Discovery through Machine Learning and Preclinical Validation of Novel Anti-Diabetic Peptides

New Developments in Pharmacological Treatment of Obesity and Type 2 Diabetes-Beyond and within GLP-1 Receptor Agonists

Guidelines for the management of obesity and type 2 diabetes (T2DM) emphasize the importance of lifestyle changes, including a reduced-calorie diet and increased physical activity. However, for many people, these changes can be difficult to maintain over the long term. Medication options are already available to treat obesity, which can help reduce appetite and/or reduce caloric intake. Incretin-based peptides exert their effect through G-protein-coupled receptors, the receptors for glucagon-like peptide-1 (GLP-1) and glucose-dependent insulinotropic polypeptide (GIP), and glucagon peptide hormones are important regulators of insulin secretion and energy metabolism. Understanding the role of intercellular signaling pathways and inflammatory processes is essential for the development of effective pharmacological agents in obesity. GLP-1 receptor agonists have been successfully used, but it is assumed that their effectiveness may be limited by desensitization and downregulation of the target receptor. A growing number of new agents acting on incretin hormones are becoming available for everyday clinical practice, including oral GLP-1 receptor agonists, the dual GLP-1/GIP receptor agonist tirzepatide, and other dual and triple GLP-1/GIP/glucagon receptor agonists, which may show further significant therapeutic potential. This narrative review summarizes the therapeutic effects of different incretin hormones and presents future prospects in the treatment of T2DM and obesity.

Recent Advances in the Discovery of Novel Drugs on Natural Molecules

Natural products (NPs) are always a promising source of novel drugs for tackling unsolved diseases [...].

Mass spectrometry as a lens into molecular human nutrition and health

Mass spectrometry (MS) has developed over the last decades into the most informative and versatile analytical technology in molecular and structural biology (). The platform enables discovery, identification, and characterisation of non-volatile biomolecules, such as proteins, peptides, DNA, RNA, nutrients, metabolites, and lipids at both speed and scale and can elucidate their interactions and effects. The versatility, robustness, and throughput have rendered MS a major research and development platform in molecular human health and biomedical science. More recently, MS has also been established as the central tool for 'Molecular Nutrition', enabling comprehensive and rapid identification and characterisation of macro- and micronutrients, bioactives, and other food compounds. 'Molecular Nutrition' thereby helps understand bioaccessibility, bioavailability, and bioefficacy of macro- and micronutrients and related health effects. Hence, MS provides a lens through which the fate of nutrients can be monitored along digestion via absorption to metabolism. This in turn provides the bioanalytical foundation for 'Personalised Nutrition' or 'Precision Nutrition' in which design and development of diets and nutritional products is tailored towards consumer and patient groups sharing similar genetic and environmental predisposition, health/disease conditions and lifestyles, and/or objectives of performance and wellbeing. The next level of integrated nutrition science is now being built as 'Systems Nutrition' where public and personal health data are correlated with life condition and lifestyle factors, to establish directional relationships between nutrition, lifestyle, environment, and health, eventually translating into science-based public and personal heath recommendations and actions. This account provides a condensed summary of the contributions of MS to a precise, quantitative, and comprehensive nutrition and health science and sketches an outlook on its future role in this fascinating and relevant field.

Bioactive compounds for human and planetary health

Bioactive compounds found in edible plants and foods are vital for human and planetary health, yet their significance remains underappreciated. These natural bioactives, as part of whole diets, ingredients, or supplements, can modulate multiple aspects of human health and wellness. Recent advancements in omic sciences and computational biology, combined with the development of Precision Nutrition, have contributed to the convergence of nutrition and medicine, as well as more efficient and affordable healthcare solutions that harness the power of food for prevention and therapy. Innovation in this field is crucial to feed a growing global population sustainably and healthily. This requires significant changes in our food system, spanning agriculture, production, distribution and consumption. As we are facing pressing planetary health challenges, investing in bioactive-based solutions is an opportunity to protect biodiversity and the health of our soils, waters, and the atmosphere, while also creating value for consumers, patients, communities, and stakeholders. Such research and innovation targets include alternative proteins, such as cellular agriculture and plant-derived protein; natural extracts that improve shelf-life as natural preservatives; upcycling of agricultural by-products to reduce food waste; and the development of natural alternatives to synthetic fertilizers and pesticides. Translational research and innovation in the field of natural bioactives are currently being developed at two levels, using a systems-oriented approach. First, at the biological level, the interplay between these compounds and the human host and microbiome is being elucidated through omics research, big data and artificial intelligence, to accelerate both discovery and validation. Second, at the ecosystem level, efforts are focused on producing diverse nutrient-rich, flavorful, and resilient, yet high-yield agricultural crops, and educating consumers to make informed choices that benefit both their health and the planet. Adopting a system-oriented perspective helps: unravel the intricate and dynamic relationships between bioactives, nutrition, and sustainability outcomes, harnessing the power of nature to promote human health and wellbeing; foster sustainable agriculture and protect the ecosystem. Interdisciplinary collaboration in this field is needed for a new era of research and development of practical food-based solutions for some of the most pressing challenges humanity and our planet are facing today.

Smart systems in producing algae-based protein to improve functional food ingredients industries

Production and extraction systems of algal protein and handling process of functional food ingredients need to control several parameters such as temperature, pH, intensity, and turbidity. Many researchers have investigated the Internet of Things (IoT) approach for enhancing the yield of microalgae biomass and machine learning for identifying and classifying microalgae. However, there have been few specific studies on using IoT and artificial intelligence (AI) for production and extraction of algal protein as well as functional food ingredients processing. In order to improve the production of algal protein and functional food ingredients, the implementation of smart system is a must to have real-time monitoring, remote control system, quick response to sudden events, prediction and characterisation. Techniques of IoT and AI are expected to help functional food industries to have a big breakthrough in the future. Manufacturing and implementation of beneficial smart systems are important to provide convenience and to increase the efficiency of work by using the interconnectivity of IoT devices to have good capturing, processing, archiving, analyzing, and automation. This review investigates the possibilities of implementation of IoT and AI in production and extraction of algal protein and processing of functional food ingredients.

Application of machine learning on understanding biomolecule interactions in cellular machinery

Machine learning (ML) applications have become ubiquitous in all fields of research including protein science and engineering. Apart from protein structure and mutation prediction, scientists are focusing on knowledge gaps with respect to the molecular mechanisms involved in protein binding and interactions with other components in the experimental setups or the human body. Researchers are working on several wet-lab techniques and generating data for a better understanding of concepts and mechanics involved. The information like biomolecular structure, binding affinities, structure fluctuations and movements are enormous which can be handled and analyzed by ML. Therefore, this review highlights the significance of ML in understanding the biomolecular interactions while assisting in various fields of research such as drug discovery, nanomedicine, nanotoxicity and material science. Hence, the way ahead would be to force hand-in hand of laboratory work and computational techniques.

Prediction, Discovery, and Characterization of Plant- and Food-Derived Health-Beneficial Bioactive Peptides

Nature may have the answer to many of our questions about human, animal, and environmental health. Natural bioactives, especially when harvested from sustainable plant and food sources, provide a plethora of molecular solutions to nutritionally actionable, chronic conditions. The spectrum of these conditions, such as metabolic, immune, and gastrointestinal disorders, has changed with prolonged human life span, which should be matched with an appropriately extended health span, which would in turn favour more sustainable health care: "adding years to life and adding life to years". To date, bioactive peptides have been undervalued and underexploited as food ingredients and drugs. The future of translational science on bioactive peptides-and natural bioactives in general-is being built on (a) systems-level rather than reductionist strategies for understanding their interdependent, and at times synergistic, functions; and (b) the leverage of artificial intelligence for prediction and discovery, thereby significantly reducing the time from idea and concept to finished solutions for consumers and patients. This new strategy follows the path from benefit definition via design to prediction and, eventually, validation and production.

Predicting High Blood Pressure Using DNA Methylome-Based Machine Learning Models

DNA methylation modification plays a vital role in the pathophysiology of high blood pressure (BP). Herein, we applied three machine learning (ML) algorithms including deep learning (DL), support vector machine, and random forest for detecting high BP using DNA methylome data. Peripheral blood samples of 50 elderly individuals were collected three times at three visits for DNA methylome profiling. Participants who had a history of hypertension and/or current high BP measure were considered to have high BP. The whole dataset was randomly divided to conduct a nested five-group cross-validation for prediction performance. Data in each outer training set were independently normalized using a min–max scaler, reduced dimensionality using principal component analysis, then fed into three predictive algorithms. Of the three ML algorithms, DL achieved the best performance (AUPRC = 0.65, AUROC = 0.73, accuracy = 0.69, and F1-score = 0.73). To confirm the reliability of using DNA methylome as a biomarker for high BP, we constructed mixed-effects models and found that 61,694 methylation sites located in 15,523 intragenic regions and 16,754 intergenic regions were significantly associated with BP measures. Our proposed models pioneered the methodology of applying ML and DNA methylome data for early detection of high BP in clinical practices.

Artificial Intelligence in Functional Food Ingredient Discovery and Characterisation: A Focus on Bioactive Plant and Food Peptides

Scientific research consistently demonstrates that diseases may be delayed, treated, or even prevented and, thereby, health may be maintained with health-promoting functional food ingredients (FFIs). Consumers are increasingly demanding sound information about food, nutrition, nutrients, and their associated health benefits. Consequently, a nutrition industry is being formed around natural foods and FFIs, the economic growth of which is increasingly driven by consumer decisions. Information technology, in particular artificial intelligence (AI), is primed to vastly expand the pool of characterised and annotated FFIs available to consumers, by systematically discovering and characterising natural, efficacious, and safe bioactive ingredients (bioactives) that address specific health needs. However, FFI-producing companies are lagging in adopting AI technology for their ingredient development pipelines for several reasons, resulting in a lack of efficient means for large-scale and high-throughput molecular and functional ingredient characterisation. The arrival of the AI-led technological revolution allows for the comprehensive characterisation and understanding of the universe of FFI molecules, enabling the mining of the food and natural product space in an unprecedented manner. In turn, this expansion of bioactives dramatically increases the repertoire of FFIs available to the consumer, ultimately resulting in bioactives being specifically developed to target unmet health needs.

Measuring the oral bioavailability of protein hydrolysates derived from food sources: A critical review of current bioassays

BACKGROUND

Food proteins are a source of hydrolysates with potentially useful biological attributes. Bioactive peptides from food-derived proteins are released from hydrolysates using exogenous industrial processes or endogenous intestinal enzymes. Current in vitro permeability assays have limitations in predicting the oral bioavailability (BA) of bioactive peptides in humans. There are also difficulties in relating the low blood levels of food-derived bioactive peptides detected in preclinical in vivo models to pharmacodynamic read-outs relevant for humans.

SCOPE AND APPROACH

In this review, we describe in vitro assays of digestion, permeation, and metabolism as indirect predictors of the potential oral BA of hydrolysates and their constituent bioactive peptides. We discuss the relationship between industrial hydrolysis processes and the oral BA of hydrolysates and their peptide by-products.

KEY FINDINGS

Hydrolysates are challenging for analytical detection methods due to capacity for enzymatic generation of peptides with novel sequences and also new modifications of these peptides during digestion. Mass spectrometry and peptidomics can improve the capacity to detect individual peptides released from complex hydrolysates in biological milieu.

An Artificial-Intelligence-Discovered Functional Ingredient, NRT_N0G5IJ, Derived from Pisum sativum, Decreases HbA1c in a Prediabetic Population

The prevalence of prediabetes is rapidly increasing, and this can lead to an increased risk for individuals to develop type 2 diabetes and associated diseases. Therefore, it is necessary to develop nutritional strategies to maintain healthy glucose levels and prevent glucose metabolism dysregulation in the general population. Functional ingredients offer great potential for the prevention of various health conditions, including blood glucose regulation, in a cost-effective manner. Using an artificial intelligence (AI) approach, a functional ingredient, NRT_N0G5IJ, was predicted and produced from Pisum sativum (pea) protein by hydrolysis and then validated. Treatment of human skeletal muscle cells with NRT_N0G5IJ significantly increased glucose uptake, indicating efficacy of this ingredient in vitro. When db/db diabetic mice were treated with NRT_N0G5IJ, we observed a significant reduction in glycated haemoglobin (HbA1c) levels and a concomitant benefit on fasting glucose. A pilot double-blinded, placebo controlled human trial in a population of healthy individuals with elevated HbA1c (5.6% to 6.4%) showed that HbA1c percentage was significantly reduced when NRT_N0G5IJ was supplemented in the diet over a 12-week period. Here, we provide evidence of an AI approach to discovery and demonstrate that a functional ingredient identified using this technology could be used as a supplement to maintain healthy glucose regulation.