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A diverse array of microbes in and on the human body constitute the microbiota. These micro-residents continuously interact with the human host through the language of metabolites to dictate the host's physiology in health and illnesses. Any biotic and abiotic component ensuring a balanced host-microbiota interaction are potential microbiome therapeutic agents to overcome human diseases. Plant metabolites are continually being used to treat various illnesses. These metabolites target the host's metabolic machinery and host-gut microbiota interactions to overcome human diseases. Despite the paramount therapeutic significance of the factors affecting host-microbiota interactions, a comprehensive overview of the modulatory role of plant-derived metabolites in host-microbiota interactions is lacking. The current review puts an effort into comprehending the role of medicinal plants in gut microbiota modulation to mitigate various human illnesses. It would develop a holistic understanding of host-microbiota interactions and the role of effectors in health and diseases. Graphic Abstract [ABSTRACT FROM AUTHOR]

Pancreatic cancer is often diagnosed at an advanced stage when treatment options are limited. Being one of the deadliest cancers that mandates longer medication and treatment phases, there is an inevitable need to have the knowledge of drug response of anti-pancreatic cancer drugs before it is recommended for a patient. AI-driven drug response prediction has proven potential to personalize treatment strategies, improve therapeutic outcomes, and reduce adverse effects and treatment costs for cancer patients. In this research work, we have accounted for the use of different drug descriptors and their core structures known as scaffolds along with three cell line features, chromatin profiling, reverse phase protein array, and metabolomics data to build a feature engineered dataset for drug response prediction tested on various computational learning models. The 53 unique drugs against 18 unique pancreatic cancer cell lines were taken as the raw dataset. The initial dataset having a large dimension was feature selected using an ensemble method derived from five different techniques. The dataset was evaluated on various computational methods and an accuracy of 89% was achieved using the TabNet architecture. Furthermore, the common scaffolds that were persistently found among the drugs that possess high IC50-valued drug clusters were also recorded. [ABSTRACT FROM AUTHOR]

Effective diagnostics and therapeutics are foundational to modern medical technology. As the drug modality transitions from traditional small-molecule drugs to more intricate protein and nucleic acid-based therapies, there is mounting momentum towards personalized therapeutic approaches. These evolving paradigms challenge conventional biomanufacturing practices, highlighting the need for agile and patient-centric methodologies. Cell-free protein synthesis (CFPS), which facilitates the direct and programmable production of recombinant proteins, emerges as a compelling alternative. It offers rapid, adaptable, and customized protein production capabilities. Furthermore, by leveraging the core molecular mechanisms of gene expression, CFPS is gaining traction in the diagnostics realm, addressing the growing need for sophisticated diagnostic tools. In this review, we survey recent advancements and discuss how CFPS is poised to markedly reshape therapeutic production and diagnostic methods in the foreseeable future. [ABSTRACT FROM AUTHOR]

Digital twins are virtual models of physical artefacts that may or may not be synchronously connected, and that can be used to simulate their behavior. They are widely used in several domains such as manufacturing and automotive to enable achieving specific quality goals. In the health domain, so-called digital patient twins have been understood as virtual models of patients generated from population data and/or patient data, including, for example, real-time feedback from wearables. Along with the growing impact of data science technologies like artificial intelligence, novel health data ecosystems centered around digital patient twins could be developed. This paves the way for improved health monitoring and facilitation of personalized therapeutics based on management, analysis, and interpretation of medical data via digital patient twins. The utility and feasibility of digital patient twins in routine medical processes are still limited, despite practical endeavors to create digital twins of physiological functions, single organs, or holistic models. Moreover, reliable simulations for the prediction of individual drug responses are still missing. However, these simulations would be one important milestone for truly personalized therapeutics. Another prerequisite for this would be individualized pharmaceutical manufacturing with subsequent obstacles, such as low automation, scalability, and therefore high costs. Additionally, regulatory challenges must be met thus calling for more digitalization in this area. Therefore, this narrative mini-review provides a discussion on the potentials and limitations of digital patient twins, focusing on their potential bridging function for personalized therapeutics and an individualized pharmaceutical manufacturing while also looking at the regulatory impacts. [ABSTRACT FROM AUTHOR]

Digital twins are virtual models of physical artefacts that may or may not be synchronously connected, and that can be used to simulate their behavior. They are widely used in several domains such as manufacturing and automotive to enable achieving specific quality goals. In the health domain, so-called digital patient twins have been understood as virtual models of patients generated from population data and/or patient data, including, for example, real-time feedback from wearables. Along with the growing impact of data science technologies like artificial intelligence, novel health data ecosystems centered around digital patient twins could be developed. This paves the way for improved health monitoring and facilitation of personalized therapeutics based on management, analysis, and interpretation of medical data via digital patient twins. The utility and feasibility of digital patient twins in routine medical processes are still limited, despite practical endeavors to create digital twins of physiological functions, single organs, or holistic models. Moreover, reliable simulations for the prediction of individual drug responses are still missing. However, these simulations would be one important milestone for truly personalized therapeutics. Another prerequisite for this would be individualized pharmaceutical manufacturing with subsequent obstacles, such as low automation, scalability, and therefore high costs. Additionally, regulatory challenges must be met thus calling for more digitalization in this area. Therefore, this narrative mini-review provides a discussion on the potentials and limitations of digital patient twins, focusing on their potential bridging function for personalized therapeutics and an individualized pharmaceutical manufacturing while also looking at the regulatory impacts.

ABCA4-associated Stargardt disease (STGD1) leads to bilateral central vision loss and is responsible for 12% of inherited retinal disease-related blindness. The lack of approved treatments highlights the urgent need for effective therapies. This review explores personalized treatments for STGD1, focusing on therapeutic alternative splicing, genome editing, and translational read-through technologies. Literature searches as of July 2023 were undertaken via PubMed. Significant progress has been made in sequencing technology, revealing the complexities of the ABCA4 locus. Comprehensive functional assays can now enable the determination of pathogenicity for ABCA4 variants of uncertain significance. These breakthroughs facilitate the application of gene expression modulation technologies, ushering in a new era of personalized therapeutics. By targeting the ABCA4 gene and manipulating its expression, tailored treatments can address ABCA4-associated STGD1, offering enhanced efficacy and precise interventions based on the individual's genetic profile. These advancements provide hope to those affected by STGD1, with improved treatment options and the potential to prevent vision loss. The convergence of genetic analysis breakthroughs and gene expression modulation technologies revolutionizes our understanding and treatment of inherited disorders, unlocking a promising frontier in personalized therapeutics. This transformative approach to STGD1 holds promise for similar breakthroughs in other inherited conditions. [ABSTRACT FROM AUTHOR]

The past decades have seen tremendous developments with respect to "specific" therapeutics that target key signaling molecules to conquer cancer. The key advancements with multiomics technologies, especially genomics, have allowed physicians and molecular oncologists to design "tailor‐made" solutions to the specific oncogenes that are deregulated in individual patients, a strategy which has turned out to be successful though the patients quickly develop resistance. The swift integration of multidisciplinary approaches has led to the development of "next generation" therapeutics and, with synergistic therapeutic regimes combined with immune checkpoint inhibitors to reactivate the dampened immune response, has provided the much‐needed promise for cancer patients. Despite these advances, a large portion of the druggable genome remains understudied, and the role of druggable genome in the immune system needs further attention. Establishment of patient‐derived organoid models has fastened the preclinical validation of novel therapeutics for swift clinical translation. We summarized the current advances and challenges and also stress the importance of biobanking and collection of longitudinal data sets with structured clinical information, as well as the critical role these "high content data sets" will play in designing new therapeutic regimes in a tailor‐made fashion. [ABSTRACT FROM AUTHOR]

Worldwide non-alcoholic fatty liver disease (NAFLD) is recognized as the most common type of liver disease and its burden increasing at an alarming rate. NAFLD entails steatosis, fibrosis, cirrhosis, and, finally, hepatocellular carcinoma (HCC). The substantial inter-patient variation during disease progression is the hallmark of individuals with NAFLD. The variability of NAFLD development and related complications among individuals is determined by genetic and environmental factors. Genome-wide association studies (GWAS) have discovered reproducible and robust associations between gene variants such as PNPLA3, TM6SF2, HSD17B13, MBOAT7, GCKR and NAFLD. Evidences have provided the new insights into the NAFLD biology and underlined potential pharmaceutical targets. Ideally, the candidate genes associated with the hereditability of NAFLD are mainly involved in assembly of lipid droplets, lipid remodeling, lipoprotein packing and secretion, redox status mitochondria, and de novo lipogenesis. In recent years, the ability to translate genetics into a clinical context has emerged substantially by combining genetic variants primarily associated with NAFLD into polygenic risk scores (PRS). These score in combination with metabolic factors could be utilized to identify the severe liver diseases in patients with the gene regulatory networks (GRNs). Hereby, we even have highlighted the current understanding related to the schedule therapeutic approach of an individual based on microbial colonization and dysbiosis reversal as a therapy for NAFLD. The premise of this review is to concentrate on the potential of genetic factors and their translation into the design of novel therapeutics, as well as their implications for future research into personalized medications using microbiota., (Copyright © 2022 Elsevier Masson SAS. All rights reserved.)

Cancer drug resistance presents a challenge for precision medicine. Drug-resistant mutations are always emerging. In this study, we explored the relationship between drug-resistant mutations and drug resistance from the perspective of protein structure. By combining data from previously identified drug-resistant mutations and information of protein structure and function, we used machine learning-based methods to build models to predict cancer drug resistance mutations. The performance of our combined model achieved an accuracy of 86%, a Matthews correlation coefficient score of 0.57, and an F1 score of 0.66. We have constructed a fast, reliable method that predicts and investigates cancer drug resistance in a protein structure. Nonetheless, more information is needed concerning drug resistance and, in particular, clarification is needed about the relationships between the drug and the drug resistance mutations in proteins. Highly accurate predictions regarding drug resistance mutations can be helpful for developing new strategies with personalized cancer treatments. Our novel concept, which combines protein structure information, has the potential to elucidate physiological mechanisms of cancer drug resistance. [ABSTRACT FROM AUTHOR]

The human body harbors approximately 10 14 cells belonging to a diverse group of microorganisms. Bacteria outnumbers protozoa, fungi and viruses inhabiting our gastrointestinal tract (GIT), commonly referred to as the "human gut microbiome". Dysbiosis occurs when the balanced relationship between the host and the gut microbiota is disrupted, altering the usual microbial population there. This increases the susceptibility of the host to pathogens, and chances of its morbidity. It is due to the fact that the gut microbiome plays an important role in human health; it influences the progression of conditions varying from colorectal cancer to GIT disorders linked with the nervous system, autoimmunity, metabolism and inheritance. A rare disease is a lethal and persistent condition affecting 2-3 people per 5,000 populaces. This review article intends to discuss such rare neurological, autoimmune, cardio-metabolic and genetic disorders of man, focusing on the fundamental mechanism that links them with their gut microbiome. Ten rare diseases, including Pediatric Crohn's disease (PCD), Lichen planus (LP), Hypophosphatasia (HPP), Discitis, Cogan's syndrome, Chancroid disease, Sennetsu fever, Acute cholecystitis (AC), Grave's disease (GD) and Tropical sprue (TS) stands to highlight as key examples, along with personalized therapeutics meant for them. This medicinal approach addresses the individual's genetic and genomic pathography, and tackles the illness with specific and effective treatments., Competing Interests: The authors have no conflicts of interest to disclose., (2022, International Research and Cooperation Association for Bio & Socio - Sciences Advancement.)