Improving Wildfire Readiness Among Patients With Chronic Obstructive Pulmonary Disease and Asthma: Applying a Population Health Approach to Climate Change.

As a result of climate change, wildfire frequency, duration, and severity are increasing in the United States. Exposure to wildfire-related air pollutants can lead to negative health outcomes, particularly among patients with pre-existing respiratory diseases (eg, asthma and chronic obstructive pulmonary disease) and those who are at higher risk for developing these conditions. Underserved communities are disproportionately affected for multiple reasons, including lack of financial and social resources, increased exposure to air pollutants at home and at work, and impaired access to healthcare. To best serve clinically high-risk and underserved populations, health systems must leverage community public health data, develop and mobilize a wildfire preparedness action plan to identify populations at high risk, and implement interventions to mitigate the consequences of poor air quality. University of California, Davis Health, located at the epicenter of the largest wildfires in California's history, has developed the 5 pillar Wildfire Population Health Approach: (1) identify clinically at-risk and underserved patient populations using well-validated, condition-targeted registries; (2) assemble multidisciplinary care teams to understand the needs of these communities and patients; (3) create custom analytics and wildfire-risk stratification; (4) develop care pathways based on wildfire-risk tiers by disease, risk of exposure, and healthcare access; and (5) identify outcome measures tailored to interventions with a commitment to continuous, iterative improvement efforts. The Wildfire Population Health Approach provides an action plan for health systems and care teams to meet the needs of clinically at-risk and underserved patients affected by the increasing health threat posed by climate change-related wildfires.

Recombinant Alpha-1 Antitrypsin-Fc Fusion Protein INBRX-101 in Adults With Alpha-1 Antitrypsin Deficiency: A Phase 1 Study.

Background: Alpha-1 antitrypsin deficiency (AATD) is characterized by low alpha-1 antitrypsin (AAT) levels, predisposing individuals to lung disease. The standard of care, plasma-derived AAT (pdAAT), is delivered as weekly infusions to maintain serum AAT concentrations ≥11µM (≈50% of those in healthy individuals). INBRX-101, a recombinant human AAT-Fc fusion protein, was designed to have a longer half-life and achieve higher AAT levels than pdAAT. Methods: In this phase 1 dose-escalation study (N=31), adults with AATD received 1 dose (part 1) or 3 doses (part 2) of 10 (part 1), 40, 80, or 120mg/kg INBRX-101 every 3 weeks (Q3W) via intravenous infusion. The primary endpoint was safety and tolerability. Secondary endpoints were pharmacokinetics (PK), pharmacodynamics (PD), and immunogenicity of INBRX-101. Results: INBRX-101 was well tolerated. Most treatment-emergent adverse events were grade ≤2. In part 2 (n=18; each dose, n=6), dose-related increases in serum functional AAT (fAAT) were observed; mean fAAT levels remained above the 21 µM target for up to 4 weeks after the final dose in the 120-mg/kg cohort. Antidrug antibodies had no meaningful impact on PK or PD. INBRX-101 was detected in pulmonary epithelial lining fluid (PELF) from all patients assessed (n=11), and PELF fAAT increased after dosing. PK/PD modeling projected steady-state serum fAAT ≥21µM at 120 mg/kg Q3W (average concentration ≈43µM; trough concentration ≈28µM) and Q4W (≈34µM; ≈21µM). Conclusion: The favorable safety profile and ability to maintain serum fAAT levels >21µM with extended-interval dosing, support a phase 2 trial evaluating Q3W and Q4W dosing of INBRX-101.

Using the ROX Index to Predict Treatment Outcome for High-Flow Nasal Cannula and/or Noninvasive Ventilation in Patients With COPD Exacerbations.

BACKGROUND: The ratio of oxygen saturation index (ROX index; or SpO2 /FIO2 /breathing frequency) has been shown to predict risk of intubation after high-flow nasal cannula (HFNC) support among adults with acute hypoxemic respiratory failure primarily due to pneumonia. However, its predictive value for other subtypes of respiratory failure is unknown. This study investigated whether the ROX index predicts liberation from HFNC or noninvasive ventilation (NIV), intubation with mechanical ventilation, or death in adults admitted for respiratory failure due to an exacerbation of COPD. METHODS: We performed a retrospective study of 260 adults hospitalized with a COPD exacerbation and treated with HFNC and/or NIV (continuous or bi-level). ROX index scores were collected at treatment initiation and predefined time intervals throughout HFNC and/or NIV treatment or until the subject was intubated or died. A ROX index score of ≥ 4.88 was applied to the cohort to determine if the same score would perform similarly in this different cohort. Accuracy of the ROX index was determined by calculating the area under the receiver operator curve. RESULTS: A total of 47 subjects (18%) required invasive mechanical ventilation or died while on HFNC/NIV. The ROX index at treatment initiation, 1 h, and 6 h demonstrated the best prediction accuracy for avoidance of invasive mechanical ventilation or death (area under the receiver operator curve 0.73 [95% CI 0.66-0.80], 0.72 [95% CI 0.65-0.79], and 0.72 [95% CI 0.63-0.82], respectively). The optimal cutoff value for sensitivity (Sn) and specificity (Sp) was a ROX index score > 6.88 (sensitivity 62%, specificity 57%). CONCLUSIONS: The ROX index applied to adults with COPD exacerbations treated with HFNC and/or NIV required higher scores to achieve similar prediction of low risk of treatment failure when compared to subjects with hypoxemic respiratory failure/pneumonia. ROX scores < 4.88 did not accurately predict intubation or death.

Work of Breathing During Proportional Assist Ventilation as a Predictor of Extubation Failure.

BACKGROUND: Despite decades of research on predictors of extubation success, use of ventilatory support after extubation is common and 10-20% of patients require re-intubation. Proportional assist ventilation (PAV) mode automatically calculates estimated total work of breathing (total WOB). Here, we assessed the performance of total WOB to predict extubation failure in invasively ventilated subjects. METHODS: This prospective observational study was conducted in 6 adult ICUs at an academic medical center. We enrolled intubated subjects who successfully completed a spontaneous breathing trial, had a rapid shallow breathing index < 105 breaths/min/L, and were deemed ready for extubation by the primary team. Total WOB values were recorded at the end of a 30-min PAV trial. Extubation failure was defined as any respiratory support and/or re-intubation within 72 h of extubation. We compared total WOB scores between groups and performance of total WOB for predicting extubation failure with receiver operating characteristic curves. RESULTS: Of 61 subjects enrolled, 9.8% (n = 6) required re-intubation, and 50.8% (n = 31) required any respiratory support within 72 h of extubation. Median total WOB at 30 min on PAV was 0.9 J/L (interquartile range 0.7-1.3 J/L). Total WOB was significantly different between subjects who failed or were successfully extubated (median 1.1 J/L vs 0.7 J/L, P = .004). The area under the curve was 0.71 [95% CI 0.58-0.85] for predicting any requirement of respiratory support and 0.85 [95% CI 0.69-1.00] for predicting re-intubation alone within 72 h of extubation. Total WOB cutoff values maximizing sensitivity and specificity equally were 1.0 J/L for any respiratory support (positive predictive value [PPV] 70.0%, negative predictive value [NPV] 67.7%) and 1.3 J/L for re-intubation (PPV 26.3%, NPV 97.6%). CONCLUSIONS: The discriminative performance of a PAV-derived total WOB value to predict extubation failure was good, indicating total WOB may represent an adjunctive tool for assessing extubation readiness. However, these results should be interpreted as preliminary, with specific thresholds of PAV-derived total WOB requiring further investigation in a large multi-center study.

Remote patient monitoring in the management of chronic obstructive pulmonary disease.

Remote patient monitoring allows monitoring high-risk patients through implementation of an expanding number of technologies in coordination with a healthcare team to augment care, with the potential to provide early detection of exacerbation, prompt access to therapy and clinical services, and ultimately improved patient outcomes and decreased healthcare utilization.In this review, we describe the application of remote patient monitoring in chronic obstructive pulmonary disease including the potential benefits and possible barriers to implementation both for the individual and the healthcare system.

A 66-Year-Old Man With Subacute Cough and Worsening Dyspnea Previously Diagnosed With COVID-19 Pneumonia.

A 66-year-old man presented with subacute cough and worsening dyspnea. Labs were notable for moderate peripheral eosinophilia, and computed tomography (CT) scan demonstrated extensive crazy-paving throughout bilateral upper lung fields. Bronchoalveolar lavage (BAL) revealed macrophages with lipid-filled vacuoles and negative periodic acid-Schiff (PAS) stain. Further history obtained from the patient and family was notable for daily application of commercially available vapor rub to nares and intentional deep inhalation of nebulized fluids containing scented oils. The patient was diagnosed with exogenous lipoid pneumonia through an unusual route of lipid administration.

Interprofessional Near-Peer Mentoring Teams Enhance Cancer Research Training: Sustainable Approaches for Biomedical Workforce Development of Historically Underrepresented Students.

A cancer research training program explored different approaches for staffing their in-person and virtual programs for high school students. The inclusion of undergraduate near-peer mentors had a universal benefit when implemented across in-person and virtual training programs of one- and ten-week durations. Benefits are described for four stakeholder groups: the high school trainees, program staff, scientist partners, and peer mentors themselves. Peer mentors described that their involvement enhanced their own professional development and, for some, drove a new interest in cancer research. Scientist partners described that peer mentors helped translate their work in the virtual environment for high school students. High school trainees reported their sessions with peer mentors to be one of their favorite parts of the program. Interprofessional peer mentors were highly relatable to students and modeled communication and paths in biomedical research. Staff reported that peer mentors supported student engagement during community shadowing sessions, allowing staff to focus on developing the shadowing experiences with partners. The benefit of including peer mentors was substantial from all viewpoints explored. Their intensive inclusion in cancer research training programs supports sustainability and capacity building in biomedical workforce development.

Physiologically-Based Pharmacokinetic Modeling to Investigate the Effect of Maturation on Buprenorphine Pharmacokinetics in Newborns with Neonatal Opioid Withdrawal Syndrome.

Neonatal opioid withdrawal syndrome (NOWS) is a major public health concern whose incidence has paralleled the opioid epidemic in the United States. Sublingual buprenorphine is an emerging treatment for NOWS, but given concerns about long-term adverse effects of perinatal opioid exposure, precision dosing of buprenorphine is needed. Buprenorphine pharmacokinetics (PK) in newborns, however, is highly variable. To evaluate underlying sources of PK variability, a neonatal physiologically-based pharmacokinetic (PBPK) model of sublingual buprenorphine was developed using Simcyp (version 19.1). The PBPK model included metabolism by cytochrome P450 (CYP) 3A4, CYP2C8, UDP-glucuronosyltransferase (UGT) 1A1, UGT1A3, UGT2B7, and UGT2B17, with additional biliary excretion. Maturation of metabolizing enzymes was incorporated, and default CYP2C8 and UGT2B7 ontogeny profiles were updated according to recent literature. A biliary clearance developmental profile was outlined using clinical data from neonates receiving sublingual buprenorphine as NOWS treatment. Extensive PBPK model validation in adults demonstrated good predictability, with geometric mean (95% confidence interval (CI)) predicted/observed ratios (P/O ratios) of area under the curve from zero to infinity (AUC0-∞ ), peak concentration (Cmax ), and time to reach peak concentration (Tmax ) equaling 1.00 (0.74-1.33), 1.04 (0.84-1.29), and 0.95 (0.72-1.26), respectively. In neonates, the geometric mean (95% CI) P/O ratio of whole blood concentrations was 0.75 (95% CI 0.64-0.87). PBPK modeling and simulation demonstrated that variability in biliary clearance, sublingual absorption, and CYP3A4 abundance are likely important drivers of buprenorphine PK variability in neonates. The PBPK model could be used to guide development of improved buprenorphine starting dose regimens for the treatment of NOWS.

Cosmic Ray Background Removal With Deep Neural Networks in SBND.

In liquid argon time projection chambers exposed to neutrino beams and running on or near surface levels, cosmic muons, and other cosmic particles are incident on the detectors while a single neutrino-induced event is being recorded. In practice, this means that data from surface liquid argon time projection chambers will be dominated by cosmic particles, both as a source of event triggers and as the majority of the particle count in true neutrino-triggered events. In this work, we demonstrate a novel application of deep learning techniques to remove these background particles by applying deep learning on full detector images from the SBND detector, the near detector in the Fermilab Short-Baseline Neutrino Program. We use this technique to identify, on a pixel-by-pixel level, whether recorded activity originated from cosmic particles or neutrino interactions.

Comparative Effectiveness of Roflumilast and Azithromycin for the Treatment of Chronic Obstructive Pulmonary Disease.

BACKGROUND: In chronic obstructive pulmonary disease (COPD) patients with exacerbations despite optimized bronchodilator therapy, roflumilast and chronic azithromycin are recommended options. Roflumilast is recommended in severe COPD patients with chronic bronchitis, whereas chronic azithromycin is more broadly indicated. The comparative effectiveness between these 2 treatments to reduce exacerbation rate remains unclear. OBJECTIVES: Our objective was analysis of the Veterans Health Administration (VHA) database (medication and claims data without lung function or presence of chronic bronchitis or tobacco use) to compare the effectiveness of roflumilast and azithromycin on hospitalizations and mortality. METHODS: The primary outcome of the study was cumulative incidences of first COPD-related and all-cause hospitalization. Sensitivity analysis on hospitalizations was conducted for VHA patients who also had Medicare. RESULTS: In 1302 roflumilast and 2573 azithromycin patients, the all-cause mortality rates at 1 year were 19% and 15%, respectively. The median times-to-all-cause death were 47 months (interquartile range [IQR] 16-81) for the roflumilast and 48 months (IQR 20-83) for the azithromycin groups. Roflumilast was associated with higher mortality (hazard ratio [HR] 1.16; 95% confidence interval [CI], 1.04-1.29). Roflumilast showed no significant association for COPD-related hospitalization (subdistribution HR [SHR]=1.14, 95% CI, 1.00-1.29) and all-cause hospitalization (HR 1.07, 95% CI, 0.97-1.18). For patients with Medicare (N=2030), roflumilast was associated with higher COPD-related (SHR 1.21; 95% CI, 1.05-1.41) and all-cause (SHR 1.23; 95% CI, 1.09-1.38) hospitalizations. CONCLUSIONS: Roflumilast was associated with higher hazard ratios for death, COPD-related hospitalizations, and all-cause hospitalizations in COPD patients only after adjustment for VHA and external Medicare events. Prospective clinical trials are needed to directly compare the relative efficacy of these therapies.

Patient and Family Preferences on Health System-Led Direct Contact for Cascade Screening.

Health benefits to relatives of people at known genetic risk for hereditary cancer syndromes is key to realizing the promise of precision medicine. We conducted a qualitative study to design a patient- and family-centered program for direct contact of relatives to recommend cascade genetic testing. We conducted two rounds of data collection using focus groups followed by individual interviews with patients with HBOC or Lynch syndrome and a separate sample of people with a family history of hereditary cancers. Results indicate that U.S.-based health system-led direct contact of relatives is acceptable to patients and families, should take a programmatic approach, include consent of relatives before proband testing, complement to existing patient-mediated disclosure, and allow for relative control of information. Our findings suggest a set of requirements for U.S.-based direct contact programs that could ultimately benefit more relatives than current approaches.

A 56-Year-Old Male With Weight Loss, Night Sweats, Dyspnea, and Bladder Cancer.

A 56-year-old man presented with subacute night sweats, fever, and weight loss with worsening dyspnea. Computed tomography (CT) scan demonstrated miliary pattern of nodules evenly distributed throughout all lung fields. Given the patient's CT findings and temporal association with Bacille Calmette-Guerin (BCG) immunotherapy for bladder cancer, the patient was diagnosed with disseminated Mycobacterium bovis secondary to BCG bladder instillations.

Use of Machine Learning to Screen for Acute Respiratory Distress Syndrome Using Raw Ventilator Waveform Data.

To develop and characterize a machine learning algorithm to discriminate acute respiratory distress syndrome from other causes of respiratory failure using only ventilator waveform data. DESIGN: Retrospective, observational cohort study. SETTING: Academic medical center ICU. PATIENTS: Adults admitted to the ICU requiring invasive mechanical ventilation, including 50 patients with acute respiratory distress syndrome and 50 patients with primary indications for mechanical ventilation other than hypoxemic respiratory failure. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Pressure and flow time series data from mechanical ventilation during the first 24-hours after meeting acute respiratory distress syndrome criteria (or first 24-hr of mechanical ventilation for non-acute respiratory distress syndrome patients) were processed to extract nine physiologic features. A random forest machine learning algorithm was trained to discriminate between the patients with and without acute respiratory distress syndrome. Model performance was assessed using the area under the receiver operating characteristic curve, sensitivity, specificity, positive predictive value, and negative predictive value. Analyses examined performance when the model was trained using data from the first 24 hours and tested using withheld data from either the first 24 hours (24/24 model) or 6 hours (24/6 model). Area under the receiver operating characteristic curve, sensitivity, specificity, positive predictive value, and negative predictive value were 0.88, 0.90, 0.71, 0.77, and 0.90 (24/24); and 0.89, 0.90, 0.75, 0.83, and 0.83 (24/6). CONCLUSIONS: Use of machine learning and physiologic information derived from raw ventilator waveform data may enable acute respiratory distress syndrome screening at early time points after intubation. This approach, combined with traditional diagnostic criteria, could improve timely acute respiratory distress syndrome recognition and enable automated clinical decision support, especially in settings with limited availability of conventional diagnostic tests and electronic health records.

Opioid Treatment for Neonatal Opioid Withdrawal Syndrome: Current Challenges and Future Approaches.

Chronic intrauterine exposure to psychoactive drugs often results in neonatal opioid withdrawal syndrome (NOWS). When nonpharmacologic measures are insufficient in controlling NOWS, morphine, methadone, and buprenorphine are first-line medications commonly used to treat infants with NOWS because of in utero exposure to opioids. Research suggests that buprenorphine may be the leading drug therapy used to treat NOWS when compared with morphine and methadone. Currently, there are no consensus or standardized treatment guidelines for medications prescribed for NOWS. Opioids used to treat NOWS exhibit large interpatient variability in pharmacokinetics (PK) and pharmacodynamic (PD) response in neonates. Organ systems undergo rapid maturation after birth that may alter drug disposition and exposure for any given dose during development. Data regarding the PK and PD of opioids in neonates are sparse. Pharmacometric methods such as physiologically based pharmacokinetic and population pharmacokinetic modeling can be used to explore factors predictive of some of the variability associated with the PK/PD of opioids in newborns. This review discusses the utility of pharmacometric techniques for enhancing precision dosing in infants requiring opioid treatment for NOWS. Applying these approaches may contribute to optimizing the outcome by reducing cumulative drug exposure, mitigating adverse drug effects, and reducing the burden of NOWS in neonates.

Physiologic Indirect Response Modeling to Describe Buprenorphine Pharmacodynamics in Newborns Treated for Neonatal Opioid Withdrawal Syndrome.

BACKGROUND AND OBJECTIVE: Buprenorphine has been shown to be effective in treating infants with neonatal opioid withdrawal syndrome. However, an evidence-based buprenorphine dosing strategy has not been established in the treatment of neonatal opioid withdrawal syndrome because of a lack of exposure-response data. The aim of this study was to develop an integrated pharmacokinetic and pharmacodynamic model to predict buprenorphine treatment outcomes in newborns with neonatal opioid withdrawal syndrome. METHODS: Clinical data were obtained from 19 newborns with a median (range) gestational age of 37 (34-41) weeks enrolled in a pilot pharmacokinetic study of buprenorphine. Sparse blood sampling, comprising three specimens obtained around the second dose of buprenorphine, was performed using heel sticks with dried blood spot technology. Standardized neonatal opioid withdrawal syndrome severity scores (Finnegan scores) were collected every 3-4 h based on symptoms by bedside nursing staff. Mean Finnegan scores were used as a pharmacodynamic marker in the exposure-response modeling. The blood concentration-Finnegan score relationship was described using a physiologic indirect response model with inclusion of natural disease remission. RESULTS: A total of 52 buprenorphine blood concentrations and 780 mean Finnegan scores were available for the pharmacokinetic/pharmacodynamic modeling and exposure-response analysis. A one-compartment model with first-order absorption adequately described the pharmacokinetic data. The buprenorphine blood concentration at 50% of maximum effect for the inhibition of disease progression was 0.77 ng/mL (95% confidence interval 0.32-1.2). The inclusion of natural disease remission described as a function of postnatal age significantly improved the model fit. CONCLUSIONS: A buprenorphine pharmacokinetic/pharmacodynamic model was successfully developed. The model could facilitate model-informed optimization of the buprenorphine dosing regimen in the treatment of neonatal opioid withdrawal syndrome.

Alpha-1 antitrypsin deficiency and recombinant protein sources with focus on plant sources: Updates, challenges and perspectives.

Alpha-1 antitrypsin deficiency (A1ATD) is an autosomal recessive disease characterized by low plasma levels of A1AT, a serine protease inhibitor representing the most abundant circulating antiprotease normally present at plasma levels of 1-2 g/L. The dominant clinical manifestations include predispositions to early onset emphysema due to protease/antiprotease imbalance in distal lung parenchyma and liver disease largely due to unsecreted polymerized accumulations of misfolded mutant A1AT within the endoplasmic reticulum of hepatocytes. Since 1987, the only FDA licensed specific therapy for the emphysema component has been infusions of A1AT purified from pooled human plasma at the 2020 cost of up to US $200,000/year with the risk of intermittent shortages. In the past three decades various, potentially less expensive, recombinant forms of human A1AT have reached early stages of development, one of which is just reaching the stage of human clinical trials. The focus of this review is to update strategies for the treatment of the pulmonary component of A1ATD with some focus on perspectives for therapeutic production and regulatory approval of a recombinant product from plants. We review other competitive technologies for treating the lung disease manifestations of A1ATD, highlight strategies for the generation of data potentially helpful for securing FDA Investigational New Drug (IND) approval and present challenges in the selection of clinical trial strategies required for FDA licensing of a New Drug Approval (NDA) for this disease.

Pharmacotherapy of neonatal opioid withdrawal syndrome: a review of pharmacokinetics and pharmacodynamics.

INTRODUCTION: Neonatal opioid withdrawal syndrome (NOWS) often arises in infants born to mothers who used opioids during pregnancy. Morphine, methadone, and buprenorphine are the most common first-line treatments, whereas clonidine and phenobarbital are generally reserved for adjunctive therapy. These drugs exhibit substantial pharmacokinetic (PK) and pharmacodynamic (PD) variability. Current pharmacological treatments for NOWS are based on institutional protocols and largely rely on empirical treatment of patient symptoms. AREAS COVERED: This article reviews the PK/PD of NOWS pharmacotherapies with a focus on the implication of physiological development and maturation. Body size-standardized clearance is consistently low in neonates, except for methadone. This can be ascribed to underdeveloped metabolic and elimination pathways. The effects of pharmacogenetics have been clarified especially for morphine. The PK/PD relationship of medications used in the treatment of NOWS is generally understudied. EXPERT OPINION: Providing an appropriate opioid dose in neonates is challenging. Advancements in quantitative pharmacology and PK/PD modeling approaches facilitate identification of key factors driving PK/PD variability and characterization of exposure-response relationships. PK/PD model-informed simulations have been widely employed to define age-appropriate pediatric dosing regimens. The model-informed approach holds promise to aid more rational use of medications in the treatment of NOWS.

Leveraging IoTs and Machine Learning for Patient Diagnosis and Ventilation Management in the Intensive Care Unit.

Future healthcare systems will rely heavily on clinical decision support systems (CDSS) to improve the decision-making processes of clinicians. To explore the design of future CDSS, we developed a research-focused CDSS for the management of patients in the intensive care unit that leverages Internet of Things (IoT) devices capable of collecting streaming physiologic data from ventilators and other medical devices. We then created machine learning (ML) models that could analyze the collected physiologic data to determine if the ventilator was delivering potentially harmful therapy and if a deadly respiratory condition, acute respiratory distress syndrome (ARDS), was present. We also present work to aggregate these models into a mobile application that can provide responsive, real-time alerts of changes in ventilation to providers. As illustrated in the recent COVID-19 pandemic, being able to accurately predict ARDS in newly infected patients can assist in prioritizing care. We show that CDSS may be used to analyze physiologic data for clinical event recognition and automated diagnosis, and we also highlight future research avenues for hospital CDSS.

Utilizing Pediatric Physiologically Based Pharmacokinetic Models to Examine Factors That Contribute to Methadone Pharmacokinetic Variability in Neonatal Abstinence Syndrome Patients.

Chronic intrauterine exposure to psychoactive drugs often results in neonatal abstinence syndrome (NAS). NAS is the symptomatic drug withdrawal in newborns that generally occurs after in utero chronic opioid exposure. Methadone is an opioid analgesic commonly prescribed for pharmacologic management of NAS. It exhibits high pharmacokinetic (PK) variability. The current study used physiologically based PK modeling to predict the PK profile of methadone in 20 newborns treated for NAS. The physiologically based PK simulations adequately predicted the PK profile of the clinical data for 45% of the patients. Sensitivity analyses were conducted to explore contributing factors to methadone PK variability. The data suggest that P450 enzymatic activity impacts the clearance of methadone in virtual adults and neonates, while the contribution of cardiac output may be negligible. Understanding maturational and/or pharmacogenetic changes in cytochrome P450 enzymatic activity may further explain the large PK variability of methadone in newborns with NAS and will help individualized treatment.