General quasi-equilibrium multivalent binding model to study diverse and complex drug-receptor interactions of biologics.

Pharmacokinetics and pharmacodynamics of many biologics are influenced by their complex binding to biological receptors. Biologics consist of diverse groups of molecules with different binding kinetics to its receptors including IgG with simple one-to-one drug receptor bindings, bispecific antibody (BsAb) that binds to two different receptors, and antibodies that can bind to six or more identical receptors. As the binding process is typically much faster than elimination (or internalization) and distribution processes, quasi-equilibrium (QE) binding models are commonly used to describe drug-receptor binding kinetics of biologics. However, no general QE modeling framework is available to describe complex binding kinetics for diverse classes of biologics. In this paper, we describe novel approaches of using differential algebraic equations (DAE) to solve three QE multivalent drug-receptor binding (QEMB) models. The first example describes the binding kinetics of three-body equilibria of BsAb that binds to 2 different receptors for trimer formation. The second example models an engineered IgG variant (Multabody) that can bind to 24 identical target receptors. The third example describes an IgG with modified neonatal Fc receptor (FcRn) binding affinity that competes for the same FcRn receptor as endogenous IgG. The model parameter estimates were obtained by fitting the model to all data simultaneously. The models allowed us to study potential roles of cooperative binding on bell-shaped drug exposure-response relationships of BsAb, and concentration-depended distribution of different drug-receptor complexes for Multabody. This DAE-based QEMB model platform can serve as an important tool to better understand complex binding kinetics of diverse classes of biologics.

Target-mediated drug disposition model for drugs with N > 2 binding sites that bind to a target with one binding site.

The paper extended the TMDD model to drugs with more than two (N > 2) identical binding sites (N-to-one TMDD). The quasi-steady-state (N-to-one QSS), quasi-equilibrium (N-to-one QE), irreversible binding (N-to-one IB), and Michaelis-Menten (N-to-one MM) approximations of the model were derived. To illustrate properties of new equations and approximations, N = 4 case was investigated numerically. Using simulations, the N-to-one QSS approximation was compared with the full N-to-one TMDD model. As expected, and similarly to the standard TMDD for monoclonal antibodies (mAb), N-to-one QSS predictions were nearly identical to N-to-one TMDD predictions, except for times of fast changes following initiation of dosing, when equilibrium has not yet been reached. Predictions for mAbs with soluble targets (slow elimination of the complex) were simulated from the full 4-to-one TMDD model and were fitted to the 4-to-one TMDD model and to its QSS approximation. It was demonstrated that the 4-to-one QSS model provided nearly identical description of not only the observed (simulated) total drug and total target concentrations, but also unobserved concentrations of the free drug, free target, and drug-target complexes. For mAb with a membrane-bound target, the 4-to-one MM approximation adequately described the data. The 4-to-one QSS approximation converged 8 times faster than the full 4-to-one TMDD.

Rational Clinical Dose Selection of Adeno-Associated Virus-Mediated Gene Therapy Based on Allometric Principles.

One of the challenges in translational medicine is to select first-in-human doses of investigational drugs based on findings in preclinical studies. Despite substantial progress in the optimization of recombinant adeno-associated virus (AAV) vectors of in vivo gene therapy for treating various diseases, there remain significant limitations to the use of preclinical data to guide dose selection in clinical trials. Here we introduce a novel concept of gene efficiency factor (GEF) to describe the efficiency of the gene transfer system and describe and apply the concept of GEF in AAV-mediated in vivo gene transfer systems. We explore the utility of allometric scaling to translate GEF across species using AAV-mediated in vivo factor IX (FIX) gene therapy for hemophilia B and to demonstrate the use of GEF in predicting efficacious AAV vector doses in humans. We show for the first time that an allometric relationship exists for GEF of AAV-mediated in vivo gene therapy. Furthermore, we demonstrate the feasibility of using the allometric relationship of GEF to select efficacious first-in-human doses of virus-mediated invivo gene therapy. Based on our findings, allometry of GEF can be used to translate biological efficiency from animal studies to clinical studies and provide a rational basis of setting first-in-human doses for new virus-mediated invivo gene therapy products.

A Modified Hybrid Wald's Approximation Method for Efficient Covariate Selection in Population Pharmacokinetic Analysis.

One important objective of population pharmacokinetic (PPK) analyses is to identify and quantify relationships between covariates and model parameters such as clearance and volume. To improve upon existing covariate model development methods including stepwise procedures and Wald's approximation method (WAM), this paper introduces an innovative method named the hybrid first-order conditional estimation (FOCE)/Monte-Carlo parametric expectation maximization (MCPEM)-based Wald's approximation method with backward elimination (BE), or H-WAM-BE. Compared with WAM, this new method uses MCPEM to obtain full covariance matrix after running FOCE to obtain full model parameter estimates, followed by BE to select the final covariate model. Two groups of datasets (simulation datasets and rituximab datasets) were used to compare the performance of H-WAM-BE with two other methods, likelihood ratio test (LRT)-based stepwise covariate method (SCM) and H-WAM with full subset approach (H-WAM-F) in NONMEM. Different scenarios with different sample sizes and sampling schemes were used for simulating datasets. The nominal model was used as the reference to evaluate the three methods for their ability to accurately identify parameter-covariate relationships. The methods were compared using the number of true and false positive covariates identified, number of times that they identified the reference model, computation times, and predictive performance. Best-performing H-WAM-BE methods (M2 and M4) showed comparable results with LRT-based SCM. H-WAM-BE required shorter or comparable computation times than LRT-based SCM and H-WAM-F regardless of the model structure, sample size, or sampling design used in this study.

Clinical pharmacology and dosing regimen optimization of neonatal opioid withdrawal syndrome treatments.

In this paper, we review the management of neonatal opioid withdrawal syndrome (NOWS) and clinical pharmacology of primary treatment agents in NOWS, including morphine, methadone, buprenorphine, clonidine, and phenobarbital. Pharmacologic treatment strategies in NOWS have been mostly empirical, and heterogeneity in dosing regimens adds to the difficulty of extrapolating study results to broader patient populations. As population pharmacokinetics (PKs) of pharmacologic agents in NOWS become more well-defined and knowledge of patient-specific factors affecting treatment outcomes continue to accumulate, PK/pharmacodynamic modeling and simulation will be powerful tools to aid the design of optimal dosing regimens at the patient level. Although there is an increasing number of clinical trials on the comparative efficacy of treatment agents in NOWS, here, we also draw attention to the importance of optimizing the dosing regimen, which can be arguably equally important at identifying the optimal treatment agent.

Genotype-Guided Dosing of Warfarin in Chinese Adults: A Multicenter Randomized Clinical Trial.

BACKGROUND: Warfarin is an effective treatment for thromboembolic disease but has a narrow therapeutic index; optimal anticoagulation dosage can differ tremendously among individuals. We aimed to evaluate whether genotype-guided warfarin dosing is superior to routine clinical dosing for the outcomes of interest in Chinese patients. METHODS: We conducted a multicenter, randomized, single-blind, parallel-controlled trial from September 2014 to April 2017 in 15 hospitals in China. Eligible patients were ≥18 years of age, with atrial fibrillation or deep vein thrombosis without previous treatment of warfarin or a bleeding disorder. Nine follow-up visits were performed during the 12-week study period. The primary outcome measure was the percentage of time in the therapeutic range of the international normalized ratio during the first 12 weeks after starting warfarin therapy. RESULTS: A total of 660 participants were enrolled and randomly assigned to a genotype-guided dosing group or a control group under standard dosing. The genotype-guided dosing group had a significantly higher percentage of time in the therapeutic range than the control group (58.8% versus 53.2% [95% CI of group difference, 1.1-10.2]; P=0.01). The genotype-guided dosing group also achieved the target international normalized ratio sooner than the control group. In subgroup analyses, warfarin normal sensitivity group had an even higher percentage of time in the therapeutic range during the first 12 weeks compared with the control group (60.8% versus 48.9% [95% CI, 1.1-24.4]). The incidence of adverse events was low in both groups. CONCLUSIONS: The outcomes of genotype-guided warfarin dosing were superior to those of clinical standard dosing. These findings raise the prospect of precision warfarin treatment in China. Registration: URL: https://www.clinicaltrials.gov; Unique identifier: NCT02211326.

Using medical claims database to develop a population disease progression model for leuprorelin-treated subjects with hormone-sensitive prostate cancer.

Androgen deprivation therapy (ADT) is a widely used treatment for patients with hormone-sensitive prostate cancer (PCa). However, duration of treatment response varies, and most patients eventually experience disease progression despite treatment. Leuprorelin is a luteinizing hormone-releasing hormone (LHRH) agonist, a commonly used form of ADT. Prostate-specific antigen (PSA) is a biomarker for monitoring disease progression and predicting treatment response and survival in PCa. However, time-dependent profile of tumor regression and growth in patients with hormone-sensitive PCa on ADT has never been fully characterized. In this analysis, nationwide medical claims database provided by Humana from 2007 to 2011 was used to construct a population-based disease progression model for patients with hormone-sensitive PCa on leuprorelin. Data were analyzed by nonlinear mixed effects modeling utilizing Monte Carlo Parametric Expectation Maximization (MCPEM) method in NONMEM. Covariate selection was performed using a modified Wald's approximation method with backward elimination (WAM-BE) proposed by our group. 1113 PSA observations from 264 subjects with malignant PCa were used for model development. PSA kinetics were well described by the final covariate model. Model parameters were well estimated, but large between-patient variability was observed. Hemoglobin significantly affected proportion of drug-resistant cells in the original tumor, while baseline PSA and antiandrogen use significantly affected treatment effect on drug-sensitive PCa cells (Ds). Population estimate of Ds was 3.78 x 10-2 day-1. Population estimates of growth rates for drug-sensitive (Gs) and drug-resistant PCa cells (GR) were 1.96 x 10-3 and 6.54 x 10-4 day-1, corresponding to a PSA doubling time of 354 and 1060 days, respectively. Proportion of the original PCa cells inherently resistant to treatment was estimated to be 1.94%. Application of population-based disease progression model to clinical data allowed characterization of tumor resistant patterns and growth/regression rates that enhances our understanding of how PCa responds to ADT.

Population pharmacokinetic analysis of AR-67, a lactone stable camptothecin analogue, in cancer patients with solid tumors.

Background AR-67 is a novel camptothecin analogue at early stages of drug development. The phase 1 clinical trial in cancer patients with solid tumors was completed and a population pharmacokinetic model (POP PK) was developed to facilitate further development of this investigational agent. Methods Pharmacokinetic data collected in the phase 1 clinical trial were utilized for the development of a population POP PK by implementing the non-linear mixed effects approach. Patient characteristics at study entry were evaluated as covariates in the model. Subjects (N = 26) were treated at nine dosage levels (1.2-12.4 mg/m2/day) on a daily × 5 schedule. Hematological toxicity data were modeled against exposure metrics. Results A two-compartment POP PK model best described the disposition of AR-67 by fitting a total of 328 PK observations from 25 subjects. Following covariate model selection, age remained as a significant covariate on central volume. The final model provided a good fit for the concentration versus time data and PK parameters were estimated with good precision. Clearance, inter-compartmental clearance, central volume and peripheral volume were estimated to be 32.2 L/h, 28.6 L/h, 6.83 L and 25.0 L, respectively. Finally, exposure-pharmacodynamic analysis using Emax models showed that plasma drug concentration versus time profiles are better predictors of AR-67-related hematologic toxicity were better predictors of leukopenia and thrombocytopenia, as compared to total dose. Conclusions A POP PK model was developed to characterize AR-67 pharmacokinetics and identified age as a significant covariate. Exposure PK metrics Cmax and AUC were shown to predict hematological toxicity. Further efforts to identify clinically relevant determinants of AR-67 disposition and effects in a larger patient population are warranted.

Validation of a HPLC/MS method for simultaneous quantification of clonidine, morphine and its metabolites in human plasma.

A high-performance liquid chromatography-tandem mass spectrometry method was developed and validated for the simultaneous quantification of morphine, morphine's major metabolites morphine-3-glucuronide and morphine-6-glucuronide, and clonidine, to support the pharmacokinetic analysis of an ongoing double-blinded randomized clinical trial that compares the use of morphine and clonidine in infants diagnosed with neonatal abstinence syndrome. Plasma samples were processed by solid-phase extraction and separated on an Inertsil ODS-3 (4 µm) column using an 0.1% formic acid in water-0.1% formic acid in methanol gradient. Detection of the analytes was conducted in the positive multiple reaction monitoring mode. The range of quantitation was 1-1000 ng/mL for morphine, morphine-3-glucuronide and morphine-6-glucuronide, and 0.25-100 ng/mL for clonidine. Intra-day and inter-day accuracy and precision were ≤15% for all analytes across the quantitation range. Extraction recovery rates were ≥94% for morphine, ≥90% for M3G, ≥87% for M6G and ≥ 79% for clonidine. Matrix effect ranged from 85-94% for clonidine to 101-106% for M3G. The method fulfilled all predetermined acceptance criteria and required only 100 µL of starting plasma volume. Furthermore, it was successfully applied to 30 clinical trial plasma samples.

The Pharmacokinetics and Pharmacodynamics of Buprenorphine in Neonatal Abstinence Syndrome.

Neonatal abstinence syndrome (NAS) is a condition affecting newborns that are exposed to an opioid in utero. In a randomized, controlled trial assessing the efficacy of buprenorphine and morphine in NAS, blood samples were analyzed from a subset of patients receiving buprenorphine along with NAS scores. The data were used to validate and adapt an existing model of buprenorphine in neonates and to identify relationships between buprenorphine or norbuprenorphine pharmacokinetics (PK) and efficacy or safety. The time to NAS stabilization was found to decrease with increasing buprenorphine exposure. This pharmacokinetic-pharmacodynamic (PK-PD) relationship was able to be quantified and adequately described with a mathematical model. The findings confirm a previous PK model of buprenorphine and extend the model to describe the PK of norbuprenorphine and to identify a novel PK-PD relationship of buprenorphine in NAS. This model will allow optimization of dosing strategies in future clinical trials.

Population pharmacokinetics of exendin-(9-39) and clinical dose selection in patients with congenital hyperinsulinism.

AIMS: Congenital hyperinsulinism (HI) is the most common cause of persistent hypoglycaemia in infants and children. Exendin-(9-39), an inverse glucagon-like peptide 1 (GLP-1) agonist, is a novel therapeutic agent for HI that has demonstrated glucose-raising effect. We report the first population pharmacokinetic (PopPK) model of the exendin-(9-39) in patients with HI and propose the optimal dosing regimen for future clinical trials in neonates with HI. METHODS: A total of 182 pharmacokinetic (PK) observations from 26 subjects in three clinical studies were included for constructing the PopPK model using first order conditional estimation (FOCE) with interaction method in nonlinear mixed-effects modelling (NONMEM). Exposure metrics (area under the curve [AUC] and maximum plasma concentration [Cmax ]) at no observed adverse effect levels (NOAELs) in rats and dogs were determined in toxicology studies. RESULTS: Observed concentration-time profiles of exendin-(9-39) were described by a linear two-compartmental PK model. Following allometric scaling of PK parameters, age and creatinine clearance did not significantly affect clearance. The calculated clearance and elimination half-life for adult subjects with median weight of 69 kg were 11.8 l h-1 and 1.81 h, respectively. The maximum recommended starting dose determined from modelling and simulation based on the AUC0-last at the NOAEL and predicted AUC0-inf using the PopPK model was 27 mg kg-1  day-1 intravenously. CONCLUSIONS: This is the first study to investigate the PopPK of exendin-(9-39) in humans. The final PopPK model was successfully used with preclinical toxicology findings to propose the optimal dosing regimen of exendin-(9-39) for clinical studies in neonates with HI, allowing for a more targeted dosing approach to achieve desired glycaemic response.

Mechanism-Based Competitive Binding Model to Investigate the Effect of Neonatal Fc Receptor Binding Affinity on the Pharmacokinetic of Humanized Anti-VEGF Monoclonal IgG1 Antibody in Cynomolgus Monkey.

The quantitative relationship between neonatal Fc receptor (FcRn) binding affinity at both acidic and physiological pH and the pharmacokinetics of protein engineered FcRn IgG1 variants has not yet been reported. Our objective was to develop a quantitatively mechanism-based competitive binding model to describe the effects of FcRn binding affinity at acidic and physiological pH on the pharmacokinetics of anti-VEGF IgG1 antibodies when both endogenous and exogenous antibodies are competing for the same FcRn. Pharmacokinetic (PK) and FcRn binding data from five Fc variants of humanized anti-VEGF IgG1 monoclonal antibodies with wide range of FcRn binding affinity were used for the analysis. Sixty-seven anti-VEGF IgG1 antibody-treated animals and 25 control animals with simulated endogenous IgG levels were used to develop the final model. A hybrid iterative two stages and Monte Carlo parametric expectation-maximization method was used to obtain the final model parameters estimates. The final model well described the observed PK data. Quantitative FcRn binding affinity-pharmacokinetics relationships was constructed to provide important biological insights in better understanding of the FcRn binding effect on pharmacokinetics of anti-VEGF IgG1 antibodies in cynomolgus monkeys and served as an important model-based drug discovery platform to guide the design and development of the future generation of anti-VEGF or other therapeutic IgG1 antibodies.

Incorporation of FcRn-mediated disposition model to describe the population pharmacokinetics of therapeutic monoclonal IgG antibody in clinical patients.

PURPOSE: The two-compartment linear model used to describe the population pharmacokinetics (PK) of many therapeutic monoclonal antibodies (TMAbs) offered little biological insight to antibody disposition in humans. The purpose of this study is to develop a semi-mechanistic FcRn-mediated IgG disposition model to describe the population PK of TMAbs in clinical patients. METHODS: A standard two-compartment linear PK model from a previously published population PK model of pertuzumab was used to simulate intensive PK data of 100 subjects for model development. Two different semi-mechanistic FcRn-mediated IgG disposition models were developed and First Order Conditional Estimation (FOCE) with the interaction method in NONMEM was used to obtain the final model estimates. The performances of these models were then compared with the two-compartment linear PK model used to simulate the data for model development. RESULTS: A semi-mechanistic FcRn-mediated IgG disposition model consisting of a peripheral tissue compartment and FcRn-containing endosomes in the central compartment best describes the simulated pertuzumab population PK data. This developed semi-mechanistic population PK model had the same number of model parameters, produced very similar concentration-time profiles but provided additional biological insight to the FcRn-mediated IgG disposition in human subjects compared with the standard linear two-compartment linear PK model. CONCLUSION: This first reported semi-mechanistic model may serve as an important model framework for developing future population PK models of TMAbs in clinical patients.

Population Pharmacokinetic Model of Sublingual Buprenorphine in Neonatal Abstinence Syndrome.

OBJECTIVE: Neonatal abstinence syndrome (NAS)--a clinical entity of infants from in utero exposure to psychoactive xenobiotic and buprenorphine--has been successfully used to treat NAS. However, nothing is known about the pharmacokinetics (PK) of buprenorphine in neonates with NAS. To our knowledge, this is the first study to investigate the population pharmacokinetic of sublingual buprenorphine in neonates with NAS. DESIGN: A retrospective population PK analysis of: (1) neonates with NAS treated with sublingual buprenorphine in randomized, double blinded clinical study and (2) data from healthy adults from a previously published pharmacokinetic study. SETTING: Neonatal intensive care unit and general clinical research unit. PATIENTS: Twenty-four neonates with NAS and five healthy adults. INTERVENTIONS: All participants received sublingual buprenorphine per study protocol. MEASUREMENTS AND MAIN RESULTS: A total of 303 PK data from 29 neonates and adults were used for model development. A population pharmacokinetic analysis was conducted using a first order conditional estimation with interaction in the NONMEM software program. A two-compartment linear PK model with first-order absorption process best described the pharmacokinetics of sublingual buprenorphine in neonates. The apparent clearance (CL) of buprenorphine was linearly related to body weight and matured with increasing age via two distinct saturated pathways. A typical neonate with NAS (body weight, 2.9 kg; postnatal age; 5.4 days) had a CL of 3.5 L/kg/hour and elimination half-life of 11 hours. Phenobarbital did not affect the clearance of buprenorphine compared to neonates of similar age and weight. CONCLUSIONS: This is the first study to investigate the population PK of sublingual buprenorphine in neonatal NAS. To our knowledge, this is also the first report to describe the age-dependent changes of buprenorphine PK in this patient population. No buprenorphine dose adjustment is needed for neonates with NAS treated with buprenorphine and concurrent phenobarbital.

GPU-accelerated compartmental modeling analysis of DCE-MRI data from glioblastoma patients treated with bevacizumab.

The compartment model analysis using medical imaging data is the well-established but extremely time consuming technique for quantifying the changes in microvascular physiology of targeted organs in clinical patients after antivascular therapies. In this paper, we present a first graphics processing unit-accelerated method for compartmental modeling of medical imaging data. Using this approach, we performed the analysis of dynamic contrast-enhanced magnetic resonance imaging data from bevacizumab-treated glioblastoma patients in less than one minute per slice without losing accuracy. This approach reduced the computation time by more than 120-fold comparing to a central processing unit-based method that performed the analogous analysis steps in serial and more than 17-fold comparing to the algorithm that optimized for central processing unit computation. The method developed in this study could be of significant utility in reducing the computational times required to assess tumor physiology from dynamic contrast-enhanced magnetic resonance imaging data in preclinical and clinical development of antivascular therapies and related fields.

Semi-mechanistic Multiple-Analyte Pharmacokinetic Model for an Antibody-Drug-Conjugate in Cynomolgus Monkeys.

PURPOSE: A semi-mechanistic multiple-analyte population pharmacokinetics (PK) model was developed to describe the complex relationship between the different analytes of monomethyl auristatin E (MMAE) containing antibody-drug conjugates (ADCs) and to provide insight regarding the major pathways of conjugate elimination and unconjugated MMAE release in vivo. METHODS: For an anti-CD79b-MMAE ADC the PK of total antibody (Tab), conjugate (evaluated as antibody conjugated MMAE or acMMAE), and unconjugated MMAE were quantified in cynomolgus monkeys for single (0.3, 1, or 3 mg/kg), and multiple doses (3 or 5 mg/kg, every-three-weeks for 4 doses). The PK data of MMAE in cynomolgus monkeys, after intravenous administration of MMAE at single doses (0.03 or 0.063 mg/kg), was included in the analysis. A semi-mechanistic model was developed and parameter estimates were obtained by simultaneously fitting the model to all PK data using a hybrid ITS-MCPEM method. RESULTS: The final model well described the observed Tab, acMMAE and unconjugated MMAE concentration-time profiles. Analysis suggested that conjugate is lost via both proteolytic degradation and deconjugation, while unconjugated MMAE in systemic circulation appears to be mainly released via proteolytic degradation of the conjugate. CONCLUSIONS: Our model improves the understanding of ADC catabolism, which may provide useful insights when designing future ADCs.

Novel method to predict body weight in children based on age and morphological facial features.

A new and novel approach of predicting the body weight of children based on age and morphological facial features using a three-layer feed-forward artificial neural network (ANN) model is reported. The model takes in four parameters, including age-based CDC-inferred median body weight and three facial feature distances measured from digital facial images. In this study, thirty-nine volunteer subjects with age ranging from 6-18 years old and BW ranging from 18.6-96.4 kg were used for model development and validation. The final model has a mean prediction error of 0.48, a mean squared error of 18.43, and a coefficient of correlation of 0.94. The model shows significant improvement in prediction accuracy over several age-based body weight prediction methods. Combining with a facial recognition algorithm that can detect, extract and measure the facial features used in this study, mobile applications that incorporate this body weight prediction method may be developed for clinical investigations where access to scales is limited.

Population pharmacokinetics of unbound hydrocortisone in critically ill neonates and infants with vasopressor-resistant hypotension.

OBJECTIVES: To determine the population pharmacokinetics of unbound hydrocortisone in critically ill neonates and infants receiving IV hydrocortisone for treatment of vasopressor-resistant hypotension and to identify patient-specific sources of pharmacokinetic variability. DESIGN: Prospective observational cohort study. SETTING: Level 3 neonatal ICU. PATIENTS: Sixty-two critically ill neonates and infants receiving IV hydrocortisone as part of standard of care for the treatment of vasopressor-resistant hypotension: median gestational age 28 weeks (range, 23-41), median weight 1.2 kg (range, 0.5-4.4), and 29 females. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Unbound baseline cortisol and postdose hydrocortisone concentrations measured from blood samples being drawn for routine laboratory tests. A one-compartment model best described the data. Allometric weight and postmenstrual age were significant covariates on unbound hydrocortisone clearance and volume of distribution. Final population estimates for clearance, volume of distribution, and baseline cortisol concentration were 20.2 L/hr, 244 L, and 1.37 ng/mL, respectively. Using the median weight and postmenstrual age of our subjects (i.e., 1.2 kg and 28 wk) in the final model, the typical unbound hydrocortisone clearance and volume of distribution were 1.0 L/hr and 4.2 L, respectively. The typical half-life for unbound hydrocortisone was 2.9 hours. A sharp and continuous increase in unbound hydrocortisone clearance was observed at 35 weeks postmenstrual age. CONCLUSIONS: We report the first pharmacokinetic data for unbound hydrocortisone, the pharmacologically active moiety, in critically ill neonates and infants with vasopressor-resistant hypotension. Unbound hydrocortisone clearance increased with body weight and was faster in children with an older postmenstrual age. Unbound hydrocortisone clearance increased sharply at 35 weeks postmenstrual age and continued to mature thereafter. This study lays the groundwork for evaluating unbound hydrocortisone exposure-response relationships and drawing definitive conclusions about the dosing of IV hydrocortisone in critically ill neonates and infants with vasopressor-resistant hypotension.

Effectiveness of a clinical pathway with methadone treatment protocol for treatment of neonatal abstinence syndrome following in utero drug exposure to substances of abuse*.

OBJECTIVE: To evaluate the effectiveness of methadone for the treatment of neonatal abstinence syndrome when used according to a preexisting clinical pathway. DESIGN: This is a 3-year retrospective study conducted at a single institution. In this study, neonates who received methadone for the treatment of neonatal abstinence syndrome according to a predefined clinical treatment pathway were evaluated for treatment success: defined as adherence to the methadone regimen with no residual signs of withdrawal. Data were collected for methadone dosages, Lipsitz scores, length of methadone treatment, total length of hospital stay, and relevant clinical data. SETTING: Level III neonatal ICU. PATIENTS: Newborn infants with in utero exposure to substances of abuse. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Sixty patients were included. The mean gestational age and birth weight were 37.07 ± 3.05 weeks and 2.77 ± 0.6 kg. All 60 patients exhibited neonatal abstinence syndrome within first 72 hours of life. Fifty-seven of 60 patients (95%) initiated methadone treatment according to protocol. There was deviation from the protocol at 48 and 72 hours of treatment with approximately 59% and 13% of the patients still on methadone at more than the prescribed amount to control neonatal abstinence syndrome. The mean ± SD total methadone exposure was 1.99 ± 1.63 mg/kg, length of treatment 11.66 ± 9.02 days, and total hospital length of stay 22.43 ± 29.3 days, suggesting significant variability in response. No significant correlation was found between birth weight or gestational age and length of treatment. CONCLUSION: Clinical pathway for treating neonatal abstinence syndrome was closely followed at the initial diagnosis. The doses of methadone used in the first 24-48 hours of this study were insufficient for adequate symptom control. Despite a formal treatment protocol, there was substantial variability in total methadone exposure, length of treatment, and length of stay, suggesting other contributory factors for the observed variability.