Summary:
Coding tools have progressed at warp speed, moving rapidly from syntax troubleshooting to autonomous multi-agent workflows, the potential for AI in programming is spectacular. People with experience writing programs will be curious about the capabilities of AI tools, those who don't write code will wonder if they should ever learn, and everyone will want to understand the risks and benefits. This workshop will focus on "Vibe Coding", using AI tools to rapidly develop, prototype, and iterate software. Participants will hear about and discuss the realm of the possible, explore different AI coding tools, and consider if, how, and when an AI-developed application should be developed securely, safely, and effectively. Through the workshop, the group will create a new application, tackling technical and validation challenges, while identifying and mitigating potential risks and failure modes.

Syllabus:

• Landscape of AI tools for coding: from chatbots to multi-agent frameworks
• Vibe coding, who, what, when, where, why, how
• Failure modes, ethics, risks
• Guardrails, best practices, and safety
• From idea to implementation, vibe coding an app

Objectives:

1. Differentiate vibe coding from traditional software development
2. Describe how AI tools can be used for coding
3. Identify risks and benefits of using AI to write code
4. Critically evaluate a vibe-coded application

Summary

Liquid-chromatography mass spectrometry (LC-MS) is utilized in many larger clinical laboratories, reference laboratories or research laboratories for developing assays for new biomarkers, for analytes for which alternative technologies lack specificity, or for high volume assays requiring greater throughput. In the life cycle of a new assay, CLSI guidance documents are used by laboratory developed test (LDT) developers to meet regulatory and accreditation requirements during the design, development, validation and implementation of LDTs into the clinical lab setting. These same documents may also be useful for researchers to use for good practice study design, clinical trials and/or publishing study results. Since these consensus-driven documents are developed to be broadly applicable, they can be challenging to navigate, may not fully address technology-specific study design considerations, use terminology and examples not common to the general laboratory and may not be specific or applicable to LC-MS. In response, CLSI has undertaken a strategic re-envisioning of the Method Evaluation (formerly referred to as Evaluation Protocol) category of documents.

This workshop will showcase improvements that CLSI is implementing to improve the experience for those who utilize their documents. The presenters will provide before-and-after examples, in an interactive format with the audience, to demonstrate how CLSI can better support those who work with LC-MS technology. Finally, the workshop will also consist of interactive brainstorming between the presenters and audience on topics such as gaps seen with current CLSI documents and how researchers and LDT developers who utilize LC-MS may take advantage of the new CLSI model to propose new targeted or niche-scoped guidance, examples, and tools.

Syllabus

• Short overview of CLSI with a focus on guidance documents and tools specific for Method Evaluation and LC-MS technology.
• Exploration of the updated Method Navigator tool to showcase its utility as a resource to find CLSI guidance to support regulatory and accreditation requirements throughout the Test Life Phases Model.
• Introduction to a new framework for CLSI Method Evaluation guidelines designed to provide more concise, phase- and technology-specific guidance using examples for linearity and method comparison studies.
• Review of recently published Method Evaluation documents of interest to LC-MS researchers and LDT developers.
• Open discussion on applying CLSI guidance in practice and identifying opportunities for improvement to CLSI’s current Method Evaluation portfolio.

Objectives

1. Describe recent improvements in CLSI Method Evaluation documents and tools that support LC-MS users to include both researchers and developers of LDTs.
2. Compare historical CLSI guidance approaches with the new, targeted strategies.
3. Use the CLSI Method Navigator tool to identify guidance documents relevant to each phase of the laboratory test life cycle.
4. Identify gaps in current guidance and discuss opportunities to enhance CLSI guidance for LC-MS technology.
5. Develop targeted study designs for LC-MS using the updated CLSI framework.

Summary

Design of experiments (DoE) is an efficient strategy for developing and optimizing LC-MS/MS methods used to quantify biomarkers in complex biological matrices. Because LC‑MS/MS workflows involve multiple interdependent processes and numerous experimental variables, DoE provides a structured and efficient framework for identifying influential factors, modelling system behavior, and achieving maximum analytical performance with minimal experimental effort. This workshop will introduce the fundamental principles of DoE with a strong focus on practical implementation. Participants will be guided through the three main stages of a DoE‑driven method development strategy: factor screening, response optimization, and robustness assessment. To demonstrate the value of DoE compared with traditional one‑factor‑at‑a‑time approaches, two applied case studies will be presented. The first case study focuses on optimizing sample preparation in a bottom-up targeted protein LC-MS workflow. The second demonstrates DoE based optimization of a UPLC-MS/MS assay for clinical diagnostic and therapeutic drug monitoring in patients with adenine phosphoribosyltransferase (APRT) deficiency. In both studies, polynomial models were constructed, central composite designs were used to evaluate significant factors, and partial least squares (PLS) regression was applied to relate variables to analytical responses. These studies demonstrate how DoE can reduce sample preparation time and enable efficient optimization of biomarker quantification, including urinary 2,8‑dihydroxyadenine (DHA) and adenine. Finally, a demonstration of DoE‑based robustness testing, showing how intentional variation of key parameters can reveal critical factors and interactions that influence method performance. Attendees will gain practical insight into applying DoE to assessing method reliability and supporting long‑term method robustness.

Syllabus

• Design of Experiments (DoE) – Get it right from the beginning
• Basic concept and assessment of DoE
• Optimization of sample preparation and LC-MS/MS clinical assay by DoE
• Evaluation of robustness of an analytical method by DoE

Objectives

1. Explain basic principles and concepts of experimental design.
2. Discuss different types of experimental designs.
3. Discuss interpretation of the results and implications of the findings.
4. Provide examples of experimental design application in the process of method development and evaluation.

Summary

Diabetes represents a collection of endocrine disorders with severe systemic complications, including type 1 diabetes, an autoimmune condition characterized by insulin deficiency. The disease pathogenesis, trajectory, and end-organ damage are variable from patient to patient. As a result, the precise and accurate quantification of proteins and peptides involved in diabetes will help facilitate research into disease pathogenesis and ultimately improve the diagnosis, prognosis, and therapeutic management of patients with diabetes. Unfortunately, most of the studies to date have relied on immunoassays, with little effort put into demonstrating the specificity of the reagents or the robustness of the assays. Furthermore, recent publications have highlighted the limitations of many commercial assays, including a failure to detect the intended target. Rigor and reproducibility could be substantially improved by applying mass spectrometry to the quantification of these biomarkers. Major improvements in sample preparation and instrumentation have made mass spectrometry–based targeted proteomics a highly reproducible methodology for detecting and quantifying proteins and peptides. In addition, the ability to quantify specific proteoforms provides insight into prohormone processing and post-translational modifications and creates an opportunity to identify and validate new biomarkers that can be used for disease stratification.

The NIDDK continues to fund several projects that aim to use targeted mass spectrometry to quantify human plasma/serum proteins and peptides of interest to the diabetes clinical research community. During this workshop, the presenters will provide an update on their recent progress toward this goal that have been made by the Targeted Mass spectrometry Assays for Diabetes and Obesity Research (TaMADOR) consortium, with a special focus on biomarkers important in type 1 diabetes.

Syllabus

• Detecting proteins and peptides in human serum and plasma
• Preparing samples for targeted proteomic analysis
• The role of antibodies and immunoprecipitation in LC-MS quantification of protein and peptide biomarkers
• Examples of assays that can be translated to clinical research or clinical care

Objectives

1. Outline the potential utility of biomarkers in clinical research and clinical care in diabetes
2. Provide the rationale for the use of LC-MS/MS methods in the quantification of peptide and protein biomarkers, including proteoform-specific biomarkers
3. List the advances in sample preparation and instrumentation that enable the development of assays to peptide and protein biomarkers in human serum/plasma
4. Identify the hurdles that exist for the development of novel protein and peptide biomarker assays
5. Demonstrate the reproducibility and robustness of LC-MS assays through inter-lab assessments.