Bioanalysis is used in drug development to determine pharmacology, bioavailability, and bioequivalence and includes pharmacokinetic, toxicokinetic, or biomarker concentration evaluation (1). Regulators require bioanalytical methods to be validated to ensure their effectiveness (1). Advanced tools, such as artificial intelligence (AI) and machine learning (ML), are helping drug developers evaluate the complex data involved in these types of studies, especially for novel therapeutics, and generate new insights from these data.
Evolution in the field of bioanalysis techniques is often driven by the need to address analyte modalities, specifically biologic drugs, metabolites, and biomarkers, according to Steve Lowes, senior director of Scientific Affairs at IQVIA Laboratories. “Sensitivity is the typical challenge, heightened by the interest to achieve needed quantification from a smaller sample as possible,” he explains. “We, thus, have three compounding factors. One is more sensitivity. Second is a smaller sample we’re expected to work from, and third is an increasing complexity of the analyte modalities themselves.”
How are new technologies making bioanalysis more accurate, more efficient, and quicker? And what is driving, or challenging, their implementation?
How do AI/ML benefit bioanalysis?
While early use of AI/ML was in reports, quality control of assay validation, and study sample analysis, these technologies are being adopted more widely in bioanalysis for validated applications in development, according to Mark Arnold, owner and principal, Bioanalytical Solution Integration. “As the capabilities of the AIs have advanced, so have the applications in bioanalysis,” he says. “New applications are looking at taking GLP (good laboratory practice) or GCP (good clinical practice) protocols and bioanalytical contracts to write bioanalytical plans, perform quality control checks that integrate ELN (electronic lab notebook), instrument, and LIMS (laboratory information management system) data on a daily basis to catch errors early and identify trends that lead to failure before they happen.”
AI/ML help automate data processing and integrate complex datasets from analytical platforms, enhancing assay precision and decision-making, according to Stuart McDougall, principal research fellow, at Arcinova. “AI-driven predictive models support real-time quality control, while machine learning algorithms identify trends and anomalies to ensure regulatory compliance,” he notes. “Together, they reduce development timelines, improve operational efficiency, and support scalable, data-driven innovation across the drug development lifecycle. When integrated into systems like LIMS, these insights are directly tied to scientific context, not just numbers on a screen.”
Yasmin Emamgholi, PhD, product manager, LabVantage Solutions, adds, “Instead of spending hours combing through assay data or chromatograms, these solutions can pick up subtle trends and patterns in real time. In development, models can suggest how a compound might behave, flag outliers, or even predict interactions before they show up in the lab. On the manufacturing side, AI can spot early signals in quality control, so issues do not turn into costly failures.”
Emamgholi also points to agentic AI, which can analyze data, as well as perform tasks across the bioanalysis workflow. “For example, agentic AI solutions can help in assay development and optimization with autonomous experiment design and closed-loop optimization,” Emamgholi explains. “Such solutions can also help in manufacturing and quality control by autonomously analyzing in-process bioassay data, tracking anomalies, and helping with predictive troubleshooting, including detecting early assay or equipment issues and triggering preventative maintenance before failures occur.”
These technologies can also support predictive modeling, according to Theo de Boer, PhD, principal scientist–scientific director LCMS, at Ardena, enabling quick detection of complex biomarker patterns and enhanced pharmacokinetic and pharmacodynamic modeling.
Long Yuan, director, Department of Drug Metabolism and Pharmacokinetics, Biogen, also sees AI/ML as beneficial for data interpretation. “For example, AI can help to do the automated peak detection integration, calculate and generate the concentration data, and even integrate with the downstream report generation process,” he states, adding that AI/ML can generate study protocols and validation reports.
“Ultimately, biotech and pharma companies will be using AI to write the bioanalytical sections of filings,” Arnold assures. “Health authorities are also looking at AI as a tool to analyze data in submissions and during on-site or remote inspections of bioanalytical labs. The resulting analyses will highlight both compliance with regulations and detect problems and errors that need further review.”
Arnold cautions that controls must be put in place for AI use in regulated bioanalysis to prevent “hallucinations,” or the creation of data that have not been provided. “Not knowing if the report content is correct is not something bioanalytical labs can risk and would result in utilizing the same or greater QC (quality control) and QA (quality assurance) resources to make sure of the report’s accuracy,” he says. “Which is contrary to what AI is supposed to do: reduce human involvement and improve accuracy.” Audit trails that demonstrate how/if the data reached the correct location can help prevent this problem, Arnold suggests.
How do LLMs improve workflows?
Bioanalytical workflows can be enhanced, and regulatory compliance can be supported, by incorporating large language models (LLMs) in bioanalysis, says McDougall. LLMs can automate documentation and interpret complex datasets.
“They streamline reporting by generating method summaries, validation protocols, and audit-ready records from structured and unstructured data,” McDougall says. “LLMs also assist in protocol optimization by analyzing historical assay performance and suggesting refinements. Integrated with lab systems, they enable natural language querying of experimental data, accelerating decision making. Additionally, LLMs support training and knowledge transfer by summarizing scientific literature and (standard operating procedures (SOPs)), improving consistency and reducing onboarding time. Their ability to contextualize and communicate scientific insights makes them valuable tools in modern bioanalysis.”
Yuan believes that LLMs can improve regulatory compliance for QC and QA practices by helping review ELNs and audit trails and create SOPs. “(LLMs) can be more interactive with humans. It’s more like a real human. So, (a person) can… get guidance and assistance on troubleshooting and regular bioanalytical workflows,” Yuan suggests.
“LLMs can help create a conversational digital twin by providing a natural-language interface to bio-analytical workflows, enabling scientists to converse with their assay datasets,” says Panchali Roychoudhury, senior director, TCG Digital (a part of LabVantage’s parent company, The Chatterjee Group). LabVantage analytics uses a hybrid approach to integrate semantic knowledge graphs with retrieval-augmented generation (RAG)-based GenAI models to create “a more accurate, comprehensive, contextually aware, and scalable system, with substantial reduction in hallucinations,” according to Roychoudhury. “Responses are also traceable and linked to related experiments or compounds. This combination reduces noise, expedites decision making, and strengthens confidence in the bioanalytical workflows.”
De Boer notes that “LLMs could play a role in automating data interpretation, supporting audit readiness, and improving knowledge management across projects. While their adoption in bioanalysis remains limited today, ongoing advancements suggest LLMs will increasingly help accelerate research, reduce administrative burdens, and enhance the integration of complex scientific data.”
Unique new instrumentation
Key to developing safe and effective drug products is the ability to perform precise quantification and structural characterization of complex molecules, according to McDougall, which can be achieved with high-resolution mass spectrometry (HRMS). He also points to the use of automated liquid handling systems and microfluidics to enhance sample processing. “These instruments are unique for their integration with digital systems, compatibility with AI-driven analytics, and ability to handle biologics and multiplexed assays—making them essential for accelerating development timelines and ensuring robust, regulatory-compliant bioanalytical data,” McDougall says.
According to Yuan, HRMS provides exceptional specificity. “For example, for oligonucleotides, sometimes the commonly used quadrupole mass spectrometer may not be able to differentiate the parent and metabolites. HRMS had a unique advantage to provide additional specificity for these types of applications,” Yuan says. He adds that HRMS can also obtain quantitative and qualitative information in the same run, allowing for data mining at a later date that can avoid sample reassessment.
Liquid chromatography (LC)–mass spectrometry (MS), automated enzyme-linked immunosorbent assay) (ELISA) (ELLA), Meso Scale Discovery (MSD), ddPCR, and flow cytometry may be used for pharmacokinetics and/or biomarker analysis, according to de Boer. Robust, reproducible results for complex modalities may be achieved by combining hybrid immunoaffinity LC–MS techniques with automated sample preparation, de Boer explains.
Although LC–MS can be more expensive and not as sensitive, Lowes sees it as a alternative to ligand binding assays (LBAs). “The benefits of mitigating the challenges and the reliance on critical reagents associated with immunoassays leads to some interesting cost and efficiency assessments of LC–MS versus LBA,” he explains. “With increasing frequency, we’re seeing LC–MS to be the best tool for the job, including in biologic bioanalysis for protein-based biologic drugs and ligand oligonucleotide therapeutics. Integral to both are also the conjugated drug structures in many oncology applications for which an antibody is directed to a tumor-associated antigen to deliver a cytotoxic payload. These are inherently complex molecules, and subsequently, so are the assays that we develop to address them. That is, we often need a free payload drug assay, a conjugated payload, and then the total antibody measurement as well.”
According to Arnold, “the improved flow cytometry instruments, with some instruments having 30 and 40 color detection ability, are being used for cellular characterization, and the improved sensitivities allow them to be applied to circulating exosome biomarkers that can detect and characterize many diseases. Mass cytometry is being used in research, and once suitable applications are identified, likely in measuring biomarkers, it will move into regulated bioanalysis.”
How do changing regulations impact bioanalysis advancements?
Development and implementation of new technologies is directly impacted by bio/pharmaceutical development and manufacturing regulations. Good manufacturing practices are often necessary to follow, and each country and/or region has its own rules. The International Council for Harmonisation (ICH) works to harmonize these regulations to help both manufacturers and regulators navigate a complex system. Bioanalysis, in particular, says McDougall, is guided by ICH M10 (2).
“Having a standard—(M10)—accepted in many major markets eliminated the need for bioanalysts to keep up with changes in each of the individual countries and adjust their practices, especially when there were conflicting expectations,” Arnold explains.
De Boer adds that “in 2025, FDA released updated guidance on bioanalytical method validation for biomarkers, emphasizing a fit-for-purpose approach that ensures sensitivity, specificity, and reproducibility (3). These evolving standards are accelerating adoption of standardized SOPs, automation, advanced assay platforms, and comprehensive documentation, enabling compliant, reproducible, and high-quality bioanalysis that supports precision medicine and faster regulatory approvals.”
FDA’s initiation of new approach methodologies to reduce animal testing in pharmaceuticals has also impacted bioanalysis (4). “The FDA announcement has certainly catalyzed ongoing efforts and support for in silico, in vitro, and ex vitro methods,” Lowes says. “The evolving developments around the organoids and organ-on-a-chip platform technologies I find particularly exciting. How bioanalysis fits into this space is still to be determined, but I suspect it will have a lasting influence on how we conduct pre-clinical stages of drug development.”
“Regulatory updates from agencies like FDA and (European Medicines Agency) demand greater precision, sensitivity, and documentation, driving innovation in instrumentation and data management,” McDougall says. “Compliance with good laboratory and clinical practices ensures data integrity and ethical standards. As expectations evolve, bioanalytical teams must adapt workflows and technologies to meet stricter requirements, ultimately improving the reliability and regulatory acceptance of drug development data.”
McDougall highlights a paper authored by members of the European Bioanalysis Forum (EBF) AI team, published in July 2025, that questions the adequacy of traditional validation frameworks for AI in bioanalysis (5). The paper “argues that static, checklist-driven validation is ill-suited for adaptive, learning systems,”
McDougall summarizes. “Instead, they propose ‘adaptive qualification,’ a dynamic, context-sensitive approach grounded in scientific oversight and continuous engagement. AI is reframed not as a tool but as a trainee, requiring lifecycle monitoring rather than one-time validation. EBF emphasizes the need for scientific stewardship, collaborative ownership across disciplines, trust built on transparency and relevance, and a shift from rigid compliance to thoughtful, fit-for-purpose oversight.”
“To be successful in delivering data accepted globally, bioanalysts must be aware of a variety of regulations and how they apply to the purpose of the analysis, type of samples they are analyzing, and the technologies,” Arnold warns. “When no regulations exist, scientists must continue to collaborate on science-based best practices.”
References
- FDA. Bioanalytical Method Validation, Guidance for Industry (CDER, May 2018).
- ICH. M10 Bioanalytical Method Validation and Study Sample Analysis (ICH, May 24, 2022).
- FDA. Bioanalytical Method Validation for Biomarkers, Guidance for Industry (CDER, CBER, January 2025).
- FDA. FDA Announces Plan to Phase Out Animal Testing Requirement for Monoclonal Antibodies and Other Drugs. Press Release. April 10, 2025.
- Timmerman, P.; et al. Why Traditional Validation May Fall Short for Artificial Intelligence in Bioanalysis: A Perspective from the European Bioanalysis Forum. Bioanalysis 2025 17(13), 835–837. DOI: 10.1080/17576180.2025.2535219
About the author
Susan Haigney is lead editor for Pharmaceutical Technology®.
Article details
Pharmaceutical Technology®
Vol. 49, No. 8
October 2025
Pages: 10–13
Citation
When referring to this article, please cite it as Haigney, S. Enhancing Bioanalysis and Drug Development with Advanced Tools and Technologies. Pharmaceutical Technology® 2025 49 (8).
