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Motivation: Germline variant classification allows accurate genetic diagnosis and risk assessment. However, it is a tedious iterative process integrating information from several sources and types of evidence. It should follow gene-specific (if available) or general updated international guidelines. Thus, it is the main burden of the incorporation of next-generation sequencing into the clinical setting., Results: We created the vaRiants in HC (vaRHC) R package to assist the process of variant classification in hereditary cancer by: (i) collecting information from diverse databases; (ii) assigning or denying different types of evidence according to updated American College of Molecular Genetics and Genomics/Association of Molecular Pathologist gene-specific criteria for ATM, CDH1, CHEK2, MLH1, MSH2, MSH6, PMS2, PTEN, and TP53 and general criteria for other genes; (iii) providing an automated classification of variants using a Bayesian metastructure and considering CanVIG-UK recommendations; and (iv) optionally printing the output to an .xlsx file. A validation using 659 classified variants demonstrated the robustness of vaRHC, presenting a better criteria assignment than Cancer SIGVAR, an available similar tool., Availability and Implementation: The source code can be consulted in the GitHub repository (https://github.com/emunte/vaRHC) Additionally, it will be submitted to CRAN soon., (© The Author(s) 2023. Published by Oxford University Press.)

Pregnant women have historically been an understudied population and have been excluded from clinical trials. Recent efforts by stakeholders have raised awareness of the importance of clinical research in pregnant women to inform prescribing decisions. The Food and Drug Administration continues working to improve the format and content of prescription drug labeling for pregnant and lactating women, as demonstrated with the Pregnancy and Lactation Labeling Rule (PLLR), effective in 2015. The pregnancy labeling subsection now includes a subheading dedicated to the inclusion of pharmacokinetic (PK) data that inform the need for dose adjustments during pregnancy and the postpartum period. In addition, the PLLR also requires prescription drug labeling to be updated when important pregnancy information becomes available. Although PLLR improved the presentation of pregnancy-related information in labeling, there is a need to increase the quality and quantity of human data on the use of prescription drugs during pregnancy. PK studies in pregnant women should be incorporated into drug development programs and prioritized to obtain important information about safe and appropriate doses of a drug when used during pregnancy. In addition, opportunistic PK studies, postapproval pregnancy safety studies, ex vivo studies, and in silico modeling can be leveraged to better inform the risks and benefits of using a drug during pregnancy to inform study design and to further understand various mechanisms impacting pharmacokinetic/pharmacodynamic of drugs during pregnancy. It is important to address the significant existing data gaps and better inform the safety and dosing of prescription drugs for pregnant women., (Published 2020. This article is a U.S. Government work and is in the public domain in the USA.)