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A knowledge gap exists in infant tetrahydrocannabinol (THC) data to guide breastfeeding recommendations for mothers who use cannabis. In the present study, a paired lactation and infant physiologically based pharmacokinetic (PBPK) model was developed and verified. The verified model was used to simulate one hundred virtual lactating mothers (mean age: 28 years, body weight: 78 kg) who smoked 0.32 g of cannabis containing 14.14% THC, either once or multiple times. The simulated breastfeeding conditions included one-hour post smoking and subsequently every three hours. The mean peak concentration (Cmax) and area under the concentration–time curve (AUC(0–24 h)) for breastmilk were higher than in plasma (Cmax: 155 vs. 69.9 ng/mL; AUC(0–24 h): 924.9 vs. 273.4 ng·hr/mL) with a milk-to-plasma AUC ratio of 3.3. The predicted relative infant dose ranged from 0.34% to 0.88% for infants consuming THC-containing breastmilk between birth and 12 months. However, the mother-to-infant plasma AUC(0–24 h) ratio increased up to three-fold (3.4–3.6) with increased maternal cannabis smoking up to six times. Our study demonstrated the successful development and application of a lactation and infant PBPK model for exploring THC exposure in infants, and the results can potentially inform breastfeeding recommendations.

Opioid use disorders (OUD) are a major issue in the U.S. Current treatments for pregnant women, like methadone and buprenorphine require daily dosing and have adverse effects. Monthly injectable naltrexone (XR‐NTX) mitigates these adverse effects but is not recommended during pregnancy due to limited pharmacokinetic and safety data. This study developed a physiologically based pharmacokinetic (PBPK) model to describe XR‐NTX pharmacokinetics during pregnancy, and to predict dosing recommendations. Model predictions were successfully validated with observed data. Maternal plasma XR‐NTX profiles were simulated for 400 non‐pregnant virtual females at the approved dose of 380 mg, then randomized to continue with either 380, 285, 190, or 95 mg during pregnancy. The non‐pregnant virtual females had a mean predicted Cmax, AUC0‐7days, and AUC0‐28days of 23.3 ng/mL, 142 ng·d/mL, and 148 ng·d/mL, respectively. Maternal XR‐NTX exposure (AUC0‐28days) were predicted to increase by 1.37, 1.43, and 1.72 times during the first, second, and third trimester of pregnancy. However, the fetal‐to‐maternal exposure (AUC0‐28days) was lower in the first (15%), second (7%), and third (9%) trimesters. A dose of 285 mg of XR‐NTX in pregnancy during the first/second trimester and dose of 190 mg in the third trimester were predicted to provide maternal exposures that were comparable to non‐pregnant levels at the standard dose. This study provides crucial insights into XR‐NTX pharmacokinetics and proposes a dosing strategy during pregnancy, potentially aiding further clinical investigations and decision making regarding XR‐NTX use during pregnancy.

The severity of the ongoing opioid crisis, recently exacerbated by the COVID-19 pandemic, emphasizes the importance for individuals suffering from opioid use disorder (OUD) to have access to and receive efficacious, evidence-based treatments. Optimal treatment of OUD should aim at blocking the effects of illicit opioids while controlling opioid craving and withdrawal to facilitate abstinence from opioid use and promote recovery. The present work analyses the relationship between buprenorphine plasma exposure and clinical efficacy in participants with moderate to severe OUD using data from two clinical studies (39 and 504 participants). Leveraging data from placebo-controlled measures assessing opioid blockade, craving, withdrawal and abstinence, we found that buprenorphine plasma concentrations sustained at 2–3 ng/ml (corresponding to ≥70% brain mu -opioid receptor occupancy) optimized treatment outcomes in the majority of participants, while some individuals (e.g., injecting opioid users) needed higher concentrations. Our work also included non-linear mixed effects modeling and survival analysis, which identified a number of demographic, genetic and social factors modulating treatment response and retention. Altogether, these findings provide key information on buprenorphine plasma levels that optimize clinical outcomes and increase the likelihood of individual treatment success. NLM identifiers: NCT02044094, NCT02357901.

Tacrolimus, an immunosuppressant used to prevent organ rejection in renal transplant patients, exhibits high inter-patient variability, necessitating therapeutic drug monitoring. Early post-transplant tacrolimus exposure in Hispanics is understudied. Although genotypic information is linked to pharmacokinetic differences, its clinical application remains limited. This study aimed to use a real-world data-driven, pharmacokinetic model-based approach for tacrolimus in Hispanics to determine a suitable initial dose and design an optimal dose titration strategy by simulations to achieve plasma trough concentration target levels of 10-12 ng/mL at the earliest. Sparse concentration-time data of tacrolimus were obtained from electronic medical records for self-identified Hispanic subjects following renal transplant. Rich pharmacokinetic literature data was leveraged to estimate structural pharmacokinetic model parameters, which were then fixed in the current analysis. Only apparent clearance was estimated with the sparse tacrolimus data and potential covariates were identified. Simulations of various starting doses and different dose titration strategies were then evaluated. The analysis included 121 renal transplant patients with 2,215 trough tacrolimus concentrations. A two-compartment transit absorption model with allometrically scaled body weight and time-varying hematocrit on apparent clearance adequately described the data. The estimated apparent clearance was 13.7 L/h for a typical patient weighing 70 kg and at 30% hematocrit, demonstrating a 40% decrease in clearance compared to other patient populations. Model based simulations indicated the best initial dose for the Hispanic population is 0.1 mg/kg/day. The proposed titration strategy, with three dose adjustments based on trough levels of tacrolimus, increased the proportion of patients within the target range (10-12 ng/mL) more than 2.5-fold and decreased the proportion of patients outside the therapeutic window by 50% after the first week of treatment. Hispanic renal transplant population showed an estimated 40% decrease of apparent clearance in the typical patient compared to other populations with similar characteristics. The proposed dose adjustment attained the target range rapidly and safely. This study advocates for tailored tacrolimus dosing regimens based on population pharmacokinetics to optimize therapy in Hispanic renal transplant recipients.

Deep learning is the fastest growing field in artificial intelligence and has led to many transformative innovations in various domains. However, lack of interpretability sometimes hinders its application in hypothesis-driven domains such as biology and healthcare. In this paper, we propose a novel deep learning model with individual feature ranking. Several simulated datasets with the scenarios that contributing features are correlated and buried among non-contributing features were used to characterize the novel analysis approach. A publicly available clinical dataset was also applied. The performance of the individual level dropout feature ranking model was compared with commonly used artificial neural network model, random forest model, and population level dropout feature ranking model. The individual level dropout feature ranking model provides a reasonable prediction of the outcomes. Unlike the random forest model and population level dropout feature ranking model, which can only identify global-wise contributing features (i.e., at population level), the individual level dropout feature ranking model allows further identification of impactful features on response at individual level. Therefore, it provides a basis for clustering patients into subgroups. This may provide a new tool for enriching patients in clinical drug development and developing personalized or individualized medicine.

Taxanes are currently the most frequently used chemotherapeutic agents in cancer care, where real‐world use has focused on minimizing adverse events and standardizing the delivery. Myelosuppression is a well‐characterized, adverse pharmacodynamic effect of taxanes. Electronic health records (EHRs) comprise data collected during routine clinical care that include patients with heterogeneous demographic, clinical, and treatment characteristics. Application of pharmacokinetic/pharmacodynamic (PK/PD) modeling to EHR data promises new insights on the real‐world use of taxanes and strategies to improve therapeutic outcomes especially for populations who are typically excluded from clinical trials, including the elderly. This investigation: (i) leveraged previously published PK/PD models developed with clinical trial data and addressed challenges to fit EHR data, and (ii) evaluated predictors of paclitaxel‐induced myelosuppression. Relevant EHR data were collected from patients treated with paclitaxel‐containing chemotherapy at Inova Schar Cancer Institute between 2015 and 2019 (n = 405). Published PK models were used to simulate mean individual exposures of paclitaxel and carboplatin, which were linearly linked to absolute neutrophil count (ANC) using a published semiphysiologic myelosuppression model. Elderly patients (≥70 years) constituted 21.2% of the dataset and 2274 ANC measurements were included in the analysis. The PD parameters were estimated and matched previously reported values. The baseline ANC and chemotherapy regimen were significant predictors of paclitaxel‐induced myelosuppression. The nadir ANC and use of supportive treatments, such as growth factors and antimicrobials, were consistent across age quantiles suggesting age had no effect on paclitaxel‐induced myelosuppression. In conclusion, EHR data could complement clinical trial data in answering key therapeutic questions.

Background Despite treatment with highly effective antimalarial drugs, malaria annually claims the lives of over half a million children under 5-years of age in sub-Saharan Africa. Cerebral malaria (CM), defined as Plasmodium falciparum infection with coma, is the severe malaria syndrome with the highest mortality. Studies in the CM mouse model suggest that a T cell-mediated response underlies CM pathology, opening a new target for therapy in humans. This trial aims to establish the preliminary safety of one such novel therapy, the glutamine antagonist 6-diazo-5-oxo-L-norleucine (DON). Methods In this phase I/IIa dose-escalation clinical trial, a single dose of intravenous (IV) DON is administered to three participants groups—healthy adults and adults with uncomplicated malaria, then pediatric participants with CM—to primarily assess safety. The secondary objective of this trial is to assess pharmacokinetics of DON over a range of doses. The open-label adult portion of the trial enrolls 40 healthy adults concurrently with 40 adults with uncomplicated malaria. Cohorts of 10 participants receive a single IV dose of DON with doses escalating between cohorts from 0.1 mg/kg, 1.0 mg/kg, 5.0 mg/kg, to 10 mg/kg. Following subsequent safety review, a randomized, double-blind, and placebo-controlled pediatric study enrolls 72 participants aged 6 months to 14 years with CM. The pediatric portion of the study minimally spans three malaria seasons including a planned interim analysis after 50% of pediatric enrollments. The first half of pediatric participants receive DON 0.1 mg/kg, 1.0 mg/kg, or placebo. Dosing for the second half of pediatric participants is informed by the safety and preliminary efficacy results of those previously enrolled. The pediatric portion of the study has an exploratory outcome evaluating the preliminary efficacy of DON. Efficacy is assessed by diagnostics predictive of CM outcome: electroencephalography (EEG), magnetic resonance imaging (MRI), and transcranial doppler (TCD), measured before and after DON administration. All participants with malaria receive standard of care antimalarials in accordance with local guidelines, regardless of study drug dose group. Discussion This preliminary safety and efficacy study evaluates DON, a candidate adjunctive therapy for pediatric CM. If results support DON preliminary safety and efficacy, follow-up phase II and III clinical trials will be indicated. Trial registration This trial was registered on ClinicalTrials.gov on 28 July 2022 (NCT05478720).

Randomized, placebo‐controlled trials for binge eating disorder (BED) have revealed highly variable, and often marked, rates of short‐term placebo response. Several quantitative based analyses in patients with BED have inconsistently demonstrated which patient factors attribute to an increase in placebo response. The objective of this study is to utilize machine learning (ML) algorithms to identify moderators of placebo response in patients with BED. Data were pooled from 12 randomized placebo‐controlled trials evaluating different treatment options for BED. The final dataset consisted of 189 adults receiving placebo with complete information of baseline variables. Placebo responders were defined as patients experiencing ≥75% reduction in binge eating frequency (BEF) at study end point. Nine patient prerandomization variables were included as predictors. Patients were divided into training and testing subsets according to an 75%:25% distribution while preserving the proportion of placebo responders. All analysis was performed in the software Pumas 2.0. Gaussian Naïve Bayes algorithm showed the best cross‐validation accuracy (~64%) and was chosen as the final algorithm. Shapley analysis suggested that patients with low baseline BEF and anxiety status were strong moderators of placebo response. Upon applying the final algorithm on the test dataset, the resulting sensitivity was 88% and prediction accuracy was 72%. This is the first application of ML to identify moderators of placebo response in BED. The results of this analysis confirm previous findings of lesser baseline disease severity and adds that patients with no anxiety are more susceptible to placebo response.

Limited data exist on the effect of continuous renal replacement therapy (CRRT) methods on anti‐epileptic drug pharmacokinetics (PK). This prospective practice‐based PK study aims to assess the impact of continuous venovenous hemofiltration (CVVH), a modality of CRRT, on levetiracetam PK in critically ill patients and to derive individualized dosing recommendations. Eleven patients receiving oral or intravenous levetiracetam and CVVH in various intensive care units at a large academic medical center were enrolled to investigate the need for dosing adjustments. Prefilter, postfilter, and ultrafiltrate samples were obtained before dosing, after the completion of the infusion or 1‐hour postoral dose, and up to 6 additional time points postinfusion or postoral administration. Patient‐specific blood and ultrafiltrate flow rates and laboratory values were also collected at the time of sampling. The average sieving coefficient (SC) for levetiracetam was 0.89 ± 0.1, indicating high filter efficiency. Six of the 11 patients experienced concentrations outside the reported therapeutic range (12–46 mg/L). The average volume of distribution was 0.73 L/kg. CVVH clearance contributes a major fraction of the total levetiracetam clearance (36–73%) in neurocritically ill patients. The average bias and precision of the estimated vs. observed total clearance value was ~ 10.6% and 21.5%. Major dose determinants were identified to be SC and effluent flow rate. Patients with higher ultrafiltrate rates will have increased drug clearance and, therefore, will require higher doses in order to match exposures seen in patients with normal renal function.