Synergistic drug combinations from electronic health records and gene expression.

ObjectiveUsing electronic health records (EHRs) and biomolecular data, we sought to discover drug pairs with synergistic repurposing potential. EHRs provide real-world treatment and outcome patterns, while complementary biomolecular data, including disease-specific gene expression and drug-protein interactions, provide mechanistic understanding.MethodWe applied Group Lasso INTERaction NETwork (glinternet), an overlap group lasso penalty on a logistic regression model, with pairwise interactions to identify variables and interacting drug pairs associated with reduced 5-year mortality using EHRs of 9945 breast cancer patients. We identified differentially expressed genes from 14 case-control human breast cancer gene expression datasets and integrated them with drug-protein networks. Drugs in the network were scored according to their association with breast cancer individually or in pairs. Lastly, we determined whether synergistic drug pairs found in the EHRs were enriched among synergistic drug pairs from gene-expression data using a method similar to gene set enrichment analysis.ResultsFrom EHRs, we discovered 3 drug-class pairs associated with lower mortality: anti-inflammatories and hormone antagonists, anti-inflammatories and lipid modifiers, and lipid modifiers and obstructive airway drugs. The first 2 pairs were also enriched among pairs discovered using gene expression data and are supported by molecular interactions in drug-protein networks and preclinical and epidemiologic evidence.ConclusionsThis is a proof-of-concept study demonstrating that a combination of complementary data sources, such as EHRs and gene expression, can corroborate discoveries and provide mechanistic insight into drug synergism for repurposing

Pre-Clinical Tools for Predicting Drug Efficacy in Treatment of Tuberculosis

Combination therapy has, to some extent, been successful in limiting the emergence of drug-resistant tuberculosis. Drug combinations achieve this advantage by simultaneously acting on different targets and metabolic pathways. Additionally, drug combination therapies are shown to shorten the duration of therapy for tuberculosis. As new drugs are being developed, to overcome the challenge of finding new and effective drug combinations, systems biology commonly uses approaches that analyse mycobacterial cellular processes. These approaches identify the regulatory networks, metabolic pathways, and signaling programs associated with M. tuberculosis infection and survival. Different preclinical models that assess anti-tuberculosis drug activity are available, but the combination of models that is most predictive of clinical treatment efficacy remains unclear. In this structured literature review, we appraise the options to accelerate the TB drug development pipeline through the evaluation of preclinical testing assays of drug combinations

The landscape of the methodology in drug repurposing using human genomic data:a systematic review

The process of drug development is expensive and time-consuming. In contrast, drug repurposing can be introduced to clinical practice more quickly and at a reduced cost. Over the last decade, there has been a significant expansion of large biobanks that link genomic data to electronic health record (EHR) data, public availability of various databases containing biological and clinical information, and rapid development of novel methodologies and algorithms in integrating different sources of data. This review aims to provide a thorough summary of different strategies that utilize genomic data to seek drug-repositioning opportunities. We searched MEDLINE and EMBASE databases to identify eligible studies up until 1st May 2023, with a total of 102 studies finally included after two-step parallel screening. We summarized commonly used strategies for drug repurposing, including Mendelian randomization, multi-omic-based and network-based studies, and illustrated each strategy with examples, as well as the data sources implemented. By leveraging existing knowledge and infrastructure to expedite the drug discovery process and reduce costs, drug repurposing potentially identifies new therapeutic uses for approved drugs in a more efficient and targeted manner. However, technical challenges when integrating different types of data and biased or incomplete understanding of drug interactions are important hindrances that cannot be disregarded in the pursuit of identifying novel therapeutic applications. This review offers an overview of drug repurposing methodologies, providing valuable insights and guiding future directions for advancing drug repurposing studies

Honey combination therapies for skin and wound infections: A systematic review of the literature

Replaced AM with VoR 2020-11-24.Topical application of medical grade honey is recommended for the clinical management of wound infections. The suitability of honey as a wound healing agent is largely due to its antibacterial activity, immune modulatory properties, and biocompatibility. Despite the usefulness of honey in wound healing, chronic wound infections continue to be a global problem requiring new and improved therapeutic interventions. Several recent studies have investigated the effects of combining honey with other therapies or agents with the aim of finding more efficacious treatments. In this systematic review, the database PubMed was used to carry out a search of the scientific literature on the combined effects of honey and other therapies on antimicrobial activity and wound and skin healing. The search revealed that synergistic or additive antimicrobial effects were observed in vitro when honey was combined with antibiotics, bacteriophages, antimicrobial peptides, natural agents e.g. ginger or propolis and other treatment approaches such as the use of chitosan hydrogel. Outcomes depended on the type of honey, the combining agent or treatment and the microbial species or strain. Improved wound healing was also observed in vivo in mice when honey was combined with laser therapy or bacteriophage therapy. More clinical studies in humans are required to fully understand the effectiveness of honey combination therapies for the treatment of skin and wound infections.https://doi.org/10.2147/CCID.S28214313pubpu

Advancing Biomedicine with Graph Representation Learning: Recent Progress, Challenges, and Future Directions

Graph representation learning (GRL) has emerged as a pivotal field that has contributed significantly to breakthroughs in various fields, including biomedicine. The objective of this survey is to review the latest advancements in GRL methods and their applications in the biomedical field. We also highlight key challenges currently faced by GRL and outline potential directions for future research.Comment: Accepted by 2023 IMIA Yearbook of Medical Informatic

Network and systems medicine: Position paper of the European Collaboration on Science and Technology action on Open Multiscale Systems Medicine

Introduction: Network and systems medicine has rapidly evolved over the past decade, thanks to computational and integrative tools, which stem in part from systems biology. However, major challenges and hurdles are still present regarding validation and translation into clinical application and decision making for precision medicine. Methods: In this context, the Collaboration on Science and Technology Action on Open Multiscale Systems Medicine (OpenMultiMed) reviewed the available advanced technologies for multidimensional data generation and integration in an open-science approach as well as key clinical applications of network and systems medicine and the main issues and opportunities for the future. Results: The development of multi-omic approaches as well as new digital tools provides a unique opportunity to explore complex biological systems and networks at different scales. Moreover, the application of findable, applicable, interoperable, and reusable principles and the adoption of standards increases data availability and sharing for multiscale integration and interpretation. These innovations have led to the first clinical applications of network and systems medicine, particularly in the field of personalized therapy and drug dosing. Enlarging network and systems medicine application would now imply to increase patient engagement and health care providers as well as to educate the novel generations of medical doctors and biomedical researchers to shift the current organ- and symptom-based medical concepts toward network- and systems-based ones for more precise diagnoses, interventions, and ideally prevention. Conclusion: In this dynamic setting, the health care system will also have to evolve, if not revolutionize, in terms of organization and management

A Perspective Review of Cancer Therapy (Part II): Adoptive Cell Transfer, Metabolic Therapy, and Artificial Intelligence

In recent years, there has been significant advancement in cancer therapy, with the emergence of novel and inventive methods that provide hope to patients. An advanced approach is adoptive cell transfer, which utilizes the immune system’s potential by modifying T cells to identify and eliminate cancer cells. The implementation of this individualized method has demonstrated encouraging effects across different cancer types, resulting in enhanced prognoses for numerous individuals. Metabolic therapy has emerged as a possible treatment technique, alongside adoptive cell transfer. This therapeutic method seeks to interrupt the growth and survival of cancer cells by specifically targeting their abnormal metabolism. Moreover, artificial intelligence (AI) is transforming cancer care by assisting in the identification of diseases, forecasting the likely course of illness, and devising treatment strategies. Artificial intelligence algorithms employ extensive data analysis to identify patterns and anomalies that may not be readily apparent to human specialists in isolation. Oncologists can enhance patient outcomes and reduce unwanted effects by integrating adoptive cell transfer, metabolic therapy, and AI technologies to provide personalized treatments