High-Content Screening for Cryptosporidium Drug Discovery

Data stewardship and curation practices in AI-based genomics and automated microscopy image analysis for high-throughput screening studies: promoting robust and ethical AI applications

BACKGROUND

Researchers have increasingly adopted AI and next-generation sequencing (NGS), revolutionizing genomics and high-throughput screening (HTS), and transforming our understanding of cellular processes and disease mechanisms. However, these advancements generate vast datasets requiring effective data stewardship and curation practices to maintain data integrity, privacy, and accessibility. This review consolidates existing knowledge on key aspects, including data governance, quality management, privacy measures, ownership, access control, accountability, traceability, curation frameworks, and storage systems.

METHODS

We conducted a systematic literature search up to January 10, 2024, across PubMed, MEDLINE, EMBASE, Scopus, and additional scholarly platforms to examine recent advances and challenges in managing the vast and complex datasets generated by these technologies. Our search strategy employed structured keyword queries focused on four key thematic areas: data governance and management, curation frameworks, algorithmic bias and fairness, and data storage, all within the context of AI applications in genomics and microscopy. Using a realist synthesis methodology, we integrated insights from diverse frameworks to explore the multifaceted challenges associated with data stewardship in these domains. Three independent reviewers, who systematically categorized the information across critical themes, including data governance, quality management, security, privacy, ownership, and access control conducted data extraction and analysis. The study also examined specific AI considerations, such as algorithmic bias, model explainability, and the application of advanced cryptographic techniques. The review process included six stages, starting with an extensive search across multiple research databases, resulting in 273 documents. Screening based on broad criteria, titles, abstracts, and full texts followed this, narrowing the pool to 38 highly relevant citations.

RESULTS

Our findings indicated that significant research was conducted in 2023 by highlighting the increasing recognition of robust data governance frameworks in AI-driven genomics and microscopy. While 36 articles extensively discussed data interoperability and sharing, AI-model explain ability and data augmentation remained underexplored, indicating significant gaps. The integration of diverse data types-ranging from sequencing and clinical data to proteomic and imaging data-highlighted the complexity and expansive scope of AI applications in these fields. The current challenges identified in AI-based data stewardship and curation practices are lack of infrastructure and cost optimization, ethical and privacy considerations, access control and sharing mechanisms, large scale data handling and analysis and transparent data-sharing policies and practice. Proposed solutions to address issues related to data quality, privacy, and bias management include advanced cryptographic techniques, federated learning, and blockchain technology. Robust data governance measures, such as GA4GH standards, DUO versioning, and attribute-based access control, are essential for ensuring data integrity, security, and ethical use. The study also emphasized the critical role of Data Management Plans (DMPs), meticulous metadata curation, and advanced cryptographic techniques in mitigating risks related to data security and identifiability. Despite advancements, significant challenges persisted in balancing data ownership with research accessibility, integrating heterogeneous data sources, ensuring platform interoperability, and maintaining data quality. Ongoing risks of unauthorized access and data breaches underscored the need for continuous innovation in data management practices and stricter adherence to legal and ethical standards.

CONCLUSIONS

These findings explored the current practices and challenges in data stewardship, offering a roadmap for strengthening the governance, security, and ethical use of AI in genomics and microscopy. While robust governance frameworks and ethical practices have established a foundation for data integrity and transparency, there remains an urgent need for collaborative efforts to develop interoperable platforms and transparent data-sharing policies. Additionally, evolving legal and ethical frameworks will be crucial to addressing emerging challenges posed by AI technologies. Fostering transparency, accountability, and ethical responsibility within the research community will be key to ensuring trust and driving ethically sound scientific advancements.

Treating cryptosporidiosis: A review on drug discovery strategies

Despite several decades of research on therapeutics, cryptosporidiosis remains a major concern for human and animal health. Even though this field of research to assess antiparasitic drug activity is highly active and competitive, only one molecule is authorized to be used in humans. However, this molecule was not efficacious in immunocompromised people and the lack of animal therapeutics remains a cause of concern. Indeed, the therapeutic arsenal needs to be developed for both humans and animals. Our work aims to clarify research strategies that historically were diffuse and poorly directed. This paper reviews in vitro and in vivo methodologies to assess the activity of future therapeutic compounds by screening drug libraries or through drug repurposing. It focuses on High Throughput Screening methodologies (HTS) and discusses the lack of knowledge of target mechanisms. In addition, an overview of several specific metabolic pathways and enzymatic activities used as targets against Cryptosporidium is provided. These metabolic processes include glycolytic pathways, fatty acid production, kinase activities, tRNA elaboration, nucleotide synthesis, gene expression and mRNA maturation. As a conclusion, we highlight emerging future strategies for screening natural compounds and assessing drug resistance issues.

Paving the Way: Contributions of Big Data to Apicomplexan and Kinetoplastid Research

In the age of big data an important question is how to ensure we make the most out of the resources we generate. In this review, we discuss the major methods used in Apicomplexan and Kinetoplastid research to produce big datasets and advance our understanding of Plasmodium, Toxoplasma, Cryptosporidium, Trypanosoma and Leishmania biology. We debate the benefits and limitations of the current technologies, and propose future advancements that may be key to improving our use of these techniques. Finally, we consider the difficulties the field faces when trying to make the most of the abundance of data that has already been, and will continue to be, generated.

Phenotypic screening techniques for Cryptosporidium drug discovery

Introduction: Two landmark epidemiological studies identified Cryptosporidium spp. as a significant cause of diarrheal disease in pediatric populations in resource-limited countries. Notably, nitazoxanide is the only approved drug for treatment of cryptosporidiosis but shows limited efficacy. As a result, many drug discovery efforts have commenced to find improved treatments. The unique biology of Cryptosporidium presents challenges for traditional drug discovery methods, which has inspired new assay platforms to study parasite biology and drug screening. Areas covered: The authors review historical advancements in phenotypic-based assays and techniques for Cryptosporidium drug discovery, as well as recent advances that will define future drug discovery. The reliance on phenotypic-based screens and repositioning of phenotypic hits from other pathogens has quickly created a robust pipeline of potential cryptosporidiosis therapeutics. The latest advances involve new in vitro culture methods for oocyst generation, continuous culturing capabilities, and more physiologically relevant assays for testing compounds. Expert opinion: Previous phenotypic screening techniques have laid the groundwork for recent cryptosporidiosis drug discovery efforts. The resulting improved methodologies characterize compound activity, identify, and validate drug targets, and prioritize new compounds for drug development. The most recent improvements in phenotypic assays are poised to help advance compounds into clinical development.