Fabrication of Electrospun Levodopa-Carbidopa Fixed-Dose Combinations

We report in this work coaxial electrospun fibers with potential applications in the treatment of Parkinson’s disease. The fibers comprise a fixed dose combination (FDC) containing the active ingredients levodopa and carbidopa, loaded in a fast dissolving polyvinylpyrrolidone (PVP) shell and an insoluble but swellable Eudragit® RLPO core. Under appropriate processing conditions we are able to prepare fibers with distinct core/shell architectures and diameters of approximately 400 nm. X-ray diffraction and differential scanning calorimetry analyses revealed that the drugs are dispersed on the molecular level within the polymer carriers, and IR spectroscopy indicated the presence of intermolecular interactions. At pH 1, the composite fibers yields extended release over more than 8 h, with an initial loading dose being freed from the PVP shell and then a sustained release phase following from the insoluble core. This is markedly extended over the release period of the commercial FDC product, and thus the fibers generated here have the potential to be used to reduce the required dosing frequency

Electrospun fixed dose combination fibers for the treatment of cardiovascular disease

Fixed dose combinations (FDCs) offer an accessible way to simplify complex therapeutic regimens by the simultaneous presentation of multiple drugs in a single entity to the patient. However, encapsulation of hydrophobic drugs into FDCs possess a number of technical challenges. Electrospun nanofibers offer a convenient way to incorporate multiple hydrophobic drugs into a single formulation in a single step, via the use of an appropriate organic solvent system during fabrication. In this study, we report a series of novel fiber formulations comprising ethyl cellulose loaded with two hydrophobic drugs, spironolactone and nifedipine, either individually or in combination. The drugs are found to be present in the fibers in the form of amorphous solid dispersions, and these are stable at room temperature for 4 months. The products showed extended release profiles over more than 30 h. This formulation strategy offers potential to manage chronic cardiovascular conditions and overcome patient related non-adherence by providing a simplified treatment model

Core/shell microencapsulation of indomethacin/paracetamol by co-axial electrohydrodynamic atomization

Core/shell microparticles for development of drug delivery systems were prepared using co-axial electrohydrodynamic atomization technique in order to develop fixed dose combined formulations incorporating paracetamol and indomethacin as model drugs. The developed drug delivery systems offered successful co-encapsulation of paracetamol and indomethacin with high drug encapsulation efficiencies of 54% and 69% for paracetamol and indomethacin, respectively. The developed formulations were further characterised with respect to their morphology, drug release profile and possible interactions. In comparison to the release rate of the free indomethacin, the developed formulation resulted in enhanced dissolution rate of indomethacin. This study demonstrates a versatile polymeric platform where multiple drug encapsulation and co-delivery is made possible by utilizing co-axial electrohydrodynamic atomization. The proposed system offered high processing yield of 60–70%, as a single-step platform for preparation of fixed dose formulations for oral drug delivery, particularly in geriatric therapy

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

Predicting drug-microbiome interactions with machine learning

Pivotal work in recent years has cast light on the importance of the human microbiome in maintenance of health and physiological response to drugs. It is now clear that gastrointestinal microbiota have the metabolic power to promote, inactivate, or even toxify the efficacy of a drug to a level of clinically relevant significance. At the same time, it appears that drug intake has the propensity to alter gut microbiome composition, potentially affecting health and response to other drugs. Since the precise composition of an individual's microbiome is unique, one's drug-microbiome relationship is similarly unique. Thus, in the age of evermore personalised medicine, the ability to predict individuals' drug-microbiome interactions is highly sought. Machine learning (ML) offers a powerful toolkit capable of characterising and predicting drug-microbiota interactions at the individual patient level. ML techniques have the potential to learn the mechanisms operating drug-microbiome activities and measure patients' risk of such occurrences. This review will outline current knowledge at the drug-microbiota interface, and present ML as a technique for examining and forecasting personalised drug-microbiome interactions. When harnessed effectively, ML could alter how the pharmaceutical industry and healthcare professionals consider the drug-microbiome axis in patient care

3D printed infliximab suppositories for rectal biologic delivery

Infliximab is a monoclonal antibody that plays an important role in the management and treatment of chronic inflammatory bowel diseases (IBD). Due to its macromolecular structure, its delivery through the oral route is challenging, limiting its administration to only via the parenteral route. The rectal route offers an alternative way for administering infliximab, allowing it to be localised at the disease site and circumventing its passage across the alimentary canal and thus, maintaining its integrity and bioactivity. Three-dimensional (3D) printing is an advanced production technology that permits the creation of dose-flexible drug products from digital designs. The current study assessed the feasibility of utilising semi-solid extrusion 3D printing for the fabrication of infliximab-loaded suppositories for the local treatment of IBD. Various printing inks composed of Gelucire® (48/16 or 44/14) mixed with coconut oil and/or purified water were investigated. It was shown that following reconstitution in water, the infliximab solution can be directly incorporated into the printing ink of Gelucire® 48/16 and can withstand the extrusion process, resulting in well-defined suppositories. Since water content and temperature are critical for safeguarding infliximab's potency, the effect of changing the composition of the printing inks and printing parameters on infliximab's biologic efficiency was evaluated by measuring its binding capacity (i.e., the amount of infliximab that actively binds to its antigen to exert an effect). Despite drug loading assays showing that infliximab remains intact following printing, it was found that the incorporation of water in isolation results in only ∼65% binding capacity. However, when oil is added to the mixture, infliximab's binding capacity increases up to ∼85%. These promising results demonstrate that 3D printing has the potential to be exploited as a novel platform for fabricating dosage forms containing biopharmaceuticals, avoiding patients' compliance issues observed with injectables and addressing their unmet needs

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

Using public engagement and consultation to inform the development of ageing-and dementia-friendly pharmacies – Innovative practice

This study explored public perceptions about the importance of, and how to create, ageing- and dementia-friendly pharmacists and pharmacies. In September 2016, four focus groups (45 minutes each) were conducted with 16 participants who represented organisations, groups or forums working with and/or for older people and people with dementia in Greater London. Discussions were recorded via handwritten notes and thematically analysed. Participants confirmed the importance of pharmacists and pharmacies being ageing- and dementia-friendly and described variability in whether this is currently the case. Suggested strategies for improvement included targeting communication, pharmacist leadership and shop layout

P-glycoprotein expression in the gastrointestinal tract of male and female rats is influenced differently by food

The aim of this study was to explore the influence of food on P-glycoprotein (P-gp) relative expression in both male and female rats, and its effect on intestinal permeation of P-gp substrates (ranitidine and ganciclovir) and a P-gp non-substrate (metformin). The intestine of 12 male and 12 female Wistar rats were excised and segmented into the duodenum, jejunum, ileum and colon. P-gp extracted from each segment was then determined via Western-blotting. In male rats, the relative P-gp expression decreased significantly after food intake in all segments of the intestine except in the duodenum. The most notable change was demonstrated in the colon where relative expression decreased from 1.75 ± 0.36 in the fasted-state to 0.31 ± 0.15 in the fed-state. In female rats, a fundamentally different result was observed. Food ingestion resulted in a significant increase in relative P-gp expression in all regions of the intestine except in the colon. The largest difference was observed in the jejunum of the fed-state female rat intestine where P-gp expression was 1.76 ± 0.95 which was a six-fold increase from the fasted state at 0.34 ± 0.13. Intestinal permeation studies in an Ussing chamber showed that both ganciclovir and ranitidine exhibited a sex difference in intestinal permeability in the fasted-state. No sex differences and food effects were observed on metformin small intestine permeability. The permeability results of the three drugs highly supported that there was a sex-related food effect on P-gp function in the small intestine. In summary, the current study reports stark differences between male and female rats at a physiological level relating to P-gp expression and the influence of food