted concentrations. Absolute and relative bias and precision of the models as predictors of observed concentrations were determined. The clinical implications of the different results were evaluated according to whether the predicted concentration would have required dose adjustment for a target of 100% ƒ T>MIC (chosen MIC = 2 mg/L).
Results
157 free meropenem concentrations from 56 patients were available for analysis. Eight published pharmacokinetic models were evaluated using percentage difference plots. The models studied showed an absolute bias in predicting serum concentrations that ranged from a mean (95%CI) % difference of -108.6 (-119.91, -97.30) % to19.86 (7.26, 32.47) %, while absolute precision ranged from-249.13 (-263.42, -234.84) % to 31.91 (17.62, 46.21) % and -178.93 (-196.93, -160.93) to 175.04 (157.04, 193.04). A dose change prompted by individuals was required in 44% to 64% of the concentrations. When compared to an absolute standard, the one compartment model by Muro et al. developed in Japanese patients was found to be the least biased and most precise at predicting free meropenem concentrations.
Conclusions
Seven of the eight equations evaluated here are likely to under-predict free meropenem concentrations coupled with variable magnitudes of precision that prompted similar dosing decisions using four of the targeted models. When compared to the absolute standard, the model by Muro et al. ranked the highest at accurately and precisely predicting free meropenem concentrations. The calculated results of this dosing approach also led to similar choices of dose adjustments most often as compared to the results based on the observed concentrations. The best performed model thus may be adapted into a TDM program focusing on the optimal dosing of meropenem.
6.
Farkas A, Wong G, Daroczi G, Lipman J, Roberts JA (2014): Use of Bayesian Pharmacokinetic Models to Predict Plasma Meropenem Concentrations in Critically Ill Patients – Application to Therapeutic Drug Monitoring. .

Objective
Therapeutic Drug Monitoring (TDM) is an efficient tool for optimizing the therapeutics of drugs. Applying Bayesian feedback to TDM is a method that can accurately describe patient pharmacokinetics based with only minimal data. The goal of this study was to establish the agreement between observed pharmacokinetic data with a Bayesian feedback as well as a non-Bayesian model of meropenem in critically ill patients
Methods
Total meropenem concentrations were measured in critically ill patients as part of a clinical TDM program. Meropenem was administered by either intermittent or extended infusion. A two compartment population pharmacokinetic model incorporating body weight and creatinine clearance as covariates was coded in the R language into Individually Designed Optimum Dosing Strategies – Scientific (ID-ODS - S®) and was used to calculate the meropenem concentrations, allowing for the change in model parameters from dose to dose. Difference plots of observed versus predicted concentrations (with or without feedback) were constructed to evaluate the agreement between the observed and predicted concentrations. Bias and precision of the two models as predictors of observed concentrations were determined by the mean differences,95% limits of agreement and their corresponding 95% confidence intervals (95%CI).
Results
290 meropenem concentrations from 34 patients were available for analysis. Relative percentage difference plots were constructed to increase the normality of the data. Here observed versus model predicted concentrations with or without feedback showed non-significant bias with a mean (95%CI) % difference of 1.81 (-1.82, 5.45) % and -0.93 (-8.17, 6.30) %, respectively. Predictions with as compared to without feedback showed improved precision illustrated by a narrower range of 95% (95%CI) lower and upper limits of agreements of -59.89 (-66.17, -53.61) % to 63.52 (57.25,69.80) % and -123.65 (-136.13, -111.17) % to 121.78 (109.30,134.26) %, respectively.
Conclusions
Model based simulations with and without Bayesian feedback were found to be similarly accurate, while the method with Bayesian feedback resulted in better precision of predicted meropenem concentrations in this group of critically ill patients. These highly individualized Bayesian models further enable practitioners to correctly describe the pharmacokinetics of meropenem in their patients, which in turn ensures more accurate dose adjustments in a clinical TDM program focusing on the optimal dosing of meropenem.