Pure platinum nanostructures grown by electron beam induced deposition

Platinum has numerous applications in catalysis, nanoelectronics, and sensing devices. Here we report a method for localized, mask-free deposition of high-purity platinum that employs a combination of room-temperature, direct-write electron beam induced deposition (EBID) using the precursor Pt(PF3)4, and low temperature (≤400 C) postgrowth annealing in H2O. The annealing treatment removes phosphorus contaminants through a thermally activated pathway involving dissociation of H2O and the subsequent formation of volatile phosphorus oxides and hydrides that desorb during annealing. The resulting Pt is indistinguishable from pure Pt films by wavelength dispersive X-ray spectroscopy (WDS). © 2013 American Chemical Society

Localized deposition of pure platinum nanostructures

© 2014 IEEE. Localized deposition of pure platinum nanostructures was achieved using a combination of focused electron beam induced processing (FEBID) of an inorganic platinum precursor and low temperature annealing in water vapour. This technique enables fabrication of Pt nanostructures with high spatial resolution and purity, for applications in nanoelectronics, sensing devices and catalysis

Advanced machine-learning techniques in drug discovery

The popularity of machine learning (ML) across drug discovery continues to grow, yielding impressive results. As their use increases, so do their limitations become apparent. Such limitations include their need for big data, sparsity in data, and their lack of interpretability. It has also become apparent that the techniques are not truly autonomous, requiring retraining even post deployment. In this review, we detail the use of advanced techniques to circumvent these challenges, with examples drawn from drug discovery and allied disciplines. In addition, we present emerging techniques and their potential role in drug discovery. The techniques presented herein are anticipated to expand the applicability of ML in drug discovery

Energy consumption and carbon footprint of 3D printing in pharmaceutical manufacture

Achieving carbon neutrality is seen as an important goal in order to mitigate the effects of climate change, as carbon dioxide is a major greenhouse gas that contributes to global warming. Many countries, cities and organizations have set targets to become carbon neutral. The pharmaceutical sector is no exception, being a major contributor of carbon emissions (emitting approximately 55% more than the automotive sector for instance) and hence is in need of strategies to reduce its environmental impact. Three-dimensional (3D) printing is an advanced pharmaceutical fabrication technology that has the potential to replace traditional manufacturing tools. Being a new technology, the environmental impact of 3D printed medicines has not been investigated, which is a barrier to its uptake by the pharmaceutical industry. Here, the energy consumption (and carbon emission) of 3D printers is considered, focusing on technologies that have successfully been demonstrated to produce solid dosage forms. The energy consumption of 6 benchtop 3D printers was measured during standby mode and printing. On standby, energy consumption ranged from 0.03 to 0.17 kWh. The energy required for producing 10 printlets ranged from 0.06 to 3.08 kWh, with printers using high temperatures consuming more energy. Carbon emissions ranged between 11.60 and 112.16 g CO2 (eq) per 10 printlets, comparable with traditional tableting. Further analyses revealed that decreasing printing temperature was found to reduce the energy demand considerably, suggesting that developing formulations that are printable at lower temperatures can reduce CO2 emissions. The study delivers key initial insights into the environmental impact of a potentially transformative manufacturing technology and provides encouraging results in demonstrating that 3D printing can deliver quality medicines without being environmentally detrimental

Machine learning uncovers adverse drug effects on intestinal bacteria

The human gut microbiome, composed of trillions of microorganisms, plays an essential role in human health. Many factors shape gut microbiome composition over the life span, including changes to diet, lifestyle, and medication use. Though not routinely tested during drug development, drugs can exert profound effects on the gut microbiome, potentially altering its functions and promoting disease. This study develops a machine learning (ML) model to predict whether drugs will impair the growth of 40 gut bacterial strains. Trained on over 18,600 drug–bacteria interactions, 13 distinct ML models are built and compared, including tree-based, ensemble, and artificial neural network techniques. Following hyperparameter tuning and multi-metric evaluation, a lead ML model is selected: a tuned extra trees algorithm with performances of AUROC: 0.857 (±0.014), recall: 0.587 (±0.063), precision: 0.800 (±0.053), and f1: 0.666 (±0.042). This model can be used by the pharmaceutical industry during drug development and could even be adapted for use in clinical settings

3D printing tablets: Predicting printability and drug dissolution from rheological data

Rheology is an indispensable tool for formulation development, which when harnessed, can both predict a material’s performance and provide valuable insight regarding the material’s macrostructure. However, rheological characterizations are under-utilized in 3D printing of drug formulations. In this study, viscosity measurements were used to establish a mathematical model for predicting the printability of fused deposition modelling 3D printed tablets (Printlets). The formulations were composed of polycaprolactone (PCL) with different amounts of ciprofloxacin and polyethylene glycol (PEG), and different molecular weights of PEG. With all printing parameters kept constant, both binary and ternary blends were found to extrude at nozzle temperatures of 130, 150 and 170 °C. In contrast PCL was unextrudable at 130 and 150 °C. Three standard rheological models were applied to the experimental viscosity measurements, which revealed an operating viscosity window of between 100 and 1000 Pa·s at the apparent shear rate of the nozzle. The drug release profiles of the printlets were experimentally measured over seven days. As a proof-of-concept, machine learning models were developed to predict the dissolution behaviour from the viscosity measurements. The machine learning models were discovered to accurately predict the dissolution profile, with the highest f2 similarity score value of 90.9 recorded. Therefore, the study demonstrated that using only the viscosity measurements can be employed for the simultaneous high-throughput screening of formulations that are printable and with the desired release profile

Harnessing machine learning for development of microbiome therapeutics

The last twenty years of seminal microbiome research has uncovered microbiota's intrinsic relationship with human health. Studies elucidating the relationship between an unbalanced microbiome and disease are currently published daily. As such, microbiome big data have become a reality that provide a mine of information for the development of new therapeutics. Machine learning (ML), a branch of artificial intelligence, offers powerful techniques for big data analysis and prediction-making, that are out of reach of human intellect alone. This review will explore how ML can be applied for the development of microbiome-targeted therapeutics. A background on ML will be given, followed by a guide on where to find reliable microbiome big data. Existing applications and opportunities will be discussed, including the use of ML to discover, design, and characterize microbiome therapeutics. The use of ML to optimize advanced processes, such as 3D printing and in silico prediction of drug-microbiome interactions, will also be highlighted. Finally, barriers to adoption of ML in academic and industrial settings will be examined, concluded by a future outlook for the field

EVALUATION OF APPLICATION OF INTERNATIONAL PROSTATE SYMPTOMS SCORE IN SUDANESE PATIENTS WITH BENIGN PROSTATIC HYPERPLASIA

Objectives: The purpose of the present study is to assess the reliability and validity of IPSS in pre- and treatment of Sudanese patients with BPH. Material and methods: In a descriptive prospective study, 350 Sudanese patients with LUTS attended the Urology department of Gezira Hospital for Renal Diseases and Surgery from September 2003 to April 2006. They were interviewed by urologists and answered the International Prostate Symptom Score (IPSS) questionnaire. Clinical work up was done for all only patients with benign hyperplasia were included in the analysis .Europeans guideline was adopted in the management and the out come was also subjected to IPSS evaluation. Results: The most common voiding symptoms was a weak stream (93% of subjects), followed by intermittency (57%) and hesitancy (54%). The most prevalence of storage symptoms was nocturia (87% of subjects), followed by frequency (73%) and urgency (64%). 64% of the study subjects   presented with severe IPSS, 33% with moderate IPSS, while only 3% of the patients presented with mild IPSS. Digital rectal examination (DRE) was done to all study subjects. There was no significant correlation between DRE and IPSS reported. The postoperative IPSS post treatment follow up was mild in 87%, moderate in 4% and sever in 9% of the patients.   Conclusion: Our study indicates that IPSS is informative and reproducible in assessment of patients with BPH. &nbsp

Pressure-assisted microsyringe 3D printing of oral films based on pullulan and hydroxypropyl methylcellulose

Oral films (OFs) continue to attract attention as drug delivery systems, particularly for pedatric and geriatric needs. However, immiscibility between different polymers limits the full potential of OFs from being explored. One example is pullulan (PUL), a novel biopolymer which often has to be blended with other polymers to reduce cost and alter its mechanical properties. In this study, the state-of-the-art in fabrication techniques, three-dimensional (3D) printing was used to produce hybrid film structures of PUL and hydroxypropyl methylcellulose (HPMC), which were loaded with caffeine as a model drug. 3D printing was used to control the spatial deposition of films. HPMC was found to increase the mean mechanical properties of PUL films, where the tensile strength, elastic modulus and elongation break increased from 8.9 to 14.5 MPa, 1.17 to 1.56 GPa and from 1.48% to 1.77%, respectively. In addition, the spatial orientation of the hybrid films was also explored to determine which orientation could maximize the mechanical properties of the hybrid films. The results revealed that 3D printing could modify the mechanical properties of PUL whilst circumventing the issues associated with immiscibility

Machine Learning and Machine Vision Accelerate 3D Printed Orodispersible Film Development

Orodispersible films (ODFs) are an attractive delivery system for a myriad of clinical applications and possess both large economical and clinical rewards. However, the manufacturing of ODFs does not adhere to contemporary paradigms of personalised, on-demand medicine, nor sustainable manufacturing. To address these shortcomings, both three-dimensional (3D) printing and machine learning (ML) were employed to provide on-demand manufacturing and quality control checks of ODFs. Direct ink writing (DIW) was able to fabricate complex ODF shapes, with thicknesses of less than 100 µm. ML algorithms were explored to classify the ODFs according to their active ingredient, by using their near-infrared (NIR) spectrums. A supervised model of linear discriminant analysis was found to provide 100% accuracy in classifying ODFs. A subsequent partial least square algorithm was applied to verify the dose, where a coefficient of determination of 0.96, 0.99 and 0.98 was obtained for ODFs of paracetamol, caffeine, and theophylline, respectively. Therefore, it was concluded that the combination of 3D printing, NIR and ML can result in a rapid production and verification of ODFs. Additionally, a machine vision tool was used to automate the in vitro testing. These collective digital technologies demonstrate the potential to automate the ODF workflow