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Chemical mechanisms describe the atmospheric transformations of organic and inorganic species and connect air emissions to secondary species such as ozone, fine particles, and hazardous air pollutants (HAPs) like formaldehyde. Recent advances in our understanding of several chemical systems and shifts in the drivers of atmospheric chemistry warrant updates to mechanisms used in chemical transport models such as the Community Multiscale Air Quality (CMAQ) modeling system. This work builds on the Regional Atmospheric Chemistry Mechanism version 2 (RACM2) and develops the Community Regional Atmospheric Chemistry Multiphase Mechanism (CRACMM) version 1.0, which demonstrates a fully coupled representation of chemistry leading to ozone and secondary organic aerosol (SOA) with consideration of HAPs. CRACMMv1.0 includes 178 gas-phase species, 51 particulate species, and 508 reactions spanning gas-phase and heterogeneous pathways. To support estimation of health risks associated with HAPs, nine species in CRACMM cover 50 % of the total cancer and 60 % of the total non-cancer emission-weighted toxicity estimated for primary HAPs from anthropogenic and biomass burning sources in the US, with the coverage of toxicity higher (> 80 %) when secondary formaldehyde and acrolein are considered. In addition, new mechanism species were added based on the importance of their emissions for the ozone, organic aerosol, or atmospheric burden of total reactive organic carbon (ROC): sesquiterpenes, furans, propylene glycol, alkane-like low- to intermediate-volatility organic compounds (9 species), low- to intermediate-volatility oxygenated species (16 species), intermediate-volatility aromatic hydrocarbons (2 species), and slowly reacting organic carbon. Intermediate- and lower-volatility organic compounds were estimated to increase the coverage of anthropogenic and biomass burning ROC emissions by 40 % compared to current operational mechanisms. Autoxidation, a gas-phase reaction particularly effective in producing SOA, was added for C10 and larger alkanes, aromatic hydrocarbons, sesquiterpenes, and monoterpene systems including second-generation aldehydes. Integrating the radical and SOA chemistry put additional constraints on both systems and enabled the implementation of previously unconsidered SOA pathways from phenolic and furanone compounds, which were predicted to account for ∼ 30 % of total aromatic hydrocarbon SOA under typical atmospheric conditions. CRACMM organic aerosol species were found to span the atmospherically relevant range of species carbon number, number of oxygens per carbon, and oxidation state with a slight high bias in the number of hydrogens per carbon. In total, 11 new emitted species were implemented as precursors to SOA compared to current CMAQv5.3.3 representations, resulting in a bottom-up prediction of SOA, which is required for accurate source attribution and the design of control strategies. CRACMMv1.0 is available in CMAQv5.4.

Triggered drug delivery strategies have been shown to enhance drug accumulation at target diseased sites in comparison to administration of free drug. In particular, many studies have demonstrated improved targetability of chemotherapeutics when delivered via thermosensitive liposomes. However, most studies continue to focus on encapsulating doxorubicin while many other drugs would benefit from this targeted and localized delivery approach. The proposed study explores the therapeutic potential of a thermosensitive liposome formulation of the commonly used chemotherapy drug vinorelbine in combination with mild hyperthermia (39-43 °C) in a murine model of rhabdomyosarcoma. Rhabdomyosarcoma, the most common soft tissue sarcoma in children, is largely treated using conventional chemotherapy which is associated with significant adverse long-term sequelae. In this study, mild hyperthermia was pursued as a non-invasive, non-toxic means to improve the efficacy and safety profiles of vinorelbine. Thorough assessment of the pharmacokinetics, biodistribution, efficacy and toxicity of vinorelbine administered in the thermosensitive liposome formulation was compared to administration in a traditional, non-thermosensitive liposome formulation. This study shows the potential of an advanced formulation technology in combination with mild hyperthermia as a means to target an untargeted therapeutic agent and result in a significant improvement in its therapeutic index.Graphical abstract

Chemotherapy plays an important role in debulking tumors in advance of surgery and/or radiotherapy, tackling residual disease, and treating metastatic disease. In recent years many promising advanced drug delivery strategies have emerged that offer more targeted delivery approaches to chemotherapy treatment. For example, thermosensitive liposome mediated drug delivery in combination with localized mild hyperthermia can increase local drug concentrations resulting in a reduction in systemic toxicity and an improvement in local disease control. However, the majority of solid tumor associated deaths are due to metastatic spread. A therapeutic approach focused on a localized target area harbors the risk of overlooking and undertreating potential metastatic spread. Previous studies reported systemic, albeit limited, anti-tumor effects following treatment with thermosensitive liposomal chemotherapy and localized mild hyperthermia. This work explores the systemic treatment capabilities of a thermosensitive liposome formulation of the vinca alkaloid vinorelbine in combination with mild hyperthermia in an immunocompetent murine model of rhabdomyosarcoma. This treatment approach was found to be highly effective at heated, primary tumor sites. However, it demonstrated limited anti-tumor effects in secondary, distant tumors. As a result, the addition of immune checkpoint inhibition therapy was pursued to further enhance the systemic anti-tumor effect of this treatment approach. Once combined with immune checkpoint inhibition therapy, a significant improvement in systemic treatment capability was achieved. We believe this is one of the first studies to demonstrate that a triple combination of thermosensitive liposomes, localized mild hyperthermia, and immune checkpoint inhibition therapy can enhance the systemic treatment capabilities of thermosensitive liposomes.Graphical abstract

Introduction We use machine learning to create predictive models from preoperative data to predict the Shoulder Arthroplasty Smart (SAS) score, the American Shoulder and Elbow Surgeons (ASES) score, and the Constant score at multiple postoperative timepoints and compare the accuracy of each algorithm for anatomic (aTSA) and reverse (rTSA) total shoulder arthroplasty. Methods Clinical data from 2,270 aTSA and 4,198 rTSA patients were analyzed using 3 supervised machine learning techniques to create predictive models for the SAS, ASES, and Constant score at 6 different postoperative timepoints using a full input feature set and the 2 different minimal feature sets. Mean absolute errors (MAE) quantified the difference between actual and predicted outcome scores for each model at each postoperative timepoint. The performance of each model was also quantified by its ability to predict improvement greater than the minimal clinically important difference (MCID) and the substantial clinical benefit (SCB) patient satisfaction thresholds for each outcome measure at 2-3 years after surgery. Results All 3 machine learning techniques were more accurate at predicting aTSA and rTSA outcomes using the SAS score (aTSA: ±7.41 MAE; rTSA: ±7.79 MAE), followed by the Constant score (aTSA: ±8.32 MAE; rTSA: ±8.30 MAE), and finally the ASES score (aTSA: ±10.86 MAE; rTSA: ±10.60 MAE). These prediction accuracy trends were maintained across the 3 different model input categories for each of the SAS, ASES, and Constant models at each postoperative timepoint. For aTSA patients, the XGBoost predictive models achieved 94-97% accuracy in MCID with an AUROC between 0.90-0.97 and 89-94% accuracy in SCB with an AUROC between 0.89-0.92 for the 3 clinical scores using the full feature set of inputs. For rTSA patients, the XGBoost predictive models achieved 95-99% accuracy in MCID with an AUROC between 0.88-0.96 and 88-92% accuracy in SCB with an AUROC between 0.81-0.89 for the 3 clinical scores using the full feature set of inputs. Discussion Our study demonstrated that the SAS score predictions are more accurate than the ASES and Constant predictions for multiple supervised machine learning techniques, despite requiring less input data for the SAS model. Additionally, we predicted which patients will, and will not achieve clinical improvement that exceeds the MCID and SCB thresholds for each score; this highly accurate predictive capability effectively risk-stratifies patients for a variety of outcome measures using only preoperative data.

Background Improvement in internal rotation (IR) after anatomic (aTSA) and reverse (rTSA) total shoulder arthroplasty is difficult to predict, with rTSA patients experiencing greater variability and more limited IR improvements than aTSA patients. The purpose of this study is to quantify and compare the IR score for aTSA and rTSA patients and create supervised machine learning that predicts IR after aTSA and rTSA at multiple postoperative timepoints. Methods Clinical data from 2,270 aTSA and 4,198 rTSA patients were analyzed using 3 supervised machine learning techniques to create predictive models for internal rotation as measured by the IR score at 6 postoperative timepoints. Predictions were performed using the full input feature set and 2 minimal input feature sets. The mean absolute error (MAE) quantified the difference between actual and predicted IR scores for each model at each timepoint. The predictive accuracy of the XGBoost algorithm was also quantified by its ability to distinguish which patients would achieve clinical improvement greater than the minimal clinically important difference (MCID) and substantial clinical benefit (SCB) patient satisfaction thresholds for IR score at 2-3 years after surgery. Results RTSA patients had significantly lower mean IR scores and significantly less mean IR score improvement than aTSA patients at each postoperative timepoint. Both aTSA and rTSA patients experienced significant improvements in their ability to perform ADLs; however, aTSA patients were significantly more likely to perform these ADLs. Using a minimal feature set of preoperative inputs, our machine learning algorithms had equivalent accuracy when predicting IR score for both aTSA (0.92-1.18 MAE) and rTSA (1.03-1.25 MAE) from 3 months to >5 years after surgery. Furthermore, these predictive algorithms identified with 90% accuracy for aTSA and 85% accuracy for rTSA which patients will achieve MCID IR score improvement and predicted with 85% accuracy for aTSA patients and 77% accuracy for rTSA which patients will achieve SCB IR score improvement at 2-3 years after surgery. Discussion Our machine learning study demonstrates that active internal rotation can be accurately predicted after aTSA and rTSA at multiple postoperative timepoints using a minimal feature set of preoperative inputs. These predictive algorithms accurately identified which patients will, and will not achieve clinical improvement in IR score that exceeds the MCID and SCB patient satisfaction thresholds.

Mobile sources are responsible for a substantial controllable portion of the reactive organic carbon (ROC) emitted to the atmosphere, especially in urban environments of the United States (U.S.). We update existing methods for calculating mobile source organic particle and vapor emissions in the U.S. with over a decade of laboratory data that parameterize the volatility and organic aerosol (OA) potential of emissions from onroad vehicles, nonroad engines, aircraft, marine vessels, and locomotives. We find that existing emission factor information from teflon filters combined with quartz filters collapses into simple relationships and can be used to reconstruct the complete volatility distribution of ROC emissions. This new approach consists of source-specific filter artifact corrections and state-of-the-science speciation including explicit intermediate volatility organic compounds (IVOCs), yielding the first bottom-up volatility-resolved inventory of U.S. mobile source emissions. Using the Community Multiscale Air Quality model, we estimate mobile sources account for 20–25 % of the IVOC concentrations and 4.4–21.4 % of ambient OA. The updated emissions and air quality model reduce biases in predicting fine-particle organic carbon in winter, spring, and autumn throughout the U.S. (4.3–11.3 % reduction in normalized bias). We identify key uncertain parameters that align with current state-of-the-art research measurement challenges.

Supplementary Figure 1 from Chemotherapy Dosing Schedule Influences Drug Resistance Development in Ovarian Cancer

Drug resistance leads to chemotherapy failure and is responsible for the death of a great majority of patients with metastatic, late-stage ovarian cancer. The present study addressed whether changes in the chemotherapy dosing schedule affect the development, further worsening, or circumvention of drug resistance in chemosensitive and chemoresistant ovarian cancer. Severe combined immunodeficient mice bearing HeyA8 and HeyA8-MDR xenografts were treated with docetaxel intermittently (1×/wk or 3×/wk) or continuously for 21 days. Tumor mRNA expression of genes implicated in docetaxel resistance was measured by quantitative real-time-PCR. Analyzed genes included those encoding for the drug efflux transporters mdr1 and mrp7 and for molecules that interfere with or overcome the effects of docetaxel, including β-tubulinIII, actinin4, stathmin1, bcl2, rpn2, thoredoxin, and akt2. In both models, continuous docetaxel resulted in greater antitumor efficacy than 1×/wk or 3×/wk dosing and did not induce upregulation of any analyzed genes. Once weekly dosing caused upregulation of various drug resistance–related genes, especially in chemoresistant xenografts. More frequent, 3×/wk dosing diminished this effect, although levels of various genes were higher than for continuous chemotherapy. Drug efflux transporter expression was further examined by Western blotting, confirming that intermittent, but not continuous, docetaxel induced significant upregulation. Overall, our results show that the presence and length of treatment-free intervals contribute to the development of drug resistance. Elimination of these intervals by continuous dosing resulted in superior antitumor efficacy and prevented drug resistance induction in chemosensitive and chemoresistant disease. These results encourage the clinical implementation of continuous chemotherapy to overcome and/or prevent drug resistance in newly diagnosed and recurrent, refractory ovarian cancer. Mol Cancer Ther; 10(7); 1289–99. ©2011 AACR.

Clinicians may be reluctant to feed patients on high-flow nasal cannula (HFNC) therapy, despite studies suggesting it is beneficial and safe. We describe the implementation of a feeding protocol for patients with bronchiolitis on HFNC and determine its effect on nutrition goals.Prospective bedside data on enteral volume, feed interruptions, and aspiration events were collected on patients with bronchiolitis who were24 months of age, treated with HFNC, and fed per a developed protocol. Exclusion criteria included history of prematurity32 weeks, congenital heart disease, or positive-pressure ventilation before feeding. Length of intensive care unit and hospital stay was compared with both a concurrent cohort (CC) of patients not fed per the protocol and a retrospective cohort (RC) admitted prior to protocol creation.Seventy-eight patients met the criteria for the prospective study arm: 24 patients were included in the CC, and 74 were included in the RC. Seventy-one percent of prospective patients received enteral nutrition (EN) on HFNC day 1 vs 42% of the CC. In the prospective cohort, feed interruption occurred in 23% of patients and was associated with higher flow rates; however, no aspiration events occurred. Patients fed per protocol were fed 8-10 h sooner and discharged 1 day earlier than those in the RC.The use of a feeding protocol for patients with bronchiolitis on HFNC was safe and associated with shorter time to initiate EN and shorter length of hospital stay.

Introduction Interest in nanomedicines has surged in recent years due to the critical role they have played in the COVID-19 pandemic. Nanoformulations can turn promising therapeutic cargo into viable products through improvements in drug safety and efficacy profiles. However, the developmental pathway for such formulations is non-trivial and largely reliant on trial-and-error. Beyond the costly demands on time and resources, this traditional approach may stunt innovation. The emergence of automation, artificial intelligence (AI) and machine learning (ML) tools, which are currently underutilized in pharmaceutical formulation development, offers a promising direction for an improved path in the design of nanomedicines. Areas covered This article highlights the potential of harnessing experimental automation and AI/ML to drive innovation in nanomedicine development. The discussion centers on the current challenges in drug formulation research and development, and the major advantages afforded through the application of data-driven methods. Expert opinion The development of integrated workflows based on automated experiments and AI/ML may accelerate nanomedicine development. A crucial step in achieving this is the generation of high-quality, accessible datasets. Future efforts to make full use of these tools can ultimately contribute to the development of more innovative nanomedicines and improved clinical translation of formulations that rely on advanced drug delivery systems.

BACKGROUND The use of electronic clinical decision support (CDS) systems for pediatric critical care trials is rare. We sought to describe in detail the use of a CDS tool (Children's Hospital Euglycemia for Kids Spreadsheet [CHECKS]), for the management of hyperglycemia during the 32 multicenter Heart And Lung Failure-Pediatric Insulin Titration trial. RESEARCH QUESTION In critically ill pediatric patients who were treated with CHECKS, how was user compliance associated with outcomes; and what patient and clinician factors might account for the observed differences in CHECKS compliance? STUDY DESIGN AND METHODS During an observational retrospective study of compliance with a CDS tool used during a prospective randomized controlled trial, we compared patients with high and low CHECKS compliance. We investigated the association between compliance and blood glucose metrics. We describe CHECKS and use a computer interface analysis framework (the user, function, representation, and task analysis framework) to categorize user interactions. We discuss implications for future randomized controlled trials. RESULTS Over a 4.5-year period, 658 of 698 children were treated with the CHECKS protocol for ≥24 hours with a median of 119 recommendations per patient. Compliance per patient was high (median, 99.5%), with only 30 patients having low compliance (

Nanomedicines have transformed promising therapeutic agents into clinically approved medicines with optimal safety and efficacy profiles. This is exemplified by the mRNA vaccines against COVID-19, which were made possible by lipid nanoparticle technology. Despite the success of nanomedicines to date, their design remains far from trivial in part due to the complexity associated with their preclinical development. Herein we propose a nanomedicine materials acceleration platform (NanoMAP) to streamline the preclinical development of these formulations. NanoMAP combines high-throughput experimentation with state-of-the-art advances in artificial intelligence (including active learning and few-shot learning) as well as a web-based application for data sharing. The deployment of NanoMAP requires interdisciplinary collaboration between leading figures in drug delivery and artificial intelligence to enable this data-driven design approach. The proposed approach will not only expedite the development of next generation nanomedicines, but also encourage participation of the pharmaceutical science community in a large data curation initiative.

Chemical mechanisms describe the atmospheric transformations of organic and inorganic species and connect air emissions to secondary species such as ozone, fine particles, and hazardous air pollutants (HAPs) like formaldehyde. Recent advances in our understanding of several chemical systems and shifts in the drivers of atmospheric chemistry warrant updates to mechanisms used in chemical transport models such as the Community Multiscale Air Quality (CMAQ) modeling system. This work builds on the Regional Atmospheric Chemistry Mechanism version 2 (RACM2) and develops the Community Regional Atmospheric Chemistry Multiphase Mechanism (CRACMM) version 1.0, which fully couples the chemistry leading to ozone and secondary organic aerosol (SOA) with consideration of HAPs. CRACMM v1.0 includes 178 gas-phase species, 51 particulate species, and 508 reactions spanning gas-phase and heterogeneous pathways. To support estimation of health risks associated with HAPs, nine species in CRACMM cover 50 % of the total cancer and 60 % of the total noncancer health risk estimated for primary HAPs from anthropogenic and biomass burning sources in the U.S., with the coverage of risk higher (>80 %) when secondary formaldehyde and acrolein are considered. In addition, new mechanism species were added based on the importance of their emissions for ozone, organic aerosol, or atmospheric burden of total reactive organic carbon (ROC): sesquiterpenes, furans, propylene glycol, alkane-like low to intermediate volatility organic compounds (9 species), low to intermediate volatility oxygenated species (16 species), intermediate volatility aromatic hydrocarbons (2 species), and slowly reacting organic carbon. Intermediate and lower volatility organic compounds were estimated to increase the coverage of anthropogenic and biomass burning ROC emissions by 40 % compared to current operational mechanisms. Autoxidation, a gas-phase reaction particularly effective in producing SOA, was added for C10 and larger alkanes, aromatic hydrocarbons, sesquiterpenes, and monoterpene systems including second generation aldehydes. Integrating the radical and SOA chemistry put additional constraints on both systems and enabled the implementation of previously unconsidered SOA pathways from phenolic and furanone compounds, which were predicted to account for ~30 % of total aromatic hydrocarbon SOA under typical atmospheric conditions. CRACMM organic aerosol species were found to span the atmospherically relevant range of carbon number, number of oxygens per carbon, and oxidation state with a slight high bias in number of hydrogens per carbon. In total, eleven new emitted species were implemented as precursors to SOA compared to current CMAQv5.3.3 representations resulting in a bottom-up prediction of SOA, which is required for accurate source attribution and design of control strategies. CRACMMv1.0 will be available in CMAQv5.4.

Long-acting injectables are considered one of the most promising therapeutic strategies for the treatment of chronic diseases as they can afford improved therapeutic efficacy, safety, and patient compliance. The use of polymer materials in such a drug formulation strategy can offer unparalleled diversity owing to the ability to synthesize materials with a wide range of properties. However, the interplay between multiple parameters, including the physicochemical properties of the drug and polymer, make it very difficult to intuitively predict the performance of these systems. This necessitates the development and characterization of a wide array of formulation candidates through extensive and time-consuming in vitro experimentation. Machine learning is enabling leap-step advances in a number of fields including drug discovery and materials science. Our study takes a critical step towards data-driven drug formulation development with an emphasis on long-acting injectables. A series of machine learning algorithms were trained and refined for accurate prediction of experimental drug release profiles using this dataset. The dataset was constructed from previously published studies by our research group and other research groups.The studies performed by our group include spherical and cylinder shaped polymeric LAIs. Data from external sources was identified using the Web of Science search engine and the keyword combination “polymeric microparticle” and “drug delivery”. Information related to the preparation, final composition, and release kinetics of drug from LAIs was collected. The latter was primarily extracted from figures of in vitro drug release profiles using the “GetData Graph Digitizer” application. The final dataset contained 181 drug release profiles for 43 unique drug-polymer combinations. In total this comprised 3783 individual fractional release measurements. The initially collected dataset was composed of a table of drug and polymer names, as well as physicochemical properties of the formulation, and fractional drug release values at various timepoints. In order to use this data to construct and train ML models it is necessary to describe various elements using machine-readable descriptors which were generated using RDkit. The polymers and LAI formulations were described exclusively using information reported in the relevant published articles, these included; polymer molecular weight (Polymer_MW), lactide-to-glycolide ratio (LA/GA; for non-PLGA systems this was set as zero), molecular crosslinking ratio of polymers (CL_Ratio; for non-cross-linked systems this was set as zero), initial drug-to-polymer ratio (Initial D/M ratio), drug loading capacity (DLC), surface area-to-volume (SA-V) ratio for the LAI system, fractional drug release at 6 h (T=0.25), fractional drug release at 12 h (T=0.5), fractional drug release at 24 h (T=1.0), and the precent of surfactant present in the release media (SE; where no surfactant was present in the release media, this was set as zero). With the exception of SA-V, T=0.25, T=0.5, and T=1.0, the 17 input features were either extracted from original publications or calculated using the RDkit package. SA-V was constructed and implemented for this study as it confers information that is related to the size and shape of the LAI system. This enables the inclusion of both spherical and cylindrical shaped LAIs in one model. For initial fractional drug release timepoints (i.e., T=0.25, T=0.5, and T=1.0), where these values were not available from the previously published studies, they were imputed using best fit polynomial curves that range from T = 0 to T = 2 days. The code and results that support the findings of our study are available at the Aspuru-Guzik Group’s GitHub page (https://github.com/aspuru-guzik-group/long-acting-injectables) and in the preprint of the related manuscript available on ChemRxiv (https://doi.org/10.26434/chemrxiv-2021-mxrxw-v2)., NSERC Discovery grant (RGPIN-2022-04910) to C.A. F.H., M.A., and A.A.G. acknowledge support from the Defense Advanced Research Projects Agency under the Accelerated Molecular Discovery Program under Cooperative Agreement No. HR00111920027 dated August 1, 2019. R.J.H. gratefully acknowledges NSERC for a postgraduate scholarship (PGSD3-534584-2019), as well as support from the Vector Institute. A.A.G. would like to thank Dr. Anders Frøseth for his support. M.A. is supported by a postdoctoral fellowship from the Vector Institute.