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Helma, C., Cramer, T., Kramer, S. and De Raedt, L. (2004) Data Mining and Machine Learning Techniques for the Identification of Mutagenicity Inducing Substructures and Structure Activity Relationships of Noncongeneric Compounds. Journal of Chemical Information and Computer Sciences, 44, 1402-1411.
https://doi.org/10.1021/ci034254q

has been cited by the following article:

• TITLE: Strengthening Extractable & Leachable Study Submissions: Best Practices to Avoid Regulatory Deficiencies

  AUTHORS: Subathra Ramamoorthy, Ngee Sing Chong, Kishore Kumar Hotha

  KEYWORDS: Extractables and Leachables, Pharmaceutical Container Closure Systems, Manufacturing Equipment, Guidance, Regulatory Standards, Prototype Procedures

  JOURNAL NAME: American Journal of Analytical Chemistry, Vol.15 No.12, December 26, 2024

  ABSTRACT: The assessment of extractable and leachables (E&L) has become a cornerstone of pharmaceutical product safety, yet no unified global standards currently exist to quantify or report E&L from container closure systems. While initially limited to primary packaging, E&L evaluations now encompass secondary packaging, manufacturing equipment, and medical devices. Key milestones include the FDA’s 1999 guidance on container closure systems and more recent frameworks from USP chapters , , and , as well as PQRI recommendations. These guidelines have set the foundation for conducting E&L studies, especially for high-risk dosage forms, and are complemented by the forthcoming ICH Q3E, which aims to standardize E&L considerations across dosage forms. This article outlines a structured procedure for E&L assessments, integrating regulatory expectations with practical insights.