Knowledge-based approaches to drug discovery for rare diseases

Alves, Vinicius M.; Korn, Daniel; Pervitsky, Vera; Thieme, Andrew; Capuzzi, Stephen J.; Baker, Nancy C.; Chirkova, Rada; Ekins, Sean; Muratov, Eugene; Hickey, Anthony; Tropsha, Alexander

doi:10.1016/j.drudis.2021.10.014

Cited by 27 publications

(19 citation statements)

References 103 publications

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“…Traylor et al (115) compared the differentially expressed genes (DEGs) of chordoma tissue samples with pharmacogenomic interactions in the Comparative Toxicogenomics Database using a drug repositioning platform named ksRepo. Alves et al (102) reported a case of knowledge-based approach for drug repurposing with chordoma by building a connection between metformin and chordoma using the ROBOKOP platform. Their results suggested a potential treatment value by targeting osteoblast differentiation.…”

Section: Multidimensional Drug Discovery Strategies For Chordomamentioning

confidence: 99%

“…The main obstacles for the identification of novel therapeutic avenues include the extremely low incidence of morbidity and the lack of appropriate models for preclinical research. Conventional drug discovery pipeline is long-lasting and is not cost-effective for rare diseases such as chordoma, for which new strategies are required ( 102 ).…”

Section: New Era: Modern Personalized Precision Drug Treatmentmentioning

confidence: 99%

“…It offers an opportunity for secondary analysis of vetted therapies with optimized pharmacokinetics and acceptable toxicity profiles for candidates with rare diseases in clinical trials ( 110 ). A deep learning strategy featured with combination and comprehensive analyses of gene expression features, signaling pathways, clinical prognoses, and pharmacochemical characteristics has promoted the implementation of precision medicine and our understanding and management of rare diseases including chordoma ( 102 ). Integrative mining approaches include ksRepo processing, Swanson’s ABC approach, Chemotext, and ROBOKOP (Reasoning Over Biomedical Objects linked Knowledge-Oriented Pathways) ( 111 – 114 ).…”

Section: New Era: Modern Personalized Precision Drug Treatmentmentioning

confidence: 99%

“…Alves et al. ( 102 ) reported a case of knowledge-based approach for drug repurposing with chordoma by building a connection between metformin and chordoma using the ROBOKOP platform. Their results suggested a potential treatment value by targeting osteoblast differentiation.…”

Section: New Era: Modern Personalized Precision Drug Treatmentmentioning

confidence: 99%

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

et al. 2022

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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.

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Section: Multidimensional Drug Discovery Strategies For Chordomamentioning

confidence: 99%

Section: New Era: Modern Personalized Precision Drug Treatmentmentioning

confidence: 99%

Section: New Era: Modern Personalized Precision Drug Treatmentmentioning

confidence: 99%

Section: New Era: Modern Personalized Precision Drug Treatmentmentioning

confidence: 99%

See 2 more Smart Citations

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

et al. 2022

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“…Thus, the prognosis of patients may change depending on various genetic and environmental factors, but it is complicated to arrive at standards of care for treatment and rehabilitation because health research is necessarily conducted on a small scale and cannot be based on evidence or experience [ 14 ]. Conventionally, it takes 10 to 15 years to bring a drug to market, with an average R&D cost of $2.6 billion [ 15 ]. These two factors represent a bottleneck in the drug discovery pipeline for rare disorders, as research costs are high while revenues are low due to the small number of patients.…”

Section: Introductionmentioning

confidence: 99%

The Impact of Artificial Intelligence in the Odyssey of Rare Diseases

et al. 2023

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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.

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Applications of artificial intelligence in clinical laboratory genomics

Aradhya

Facio

Metz

et al. 2023

American J of Med Genetics Pt C

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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.

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Knowledge-based approaches to drug discovery for rare diseases

Cited by 27 publications

References 103 publications

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

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

The Impact of Artificial Intelligence in the Odyssey of Rare Diseases

Applications of artificial intelligence in clinical laboratory genomics

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