Systematic DOE Approach for Dry Granulation Study at Early Clinical Stage of Novel Drugs: An Industrial Case.

In order to introduce a cost-effective strategy method for commercial scale dry granulation at the early clinical stage of drug product development, we developed dry granulation process using formulation without API, fitted and optimized the process parameters adopted Design of Experiment (DOE). Then, the process parameters were confirmed using one formulation containing active pharmaceutical ingredient (API). The results showed that the roller pressure had significant effect on particle ratio (retained up to #60 mesh screen), bulk density and tapped density. The roller gap had significant influence on particle ratio and specific energy. The particle ratio was significantly affected by the mill speed (second level). The tabletability of the powder decreased after dry granulation. The effect of magnesium stearate on the tabletability was significant. In the process validation study, the properties of the prepared granules met the requirements for each response studied in the DOE. The prepared tablets showed higher tensile strength, good content uniformity of filled capsules, and the dissolution profiles of which were consistent with that of clinical products. This drug product process development and research strategies could be used as a preliminary experiment for the dry granulation process in the early clinical stage.

Implementation and evaluation of an additional GPT-4-based reviewer in PRISMA-based medical systematic literature reviews.

BACKGROUND: PRISMA-based literature reviews require meticulous scrutiny of extensive textual data by multiple reviewers, which is associated with considerable human effort. OBJECTIVE: To evaluate feasibility and reliability of using GPT-4 API as a complementary reviewer in systematic literature reviews based on the PRISMA framework. METHODOLOGY: A systematic literature review on the role of natural language processing and Large Language Models (LLMs) in automatic patient-trial matching was conducted using human reviewers and an AI-based reviewer (GPT-4 API). A RAG methodology with LangChain integration was used to process full-text articles. Agreement levels between two human reviewers and GPT-4 API for abstract screening and between a single reviewer and GPT-4 API for full-text parameter extraction were evaluated. RESULTS: An almost perfect GPT-human reviewer agreement in the abstract screening process (Cohen's kappa > 0.9) and a lower agreement in the full-text parameter extraction were observed. CONCLUSION: As GPT-4 has performed on a par with human reviewers in abstract screening, we conclude that GPT-4 has an exciting potential of being used as a main screening tool for systematic literature reviews, replacing at least one of the human reviewers.

Assessment of spatial air quality on the East Coast of Peninsular Malaysia utilizing environmetric techniques.

The presence of harmful substances in the atmosphere poses significant risks to the environment and public health. These pollutants can come from natural sources like dust and wildfires, or from human activities such as industrial, transportation, and agricultural practices. The objective of this study was to assess air quality on the East Coast of Peninsular Malaysia by analyzing historical data from the Department of Environment, Malaysia. Daily measurements of PM10, O3, SO2, NO2, and CO were collected from eight monitoring stations over 11 years (2011-2021) and analyzed using environmetric techniques. Hierarchical agglomerative cluster analysis (HACA) classified two stations as belonging to the high pollution cluster (HPC), three stations as part of the moderate pollution cluster (MPC), and three stations as the low pollution cluster (LPC). Discriminant analysis revealed a correct assignment rate of 90.50%, indicating that all five parameters were able to differentiate pollution levels with high significance (p < 0.0001). Principal component analysis (PCA) was conducted to validate the pattern of air quality variables in relation to the identified clusters (HPC, MPC, and LPC). The results showed that two verifactors (VFs) were extracted in HPC and LPC, while three VFs were identified in MPC. The cumulative variance explained by the PCA for HPC, MPC, and LPC was 69.43%, 82.32%, and 62.16%, respectively. Finally, an artificial neural network (ANN) was used to forecast the air pollutant index (API) levels, using the R2 and RMSE performance metrics. The PCA-MLP Model A yielded an R2 value of 0.8470 and an RMSE of 6.6470, while PCA-MLP Model B achieved an R2 value of 0.8591 and an RMSE of 6.3000, both indicating a significant and strong correlation.

Sustainable Synthesis of the Active Pharmaceutical Ingredient Atenolol in Deep Eutectic Solvents.

Atenolol, one of the top five best-selling drugs in the world today used to treat angina and hypertension, and to reduce the risk of death after a heart attack, faces challenges in current synthetic methods to address inefficiencies and environmental concerns. The traditional synthesis of this drug involves a process that generates a large amount of waste and other by-products that need disposal. This study presents a one-pot DES-based sustainable protocol for synthesizing atenolol. The use of the DES allowed the entire process to be conducted with no need for additional bases or catalysts, in short reaction times, under mild conditions, and avoiding chromatographic purification. The overall yield of atenolol was 95%. The scalability of the process to gram-scale production was successfully demonstrated, emphasizing its potential in industrial applications. Finally, the 'greenness' evaluation, performed using the First Pass CHEM21 Metrics Toolkit, highlighted the superiority in terms of the atom economy, the reaction mass efficiency, and the overall process mass intensity of the DES-based synthesis compared with the already existing methods.

Advantages and drawbacks of life cycle assessment application to the pharmaceuticals: a short critical literature review.

Pharmaceuticals are among the most challenging products to assess by life cycle assessment (LCA). The main drawback highlighted by LCA practitioners is the lack of inventory data, both regarding the synthesis of active pharmaceutical ingredient (API) precursors (upstream) and the details concerning the downstream phases (use and end of life). A short critical review of pharma-LCAs found in the literature is here proposed, with discussion of several tools and models used to predict the environmental impacts derived from the life cycle of pharmaceuticals, emphasizing current strengths and weaknesses, and exploring the possibilities for improvements. The case of antibiotics is selected as a representative class of pharmaceuticals, due to their massive use worldwide and the growing related issue of antimicrobial resistance enrichment, which is generally not included in most of LCAs. Also, we comment on drafting product category rules (PCRs) in the relevant field to develop standard methodologies and enhance the comparability of the studies, ultimately advocating collaboration with companies and improving inventory data quality and availability for the whole value chain of products.

Facilitating direct compaction tableting of fine cohesive APIs using dry coated fine excipients: Effect of the excipient size and amount of coated silica.

The possibility of attaining direct compression (DC) tableting using silica coated fine particle sized excipients was examined for high drug loaded (DL) binary blends of APIs. Three APIs, very-cohesive micronized acetaminophen (mAPAP, 7 µm), cohesive acetaminophen (cAPAP, 23 µm), and easy-flowing ibuprofen (IBU, 53 µm), were selected. High DL (60 wt%) binary blends were prepared with different fine-milled MCC-based excipients (ranging 20- 37 µm) with or without A200 silica coating during milling. The blend flowability (flow function coefficient -FFC) and bulk density (BD) of the blends for all three APIs were significantly improved by 1 wt% A200 dry coated MCCs; reaching FFC of 4.28 from 2.14, 7.82 from 2.96, and > 10 from 5.57, for mAPAP, cAPAP, and IBU blends, respectively, compared to the uncoated MCC blends. No negative impact was observed on the tablet tensile strength (TS) by using dry coated MCCs despite lower surface energy of silica. Instead, the desired tablet TS levels were reached or exceeded, even above that for the blends with uncoated milled MCCs. The novelty here is that milled and silica coated fine MCCs could promote DC tableting for cAPAP and IBU blends at 60 wt% DL through adequate flowability and tensile strength, without having to dry coat the APIs. The effect of the silica amount was investigated, indicating lesser had a positive impact on TS, whereas the higher amount had a positive impact on flowability. Thus, the finer excipient size and silica amounts may be adjusted to potentially attain blend DC processability for high DL blends of fine APIs.

Unveiling green corrosion inhibitor of Aloe vera extracts for API 5L steel in seawater environment.

This study evaluated Aloe vera extract as a green inhibitor to prevent corrosion in seawater environments. A. vera extract was produced by maceration with methanol-water at room temperature. Electrochemical techniques were used to evaluate the corrosion inhibitor effectiveness of the A. vera extract. The morphology of the corrosion products was analyzed by FE-SEM equipped with EDS and AFM. FT-IR and LCMS characterized the functional and structural groups in this extract. The electrochemical measurements show that A. vera extract could effectively reduce the corrosion of API 5L steel in seawater environments. Inhibition efficiency (IE) increases with increasing concentration. Optimal corrosion inhibition efficiency of around 83.75% (PDP) and 88.60% (EIS) was obtained by adding 300 mg L-1 of extract at 310 K. Furthermore, the higher the concentration of A. vera extract, the greater the activation energy (Ea), with the highest activation energy being 48.24 kJ mol-1 for the concentration of 300 mg L-1. Conversely, increasing the temperature and exposure duration reduces the corrosion inhibition efficiency (IE) values; the best exposure period was 30 min with 88.34% IE by a concentration of 300 mg L-1 at 300 K. This corrosion inhibition is achieved by the adsorption process of A. vera bioactive on metal surfaces with a mixed inhibitor through a physisorption-chemisorption mechanism. This finding was confirmed by the smoother surface morphology of the steel treated with A. vera extract than without. This unveiling investigation found that A. vera extract has the potential to be an environmentally friendly corrosion inhibitor in the seawater environment.

API integration and organisational agility outcomes in digital music platforms: A qualitative case study.

Organisations deploy digital platforms to maximise value and transform their businesses. The success of most platforms is attributed to Application Programming Interfaces (APIs), the protocols enabling different software to communicate with each other. However, previous research on APIs has predominantly focused on the technical dimensions, such as design, and unintentionally neglected other social areas, such as organisational outcomes. This study seeks to advance organisational API research by adopting an agility perspective to explore the agility outcomes after API integration. Through rich qualitative data from a music digital firm, the findings revealed four primary agility outcomes: customer agility in the form of swift customer feedback, operational agility in the form of improved business process and delay reduction, partner agility in the form of embracing flexibility in processes and structures and expanding their ecosystem and decision agility in the form of fast decision making. A model showing the interplay and interdependencies of the agility outcomes was developed and provided depth and clarity to the findings. This study extends the literature by establishing how API integration influences organisational agility under conditions such as possessing capabilities and managing tensions during the integration process.

Nutrient Composition and Quality Assessment of Royal Jelly Samples Relative to Feed Supplements.

Royal jelly is a substance secreted by the hypopharyngeal and mandibular glands of nurse honey bees, serving as crucial nutritional source for young larvae, queen honey bees, and also valuable product for humans. In this study, the effect of the feed supplements on the nutritional composition and qualities of royal jelly was investigated. Two types of royal jelly samples were acquired: one from honey bees fed with sugar syrup as a feed supplement and the other from honey bees fed with honey. The production, harvesting, and storage of all royal jelly samples followed standard procedures. Parameters for quality assessment and nutritional value, including stable carbon isotopic ratio, moisture content, 10-hydroxy-2-decenoic acid (10-HDA) level, carbohydrate composition, amino acid composition, and mineral contents, were analyzed. The results revealed that despite variability in moisture content and carbohydrate composition, fructose was lower (2.6 and 4.1 g/100 g as is for sugar-fed and honey-fed royal jelly, respectively) and sucrose was higher (7.5 and 2.7 g/100 g as is for sugar-fed and honey-fed royal jelly, respectively) in the sugar-fed group. The stable isotope ratio (-16.4608‱ for sugar-fed and -21.9304‱ for honey-fed royal jelly) clearly distinguished the two groups. 10-HDA, amino acid composition, and total protein levels were not significantly different. Certain minerals, such as potassium, iron, magnesium, manganese, and phosphorus were higher in the honey-fed group. Hierarchical analysis based on moisture, sugar composition, 10-HDA, and stable carbon isotopes categorized the samples into two distinct groups. This study demonstrated that the feed source could affect the nutritional quality of royal jelly.

MALT1 substrate cleavage: what is it good for?

CARD-BCL10-MALT1 (CBM) signalosomes connect distal signaling of innate and adaptive immune receptors to proximal signaling pathways and immune activation. Four CARD scaffold proteins (CARD9, 10, 11, 14) can form seeds that nucleate the assembly of BCL10-MALT1 filaments in a cell- and stimulus-specific manner. MALT1 (also known as PCASP1) serves a dual function within the assembled CBM complexes. By recruiting TRAF6, MALT1 acts as a molecular scaffold that initiates IκB kinase (IKK)/NF-κB and c-Jun N-terminal kinase (JNK)/AP-1 signaling. In parallel, proximity-induced dimerization of the paracaspase domain activates the MALT1 protease which exerts its function by cleaving a set of specific substrates. While complete MALT1 ablation leads to immune deficiency, selective destruction of either scaffolding or protease function provokes autoimmune inflammation. Thus, balanced MALT1-TRAF6 recruitment and MALT1 substrate cleavage are critical to maintain immune homeostasis and to promote optimal immune activation. Further, MALT1 protease activity drives the survival of aggressive lymphomas and other non-hematologic solid cancers. However, little is known about the relevance of the cleavage of individual substrates for the pathophysiological functions of MALT1. Unbiased serendipity, screening and computational predictions have identified and validated ~20 substrates, indicating that MALT1 targets a quite distinct set of proteins. Known substrates are involved in CBM auto-regulation (MALT1, BCL10 and CARD10), regulation of signaling and adhesion (A20, CYLD, HOIL-1 and Tensin-3), or transcription (RelB) and mRNA stability/translation (Regnase-1, Roquin-1/2 and N4BP1), indicating that MALT1 often targets multiple proteins involved in similar cellular processes. Here, we will summarize what is known about the fate and functions of individual MALT1 substrates and how their cleavage contributes to the biological functions of the MALT1 protease. We will outline what is needed to better connect critical pathophysiological roles of the MALT1 protease with the cleavage of distinct substrates.

UV imaging for the rapid at-line content determination of different colourless APIs in their tablets with artificial neural networks.

This paper presents a novel high-resolution and rapid (50 ms) UV imaging system, which was used for at-line, non-destructive API content determination of tablets. For the experiments, amlodipine and valsartan were selected as two colourless APIs with different UV induced fluorescent properties according to the measured solid fluorescent spectra. Images were captured with a LED-based UV illumination (385-395 nm) of tablets containing amlodipine or valsartan and common tableting excipients. Blue or green colour components from the RGB colour space were extracted from the images and used as an input dataset to execute API content prediction with artificial neural networks. The traditional destructive, solution-based transmission UV measurement was applied as reference method. After the optimization of the number of hidden layer neurons it was found that the relative error of the content prediction was 4.41 % and 3.98 % in the case of amlodipine and valsartan containing tablets respectively. The results open the possibility to use the proposed UV imaging-based system as a rapid, in-line tool for 100 % API content screening in order to greatly improve pharmaceutical quality control and process understanding.

PermDroid a framework developed using proposed feature selection approach and machine learning techniques for Android malware detection.

The challenge of developing an Android malware detection framework that can identify malware in real-world apps is difficult for academicians and researchers. The vulnerability lies in the permission model of Android. Therefore, it has attracted the attention of various researchers to develop an Android malware detection model using permission or a set of permissions. Academicians and researchers have used all extracted features in previous studies, resulting in overburdening while creating malware detection models. But, the effectiveness of the machine learning model depends on the relevant features, which help in reducing the value of misclassification errors and have excellent discriminative power. A feature selection framework is proposed in this research paper that helps in selecting the relevant features. In the first stage of the proposed framework, t-test, and univariate logistic regression are implemented on our collected feature data set to classify their capacity for detecting malware. Multivariate linear regression stepwise forward selection and correlation analysis are implemented in the second stage to evaluate the correctness of the features selected in the first stage. Furthermore, the resulting features are used as input in the development of malware detection models using three ensemble methods and a neural network with six different machine-learning algorithms. The developed models' performance is compared using two performance parameters: F-measure and Accuracy. The experiment is performed by using half a million different Android apps. The empirical findings reveal that malware detection model developed using features selected by implementing proposed feature selection framework achieved higher detection rate as compared to the model developed using all extracted features data set. Further, when compared to previously developed frameworks or methodologies, the experimental results indicates that model developed in this study achieved an accuracy of 98.8%.

Comparison of permitted daily exposure (PDE) values for active pharmaceutical ingredients (APIs) - Evidence of a robust approach.

Permitted Daily Exposure Limits (PDEs) are set for Active Pharmaceutical Ingredients (APIs) to control cross-contamination when manufacturing medicinal products in shared facilities. With the lack of official PDE lists for pharmaceuticals, PDEs have to be set by each company separately. Although general rules and guidelines for the setting of PDEs exist, inter-company variations in the setting of PDEs occur and are considered acceptable within a certain range. To evaluate the robustness of the PDE approach between different pharmaceutical companies, data on PDE setting of five marketed APIs (amlodipine, hydrochlorothiazide, metformin, morphine, and omeprazole) were collected and compared. Findings show that the variability between PDE values is within acceptable ranges (below 10-fold) for all compounds, with the highest difference for morphine due to different Point of Departures (PODs) and Adjustment Factors (AFs). Factors of PDE variability identified and further discussed are: (1) availability of data, (2) selection of POD, (3) assignment of AFs, (4) route-to-route extrapolation, and (5) expert judgement and differences in company policies. We conclude that the investigated PDE methods and calculations are robust and scientifically defensible. Additionally, we provide further recommendations to harmonize PDE calculation approaches across the pharmaceutical industry.

Apigenin intervenes in liver fibrosis by regulating PKM2-HIF-1α mediated oxidative stress.

Apigenin (API) is a natural flavonoid compound with antioxidant, anti fibrotic, anti-inflammatory and other effects, but there is limited research on the effect of API on liver fibrosis. This study aims to explore the effect and potential mechanism of API on liver fibrosis induced by CCl4 in mice. The results indicate that API reduces oxidative stress levels, inhibits hepatic stellate cell (HSC) activation, and exerts anti liver fibrosis effects by regulating the PKM2-HIF-1α pathway. We observed that API alleviated liver tissue pathological damage and collagen deposition in CCl4 induced mouse liver fibrosis model, promoting the recovery of liver function in mice with liver fibrosis. In addition, the API inhibits the transition of Pyruvate kinase isozyme type M2 (PKM2) from dimer to tetramer formation by regulating the EGFR-MEK1/2-ERK1/2 pathway, thereby preventing dimer from entering the nucleus and blocking PKM2-HIF-1α access. This change leads to a decrease in malondialdehyde (MDA) and Catalase (CAT) levels and an increase in glutathione (GSH), superoxide dismutase (SOD), glutathione peroxidase (GSH-PX) levels, as well as total antioxidant capacity (T-AOC) in the liver of liver fibrosis mice. At the same time, API downregulated the expression of α-smooth muscle actin (α-SMA), Vimentin and Desmin in the liver tissue of mice with liver fibrosis, inhibited the activation of HSC, and reduced collagen deposition. These results indicate that API can inhibit HSC activation and alleviate CCl4 induced liver fibrosis by inhibiting the PKM2-HIF-1α pathway and reducing oxidative stress, laying an important foundation for the development and clinical application of API as a novel drug for treating liver fibrosis.

Integrated Portable and Stationary Health Impact-Monitoring System for Firefighters.

The multi-layered negative effects caused by pollutants released into the atmosphere as a result of fires served as the stimulus for the development of a system that protects the health of firefighters operating in the affected area. A collaborative network comprising mobile and stationary Internet of Things (IoT) devices that are furnished with gas sensors, along with a remote server, constructs a resilient framework that monitors the concentrations of harmful emissions, characterizes the ambient air quality of the vicinity where the fire transpires, adopting European Air Quality levels, and communicates the outcomes via suitable applications (RESTful APIs and visualizations) to the stakeholders responsible for fire management decision making. Different experimental evaluations adopting separate contexts illustrate the operation of the infrastructure.

[Feeling analysis on allergen immunotherapy on Twitter using an unsupervised machine learning model]. / Análisis de sentimientos acerca de la inmunoterapia con alérgenos en Twitter mediante un modelo de aprendizaje automático no supervisado.

OBJECTIVE: Analyze feelings about allergen-specific immunotherapy on Twitter using the VADER model VADER (Valence Aware Dictionary and sEntiment Reasoner) model. METHODS: tweets related to specific allergen immunotherapy were obtained through the Twitter Application Programming Interface (API). The keywords "allergy shot" were used between January 1, 2012, and December 31, 2022. The data was processed by removing URLs, usernames, hashtags, multiple spaces, and duplicate tweets. Subsequently, a sentiment analysis was performed using the VADER model. RESULTS: A total of 34,711 tweets were retrieved, of which 1928 were eliminated. Of the remaining 32,783 tweets, 32.41% expressed a negative sentiment, 31.11% expressed a neutral sentiment, and 36.47% expressed a positive sentiment, with an average polarity of 0.02751 (neutral) over the 11-year period. CONCLUSIONS: The average polarity of tweets about allergen-specific immunotherapy is neutral over the 11 years analyzed. There was an annual increase in the average polarity over the years, with 2017, 2018, and 2022 having positive polarity averages. Additionally, the number of tweets decreased over time.

OBJETIVO: Analizar los sentimientos acerca de la inmunoterapia alérgeno-específica en Twitter mediante el modelo VADER (Valence Aware Dictionary and sEntiment Reasoner). MÉTODOS: Se utilizaron tweets relacionados con la inmunoterapia alérgeno-específica obtenidos a través del API (Application Programming Interface) de Twitter. Se incorporaron las palabras clave "allergy shot" en el período comprendido entre el 1 de enero de 2012 y el 31 de diciembre de 2022. Los datos obtenidos fueron procesados, eliminando las URL, nombres de usuarios, hashtags, espacios múltiples y tweets duplicados. Posteriormente, se realizó un análisis de sentimientos utilizando el modelo VADER. RESULTADOS: Se recolectaron 34,711 tweets, de los que se eliminaron 1928. De los 32,783 tweets restantes, se encontró que el 32.41% de los usuarios expresó un sentimiento negativo, el 31.11% un sentimiento neutral y el 36.47% un sentimiento positivo, con una media de polaridad de 0.02751 (neutral) a lo largo de los 11 años. CONCLUSIONES: La polaridad media de los tweets acerca de la inmunoterapia alérgeno-específica es neutral a lo largo de los 11 años analizados. Existe un aumento anual en la polaridad media positiva a lo largo de los años, sobre todo entre 2017, 2018 y 2022. La cantidad de tweets disminuyó con el tiempo.

Inserting Pre-analytical Chromatographic Priming Runs Significantly Improves Targeted Pathway Proteomics with Sample Multiplexing.

GoDig, a platform for targeted pathway proteomics without the need for manual assay scheduling or synthetic standards, is a powerful, flexible, and easy-to-use method that uses tandem mass tags to increase sample throughput up to 18-fold relative to label-free methods. Though the protein-level success rates of GoDig are high, the peptide-level success rates are more limited, hampering assays of harder-to-quantify proteins and site-specific phenomena. To guide the optimization of GoDig assays as well as improvements to the GoDig platform, we created GoDigViewer, a new stand-alone software that provides detailed visualizations of GoDig runs. GoDigViewer guided the implementation of "priming runs," an acquisition mode with significantly higher success rates. In this mode, two or more chromatographic priming runs are automatically performed to improve the accuracy and precision of target elution orders, followed by analytical runs which quantify targets. Using priming runs, success rates exceeded 97% for a list of 400 peptide targets and 95% for a list of 200 targets that are usually not quantified using untargeted mass spectrometry. We used priming runs to establish a quantitative assay of 125 macroautophagy proteins that had a >95% success rate and revealed differences in macroautophagy expression profiles across four human cell lines.

Automated contouring, treatment planning, and quality assurance for VMAT craniospinal irradiation (VMAT-CSI).

Purpose: Create a comprehensive automated solution for pediatric and adult VMAT-CSI including contouring, planning, and plan check to reduce planning time and improve plan quality. Methods: Seventy-seven previously treated CSI patients (age, 2-67 years) were used for creation of an auto-contouring model to segment 25 organs at risk (OARs). The auto-contoured OARs were evaluated using the Dice Similarity Coefficient (DSC), 95% Hausdorff Distance (HD95), and a qualitative ranking by one physician and one physicist (scale: 1-acceptable, 2-minor edits, 3-major edits). The auto-planning script was developed using the Varian Eclipse Scripting API and tested with 20 patients previously treated with either low-dose VMAT-CSI (12 Gy) or high-dose VMAT-CSI (36 Gy + 18 Gy boost). Clinically relevant metrics, planning time, and blinded physician review were used to evaluate significance of differences between the auto and manual plans. Finally, the plan preparation for treatment and plan check processes were automated to improve efficiency and safety of VMAT-CSI. Results: The auto-contours achieved an average DSC of 0.71 ± 0.15, HD95 of 4.81 ± 4.68, and reviewers' ranking of 1.22 ± 0.39, indicating close to "acceptable-as-is" contours. Compared to the manual CSI plans, the auto-plans for both dose regimens achieved statistically significant reductions in body V50% and Dmean for parotids, submandibular, and thyroid glands. The variance in the dosimetric parameters decreased for the auto-plans as compared to the manual plans indicating better plan consistency. From the blinded review, the auto-plans were marked as equivalent or superior to the manual-plans 88.3% of the time. The required time for the auto-contouring and planning was consistently between 1-2 hours compared to an estimated 5-6 hours for manual contouring and planning. Conclusions: Reductions in contouring and planning time without sacrificing plan quality were obtained using the developed auto-planning process. The auto-planning scripts and documentation will be made freely available to other institutions and clinics.