The precision medicine process for treating rare disease using the artificial intelligence tool mediKanren

Transforming Physiology and Healthcare through Foundation Models

Recent developments in artificial intelligence (AI) may significantly alter physiological research and healthcare delivery. Whereas AI applications in medicine have historically been trained for specific tasks, recent technological advances have produced models trained on more diverse datasets with much higher parameter counts. These new, "foundation" models raise the possibility that more flexible AI tools can be applied to a wider set of healthcare tasks than in the past. This review describes how these newer models differ from conventional task-specific AI, which relies heavily on focused datasets and narrow, specific applications. By examining the integration of AI into diagnostic tools, personalized treatment strategies, biomedical research, and healthcare administration, we highlight how these newer models are revolutionizing predictive healthcare analytics and operational workflows. In addition, we address ethical and practical considerations associated with the use of foundation models by highlighting emerging trends, calling for changes to existing guidelines, and emphasizing the importance of aligning AI with clinical goals to ensure its responsible and effective use.

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.

Omics and rare diseases: challenges, applications, and future perspectives

INTRODUCTION

Rare diseases (RDs) are a heterogeneous group of diseases recognized as a relevant global health priority but posing aspects of complexity, such as geographical scattering of affected individuals, improper/late diagnosis, limited awareness, difficult surveillance and monitoring, limited understanding of natural history, and lack of treatment. Usually, RDs have a pediatric onset and are life-long, multisystemic, and associated with a poor prognosis.

AREAS COVERED

In this work, we review how high-throughput omics technologies such as genomics, transcriptomics, proteomics, metabolomics, epigenomics, and other well-established omics, which are increasingly more affordable and efficient, can be applied to the study of RDs promoting diagnosis, understanding of pathological mechanisms, biomarker discovery, and identification of treatments.

EXPERT OPINION

RDs, despite their challenges, offer a niche where collaborative efforts and personalized treatment strategies might be feasible using omics technologies. Specialized consortia fostering multidisciplinary collaboration, data sharing, and the development of biobanks and registries can be built; multi-omics approaches, including so far less exploited omics technologies, along with the implementation of AI tools can be undertaken to deepen our understanding of RDs, driving biomarker discovery and clinical interventions. Nevertheless, technical, ethical, legal, and societal issues must be clearly defined and addressed.

[Artificial intelligence for the comprehensive approach to orphan/rare diseases: A scoping review]

INTRODUCTION

Orphan diseases (OD) are rare but collectively common, presenting challenges such as late diagnoses, disease progression, and limited therapeutic options. Recently, artificial intelligence (AI) has gained interest in the research of these diseases.

OBJECTIVE

To synthesize the available evidence on the use of AI in the comprehensive approach to orphan diseases.

METHODS

An exploratory systematic review of the Scoping Review type was conducted in PubMed, Bireme, and Scopus from 2019 to 2024.

RESULTS

fifty-six articles were identified, with 21.4% being experimental studies; 28 documents did not specify an OD, 8 documents focused primarily on genetic diseases; 53.57% focused on diagnosis, and 36 different algorithms were identified.

CONCLUSIONS

The information found shows the development of AI algorithms in different clinical settings, confirming the potential benefits in diagnosis times, therapeutic options, and greater awareness among health professionals.

Variants in RHOBTB2 associated with cancer and rare developmental and epileptic encephalopathy

RHOBTB2 is a member of the Rho GTPases subfamily of signaling proteins, known tumor suppressors whose loss of function and decreased expression is associated with cancer onset. Beyond its cancer-related role, RHOBTB2 is implicated in rare neurodevelopmental disorders, specifically RHOBTB2-related disorders, recognized in 2018 as a subtype of developmental and epileptic encephalopathies (DEE). Common symptoms of these disorders include early-onset epilepsy, severe intellectual disability, microcephaly, and movement disorders. Few studies have investigated patient variants associated with RHOBTB2-related disorders, and the impact of these variants on protein function remains unclear. Limited research suggests that the accumulation of RHOBTB2 in neural tissues contributes to the development of DEE. Similarly, preclinical studies indicate that missense variants near or in the BTB domain of RHOBTB2 result in decreased degradation of RHOBTB2 and the onset of DEE, whereas variants in the GTPase domain cause more variable neurodevelopmental symptoms, but do not impair proteasomal degradation of RHOBTB2. However, the exact pathophysiological mechanisms are unclear and may differ across variants. Current treatment approaches for individuals with RHOBTB2-related DEE involve the use of antiseizure medications to decrease seizures; however, no treatments have been identified that address the other symptoms or the underlying pathophysiological mechanisms associated with these disorders. Overall, RHOBTB2 remains an understudied protein with limited information on its function and how it contributes to disease mechanisms. This review provides an overview of the current knowledge of RHOBTB2 function, with an emphasis on its association with neurodevelopmental disorders through an analysis of preclinical studies and case reports that link individual variants with clinical features.

The scienthetic method: from Aristotle to AI and the future of medicine

While AI holds immense potential for accelerating advances in oncology, we must be intentional in developing and applying these technologies responsibly, equitably, and ethically. One path forward is for cancer care providers and researchers to be among the architects of AI and its adoption in medicine. Given the limitations of traditional top-down, hypothesis-driven design in an exponentially expanding data universe, on one hand, and the danger of spiraling into artificial ignorance (ai) from rushing into a purely 'synthetic' method on the other, this article proposes a 'scienthetic' method that synergizes AI with human wisdom. Tracing philosophical underpinnings of the scientific method from Socrates, Plato, and Aristotle to the present, it examines the critical juncture at which AI stands to either augment or undermine new knowledge. The scienthetic method seeks to harness the power and capabilities of AI responsibly, equitably, and ethically to transcend the limitations of both the traditional scientific method and purely synthetic methods, by intentionally weaving machine intelligence together with human wisdom.

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.

Artificial intelligence in drug repurposing for rare diseases: a mini-review

Drug repurposing, the process of identifying new uses for existing drugs beyond their original indications, offers significant advantages in terms of reduced development time and costs, particularly in addressing unmet medical needs in rare diseases. Artificial intelligence (AI) has emerged as a transformative force in healthcare, and by leveraging AI technologies, researchers aim to overcome some of the challenges associated with rare diseases. This review presents concrete case studies, as well as pre-existing platforms, initiatives, and companies that demonstrate the application of AI for drug repurposing in rare diseases. Despite representing a modest part of the literature compared to other diseases such as COVID-19 or cancer, the growing interest, and investment in AI for drug repurposing in rare diseases underscore its potential to accelerate treatment availability for patients with unmet medical needs.

The use of artificial intelligence in the treatment of rare diseases: A scoping review

With the increasing application of artificial intelligence (AI) in medicine and healthcare, AI technologies have the potential to improve the diagnosis, treatment, and prognosis of rare diseases. Presently, existing research predominantly focuses on the areas of diagnosis and prognosis, with relatively fewer studies dedicated to the domain of treatment. The purpose of this review is to systematically analyze the existing literature on the application of AI in the treatment of rare diseases. We searched three databases for related studies, and established criteria for the selection of retrieved articles. From the 407 unique articles identified across the three databases, 13 articles from 8 countries were selected, which investigated 10 different rare diseases. The most frequently studied rare disease group was rare neurologic diseases (n = 5/13, 38.46%). Among the four identified therapeutic domains, 7 articles (53.85%) focused on drug research, with 5 specifically focused on drug discovery (drug repurposing, the discovery of drug targets and small-molecule inhibitors), 1 on pre-clinical studies (drug interactions), and 1 on clinical studies (information strength assessment of clinical parameters). Across the selected 13 articles, we identified total 32 different algorithms, with random forest (RF) being the most commonly used (n = 4/32, 12.50%). The predominant purpose of AI in the treatment of rare diseases in these articles was to enhance the performance of analytical tasks (53.33%). The most common data source was database data (35.29%), with 5 of these studies being in the field of drug research, utilizing classic databases such as RCSB, PDB and NCBI. Additionally, 47.37% of the articles highlighted the existing challenge of data scarcity or small sample sizes.

Exploring NCATS in-house biomedical data for evidence-based drug repurposing

Drug repurposing is a strategy for identifying new uses of approved or investigational drugs that are outside the scope of the original medical indication. Even though many repurposed drugs have been found serendipitously in the past, the increasing availability of large volumes of biomedical data has enabled more systemic, data-driven approaches for drug candidate identification. At National Center of Advancing Translational Sciences (NCATS), we invent new methods to generate new data and information publicly available to spur innovation and scientific discovery. In this study, we aimed to explore and demonstrate biomedical data generated and collected via two NCATS research programs, the Toxicology in the 21st Century program (Tox21) and the Biomedical Data Translator (Translator) for the application of drug repurposing. These two programs provide complementary types of biomedical data from uncovering underlying biological mechanisms with bioassay screening data from Tox21 for chemical clustering, to enrich clustered chemicals with scientific evidence mined from the Translator towards drug repurposing. 129 chemical clusters have been generated and three of them have been further investigated for drug repurposing candidate identification, which is detailed as case studies.

Functional genomics and small molecules in mitochondrial neurodevelopmental disorders

Mitochondria are critical for brain development and homeostasis. Therefore, pathogenic variation in the mitochondrial or nuclear genome which disrupts mitochondrial function frequently results in developmental disorders and neurodegeneration at the organismal level. Large-scale application of genome-wide technologies to individuals with mitochondrial diseases has dramatically accelerated identification of mitochondrial disease-gene associations in humans. Multi-omic and high-throughput studies involving transcriptomics, proteomics, metabolomics, and saturation genome editing are providing deeper insights into the functional consequence of mitochondrial genomic variation. Integration of deep phenotypic and genomic data through allelic series continues to uncover novel mitochondrial functions and permit mitochondrial gene function dissection on an unprecedented scale. Finally, mitochondrial disease-gene associations illuminate disease mechanisms and thereby direct therapeutic strategies involving small molecules and RNA-DNA therapeutics. This review summarizes progress in functional genomics and small molecule therapeutics in mitochondrial neurodevelopmental disorders.

Literature-based predictions of Mendelian disease therapies

In the effort to treat Mendelian disorders, correcting the underlying molecular imbalance may be more effective than symptomatic treatment. Identifying treatments that might accomplish this goal requires extensive and up-to-date knowledge of molecular pathways-including drug-gene and gene-gene relationships. To address this challenge, we present "parsing modifiers via article annotations" (PARMESAN), a computational tool that searches PubMed and PubMed Central for information to assemble these relationships into a central knowledge base. PARMESAN then predicts putatively novel drug-gene relationships, assigning an evidence-based score to each prediction. We compare PARMESAN's drug-gene predictions to all of the drug-gene relationships displayed by the Drug-Gene Interaction Database (DGIdb) and show that higher-scoring relationship predictions are more likely to match the directionality (up- versus down-regulation) indicated by this database. PARMESAN had more than 200,000 drug predictions scoring above 8 (as one example cutoff), for more than 3,700 genes. Among these predicted relationships, 210 were registered in DGIdb and 201 (96%) had matching directionality. This publicly available tool provides an automated way to prioritize drug screens to target the most-promising drugs to test, thereby saving time and resources in the development of therapeutics for genetic disorders.

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.

Exploring NCATS In-House Biomedical Data for Evidence-based Drug Repurposing

Drug repurposing is a strategy for identifying new uses of approved or investigational drugs that are outside the scope of the original medical indication. Even though many repurposed drugs have been found serendipitously in the past, the increasing availability of large volumes of biomedical data has enabled more systemic, data-driven approaches for drug candidate identification. At National Center of Advancing Translational Sciences (NCATS), we invent new methods to generate new data and information publicly available to spur innovation and scientific discovery. In this study, we aimed to explore and demonstrate biomedical data generated and collected via two NCATS research programs, the Toxicology in the 21st Century program (Tox21) and the Biomedical Data Translator (Translator) for the application of drug repurposing. These two programs provide complementary types of biomedical data from uncovering underlying biological mechanisms with bioassay screening data from Tox21 for chemical clustering, to enrich clustered chemicals with scientific evidence mined from the Translator towards drug repurposing. 129 chemical clusters have been generated and three of them have been further investigated for drug repurposing candidate identification, which is detailed as case studies.

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.

Learning channel-selective and aberrance repressed correlation filter with memory model for unmanned aerial vehicle object tracking

To ensure that computers can accomplish specific tasks intelligently and autonomously, it is common to introduce more knowledge into artificial intelligence (AI) technology as prior information, by imitating the structure and mindset of the human brain. Currently, unmanned aerial vehicle (UAV) tracking plays an important role in military and civilian fields. However, robust and accurate UAV tracking remains a demanding task, due to limited computing capability, unanticipated object appearance variations, and a volatile environment. In this paper, inspired by the memory mechanism and cognitive process in the human brain, and considering the computing resources of the platform, a novel tracking method based on Discriminative Correlation Filter (DCF) based trackers and memory model is proposed, by introducing dynamic feature-channel weight and aberrance repressed regularization into the loss function, and by adding an additional historical model retrieval module. Specifically, the feature-channel weight integrated into the spatial regularization (SR) enables the filter to select features. The aberrance repressed regularization provides potential interference information to the tracker and is advantageous in suppressing the aberrances caused by both background clutter and appearance changes of the target. By optimizing the aforementioned two jointly, the proposed tracker could restrain the potential distractors, and train a robust filter simultaneously by focusing on more reliable features. Furthermore, the overall loss function could be optimized with the Alternative Direction Method of Multipliers (ADMM) method, thereby improving the calculation efficiency of the algorithm. Meanwhile, with the historical model retrieval module, the tracker is encouraged to adopt some historical models of past video frames to update the tracker, and it is also incentivized to make full use of the historical information to construct a more reliable target appearance representation. By evaluating the method on two challenging UAV benchmarks, the results prove that this tracker shows superior performance compared with most other advanced tracking algorithms.