Knowledge-based approaches to drug discovery for rare diseases

Drug likeliness, pharmacokinetics profiling and efficacy of Polyscias fulva bioactive compounds in the management of uterine fibroids; An integrative in silico and in vivo approach

Polyscias fulva is traditionally used in Uganda for the management of Uterine fibroids (UF). However, there is paucity of data regarding its efficacy, biological targets and potential mechanisms of action hence prompting scientific validation process through insilico and invivo approaches. In this study, we utilized network pharmacology, molecular docking, molecular dynamic simulations and invivo assays to investigate the drug likeliness, pharmacokinetics and efficacy of Polyscias fulva against Uterine fibroids. Four Polyscias fulva bioactive compounds; pinoresinol, lichexanthone, methyl atarate, β-sitosterol exhibited drug likeness properties with moderate safety profiles. Forty-eight (48) uterine fibroid targets were identified as potential targets for the eleven Polyscias fulva compounds. Protein-protein interaction (PPI) analysis revealed four key targets (HIF1A, ESR1, EGFR, and CASP3). The KEGG pathway and GO enrichment analyses revealed that these key targets play significant roles in regulating the positive regulation of cyclin-dependent protein serine/threonine kinase activity, positive regulation of nitric-oxide synthase activity and positive regulation of transcription, DNA-templated. β-sitosterol demonstrated the strongest binding affinity with the four targets, showing particularly strong affinities for EGFR (-9.75 kcal/mol) and HIF1A (-9.21 kcal/mol). Molecular dynamics (MD) simulations revealed high stability in these protein-ligand complexes, with CASP3 displaying the lowest deviation and most consistent RMSD (0.14 nm) of the protein, followed by EGFR (0.25), HIF1A (0.29), and ESR1 (0.79). In-vivo evaluation on female Wistar rats with Polyscias fulva ethanolic extract showed an ameliorative effect of the extracts against monosodium glutamate-induced (MSG) UF. Treated animals exhibited a decrease in serum proteins, cholesterol, estrogen, and progesterone levels (P < 0.05) and the extract preserved uterine tissue histoachitecture as compared to controls. In conclusion, Polyscias fulva demonstrates potential ameliorative activity against UF with promising pharmacokinetic properties and safety profiles.

Harnessing Artificial Intelligence in Pediatric Oncology Diagnosis and Treatment: A Review

Artificial intelligence (AI) is rapidly transforming pediatric oncology by creating new means to improve the accuracy and efficacy of cancer diagnosis and treatment in children. This review critically examines current applications of AI technologies like machine learning (ML) and deep learning (DL) to the main types of pediatric cancers. However, the application of AI to pediatric oncology is prone to certain challenges, including the heterogeneity and rarity of pediatric cancer data, rapid technological development in imaging, and ethical concerns pertaining to data privacy and algorithmic transparency. Collaborative efforts and data-sharing schemes are important to surpass these challenges and facilitate effective training of AI models. This review also points to emerging trends, including AI-based radiomics and proteomics applications, and provides future directions to realize the full potential of AI in pediatric oncology. Finally, AI is a promising paradigm shift toward precision medicine in childhood cancer treatment, which can enhance the survival rates and quality of life for pediatric patients.

AI-Driven Drug Discovery for Rare Diseases

Rare diseases (RDs), affecting 300 million people globally, present a daunting public health challenge characterized by complexity, limited treatment options, and diagnostic hurdles. Despite legislative efforts, such as the 1983 US Orphan Drug Act, more than 90% of RDs lack effective therapies. Traditional drug discovery models, marked by lengthy development cycles and high failure rates, struggle to meet the unique demands of RDs, often yielding poor returns on investment. However, the advent of artificial intelligence (AI), encompassing machine learning (ML) and deep learning (DL), offers groundbreaking solutions. This review explores AI's potential to revolutionize drug discovery for RDs by overcoming these challenges. It discusses AI-driven advancements, such as drug repurposing, biomarker discovery, personalized medicine, genetics, clinical trial optimization, corporate innovations, and novel drug target identification. By synthesizing current knowledge and recent breakthroughs, this review provides crucial insights into how AI can accelerate therapeutic development for RDs, ultimately improving patient outcomes. This comprehensive analysis fills a critical gap in the literature, enhancing understanding of AI's pivotal role in transforming RD research and guiding future research and development efforts in this vital area of medicine.

AI-enabled language models (LMs) to large language models (LLMs) and multimodal large language models (MLLMs) in drug discovery and development

BACKGROUND

Due to the recent revolution of artificial intelligence (AI), AI-enabled large language models (LLMs) have flourished and started to be applied in various sectors of science and medicine. Drug discovery and development are time-consuming, complex processes that require high investment. The conventional method of drug discovery is costly and has a high failure rate. AI-enabled LLMs are used in various steps of drug discovery to solve the challenges of time and cost.

AIM OF REVIEW

The article aims to provide a comprehensive understanding of AI-enabled LLMs and their use in various steps of drug discovery to ease the challenges.

KEY SCIENTIFIC CONCEPTS OF REVIEW

The review provides an overview of the LLMs and their current state-of-the-art application in structure-based drug molecule design and de novo drug design. The different applications of AI-enabled LLMshave been illustrated, such as drug target identification, validation, interaction, and ADME/ADMET. Several domain-specific models of LLMs are developed in this direction and applied in drug discovery and development to speed up the process. We discussed all these domain-specific models of LLMs and their applications in this field. Finally, we illustrated the challenges and future perspectives on the applications of AI-enabled LLMs in drug discovery and development.

An Optimised Mobilenet V2 Attention Parallel Network for Predicting Drug-Drug Interactions Through Combining Local and Global Features

Drug-drug interactions (DDIs) are an important concern in the clinical practice and drug development process as these may lead to serious adverse effects on patient safety. Thorough DDI prediction is important for effective medication management and reduced risk factors. This work presents a new technique, namely MV2SAPCNNO: MobileNetV2 with simplicial attention network-based parallel convolutional neural network and narwhal optimiser, for improving the precision of DDI prediction. The proposed method starts with data preprocessing, including normalisation and noise reduction, to enhance the quality of the data. Then, MobileNetV2 with simplicial attention network (MV2SAN) is used to extract both local and global features from the dataset. These features are processed using a parallel convolutional neural network (PCNN), optimised by the narwhal optimiser (NO) to improve parameter tuning, minimise error and reduce computational complexity. The performance of the model is evaluated using accuracy, precision, recall and F-score. Experimental results prove that MV2SAPCN-NO achieves better performance over the current models of DDI prediction in accuracy and enhanced classification metrics. The narwhal optimiser enhances the model's convergence efficiency and decreases computational time with an excellent predictive performance. An efficient and accurate DDI prediction model was proposed called MV2SAPCNNO. This model actually outperformed traditional models, and such findings were exhibited to contribute towards secure medication administration, drug development processes and protection of patients in clinical practice.

Petagraph: A large-scale unifying knowledge graph framework for integrating biomolecular and biomedical data

Over the past decade, there has been substantial growth in both the quantity and complexity of available biomedical data. In order to more efficiently harness this extensive data and alleviate challenges associated with integration of multi-omics data, we developed Petagraph, a biomedical knowledge graph that encompasses over 32 million nodes and 118 million relationships. Petagraph leverages more than 180 ontologies and standards in the Unified Biomedical Knowledge Graph (UBKG) to embed millions of quantitative genomics data points. Petagraph provides a cohesive data environment that enables users to efficiently analyze, annotate, and discern relationships within and across complex multi-omics datasets supported by UBKG's annotation scaffold. We demonstrate how queries on Petagraph can generate meaningful results across various research contexts and use cases.

Dona Flor and her two husbands: Discovery of novel HDAC6/AKT2 inhibitors for myeloid cancer treatment

Hematological cancer treatment with hybrid kinase/HDAC inhibitors is a novel strategy to overcome the challenge of acquired resistance to drugs. We collected IC 50 datasets from the ChEMBL database for 13 cancer cell lines (72 h cytotoxicity, measured by MTT), known inhibitors for 38 kinases, and 10 HDACs isoforms, that we identified by target fishing and literature review. The data was subjected to rigorous biological and chemical curation leaving the final datasets ranging from 76 to 8173 compounds depending on the target. We generated Random Forest classification models, whereby 14 showed greater than 80% predictability after 5-fold external cross-validation. We screened 30 hybrid kinase/HDAC inhibitor analogs through each of these models. Fragment-contribution maps were constructed to aid the understanding of SARs and the optimization of these compounds as selective kinase/HDAC inhibitors for cancer treatment. Among the predicted compounds, 9 representative hybrids were synthesized and subjected to biological evaluation to validate the models. We observed high hit rates after biological testing for the following models: K562 (62.5%), MV4-11 (75.0%), MM1S (100%), NB-4 (62.5%), U937 (75.0), and HDAC6 (86.0%). This aided the identification of 6b and 6k as potent anticancer inhibitors with IC 50 of 0.2-0.8 µM in three cancer cell lines, linked to HDAC6 inhibition below 2 nM, and blockade of AKT2 phosphorylation at 2 μM, validating the ability of our models to predict novel drug candidates.

Highlights

Novel kinase/HDAC inhibitors for cancer treatment were found using machine learning61 QSAR models for hematological cancers and its targets were built and validatedK562, MV4-11, MM1S, NB-4, U937, and HDAC6 models had hit rates above 62.5% in tests 6b and 6k presented potent IC 50 of 0.2-0.8 µM in three cancer cell lines 6b and 6k inhibited HDAC6 below 2 nM, and blockade of AKT2 phosphorylation at 2 μM.

Patient-Reported Experience Measurements From Individuals With Inherited Retinal Disorders Involved in Observational Research

Purpose

Inherited retinal disorders (IRD) are a complex group of conditions. By developing the first patient-reported experience measurement (PREM) questionnaire tailored for individuals with IRD participating in natural history studies, we gathered information on individuals' views of their experience while they are involved in research.

Methods

Adults with IRD who (i) were enrolled in a natural history study taking place at Moorfields Eye Hospital (London, UK), (ii) had attended at least two study visits, (iii) the most recent one being less than two weeks before the questionnaire, and (iv) who were not involved in interventional research, were considered for participation.

Results

Fifty individuals completed the PREM questionnaire at a mean age of 31.1 ± 11 years old and were diagnosed at a mean age of 14 ± 9.7 years old. Most individuals rated "getting closer to receiving treatment' as their main motivation to enroll in the study, and their biggest influence was their own curiosity. Individuals were more satisfied with the care they received, and least satisfied with the efficiency of the visit. After validity and reliability assessments, the final PREM was created, with 27 questions and five sections, and Cronbach alpha coefficient between 0.316 and 0.756 in each section.

Conclusions

The PREM instrument allowed us to assess the overall satisfaction of individuals with IRD involved in research, detect possible barriers to research participation, and ways of improving our care.

Translational Relevance

The final version can be included in future research and other sites worldwide, to maintain high quality standards.

Preclinical alternative drug discovery programs for monogenic rare diseases. Should small molecules or gene therapy be used? The case of hereditary spastic paraplegias

Patients diagnosed with rare diseases and their and families search desperately to organize drug discovery campaigns. Alternative models that differ from default paradigms offer real opportunities. There are, however, no clear guidelines for the development of such models, which reduces success rates and raises costs. We address the main challenges in making the discovery of new preclinical treatments more accessible, using rare hereditary paraplegia as a paradigmatic case. First, we discuss the necessary expertise, and the patients' clinical and genetic data. Then, we revisit gene therapy, de novo drug development, and drug repurposing, discussing their applicability. Moreover, we explore a pool of recommended in silico tools for pathogenic variant and protein structure prediction, virtual screening, and experimental validation methods, discussing their strengths and weaknesses. Finally, we focus on successful case applications.

The impact and future of artificial intelligence in medical genetics and molecular medicine: an ongoing revolution

Artificial intelligence (AI) platforms have emerged as pivotal tools in genetics and molecular medicine, as in many other fields. The growth in patient data, identification of new diseases and phenotypes, discovery of new intracellular pathways, availability of greater sets of omics data, and the need to continuously analyse them have led to the development of new AI platforms. AI continues to weave its way into the fabric of genetics with the potential to unlock new discoveries and enhance patient care. This technology is setting the stage for breakthroughs across various domains, including dysmorphology, rare hereditary diseases, cancers, clinical microbiomics, the investigation of zoonotic diseases, omics studies in all medical disciplines. AI's role in facilitating a deeper understanding of these areas heralds a new era of personalised medicine, where treatments and diagnoses are tailored to the individual's molecular features, offering a more precise approach to combating genetic or acquired disorders. The significance of these AI platforms is growing as they assist healthcare professionals in the diagnostic and treatment processes, marking a pivotal shift towards more informed, efficient, and effective medical practice. In this review, we will explore the range of AI tools available and show how they have become vital in various sectors of genomic research supporting clinical decisions.

Rare diseases and pyruvate kinase M2: a promising therapeutic connection

Pyruvate kinase M2 (PKM2) is a key glycolytic enzyme that regulates proliferating cell metabolism. The role of PKM2 in common diseases has been well established, but its role in rare diseases (RDs) is less understood. Over the past few years, PKM2 has emerged as a crucial player in RDs, including, neoplastic, respiratory, metabolic, and neurological disorders. Herein, we summarize recent findings and developments highlighting PKM2 as an emerging key player in RDs. We also discuss the current status of PKM2 modulation in RDs with particular emphasis on preclinical and clinical studies in addition to current challenges in the field.

Computational biology approaches for drug repurposing

The drug discovery and development (DDD) process greatly relies on the data available in various forms to generate hypotheses for novel drug design. The complex and heterogeneous nature of biological data makes it difficult to utilize or gather meaningful information as such. Computational biology techniques have provided us with opportunities to better understand biological systems through refining and organizing large amounts of data into actionable and systematic purviews. The drug repurposing approach has been utilized to overcome the expansive time periods and costs associated with traditional drug development. It deals with discovering new uses of already approved drugs that have an established safety and efficacy profile, thereby, requiring them to go through fewer development phases. Thus, drug repurposing through computational biology provides a systematic approach to drug development and overcomes the constraints of traditional processes. The current chapter covers the basics, approaches and tools of computational biology that can be employed to effectively develop repurposing profile of already approved drug molecules.

Potential of Artificial Intelligence to Accelerate Drug Development for Rare Diseases

The growth in breadth and depth of artificial intelligence (AI) applications has been fast, running hand in hand with the increasing amount of digital data available. Here, we comment on the application of AI in the field of drug development, with a strong focus on the specific achievements and challenges posed by rare diseases. Data paucity and high costs make drug development for rare diseases especially hard. AI can enable otherwise inaccessible approaches based on the large-scale integration of heterogeneous datasets and knowledge bases, guided by expert biological understanding. Obstacles still exist for the routine use of AI in the usually conservative pharmaceutical domain, which can easily become disillusioned. It is crucial to acknowledge that AI is a powerful, supportive tool that can assist but not replace human expertise in the various phases and aspects of drug discovery and development.

Rare and complex diseases in focus: ChatGPT's role in improving diagnosis and treatment

Rare and complex diseases pose significant challenges to both patients and healthcare providers. These conditions often present with atypical symptoms, making diagnosis and treatment a formidable task. In recent years, artificial intelligence and natural language processing technologies have shown great promise in assisting medical professionals in diagnosing and managing such conditions. This paper explores the role of ChatGPT, an advanced artificial intelligence model, in improving the diagnosis and treatment of rare and complex diseases. By analyzing its potential applications, limitations, and ethical considerations, we demonstrate how ChatGPT can contribute to better patient outcomes and enhance the healthcare system's overall effectiveness.

Biomedical Big Data Technologies, Applications, and Challenges for Precision Medicine: A Review

The explosive growth of biomedical Big Data presents both significant opportunities and challenges in the realm of knowledge discovery and translational applications within precision medicine. Efficient management, analysis, and interpretation of big data can pave the way for groundbreaking advancements in precision medicine. However, the unprecedented strides in the automated collection of large-scale molecular and clinical data have also introduced formidable challenges in terms of data analysis and interpretation, necessitating the development of novel computational approaches. Some potential challenges include the curse of dimensionality, data heterogeneity, missing data, class imbalance, and scalability issues. This overview article focuses on the recent progress and breakthroughs in the application of big data within precision medicine. Key aspects are summarized, including content, data sources, technologies, tools, challenges, and existing gaps. Nine fields-Datawarehouse and data management, electronic medical record, biomedical imaging informatics, Artificial intelligence-aided surgical design and surgery optimization, omics data, health monitoring data, knowledge graph, public health informatics, and security and privacy-are discussed.

Artificial intelligence in rare disease diagnosis and treatment

Artificial intelligence (AI) utilization in health care has grown over the past few years. It also has demonstrated potential in improving the efficiency of diagnosis and treatment. Some types of AI, such as machine learning, allow for the efficient analysis of vast datasets, identifying patterns, and generating key insights. Predictions can then be made for medical diagnosis and personalized treatment recommendations. The use of AI can bypass some conventional limitations associated with rare diseases. Namely, it can optimize traditional randomized control trials, and may eventually reduce costs for drug research and development. Recent advancements have enabled researchers to train models based on large datasets and then fine-tune these models on smaller datasets typically associated with rare diseases. In this mini-review, we discuss recent advancements in AI and how AI can be applied to streamline rare disease diagnosis and optimize treatment.

The Impact of Artificial Intelligence on Optimizing Diagnosis and Treatment Plans for Rare Genetic Disorders

Rare genetic disorders (RDs), characterized by their low prevalence and diagnostic complexities, present significant challenges to healthcare systems. This article explores the transformative impact of artificial intelligence (AI) and machine learning (ML) in addressing these challenges. It emphasizes the need for accurate and early diagnosis of RDs, often hindered by genetic and clinical heterogeneity. This article discusses how AI and ML are reshaping healthcare, providing examples of their effectiveness in disease diagnosis, prognosis, image analysis, and drug repurposing. It highlights AI's ability to efficiently analyze extensive datasets and expedite diagnosis, showcasing case studies like Face2Gene. Furthermore, the article explores how AI tailors treatment plans for RDs, leveraging ML and deep learning (DL) to create personalized therapeutic regimens. It emphasizes AI's role in drug discovery, including the identification of potential candidates for rare disease treatments. Challenges and limitations related to AI in healthcare, including ethical, legal, technical, and human aspects, are addressed. This article underscores the importance of data ethics, privacy, and algorithmic fairness, as well as the need for standardized evaluation techniques and transparency in AI research. It highlights second-generation AI systems that prioritize patient-centric care, efficient patient recruitment for clinical trials, and the significance of high-quality data. The integration of AI with telemedicine, the growth of health databases, and the potential for personalized therapeutic recommendations are identified as promising directions for the field. In summary, this article provides a comprehensive exploration of how AI and ML are revolutionizing the diagnosis and treatment of RDs, addressing challenges while considering ethical implications in this rapidly evolving healthcare landscape.

RDKG-115: Assisting drug repurposing and discovery for rare diseases by trimodal knowledge graph embedding

Rare diseases (RDs) may affect individuals in small numbers, but they have a significant impact on a global scale. Accurate diagnosis of RDs is challenging, and there is a severe lack of drugs available for treatment. Pharmaceutical companies have shown a preference for drug repurposing from existing drugs developed for other diseases due to the high investment, high risk, and long cycle involved in RD drug development. Compared to traditional approaches, knowledge graph embedding (KGE) based methods are more efficient and convenient, as they treat drug repurposing as a link prediction task. KGE models allow for the enrichment of existing knowledge by incorporating multimodal information from various sources. In this study, we constructed RDKG-115, a rare disease knowledge graph involving 115 RDs, composed of 35,643 entities, 25 relations, and 5,539,839 refined triplets, based on 372,384 high-quality literature and 4 biomedical datasets: DRKG, Pathway Commons, PharmKG, and PMapp. Subsequently, we developed a trimodal KGE model containing structure, category, and description embeddings using reverse-hyperplane projection. We utilized this model to infer 4199 reliable new inferred triplets from RDKG-115. Finally, we calculated potential drugs and small molecules for each of the 115 RDs, taking multiple sclerosis as a case study. This study provides a paradigm for large-scale screening of drug repurposing and discovery for RDs, which will speed up the drug development process and ultimately benefit patients with RDs. The source code and data are available at https://github.com/ZhuChaoY/RDKG-115.

Applications of artificial intelligence in clinical laboratory genomics

The transition from analog to digital technologies in clinical laboratory genomics is ushering in an era of "big data" in ways that will exceed human capacity to rapidly and reproducibly analyze those data using conventional approaches. Accurately evaluating complex molecular data to facilitate timely diagnosis and management of genomic disorders will require supportive artificial intelligence methods. These are already being introduced into clinical laboratory genomics to identify variants in DNA sequencing data, predict the effects of DNA variants on protein structure and function to inform clinical interpretation of pathogenicity, link phenotype ontologies to genetic variants identified through exome or genome sequencing to help clinicians reach diagnostic answers faster, correlate genomic data with tumor staging and treatment approaches, utilize natural language processing to identify critical published medical literature during analysis of genomic data, and use interactive chatbots to identify individuals who qualify for genetic testing or to provide pre-test and post-test education. With careful and ethical development and validation of artificial intelligence for clinical laboratory genomics, these advances are expected to significantly enhance the abilities of geneticists to translate complex data into clearly synthesized information for clinicians to use in managing the care of their patients at scale.

The Impact of Artificial Intelligence in the Odyssey of Rare Diseases

Emerging machine learning (ML) technologies have the potential to significantly improve the research and treatment of rare diseases, which constitute a vast set of diseases that affect a small proportion of the total population. Artificial Intelligence (AI) algorithms can help to quickly identify patterns and associations that would be difficult or impossible for human analysts to detect. Predictive modeling techniques, such as deep learning, have been used to forecast the progression of rare diseases, enabling the development of more targeted treatments. Moreover, AI has also shown promise in the field of drug development for rare diseases with the identification of subpopulations of patients who may be most likely to respond to a particular drug. This review aims to highlight the achievements of AI algorithms in the study of rare diseases in the past decade and advise researchers on which methods have proven to be most effective. The review will focus on specific rare diseases, as defined by a prevalence rate that does not exceed 1-9/100,000 on Orphanet, and will examine which AI methods have been most successful in their study. We believe this review can guide clinicians and researchers in the successful application of ML in rare diseases.

DeepmRNALoc: A Novel Predictor of Eukaryotic mRNA Subcellular Localization Based on Deep Learning

The subcellular localization of messenger RNA (mRNA) precisely controls where protein products are synthesized and where they function. However, obtaining an mRNA's subcellular localization through wet-lab experiments is time-consuming and expensive, and many existing mRNA subcellular localization prediction algorithms need to be improved. In this study, a deep neural network-based eukaryotic mRNA subcellular location prediction method, DeepmRNALoc, was proposed, utilizing a two-stage feature extraction strategy that featured bimodal information splitting and fusing for the first stage and a VGGNet-like CNN module for the second stage. The five-fold cross-validation accuracies of DeepmRNALoc in the cytoplasm, endoplasmic reticulum, extracellular region, mitochondria, and nucleus were 0.895, 0.594, 0.308, 0.944, and 0.865, respectively, demonstrating that it outperforms existing models and techniques.

A chronicle review of new techniques that facilitate the understanding and development of optimal individualized therapeutic strategies for chordoma

Chordoma is a rare malignant bone tumor that mainly occurs in the sacrum and the clivus/skull base. Surgical resection is the treatment of choice for chordoma, but the local recurrence rate is high with unsatisfactory prognosis. Compared with other common tumors, there is not much research and individualized treatment for chordoma, partly due to the rarity of the disease and the lack of appropriate disease models, which delay the discovery of therapeutic strategies. Recent advances in modern techniques have enabled gaining a better understanding of a number of rare diseases, including chordoma. Since the beginning of the 21st century, various chordoma cell lines and animal models have been reported, which have partially revealed the intrinsic mechanisms of tumor initiation and progression with the use of next-generation sequencing (NGS) techniques. In this study, we performed a systematic overview of the chordoma models and related sequencing studies in a chronological manner, from the first patient-derived chordoma cell line (U-CH1) to diverse preclinical models such as the patient-derived organoid-based xenograft (PDX) and patient-derived organoid (PDO) models. The use of modern sequencing techniques has discovered mutations and expression signatures that are considered potential treatment targets, such as the expression of Brachyury and overactivated receptor tyrosine kinases (RTKs). Moreover, computational and bioinformatics techniques have made drug repositioning/repurposing and individualized high-throughput drug screening available. These advantages facilitate the research and development of comprehensive and personalized treatment strategies for indicated patients and will dramatically improve their prognoses in the near feature.

Defining clinical outcome pathways

Here, we propose a broad concept of 'Clinical Outcome Pathways' (COPs), which are defined as a series of key molecular and cellular events that underlie therapeutic effects of drug molecules. We formalize COPs as a chain of the following events: molecular initiating event (MIE) → intermediate event(s) → clinical outcome. We illustrate the concept with COP examples both for primary and alternative (i.e., drug repurposing) therapeutic applications. We also describe the elucidation of COPs for several drugs of interest using the publicly accessible Reasoning Over Biomedical Objects linked in Knowledge-Oriented Pathways (ROBOKOP) biomedical knowledge graph-mining tool. We propose that broader use of COP uncovered with the help of biomedical knowledge graph mining will likely accelerate drug discovery and repurposing efforts.