The colon targeting efficacies of mesalazine medications and their impacts on the gut microbiome.

Successful treatment of ulcerative colitis (UC) is highly dependent on several parameters, including dosing regimen and the ability to deliver drugs to the disease site. In this study two strategies for delivering mesalazine (5-aminosalicylic acid, 5-ASA) to the colon were compared in an advanced in vitro model of the human gastrointestinal (GI) tract, the SHIME® system. Herein, a prodrug strategy employing bacteria-mediated drug release (sulfasalazine, Azulfidine®) was evaluated alongside a formulation strategy that utilised pH and bacteria-mediated release (5-ASA, Octasa® 1600 mg). SHIME® experiments were performed simulating both the GI physiology and colonic microbiota under healthy and inflammatory bowel disease (IBD) conditions, to study the impact of the disease state and ileal pH variability on colonic 5-ASA delivery. In addition, the effects of the products on the colonic microbiome were investigated by monitoring bacterial growth and metabolites. Results demonstrated that both the prodrug and formulation approaches resulted in a similar percentage of 5-ASA recovery under healthy conditions. On the contrary, during experiments simulating the GI physiology and microbiome of IBD patients (the target population) the formulation strategy resulted in a higher proportion of 5-ASA delivery to the colonic region as compared to the prodrug approach (P < 0.0001). Interestingly, the two products had distinct effects on the synthesis of key bacterial metabolites, such as lactate and short chain fatty acids, which varied according to disease state and ileal pH variability. Further, both 5-ASA and sulfasalazine significantly reduced the growth of the faecal microbiota sourced from six healthy humans. The findings support that the approach selected for colonic drug delivery could significantly influence the effectiveness of UC treatment, and highlight that drugs licensed for UC may differentially impact the growth and functioning of the colonic microbiota.

Exploring the interactions of JAK inhibitor and S1P receptor modulator drugs with the human gut microbiome: Implications for colonic drug delivery and inflammatory bowel disease.

The gut microbiota is a complex ecosystem, home to hundreds of bacterial species and a vast repository of enzymes capable of metabolising a wide range of pharmaceuticals. Several drugs have been shown to affect negatively the composition and function of the gut microbial ecosystem. Janus Kinase (JAK) inhibitors and Sphingosine-1-phosphate (S1P) receptor modulators are drugs recently approved for inflammatory bowel disease through an immediate release formulation and would potentially benefit from colonic targeted delivery to enhance the local drug concentration at the diseased site. However, their impact on the human gut microbiota and susceptibility to bacterial metabolism remain unexplored. With the use of calorimetric, optical density measurements, and metagenomics next-generation sequencing, we show that JAK inhibitors (tofacitinib citrate, baricitinib, filgotinib) have a minor impact on the composition of the human gut microbiota, while ozanimod exerts a significant antimicrobial effect, leading to a prevalence of the Enterococcus genus and a markedly different metabolic landscape when compared to the untreated microbiota. Moreover, ozanimod, unlike the JAK inhibitors, is the only drug subject to enzymatic degradation by the human gut microbiota sourced from six healthy donors. Overall, given the crucial role of the gut microbiome in health, screening assays to investigate the interaction of drugs with the microbiota should be encouraged for the pharmaceutical industry as a standard in the drug discovery and development process.

Poly(D,l-lactide-co-glycolide) particles are metabolised by the gut microbiome and elevate short chain fatty acids.

The production of short chain fatty acids (SCFAs) by the colonic microbiome has numerous benefits for human health, including maintenance of epithelial barrier function, suppression of colitis, and protection against carcinogenesis. Despite the therapeutic potential, there is currently no optimal approach for elevating the colonic microbiome's synthesis of SCFAs. In this study, poly(D,l-lactide-co-glycolide) (PLGA) was investigated for this application, as it was hypothesised that the colonic microbiota would metabolise PLGA to its lactate monomers, which would promote the resident microbiota's synthesis of SCFAs. Two grades of spray dried PLGA, alongside a lactate bolus control, were screened in an advanced model of the human colon, known as the M-SHIME® system. Whilst the high molecular weight (Mw) grade of PLGA was stable in the presence of the microbiota sourced from three healthy humans, the low Mw PLGA (PLGA 2) was found to be metabolised. This microbial degradation led to sustained release of lactate over 48 h and increased concentrations of the SCFAs propionate and butyrate. Further, microbial synthesis of harmful ammonium was significantly reduced compared to untreated controls. Interestingly, both types of PLGA were found to influence the composition of the luminal and mucosal microbiota in a donor-specific manner. An in vitro model of an inflamed colonic epithelium also showed the polymer to affect the expression of pro- and anti-inflammatory markers, such as interleukins 8 and 10. The findings of this study reveal PLGA's sensitivity to enzymatic metabolism in the gut, which could be harnessed for therapeutic elevation of colonic SCFAs.

Colonic drug delivery: Formulating the next generation of colon-targeted therapeutics.

Colonic drug delivery can facilitate access to unique therapeutic targets and has the potential to enhance drug bioavailability whilst reducing off-target effects. Delivering drugs to the colon requires considered formulation development, as both oral and rectal dosage forms can encounter challenges if the colon's distinct physiological environment is not appreciated. As the therapeutic opportunities surrounding colonic drug delivery multiply, the success of novel pharmaceuticals lies in their design. This review provides a modern insight into the key parameters determining the effective design and development of colon-targeted medicines. Influential physiological features governing the release, dissolution, stability, and absorption of drugs in the colon are first discussed, followed by an overview of the most reliable colon-targeted formulation strategies. Finally, the most appropriate in vitro, in vivo, and in silico preclinical investigations are presented, with the goal of inspiring strategic development of new colon-targeted therapeutics.

Advancing oral delivery of biologics: Machine learning predicts peptide stability in the gastrointestinal tract.

The oral delivery of peptide therapeutics could facilitate precision treatment of numerous gastrointestinal (GI) and systemic diseases with simple administration for patients. However, the vast majority of licensed peptide drugs are currently administered parenterally due to prohibitive peptide instability in the GI tract. As such, the development of GI-stable peptides is receiving considerable investment. This study provides researchers with the first tool to predict the GI stability of peptide therapeutics based solely on the amino acid sequence. Both unsupervised and supervised machine learning techniques were trained on literature-extracted data describing peptide stability in simulated gastric and small intestinal fluid (SGF and SIF). Based on 109 peptide incubations, classification models for SGF and SIF were developed. The best models utilized k-Nearest Neighbor (for SGF) and XGBoost (for SIF) algorithms, with accuracies of 75.1% (SGF) and 69.3% (SIF), and f1 scores of 84.5% (SGF) and 73.4% (SIF) under 5-fold cross-validation. Feature importance analysis demonstrated that peptides' lipophilicity, rigidity, and size were key determinants of stability. These models are now available to those working on the development of oral peptide therapeutics.

Impact of Peptide Structure on Colonic Stability and Tissue Permeability.

Most marketed peptide drugs are administered parenterally due to their inherent gastrointestinal (GI) instability and poor permeability across the GI epithelium. Several molecular design techniques, such as cyclisation and D-amino acid (D-AA) substitution, have been proposed to improve oral peptide drug bioavailability. However, very few of these techniques have been translated to the clinic. In addition, little is known about how synthetic peptide design may improve stability and permeability in the colon, a key site for the treatment of inflammatory bowel disease and colorectal cancer. In this study, we investigated the impact of various cyclisation modifications and D-AA substitutions on the enzymatic stability and colonic tissue permeability of native oxytocin and 11 oxytocin-based peptides. Results showed that the disulfide bond cyclisation present in native oxytocin provided an improved stability in a human colon model compared to a linear oxytocin derivative. Chloroacetyl cyclisation increased native oxytocin stability in the colonic model at 1.5 h by 30.0%, whereas thioether and N-terminal acetylated cyclisations offered no additional protection at 1.5 h. The site and number of D-AA substitutions were found to be critical for stability, with three D-AAs at Tyr, Ile and Leu, improving native oxytocin stability at 1.5 h in both linear and cyclic structures by 58.2% and 79.1%, respectively. Substitution of three D-AAs into native cyclic oxytocin significantly increased peptide permeability across rat colonic tissue; this may be because D-AA substitution favourably altered the peptide's secondary structure. This study is the first to show how the strategic design of peptide therapeutics could enable their delivery to the colon via the oral route.

Current clinical translation of microbiome medicines.

The microbiome is experiencing increasing scrutiny for its role in disease, and the number of new research reports describing microbiome-disease relationships is growing exponentially. Researchers are increasingly working to translate the emerging fundamental science into microbiome medicines that will address important unmet needs in the clinic. We summarise the types of microbiome medicines that have the most translational potential, and provide a detailed analysis of the current global microbiome medicines pipeline and the challenges facing clinical translation. The regulatory pipeline is currently dominated by probiotics intended for oral delivery to the gastrointestinal (GI) tract; however, several non-living biologics and small molecules provide notable exceptions. With the first microbiome medicine set to begin the regulatory submission process in 2022, it is an exciting time for the field.

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.

Active Machine learning for formulation of precision probiotics.

It is becoming clear that the human gut microbiome is critical to health and well-being, with increasing evidence demonstrating that dysbiosis can promote disease. Increasingly, precision probiotics are being investigated as investigational drug products for restoration of healthy microbiome balance. To reach the distal gut alive where the density of microbiota is highest, oral probiotics should be protected from harsh conditions during transit through the stomach and small intestines. At present, few probiotic formulations are designed with this delivery strategy in mind. This study employs an emerging machine learning (ML) technique, known as active ML, to predict how excipients at pharmaceutically relevant concentrations affect the intestinal proliferation of a common probiotic, Lactobacillus paracasei. Starting with a labelled dataset of just 6 bacteria-excipient interactions, active ML was able to predict the effects of a further 111 excipients using uncertainty sampling. The average certainty of the final model was 67.70% and experimental validation demonstrated that 3/4 excipient-probiotic interactions could be correctly predicted. The model can be used to enable superior probiotic delivery to maximise proliferation in vivo and marks the first use of active ML in microbiome science.

Advancing pharmacy and healthcare with virtual digital technologies.

Digitalisation of the healthcare sector promises to revolutionise patient healthcare globally. From the different technologies, virtual tools including artificial intelligence, blockchain, virtual, and augmented reality, to name but a few, are providing significant benefits to patients and the pharmaceutical sector alike, ranging from improving access to clinicians and medicines, as well as improving real-time diagnoses and treatments. Indeed, it is envisioned that such technologies will communicate together in real-time, as well as with their physical counterparts, to create a large-scale, cyber healthcare system. Despite the significant benefits that virtual-based digital health technologies can bring to patient care, a number of challenges still remain, ranging from data security to acceptance within the healthcare sector. This review provides a timely account of the benefits and challenges of virtual health interventions, as well an outlook on how such technologies can be transitioned from research-focused towards real-world healthcare and pharmaceutical applications to transform treatment pathways for patients worldwide.

A customizable 3D printed device for enzymatic removal of drugs in water.

The infiltration of drugs into water is a key global issue, with pharmaceuticals being detected in all nearly aqueous systems at often alarming concentrations. Pharmaceutical contamination of environmental water supplies has been shown to negatively impact ecological equilibrium and pose a risk to human health. In this study, we design and develop a novel system for the removal of drugs from water, termed as Printzyme. The device, fabricated with stereolithography (SLA) 3D printing, immobilises laccase sourced from Trametes Versicolor within a poly(ethylene glycol) diacrylate hydrogel. We show that SLA printing is a sustainable method for enzyme entrapment under mild conditions, and measure the stability of the system when exposed to extremes of pH and temperature in comparison to free laccase. When tested for its drug removal capacity, the 3D printed device substantially degraded two dissolved drugs on the European water pollution watch list. When configured in the shape of a torus, the device effectively removed 95% of diclofenac and ethinylestradiol from aqueous solution within 24 and 2 h, respectively, more efficiently than free enzyme. Being customizable and reusable, these 3D printed devices could help to efficiently tackle the world's water pollution crisis, in a flexible, easily scalable, and cost-efficient manner.

Clinical translation of advanced colonic drug delivery technologies.

Targeted drug delivery to the colon offers a myriad of benefits, including treatment of local diseases, direct access to unique therapeutic targets and the potential for increasing systemic drug bioavailability and efficacy. Although a range of traditional colonic delivery technologies are available, these systems exhibit inconsistent drug release due to physiological variability between and within individuals, which may be further exacerbated by underlying disease states. In recent years, significant translational and commercial advances have been made with the introduction of new technologies that incorporate independent multi-stimuli release mechanisms (pH and/or microbiota-dependent release). Harnessing these advanced technologies offers new possibilities for drug delivery via the colon, including the delivery of biopharmaceuticals, vaccines, nutrients, and microbiome therapeutics for the treatment of both local and systemic diseases. This review details the latest advances in colonic drug delivery, with an emphasis on emerging therapeutic opportunities and clinical technology translation.

Poly(lactic acid)-hyperbranched polyglycerol nanoparticles enhance bioadhesive treatment of esophageal disease and reduce systemic drug exposure.

The effective treatment of esophageal disease represents a significant unmet clinical need, as existing treatments often lead to unnecessary systemic drug exposure and suboptimal concentrations at the disease site. Here, surface-modified bioadhesive poly(lactic acid)-hyperbranched polyglycerol nanoparticles (BNPs), with an average 100-200 nm diameter, were developed for local and sustained esophageal drug delivery. BNPs showed significantly higher adhesion and permeation into ex vivo human and rat esophageal tissue than non-adhesive nanoparticles (NNPs) and had longer residence times within the rat esophagus in vivo. Incubation with human esophagus (Het-1A) cells confirmed BNPs' biocompatibility at clinically relevant concentrations. In a rat model of achalasia, nifedipine-loaded BNPs significantly enhanced esophageal drug exposure, increased therapeutic efficacy, and reduced systemic drug exposure compared to NNPs and free drug. The safety of BNPs was demonstrated by an absence of intestinal, hepatic, and splenic toxicity following administration. This study is the first to demonstrate the efficacy of BNPs for esophageal drug delivery and highlight their potential for improving the lives of patients suffering with esophageal conditions.

Ileocolonic-Targeted JAK Inhibitor: A Safer and More Effective Treatment for Inflammatory Bowel Disease.

Janus kinase (JAK) inhibitors, such as tofacitinib (Xeljanz) and filgotinib (Jyseleca), have been approved for treatment of ulcerative colitis with several other JAK inhibitors in late-stage clinical trials for inflammatory bowel disease (IBD). Despite their impressive efficacy, the risk of adverse effects accompanying the use of JAK inhibitors has brought the entire class under scrutiny, leading to them receiving an FDA black box warning. In this study we investigated whether ileocolonic-targeted delivery of a pan-JAK inhibitor, tofacitinib, can lead to increased tissue exposure and reduced systemic exposure compared to untargeted formulations. The stability of tofacitinib in the presence of rat colonic microbiota was first confirmed. Next, in vivo computed tomography imaging was performed in rats to determine the transit time and disintegration site of ileocolonic-targeted capsules compared to gastric release capsules. Pharmacokinetic studies demonstrated that systemic drug exposure was significantly decreased, and colonic tissue exposure increased at 10 mg/kg tofacitinib dosed in ileocolonic-targeted capsules compared to gastric release capsules and an oral solution. Finally, in a rat model of LPS-induced colonic inflammation, targeted tofacitinib capsules significantly reduced concentrations of proinflammatory interleukin 6 in colonic tissue compared to a vehicle-treated control (p = 0.0408), unlike gastric release tofacitinib capsules and orally administered dexamethasone. Overall, these results support further development of ileocolonic-targeted tofacitinib, and potentially other specific JAK inhibitors in pre-clinical and clinical development, for the treatment of IBD.

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.

Machine Learning Predicts Drug Metabolism and Bioaccumulation by Intestinal Microbiota.

Over 150 drugs are currently recognised as being susceptible to metabolism or bioaccumulation (together described as depletion) by gastrointestinal microorganisms; however, the true number is likely higher. Microbial drug depletion is often variable between and within individuals, depending on their unique composition of gut microbiota. Such variability can lead to significant differences in pharmacokinetics, which may be associated with dosing difficulties and lack of medication response. In this study, literature mining and unsupervised learning were used to curate a dataset of 455 drug-microbiota interactions. From this, 11 supervised learning models were developed that could predict drugs' susceptibility to depletion by gut microbiota. The best model, a tuned extremely randomised trees classifier, achieved performance metrics of AUROC: 75.1% ± 6.8; weighted recall: 79.2% ± 3.9; balanced accuracy: 69.0% ± 4.6; and weighted precision: 80.2% ± 3.7 when validated on 91 drugs. This machine learning model is the first of its kind and provides a rapid, reliable, and resource-friendly tool for researchers and industry professionals to screen drugs for susceptibility to depletion by gut microbiota. The recognition of drug-microbiome interactions can support successful drug development and promote better formulations and dosage regimens for patients.

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.

Harnessing artificial intelligence for the next generation of 3D printed medicines.

Artificial intelligence (AI) is redefining how we exist in the world. In almost every sector of society, AI is performing tasks with super-human speed and intellect; from the prediction of stock market trends to driverless vehicles, diagnosis of disease, and robotic surgery. Despite this growing success, the pharmaceutical field is yet to truly harness AI. Development and manufacture of medicines remains largely in a 'one size fits all' paradigm, in which mass-produced, identical formulations are expected to meet individual patient needs. Recently, 3D printing (3DP) has illuminated a path for on-demand production of fully customisable medicines. Due to its flexibility, pharmaceutical 3DP presents innumerable options during formulation development that generally require expert navigation. Leveraging AI within pharmaceutical 3DP removes the need for human expertise, as optimal process parameters can be accurately predicted by machine learning. AI can also be incorporated into a pharmaceutical 3DP 'Internet of Things', moving the personalised production of medicines into an intelligent, streamlined, and autonomous pipeline. Supportive infrastructure, such as The Cloud and blockchain, will also play a vital role. Crucially, these technologies will expedite the use of pharmaceutical 3DP in clinical settings and drive the global movement towards personalised medicine and Industry 4.0.

5-Aminolevulinic Acid as a Novel Therapeutic for Inflammatory Bowel Disease.

5-Aminolevulinic acid (5-ALA) is a naturally occurring nonprotein amino acid licensed as an optical imaging agent for the treatment of gliomas. In recent years, 5-ALA has been shown to possess anti-inflammatory and immunoregulatory properties through upregulation of heme oxygenase-1 via enhancement of porphyrin, indicating that it may be beneficial for the treatment of inflammatory conditions. This study systematically examines 5-ALA for use in inflammatory bowel disease (IBD). Firstly, the ex vivo colonic stability and permeability of 5-ALA was assessed using human and mouse fluid and tissue. Secondly, the in vivo efficacy of 5-ALA, in the presence of sodium ferrous citrate, was investigated via the oral and intracolonic route in an acute DSS colitis mouse model of IBD. Results showed that 5-ALA was stable in mouse and human colon fluid, as well as in colon tissue. 5-ALA showed more tissue restricted pharmacokinetics when exposed to human colonic tissue. In vivo dosing demonstrated significantly improved colonic inflammation, increased local heme oxygenase-1 levels, and decreased concentrations of proinflammatory cytokines TNF-α, IL-6, and IL-1ß in both plasma and colonic tissue. These effects were superior to that measured concurrently with established anti-inflammatory treatments, ciclosporin and 5-aminosalicylic acid (mesalazine). As such, 5-ALA represents a promising addition to the IBD armamentarium, with potential for targeted colonic delivery.

Disrupting 3D printing of medicines with machine learning.

3D printing (3DP) is a progressive technology capable of transforming pharmaceutical development. However, despite its promising advantages, its transition into clinical settings remains slow. To make the vital leap to mainstream clinical practice and improve patient care, 3DP must harness modern technologies. Machine learning (ML), an influential branch of artificial intelligence, may be a key partner for 3DP. Together, 3DP and ML can utilise intelligence based on human learning to accelerate drug product development, ensure stringent quality control (QC), and inspire innovative dosage-form design. With ML's capabilities, streamlined 3DP drug delivery could mark the next era of personalised medicine. This review details how ML can be applied to elevate the 3DP of pharmaceuticals and importantly, how it can expedite 3DP's integration into mainstream healthcare.